Improvement of Storage System: Case study: Cutting Pattern of Jewelry Box
Main Article Content
Abstract
The purpose of this study is to improve a cloth cutting storage system. The main problem of this process is the lack of storage shelves which is not able to cover with the number of cutting patterns and unstandardized storage system which lead to longer picking time and missing search. After analyzing on the current situations and problems, two methods of new systems for storage of cutting patterns were presented, one is based on frequency of using, and another one is based on cutting pattern types. The technique of Analytical Hierarchy Process (AHP) was used to compare between two alternatives. Under the multi-criteria which are distance, picking time, ease of implementation, company policies and flexibility, the result showed that the second storage system based on pattern types is more suitable as it can solve the insufficient storage spaces and more employees can pick up cutting patterns. The visual control is also applied on. After improvement, picking time is reduced from 91.2 seconds to 16.7 seconds. Moreover, numbers of cutting patterns which can be stored on shelves are increased from 235 patterns to 563 patterns.
Article Details
Copyright @2021 Engineering Transactions
Faculty of Engineering and Technology
Mahanakorn University of Technology
References
อนุวัฒน์ ทรัพย์พืชผล และ ไพบูลย์ กิจวรวุฒิ, การจัดการคลังสินค้าระดับโลกกรุงเทพมหานคร: บจก.พิมพ์ดี, 2549
Weber, C.A., Current, J.R. A multiple objective approach to vendor selection. European Journal of Operation Research, Vol.68, pp 173-184, 1993.
Vergara, F.E., Khouja, M., Michalewicz, Z. “An evolutionary algorithm for optimizing material flow in supply chains”, Computers & Industrial Engineering, Vol.43, pp 407-421, 2002.
T.L. Saaty, How to make a decision: the analytical hirearchy process, European Journal of Operational Research 48 (1) (1990) 9-26.
A.A. Auilar-Lasserre et al., An AHP-based decision-making tool for the solution of multiproduct batch plant design problem under imprecise demand, Computer & Operations Research 36 (2009) 711-736.