Opções reais e estratégia de negócios - aplicativos para tomada de decisão

Opções reais e estratégia de negócios - aplicativos para tomada de decisões
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Sistemas especialistas com aplicativos.
Destaques.
Duas décadas foram sistematicamente revistas em técnicas de MCDM fuzzy de 1994 a 2014.
O banco de dados para análise foi de 403 artigos de mais de 150 periódicos de alto escalão.
403 documentos acadêmicos foram agrupados em quatro campos principais diferentes.
Os trabalhos foram classificados com base na utilização, desenvolvimento e proposição de trabalhos de pesquisa.
O MCDM é considerado como uma ferramenta complexa de tomada de decisão, envolvendo fatores quantitativos e qualitativos. Nos últimos anos, várias ferramentas fuzzy de FMCDM foram sugeridas para escolher as opções ideais possíveis. O objetivo deste artigo é revisar sistematicamente as aplicações e metodologias das técnicas de multi-decisão fuzzy (FMCDM). Este estudo revisou um total de 403 artigos publicados de 1994 a 2014 em mais de 150 periódicos revisados ​​por pares (extraídos de bancos de dados on-line como ScienceDirect, Springer, Emerald, Wiley, ProQuest e Taylor & Francis). De acordo com as opiniões dos especialistas, esses documentos foram agrupados em quatro áreas principais: engenharia, administração e negócios, ciência e tecnologia. Além disso, esses artigos foram categorizados com base em autores, data de publicação, país de origem, métodos, ferramentas e tipo de pesquisa (FMCDM utilizando pesquisa, FMCDM desenvolvendo pesquisa e FMCDM propondo pesquisa). Os resultados deste estudo indicaram que, em 2013, os acadêmicos publicaram trabalhos mais do que em outros anos. Além disso, MCDM difuso híbrido no método integrado e AHP fuzzy na seção individual foram classificados como o primeiro e segundo métodos em uso. Além disso, Taiwan foi classificado como o primeiro país que contribuiu para essa pesquisa, e a engenharia foi classificada como o primeiro campo que aplicou ferramentas e técnicas de DM difusas.
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Opções reais e estratégia de negócios - aplicativos para tomada de decisões
Ferramentas para Análise de Decisão:
Análise de Decisões Arriscadas.
Se você começar com certezas, acabará com dúvidas, mas se quiser se contentar em começar com dúvidas, acabará com quase certezas.
Para mis visitantes do mundo de habla hispana, este sitio se encuentra disponible en español en:
Tomar decisões é, sem dúvida, a tarefa mais importante de um gerente e muitas vezes é muito difícil. Este site oferece um procedimento de tomada de decisão para resolver problemas complexos passo a passo. Ele apresenta o processo de análise de decisão tanto para tomada de decisão pública quanto privada, usando diferentes critérios de decisão, diferentes tipos de informação e informações de qualidade variável. Descreve os elementos na análise das alternativas e escolhas de decisão, bem como os objetivos e metas que orientam a tomada de decisão. As principais questões relacionadas às preferências de um tomador de decisão em relação a alternativas, critérios de escolha e modos de escolha, juntamente com as ferramentas de avaliação de risco também são apresentadas.
Para pesquisar o site, tente E dit | F ind na página [Ctrl + f]. Digite uma palavra ou frase na caixa de diálogo, por ex. & quot; risco & quot; ou & quot; utilidade & quot; Se a primeira aparição da palavra / frase não for o que você está procurando, tente F ind Next.
Introdução e Resumo.
Os analistas de decisão fornecem suporte quantitativo para os tomadores de decisão em todas as áreas, incluindo engenheiros, analistas em escritórios de planejamento e agências públicas, consultores de gerenciamento de projetos, planejadores de processos de fabricação, analistas financeiros e econômicos, especialistas em diagnósticos médicos / tecnológicos e assim por diante.
Abordagem Progressiva à Modelagem: A modelagem para a tomada de decisão envolve duas partes distintas, uma é a tomadora de decisões e a outra é a construtora de modelos conhecida como analista. O analista deve auxiliar o decisor em seu processo de tomada de decisão. Portanto, o analista deve estar equipado com mais de um conjunto de métodos analíticos.
Especialistas em construção de modelos são frequentemente tentados a estudar um problema e, em seguida, saem em isolamento para desenvolver um modelo matemático elaborado para uso pelo gerente (ou seja, o tomador de decisão). Infelizmente, o gerente pode não entender esse modelo e pode usá-lo cegamente ou rejeitá-lo completamente. O especialista pode achar que o gerente é muito ignorante e pouco sofisticado para apreciar o modelo, enquanto o gerente pode sentir que o especialista vive em um mundo de sonhos de suposições irrealistas e linguagem matemática irrelevante.
Tal falha de comunicação pode ser evitada se o gerente trabalhar com o especialista para desenvolver primeiro um modelo simples que forneça uma análise crua, mas compreensível. Depois que o gerente tiver adquirido confiança nesse modelo, detalhes adicionais e sofisticação podem ser adicionados, talvez progressivamente apenas um pouco por vez. Este processo requer um investimento de tempo por parte do gestor e interesse sincero por parte do especialista em resolver o problema real do gestor, em vez de criar e tentar explicar modelos sofisticados. Esse modelo progressivo é frequentemente chamado de bootstrapping e é o fator mais importante na determinação da implementação bem-sucedida de um modelo de decisão. Além disso, a abordagem de bootstrapping simplifica a difícil tarefa dos processos de validação e verificação de modelos.
O que é um Sistema: Os sistemas são formados com partes reunidas de uma maneira particular, a fim de buscar um objetivo. A relação entre as partes determina o que o sistema faz e como ele funciona como um todo. Portanto, o relacionamento em um sistema geralmente é mais importante que as partes individuais. Em geral, os sistemas que são blocos de construção para outros sistemas são chamados de subsistemas.
A Dinâmica de um Sistema: Um sistema que não muda é um sistema estático (isto é, determinístico). Muitos dos sistemas dos quais fazemos parte são sistemas dinâmicos, que são alterados ao longo do tempo. Referimo-nos à maneira como um sistema muda ao longo do tempo como o comportamento do sistema. E quando o desenvolvimento do sistema segue um padrão típico, dizemos que o sistema tem um padrão de comportamento. Se um sistema é estático ou dinâmico, depende do horizonte de tempo escolhido e de quais variáveis ​​você se concentra. O horizonte de tempo é o período de tempo dentro do qual você estuda o sistema. As variáveis ​​são valores alteráveis ​​no sistema.
Em modelos deterministas, uma boa decisão é julgada apenas pelo resultado. No entanto, nos modelos probabilísticos, o tomador de decisão está preocupado não apenas com o valor do resultado, mas também com a quantidade de risco que cada decisão carrega.
Como exemplo de modelos determinísticos versus probabilísticos, considere o passado e o futuro: nada que possamos fazer pode mudar o passado, mas tudo o que fazemos influencia e modifica o futuro, embora o futuro tenha um elemento de incerteza. Os gerentes são muito mais atraídos por moldar o futuro do que a história do passado.
A incerteza é o fato da vida e dos negócios; A probabilidade é o guia para uma vida "boa" e um negócio de sucesso. O conceito de probabilidade ocupa um lugar importante no processo de tomada de decisão, seja o problema enfrentado nos negócios, no governo, nas ciências sociais ou apenas na vida pessoal cotidiana. Em poucas situações de tomada de decisão, a informação perfeita - todos os fatos necessários - está disponível. A maioria das decisões é tomada em face da incerteza. Probabilidade entra no processo, desempenhando o papel de um substituto para a certeza - um substituto para o conhecimento completo.
A modelagem probabilística é amplamente baseada na aplicação de estatísticas para avaliação de probabilidade de eventos incontroláveis ​​(ou fatores), bem como na avaliação de risco de sua decisão. A ideia original de estatística era a coleta de informações sobre e para o Estado. A palavra estatística não é derivada de nenhuma raiz clássica grega ou latina, mas da palavra italiana para estado. A probabilidade tem uma história muito mais longa. A probabilidade é derivada do verbo para testar o significado para "descobrir" o que não é facilmente acessível ou compreensível. A palavra "prova" tem a mesma origem que fornece detalhes necessários para entender o que é reivindicado como verdadeiro.
Modelos probabilísticos são vistos como similares aos de um jogo; as ações são baseadas nos resultados esperados. O centro de interesse se move dos modelos determinísticos para os modelos probabilísticos usando técnicas estatísticas subjetivas para estimativa, testes e previsões. Na modelagem probabilística, risco significa incerteza para a qual a distribuição de probabilidade é conhecida. Portanto, avaliação de risco significa um estudo para determinar os resultados das decisões junto com suas probabilidades.
Os tomadores de decisão muitas vezes enfrentam uma grave falta de informação. A avaliação de probabilidade quantifica a lacuna de informações entre o que é conhecido e o que precisa ser conhecido para uma decisão ideal. Os modelos probabilísticos são usados ​​para proteção contra incertezas adversas e exploração da incerteza propícia.
A dificuldade na avaliação da probabilidade decorre de informações escassas, vagas, inconsistentes ou incompletas. Uma declaração como "a probabilidade de uma queda de energia está entre 0,3 e 0,4" é mais natural e realista do que sua contraparte "exata", como "a probabilidade de uma queda de energia é de 0,36342".
É uma tarefa desafiadora comparar vários cursos de ação e depois selecionar uma ação a ser implementada. Às vezes, a tarefa pode ser muito desafiadora. Dificuldades na tomada de decisão surgem através de complexidades nas alternativas de decisão. A limitada capacidade de processamento de informações de um tomador de decisão pode ser tensa ao considerar as conseqüências de apenas um curso de ação. No entanto, a escolha requer que as implicações de vários cursos de ação sejam visualizadas e comparadas. Além disso, fatores desconhecidos sempre se intrometem na situação problemática e raramente são resultados conhecidos com certeza. Quase sempre, um resultado depende das reações de outras pessoas que podem estar indecisas. Não é de admirar que os tomadores de decisão às vezes adiem as escolhas pelo maior tempo possível. Então, quando finalmente decidem, negligenciam considerar todas as implicações de sua decisão.
Emoções e Decisão Arriscada: A maioria dos tomadores de decisão confia em emoções para fazer julgamentos sobre decisões arriscadas. Muitas pessoas têm medo das possíveis consequências indesejáveis. No entanto, precisamos de emoções para podermos julgar se uma decisão e seus riscos concomitantes são moralmente aceitáveis. Esta questão tem implicações práticas diretas: engenheiros, cientistas e formuladores de políticas envolvidos no desenvolvimento da regulação de risco devem levar a sério as emoções do público ou não? Mesmo que as emoções sejam subjetivas e irracionais (ou a-racionais), elas devem fazer parte do processo de tomada de decisão, uma vez que elas nos mostram nossas preferências. Desde emoções e racionalidade não são mutuamente exclusivas, porque para ser praticamente racional, precisamos ter emoções. Isso pode levar a uma visão alternativa sobre o papel das emoções na avaliação de risco: as emoções podem ser um guia normativo na tomada de decisões sobre riscos moralmente aceitáveis.
A maioria das pessoas muitas vezes faz escolhas por hábito ou tradição, sem passar pelos passos do processo de tomada de decisão sistematicamente. Decisões podem ser tomadas sob pressão social ou restrições de tempo que interfiram com uma consideração cuidadosa das opções e consequências. As decisões podem ser influenciadas pelo estado emocional no momento em que uma decisão é tomada. Quando as pessoas não têm informações ou habilidades adequadas, elas podem tomar decisões menos que ótimas. Mesmo quando ou se as pessoas têm tempo e informações, muitas vezes fazem um trabalho ruim em entender as probabilidades das consequências. Mesmo quando eles conhecem as estatísticas; eles são mais propensos a confiar em experiências pessoais do que informações sobre probabilidades. As preocupações fundamentais da tomada de decisão estão combinando informações sobre probabilidade com informações sobre desejos e interesses. Por exemplo: quanto você quer conhecê-la, quão importante é o piquenique, quanto vale o prêmio?
A tomada de decisões de negócios é quase sempre acompanhada de condições de incerteza. Claramente, quanto mais informações o tomador de decisão tiver, melhor será a decisão. Tratar decisões como se fossem apostas é a base da teoria da decisão. Isso significa que temos que negociar o valor de um determinado resultado em relação à sua probabilidade.
Para operar de acordo com os cânones da teoria da decisão, devemos calcular o valor de um determinado resultado e suas probabilidades; daqui, determinando as conseqüências de nossas escolhas.
A origem da teoria da decisão é derivada da economia usando a função utilidade dos payoffs. Sugere que as decisões sejam tomadas computando-se a utilidade e a probabilidade, os intervalos de opções e também estabelece estratégias para boas decisões:
Este site apresenta o processo de análise de decisão tanto para tomada de decisão pública quanto privada sob diferentes critérios de decisão, tipo e qualidade das informações disponíveis. Este site descreve os elementos básicos na análise de alternativas de decisão e escolha, bem como as metas e objetivos que orientam a tomada de decisão. Nas seções subseqüentes, examinaremos as principais questões relacionadas às preferências de um decisor em relação a alternativas, critérios de escolha e modos de escolha.
Os objetivos são importantes tanto na identificação de problemas quanto na avaliação de soluções alternativas. A avaliação de alternativas requer que os objetivos do tomador de decisão sejam expressos como critério que reflete os atributos das alternativas relevantes para a escolha.
O estudo sistemático da tomada de decisão fornece uma estrutura para a escolha de cursos de ação em uma situação complexa, incerta ou sujeita a conflitos. As escolhas de ações possíveis e a previsão dos resultados esperados derivam de uma análise lógica da situação de decisão.
Uma possível desvantagem na abordagem de análise de decisão: Você já deve ter percebido que os critérios acima sempre resultam na seleção de apenas um curso de ação. No entanto, em muitos problemas de decisão, o tomador de decisão pode considerar uma combinação de algumas ações. Por exemplo, no problema de Investimento, o investidor pode desejar distribuir os ativos entre uma mistura das opções de forma a otimizar o retorno do portfólio. Visite o site The Game Theory with Applications para criar uma estratégia mista ideal.
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Modelagem Probabilística: De Dados a um Conhecimento Decisivo.
Conhecimento é o que conhecemos bem. Informação é a comunicação do conhecimento. Em toda troca de conhecimento, existe um remetente e um receptor. O remetente faz comum o que é privado, informando, comunicando. As informações podem ser classificadas como formas explícitas e tácitas. A informação explícita pode ser explicada de forma estruturada, enquanto a informação tácita é inconsistente e confusa para explicar. Saiba que os dados são apenas informação grosseira e não conhecimento por si mesmos.
Os dados são conhecidos por serem informações brutas e não por si só. A seqüência de dados para conhecimento é: de dados para informações, de informações para fatos e, finalmente, de fatos para o conhecimento. Os dados tornam-se informações, quando se tornam relevantes para o seu problema de decisão. A informação torna-se factível, quando os dados podem suportá-lo. Fatos são o que os dados revelam. Contudo, o conhecimento instrumental decisivo (isto é, aplicado) é expresso em conjunto com algum grau estatístico de confiança.
Fato se torna conhecimento, quando é usado na conclusão bem-sucedida de um processo de decisão. Uma vez que você tenha uma enorme quantidade de fatos integrados como conhecimento, então sua mente será sobre-humana no mesmo sentido que a humanidade com a escrita é sobre-humana em comparação com a humanidade antes de escrever. A figura a seguir ilustra o processo de pensamento estatístico baseado em dados na construção de modelos estatísticos para tomada de decisão sob incertezas.
A figura acima mostra o fato de que, à medida que a exatidão de um modelo estatístico aumenta, o nível de melhorias na tomada de decisões aumenta. É por isso que precisamos de modelagem probabilística. A modelagem probabilística surgiu da necessidade de colocar conhecimento em uma base de evidências sistemáticas. Isso exigiu um estudo das leis da probabilidade, o desenvolvimento de medidas de propriedades e relações de dados e assim por diante.
