Spare parts management at automobile service centre using artificial neural network

Automobile industry is one of the rising industries in today's economy. One of major factors to sustain the growth is its efficient after-sales service network. The Automobile Service Centre (ASC) has large product variety as every vehicle has different repair or service needs. Owing to large variety of vehicles with different service requirement, the management of expensive spare parts is major challenge in efficient management of ASC. Inventory models have been used in past to determine the optimal inventory levels and reorder quantity but these models only consider the inventory carrying cost and ordering cost. The decision, in present context, is also influenced by size of service centre, criticality of item, unit price, etc. In this paper, ANN is used in two phases: first to determine the inventory policy and then to determine the parameters of the policy by taking the real case of an ASC in a metropolitan city.