Random network models and sensitivity algorithms for the analysis of ordering time and inventory state in multi-stage supply chains |
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| Affiliation: | 1. School of Management, Fuzhou University, 2 Xue Yuan Rd., University Town, Fuzhou 350108, China;2. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing 210016, China;1. Applied Innovation Center for Advanced Analytics, Desert Research Institute, USA;2. Department of Computer Science and Engineering, Mississippi State University, USA;3. Department of Electrical and Computer Engineering, Mississippi State University, USA;1. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China;2. Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, China;1. School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China;2. School of International Business, Inner Mongolia University of Technology, Hohhot 010080, China;1. School of Mathematics and Statistics, University of Melbourne, Victoria 3010, Australia;2. Department of Mathematics, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia;3. Melbourne School of Population and Global Health, University of Melbourne, Victoria 3010, Australia;4. Modelling and Simulation, Infection and Immunity Theme, Murdoch Childrens Research Institute, Royal Children''s Hospital, Parkville, Victoria 3052, Australia |
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| Abstract: | Supply chains in reality face a highly dynamic and uncertain environment, especially the uncertain end-customer demands and orders. Since the condition of product market changes frequently, the tasks of order management, product planning, and inventory management are complex and difficult. It is imperative for companies to develop new ways to manage the randomness and uncertainty in market demands. Based on the graphical evaluation and review technique, this paper provides a simple but integrated stochastic network mathematical model for supply chain ordering time distribution analysis. Then the ordering time analysis model is extended so that the analysis of inventory level distribution characteristics of supply chain members is allowed. Further, to investigate the effects of different end-customer demands on upstream orders and relative inventory levels, model-based sensitivity analysis algorithms for ordering fluctuations and inventory fluctuations are developed. A detailed numerical example is presented to illustrate the application of the proposed models to a multi-stage supply chain system, and the results of which shows the effectiveness and flexibility of the proposed stochastic network models and algorithms in order and inventory management. |
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| Keywords: | Supply chain networks Ordering time distribution Inventory management Sensitivity analysis |
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