Complexities in markets: Introduction to the special issue

Gilbert, Nigel; Jager, Wander; Deffuant, Guillaume; Adjali, Iqbal

doi:10.1016/j.jbusres.2007.01.016

Cited by 34 publications

(9 citation statements)

References 5 publications

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“…Traditionally, ABMs are applied in the social sciences [e.g. 69,70,71]. Applications related to technology and markets have appeared as well, such as models of electricity markets [72,73,74,75,76,77,78,79].…”

Section: Agent-based Modelingmentioning

confidence: 99%

Modeling for Transition Management

Chappin

Dijkema

2015

SSRN Journal

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A framework for the modeling and simulation of transitions is presented. A transition, "substantial change in the state of a socio-technical system", typically unfolds over a long timespan. We therefore suggest to use simulation to inform transition managers on the effect of their decisions. Transition models preferably meet five functional requirements: to allow for the representation of physical and social components, for material and immaterial interactions, to allow the system structure to change, to compute transition indicators and to capture the effect of interventions. Modeling the decarbonization of the power sector illustrates that an agent-based model allows us to let both the content and the structure of a system to emerge in a simulation.

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Section: Agent-based Modelingmentioning

confidence: 99%

Modeling for Transition Management

Chappin

Dijkema

2015

SSRN Journal

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“…Numerous factors contribute to complexity, including demand uncertainty, volatile supply markets and increased product variety. Our focus is on product variety, as in many cases the capability to provide a range of products has become a key (and often prerequisite) competitive factor as firms seek to differentiate and often customise their products in the marketplace (Gilbert, Jager, Deffuant & Adjali, 2007;Ho & Tang, 1998). In addressing 'complexity', researchers have found it useful to distinguish between 'complexity' arising from the number of components (or actors) in a system and 'dynamic complexity' arising from unpredictability or variability (Bozarth, Warsing, Flynn, & Flynn, 2009).…”

Section: Product Variety Managementmentioning

confidence: 99%

A conceptual data model to enhance SKU management in food SMEs

Sammon

O’Reilly

2013

Journal of Decision Systems

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This paper reports on an action research project (two-cycle approach -problem solving cycle and research cycle) that investigated the role of data management in supporting product variety/Stock Keeping Unit (SKU) management in a food SME (Small and Medium Enterprise). We find that the SME did not view data as a key resource and hence data redundancy and data integrity issues were fundamental problems. Application of a conceptual data model instilled discipline with regard to data management and this resulted in: (1) a change in data management behaviour within the food SME, and (2) the development and deployment of SKU management tools for the food SME. IntroductionConsumer food manufacturing is characterised by increasing demand for product variety. Recent research has investigated the role of hierarchical planning approaches and Stock Keeping Unit (SKU) classification in product variety management and order fulfilment. Appropriate data management is a key enabler of such planning and associated classifications; however, this has received less attention, especially with regard to the needs of small and medium enterprises (SMEs). Within SME environments, there is an overreliance on spreadsheet products (e.g. MS Excel) to manage data. Such 'literal' data representations are devoid of adequate 'abstraction' which is necessary to maintain an appropriate parent-child relationship between a master SKU recipe and the associated SKU varieties. Hence, this research focuses on SKU data management and adopts an action research (AR) approach in the food SME environment. This paper is structured as follows: a brief review of relevant literature establishes key concepts and trends associated with product variety with particular reference to the food industry; this is followed by a discussion of data management and data ontologies; the methodology is then outlined and findings are presented; the conclusion reflects on the contribution of the AR approach adopted and reports on practical outcomes.

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“…Moreover, potential lateral effects such as word-ofmouth can become important as well in consumers' decision making and as a result lead to non-linearity in the dynamics of a market. Consequently, CPG markets lend themselves readily to be modeled using agent based simulations [8].…”

Section: Introductionmentioning

confidence: 99%

Volatility in the Consumer Packaged Goods Industry — A Simulation Based Study

Sengupta

Glavin²

2010

Advs. Complex Syst.

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Abstract. The volatility in a CPG market is modelled using a bottom up simulation approach and validated against disaggregated supermarket transactions data. The simulation uses independent agents, each agent representing unique households in the data. A simple behavioural model incorporates household preferences for product attributes, prices and promotions. Our validation strategy tests the model predictions at both macro and micro levels. At the macro level, the model is validated against out of sample evolution of market shares while at the micro level, household level choice of individual products and product attribute combinations are used. The model captures the volatility in terms of market share of brands and flavours -with the direction of change being more accurately predicted than the magnitude. At the micro level, we achieve a reasonable degree of prediction accuracy of household level SKU choice and a substantially higher accuracy for attribute choice. We found that product size to be the most difficult to predict among all attributes.

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Complexities in markets: Introduction to the special issue

Cited by 34 publications

References 5 publications

Modeling for Transition Management

Modeling for Transition Management

A conceptual data model to enhance SKU management in food SMEs

Volatility in the Consumer Packaged Goods Industry — A Simulation Based Study

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