No abstract

International audienceThis paper discusses some aspects of the decisional challenges implied by the management of modern information technology (IT) systems. These rapidly evolving complex systems are crucial to the overall performance of industrial companies. Two challenges are delineated: (1) the complexity of modern systems requires a system-thinking approach; (2) the critical role of IT in business performance implies taking into account these technological aspects within any business plan. Some IT management frameworks are designed to face these challenges but seem not to provide enough comprehensible decisional support to IT managers in real time. That is why this paper proposes innovative decision-aid methods, designed for some of the activities related to capacity management. Attention is paid to modelling and monitoring activities, through methods that combine qualitative and quantitative analyses. These approaches can provide operational support to IT managers. The added value of the methods developed is demonstrated by practical examples implemented in a semiconductor manufacturing firm. These results are an encouraging first step towards more comprehensive and more effective solutions

International audienceThis 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

This paper presents an original research initiated by the monitoring needs of a semiconductor production plant. The industrial operations rely on an Information Technology (IT) system, and several time series data are controlled statistically. Unfortunately, these variables often contain outliers, as well as structural changes due to external decisions in the IT activity. As a consequence, it has been observed that the monitoring results obtained with standard techniques could be severely biased. This paper presents some contributions to overcome such difficulties. A new monitoring method is proposed, based on robust Holt-Winters smoothing algorithm, and coupled with a relearning procedure for structural breaks detection. Such a method is flexible enough for a large-scale industrial application. We evaluate its performances through simulations studies, and show its usefulness in industrial real applications for univariate and multivariate time series. The scope of application deals with IT activity monitoring, but the introduced statistical methods are generic enough for being used in other industrial fields.

No abstract