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A statistical tolerancing approach for design of synchronized supply chains
Authors:Alok Kumar Choudhary   Kumar Ashutosh Singh  M.K. Tiwari  
Affiliation:aWolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Leicestershire, LE11, 3TU, UK;bS. P. Jain Institute of Management, Mumbai, India;cIntelligent Decision Support System Lab, National Institute of Foundry and Forge Technology (NIFFT), Ranchi 834003, India
Abstract:Supply chain (SC) can be visualized as a means to fulfilling the customer's requirement with the objective to maximize the overall value generated. With the rapid change of world economy, firms need to deploy alternative methodologies to improve the responsiveness of SC. In this regard, one important issue is the synchronization and co-ordination of supply chain network (SCN). In order to ensure the dispatch of finished product to the customer, with in customers specified delivery window, with a fierce high probability, the delivery performance of any SCN is to be minimized by forcing the work in the system. These requirements can be achieved by SC coordination, where timing of the actions performed by each of the business entities must be perfectly synchronized in SCN.This research is first of its kind where a probabilistic model is conceptualized to allocate the tolerances on lead times of internal business processes of a SCN. Various linear and nonlinear constraints have been considered with an end goal of minimizing the total cost of given network. In the proposed approach, synchronization among the members of SC has been achieved in the similar fashion as that of tolerance allocation on mechanical assemblies.The effectiveness of the proposed model is illustrated with a case study. The probabilistic model for ensuring synchronization shows much promise for improving the entire performance of network in terms of greater profit, speed, flexibility, quality, and product development etc., in a long run manufacturing enterprise.
Keywords:Supply chain   Tolerances   Lead time   Manufacturing enterprise
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