Intelligent evaluation of supplier bids using a hybrid technique in distributed supply chains

The main idea of this research is to devise the smart module to pick the best supplier bid(s) automatically. The hybrid model is composed of three useful tools: fuzzy logic, AHP, and QFD. The approach has been carefully implemented and verified via a real-world case study in a medium-to-large industry manufacturing vehicle tires and other rubber products. A collection of 12 assessment criteria classified into two categories have been considered. Eight factors are derived from customer suggestions and the other four are design specifications required to manufacture the product. The main outcomes are: a hybrid autonomous model to evaluate supplier bids without direct human intervention; devising a hybrid three-module method and overcoming complexity of computations in resulting algorithm by means of agents; outlining the best criteria to assess suppliers; evaluating the suppliers based on voice of customer during all stages of the process; and discussing analysis, design, and implementation issues of the evaluation agent. The paper includes implications for development of an integrated total system for supply chain coordination. The most important advantages of this work over earlier researches on supplier selection are: implementation of an autonomous assessment mechanism using intelligent agents for the first time, making the best out of three widely applied methodologies all at once, evaluation process mainly based on features of customer order, coordination of supply job based on a bidding system, and portal-mediated operation and control.     

A new DEA method for supplier selection in presence of both cardinal and ordinal data

The success of a supply chain is highly dependent on selection of best suppliers. These decisions are an important component of production and logistics management for many firms. Little attention is given in the literature to the simultaneous consideration of cardinal and ordinal data in supplier selection process. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to identify most efficient supplier in presence of both cardinal and ordinal data. Then, utilizing this model, an innovative method for prioritizing suppliers by considering multiple criteria is proposed. As an advantage, our method identifies best supplier by solving only one mixed integer linear programming (MILP). Applicability of proposed method is indicated by using data set includes specifications of 18 suppliers.     

Digital twin-based smart assembly process design and application framework for complex products and its case study

With rapid advances in new generation information technologies, digital twin (DT), and cyber-physical system, smart assembly has become a core focus for intelligent manufacturing in the fourth industrial evolution. Deep integration between information and physical worlds is a key phase to develop smart assembly process design that bridge the gap between product assembly design and manufacturing. This paper presents a digital twin reference model for smart assembly process design, and proposes an application framework for DT-based smart assembly with three layers. Product assembly station components are detailed in the physical space layer; two main modules, communication connection and data processing, are introduced in the interaction layer; and we discuss working mechanisms of assembly process planning, simulation, predication, and control management in the virtual space layer in detail. A case study shows the proposed approach application for an experimental simplified satellite assembly case using the DT-based assembly application system (DT-AAS) to verify the proposed application framework and method effectiveness.     

Impact of decision sequence of pricing and quality investment in decentralized assembly system

The quality and price of suppliers’ products directly influence the potential demand in the market. This paper studies the impact of decision sequence in a two-echelon assembly system with demand uncertainty: suppliers first decide wholesale price and quality investment for their components, and then the manufacturer decides product price after the uncertainty in demand is resolved. We consider three scenarios in the stage of suppliers’ decisions. In case 1, both suppliers simultaneously determine quality investment followed by the simultaneous setting of wholesale prices. In case 2, both suppliers make quality investment and wholesale price decisions simultaneously. In case 3, one supplier acts as a leader and moves first to announce quality investment and wholesale price, and the other supplier moves later. We compare all decision models from each firms’ perspective. Our analysis reveals that the second decision sequence is the best option for the manufacturer and the integrated supply chain and the first decision sequence is the best option for suppliers as a whole, but there is no clear dominating choice for each supplier. The optimal choices of suppliers are mainly determined by the cost structure. We examine and discuss the relation between system parameters and the incentives of suppliers in choosing decision sequence.     

A framework for collaborative top-down assembly design

This paper presents a new system framework for collaborative top-down assembly design. Different from current computer-aided design (CAD) systems, the framework allows a group of designers to collaboratively conduct product design in a top-down manner. In our framework, a multi-level and distributed assembly model is adopted to effectively support collaborative top-down assembly design. Meanwhile, fine-granularity collaborative design functionalities are provided. First, the coupled structural parameters involved in the distributed skeleton models of the product can be collaboratively determined by the correlative designers based on fuzzy and utility theory. Second, agent based design variation propagation is achieved to ensure the consistency of the multi-level and distributed assembly model during the whole design process. Third, collaborative design of assembly interfaces between the components assigned to different designers is supported. The prototype implementation shows that our framework works well for supporting practical collaborative top-down assembly design.     