A inferência estatística tem como objetivo determinar se qualquer significância estatística pode ser anexada aos resultados após a devida tolerância ser feita para qualquer variação aleatória como fonte de erro. Inferências inteligentes e críticas não podem ser feitas por aqueles que não entendem o propósito, as condições e a aplicabilidade das várias técnicas para julgar significância.
Conhecimento é mais do que saber algo técnico. O conhecimento precisa de sabedoria. Sabedoria é o poder de colocar nosso tempo e nosso conhecimento no uso adequado. A sabedoria vem com a idade e a experiência. Sabedoria é a aplicação precisa de conhecimento preciso e seu principal componente é conhecer os limites de seu conhecimento. Sabedoria é saber como algo técnico pode ser melhor usado para atender às necessidades do tomador de decisões. A sabedoria, por exemplo, cria um software estatístico que é útil, em vez de tecnicamente brilhante. Por exemplo, desde que a Web entrou na consciência popular, observadores notaram que ela coloca a informação na ponta dos dedos, mas tende a manter a sabedoria fora de alcance.
Considerando o ambiente incerto, a chance de que "boas decisões" sejam tomadas aumenta com a disponibilidade de "boas informações". A chance de que "boa informação" esteja disponível aumenta com o nível de estruturação do processo de Gestão do Conhecimento. Pode-se perguntar: "Qual é o uso de técnicas de análise de decisão sem a melhor informação disponível fornecida pela Gestão do Conhecimento?" A resposta é: não se pode tomar decisões responsáveis ​​até que se possua conhecimento suficiente. No entanto, para decisões privadas, pode-se confiar, por exemplo, nas motivações psicológicas, como discutido em "Tomada de decisão sob pura incerteza" neste site. Além disso, a Gestão do Conhecimento e a Análise de Decisões estão, de fato, inter-relacionadas, uma vez que uma influencia a outra, tanto no tempo quanto no espaço. A noção de "sabedoria" no sentido da sabedoria prática entrou na civilização ocidental através de textos bíblicos. Na experiência helênica, esse tipo de sabedoria recebeu um caráter mais estrutural na forma de filosofia. Nesse sentido, a filosofia também reflete uma das expressões da sabedoria tradicional.
Tomar decisões é, sem dúvida, a tarefa mais importante de um gerente e muitas vezes é muito difícil. Este site oferece um procedimento de tomada de decisão para resolver problemas complexos passo a passo.
O processo de tomada de decisão: Ao contrário do processo de tomada de decisão determinista, no processo de tomada de decisão sob incerteza, as variáveis ​​são muitas vezes mais numerosas e mais difíceis de medir e controlar. No entanto, os passos são os mesmos. Eles são: Simplificação Construindo um modelo de decisão Testando o modelo Usando o modelo para encontrar a solução É uma representação simplificada da situação atual Ela não precisa ser completa ou exata em todos os aspectos Concentra-se nos relacionamentos mais essenciais e ignora os menos essenciais . É mais facilmente compreendido do que a situação empírica e, portanto, permite que o problema seja resolvido mais prontamente com tempo e esforço mínimos. Pode ser usado repetidas vezes para problemas semelhantes ou pode ser modificado.
Felizmente, os métodos probabilísticos e estatísticos para análise e tomada de decisão sob incerteza são mais numerosos e poderosos hoje do que antes. O computador possibilita muitas aplicações práticas. Alguns exemplos de aplicativos de negócios são os seguintes: Um auditor pode usar técnicas de amostragem aleatória para auditar a conta a receber pelo cliente. Um gerente de fábrica pode usar técnicas estatísticas de controle de qualidade para garantir a qualidade de sua produção com um mínimo de testes ou inspeção. Um analista financeiro pode usar a regressão e a correlação para ajudar a entender o relacionamento de um índice financeiro com um conjunto de outras variáveis ​​nos negócios. Um pesquisador de mercado pode usar teste de significância para aceitar ou rejeitar as hipóteses sobre um grupo de compradores para o qual a empresa deseja vender um determinado produto. Um gerente de vendas pode usar técnicas estatísticas para prever as vendas para o próximo ano.
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Análise de Decisão: Tomar Decisões Justificáveis ​​e Defensíveis.
Os humanos podem entender, comparar e manipular números. Portanto, para criar um modelo de análise de decisão, é necessário criar a estrutura do modelo e atribuir probabilidades e valores para preencher o modelo de cálculo. Isso inclui os valores de probabilidades, as funções de valor para avaliar alternativas, as ponderações de valor para medir os objetivos de compensação e a preferência de risco.
Uma vez que a estrutura e os números estejam no lugar, a análise pode começar. A análise de decisão envolve muito mais do que calcular a utilidade esperada de cada alternativa. Se parássemos ali, os tomadores de decisão não teriam muita percepção. Temos que examinar a sensibilidade dos resultados, a utilidade ponderada para as principais probabilidades e os parâmetros de preferência de peso e risco. Como parte da análise de sensibilidade, podemos calcular o valor da informação perfeita para incertezas que foram cuidadosamente modeladas.
Existem duas comparações quantitativas adicionais. A primeira é a comparação direta da utilidade ponderada para duas alternativas em todos os objetivos. A segunda é a comparação de todas as alternativas em quaisquer dois objetivos selecionados, o que mostra a otimização de Pareto para esses dois objetivos.
A complexidade no mundo moderno, juntamente com a quantidade de informações, a incerteza e o risco, torna necessário fornecer uma estrutura racional de tomada de decisão. O objetivo da análise de decisão é fornecer orientação, informação, percepção e estrutura para o processo de tomada de decisão, a fim de tomar decisões melhores e mais "racionais".
Uma decisão precisa de um tomador de decisões responsável por tomar decisões. Este tomador de decisão tem várias alternativas e deve escolher uma delas. O objetivo do decisor é escolher a melhor alternativa. Quando esta decisão for tomada, os eventos sobre os quais o tomador de decisão não tem controle podem ter ocorrido. Cada combinação de alternativas, seguida por um evento acontecendo, leva a um resultado com algum valor mensurável. Os gerentes tomam decisões em situações complexas. Árvore de decisão e matrizes de pagamento ilustram essas situações e adicionam estrutura aos problemas de decisão.
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Forman E., e M. Selly, Decisão por Objetivos: Como convencer os outros de que você está certo, World Scientific, 2001.
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Elementos de Modelos de Análise de Decisão.
Os modelos matemáticos e técnicas consideradas na análise de decisão estão preocupados com teorias prescritivas de escolha (ação). Isso responde à questão de exatamente como um tomador de decisões deve se comportar quando confrontado com uma escolha entre as ações que têm resultados regidos pelo acaso ou as ações dos concorrentes.
A análise de decisão é um processo que permite ao decisor selecionar pelo menos e no máximo uma opção a partir de um conjunto de possíveis alternativas de decisão. Deve haver incerteza quanto ao futuro, juntamente com o objetivo de otimizar o resultado (retorno) resultante em termos de algum critério numérico de decisão.
Os elementos dos problemas de análise de decisão são os seguintes:
Um único indivíduo é designado como o decisor. Por exemplo, o CEO de uma empresa, que é responsável perante os acionistas.
Um número finito de eventos possíveis (futuros) chamados de 'Estados da Natureza' (um conjunto de cenários possíveis). São as circunstâncias em que uma decisão é tomada. Os estados da natureza são identificados e agrupados no conjunto "S"; seus membros são denotados por "s (j)". Set S é uma coleção de eventos mutuamente exclusivos, o que significa que apenas um estado de natureza ocorrerá. Um número finito de possíveis alternativas de decisão (ou seja, ações) está disponível para o tomador de decisão. Apenas uma ação pode ser tomada. O que eu posso fazer? Uma boa decisão requer buscar um conjunto melhor de alternativas do que aquelas que são inicialmente apresentadas ou tradicionalmente aceitas. Seja breve na parte de lógica e razão da sua decisão. Embora existam provavelmente milhares de fatos sobre um automóvel, você não precisa deles todos para tomar uma decisão. Cerca de meia dúzia vai fazer. Payoff é o retorno de uma decisão. Diferentes combinações de decisões e estados da natureza (incerteza) geram payoffs diferentes. Os payoffs são geralmente mostrados em tabelas. Na análise de decisão, o retorno é representado pelo valor positivo (+) da receita líquida, receita ou lucro e valor negativo (-) para despesa, custo ou prejuízo líquido. A análise da tabela de payoff determina as alternativas de decisão usando diferentes critérios. Linhas e colunas recebem possíveis alternativas de decisão e possíveis estados de natureza, respectivamente.
Construir tal matriz geralmente não é uma tarefa fácil; portanto, pode levar um pouco de prática.
Fonte de erros na tomada de decisões: As principais fontes de erros em problemas de tomada de decisão arriscados são: falsas suposições, não ter uma estimativa precisa das probabilidades, confiar em expectativas, dificuldades em medir a função de utilidade e prever erros.
Considere o seguinte exemplo de tomada de decisão de investimento:
O Exemplo de Tomada de Decisão de Investimento:
Os estados da natureza são os estados de economia durante um ano. O problema é decidir qual ação tomar entre três possíveis cursos de ação com as taxas de retorno dadas, conforme mostrado no corpo da tabela.
Borden T. e W. Banta, (Ed.), Usando indicadores de desempenho para orientar a tomada de decisão estratégica, Jossey-Bass Pub., 1994.
Eilon S., A Arte do Reconhecimento: Análise dos Critérios de Desempenho, Academic Press, 1984.
Von Furstenberg G., Agindo sob Incerteza: Concepções Multidisciplinares, Kluwer Academic Publishers, 1990.
Lidar com Incertezas.
Há algumas descrições satisfatórias de incerteza, uma das quais é o conceito e a álgebra da probabilidade.
Tomar decisões empresariais sérias é enfrentar um futuro em que a ignorância e a incerteza dominam cada vez mais o conhecimento, à medida que o horizonte de planejamento recua à distância. As deficiências sobre o nosso conhecimento do futuro podem ser divididas em três domínios, cada um com limites bastante obscuros: Risco: Pode-se ser capaz de enumerar os resultados e calcular as probabilidades. No entanto, é preciso procurar por distribuições não-normais, especialmente aqueles com “fat tails”, como ilustrado no mercado de ações pelos eventos raros. Incerteza: pode-se enumerar os resultados, mas as probabilidades são obscuras. Na maioria das vezes, o melhor que se pode fazer é dar uma ordem de classificação aos possíveis resultados e, então, ter cuidado para não omitir nenhum de seus significados. Cisnes Negros: O nome vem de uma anomalia genética australiana. Este é o domínio de eventos que são extremamente improváveis ​​ou inconcebíveis, mas quando eles acontecem, e eles acontecem, eles têm sérias conseqüências, geralmente ruins. Um exemplo do primeiro tipo é o derramamento de óleo do Exxon Valdez, do segundo, o acidente de radiação em Three Mile Island.
De fato, todos os sistemas altamente artificiais, como grandes redes de comunicação, estações geradoras elétricas movidas a energia nuclear e espaçonaves, estão cheios de caminhos ocultos para o fracasso, tão numerosos que não podemos pensar em todos eles, ou não. capaz de arcar com o tempo e o dinheiro necessários para testá-los e eliminá-los. Individualmente, cada um desses caminhos é um cisne negro, mas há muitos deles que a probabilidade de um deles ser ativado é bastante significativa. Ao tomar decisões de negócios, estamos amplamente preocupados com o domínio do risco e geralmente assumimos que as probabilidades seguem distribuições normais. No entanto, devemos nos preocupar com os três domínios e ter uma mente aberta sobre a forma das distribuições.
Continuum de pura incerteza e certeza: O domínio dos modelos de análise de decisão situa-se entre dois casos extremos. Isso depende do grau de conhecimento que temos sobre o resultado de nossas ações, conforme mostrado abaixo:
Um "polo" nessa escala é determinístico, como o problema do carpinteiro. O "pólo" oposto é pura incerteza. Entre esses dois extremos estão os problemas sob risco. A idéia principal aqui é que, para qualquer problema, o grau de certeza varia entre os gerentes, dependendo de quanto conhecimento cada um deles tem sobre o mesmo problema. Isso reflete a recomendação de uma solução diferente para cada pessoa.
Probabilidade é um instrumento usado para medir a probabilidade de ocorrência de um evento. When you use probability to express your uncertainty, the deterministic side has a probability of 1 (or zero), while the other end has a flat (all equally probable) probability. For example, if you are certain of the occurrence (or non-occurrence) of an event, you use the probability of one (or zero). If you are uncertain, and would use the expression "I really don't know," the event may or may not occur with a probability of 50%. This is the Bayesian notion that probability assessment is always subjective. That is, the probability always depends upon how much the decision maker knows. If someone knows all there is to know, then the probability will diverge either to 1 or 0.
The decision situations with flat uncertainty have the largest risk. For simplicity, consider a case where there are only two outcomes, with one having a probability of p. Thus, the variation in the states of nature is p(1-p). The largest variation occurs if we set p = 50%, given each outcome an equal chance. In such a case, the quality of information is at its lowest level. Remember from your Statistics course that the quality of information and variation are inversely related . That is, larger variation in data implies lower quality data (i. e. information).
Relevant information and knowledge used to solve a decision problem sharpens our flat probability . Useful information moves the location of a problem from the pure uncertain "pole" towards the deterministic "pole".
Probability assessment is nothing more than the quantification of uncertainty. In other words, quantification of uncertainty allows for the communication of uncertainty between persons. There can be uncertainties regarding events, states of the world, beliefs, and so on. Probability is the tool for both communicating uncertainty and managing it (taming chance).
There are different types of decision models that help to analyze the different scenarios. Depending on the amount and degree of knowledge we have, the three most widely used types are:
Decision-making under pure uncertainty Decision-making under risk Decision-making by buying information (pushing the problem towards the deterministic "pole")
In decision-making under pure uncertainty, the decision maker has absolutely no knowledge, not even about the likelihood of occurrence for any state of nature. In such situations, the decision-maker's behavior is purely based on his/her attitude toward the unknown . Some of these behaviors are optimistic, pessimistic, and least regret, among others. The most optimistic person I ever met was undoubtedly a young artist in Paris who, without a franc in his pocket, went into a swanky restaurant and ate dozens of oysters in hopes of finding a pearl to pay the bill.
Optimist: The glass is half-full.
Pessimist: The glass is half-empty.
Manager: The glass is twice as large as it needs to be.
Or, as in the follwoing metaphor of a captain in a rough sea:
The pessimist complains about the wind;
the optimist expects it to change;
the realist adjusts the sails.
Optimists are right; so are the pessimists. It is up to you to choose which you will be. The optimist sees opportunity in every problem; the pessimist sees problem in every opportunity.
Both optimists and pessimists contribute to our society. The optimist invents the airplane and the pessimist the parachute.
Whenever the decision maker has some knowledge regarding the states of nature, he/she may be able to assign subjective probability for the occurrence of each state of nature. By doing so, the problem is then classified as decision making under risk.
In many cases, the decision-maker may need an expert's judgment to sharpen his/her uncertainties with respect to the likelihood of each state of nature. In such a case, the decision-maker may buy the expert's relevant knowledge in order to make a better decision. The procedure used to incorporate the expert's advice with the decision maker's probabilities assessment is known as the Bayesian approach.
For example, in an investment decision-making situation, one is faced with the following question: What will the state of the economy be next year? Suppose we limit the possibilities to Growth (G), Same (S), or Decline (D). Then, a typical representation of our uncertainty could be depicted as follows:
Howson C., and P. Urbach, Scientific Reasoning: The Bayesian Approach , Open Court Publ., Chicago, 1993.
Gheorghe A., Decision Processes in Dynamic Probabilistic Systems , Kluwer Academic, 1990.
