A local collaboration as the most successful co-ordination scenario in the supply chain
Purpose - The paper's purpose is to provide a method of reducing inventory costs in multi-product and multi-nodes supply chains (SC). Design/methodology/approach - The proposed approach is based on applying the classical inventory control models and simulation. This is a two-stage approach in which inventory cost reduction in the SC occurs as a result of the appropriate selection of inventory control policies by its members. Findings - Cost reduction in the whole SC may occur as a result of an appropriate choice of inventory control policies in every node. SC members can learn which inventory control policies should be applied. Results of the learning process are closely connected with a kind of collaboration between chain nodes. The best results of the whole system can happen with a simultaneous deterioration of operation of its component elements. Therefore, losses which occur in some chain members as a result of collaboration should be compensated. Research limitations/implications - This approach does not offer an optimal solution. The authors do not know how far they are from an optimal solution. The approach should be tested for more complicated SC. The authors have not studied the strategy in which SC cells negotiate the level of their inventories. Practical implications - SC cells should apply a local cooperation strategy. When the cost of the whole SC decreases, the cost in particular nodes may significantly increase. The start of collaboration in the SC can cause a deterioration of results in some companies; therefore this loss should be compensated. Originality/value - The paper presents the development of a framework in which an application of different policies of inventory control and application of different collaboration strategies in the SC are studied. The framework enables the use of classical inventory control models in a coordinated manner, and SC members decide which policies should be applied only on the basis of earlier collected experiences.
inventory control simulation supply chain management