An intelligent supplier evaluation,selection and development system

Supplier evaluation and selection process has a critical role and significant impact on purchasing management in supply chain. It is also a complex multiple criteria decision making problem which is affected by several conflicting factors. Due to multiple criteria effects the evaluation and selection process, deciding which criteria have the most critical roles in decision making is a very important step for supplier selection, evaluation and particularly development. With this study, a hybridization of fuzzy c-means (FCM) and rough set theory (RST) techniques is proposed as a new solution for supplier selection, evaluation and development problem. First the vendors are clustered with FCM algorithm then the formed clusters are represented by their prototypes that are used for labeling the clusters. RST is used at the next step of modeling where we discover the primary features in other words the core evaluation criteria of the suppliers and extract the decision rules for characterizing the clusters. The obtained results show that the proposed method not only selects the best supplier(s), also clusters all of the vendors with respect to fuzzy similarity degrees, decides the most critical criteria for supplier evaluation and extracts the decision rules about data.     

A complexity model for sequence planning in mixed-model assembly lines

Sequence planning is an important problem in assembly line design. It is to determine the order of assembly tasks to be performed sequentially. Significant research has been done to find good sequences based on various criteria, such as process time, investment cost, and product quality. This paper discusses the selection of optimal sequences based on complexity induced by product variety in mixed-model assembly line. The complexity was defined as operator choice complexity, which indirectly measures the human performance in making choices, such as selecting parts, tools, fixtures, and assembly procedures in a multi-product, multi-stage, manual assembly environment. The complexity measure and its model for assembly lines have been developed in an earlier paper by the authors. According to the complexity models developed, assembly sequence determines the directions in which complexity flows. Thus proper assembly sequence planning can reduce complexity. However, due to the difficulty of handling the directions of complexity flows in optimization, a transformed network flow model is formulated and solved based on dynamic programming. Methodologies developed in this paper extend the previous work on modeling complexity, and provide solution strategies for assembly sequence planning to minimize complexity.     

Analytic network process and multi-period goal programming integration in purchasing decisions

This paper presents a multi-period inventory lot sizing scenario, where there is single product and multiple suppliers. By considering multi-period planning horizon, an integrated approach of Archimedean Goal Programming (AGP) and Analytic Network Process (ANP) is suggested. This integrated approach proposes a two-stage mathematical model to evaluate the suppliers and to determine their periodic shipment allocations given a number of tangible and intangible criteria. In the evaluation stage, the suppliers are evaluated according to 14 criteria that are involved in four control hierarchies; benefit, opportunity, cost and risk (BOCR). In the shipment stage, a multi objective mixed integer linear programming (MOMILP) model is described to solve the order allocation problem. This MOMILP model is suggested to achieve target values of periodic goals: budget, aggregate quality, total value of purchasing (TVP) and demand over the planning horizon, without exceeding vendor production capacities. This multi-period model is solved by using AGP. Finally some computational experiments are conducted to test the performance of the proposed method.     

A hybrid intelligent approach for supply chain management system

The process of supplier selection is one of the key processes in the industry since the suppliers play a key role in regards to the competitive advantage. It is therefore important to identify and select good suppliers. Supplier selection can be considered a decision-making problem with many criteria, which include both qualitative and quantitative elements, such as; purchase cost, quality level, supplier risk etc. The best process to be designed for selecting the suppliers may require a compromise in terms of these elements. A general framework, which combines Analytic Hierarchy Process (AHP), Fuzzy AHP and Genetic Algorithm (GA) in order to determine the best set of suppliers, is proposed in this study.     