Kouvelis P., and G. Yu, Robust Discrete Optimization and its Applications, Kluwer Academic Publishers, 1997. Provides a comprehensive discussion of motivation for sources of uncertainty in decision process, and a good discussion on minmax regret and its advantages over other criteria.
Decision Making Under Pure Uncertainty.
Personality Types and Decision Making:
Pessimism , or Conservative (MaxMin). Worse case scenario. Bad things always happen to me.
Optimism , or Aggressive (MaxMax). Good things always happen to me.
Coefficient of Optimism (Hurwicz's Index) , Middle of the road: I am neither too optimistic nor too pessimistic.
a) Choose an a between 0 & 1, 1 means optimistic and 0 means pessimistic,
b) Choose largest and smallest # for each action,
c) Multiply largest payoff (row-wise) by a and the smallest by (1- a ),
d) Pick action with largest sum.
For example, for a = 0.7, we have.
Minimize Regret: (Savag's Opportunity Loss) I hate regrets and therefore I have to minimize my regrets. My decision should be made so that it is worth repeating. I should only do those things that I feel I could happily repeat. This reduces the chance that the outcome will make me feel regretful, or disappointed, or that it will be an unpleasant surprise.
Regret is the payoff on what would have been the best decision in the circumstances minus the payoff for the actual decision in the circumstances. Therefore, the first step is to setup the regret table:
a) Take the largest number in each states of nature column (say, L).
b) Subtract all the numbers in that state of nature column from it (i. e. L - Xi, j).
c) Choose maximum number of each action.
d) Choose minimum number from step (d) and take that action.
You may try checking your computations using Decision Making Under Pure Uncertainty JavaScript, and then performing some numerical experimentation for a deeper understanding of the concepts.
Limitations of Decision Making under Pure Uncertainty.
Decision analysis in general assumes that the decision-maker faces a decision problem where he or she must choose at least and at most one option from a set of options. In some cases this limitation can be overcome by formulating the decision making under uncertainty as a zero-sum two-person game.
In decision making under pure uncertainty, the decision-maker has no knowledge regarding which state of nature is "most likely" to happen. He or she is probabilistically ignorant concerning the state of nature therefore he or she cannot be optimistic or pessimistic. In such a case, the decision-maker invokes consideration of security.
Notice that any technique used in decision making under pure uncertainties, is appropriate only for the private life decisions . Moreover, the public person (i. e., you, the manager) has to have some knowledge of the state of nature in order to predict the probabilities of the various states of nature. Otherwise, the decision-maker is not capable of making a reasonable and defensible decision.
You might try to use Decision Making Under Uncertainty JavaScript E-lab for checking your computation, performing numerical experimentation for a deeper understanding, and stability analysis of your decision by altering the problem's parameters.
Biswas T., Decision Making Under Uncertainty , St. Martin's Press, 1997.
Driver M., K. Brousseau, and Ph. Hunsaker, The Dynamic Decisionmaker: Five Decision Styles for Executive and Business Success , Harper & Row, 1990.
Eiser J., Attitudes and Decisions , Routledge, 1988.
Flin R., et al., (Ed.), Decision Making Under Stress: Emerging Themes and Applications , Ashgate Pub., 1997.
Ghemawat P., Commitment: The Dynamic of Strategy , Maxwell Macmillan Int., 1991.
Goodwin P., and G. Wright, Decision Analysis for Management Judgment , Wiley, 1998.
Decision Making Under Risk.
The problem is defined and all feasible alternatives are considered. The possible outcomes for each alternative are evaluated. Outcomes are discussed based on their monetary payoffs or net gain in reference to assets or time. Various uncertainties are quantified in terms of probabilities. The quality of the optimal strategy depends upon the quality of the judgments. The decision-maker should identify and examine the sensitivity of the optimal strategy with respect to the crucial factors.
Whenever the decision maker has some knowledge regarding the states of nature, he/she may be able to assign subjective probability estimates for the occurrence of each state. In such cases, the problem is classified as decision making under risk. The decision-maker is able to assign probabilities based on the occurrence of the states of nature. The decision making under risk process is as follows:
a) Use the information you have to assign your beliefs (called subjective probabilities) regarding each state of the nature, p(s),
b) Each action has a payoff associated with each of the states of nature X(a, s),
c) We compute the expected payoff, also called the return (R), for each action R(a) = Sums of [X(a, s) p(s)],
d) We accept the principle that we should minimize (or maximize) the expected payoff,
e) Execute the action which minimizes (or maximize) R(a).
Expected Payoff: The actual outcome will not equal the expected value. What you get is not what you expect, i. e. the "Great Expectations!"
a) For each action, multiply the probability and payoff and then,
b) Add up the results by row,
c) Choose largest number and take that action.
The Most Probable States of Nature (good for non-repetitive decisions)
a) Take the state of nature with the highest probability (subjectively break any ties),
b) In that column, choose action with greatest payoff.
In our numerical example, there is a 40% chance of growth so we must buy stocks.
Expected Opportunity Loss (EOL):
a) Setup a loss payoff matrix by taking largest number in each state of nature column(say L), and subtract all numbers in that column from it, L - Xij,
b) For each action, multiply the probability and loss then add up for each action,
c) Choose the action with smallest EOL.
Computation of the Expected Value of Perfect Information (EVPI)
EVPI helps to determine the worth of an insider who possesses perfect information. Recall that EVPI = EOL.
a) Take the maximum payoff for each state of nature,
b) Multiply each case by the probability for that state of nature and then add them up,
c) Subtract the expected payoff from the number obtained in step (b)
Therefore, EVPI = 10.8 - Expected Payoff = 10.8 - 9.5 = 1.3. Verify that EOL=EVPI.
The efficiency of the perfect information is defined as 100 [EVPI/(Expected Payoff)]%
Therefore, if the information costs more than 1.3% of investment, don't buy it. For example, if you are going to invest $100,000, the maximum you should pay for the information is [100,000 * (1.3%)] = $1,300.
I Know Nothing: (the Laplace equal likelihood principle) Every state of nature has an equal likelihood. Since I don't know anything about the nature, every state of nature is equally likely to occur:
a) For each state of nature, use an equal probability (i. e., a Flat Probability),
b) Multiply each number by the probability,
c) Add action rows and put the sum in the Expected Payoff column,
d) Choose largest number in step (c) and perform that action.
A Discussion on Expected Opportunity Loss (Expected Regret): Comparing a decision outcome to its alternatives appears to be an important component of decision-making. One important factor is the emotion of regret. This occurs when a decision outcome is compared to the outcome that would have taken place had a different decision been made. This is in contrast to disappointment, which results from comparing one outcome to another as a result of the same decision. Accordingly, large contrasts with counterfactual results have a disproportionate influence on decision making.
Regret results compare a decision outcome with what might have been. Therefore, it depends upon the feedback available to decision makers as to which outcome the alternative option would have yielded. Altering the potential for regret by manipulating uncertainty resolution reveals that the decision-making behavior that appears to be risk averse can actually be attributed to regret aversion.
There is some indication that regret may be related to the distinction between acts and omissions. Some studies have found that regret is more intense following an action, than an omission. For example, in one study, participants concluded that a decision maker who switched stock funds from one company to another and lost money, would feel more regret than another decision maker who decided against switching the stock funds but also lost money. People usually assigned a higher value to an inferior outcome when it resulted from an act rather than from an omission. Presumably, this is as a way of counteracting the regret that could have resulted from the act.
You might like to use Making Risky Decisions JavaScript E-lab for checking your computation, performing numerical experimentation for a deeper understanding, and stability analysis of your decision by altering the problem's parameters.
Beroggi G., Decision Modeling in Policy Management: An Introduction to the Analytic Concepts , Boston, Kluwer Academic Publishers, 1999.
George Ch., Decision Making Under Uncertainty: An Applied Statistics Approach , Praeger Pub., 1991.
Rowe W., An Anatomy of Risk , R. E. Krieger Pub. Co., 1988.
Suijs J., Cooperative Decision-Making Under Risk , Kluwer Academic, 1999.
Making a Better Decision by Buying Reliable Information (Bayesian Approach)
The probabilities of the states of nature represent the decision-maker's (e. g. manager) degree of uncertainties and personal judgment on the occurrence of each state. We will refer to these subjective probability assessments as 'prior' probabilities.
The expected payoff for each action is:
A1= 0.2(3000) + 0.5(2000) + 0.3(-6000)= $ -200 and A2= 0;
so the company chooses A2 because of the expected loss associated with A1, and decides not to develop.
However, the manager is hesitant about this decision. Based on "nothing ventured, nothing gained" the company is thinking about seeking help from a marketing research firm. The marketing research firm will assess the size of the product's market by means of a survey.
Now the manager is faced with a new decision to make; which marketing research company should he/she consult? The manager has to make a decision as to how 'reliable' the consulting firm is. By sampling and then reviewing the past performance of the consultant, we can develop the following reliability matrix :
All marketing research firms keep records (i. e., historical data) of the performance of their past predictions. These records are available to their clients free of charge. To construct a reliability matrix, you must consider the marketing research firm's performance records for similar products with high sales. Then, find the percentage of which products the marketing research firm correctly predicted would have high sales (A), medium sales (B), and little (C) or almost no sales. Their percentages are presented by.
in the first column of the above table, respectively. Similar analysis should be conducted to construct the remaining columns of the reliability matrix.
Note that for consistency, the entries in each column of the above reliability matrix should add up to one. While this matrix provides the conditional probabilities such as P(A p |A) = 0.8, the important information the company needs is the reverse form of these conditional probabilities. In this example, what is the numerical value of P(A|A p )? That is, what is the chance that the marketing firm predicts A is going to happen, and A actually will happen? This important information can be obtained by applying the Bayes Law (from your probability and statistics course) as follows:
a) Take probabilities and multiply them "down" in the above matrix,
b) Add the rows across to get the sum,
c) Normalize the values (i. e. making probabilities adding up to 1) by dividing each column number by the sum of the row found in Step b,
You might like to use Computational Aspect of Bayse' Revised Probability JavaScript E-lab for checking your computation, performing numerical experimentation for a deeper understanding, and stability analysis of your decision by altering the problem's parameters.
d) Draw the decision tree. Many managerial problems, such as this example, involve a sequence of decisions . When a decision situation requires a series of decisions, the payoff table cannot accommodate the multiple layers of decision-making. Thus, a decision tree is needed.
Do not gather useless information that cannot change a decision: A question for you: In a game a player is presented two envelopes containing money. He is told that one envelope contains twice as much money as the other envelope, but he does not know which one contains the larger amount. The player then may pick one envelope at will, and after he has made a decision, he is offered to exchange his envelope with the other envelope.
If the player is allowed to see what's inside the envelope he has selected at first, should the player swap, that is, exchange the envelopes?
The outcome of a good decision may not be good, therefor one must not confuse the quality of the outcome with the quality of the decision.
As Seneca put it "When the words are clear, then the thought will be also".
Decision Tree and Influence Diagram.
You may imagine driving your car; starting at the foot of the decision tree and moving to the right along the branches. At each square you have control, to make a decision and then turn the wheel of your car. At each circle , Lady Fortuna takes over the wheel and you are powerless.
Here is a step-by-step description of how to build a decision tree: Draw the decision tree using squares to represent decisions and circles to represent uncertainty, Evaluate the decision tree to make sure all possible outcomes are included, Calculate the tree values working from the right side back to the left, Calculate the values of uncertain outcome nodes by multiplying the value of the outcomes by their probability (i. e., expected values).
On the tree, the value of a node can be calculated when we have the values for all the nodes following it. The value for a choice node is the largest value of all nodes immediately following it. The value of a chance node is the expected value of the nodes following that node, using the probability of the arcs. By rolling the tree backward, from its branches toward its root, you can compute the value of all nodes including the root of the tree. Putting these numerical results on the decision tree results in the following graph:
A Typical Decision Tree.
Click on the image to enlarge it.
Determine the best decision for the tree by starting at its root and going forward.
Based on proceeding decision tree, our decision is as follows:
Hire the consultant, and then wait for the consultant's report.
If the report predicts either high or medium sales, then go ahead and manufacture the product.
Otherwise, do not manufacture the product.
Check the consultant's efficiency rate by computing the following ratio:
(Expected payoff using consultant dollars amount) / EVPI.
Using the decision tree, the expected payoff if we hire the consultant is:
EP = 1000 - 500 = 500,
EVPI = .2(3000) + .5(2000) + .3(0) = 1600.
Therefore, the efficiency of this consultant is: 500/1600 = 31%
If the manager wishes to rely solely on the marketing research firm's recommendations , then we assign flat prior probability [as opposed to (0.2, 0.5, 0.3) used in our numerical example].
Clearly the manufacturer is concerned with measuring the risk of the above decision, based on decision tree.
Coefficient of Variation as Risk Measuring Tool and Decision Procedure: Based on the above decision, and its decision-tree, one might develop a coefficient of variation (C. V) risk-tree, as depicted below:
Coefficient of Variation as a Risk Measuring Tool and Decision Procedure.
Click on the image to enlarge it.
Notice that the above risk-tree is extracted from the decision tree, with C. V. numerical value at the nodes relevant to the recommended decision. For example the consultant fee is already subtracted from the payoffs.
From the above risk-tree, we notice that this consulting firm is likely (with probability 0.53) to recommend Bp (a medium sales), and if you decide to manufacture the product then the resulting coefficient of variation is very high (403%), compared with the other branch of the tree (i. e., 251%).
Clearly one must not consider only one consulting firm, rather one must consider several potential consulting during decision-making planning stage. The risk decision tree then is a necessary tool to construct for each consulting firm in order to measure and compare to arrive at the final decision for implementation.
The Impact of Prior Probability and Reliability Matrix on Your Decision: To study how important your prior knowledge and/or the accuracy of the expected information from the consultant in your decision our numerical example, I suggest redoing the above numerical example in performing some numerical sensitivity analysis. You may start with the following extreme and interesting cases by using this JavaScript for the needed computation: Consider a flat prior, without changing the reliability matrix. Consider a perfect reliability matrix (i. e., with an identity matrix), without changing the prior. Consider a perfect prior, without changing the reliability matrix. Consider a flat reliability matrix (i. e., with all equal elements), without changing the prior. Consider the consultant prediction probabilities as your own prior, without changing the reliability matrix.
Influence diagrams: As can be seen in the decision tree examples, the branch and node description of sequential decision problems often become very complicated. At times it is downright difficult to draw the tree in such a manner that preserves the relationships that actually drive the decision. The need to maintain validation, and the rapid increase in complexity that often arises from the liberal use of recursive structures, have rendered the decision process difficult to describe to others. The reason for this complexity is that the actual computational mechanism used to analyze the tree, is embodied directly within the trees and branches. The probabilities and values required to calculate the expected value of the following branch are explicitly defined at each node.
Influence diagrams are also used for the development of decision models and as an alternate graphical representations of decision trees. The following figure depicts an influence diagram for our numerical example.
In the influence diagram above, the decision nodes and chance nodes are similarly illustrated with squares and circles. Arcs (arrows) imply relationships, including probabilistic ones.
Finally, decision tree and influence diagram provide effective methods of decision-making because they: Clearly lay out the problem so that all options can be challenged Allow us to analyze fully the possible consequences of a decision Provide a framework to quantify the values of outcomes and the probabilities of achieving them Help us to make the best decisions on the basis of existing information and best guesses.
Bazerman M., Judgment in Managerial Decision Making , Wiley, 1993.
Connolly T., H. Arkes, and K. Hammond (eds), Judgment and Decision Making: An Interdisciplinary Reader , Cambridge University Press, 2000.
Cooke R., Experts in Uncertainty , Oxford Univ Press, 1991. Describes much of the history of the expert judgment problem. It also includes many of the methods that have been suggested to do numerical combination of expert uncertainties. Furthermore, it promotes a method that has been used extensively by us and many others, in which experts are given a weighting that judge their performance on calibration questions. This is a good way of getting around the problem of assessing the "quality" of an expert, and lends a degree of objectivity to the results that is not obtained by other methods.
