Applying fuzzy multiple attributes decision making for product configuration

Mass customization (MC) is an emergent concept in industry intended to provide customized products through flexible processes in high volumes and at reasonably low costs. The method of configuration is one of important ways to realize quickly product customization. But, in business, particularly through the Internet, a customer normally develops in his mind some sort of ambiguity, given the choice of similar alternative products. This paper proposes a new approach to product configuration by applying the theory of fuzzy multiple attribute decision making (FMADM), which focus on uncertain and fuzzy requirements the customer submits to the product supplier. The proposed method can be used either in the product data management system or e-commerce websites, with which it is easy for customers to get his preferred product according to the utility value with respect to all attributes. Finally, the digital camera is taken as an example to further verify the validity and the feasibility of the proposed method.     

An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

Reverse logistics consists of all operations related to the reuse of products. External suppliers are one of the important members of reverse logistics and closed loop supply chain (CLSC) networks. However in CLSC network configuration models, suppliers are assessed based on purchasing cost and other factors such as on-time delivery are ignored. In this research, a general closed loop supply chain network is examined that includes manufacturer, disassembly, refurbishing, and disposal sites. Meanwhile, it is managed by the manufacturer. We propose an integrated model which has two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Besides, a fuzzy method is designed to evaluate suppliers based on qualitative criteria. The output of this stage is the weight of each supplier according to each part. In the second phase, we propose a multi objective mixed-integer linear programming model to determine which suppliers and refurbishing sites should be selected (strategic decisions), and find out the optimal number of parts and products in CLSC network (tactical decisions). The objective functions maximize profit and weights of suppliers, and one of them minimizes defect rates. To our knowledge, this model is the first effort to consider supplier selection, order allocation, and CLSC network configuration, simultaneously. The mathematical programming model is validated through numerical analysis.     

Supplier selection and supply quantity allocation of common and non-common parts with multiple criteria under multiple products

With limited capacity of suppliers, how to reduce the total operating cost of the enterprise by determining the most suitable production capacity allocation has become the major issue faced by various enterprises in producing multiple types of products. In addition, when manufacturing multiple types of products, due to the high demand of common and non-common parts, which is applicable to various products, enterprises will place special emphasis on the procurement of common and non-common parts, to select most suitable suppliers of parts with the highest quality and minimum time and costs, in order to cut down on operating costs of enterprises. This research first lists parts of various products through bill of material (BOM), and constructs an optimal mathematical model suitable for multi-phase products’ parts, in order to assess the assembling relationship of various parts; it makes use of the linkage among those to select the supplier of common and non-common parts when assessing multiple products. Then considering the limited production capacity of suppliers, it selects the best combination of suppliers of special common and non-common parts. To solve the optimal mathematical model, a genetic algorithm (GA) is proposed to find the acceptable results of the supply selection and quantity allocation problem. It then provides a benchmark for enterprise in current diversified market to purchase and assess common and non-common parts, and makes such benchmark a normal standard for selection of suppliers in the future.     

A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).     

产品配置软件的开发及产品实例的定制实现

大规模定制生产是以大规模生产的效率和成本向客户提供个性化定制产品的一种生产模式,它以客户为中心组织生产,提供多样化产品来满足不同需求。本文在建立的多色图配置模型及配置算法的基础上,开发大规模定制生产中产品配置原型软件,实现产品结构信息管理、BOM管理、配置规则管理、配置结果管理等相关功能。同时编写配置软件与CATIA的接口程序,实现在三维环境中对配置结果电子样机的预览,最后结合产品实例进行验证配置模型和配置方法的正确性,以及使用配置结果和变型设计实现定制生产。     

Machine learning for dynamic multi-product supply chain formation

Recent trend in eCommerce applications toward effectively reducing supply chain costs—including spatial, temporal, and monetary resources—has spurred interest among researchers as well as practitioners to efficiently utilize supply chains. One of the least studied of these views is adaptive or dynamic configuration of supply chains. This problem is relatively new since faster communications over the Internet or by any other means and the willingness to utilize it for effective management of supply chains did not exist a few decades ago. The proposed framework addresses the problem of supply chain configuration. We incorporate machine-learning techniques to develop a dynamically configurable supply chain framework, and evaluate its effectiveness with respect to comparable static supply chains. Specifically, we consider the case where several parts go into the production of a product. A single supplier or a combination of suppliers could supply these parts. The proposed framework automatically forms the supply chain dynamically as per the dictates of incoming orders and the constraints from suppliers upstream.     

