Fuzzy Topsis Decision Method for Configuration Management

Mass customization refers to an environment in which reducing quantities and increasing varieties of products are being manufactured. A product configuration is defined as an aggregation of parts whose functions and performance parameters must be defined and controlled to achieve the overall performance of a system or product. Since the product configurations would be varied based on consumer needs, selecting effective product configurations from among several alternatives is a challenge during the mass customization design stage. This study developed a structural model which combines a fuzzy quality function deployment with a fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) to solve this problem. The configuration alternatives ranked using the proposed method can provide a useful reference for decision makers in implementing configuration management

A Quantitative Model for Decomposing & Assessing the Value for the Customer

The research presented in this paper proposes a novel quantitative model for decomposing and assessing the Value for the Customer. The proposed approach builds on the different dimensions of the Value Network analysis proposed by Verna Allee having as background the concept of Value for the Customer proposed by Woodall. In this context, the Value for the Customer is modelled as a relationship established between the exchanged deliverables and a combination of tangible and intangible assets projected into their endogenous or exogenous dimensions. The Value Network Analysis of the deliverables exchange enables an in-depth understanding of this frontier and the implicit modelling of co-creation scenarios. The proposed Conceptual Model for Decomposing Value for the Customer combines several concepts: from the marketing area we have the concept of Value for the Customer; from the area of intellectual capital the concept of Value Network Analysis; from the collaborative networks area we have the perspective of the enterprise life cycle and the endogenous and exogenous perspectives; at last, the proposed model is supported by a mathematical formal description that stems from the area of Multi-Criteria Decision Making. The whole concept is illustrated in the context of a case study of an enterprise in the footwear industry (Pontechem). The merits of this approach seem evident from the contact with Pontechem as it provides a structured approach for the enterprises to assess the adequacy of their value proposition to the client/customer needs and how these relate to their endogenous and/or exogenous tangible or intangible assets. The proposed model, as a tool, may therefore be a useful instrument in supporting the commercialisation of new products and/or services

Customer requirements based ERP customization using AHP technique

Purpose– Customization is a difficult task for many organizations implementing enterprise resource planning (ERP) systems. The purpose of this paper is to develop a new framework based on customers’ requirements to examine the ERP customization choices for the enterprise. The analytical hierarchy process (AHP) technique has been applied complementarily with this framework to prioritize ERP customization choices. \ud \ud Design/methodology/approach– Based on empirical literature, the paper proposed an ERP customization framework anchored on the customer's requirements. A case study research method was used to evaluate the applicability of the framework in a real-life setting. In a case study with 15 practitioners working on the vendor's and the client's sides in an ERP implementation, the paper applied the framework jointly with the AHP technique to prioritize the feasible customization choices for ERP implementation. \ud \ud Findings– The paper demonstrates the applicability of the framework in identifying the various feasible choices for the client organization to consider when they decide to customize their selected ERP product. \ud \ud Research limitations/implications– Further case studies need to be carried out in various contexts to acquire knowledge about the generalizability of the observations. This will also contribute to refining the proposed ERP customization framework. \ud \ud Practical implications– Very few literature sources suggest methods for exploring and evaluating customization options in ERP projects from requirements engineering perspective. The proposed framework helps practitioners and consultants anchor the customization decisions on the customer's requirements and use a well-established prioritization technique, AHP, to identify the feasible customization choices for the implementing enterprise. \ud \ud Originality/value– No previously published research studies provide an approach to prioritize customization choices for ERP anchored on the customer's requirements

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Définition des familles de produits à l'aide de la logique floue