A new approach to quality function deployment planning with financial consideration

Quality function deployment (QFD) is becoming a widely used customer-oriented approach and tool in product design. Taking into account the financial factors and uncertainties in the product design process, this paper deals with a fuzzy formulation combined with a genetic-based interactive approach to QFD planning. By introducing new concepts of planned degree, actual achieved degree, actual primary costs required and actual planned costs, two types of fuzzy optimisation models are discussed in this paper. These models consider not only the overall customer satisfaction, but also the enterprise satisfaction with the costs committed to the product. With the interactive approach, the best balance between enterprise satisfaction and overall customer satisfaction can be obtained, and the preferred solutions under different business criteria can be achieved through human–computer interaction.Scope and PurposeQuality function deployment (QFD) that originated in Japan in the late 1960s is a concept and mechanism for translating the ‘voice of customer’ into product through various stages of product planning, engineering and manufacturing. It has become a widely used customer-oriented approach to facilitating product design by analysing customer requirements (CRs). Determination of the target levels for the technical attributes (TAs) of a product with a view to achieving a high level of overall customer satisfaction is an important activity in product design and development.Traditional methods for QFD planning are mainly subjective, ad hoc and heuristic. They can hardly achieve global optimisation, and most of these models barely take into consideration the correlation between TAs. Moreover, most of these methods are technically one-sided without considering the design budget. However, the financial factor is also an important factor and should not be neglected in QFD planning. In addition, owing to uncertainties involved in the decision process, these deterministic methods could not formulate and solve it effectively.Taking into consideration the financial factors and uncertainties in the product design process, this paper deals with fuzzy formulation combined with a genetic-based interactive approach to QFD planning. By introducing new concepts of planned degree, actual achieved degree, actual primary costs required and actual planned costs, two types of fuzzy optimisation models are discussed in this paper. These models consider not only the overall customer satisfaction, but also the enterprise satisfaction with the costs committed to the product. With the interactive approach, the best balance between enterprise satisfaction and overall customer satisfaction can be obtained, and the preferred solutions under different business criteria can be achieved through human–computer interaction.     

Modular product platform configuration and co-planning of assembly lines using assembly and disassembly

Formation of products platforms is carried out during the planning stage and very often separately from the planning of corresponding assembly lines. There is a dearth of literature which considers the different aspects of fully integrating platform design, product family formation, assembly line design, delayed product differentiation, and new concepts of mass customization. A Modular Product Platform Configuration model which uses assembly and disassembly for configuring product variants and Co-Planning of products platforms (MPCC) and their assembly Lines is presented. It is used to co-plan the common platform components and the associated product families simultaneously with the planning of its corresponding mixed-model assembly line. Using both assembly and disassembly to customize the product family platform in order to generate product variants is not commonly discussed in literature. It is defined as the formation of platforms for use to derive multiple products by including many components not shared by every product. The platform is then customized by assembling or disassembling components to form different product variants. The model is formulated using mixed integer mathematical programming to minimize the number of assembly stations and cycle time. Two case studies are used for verification and demonstration. They illustrated the ability of the MPCC model to integrate the planning of product platform, product families and the number of assembly stations required to assemble and disassemble components from mass-assembled product platforms to derive new product variants.     

Knowledge-based approach and system for assembly oriented design,Part I: the approach

Assembly is one of the most important stages for product development. Assembly-oriented design (AOD) is a new approach to designing assemblies, which uses a number of design and analysis tools to help the designer plan out and analyze candidate assembly schemes prior to having detailed knowledge of the geometry of the parts. Using this approach, many assembly schemes can be inexpensively evaluated for their ability to deliver the important characteristics of the final product. This research proposes a knowledge-based approach and develops an expert design system to support top-down design for assembled products. The presentation of research report is divided into two parts: the knowledge-based approach (Part I) and the knowledge-based expert design system (Part II). This paper is the first part of the report (Part I), which mainly proposes a knowledge-based approach and framework for intelligent assembly oriented design. The proposed approach focuses on the integration of product design, assemblability analysis and evaluation, and design for assembly with economical analysis. It differs from the existing approaches adopting the part-first bottom-up modeling technique, in which a hybrid model related to design problem-solving including function–behavior–structure model, feature-based geometric model, and parametric constraint model is used as a comprehensive intelligent framework for assembly modeling and design in a top-down manner from the conceptual level to the detailed level. Through the use of intelligent approach and framework, concurrent engineering knowledge can be effectively incorporated into the assembly design process, and a knowledge-based expert design system can be implemented.     

