Robustness and Optimality as Criteria for Strategic Decisions

Rosenhead, Jonathan; Elton, Martin C.J.; Gupta, S. K.

doi:10.1057/jors.1972.72

Cited by 239 publications

(54 citation statements)

References 6 publications

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“…From an organizational concepts flexibility is defined as the capacity for variability (Fellenz 2000); ability to change strategic decisions to become adaptive to external changes (Evans 1991;Bierly and Chakrabarti 1996;Gupta and Nehra 2002); business agility infused by new information and communication technologies (Sushil 2012b); ability to be versatile depending on the needs of a particular situation and/or being resilience which enable the organization to endure when negatively affected (Evans 1991); quest for freedom (Heinl et al 1999); firm's ability to reposition themselves in a market and successful responsiveness to environmental demands (Fellenz 2000); size of the choice set (Marschak and Nelson 1962); autonomy of functions within the organizations and broadening of mind (Sushil 1997); adjustment for the unexpected consequences of predictable changes (Evans 1991;Bahrami 1996); reducing the ability to compromise (Kulatilaka and Marks 1988); resemblance to elasticity in regard of resumption to previous position before an expansion, distortion or contraction (Evans 1991); and robust systems which are capable of enduring any unanticipated changes in the business environment (Rosenhead et al 1972).…”

Section: Global Journal Of Flexible Systems Managementmentioning

confidence: 99%

Marketing Flexibility Interaction Matrix and Consumer Clusters Preference Criteria in Telecommunication Sector

Sharma

Medudula

Patro

2015

Glob J Flex Syst Manag

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The sequence of various events in Indian Telecom Sector has transformed the industry to become more competitive and dynamic in nature. With increasing competitive levels, the industry calls for more customer centric operations and flexible processes. The emphasis has been laid out in identifying various marketing functions and their use of various flexibility connotations in practise. The interaction matrix between marketing elements and flexibility connotations was developed through Delphi technique, which revealed connotations such as adaptiveness, customization and localness are mostly adopted for making the marketing processes flexible and customeroriented. The analysis of various customer preference factors with clustering technique revealed a solution of four cluster groups where the customer preferences differ on various parameters. The clustering technique points out that customer prefers the best network quality provider with better customer service and plethora of Value added services. Cluster analysis also revealed that celebrity endorsements and the advertisements are not the motivating factors for selecting the operator and they are used only as top of the mind recall. The use of flexibility concepts in various marketing activities such as branding, point of purchase, product innovations, product differentiation, product planning and development, pricing strategies and loyalty programs would help the telecom organizations to better manage their subscribers in these cluster groups.

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Section: Global Journal Of Flexible Systems Managementmentioning

confidence: 99%

Marketing Flexibility Interaction Matrix and Consumer Clusters Preference Criteria in Telecommunication Sector

Sharma

Medudula

Patro

2015

Glob J Flex Syst Manag

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“…Kreps (1979) demonstrates that preferences for flexibility can be treated axiomatically and shows that they may be equivalent to the preferences derived from expected utility theory. Formalization of the notion of flexibility in a sequential decision context and relating its value to the amount of information that the decision maker expects to receive have been attempted by Rosenhead et al (1972), Henry (1974), Mandelbaum (1978), Jones and Ostroy (1984), Miller (1986), and others. Rosenhead et al defined and measured flexibility by the number of optional alternatives left over after one has made an initial decision.…”

Section: The Economic Viewmentioning

confidence: 99%

Flexibility in manufacturing: A survey

Sethi

1990

Int J Flex Manuf Syst

1,341

994

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This article is an attempt to survey the vast literature on flexibility in manufacturing that has accumulated over the last 10 to 20 years. The survey begins with a brief review of the classical literature on fexibility in economics and organization theory, which provides a background for manufacturing flexibility. Several kinds of flexibilities in manufacturing are then defined carefully along with their purposes, the means to obtain them, and some suggested measurements and valuations. Then we examine the interrelationships among the several flexibilities. Various empirical studies and analytical/optimization models dealing with these flexibilities are reported and discussed. The article concludes with suggestions for some possible future research directions.

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“…The Robust Regions help identify key strategies that perform relatively well compared with the alternatives over a wide range of scenarios. Under conditions of deep uncertainty, decision-makers often seek such robust strategies (11). Often, such strategies are adaptive; that is, they evolve over time in response to new information.…”

Section: A New Decision Sciences For Complex Systemsmentioning

confidence: 99%

A new decision sciences for complex systems

Lempert

2002

Proc. Natl. Acad. Sci. U.S.A.

299

175

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Models of complex systems can capture much useful information but can be difficult to apply to real-world decision-making because the type of information they contain is often inconsistent with that required for traditional decision analysis. New approaches, which use inductive reasoning over large ensembles of computational experiments, now make possible systematic comparison of alternative policy options using models of complex systems. This article describes Computer-Assisted Reasoning, an approach to decisionmaking under conditions of deep uncertainty that is ideally suited to applying complex systems to policy analysis. The article demonstrates the approach on the policy problem of global climate change, with a particular focus on the role of technology policies in a robust, adaptive strategy for greenhouse gas abatement.robust adaptive planning ͉ agent-based modeling ͉ deep uncertainty ͉ complex adaptive systems ͉ computer-assisted reasoning T he study of complex systems provides powerful tools and concepts for capturing useful information about the world. For instance, the process of technology innovation and diffusion, crucial to many of today's most important policy decisions, displays emergence and a dynamics shaped by self-referential expectations in the face of imperfect information. Such properties are difficult to represent with traditional analytic approaches but spring naturally from the mathematics of complexity. Unfortunately, it has often proved difficult to apply this mathematics to policy problems. Complex systems often are characterized by uncertainty of a type that strains the traditional methods of decision analysis, vital to the systematic examination of policy alternatives.Traditional decision analysis rests on key assumptions about the types of information available to the decision-maker. Powerful Bayesian methods produce a ranking of alternative strategies, and in particular, identify the optimum strategy, assuming the decision-maker has a well characterized system model and can represent uncertainty with probability distributions over the input parameters to that model. Such methods have proved extraordinarily useful for many problems because they help structure the extensive information decision-makers often have, offer a systematic, nonbiased treatment, and expose the numerous logical fallacies to which human reasoning is prone (1).However, we often have information about complex systems different from that assumed by traditional decision analysis. For instance, complex systems often display regions of extreme sensitivity to the particular assumptions, while at the same time exhibit important regularities of macroscopic behavior. Such systems display one example of deep uncertainty, a situation where the system model and the input parameters to the system model are not known or widely agreed on by the stakeholders to the decision. Under such deep uncertainty, the standard tools of decision analysis are difficult to apply and may not accurately represent the goals of decision-makers...

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Robustness and Optimality as Criteria for Strategic Decisions

Cited by 239 publications

References 6 publications

Marketing Flexibility Interaction Matrix and Consumer Clusters Preference Criteria in Telecommunication Sector

Marketing Flexibility Interaction Matrix and Consumer Clusters Preference Criteria in Telecommunication Sector

Flexibility in manufacturing: A survey

A new decision sciences for complex systems

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