4,762 research outputs found

    A comparison of multi-criteria methods for spare parts classification

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    Spare parts classification is a fundamental step in spare parts inventory management. Through classification, the parts are grouped using a set of relevant criteria. Methodologies and methods for multicriteria decision making are used to support the classification of spare parts. In this paper, a comparative study between the use of the multi-criteria classification based on rules and the multi-criteria classification using the Analytic Hierarchy Process is presented, showing the advantages and disadvantages of each method. The study confirmed that the multi-criteria method based on rules is more easily applied in organizations. The multi-criteria method using Analytic Hierarchy Process required more calculations, turning the implementation of the method more complicated, especially for non-Analytic Hierarchy Process specialists

    The study of supply chain management in Chery Automobile Co., LTD

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    Integrating Closed-loop Supply Chains and Spare Parts Management at IBM

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    Ever more companies are recognizing the benefits of closed-loop supplychains that integrate product returns into business operations. IBMhas been among the pioneers seeking to unlock the value dormant inthese resources. We report on a project exploiting product returns asa source of spare parts. Key decisions include the choice of recoveryopportunities to use, the channel design, and the coordination ofalternative supply sources. We developed an analytic inventory controlmodel and a simulation model to address these issues. Our results showthat procurement cost savings largely outweigh reverse logistics costsand that information management is key to an efficient solution. Ourrecommendations provide a basis for significantly expanding the usageof the novel parts supply source, which allows for cutting procurementcosts.supply chain management;reverse logistics;product recovery;inventory management;service management

    In search for classification and selection of spare parts suitable for additive manufacturing: a literature review

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    This paper reviews the literature on additive manufacturing (AM) technologies and equipment, and spare parts classification criteria to propose a systematic process for selecting spare parts which are suitable for AM. This systematic process identifies criteria that can be used to select spare parts that are suitable for AM. The review found that there is limited research that addresses identifying processes for spare parts selection for AM, even though companies have identified this to be a key challenge in adopting AM. Seven areas for future research are identified relating to the methodology of spare parts selection for AM, processes for cross-functional integration in selecting spare parts for AM, broadening the spare parts portfolio that is suitable for AM (by considering usage of AM in conjunction with conventional technologies), and potential impact of AM on product modularity and integrality

    A review of the state of the art in tools and techniques used to evaluate remanufacturing feasibility

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    Remanufacturing often seems a sensible approach for companies looking to adopt sustainable business plans to achieve long term success. However, remanufacturing must not be treated as a panacea for achieving a sustainable business, as issues such as market demand, product design, end of life condition and information uncertainty can affect the success of a remanufacturing endeavour. Businesses therefore need to carefully assess the feasibility of adopting remanufacturing before committing to a particular activity or strategy. To aid this decision process, a number of tools and techniques have been published by academics. However, there is currently not a formal review and comparison of these tools and how they relate to the decision process. The main research objective of this study has therefore been to identify tools and methods which have been developed within academia to support the decision process of assessing and evaluating the viability of conducting remanufacturing, and evaluate how they have met the requirements of the decision stage. This has been achieved by conducting a content analysis. Three bibliographic databases were searched (Compendex, Web of Science and Scopus) using a structured keyword search to identify relevant literature. The identified tools were then split into 6 categories based upon the specific decision stages and applications, then evaluated against a set of key criteria which are, the decision factors (economic, environmental, social) and the inclusion of uncertainty. The key finding of this study has been that although decision factors are generally well covered, operational tools and the use of uncertainty are often neglected

    Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

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    With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to re-examine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model

    Moving forward in reverse : a review into strategic decision making in reverse logistics

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    Reverse Logistics, the process of managing the backward flows of materials from a point of use to a point of recovery or proper disposal, has gained increased industry acceptance as a strategy for both competitive advantage and sustainable development. This has stimulated a growing number of researchers to investigate Strategic management issues relating to the set up and control of effective and efficient Reverse Logistics systems. This paper systematically reviews the most important works in this field, with a focus on papers that concentrate on the strategic decision making process involved in the design and operation of a Reverse Logistics process with remanufacturing. The review found that: the majority of work is primarily focused on OEM specific issues; the sectors receiving the most attention are the ones under the greatest pressure from environmental legislation; and previous research findings from Rubio et al. (2009) and Fleischmann et al. (2000) are reaffirmed that the Reverse Logistics field is growing, but characterised by mainly quantitative, mathematical models. Future research efforts should be focused on the empirical investigation of the Reverse Logistics design process for all types of remanufacturers

    A survey of AI in operations management from 2005 to 2009

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    Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research
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