Integrated rough VIKOR for customer-involved design concept evaluation combining with customers’ preferences and designers’ perceptions

Customer-involved design concept evaluation (DCE) allows customers to take part in evaluating the design alternatives to get more popular design concept. Traditional customer-involved DCE methods still focus on the collection of customer responses and only consider cost and benefit characteristics of design criteria in multi-criteria decision-making (MCDM) based evaluation process. Few studies have customized the decision-making algorithms specifically aimed at customers’ preferences. This paper further explores the customers’ influences in the early stages of the product design development, and proposes a new rough number based MCDM model (i.e., VIKOR) incorporating customers’ preferences for design specifications along with designers’ perceptions for the characteristics of design criteria (cost and benefit) to perform concept evaluation under subjective environment, and this proposed method is named as integrated rough VIKOR (IR-VIKOR). The objective of this study is to identify the best design concept which maximizes the satisfactions of expectations from most customers as well as conforms to the characteristics of design criteria. Firstly, Shannon entropy is used to obtain the weightings and relative importance ratings of design criteria from the customers’ preferences. Secondly, the customers’ preferences for design attribute values, the importance ratings of design criteria and the characteristics of design criteria are combined together to define the ideal solutions to calculate the rough evaluation index of each design alternative in IR-VIKOR, and finally the ranking result is provided by IR-VIKOR to determine the best design concept. A practical design example is introduced to illustrate the evaluation process of this proposed method, and the empirical comparisons are further carried out to validate its superiority for DCE. Through the sensitivity analysis experiments including i) inside IR-VIKOR, and ii) between IR-VIKOR and other classical MCDM methods, the proposed method is proved to be a reliable and feasible customer-involved DCE approach.     

Web-based modular interface geometries with constraints in assembly models

The use of a modular technique in modeling for assembly can improve design efficiency and reduce the cost of product development. This paper presents an approach to an assembly model that was built using modular interface geometries. This paper proposes a novel, hybrid modular design strategy instead of the traditional, top-down design process. The curve-joint method is used as a simplified process for converting a 3D solid model to a skeleton model with interface geometries in modeling for assembly. This research builds assembly interface geometries with their constraints in the assembly model instead of using information about individual assembly parts for the product. These interface geometries are easy to share, and they deliver the design requirements properly. They also ensure that minimal efforts will be required in the design change process. By implementing this method, the constraints of the features in modular assembly parts can be transferred to interface geometries. Designers can easily add, replace, and delete design parts in the modular product. Module interaction for application programmed interface (MIAPI) is developed using HTML and JavaScript. The module structure of products can be verified via the web-based Internet in VRML format. These simplified assembly models that have fewer constraints allow design project managers to simulate the functioning of the product in the modularized design before the prototype is built. By using the assembly models, customers can easily choose various modules to assemble the exact products they are seeking via the Internet process. A desk lamp model is used as the example for implementation to validate the feasibility of this research.     

Literature review of reuse in business process modeling

Business process models play an important role in the analysis and improvement of the performance of an enterprise. Evidently, the quality of a business process model has a direct effect on the business performance. This evidence has motivated both the academic and industrial communities to look for suitable methods for creating good quality business process models. In particular, there is a wide agreement that reuse can accelerate the design process and produce high quality solutions by adopting best practices and agreed-up-on solutions. However, faced with various types of reusable artifacts, business process designers need a set of criteria to determine which type would suit best their needs and design context. To assist designers in their choice, we first present a set of criteria influencing the design phase in terms of effort required and the quality of the resulting model. Secondly, we use this set of criteria to present a state of the art on the most significant reusable design artifacts.     