Bouyssou D., et al. , Evaluation and Decision Models: A Critical Perspective , Kluwer Academic Pub, 2000.
Daellenbach H., Systems and Decision Making: A Management Science Approach , Wiley, 1994.
Goodwin P., and G. Wright, Decision Analysis for Management Judgment , Wiley, 1998.
Klein D., Decision-Analytic Intelligent Systems: Automated Explanation and Knowledge Acquisition , Lawrence Erlbaum Pub., 1994.
Thierauf R., Creative Computer Software for Strategic Thinking and Decision Making: A Guide for Senior Management and MIS Professionals , Quorum Books, 1993.
Why Managers Seek the Advice From Consulting Firms.
Work they are not -- or feel they are not В — competent to do themselves.
Work they do not want to do themselves.
Work they do not have time to do themselves.
All such work falls under the broad umbrella of consulting service. Regardless of why managers pay others to advise them, they typically have high expectations concerning the quality of the recommendations, measured in terms of reliability and cost . However, the manager is solely responsible for the final decision he/she is making and not the consultants.
The following figure depicts the process of the optimal information determination. For more details, read the Cost/Benefit Analysis.
The Determination of the Optimal Information.
Deciding about the Consulting Firm: Each time you are thinking of hiring a consultant you may face the danger of looking foolish, not to mention losing thousands or even millions of dollars. To make matters worse, most of the consulting industry's tried-and-true firms have recently merged, split, disappeared, reappeared, or reconfigured at least once.
How can you be sure to choose the right consultants?
Test the consultant's knowledge of your product. It is imperative to find out the depth of a prospective consultant's knowledge about your particular product and its potential market. Ask the consultant to provide a generic project plan, task list, or other documentation about your product.
Is there an approved budget and duration?
What potential customers' involvement is expected?
Who is expected to provide the final advice and provide sign-off?
Even the best consultants are likely to have some less-than-successful moments in their work history. Conducting the reliability analysis process is essential. Ask specific questions about the consultants' past projects, proud moments, and failed efforts. Of course it's important to check a potential consultant's references. Ask for specific referrals from as many previous clients or firms with similar businesses to yours. Get a clearly written contract, accurate cost estimates, the survey statistical sample size, and the commitment on the completion and written advice on time.
Holtz H., The Complete Guide to Consulting Contracts: How to Understand, Draft, and Negotiate Contracts and Agreements that Work , Dearborn Trade, 1997.
Weinberg G., Secrets of Consulting: A Guide to Giving and Getting Advice Successfully , Dorset House, 1986.
Revising Your Expectation and its Risk.
Application: Suppose the following information is available from two independent sources:
The combined expected value is:
The combined variance is:
For our application, using the above tabular information, the combined estimate of expected sales is 83.15 units with combined variance of 65.77, having 9.6% risk value.
You may like using Revising the Mean and Variance JavaScript to performing some numerical experimentation. You may apply it for validating the above example and for a deeper understanding of the concept where more than 2-sources of information are to be combined.
Determination of the Decision-Maker's Utility Function.
Individuals pay insurance premiums to avoid the possibility of financial loss associated with an undesirable event occurring. However, utilities of different outcomes are not directly proportional to their monetary consequences. If the loss is considered to be relatively large, an individual is more likely to opt to pay an associated premium. If an individual considers the loss inconsequential, it is less likely the individual will choose to pay the associated premium.
Individuals differ in their attitudes towards risk and these differences will influence their choices. Therefore, individuals should make the same decision each time relative to the perceived risk in similar situations. This does not mean that all individuals would assess the same amount of risk to similar situations. Further, due to the financial stability of an individual, two individuals facing the same situation may react differently but still behave rationally. An individual's differences of opinion and interpretation of policies can also produce differences.
The expected monetary reward associated with various decisions may be unreasonable for the following two important reasons:
1. Dollar value may not truly express the personal value of the outcome. This is what motivates some people to play the lottery for $1.
2. Expected monetary values may not accurately reflect risk aversion. For example, suppose you have a choice of between getting $10 dollars for doing nothing, or participating in a gamble. The gamble's outcome depends on the toss of a fair coin. If the coin comes up heads, you get $1000. However, if it is tails, you take a $950 loss.
The first alternative has an expected reward of $10, the second has an expected reward of.
0.5(1000) + 0.5(- 950) = $25. Clearly, the second choice is preferred to the first if expected monetary reward were a reasonable criterion. But, you may prefer a sure $10 to running the risk of losing $950.
Why do some people buy insurance and others do not? The decision-making process involves psychological and economical factors, among others. The utility concept is an attempt to measure the usefulness of money for the individual decision maker. It is measured in 'Utile'. The utility concept enables us to explain why, for example, some people buy one dollar lotto tickets to win a million dollars. For these people 1,000,000 ($1) is less than ($1,000,000). These people value the chance to win $1,000,000 more than the value of the $1 to play. Therefore, in order to make a sound decision considering the decision-maker's attitude towards risk, one must translate the monetary payoff matrix into the utility matrix. The main question is: how do we measure the utility function for a specific decision maker?
Consider our Investment Decision Problem. What would the utility of $12 be?
a) Assign 100 utils and zero utils to the largest and smallest ($) payoff, respectively in the payoff matrix. For our numerical example, we assign 100 utils to 15, and 0 utils to -2,
b) Ask the decision maker to choose between the following two scenarios:
1) Get $12 for doing nothing (called, the certainty equivalent , the difference between a decision maker's certainty equivalent and the expected monetary value is called the risk premium .)
2) Play the following game: win $15 with probability (p) OR -$2 with probability (1-p), where p is a selected number between 0 and 1.
By changing the value of p and repeating a similar question, there exists a value for p at which the decision maker is indifferent between the two scenarios. Say, p = 0.58.
c) Now, the utility for $12 is equal to.
d) Repeat the same process to find the utilities for each element of the payoff matrix. Suppose we find the following utility matrix:
At this point, you may apply any of the previously discussed techniques to this utility matrix (instead of monetary) in order to make a satisfactory decision. Clearly, the decision could be different.
Notice that any technique used in decision making with utility matrix is indeed very subjective ; therefore it is more appropriate only for the private life decisions.
You may like to check your computations using Determination of Utility Function JavaScript, and then perform some numerical experimentation for a deeper understanding of the concepts.
Utility Function Representations with Applications.
The aim is to represent the functional relationship between the entries of monetary matrix and the utility matrix outcome obtained earlier. You may ask what is a function?
What is a function? A function is a thing that does something. For example, a coffee grinding machine is a function that transforms the coffee beans into powder. A utility function translates (converts) the input domain (monetary values) into output range, with the two end-values of 0 and 100 utiles. In other words, a utility function determines the degrees of the decision-maker sensible preferences.
This chapter presents a general process for determining utility function . The presentation is in the context of the previous chapter's numerical results, although there are repeated data therein.
Utility Function Representations with Applications: There are three different methods of representing a function: The Tabular, Graphical, and Mathematical representation. The selection of one method over another depends on the mathematical skill of the decision-maker to understand and use it easily. The three methods are evolutionary in their construction process, respectively; therefore, one may proceed to the next method if needed.
The utility function is often used to predict the utility of the decision-maker for a given monetary value. The prediction scope and precision increases form the tabular method to the mathematical method.
Tabular Representation of the Utility Function: We can tabulate the pair of data (D, U) using the entries of the matrix representing the monetary values (D) and their corresponding utiles (U) from the utility matrix obtained already. The Tabular Form of the utility function for our numerical example is given by the following paired (D, U) table:
Utility Function (U) of the Monetary Variable (D) in Tabular Form.
Tabular Representation of the Utility Function for the Numerical Example.
As you see, the tabular representation is limited to the numerical values within the table. Suppose one wishes to obtain the utility of a dollar value, say $10. One may apply an interpolation method: however since the utility function is almost always non-linear; the interpolated result does not represent the utility of the decision maker accurately. To overcome this difficulty, one may use the graphical method.
Graphical Representation of the Utility Function: We can draw a curve using a scatter diagram obtained by plotting the Tabular Form on a graph paper. Having the scatter diagram, first we need to decide on the shape of the utility function. The utility graph is characterized by its properties of being smooth, continuous, and an increasing curve . Often a parabola shape function fits well for relatively narrow domain values of D variable. For wider domains, one may fit few piece-wise parabola functions, one for each appropriate sub-domain.
For our numerical example, the following is a graph of the function over the interval used in modeling the utility function, plotted with its associated utility (U-axis) and the associated Dollar values (D-axis). Note that in the scatter diagram the multiple points are depicted by small circles.
Graphical Representation of the Utility Function for the Numerical Example.
The graphical representation has a big advantage over the tabular representation in that one may read the utility of dollar values say $10, directly from the graph, as shown on the above graph, for our numerical example. The result is U = 40, approximately. Reading a value from a graph is not convenient; therefore, for prediction proposes, a mathematical model serves best.
Mathematical Representation of the Utility Function: We can construct a mathematical model for the utility function using the shape of utility function obtained by its representation by Graphical Method. Often a parabola shape function fits well for relatively narrow domain values of D variable. For wider domains, one may fit a few piece-wise parabola functions, one for each appropriate sub-domain.
We know that we want a quadratic function that best fits the scatter diagram that has already been constructed. Therefore, we use a regression analysis to estimate the coefficients in the function that is the best fit to the pairs of data (D, U).
Parabola models: Parabola regressions have three coefficients with a general form:
where Dbar is the mean of D i 's.
For our numerical example i = 1, 2. 12. By evaluating these coefficients using the information given in tabular form section, the "best" fit is characterized by its coefficients estimated values: c = 0.291, b = 1.323, and a = 0.227. The result is; therefore, a utility function approximated by the following quadratic function:
U = 0.291D 2 + 1.323D + 0.227, for all D such that -2 £ D £ 15
The above mathematical representation provides more useful information than the other two methods. For example, by taking the derivative of the function provides the marginal value of the utility; i. e.,
Marginal Utility = 1.323 + 0.582D, for all D such that -2 < D < 15
Notice that for this numerical example, the marginal utility is an increasing function, because variable D has a positive coefficient; therefore, one is able to classify this decision - maker as a mild risk-taker.
You might like to use Quadratic Regression JavaScript to check your hand computation. For higher degrees than quadratic, you may like to use the Polynomial Regressions JavaScript.
A Classification of Decision Maker's Relative Attitudes Toward Risk and Its Impact.
Decision theory does not describe what people actually do since there are difficulties with both computations of probability and the utility of an outcome. Decisions can also be affected by people's subjective rationality and by the way in which a decision problem is perceived.
Traditionally, the expected value of random variables has been used as a major aid to quantify the amount of risk. However, the expected value is not necessarily a good measure alone by which to make decisions since it blurs the distinction between probability and severity. To demonstrate this, consider the following example:
Suppose that a person must make a choice between scenarios 1 and 2 below:
Scenario 1: There is a 50% chance of a loss of $50, and a 50% chance of no loss.
Scenario 2: There is a 1% chance of a loss of $2,500, and a 99% chance of no loss.
Both scenarios result in an expected loss of $25, but this does not reflect the fact that the second scenario might be considered to be much more risky than the first. (Of course, this is a subjective assessment). The decision maker may be more concerned about minimizing the effect of the occurrence of an extreme event than he/she is concerned about the mean. The following charts depict the complexity of probability of an event and the impact of the occurrence of the event, and its related risk indicator, respectively:
From the previous section, you may recall that the certainty equivalent is the risk free payoff. Moreover, the difference between a decision maker's certainty equivalent and the expected monetary value (EMV) is called the risk premium. We may use the sign and the magnitude of the risk premium in classification of a decision maker's relative attitude toward risk as follows:
If the risk premium is positive, then the decision maker is willing to take the risk and the decision maker is said to be a risk seeker . Clearly, some people are more risk-accepting than others: the larger is the risk premium, the more risk-accepting the decision-maker.
If the risk premium is negative, then the decision-maker would avoid taking the risk and the decision maker is said to be risk averse .
If the risk premium is zero, then the decision maker is said to be risk neutral .
Buying Insurance: As we have noticed, often it is not probability, but expectation that acts a measuring tool and decision-guide. Many decision cases are similar to the following: The probability of a fire in your neighborhood may be very small. But, if it occurred, the cost to you could be very great. Not only property but also your "dear ones", so the negative expectation of not ensuring against fire is so much greater than the cost of premium than ensuring is the best.
Christensen C., The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail , Harvard Business School Publishing, 1997.
Eilon S., The Art of Reckoning: Analysis of Performance Criteria , Academic Press, 1984.
Hammond J., R. Keeney, and H. Raiffa, Smart Choices: A Practical Guide to Making Better Decisions , Harvard Business School Press., 1999.
Richter M., and K. Wong, Computable preference and utility, Journal of Mathematical Economics , 32(3), 339-354, 1999.
Tummala V., Decision Analysis with Business Applications , Educational Publishers, 1973.
The Discovery and Management of Losses.
A rare or unexpected event with potentially significant consequences for decision-making could be conceived as a risk or an opportunity. The main concerns are: How to predict, identify or explain chance events and their consequences? How to assess, prepare for or manage them?
A decision-maker who is engaged in planning, needs to adopt a view for the future, in order to decide goals, and to decide the best sequence of actions to achieve these goals by forecasting their consequences. Unfortunately, the unlikeness of such events makes them difficult to predict or explain by methods that use historical data. However, focusing on the decision-maker's psychological-attitude factors and its environment is mostly relevant.
The following figure provides a classification of the loss frequency function together with the ranges for the Expected, Unexpected, and the Stress, which must be determined by the decision-makers ability and resources.
The manager's ability to discover both unexpected and stress loss events and forecast their consequences is the major task. This is because, these event are very unlikely, therefore making them difficult to predict or explain. However, once a rare event has been identified, the main concern is its consequences for the organization. A good manager cannot ignore these events, as their consequences are significant. For example, although strong earthquakes occur in major urban centers only rarely such earthquakes tend to have human and economic consequences well beyond that of the typical tremor. A rational public safety body for a city in an earthquake-prone area would plan for such contingencies even though the chance of a strong quake is still very small.
Belluck D., and S. Benjamin, A Practical Guide to Understanding, Managing and Reviewing Risk Assessment Reports , CRC Press, 1999.
Koller G., Risk Assessment and Decision Making in Business and Industry: A Practical Guide , CRC Press, 1999.
Hoffman D., Managing Operational Risk: 20 Firmwide Best Practice Strategies , Wiley, 2002.
Van Asselt M., Perspectives on Uncertainty and Risk: The Prima Approach to Decision Support , Kluwer Academic Publishers, 2000.
Risk Assessment & Coping Strategies:
How Good Is Your Decision?
Considering our earlier Investment Decision-Making Example:
The states of nature are the states of economy during, an arbitrary time frame, as in one year.
The expected value (i. e., the averages) is defined by:
Expected Value = m = S X i . P i , the sum is over all i's.
The expected value alone is not a good indication of a quality decision. The variance must be known so that an educated decision may be made. Have you ever heard the dilemma of the six-foot tall statistician who drowned in a stream that had an average depth of three feet?
In the investment example, it is also interesting to compare the 'risk' between alternative courses of action. A measure of risk is generally reported by variation, or its square root called standard deviation. Variation or standard deviation are numerical values that indicate the variability inherent to your decision. For risk, smaller values indicate that what you expect is likely to be what you get. Therefore, risk must also be used when you want to compare alternate courses of action. What we desire is a large expected return, with small risk. Thus, high risk makes a manager very worried.
Variance : An important measure of risk is variance which is defined by:
Variance = s 2 = S [X i 2 . P i ] - m 2 , the sum is over all i's.