Machine learning for dynamic multi-product supply chain formation

Recent trend in eCommerce applications toward effectively reducing supply chain costs—including spatial, temporal, and monetary resources—has spurred interest among researchers as well as practitioners to efficiently utilize supply chains. One of the least studied of these views is adaptive or dynamic configuration of supply chains. This problem is relatively new since faster communications over the Internet or by any other means and the willingness to utilize it for effective management of supply chains did not exist a few decades ago. The proposed framework addresses the problem of supply chain configuration. We incorporate machine-learning techniques to develop a dynamically configurable supply chain framework, and evaluate its effectiveness with respect to comparable static supply chains. Specifically, we consider the case where several parts go into the production of a product. A single supplier or a combination of suppliers could supply these parts. The proposed framework automatically forms the supply chain dynamically as per the dictates of incoming orders and the constraints from suppliers upstream.     

Real time fuzzy scheduling rules in FMS

This paper presents a real-time fuzzy expert system to scheduling parts for a flexible manufacturing system (FMS). First, some vagueness and uncertainties in scheduling rules are indicated and then a fuzzy-logic approach is proposed to improve the system performance by considering multiple performance measures. This approach focuses on characteristics of the system's status, instead of parts, to assign priorities to the parts waiting to be processed. Secondly, a simulation model is developed and it has shown that the proposed fuzzy logic-based decision making process keeps all performance measures at a good level. The proposed approach provides a promising alternative framework in solving scheduling problems in FMSs, in contrast to traditional rules, by making use of intelligent tools.     

Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study

Supplier selection is a decision-making process to identify and evaluate suppliers for making contracts. Here, we use interval type-2 fuzzy values to show the decision makers’ preferences and also introduce a new formula to compute the distance between two interval type-2 fuzzy sets. The performance of the proposed distance formula in comparison with the normalized Hamming, normalized Hamming based on the Hausdorff metric, normalized Euclidean and the signed distances is evaluated. The results show that the signed distance has the same trend as our method, but the other three methods are not appropriate for interval type-2 fuzzy sets. Using this approach, we propose a hierarchical clustering-based method to solve a supplier selection problem and find the proximity of the suppliers. To illustrate the applicability of the proposed method, first a case study of supplier selection problem with 8 criteria and 8 suppliers are illustrated and next, an example taken from the literature is worked through. Then, to test the hierarchical clustering-based method and compare with the obtained results by two other methods, a comparative study using experimental analysis is designed. The results show that while the proposed hierarchical clustering algorithm provides acceptable results, it is also conveniently appropriate for using interval type-2 fuzzy sets and obtaining proximity of suppliers.     

Using intelligent agents to manage fuzzy business processes

Businesses are undergoing a major paradigm shift, moving from traditional management into a world of agile organizations and processes. An agile corporation should be able to rapidly respond to market changes. For this reason, corporations have been seeking to develop numerous information technology (IT) systems to assist with the management of their business processes. Many of the coming new business processes may contain embedded intelligent agent-based systems. Agent technology looks set to radically alter not only the way in which computers are interacted, but also the way complex processes, e.g., product development, are conceptualized and built. The paper presents a fuzzy approach based on an intelligent agent framework to develop modular products. This approach aims to address the research issue: "How can modular design be carried out through intelligent agents to meet a customer's fuzzy requirements using modules that come from suppliers that are geographically separated and operate on differing computer platforms?" The proposed methodology is applied to a real-world case that involves module-based synthesis at one of largest distribution centers in the world     

Supply chain management: a modular Fuzzy Inference System approach in supplier selection for new product development

The critical objectives of purchasing departments include obtaining the product requested, at the right cost, in the right quantity, with the best quality, at the right time, from the right supplier. These goals require effective decisions concerning supplier selection at the early stage of product development. This work provides an application of fuzzy set theory in supply chain management, specifically in supplier selection for new product development. Here, a Fuzzy Inference System is proposed as an alternative approach to handle effectively the impreciseness and uncertainty that are normally found in supplier selection processes. This paper also shows that the proposed decision-making model is applicable to any supply chain system.     

