In reasons of the new working conditions and globalization, manufacturing organizations seek to reduce their production costs. One of the factors that affect production costs is the supply chain and inventorying costs. This research presents a model of multi-stage supply chain system that operates under just-in-time (JIT) policy and supplies a fixed quantity of finished products to single customer, at a fixed time interval. Raw materials enter into the manufacturing system from two different channels. The first is brought to a consolidation centre where several items from several long-distance suppliers are collected according to single ordering policy, divided into small shipments, and redirected according to multi-ordering policy to the manufacturing system. The second type includes raw materials that are brought from JIT delivery suppliers according to the multi-ordering policy. Inbound logistics of raw materials are managed by third-party logistics (3PL) firms to coordinate and consolidate the transportation flow. The deliveries of raw material from suppliers, the work-in-process in the production stages, and the finished goods are all controlled by kanbans. For this supply chain system, the batch size and the number of batches in each stage that is to be shipped by kanbans, and the total quantity over one period are to be determined optimally. The supply chain system is modelled as a nonlinear integer programming (NLIP) problem.
Due to the violent market competition, organizations should respond quickly to customer needs. This strategic objective can be reached through the development of robust production planning. One of the most important factors in production planning is the workforce productivity which is a dynamic manufacturing property, i.e. the workforce productivity increases thanks to in-job training. This phenomenon is known as production progress function or work-based-learning. Considering this phenomenon in industrial planning can lead to robust manufacturing plans. The current study introduces a novel model for a medium term production planning, which used to find the yearly optimum aggregate production plan in order to minimize the total production costs in respecting the operational constraints and considering the production progress function. The resultant model is a linear mixed integer program that can be solved optimally. The data used in validating and running the model was taken from an Egyptian factory that is dedicated to produce electric motors. The model was solved optimally using ILOG CPLEX Software. By comparing the results of this study against the adopted approach in the factory; one can find that the model succeeded to minimize the production costs by about 5.43 % for first year, 2.66% for the second, and 1.86% for the third one. In monetary units these percentages can be translated respectively to 11.7 million L.E., 6.3 million L.E., and 4.7 million L.E.
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