Since the variance is a measure of risk, therefore, the greater the variance, the higher the risk. The variance is not expressed in the same units as the expected value. So, the variance is hard to understand and explain as a result of the squared term in its computation. This can be alleviated by working with the square root of the variance which is called the Standard Deviation :
Standard Deviation = s = (Variance) ВЅ.
Both variance and standard deviation provide the same information and, therefore, one can always be obtained from the other. In other words, the process of computing standard deviation always involves computing the variance. Since standard deviation is the square root of the variance, it is always expressed in the same units as the expected value.
For the dynamic decision process, the Volatility as a measure for risk includes the time period over which the standard deviation is computed. The Volatility measure is defined as standard deviation divided by the square root of the time duration.
What should you do if the course of action with the larger expected outcome also has a much higher risk? In such cases, using another measure of risk known as the Coefficient of Variation is appropriate.
Coefficient of Variation (CV) is the relative risk, with respect to the expected value, which is defined as:
Coefficient of Variation (CV) is the absolute relative deviation with respect to size provided is not zero, expressed in percentage:
Notice that the CV is independent from the expected value measurement. The coefficient of variation demonstrates the relationship between standard deviation and expected value, by expressing the risk as a percentage of the (non-zero) expected value. The inverse of CV (namely 1/CV) is called the Signal-to-Noise Ratio.
The quality of your decision may be computed by using Measuring Risk.
The following table shows the risk measurements computed for the Investment Decision Example:
The Risk Assessment columns in the above table indicate that bonds are much less risky than the stocks, while its return is lower. Clearly, deposits are risk free.
Now, the final question is: Given all this relevant information, what action do you take? It is all up to you.
The following table shows the risk measurements computed for the Investment Decision under pure uncertainty (i. e., the Laplace equal likelihood principle):
The Risk Assessment columns in the above table indicate that bonds are much less risky than the stocks. Clearly, deposits are risk free.
Again, the final question is: Given all this relevant information, what action do you take? It is all up to you.
Ranking Process for Preference among Alternatives: Referring to the Bonds and Stocks alternatives in our numerical example, we notice that based in mean-variance, the Bonds alternative Dominates the Stocks alternative. However this is not always the case.
For example, consider two independent investment alternatives: Investment I and Investment II with the characteristics outlined in the following table:
Performance of Two Investments.
To rank these two investments under the Standard Dominance Approach in Finance , first we must compute the mean and standard deviation and then analyze the results. Using the above Applet for calculation, we notice that the Investment I has mean = 6.75% and standard deviation = 3.9%, while the second investment has mean = 5.36% and standard deviation = 2.06%. First observe that under the usual mean-variance analysis, these two investments cannot be ranked. This is because the first investment has the greater mean; it also has the greater standard deviation. Therefore, the Standard Dominance Approach is not a useful tool here. We have to resort to the coefficient of variation as a systematic basis of comparison. The C. V. for Investment I is 57.74% and for investment II is 38.43%. Therefore, Investment II has preference over the other one. Clearly, this approach can be used to rank any number of alternative investments.
Application of Signal-to-Noise Ratio In Investment Decisions: Suppose you have several portfolios, which are almost uncorrelated (i. e., all paired-wise covariance's are almost equal to zero), then one may distributed the total capital among all portfolios proportional to their signal-to-noise ratios.
Consider the above two independent investments with the given probabilistic rate of returns. Given you wish to invest $12,000 over a period of one year, how do you invest for the optimal strategy?
The C. V. for Investment-I is 57.74% and for investment-II is 38.43%, therefore signal-to-noise ratio are 1/55.74 = 0.0179 and 1/38.43 = 0.0260, respectively.
Now, one may distribute the total capital ($12000) proportional to the Beta values:
Sum of signal-to-noise ratios = 0.0179 + 0.0260 = 0.0439.
Y1 = 12000 (0.0179 / 0.0439) = 12000(0.4077) = $4892, Allocating to the investment-I.
Y2 = 12000 (0.0260 / 0.0439) = 12000(0.5923) = $7108, Allocating to the investment-II.
That is, the optimal strategic decision based upon the signal-to-noise ratio criterion is: Allocate $4892 and $7108 to the investment-I and investment-II, respectively.
These kinds of mixed-strategies are known as diversifications that aim at reducing your risky.
The quality of your decision may be computed by using Performance Measures for Portfolios.
Copping with Risk Risk avoidance is refusing to undertake an activity where the risk seems too costly. Risk prevention (loss control) is using various methods to reduce the possibility of a loss occurring. Risk transfer is shifting a risk to someone outside your company. Risk assumption or self-insurance is setting aside funds to meet losses that are uncertain in size and frequency. Risk reduction by, for example, diversifications.
Crouhy M., R. Mark, and D. Galai, Risk Managemen t, McGraw-Hill, 2002.
Koller G., Risk Modeling for Determining Value and Decision Making , Chapman & Hall/CRC, 2000.
Moore P., The Business of Risk , Cambridge University Press, 1984.
Morgan M., and M. Henrion, Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , Cambridge University Press, 1998.
Shapira Z., Risk Taking: A Managerial Perspective , Russell Sage Foundation, 1997.
Vose D., Risk Analysis: A Quantitative Guide , John Wiley & Sons, 2000.
Wahlstrom B., Models, Modeling And Modellers: An Application to Risk Analysis, European Journal of Operations Research , Vol. 75, No. 3, 477-487, 1994.
Decision's Factors-Prioritization & Stability Analysis.
Steps in Sensitivity Analysis: Begin with consideration of a nominal base-case situation, using the expected values for each input. Calculate the base-case output. Consider a series of "what-if" questions, to determine by how much the output would deviate from this nominal level if input values deviated from their expected values. Each input is changed by several percentage points above and below its expected value, and the expected payoff is recalculated. The set of expected payoff is plotted against the variable that was changed. The steeper the slope (i. e., derivative) of the resulting line, the more sensitive the expected payoff is to a change in the variable.
Scenario Analysis: Scenario analysis is a risk analysis technique that considers both the sensitivity of expected payoff to changes in key variables and the likely range of variable values. The worst and best "reasonable" sets of circumstances are considered and the expected payoff for each is calculated, and compared to the expected, or base-case output.
Scenario analysis also includes the chance events , which could be rare or novel events with potentially significant consequences for decision-making in some domain. The main issues in studying the chance events are the following:
Chance Discovery: How may we predict, identify, or explain chance events and their consequences?
Chance Management: How may we assess, prepare for, or manage them?
Clearly, both scenario and sensitivity analysis can be carried out using computerized algorithms.
How Stable is Your Decision? Stability Analysis compares the outcome of each your scenarios with chance events. Computer packages such as WinQSB, are necessary and useful tools. They can be used to examine the decision for stability and sensitivity whenever there is uncertainty in the payoffs and/or in assigning probabilities to the decision analysis.
Prioritization of Uncontrollable Factors: Stability analysis also provides critical model inputs. The simplest test for sensitivity is whether or not the optimal decision changes when an uncertainty factor is set to its extreme value while holding all other variables unchanged. If the decision does not change, the uncertainty can be regarded as relatively less important than for the other factors. Sensitivity analysis focuses on the factors with the greatest impact, thus helping to prioritize data gathering while increasing the reliability of information.
Optimal Decision Making Process.
A mathematical optimization model consists of an objective function and a set of constraints expressed in the form of a system of equations or inequalities. Optimization models are used extensively in almost all areas of decision-making such as financial portfolio selection.
Integer Linear optimization Application: Suppose you invest in project (i) by buying an integral number of shares in that project, with each share costing C i and returning R i . If we let X i denotes the number of shares of project (i) that are purchased, then the decision problem is to find nonnegative integer decision variables X 1 , X 2,В…, X n --- when one can invest at most M in the n project --- is to:
Application: Suppose you have 25 to invest among three projects whose estimated cost per share and estimated return per share values are as follows:
Using any linear integer programming software package, the optimal strategy is X 1 = 2, X 2 = 0, and X 3 = 1 with $36 as its optimal return.
JavaScript E-labs Learning Objects.
Each JavaScript in this collection is deigned to assisting you in performing numerical experimentation , for at least a couple of hours as students do in, e. g. Physics labs. These leaning objects are your statistics e-labs. These serve as learning tools for a deeper understanding of the fundamental statistical concepts and techniques, by asking "what-if" questions.
Technical Details and Applications: At the end of each JavaScript you will find a link under "For Technical Details and Applications Back to:".
Decision Making in Economics and Finance: ABC Inventory Classification -- an analysis of a range of items, such as finished products or customers into three "importance" categories: A, B, and C as a basis for a control scheme. This pageconstructs an empirical cumulative distribution function (ECDF) as a measuring tool and decision procedure for the ABC inventory classification. Inventory Control Models -- Given the costs of holding stock, placing an order, and running short of stock, this page optimizes decision parameters (order point, order quantity, etc.) using four models: Classical, Shortages Permitted , Production & Consumption, Production & Consumption with Shortages. Optimal Age for Replacement -- Given yearly figures for resale value and running costs, this page calculates the replacement optimal age and average cost. Single-period Inventory Analysis -- computes the optimal inventory level over a single cycle, from up-to-28 pairs of (number of possible item to sell, and their associated non-zero probabilities), together with the "not sold unit batch cost", and the "net profit of a batch sold". Probabilistic Modeling: Bayes' Revised Probability -- computes the posterior probabilities to "sharpen" your uncertainties by incorporating an expert judgement's reliability matrix with your prior probability vector. Can accommodate up to nine states of nature. Decision Making Under Uncertainty -- Enter up-to-6x6 payoff matrix of decision alternatives (choices) by states of nature, along with a coefficient of optimism; the page will calculate Action & Payoff for Pessimism, Optimism, Middle-of-the-Road, Minimize Regret, and Insufficient Reason. Determination of Utility Function -- Takes two monetary values and their known utility, and calculates the utility of another amount, under two different strategies: certain & uncertain. Making Risky Decisions -- Enter up-to-6x6 payoff matrix of decision alternatives (choices) by states of nature, along with subjective estimates of occurrence probability for each states of nature; the page will calculate action & payoff (expected, and for most likely event), min expected regret , return of perfect information, value of perfect information, and efficiency. Multinomial Distributions -- for up to 36 probabilities and associated outcomes, calculates expected value, variance, SD, and CV. Revising the Mean and the Variance -- to combine subjectivity and evidence-based estimates. Takes up to 14 pairs of means and variances; calculates combined estimates of mean, variance, and CV. Subjective Assessment of Estimates -- (relative precision as a measuring tool for inaccuracy assessment among estimates), tests the claim that at least one estimate is away from the parameter by more than r times (i. e., a relative precision), where r is a subjective positive number less than one. Takes up-to-10 sample estimates, and a subjective relative precision (r<1); the page indicates whether at least one measurement is unacceptable. Subjectivity in Hypothesis Testing -- Takes the profit/loss measure of various correct or incorrect conclusions regarding the hypothesis, along with probabilities of Type I and II errors (alpha & beta), total sampling cost, and subjective estimate of probability that null hypothesis is true; returns the expected net profit. Time Series Analysis and Forecasting Autoregressive Time Series -- tools for the identification, estimation, and forecasting based on autoregressive order obtained from a time series. Detecting Trend & Autocrrelation in Time Series -- Given a set of numbers, this page tests for trend by Sign Test, and for autocorrelation by Durbin-Watson test. Plot of a Time Series -- generates a graph of a time series with up to 144 points. Seasonal Index -- Calculates a set of seasonal index values from a set of values forming a time series. A related page performs a Test for Seasonality on the index values. Forecasting by Smoothing -- Given a set of numbers forming a time series, this page estimates the next number, using Moving Avg & Exponential Smoothing, Weighted Moving Avg, and Double & Triple Exponential Smoothing, &and Holt's method Runs Test for Random Fluctuations -- in a time series. Test for Stationary Time Series -- Given a set of numbers forming a time series, this page calculates the mean & variance of the first & second half, and calculates one-lag-apart & two-lag-apart autocorrelations. A related page: Time Series' Statistics calculates these statistics, and also the overall mean & variance, and the first & second partial autocorrelations.
A Critical Panoramic View of Classical Decision Analysis.
The decision maker facing a pure uncertain decision has select at least and at most one option from all possible options.
This certainly limits its scope and its applications. You have already learned both decision analysis and linear programming. Now is the time to use the game theory concepts to link together these two seemingly different types of models to widen their scopes in solving more realistic decision-making problems.
The decision maker facing a risky decision has to rely on the expected value alone which is not a good indication of a quality decision. The variance must be known so that an educated decision might be made.
For example in investment portfolio selection, it is also necessary to compare the "risk" between alternative courses of action. A measure of risk is generally reported in finance textbooks by variation, or its square root called standard deviation. Variation or standard deviation is numerical values that indicate the variability inherent to your decision. For risk, smaller values indicate that what you expect is likely to be what you get. Therefore, risk must also be used in decision analysis process.
To combine the expected values and the associated risk one may use Coefficient of Variation (CV) as a measuring tool and decision process in decision analysis. As you know well, CV is the absolute relative deviation with respect to size provided is not zero, expressed in percentage:
CV =100 |S/expected value| %
Notice that the CV is independent from the expected value measurement. The coefficient of variation demonstrates the relationship between standard deviation and expected value, by expressing the risk as a percentage of the (non-zero) expected value. This dimension-less nice property of C. V. enables decision makers to compare and decide when facing several independent decision with different measurement of the payoff matrices (such as dollar, yen, etc).
Analytical Hierarchy Process: One may realize the dilemma of analytical hierarchy process whether it can truly handle the real-life situations when one takes into account the "theoretical" difficulties in using eigenvectors (versus, for example, geometrical means) and other related issue to the issue of being able to pairwise-compare more than 10 alternatives extending the questionability to whether any person can/cannot set the nine-point scale without being biased - let alone becoming exhausted when you have 15 options/alternatives to consider with 20-30 measures and 10 people sitting in a room.
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Kindly e-mail me your comments, suggestions, and concerns. Obrigado.
This site was launched on 2/25/1994, and its intellectual materials have been thoroughly revised on a yearly basis. The current version is the 9 th Edition. All external links are checked once a month.

Interação entre opções reais e hedge financeiro: fato ou ficção na tomada de decisões gerenciais.
Este estudo empírico sobre a gestão da exposição cambial das empresas dinamarquesas não financeiras mostra que as decisões sobre a proteção financeira ou não de uma exposição cambial são afetadas pela possibilidade de reagir às mudanças nas taxas de câmbio através da realização de várias ações reais (exercício opções reais) tais como entrar em novos mercados, mudar fornecedores, mudar de produção, entre outros. Os resultados mostram que a interação combinada do tamanho de uma empresa, exportações e subsidiárias estrangeiras, bem como a ênfase gerencial da empresa na flexibilidade, são importantes para o uso de opções reais. Essas descobertas são importantes para empresas em outros países com economias abertas.
Classificação JEL.
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business analytics (BA)
Streaming Analytics FAQ: What You Need to Know –SAS Institute Inc. Three Use Cases for Interactive Data Discovery and Predictive Analytics –SAS Institute Inc. See More.
Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making.
Staff Pick: The Rising Need for Analytics Accuracy.
Learn how LinkedIn overcame analytics bottlenecks, 3 data modeling flaws that cripple data science projects, and common roadblocks of advancing data science teams and hiring data pros.
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BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples.
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
The second area of business analytics involves deeper statistical analysis. This may mean doing predictive analytics by applying statistical algorithms to historical data to make a prediction about future performance of a product, service or website design change. Or, it could mean using other advanced analytics techniques, like cluster analysis, to group customers based on similarities across several data points. This can be helpful in targeted marketing campaigns, for example.
Specific types of business analytics include:
Descriptive analytics, which tracks key performance indicators to understand the present state of a business; Predictive analytics, which analyzes trend data to assess the likelihood of future outcomes; and Prescriptive analytics, which uses past performance to generate recommendations about how to handle similar situations in the future.
