In order to stay competitive in today’s overly competitive market place, businesses must be engineered to match product characteristics and customer requirements. This increased emphasis on achieving highly adaptive manufacturing with reduction in manufacturing costs, better utilization of manufacturing resources and sound environmental management practices force organisations to adopt efficient management practices in their manufacturing operations. Some of the established practices in this context belong to the Lean, Agility and Green (LAG) paradigms. Adopting these practices in order to address customer requirements may require some level of expertise and understanding of the contribution (or lack of it) of the practices in meeting those requirements. Primarily, the wide choice of LAG practices available to address customer requirements can be confusing and/or challenging for those with limited knowledge of LAG practices and their efficacy. There is currently no systematic methodology available for selecting appropriate LAG practices considering of the product life cycle (PLC). Therefore, this research provides a novel framework for selecting appropriate LAG practices based on PLC stages for reducing costs, lead time and generated waste. The methodology describes the application of analytic hierarchy process (AHP), statistical inference and regression analysis as decision support tools, ensuring a systematic approach to the analysis with appropriate performance measures. The data collected were analysed with the aid of SPSS and Excel using a variety of statistical methods. The framework was verified through a Delphi study and validated using a case study. The key findings of the research include the various contributions of lean, agile and green practices towards improving performance measures, the importance of green in improving performance measures and the importance of selecting appropriate practices based on product life cycle stages. This research makes a clear contribution to existing body of knowledge by providing a methodological framework which could serve as a guide for companies in the FMCG industry to systematically integrate and adopt lean, agile and green to better manage their processes and meet customer requirements in their organisations. However, the framework developed in this research has not been tested in other areas.

Der beschleunigte Wandel der Industriegesellschaft wird auch in den nächsten Jahrzehnten entscheidend durch technologische Innovationsprozesse beeinflusst. In diesem Bereich sind die deutschen Werkzeugmaschinenhersteller führend, was anhand der vorgestellten Innovationen zu erkennen ist. Die deutschen Hersteller haben sich als Technologieführer mit geringeren Stückzahlen positioniert während die asiatischen Hersteller vorrangig im Segment der Universalmaschinen als Massenhersteller vertreten sind. Langfristig wird es darauf ankommen, ob die einzelnen Unternehmen zu strategischen Phasensprüngen fähig sind, die angesichts der Globalisierung der Wirtschaftsstruktur zu einer wettbewerbsfähigen Technologiekultur führen. Im Hinblick auf diese Anforderungen ist insbesondere die Beschleunigung der Entwicklungsprozesse eine wesentliche Voraussetzung für die Beibehaltung und den Ausbau der Marktposition der global agierenden Unternehmen. [... aus der Einleitung]

Dans le contexte manufacturier, un ensemble de produits sont acheminés entre différents sites avant d’être vendus à des clients finaux. Chaque site possède différentes fonctions : création, stockage, mise en vente, etc. Les données de traçabilités décrivent de manière riche (temps, position, type d’action,…) les événements de création, acheminement, décoration, etc. des produits. Cependant, de nombreuses anomalies peuvent survenir, comme le détournement de produits ou la contrefaçon d’articles par exemple. La découverte des contextes dans lesquels surviennent ces anomalies est un objectif central pour les filières industrielles concernées. Dans cette thèse, nous proposons un cadre méthodologique de valorisation des traces unitaires par l’utilisation de méthodes d’extraction de connaissances. Nous montrons comment la fouille de données appliquée à des traces transformées en des structures de données adéquates permet d’extraire des motifs intéressants caractéristiques de comportements fréquents. Nous démontrons que la connaissance a priori, celle des flux de produits prévus par les experts et structurée sous la forme d’un modèle de filière, est utile et efficace pour pouvoir classifier les traces unitaires comme déviantes ou non, et permettre d’extraire les contextes (fenêtre de temps, type de produits, sites suspects,…) dans lesquels surviennent ces comportements anormaux. Nous proposons de plus une méthode originale pour détecter les acteurs de la chaîne logistique (distributeurs par exemple) qui auraient usurpé une identité (faux nom). Pour cela, nous utilisons la matrice de confusion de l’étape de classification des traces de comportement pour analyser les erreurs du classifieur. L’analyse formelle de concepts (AFC) permet ensuite de déterminer si des ensembles de traces appartiennent en réalité au même acteur. / In a manufacturing context, a product is moved through different placements or sites before it reaches the final customer. Each of these sites have different functions, e.g. creation, storage, retailing, etc. In this scenario, traceability data describes in a rich way the events a product undergoes in the whole supply chain (from factory to consumer) by recording temporal and spatial information as well as other important elements of description. Thus, traceability is an important mechanism that allows discovering anomalies in a supply chain, like diversion of computer equipment or counterfeits of luxury items. In this thesis, we propose a methodological framework for mining unitary traces using knowledge discovery methods. We show how the process of data mining applied to unitary traces encoded in specific data structures allows extracting interesting patterns that characterize frequent behaviors. We demonstrate that domain knowledge, that is the flow of products provided by experts and compiled in the industry model, is useful and efficient for classifying unitary traces as deviant or not. Moreover, we show how data mining techniques can be used to provide a characterization for abnormal behaviours (When and how did they occur?). We also propose an original method for detecting identity usurpations in the supply chain based on behavioral data, e.g. distributors using fake identities or concealing them. We highlight how the knowledge discovery in databases, applied to unitary traces encoded in specific data structures (with the help of expert knowledge), allows extracting interesting patterns that characterize frequent behaviors. Finally, we detail the achievements made within this thesis with the development of a platform of traces analysis in the form of a prototype.