Tender calls search using a procurement product named entity recogniser

A product search service in an e-Procurement Marketplace can help the suppliers to find the best suitable tenders according to their products. Various possible ways to define and specify a product by different companies make it difficult to match a tender as a product request with the similar products offered by the suppliers. Semantic search engines try to overcome this problem by understanding the intent and contextual meaning of the words within a search domain. A fundamental part of such search engines can be a named entity recogniser that extracts desired searchable elements from the search context.This paper develops a recogniser that can extract “Procurement Product” mentions from tenders and other procurement documents. A self-learning approach has been adopted in order to train the model for extracting product mentions. The proposed approach uses already known product mentions in tenders as the training data to train the model and then use the trained model to recognize the product mentions from other tenders. The accuracy of the model has been tested evaluated using tenders that have been published in public procurement e-marketplaces. The results show that the proposed approach achieved high values of precision and recall in different test datasets. The recogniser can be used as the search element extractor for semantic search in procurement e-marketplaces. Therefore, the improvement of search performance by using the recogniser is also tested in finding tenders from different public procurement resources. The results show the semantic search process which uses the recogniser improves the search precision by about 25%.     

An integrated fuzzy synthetic evaluation approach for supplier selection based on analytic network process

In order to ensure the uninterrupted supply of items, the suppliers’ performance needs to be evaluated periodically. The evaluation process typically consists of identifying the attributes and criteria relevant to the decision, and measuring the performance of a supplier by considering the relevant criteria. But the evaluation process is complex. Linguistic assessment of suppliers may be carried out based on several criteria. Much of the data are difficult to obtain and ambiguous or vague to interpret. Nonetheless, a rational process of evaluation must exist to select the most appropriate suppliers. This paper develops a supplier evaluation approach based on the analytic network process (ANP) and fuzzy synthetic evaluation under a fuzzy environment. The importance weights of various criteria are considered as linguistic variables. These linguistic ratings can be expressed in triangular fuzzy numbers by using the fuzzy extent analysis. Fuzzy synthetic evaluation is used to select a supplier alternative and the Fuzzy ANP (FANP) method is applied to calculate the importance of the criteria weights. Then an integrated FANP and fuzzy synthetic evaluation methodology is proposed for evaluating and selecting the most suitable suppliers. A hypothetical example is presented and the results indicated that the combination of ANP and fuzzy synthetic evaluation provided useful tool to select the optimal supplier.     

A fuzzy solution approach for multi objective supplier selection

In today’s severe competitive environment the selection of appropriate suppliers is a significantly important decision for effective supply chain management. Appropriate suppliers reduce purchasing costs, decrease production lead time, increase customer satisfaction and strengthen corporate competitiveness. In this study a multiple sourcing supplier selection problem is considered as a multi objective linear programming problem. Three objective functions are minimization of costs, maximization of quality and maximization of on-time delivery respectively. In order to solve the problem, a fuzzy mathematical model and a novel solution approach are proposed to satisfy the decision maker’s aspirations for fuzzy goals. The proposed approach can be efficiently used to obtain non-dominated solutions. A numerical example is given to illustrate how the approach is utilized.     

Analytical study on multi-product production planning with outsourcing

This paper studies a problem on multi-product capacitated production planning with outsourcing. The context of the problem is about an enterprise that manufactures multiple products in multiple periods for stochastic demands. Manufacturers usually have two alternative modes for the production: one is to outsource parts from outside suppliers and then assemble them; the other is to in-house manufacture parts and then assemble them. Each mode has its relative merits. In addition, the capacity constraint by in-house manufacturing is also taken into account. This paper investigates how to balance the trade-off between the two modes. An analytical approach is proposed to study the optimal decision on the above two modes for all products during each planning period. Some findings are drawn out from this analytical study. Numerical experiments show the significant cost reduction can be obtained using the proposed decision model.     