While the two components of business analytics -- business intelligence and advanced analytics -- are sometimes used interchangeably, there are some key differences between these two business analytics techniques:
Business analytics vs. data science.
The more advanced areas of business analytics can start to resemble data science, but there is a distinction. Even when advanced statistical algorithms are applied to data sets, it doesn't necessarily mean data science is involved. There are a host of business analytics tools that can perform these kinds of functions automatically, requiring few of the special skills involved in data science.
True data science involves more custom coding and more open-ended questions. Data scientists generally don't set out to solve a specific question, as most business analysts do. Rather, they will explore data using advanced statistical methods and allow the features in the data to guide their analysis.
Business analytics applications.
Business analytics tools come in several different varieties:
Self-service has become a major trend among business analytics tools. Users now demand software that is easy to use and doesn't require specialized training. This has led to the rise of simple-to-use tools from companies such as Tableau and Qlik, among others. These tools can be installed on a single computer for small applications or in server environments for enterprise-wide deployments. Once they are up and running, business analysts and others with less specialized training can use them to generate reports, charts and web portals that track specific metrics in data sets.
Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, data cleansing and integration into a single repository, such as a data warehouse or data mart. The analysis is typically performed against a smaller sample set of data.
Analytics tools range from spreadsheets with statistical functions to complex data mining and predictive modeling applications. As patterns and relationships in the data are uncovered, new questions are asked, and the analytical process iterates until the business goal is met.
Deployment of predictive models involves scoring data records -- typically in a database -- and using the scores to optimize real-time decisions within applications and business processes. BA also supports tactical decision-making in response to unforeseen events. And, in many cases, the decision-making is automated to support real-time responses.
Próximos passos.
Expert Wayne Kernochan provides an overview of the different types of business intelligence analytics tools on the market.
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Descrições dos cursos.
ENGLISH LANGUAGE – CertTESOL.
1.132 Basic Linguistic Concepts.
1.232 English Language Teaching.
Introduces the principles of language teaching and their practical application in a language learning environment.
1.332 English Language Teaching Practice.
Develops practical skills and knowledge of language teaching and learning, through practical teaching experiences.
2.101 Accounting Principles.
An introduction to the fundamental aspects of financial accounting, including the preparation, presentation and interpretation of financial information within the context of making effective business decisions.
2.102 Management Principles.
An overview of fundamental theories and principles of management covering the four functions of management, their theory and practical implementation within an organisation. Analytical techniques and contemporary events required for practical applications are examined.
2.103 Marketing Principles.
An introduction to basic marketing principles and concepts for application in real-life situations that may arise in a marketing career both domestically and internationally.
2.111 Business Communication.
This course provides the theoretical framework and practical experience to improve communication skills in the business environment.
2.112 Information Technology Concepts.
An introduction to global business use of current and emerging information technologies to manage daily operations. The course focuses on placing information technology in the context of business, and provides hands-on experience with business software.
2.113 Business Economics.
An introduction to the fundamental principles of economics in business decision making and the role it plays in everyday life and government policy-making.
2.114 Business Law.
The fundamental aspects of law for business in New Zealand, including the principles of business law and the practical application of law in the business world. Topics covered include the basis of New Zealand law, the processes of government and business, the resolution of business disputes, the law of contract, consumer protection, and aspects of commercial law including the law of sale and goods and the law of business organisations.
2.115 Business Mathematics and Statistics.
This course equips students with the mathematical knowledge and skills required for subsequent study. It contains a common field and a specific field related to business studies.
2.120 English: Study Writing.
This course develops skills in academic reading and writing required for degree-level study.
2.211 Intercultural Communication.
An introduction to the concepts of intercultural communication, including the communication skills needed in intercultural business environments and the implications of intercultural business communication for domestic and international firms.
2.213 Managerial Economics.
This course integrates traditional economic theories with practical applications, and serves as a guide to future business planning, policy formulation and decision-making. Students develop an understanding of firm level economic theories, and develop skills in analysing economic issues using economic theoretical frameworks for improved decision-making.
2.218 Organisational Behaviour.
The development of an understanding of organisations as living entities and an exploration of how individual and group behaviour, as well as organisation structure, affects organisational performance.
2.222 Strategic Management.
An understanding of the basic concepts of strategic management, such as environment scanning, strategy formulation, strategy implementation and evaluation and control. The course focuses on the organisation as a whole and examines strategies that are relevant at different levels of hierarchy, develops skills in analysis and decision-making in dealing with complex conceptual problems.
2.223 Financial Management.
An introduction to financial management theories, concepts and techniques that provide the knowledge for analysing financial information and making sound financial management decisions that affect all functional business areas.
2.224 Marketing Management.
An introduction to the wide variety of planning tools used and skills required in managing the marketing function with particular focus on the systematic and logical application of planning tools to achieve optimum strategic outcomes.
2.225 Operations Management.
An introduction to the basic concepts of production and operations management, including the role of production and operations management in relation to other functional areas of the business. Students learn to analyse a range of production and operations management decision-making situations and apply the appropriate decision-making techniques.
2.226 Human Resource Management.
The conceptual, theoretical and contextual aspects of managing human resources in contemporary multinational organisations. The course creates awareness of the critical role played by human resources in the success of organisations, and examines issues of internationalisation, diversity and employment relations with particular reference to the Asia-Pacific region.
2.227 Retail Management.
An introduction to the wide variety of planning tools and skills required in managing retail operations, with particular focus on the systematic and logical application of planning tools to achieve optimum strategic outcomes.
2.231 Introduction to International Business.
An examination of the critical environmental factors in which an international business is conducted, including an introduction to a wide variety of concepts, issues and trends in the global business environment with particular emphasis on contemporary issues such as globalisation, outsourcing, and business ethics.
2.232 Consumer Behaviour.
An investigation of how people interact with products and their marketing environment, focusing on the decision-making process that consumers go through as they select, purchase, use or dispose of services, products, ideas or experiences in order to satisfy their needs and desires.
2.233 Business Statistics.
An introductory survey of the many applications of descriptive and inferential statistics. The course contains statistical tools and methods that are essential for analysing and interpreting economic information for planning, forecasting and decision-making in today’s competitive business world.
2.234 Macroeconomics.
An insight into the workings of the macro-economy, and how economic changes impact on households, firms and markets all at once. Particular emphasis is given to how macroeconomic variables interact and their impact on decision-making.
2.235 New Zealand Taxation.
An introduction to the New Zealand taxation system and practice, including an introduction to the historical, theoretical, conceptual and practical side of taxation and the New Zealand tax system.
2.236 Managerial Accounting.
A comprehensive introduction to the concepts and methodologies of management accounting that develops an understanding of the supporting function of management accounting in business decision-making.
2.237 Introduction to Financial Accounting.
This course aims to equip students with the knowledge to understand financial statements issued by companies, and introduces students to regulatory frameworks governing external financial reporting and issues associated with corporate financial reporting.
2.238 Financial Markets and Institutions.
An introduction to financial market operations, institutions and instruments, covering economic functions, financial market structure, different types of financial institutions, and financial instruments in money and capital markets.
2.239 Financial Accounting.
An introduction to the principles, concepts, and applications of financial accounting, covering accounting for investments, accounting for business combinations, issues arising from group financial reporting and other selected accounting issues.
2.261 Research Methods.
An overview of the knowledge and skills necessary for carrying out research in the business and social science areas. Students are introduced and encouraged to apply appropriate qualitative and quantitative methods to a research project of their own choosing.
2.265 E-Commerce for Managers.
The concepts of electronic commerce, its conduct and management, including an assessment of its major opportunities, limitations, issues and risks. Because e-commerce is an interdisciplinary topic, it is useful to aspiring managers in any functional area of business, and is therefore conducted from a managerial perspective.
2.266 Enterprise Resource Planning Solutions for Small Business.
The course is designed to apply the knowledge of e-commerce in exploring strategies for small businesses. Students will be able to develop, implement and review strategies of e-commerce in small businesses.
2.270 Management Information Systems.
The implications of information technology in industry and the role of information systems within companies. Students apply the knowledge gained in developing effective information systems strategy for an organisation.
2.330 International Relations in the Asia-Pacific Region.
An understanding of changing international relations in the Asia-Pacific region, including the role of major powers in the region as well as the relationships of smaller players such as New Zealand. The role of inter-government relations through formal organisations is also examined.
2.331 International Business.
An understanding of the global environment and strategies that multinational firms deploy to be effective in a complex and dynamic world. The course explains different approaches to how managers choose appropriate strategies that are consistent with the needs of philosophy and resources available, and equips students with a rigorous theoretical base, sound analytical skills and practical applications in international business operations.
2.332 International Marketing.
An analytical and strategic framework for international marketing, enabling students to define problems and issues that they may encounter in international marketing. It will develop a student’s own thinking for solutions rather than depending on prescriptive steps, and has a managerial perspective to enable students to identify opportunities, issues, and formulate and implement solutions.
2.333 International Trade.
This course provides a sound knowledge and understanding of international trade theories and applications, and some knowledge of international economics.
2.334 International Finance.
A systematic analysis of a representative range of analytical issues in international finance and investment against the background of global financial markets. The course examines the international monetary system and analyses exchange rates, their determinants and their relationship to significant economic factors, and applies investment, financing and dividend decisions of firms to an international setting.
2.335 International Management.
A systematic and logical application of management concepts and techniques to firms working in multi-national and multi-cultural environments. The challenges for international management that reflect dynamism and the increasing unpredictability of global events.
2.336 Research Project.
An independent research study, building on their research understanding and expertise developed in other courses to examine a specific business topic, issue or area. Students must demonstrate an understanding of the practical significance of the research project undertaken, and must explain the implications of the results for further research.
2.337 Advanced Financial Accounting.
This course examines selected issues in financial reporting and accounting from both theoretical and practical application perspectives, including current developments in financial reporting in New Zealand and overseas.
2.338 Auditing.
The main concepts of auditing and their application in the functional areas in a business organisation.
2.341 Entrepreneurship and Small Business.
Students develop and systematically apply an entrepreneurial approach to create a small business and exploit opportunities that may be commercially successful. The course focuses on managing the early growth of newly established businesses, and covers the needs of businesses.
2.343 Leadership.
An insight into various aspects of leadership. The course employs theoretical concepts and models from an international business perspective and is designed to help students to develop their own leadership potential in preparation for managerial roles.
2.344 Advanced Managerial Accounting.
The theoretical and practical foundation for the conception and application of performance measurement and management control systems in an organisation. Students will make use of theoretical concepts, paradigms and frameworks in actual cases and learn to use analytical and innovative thinking to determine solutions and recommendations to issues relating to performance management and control.
2.346 Development Economics.
An integration of traditional and modern economic development theories with practical integration. The main focus of this course is the analysis of the development process of developing countries and identifying the problems and barriers third world countries face in achieving developmental goals.
2.350 Business and Social Ethics.
An examination of different theoretical arguments that underpin the ethical issues in business organisations. The course covers the ethical challenges and dilemmas faced by different stakeholders, and other issues relating to social ethics that may have a bearing on business. The emphasis is on practical issues relating to ethics and preparing students to deal with ethical challenges in managerial roles.
2.355 Services Marketing.
An examination of contemporary issues in services marketing which includes managing and delivering quality services in a dynamic global environment. The role of multinationals and marketing of their global services. Case studies from different industries, such as banking, airlines and management consultancy, are used to enable students to appreciate the critical role of services marketing.
2.361 Applied Management.
Students are required to choose a new business idea, investigate all aspects of the new venture, and prepare a comprehensive business plan, including preparation of a research-based feasibility study, development of operational and marketing strategies, forecasting and budgeting, and presentation to potential investors. The project includes undertaking a consumer and trade survey, and the results are presented orally to a panel of teaching staff.
Prerequisites: 17 courses including all compulsory courses.
2.365 Applied E-Commerce.
Basic skills for designing, configuring and maintaining a website on the internet using open source Joomla as the web authoring software tool. Though not intended to transform students into programming or IT specialists, students will gain a thorough understanding through theory and practice of web-based architecture and associated technologies.
2.366 E-Marketing.
This course will equip students with the knowledge of how to harness the web and other digital technologies as effective marketing tools for organisations, and will give them the skills to make critical decisions to leverage the benefits of an integrated e-marketing strategy for a business.
2.367 Decision Support Systems.
The design, development and implementation of information technology-based systems that support managerial and professional work, including Communications-Driven and Group Decision Support Systems (GDSS), Data-Driven DSS, Model-Driven DSS, Document-Driven DSS, and Knowledge-Driven DSS.
TOURISM MANAGEMENT.
3.119 Principles of Tourism.
An introduction to the fundamentals and basic processes within the international tourism industry, including its meaning, development, components and dynamics that will enable each student to develop and an understanding of tourism consumer behaviour, tourism activities, the impacts of tourism, and the conditions necessary for sustainable tourism development to occur. The course examines the regulatory framework, and the trends, patterns and future of world tourism.
3.120 Tourism in New Zealand.
This course presents a systematic examination of international and domestic tourism in New Zealand and introduces the concept of sustainable development in relation to the tourism industry.
3.121 Economics for Tourism and Hospitality.
An introduction to the fundamental principles of economics in business decision-making and the role it plays in everyday life and government policy-making.
3.220 Tourism and Hospitality Regulations.
An overview of the legal system with emphasis on its impact on the tourism and hospitality business environment. The course examines the nature of law and legal process on a broad basis, their interactions with political, business, tourism and hospitality industries, and provides an understanding of tourism and hospitality regulations on an international and regional basis.
3.221 Tourism and Hospitality Marketing.
An introduction to the role of marketing in the hospitality, travel and tourism industries, covering consumer behaviour, strategic planning, market segmentation and use of the marketing mix, and the creation of a marketing plan.
3.222 Tourism and Hospitality Management.
An understanding of the scope of management in the tourism and hospitality sectors.
3.223 Human Relationships in Tourism and Hospitality.
A systematic framework for human resource management and planning, including the role and importance of communication within tourism and hospitality enterprises.
3.224 Impacts of Tourism.
An understanding of the main environmental, social, cultural, economic and financial impacts of tourism on host communities, and how to optimise the positive impacts and control or minimise adverse impacts.
3.225 Asia-Pacific Tourism.
The geography and cultures and their role in tourism of the Asia-Pacific region, examining and analysing the role of social-cultural, political and economic factors in shaping the nature of tourism in the Asia-Pacific region, and developing students' understanding of the region's political and economic environments influencing tourism.
3.226 Heritage Tourism Studies.
This course provides an understanding of the meaning, development and components of heritage tourism, and enables students to develop an understanding of the central issues of authenticity and interpretation. The course incorporates field trips to heritage attractions to help students understand the nature and challenges facing heritage tourism attractions.
3.301 Tourism Industry Practice.
This course offers a placement in a tourism establishment and the expectation of at least 140 hours of practical work place experience. This practical component is matched by the requirement to keep a detailed log of experiences and to use a systematic review process to analyse and provide a wider context for the experience. The analysis will include reviewing the strategic goals of the enterprise and evaluating various departments and legislation relating to the New Zealand tourism industry, and an assessment of different customer needs and the provision of services to satisfy those different requirements.
3.310 Applied Tourism Management Project.
This course requires students to choose a new business idea within the tourism or travel industry, investigate all aspects of the new venture, and prepare a comprehensive business plan including the preparation of a research-based feasibility study, development of operational and marketing strategies, forecasts and budgets, and presentation to potential investors. Students are required to present their results orally to a panel of teaching staff.
Prerequisites: 17 courses including all compulsory courses.
3.311 Events Management.
This course will provide students with knowledge and understanding of trends and event marketing, coordination of international, national, regional and local events, and successful management.