Dans cette thèse, la contribution principale porte sur la conception des familles de produits par l'application de la logique floue, ceci afin d’améliorer le processus de prise de décisions. Nous considérons que la formation des familles de produits, permet aux entreprises d'offrir une grande variété de produits. Cela permet alors de satisfaire une grande variété de différents types de clients sur un marché cible, et d’éviter une diversification coûteuse par la conception et la fabrication de produits personnalisés pour chaque client. La logique floue permet d’entrer l'information à fournir en des termes linguistiques familièrement exprimés par les personnes. C’est-à-dire qu’elle permet de considérer une information plus conforme à celle exprimée par les consommateurs; elle n'est pas limitée au maniement de variables binaires comme la logique booléenne. La logique floue à travers la formulation de différentes fonctions d'appartenance, est capable d'évaluer une variété de réponses pour une variable et pas seulement un «oui» ou un «non». Après l'analyse de littérature en ce qui concerne la logique floue et le développement des familles de produits. Nous concluons que le processus de prise de décisions est fondamental pour une formation effective des familles de produits et que le classement flou représente la base des processus de prise de décisions aidés par la logique floue. Pour cela, dans ce travail, différents outils assistés par la logique floue ont été développés et appliqués en cherchant à atteindre l'objectif principal. Premièrement, une procédure de classement flou a été améliorée pour permettre d'évaluer les relations de préférences entre plusieurs nombres flous avec différentes fonctions d’appartenance. L’amélioration de cette procédure a été la définition de vingt-neuf cas généraux pour représenter les différentes situations qui peuvent se présenter entre deux nombres flous. Ces cas généraux ont été aussi présentés comme un cadre de référence qui permet d'inclure d'autres fonctions d’appartenance. Postérieurement, en ce qui concerne la conception de familles de produits, différents outils ont été développés, appliqués et finalement intégrés dans une méthodologie globale pour la formation de familles de produits.----------ABSTRACT In this thesis, the main contribution is concerned to the design of product families by applying fuzzy logic, in order to improve the decision making process. We consider that the formation of product families enables companies to offer a wide variety of products allowing the satisfaction of different types of customers into the target market, and avoiding a costly diversification by designing customized products for each customer. Fuzzy logic allows entering information provided in linguistic terms familiarly expressed by the people. That is to say, it allows considering more consistent information close to the expressed by customers and it is not limited to handle binary variables as the Boolean logic. Fuzzy logic through the formulation of different membership functions can evaluate more answers of a variable instead of a just a “yes” or a “not”. After carrying out the literature review, regarding to the fuzzy logic and to the product family development. We concluded that the process of decision making is fundamental for the effectively formation of families of products, and that the fuzzy ranking is the basis of such process. In this work, various fuzzy logic-aided tools have been developed and applied aiming at achieving the main objective. First, an improved fuzzy ranking procedure for decision making in product has been proposed to permit the evaluation of the fuzzy preference relations among several fuzzy numbers with different membership functions. This fuzzy ranking procedure has been supported by the definition of twenty-nine general cases, which is enough to consider all the possible situations between two normal fuzzy numbers. These general cases have been presented as a framework to facilitate the inclusion of other membership functions. Later, regarding the design of product families, different tools have been developed, implemented, and integrated into a global methodology to form families of products. These tools include: a ranking procedure for fuzzy decision-making in product design to compare different products, a method to select products based on the fuzzy preferences of the customers, an iterative method to configure products for specific customers, a method to configure different products to satisfy the different segments of the market, and finally the integration of all these tools in a global methodology for designing families of products by using fuzzy logic

A Rule-based Service Customization Strategy for Smart Home Context-aware Automation

The continuous technical progress of the smartphone built-in modules and embedded sensing techniques has created chances for context-aware automation and decision support in home environments. Studies in this area mainly focus on feasibility demonstrations of the emerging techniques and system architecture design that are applicable to the different use cases. It lacks service customization strategies tailoring the computing service to proactively satisfy users’ expectations. This investigation aims to chart the challenges to take advantage of the dynamic varying context information, and provide solutions to customize the computing service to the contextual situations. This work presents a rule-based service customization strategy which employs a semantic distance-based rule matching method for context-aware service decision making and a Rough Set Theory-based rule generation method to supervise the service customization. The simulation study reveals the trend of the algorithms in time complexity with the number of rules and context items. A prototype smart home system is implemented based on smartphones and commercially available low-cost sensors and embedded electronics. Results demonstrate the feasibility of the proposed strategy in handling the heterogeneous context for decision making and dealing with history context to discover the underlying rules. It shows great potential in employing the proposed strategy for context-aware automation and decision support in smart home applications

Exploring Multi-Criteria Decision-Making for Academic Blockchain Platform Adoption

A decentralised distributed ledger system called Blockchain Technology (BCT) enables safe, open, and impenetrable transactions without the need for a central authority. The technology was initially created for the Bitcoin cryptocurrency, but it has subsequently been applied to other areas such as voting procedures, supply chain management, and digital identity management. The technology is increasingly becoming accepted in the academic setting for a variety of purposes, including the creation and storage of academic records. There are numerous platforms accessible for this usage, though. When numerous decision-makers are engaged in the selection process, picking an appropriate platform can be a contentious affair. For decision makers, selecting among a wide range of acceptable options might be difficult. It is possible to overcome these difficulties by using Multi-criteria Decision-Making (MCDM) techniques. When there are numerous elements to take into account, one technique for making judgments is MCDM. The process entails assessing multiple options according to pre-established standards in order to identify the optimal selection. In essence, when there are several variables to consider, MCDM assists in selecting the option. The Fuzzy Analytic Hierarchy Process (FAHP) is one of the various MCDMs which this paper uses to choose the best BCT platform for academic records based on three choices (IBM, Ethereum, and Hyperledger Fabric) and five factors (cost, degree of acceptance, simplicity of use, data security, and level of customization). The analysis's findings indicate that data security is the most crucial factor, with a weight of 0.645, and that IBM is the best BCT platform, with a value of 0.448. By comparing the FAHP results to those of AHP, IBM's suitability as a platform was confirmed.