Co-development of product and supplier platform

The platform strategy has been implemented to efficiently manage the increased variety in products and manufacturing systems domains by achieving their effective and rapid re-configuration. Despite the increased development of platforms research, their back-end issues such as the supply chain and supplier selection have received little attention. In this research, a methodology that integrates the product platform synthesis with the selection of suppliers to form a supplier platform is introduced. The formed supplier platform is a collection of suppliers capable of supplying the components/modules of the product platform. The supplier platform remains unchanged for product generations, and non-platform suppliers are added or removed as needed for producing different product variants in different production periods. The presented co-development methodology consists of three phases. First, co-platforming is used to map the product requirements to the supplier’s domain; then an intuitionistic fuzzy TOPSIS method is employed to assign weights to the suppliers according to selected criteria. The suppliers are chosen next and their platform is synthesized. A laptop product family is used to illustrate the developed methodology. The significance of this research is the synthesis of a supplier platform which can be used without change for many product variants and many product generations. Its implementation enables the planning and creation of strategic alliances with the product platform suppliers.     

Fuzzy agents for product configuration in collaborative and distributed design process

Design for product configuration is an inherent collaborative and distributed process. It is characterised by fuzziness of information, fuzziness of knowledge and fuzziness of interactions. Designs for configuration organisations are heterogeneous, dynamic and fuzzy evolving systems. This paper proposes a fuzzy agent-based approach to assist the product configuration. Four heterogeneous and distributed domains: (a) requirement, (b) functional, (c) solution and (d) constraint, are considered. Based on the distributed fuzzy models, fuzziness of interactions, a fuzzy computational approach for product configuration is developed. Agentification of the configuration approach, modelling and the implementation of a multiagent system, are presented. The fuzzy consensual solution agents emerge from fuzzy interactions of fuzzy distributed agents. The optimal product configuration emerges from affinities of the fuzzy consensual solution agents. A case study is presented to demonstrate the potential of this approach.     

Modeling customer satisfaction for new product development using a PSO-based ANFIS approach

When developing new products, it is important to understand customer perception towards consumer products. It is because the success of new products is heavily dependent on the associated customer satisfaction level. If customers are satisfied with a new product, the chance of the product being successful in marketplaces would be higher. Various approaches have been attempted to model the relationship between customer satisfaction and design attributes of products. In this paper, a particle swarm optimization (PSO) based ANFIS approach to modeling customer satisfaction is proposed for improving the modeling accuracy. In the approach, PSO is employed to determine the parameters of an ANFIS from which better customer satisfaction models in terms of modeling accuracy can be generated. A notebook computer design is used as an example to illustrate the approach. To evaluate the effectiveness of the proposed approach, modeling results based on the proposed approach are compared with those based on the fuzzy regression (FR), ANFIS and genetic algorithm (GA)-based ANFIS approaches. The comparisons indicate that the proposed approach can effectively generate customer satisfaction models and that their modeling results outperform those based on the other three methods in terms of mean absolute errors and variance of errors.     

Selection of a dynamic supply portfolio in make-to-order environment withrisks

The problem of a multi-period supplier selection and order allocation in make-to-order environment in the presence of supply chain disruption and delay risks is considered. Given a set of customer orders for finished products, the decision maker needs to decide from which supplier and when to purchase product-specific parts required for each customer order to meet customer requested due date at a low cost and to mitigate the impact of supply chain risks. The selection of suppliers and the allocation of orders over time is based on price and quality of purchased parts and reliability of supplies. For selection of dynamic supply portfolio a mixed integer programming approach is proposed to incorporate risk that uses conditional value-at-risk via scenario analysis. In the scenario analysis, the low-probability and high-impact supply disruptions are combined with the high probability and low impact supply delays. The proposed approach is capable of optimizing the dynamic supply portfolio by calculating value-at-risk of cost per part and minimizing expected worst-case cost per part simultaneously. Numerical examples are presented and some computational results are reported.