3.313 Entrepreneurship and Small Business for Tourism and Hospitality.
This course will help students develop and systematically apply an entrepreneurial approach to create a small business and exploit opportunities that may be commercially successful. It focuses on managing early growth of newly established businesses and covers the needs of businesses in the tourism industry with particular emphasis on the entrepreneurial environment of the Asia-Pacific region.
3.314 Travel and Air Transport Management.
This course is intended for students who plan to pursue careers as managers, executives or entrepreneurs in travel agencies, tour operations, airlines, airports, ground transportation, the cruise industry, transportation research and planning, consultancies and government.
3.315 Tourism and Hospitality Consumer Behaviour.
Develop an understanding of tourists' behavioural characteristics that underpin evolving tourism demand.
3.316 Ecotourism Management.
An introduction to ecotourism that will provide students with the meaning, development, planning and management of this concept, and how stakeholders may reap its benefit with minimum social and ecological impact.
3.317 Tourism Policy Planning and Development.
This course will provide an in-depth knowledge of tourism policy, planning and development, and various aspects of planning in New Zealand and the Asia-Pacific region. It will provide students with the ability to analyse the economic, socio-cultural, environmental and geographical factors that affect tourism, and how this knowledge can be used to provide appropriate plans for sustainable tourism development.
3.336 Research Project.
Students will undertake an independent research study in a topic of their interest in the field of travel and tourism, building on their research understanding and expertise developed in other courses to examine a specific topic, issue or area. Students must demonstrate an understanding of the practical significance of the research project undertaken, and must explain the implications of the results for further research.
BUSINESS ADMINISTRATION.
4.701 Financial Accounting and Analysis.
Principles of financial and management accounting, and how these principles are applied in a decision-making context. The practical applications of utilising accounting data are also examined.
4.702 Managing Information and Technology.
The relationship between information systems and corporate strategy, the understanding of how information systems enable radical change, and the interaction between information systems and company stakeholders.
4.703 World Economy and Money Markets.
The impact of economic policy on managerial decision-making, market structures and corporate performance.
4.704 Management Accounting and Analysis.
Types of financial resources available to international companies, including the external capital market, acquisition cost and utilisation of capital, taxation, investment, risk and financial policy.
4.705 Marketing Management.
A study of marketing concepts and principles, environmental and competitor analysis, strategic planning and strategy formulation, market segmentation, target marketing, market positioning and the marketing mix.
4.706 International Strategic Management.
Frameworks for defining the direction of the organisation over the long-term, the achievement of advantage through the configuration of its resources, and the flexibility required to meet the needs of changing environments and expectations.
4.707 Human Resource Management.
Key elements of HRM and the role of culture, training and development, and group management on policy and practice in domestic and international organisations.
4.708 International Business Law.
How laws regulate business activity, requirements for legal knowledge by managers, decision-making in the context of dynamic legal systems, international law and agreements, as well as associated international legal principles.
4.709 Operations Management.
The major design, operation and control problems of production and operations management in manufacturing and service organisations including product and service design, facilities, location and layout, materials management and forecasting, purchasing and inventory control.
4.710 Cross-Cultural Behaviour and Negotiation.
Cultural influences on work behaviour in the context of international business including individual and group behaviour, leadership, communication, motivation, influence, change and cross-cultural relationships.
4.711 Business Quantitative Methods.
An overview of the various quantitative techniques available to management and used in contemporary business settings.
4.712 Organisational Behaviour and Leadership.
Principles and practices in leadership, motivation, teamwork and relationship management across organisational and value chain boundaries.
4.713 Economic Decision-Making.
Key microeconomic theories and tools used in business decision-making such as market structures, elasticity concepts and pricing models across disciplines.
4.714 Global Enterprise and International Trade.
Concepts of globalisation and major influences on business operations such as government, law, culture, demographics, politics, economic systems, resources, and geography.
4.716 International Marketing.
The application of marketing principles in the international marketplace including market selection, penetration and exploitation, cultural adaptation in new product development and consumer differences.
4.718 Business Research Methods.
The research process in a business setting, including defining the problem, developing a theoretical framework, the tools and techniques for the collection and analysis of data, and presenting results.
4.719 Advanced Management of Information Systems.
Key concepts needed by senior management to manage in the information age and become knowledgeable participants in ICT-related decisions. The course builds on ideas introduced in module 4.702.
4.720 Business Strategy and Change Management.
A study of strategy and understanding strategic business units (SBU) covering firm's resources, capabilities, external market environment for SBUs, five forces framework, strategies for sustainable competitive advantage and key elements of managing change.
4.724 E-Business.
Bridging the knowledge gap that exists between the technical experts implementing e-commerce applications and management who make operational and strategic decisions about e-commerce technology.
4.725 Entrepreneurship/New Ventures.
The theories and principles of entrepreneurship and of the process of new venture creation.
4.727 Services Marketing.
Characteristics of service organisations in regard to capacity, supply and demand, Customer Relationship Management (CRM) practices, marketing communications and the growth of e-business.
4.728 Operations Strategy and Technology.
American and Japanese production approaches are compared and organisational capabilities, path dependencies, capacity strategies, organisational boundaries and the value chain including vertical integration and outsourcing, are covered.
4.729 Quality Management.
Kaizan, quality circles, QFD, SPC and lean logistics are considered which gives emphasis to the six sigma statistical approach and the DMAIC/Fork model.
4.740 Accounting Framework.
This module covers a number of key financial concepts, tools and techniques that assist managers make well-informed decisions for their organisations. There is a broad coverage of accounting processes including journal entries, general ledger, trial balance and preparation of financial statements. Emphasis is placed on budgeting and cost concepts. These concepts are then applied in mini-case studies and decision-making from a manager’s perspective. While each topic is introduced from a conceptual background, focus is on utilising accounting and other financial data in practical situations.
4.750 Strategic Marketing.
Key marketing strategy concepts and principles and details of managing strategy in different contexts.
4.751 Business Research and Analytics.
Students will develop skills in descriptive, predictive, and prescriptive data analyses and business information and techniques to support business decision-making. Reference will be made to recent trends in data mining and "big data" management issues.
4.754 Product Development and Brand Management.
Market research and forecasting, including the use of test marketing as well as the exercise of adequate financial controls, are studied in the context of increasingly competitive and global markets.
4.771 Managing in Highly Regulated Environments.
This module examines management in a highly regulated sector such as the health care delivery systems of New Zealand. The module spans funding models, service delivery approaches and the management implications for supervisory, managerial and governance roles in a a bicultural and evolving muliticultural society.
4.772 Legal and Ethical Issues for Practising Professionals.
This module examines the legal and ethical frameworks that regulate and underpin health care services in New Zealand. The module focuses on the responsibilities of organisations, managers and individual staff delivering health care services including: ethical considerations, registration requirements, interpersonal and intercultural communication, supervisory competencies and methods, planning and managing staff performance, and meeting employment law obligations such as occupational safety and health and non-discriminatory workplace practices.
4.773 Managing in Dynamic Socio-Economic Sectors.
This module examines management in the dynamic NZ aged care and health sector spanning: issues relating to organisation of appropriate activities, nutrition, overcoming obstacles to activity based care options and debated strategies for aged care delivery. The challenges facing aged care providers in the contemporary NZ context and likely future scenarios are explored.
4.782 International Supply Chain Management.
Management of financial contracts which can optimise supply chains at national and international levels are the focus of this module.
4.783 Project Management.
Tools and techniques used in project management, including computer software packages, key project management skills, leadership and team management, time management, environment and health issues, contingency planning and crisis management.
4.787 Events Operations Management.
This module considers the management of events operations for the planner and for the venue provider. Students enable to apply theories that relate to the events management sector by offering them the opportunity to undertake some operational management responsibilities through the planning and organisation of a real event. Students will manage the processes of event design, planning, delivery and evaluation whilst developing their professional skills through application and reflection .
4.790 Corporate and Business Finance.
Corporate financing and the decisions made by corporations, the management of risk and return, capital budgeting, capital markets, debt and equity financing and the maximisation of shareholder value, and the roles of financial managers and the CFO.
4.792 Financial Statement Analysis.
Financial analytical frameworks, advanced techniques for the evaluation of operating fund cycles, performance measurement and projecting finance requirements utilising forecasting tools.
4.794 Valuations and Investment Decision Analysis.
Tools and techniques for the valuation of assets in private and public companies, and issues in implementation in practice including marginal cost of capital, WACC, CAPM, beta benchmarking, DCF, and common errors in estimating free cash flows.
4.795 International Finance and Risk Management.
Financing international organisations in global capital markets requires an understanding of the international mobility of capital, differing taxation regimes and foreign exchange risks. The international monetary system, foreign exchange theory and markets, and foreign exchange risk management are covered.
4.797 Field Study.
The field study provides an opportunity for students to take learning out of the classroom into the real business world under faculty supervision. Students are expected to contact and interact with practitioners, researchers, venture/private equity investors and policy makers to address the important issues related to the project. Field studies are typically completed over a two-month period and culminate in a written report and presentation.
Prerequisites: At least 20 modules, including all required specialisation modules, and 4.706 International Strategic Management.
4.798 Internship.
The internship is designed for students interested in gaining hands-on experience of working in a business organisation and enables students to apply the learning of the classroom to real life problems of an organisation. Students will have an opportunity to work as an intern under a business practitioner/industry supervisor. The internship is typically completed over a three-month period and culminates in a written report and presentation.
Prerequisites: At least 20 modules, including all required specialisation modules, and 4.706 International Strategic Management.
4.799 Dissertation.
Students apply theory, research, and methodologies learned from coursework to an individual research project involving a significant problem or process with a focus on their specialisation of international business, finance, marketing, or operations and logistics. Prior to embarking on their research projects, students attend workshops on quantitative and qualitative research methods. A senior member of academic staff will supervise the project. The dissertation represents a significant proportion of the programme and is typically completed over a six-month period. The successful conclusion of the dissertation is an oral presentation of the project and its findings to staff and other interested graduate students.
Prerequisites: 20 compulsory modules in chosen specialisation.
HOSPITALITY MANAGEMENT.
5.101 Principles of Hospitality Management.
This course provides a thorough understanding of the essential fundamentals of the hospitality sector in order to provide an efficient hospitality service.
5.102 Food Production Operations.
An introduction to food and beverage operations in New Zealand, including the processes and practices involved in preparation and operation of a restaurant, café, takeaway or catering service.
5.103 Food and Beverage Service Operations.
How to acquire and apply correct the principles of service operation within a dining room and/or banquet environment.
5.104 Written and Oral Communication Skills for the Hospitality Industry.
This course provides a theoretical framework and practical experience as a basis for improving communication skills in the business environment.
5.105 Accounting and Finance for Hospitality and Tourism.
An introduction to accounting principles and practices as they apply to financial record keeping in the hospitality industry.
5.106 Business Environment for the Hospitality Industry.
An introduction to the principles and practices involved in managing, marketing, economic environment, entrepreneurship, political, social, cultural and environmental issues in the hospitality industry.
5.107 Reception and Front Office Management.
Understand the operation of front office and its inter-relationship with other departments in an accommodation establishment.
5.202 Food and Beverage Management.
This course will enable students to understand and develop their knowledge, skills and techniques related to the management of a food and beverage operation.
5.203 Accommodation Management.
This course will provide students with the knowledge and skills necessary for the efficient operation of the housekeeping department within an accommodation facility.
5.204 Hospitality and Tourism Strategic Management.
A comprehensive and managerial overview of strategic management in the hospitality industry, including relevant models, theories and hospitality practices in today's competitive international hospitality industry.
5.205 Hospitality Facilities Management and Design.
An introduction to the issues involved in the design and management of hospitality facilities.
5.301 Hospitality Industry Practice.
This course offers a placement to a hospitality establishment and the expectation of at least 140 hours practical work place experience. This practical component is matched by the requirement to keep a detailed log of experiences and then using a systematic review process to analyse and provide a wider context for the experience. The analysis will include reviewing the strategic goals of the enterprise and evaluating various departments and legislation relating to the hospitality industry, and an assessment of different guest needs and the provision of services to satisfy those different requirements. Students will work for 140 hours in the establishment and a contract of services between the student and the establishment will be provided including a job description and work hours. A presentation of their work experience will also be required.
Prerequisites: All compulsory Stage I and II BHM courses.
5.302 International Food and Beverage Management.
This course emphasises international cuisine production and management, and examines international hospitality companies, management practices and trends in the marketing and selling of food and beverage products and services.
5.303 An Integrated Approach to Hospitality Management.
This course brings together all the theoretical knowledge, analytical tools and the implementation skills previously covered in the programme, and applies them in an integrated manner to the task of managing a hospitality enterprise. It addresses issues of management efficiency, performance enhancement and analytical skills in workplace situations.
Prerequisites: All compulsory Stage I and II courses.
5.310 Applied Hospitality Management Project.
Students are required to choose a new business idea within the hospitality industry, investigate all aspects of the new venture, and prepare a comprehensive business plan including preparation of a research-based feasibility study, development of operational and marketing strategies, forecasts and budgets, and presentation to potential investors. Students are required to present their results orally to a panel of teaching staff.
Prerequisites: 17 courses including all compulsory courses.
5.312 Resorts and Hospitality Management.
This course provides an understanding of the management of resorts and hospitality enterprises, including current issues in the industry, service concepts and strategic planning.
5.336 Hospitality Management Research Project.
Students will undertake an independent research study in a topic of their interest in the field of hospitality management, building on their research understanding and expertise developed in other courses to examine a specific topic, issue or area. Students must demonstrate an understanding of the practical significance of the research project undertaken, and must explain the implications of the results for further research.
5.398 Hospitality Internship Project.
This course is designed for those seeking an opportunity to investigate the functioning of a hospitality enterprise through participative observation, and to correlate an applied management project using the knowledge gained in previous courses, and time in industry, to allow students to gain experience in analytical and applied research in the hospitality field. Students will work for at least 240 hours in a hospitality establishment to gain practical knowledge and experience in planning and production, dealing with customers, risk assessment, and planning the implementation of operations for daily events. Students can gain first-hand appreciation of research for management efficiency, improved performances and analytical skills in a workplace situation. Students will be required to keep a detailed log of their experiences and provide a systematic analytical framework for recording and analysing their observations.
Prerequisites: All compulsory Stage I and II GDHM courses.
TECNOLOGIA DA INFORMAÇÃO.
7.101 The Information Technology System.
A basic understanding of computer concepts and the components of information technology system, including system software, application software, hardware assembling, installation and testing, understanding IS security threats, and ways to protect, prevent and mitigate potential threats.
7.102 Business Communication.
This course provides students with a theoretical framework and practical experience as a basis for improving communication skills in the business environment.
7.103 Fundamentals of Computer Programming.
An introduction to the fundamental principles of computing logic and the development of problem solving skills using structured programming techniques. Students will acquire basic competence in the chosen programming language and will apply this language to simple tasks using good programming techniques.
7.104 Database Engineering I.
An introduction to the concepts and fundamentals of database system (DB) and database management system (DBMS) through MS SQL Server 2008. The course will enhance students' skills in the basic elements of database design and implementation, including data modelling, logical and physical database design, and structured querying language (SQL).
7.105 Computer Networks I.
An introduction to the concepts of basic networking technology, network monitoring, availability and security. Students will gain an understanding of the OSI model, and the functionalities and protocols involved in each layer.
7.106 Business Environment.
An introductory course that will enable students to understand today's business environment, both nationally and internationally, at a basic level.
7.107 Mathematics for Computing.
This course provides students with a foundation in the mathematical knowledge and skills relevant to their interests and subsequent years of study. It contains two components: a common field and a specific field related to IT.
7.201 Systems Analysis and Design.
This course provides the necessary knowledge and skills that an IT professional must have on how information technology systems are constructed, tested and assessed for quality in order to manage, develop or provide innovative business solutions. Systems analysis and design introduces systems development process concepts and activities, with a strong focus on understanding the problem and solution through modelling using Structured (SA&D) and OO methodologies.