支持产品全生命周期设计的广义装配建模的研究

装配建模的实质就在于如何在计算机内有效地表达装配体外在的和内在的关系。本文在分析装配体几何实体信息模型、配合联接信息及设计约束机制的基础上,提出了一种以层次结构信息为逻辑主干,以拓朴联接信息为重要组成部分、支持产品全生命周期设计的广义装配模型的表达机制。该模型具有在数据结构与设计者的概念模型相同,便于理解,支持自上而下逐步求精的设计过程,便于装配模型根据需要进行修改和扩充,支持产品生命周期设计的全过程等特点。     

Geometric skeleton computation enabling concurrent product engineering and assembly sequence planning

This paper introduces a novel modelling approach to geometric skeleton computation enabling concurrent product engineering and assembly sequence planning. Current engineering vision has recently moved towards new modelling and management paradigms to maintain competitive edges all along the product lifecycle. Consistent with concurrent engineering and design for X stakes, this recent shift promotes cross-X and knowledge-intensive philosophies in the product development process, principally focused on lifecycle engineering.The main objective of this research is to integrate assembly process engineering information and knowledge in the early phases of the product development process in a top-down and proactive manner, in order to provide a geometric skeleton-based assembly context for designers. The definition of the product and its related assembly sequence requires both the enhancement and the entire understanding of product relationships between the various product components, and its related assembly rationale. As a consequence, this new modelling approach highlights the need to integrate various stakeholders’ viewpoints involved in the beginning of the product lifecycle. In such a context, earlier work has achieved the early generation of an optimal assembly sequence in the product development process, before the product geometry is completely defined. As a result, previous research has made possible to control and bind the product modelling phase through an assembly oriented product structure.The aim of the proposed approach is to compute and define a geometric skeleton model based on product relational information and the early-defined assembly sequence. The proposed approach–called SKeLeton geometry-based Assembly Context Definition (SKL-ACD)–enables the control of the product modelling phase by introducing skeleton entities consistent with product relationships and assembly sequence planning information. A prototype application within a CAD tool has been developed for aiding geometric skeleton computation and generation. Lastly, an industrial case study is introduced to highlight the feasibility and the relevance of the proposed modelling approach.     

An assembly oriented design framework for product structure engineering and assembly sequence planning

The paper describes a novel framework for an assembly-oriented design (AOD) approach as a new functional product lifecycle management (PLM) strategy, by considering product design and assembly sequence planning phases concurrently. Integration issues of product life cycle into the product development process have received much attention over the last two decades, especially at the detailed design stage. The main objective of the research is to define assembly sequence into preliminary design stages by introducing and applying assembly process knowledge in order to provide an assembly context knowledge to support life-oriented product development process, particularly for product structuring. The proposed framework highlights a novel algorithm based on a mathematical model integrating boundary conditions related to DFA rules, engineering decisions for assembly sequence and the product structure definition. This framework has been implemented in a new system called PEGASUS considered as an AOD module for a PLM system. A case study of applying the framework to a catalytic-converter and diesel particulate filter sub-system, belonging to an exhaust system from an industrial automotive supplier, is introduced to illustrate the efficiency of the proposed AOD methodology.     

Optimal assembly plan generation: a simplifying approach

The main difficulty in the overall process of optimal assembly plan generation is the great number of different ways to assemble a product (typically thousands of solutions). This problem confines the application of most existing automated planning methods to products composed of only a limited number of components. The presented method of assembly plan generation belongs to the approach called “disassembly” and is founded on a new representation of the assembly process, with introduction of a new concept, the equivalence of binary trees. This representation allows to generate the minimal list of all non-redundant (really different) assembly plans. Plan generation is directed by assembly operation constraints and plan-level performance criteria. The method was tested for various assembly applications and compared to other generation approaches. Results show a great reduction in the combinatorial explosion of the number of plans. Therefore, this simplifying approach of assembly sequence modeling allows to handle more complex products with a large number of parts.     

A modified failure mode and effects analysis method for supplier selection problems in the supply chain risk environment: A case study

In the emerging supply chain environment, supply chain risk management plays a more important role than ever. Companies must focus not only on the efficiency of supply chain, but also on its risks. If an unanticipated event occurs, all of the supply chain members will be impacted, and the result will cause significant loss. Therefore, this research proposes a modified failure mode and effects analysis (MFMEA) method to select new suppliers from the supply chain risk’s perspective and applies the analytic hierarchy process (AHP) method to determine the weight of each criterion and sub-criterion for supplier selection. An IC assembly company is then studied to validate this model. The result shows that the case company can categorize its suppliers more effectively and at the same time select a low-risk supply chain partner. Moreover, the case company can provide unsatisfactory suppliers with valuable feedback that will help them improve and become its partners in the future.