Operator Allocation in Procurement Process Using Computer Simulation and Data Envelopment Analysis

Abstract

Thailand’s commercial banks have been in fierce competition for quite some years. Procurement is an important department where strategic decisions are made. With various procurement types and demand fluctuation over the year, the bank could find it difficult to allocate operators to efficiently handle the tasks at hand. In this research, a procurement department of an existing Thai bank is used as a case study. In this particular bank, procurement department is divided into three teams which handle different types of procurement: Building, Outsourcing and General procurement. Currently, they are facing with a high rate of overall delayed work caused by a seasonal pattern of demand. Quarterly dynamic operator allocation alternatives are proposed in order to mitigate the effects of demand fluctuation and reduce the delayed work rate. The ARENA computer simulation is applied to help to assess alternatives. Parameters from the simulation model are collected and used for evaluating the alternative in Data Envelopment Analysis (DEA). Two DEA models which are BCC and MCDEA are used to determine the best allocation plan among alternatives. From simulation result, with proper operator allocation, the department can balance the operator utilization among teams which leads to lower cycle time and also the number of delayed work. Then these allocation plans are evaluated with DEA model. MCDEA shows superior discriminating power over BCC model by awarding only one scenario while BCC award to 6 out 10 scenarios to be an efficient alternative. Finally, with the best efficient allocation plan, the procurement department can reduce the number of delayed works by 6% without adding additional operator.