7.202 System Testing.
An introduction to the concepts and principles of software validation and verification techniques that are normally involved in the software testing process, including industry standards and available tools. Software testing is one of the important components of quality assurance of products and services.
7.203 Computer Algorithms and Discrete Mathematics.
A study in the theory, concepts and application of widely-used computer algorithms and data structures, and other topics in discrete mathematics. Students will develop strong logic analysis and problem-solving skills, and will be able to analyse the complexity and performance of software application systems.
7.204 Computer Organisation.
An understanding of how a computer is organised, covering the elements and structure of a processor as well as programming a processor at an assembly language level. This course will enable a student to design an embedded processor system that controls intelligent devices.
7.205 Object Oriented Programming.
A skills development course that will enable students to gain the knowledge necessary to create advanced applications for the business environment using object-oriented programming concepts.
7.206 Desktop Applications Development.
An introduction to the design and construction of an object-oriented application system based on the Microsoft Framework Technology. The course will extend the designing and programming concepts delivered in the earlier courses into a completed application system with a focus on some known standard controls: quality, modularity and reusability principles. Multi-tiers system architecture, including user interface layer, business logic layer, data access layer and database layer, is included.
7.207 Software Architecture.
An introduction to the concepts and methods in architecting, analysing, evaluating and specifying architecture for a software system.
Prerequisites: Four Stage I courses.
7.208 Web Content Management Systems.
A study of the fundamentals of internet architecture and protocols focusing mainly on web technology concepts, web languages, scripting and GUI tools which are widely used for designing software applications (including multimedia) to run on the internet.
7.209 Information Systems Security.
This course focuses on the analysis of the complete internet security of an enterprise by providing an understanding of the business risks, threats, tools required to deal with threats, and the processes needed to build more secure systems and continually improve them.
7.210 Computer Networks II.
This course provides an up-to-date survey of developments in the field of computer networking and an understanding of the central problems that confront the design of a computer network, analysis of congestion control methodologies used in different transport technologies, and provision of solutions to achieve quality of service (QoS) to different applications in a computer network.
7.211 Network Infrastructure Design.
This course provides the knowledge and skills to plan, design, develop and manage computer network infrastructure for an organisation through an in-depth study of the underlying concepts and technologies of network design.
7.212 Business Processes and Improvement.
This course provides students with knowledge and skills in integrated business process analysis and modelling techniques for the implementation of information technology systems.
7.213 Operations Management.
This course provides students with knowledge and skills in decision areas of operations management: quality, capacity and aggregate planning, inventory and transportation supporting operations research models, including forecasting, queuing and simulation, linear programming and networks. Qualitative and quantitative issues are addressed.
7.214 Database Engineering II.
An introduction to an integrated study of the theory and the practice associated with transferring user requirements into effective database design, and transfer of design into a physical database with complete functionality and high performance applications. It is also to extend understanding of emerging DB technologies and architectures.
7.215 Call Centre Technology.
This course provides students with the knowledge required to function as a call centre customer support professional, and will help develop an understanding of the processes used for help-desk services and support from a technical and non-technical perspective. Students completing this course will be able to effectively use the most common customer support tools and technologies in the industry.
7.216 Call Centre Management.
An in-depth understanding of how to maximise personal, team and operational performance, whilst managing the fast-paced environment and pressures associated with the call centre environment.
7.217 Requirement Modelling.
This course builds business analysis skills and details the application of process proven techniques, such as use-case, business object-oriented modelling and the UML, and will facilitate the development of the necessary skills for gathering, modelling and documenting requirements in the context of business and information system scenarios.
7.218 Server Administration.
Learn how to build skill sets to successfully administer Windows Server 2008 systems. Skill sets include planning for server deployment and management, application and data provisioning, business continuity and high availability, and monitoring and maintaining administrative security on a network infrastructure.
7.219 E-Commerce for Managers.
The concepts of electronic commerce, its conduct and management, including an assessment of its major opportunities, limitations, issues and risks. Because e-commerce is an interdisciplinary topic, it is useful to aspiring managers in any functional area of business, and is therefore conducted from a managerial perspective.
7.220 Enterprise Resource Planning Solutions for Small Business.
Apply the knowledge of e-commerce in developing, implementing and reviewing strategies for small businesses.
7.221 Operating Systems.
The course aims to cover the core concepts of operating systems, equip students to study some of the concepts by deploying them in a simulated environment and enable them to install, deploy and configure operating systems.
7.222 Intermediate Computer Networking.
This course provides the knowledge and skills to plan, design, configure and manage computer network infrastructure for an organisation through detailed study of the underlying concepts and technologies of network design.
7.301 Information Technology Project Management.
Gain knowledge and skills in information technology project management, and learn how to apply this knowledge in successfully managing IT projects for an organisation.
7.303 Web Applications Development.
This course will extend students' knowledge of data communications and internetworking over the world wide web, and covers issues associated with the development of applications in this environment. Emphasis is placed upon the analysis, design and development of web-based applications for a variety of purposes using current tools and techniques.
7.304 Human Computer Interaction.
This course will provide the necessary knowledge and skills in the areas of understanding, designing, implementing and evaluating user-interface to offer an enhanced interaction between users and computers.
7.305 Software Quality Assurance and Maintenance.
This course provides usable tools and techniques for software quality assurance (SQA) for the improved control of software development, and will enable students to consolidate their programming skills with a focus on the quality, modularity and reusability aspects of software development.
7.306 Multi-tier System Development.
This course will extend students' knowledge of OOP programming. Investigate and use advanced techniques that extend the standard software development environment to develop and deploy software applications for mobile device.
7.307 Intelligent Agents.
An overview of the field of information security from a management perspective. Students will be exposed to the spectrum of security activities, methods, methodologies and procedures.
7.308 Mobile Applications Development.
This course extends students' knowledge of OOP programming by investigating and using advanced techniques that extend the standard software development environment to develop and deploy software applications for mobile devices.
7.309 Network System Security.
An overview of the field of information security from a management perspective, covering the spectrum of security activities, methods, methodologies and procedures.
7.310 Advanced Networking.
This course provides an up-to-date survey of developments in the field of computer networking, and covers the main problems that confront the design of a computer network and the need to support multimedia and real-time traffic and congestion control to provide different levels of quality of service (QoS) in a computer network.
7.311 Mobile Network Design.
An understanding of the wireless and cellular network supporting mobile users all around the globe. The course includes knowledge of the cellular/wireless network, devices, protocols and how they access and interact with information and services instantly, and the design and implementation issues involved in cellular/wireless networks.
7.312 Management Information Systems.
A focus on the implications of information technology in industry and addressing the role of information systems within companies using examples of solutions such as OpenERP, SAP ERP and Microsoft dynamics. The course exposes students to the selection, implementation and benefits of IT infrastructure in an organisation so that they may apply the knowledge gained in developing effective information systems solutions for an organisation.
7.313 Enterprise Systems.
This course examines cross-functional integrated computer-based information systems, commonly referred to as Enterprise Resource Planning (ERP) systems, designed to support an organisation's information needs and operations.
7.314 E-Business Strategy.
This course provides knowledge on e-business that will enable students to plan, realise and manage e-business implementations.
7.315 Database Administration.
This course developes students' skills and knowledge of database systems through an integrated study of theory and practice associated with DBA tasks, including installation, upgrade, performance consideration, security, backup, recovery and disaster planning.
7.316 Business Intelligence.
Provides students with the design, development and implementation skills for commercial data warehouse systems, including the concepts, techniques and use of tools for data mining in the context of business and market research.
7.320 Information Technology Project.
This two-semester course will provide students with industrial experience through IT development work in an industry environment.
7.321 Intensive Information Technology Project.
This one-semester intensive course will provide students with industrial experience through IT development work in an industry environment.
7.322 Information Technology Industry Practice.
This course offers a placement to an information technology industrial establishment and the expectation of at least 100 hours practical work place experience. This practical component is matched by the requirement to keep a detailed log of experiences and then using a systematic review process to analyse and provide a wider context for the experience. The analysis will include reviewing the strategic goals of the host, an assessment of different client requirements and the provision of services to satisfy those different requirements. Students will work for no less than 100 hours in the establishment and a contract of services between the student and the establishment will be provided including a job description and work hours. A presentation of their work experience will also be required.
Prerequisites: Applicants must have passed at least 180 credits in IT courses, including: 7.102 Business Communication, 7.106 Business Environment, 7.217 Requirement Modelling, two courses from chosen specialisation.
NB Admission will be by approval only.
7.401 Research Methods.
This course introduces students to the discipline of performing research in the field of Information Technology. The course covers: selecting a research topic; forming research questions and hypotheses; performing a literature review; gathering data; experimental design; and statistically sound practices in data analysis. The skills of writing-up the steps of a research project will be continuously reinforced and critical thinking skills will be developed. This course is only available to students in the research pathway of the PGDIT.
7.402 Research Project.
The course involves a supervised individual research project leading to the production of a research report (dissertation). The project applies the skills acquired in Research Methods to select a project topic and supervisor, design hypotheses and experiments, gather data, analyse and report the results in the form of a dissertation. Specifically, students are required to employ their analytical skills to establish logical arguments in the form of hypotheses on a given research topic at the beginning of their research, and apply their research capabilities to further enhance the initial arguments and see them through the final outcome. The projects may contain both simulation and implementation studies depending on the project topic. The project prepares students for higher-level post-graduate study and provides practice applying critical research skills. This course is only available to students in the research pathway of the PGDIT.
7.403 Internship.
The course aims to provide students real world insights and exposures to actual working life and practical knowledge towards a particular occupation in an IT establishment. Students will explore and analyse the workplace practices, business processes, policies and procedures with the aim of improvement in terms of efficiency, resource utilization and sustainability in addition to their role specific tasks. This course is only available to students in the internship pathway of the PGDIT.
Prerequisites: One Level-7 specialisation course and one Level-8 specialisation course.
7.404 Industrial Project.
The course aims to provide students an opportunity to apply the academic learning, knowledge and skills to work on an industrial project and get an exposure to actual workplace within an organisation.
Prerequisites: One Level-7 specialisation course and one Level-8 specialisation course.
7.405 Specialisation Project.
This course involves an individual one-semester supervised implementation or research project. The student will reinforce their skills in implementation, individual research and time-management to produce a small software or IT system along with a formal written report. The written report will be of a similar format to those expected within the New Zealand IT industry.
7.406 Continuous Integration and Continuous Deployment.
This course aims to develop skills to automate and improve the application development process by using continuous integration, delivery and deployments tools. The course enables learners to write unit and user interface tests to ensure application functionality and interface workflows. The course emphasis is on continuous automated integration builds, testing, delivery, deployment, user interface testing, release management and recovery automation.
7.407 Cloud Application Development.
This course aims to develop theoretical and practical knowledge of cloud application development. The course enables learners to develop applications for the cloud. The course emphasis is on various cloud technologies, cloud application development, deployment and cloud application scalability, reliability and security.
7.408 Software User Experience.
This course aims to provide theoretical and practical knowledge for designing and implementing quality interfaces for interactive information systems. This includes the usage of widely used guidelines, theories and principles of designs that leads to the development of high performance, reliable and usable information system.
7.409 Topics in Cloud Computing.
This course is designed to provide students with the necessary knowledge and skills required to understand and perform the essential tasks related to the operation, maintenance, monitoring and troubleshooting of Cloud products and solutions.
7.410 Computer and Communication Network Security.
The course aims to provide an in-depth understanding of LAN, WAN and Wireless security with a focus on cryptography and management of network security. Students will explore security frameworks, policies and processes as well as practical issues involved in designing, building and deploying a secure operational environment.
7.411 Penetration Testing.
This course is designed to provide students with the necessary knowledge and skills required to understand and perform essential tasks related to penetrating information systems. Students will learn to explore and analyse security vulnerabilities in the target systems, design suitable security approaches to mitigate the detected risks and vulnerabilities, and conduct relevant security assessments to verify the effectiveness of those approaches.
7.412 Data Mining.
This course is an introduction to Data Mining techniques which includes analysis of medium to large sized datasets from different sources; data preparation which includes feature selection; visual analytics and exploratory data analysis; prediction and classification by regression and classification modelling, neural network and tree-based methods; cluster analysis; association mining with market basket methods, Text Mining; extensive use of WEKA for analysis and prediction also the use of other industry driven demand tools for Visual Analytics.
7.413 Artificial Intelligence.
This course covers the technologies and techniques of artificial intelligence currently in common use. Students will learn the contemporary models and algorithms used in intelligent information systems, their origins, and their workings. Students will also be introduced to the unique problems involved in developing an intelligent information system and the means by which real-world problems are solved by intelligent techniques.
7.414 Enterprise Cloud-based Systems.
Cloud computing is one of the trendiest technical topics today, with its scalability in the delivery of enterprise applications, cloud concepts and cloud service models, these all have a broad effect across Information Technology Information Architecture, Business, and Data Storage. This course serves to expose students to cloud service models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Business Process as a Service (BPaaS). Such cloud service models will familiarise students with vendor-maintained applications. The course also covers the Cloud security model and its challenges, implementation and support of High Performance Computing and Big Data on the Cloud.

business analytics (BA)
Streaming Analytics FAQ: What You Need to Know –SAS Institute Inc. Three Use Cases for Interactive Data Discovery and Predictive Analytics –SAS Institute Inc. See More.
Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making.
Staff Pick: The Rising Need for Analytics Accuracy.
Learn how LinkedIn overcame analytics bottlenecks, 3 data modeling flaws that cripple data science projects, and common roadblocks of advancing data science teams and hiring data pros.
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BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples.
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
The second area of business analytics involves deeper statistical analysis. This may mean doing predictive analytics by applying statistical algorithms to historical data to make a prediction about future performance of a product, service or website design change. Or, it could mean using other advanced analytics techniques, like cluster analysis, to group customers based on similarities across several data points. This can be helpful in targeted marketing campaigns, for example.
Specific types of business analytics include:
Descriptive analytics, which tracks key performance indicators to understand the present state of a business; Predictive analytics, which analyzes trend data to assess the likelihood of future outcomes; and Prescriptive analytics, which uses past performance to generate recommendations about how to handle similar situations in the future.
While the two components of business analytics -- business intelligence and advanced analytics -- are sometimes used interchangeably, there are some key differences between these two business analytics techniques:
Business analytics vs. data science.
The more advanced areas of business analytics can start to resemble data science, but there is a distinction. Even when advanced statistical algorithms are applied to data sets, it doesn't necessarily mean data science is involved. There are a host of business analytics tools that can perform these kinds of functions automatically, requiring few of the special skills involved in data science.
True data science involves more custom coding and more open-ended questions. Data scientists generally don't set out to solve a specific question, as most business analysts do. Rather, they will explore data using advanced statistical methods and allow the features in the data to guide their analysis.
Business analytics applications.
Business analytics tools come in several different varieties:
Self-service has become a major trend among business analytics tools. Users now demand software that is easy to use and doesn't require specialized training. This has led to the rise of simple-to-use tools from companies such as Tableau and Qlik, among others. These tools can be installed on a single computer for small applications or in server environments for enterprise-wide deployments. Once they are up and running, business analysts and others with less specialized training can use them to generate reports, charts and web portals that track specific metrics in data sets.
Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, data cleansing and integration into a single repository, such as a data warehouse or data mart. The analysis is typically performed against a smaller sample set of data.
Analytics tools range from spreadsheets with statistical functions to complex data mining and predictive modeling applications. As patterns and relationships in the data are uncovered, new questions are asked, and the analytical process iterates until the business goal is met.
Deployment of predictive models involves scoring data records -- typically in a database -- and using the scores to optimize real-time decisions within applications and business processes. BA also supports tactical decision-making in response to unforeseen events. And, in many cases, the decision-making is automated to support real-time responses.
Próximos passos.
Expert Wayne Kernochan provides an overview of the different types of business intelligence analytics tools on the market.
Continue Reading About business analytics (BA)
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