Operator Allocation in Procurement Process Using Computer Simulation and Data Envelopment Analysis
Article Sidebar
Main Article Content
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 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 two scenarios while BCC award to 7 out 9 scenarios to be an efficient alternative. Finally, with the best efficient allocation plan, the procurement department can reduce the number of delayed works by 2.6% without adding additional operator.
Article Details
บทความ ข้อมูล เนื้อหา รูปภาพ ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารฯ ถือเป็นลิขสิทธิ์ของวารสารฯ หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใดๆ จะได้รับอนุญาต แต่ห้ามนำไปใช้เพื่่อประโยชน์ทางธุรกิจ และห้ามดัดแปลง
References
[2] Egilmez G. et al., Stochastic skill-based manpower allocation in a cellular manufacturing system, Journal of Manufacturing Systems, 41,193-198 (2014)
[3] Watanabe S. P. et al., Internal Staff Allocation and the Changing Workload of Japanese Professoriate: A Multilevel Statistical Analysis with Simulations, University of California Berkeley,1.13, 1-10 (2013)
[4] Azadeh A. et al., An integrated fuzzy DEA–Fuzzy simulation approach for optimization of operator allocation with learning effects in multi products CMS, Applied Mathematical Modelling, 37, 9922-9933 (2013)
[5] Oh, C. et al., Use of a simulation-based decision support tool to improve emergency department throughput., Operations Research for Health Care, 9, 29-39, (2016)
[6] Azadeh A. and M. Anvari, Implementation of multivariate methods as decision making models for optimization of operator allocation by computer simulation in CMS, SIMULATION SERIES, 26:4, 316-325 (2016)
[7] Ertay T. and D. Ruan, Data envelopment analysis based decision model for optimal operator allocation in CMS, European journal of operational research, 164, 800-810 (2005)
[8] Rani R. M. et al., Determining the optimal operator allocation using a three-phase methodology, Proceedings of the Universiti Kebangsaan Malaysia, 1614, 851-858 (2014)
[9] Azadeh A., et al., An integrated fuzzy DEA–fuzzy C-means–simulation for optimization of operator allocation in cellular manufacturing systems, Applied Mathematical Modelling, 46, 361-375 (2013)