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Data-driven decision-making for IT capacity: beyond statistical analyses

Abstract : This paper reports a research work piece developed in collaboration with the semiconductor wafer production company: STMicroelectronics. This collaborative research programme aimed at implementing statistical methods, so as to improve business decisions focusing on capacity planning for information technology. The current paper presents the specification, and experimentation of a method dedicated to managing the need to integrate rigorously complex contextualisation factors, when developing statistical-based Decision Support Systems (DSS). The key challenge is to increase the end-user acceptance and success rate for DSS developments. To ensure the successful integration of DSS within the user environment, the paper formalises a so-called ‘Contextualisation process’, integrated within a larger decision-aid development framework. This Contextualisation process is specified with three methodological components, respectively ‘Qualitative Contextualisation’, ‘Statistical Modelling and Formalisation’ and ‘User Integration’. This approach is applied by STMicroelectronics, to a case study focusing on a project of change management for the infrastructure of the Information System. Based on the demonstration of the case study results, the added-value of the Contextualisation process are discussed and further perspectives for applied research in DSS are drawn.
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Submitted on : Wednesday, January 18, 2017 - 10:24:32 AM
Last modification on : Tuesday, June 29, 2021 - 10:36:06 AM



Michel Lutz, Xavier Boucher. Data-driven decision-making for IT capacity: beyond statistical analyses. Journal of Decision Systems, Abingdon Oxfordshire UK: Routledge Taylor & Francis Group, 2017, 26 (1), pp.1 - 24. ⟨10.1080/12460125.2016.1232533⟩. ⟨emse-01438820⟩



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