A real-time inventory model to manage variance of demand for decreasing inventory holding cost

Re-order point transformed into, a time driven model, real-time inventory model.Developed real-time inventory model eliminates the safety stock.Real-time model is proven to be better by some equations.Real-time model is compared with re-order point model by a simulation experiment. Variance of demand is one of the inevitable problems in the manufacturing environment. Market conditions and competition force companies reducing costs. Different approaches and methods have been developed to remedy these problems. Common points of these approaches are utilizing resources and responding in the shortest time possible. To achieve better inventory management, inventory-holding cost is tried to be decreased under the same service level. Re-order point is arranged dynamically regarding couple of factors in some studies. We try to arrange the re-order point in a time based manner by transforming the re-order point into re-order time, which eliminates the safety stock; and it makes the inventory model real-time.Recent production planning studies focus on real-time planning and dynamic scheduling to increase utilization and robustness. In these studies, real-time data is used for planning, but in most of them manufacturing systems and planning methodology are not transformed into a real-time system approach. The novel aspect of this study is the presentation of a model in which the manufacturing system has been designed as a real-time system that consists of real-time planning activities working with real-time data, and eliminating the safety stock.

[1]  George Q. Huang,et al.  Multi-agent based real-time production scheduling method for radio frequency identification enabled ubiquitous shopfloor environment , 2014, Comput. Ind. Eng..

[2]  Su Xiu Xu,et al.  Optimal modular production strategies under market uncertainty: A real options perspective , 2012 .

[3]  Ray Y. Zhong,et al.  A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing , 2015, Adv. Eng. Informatics.

[4]  Shigeru Fujimura,et al.  Dynamic routing strategies for JIT production in hybrid flow shops , 2012, Comput. Oper. Res..

[5]  Christopher S. Tang,et al.  The Value of Information Sharing in a Two-Level Supply Chain , 2000 .

[6]  Insup Lee,et al.  Parametric approach to the specification and analysis of real-time scheduling based on ACSR-VP , 2002, Sci. Comput. Program..

[7]  Nils Boysen,et al.  Jena Research Papers in Business and Economics Optimally Routing and Scheduling Tow Trains for JIT-Supply of Mixed-Model Assembly Lines , 2010 .

[8]  Rommert Dekker,et al.  An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods , 2008, Eur. J. Oper. Res..

[9]  Shih-Pin Chen,et al.  A membership function approach to lot size re-order point inventory problems in fuzzy environments , 2011 .

[10]  Ji-Bo Wang,et al.  Single machine group scheduling with decreasing time-dependent processing times subject to release dates , 2014, Appl. Math. Comput..

[11]  Young-Sik Jeong,et al.  Performance evaluation with DEVS formalism and implementation of active emergency call system for realtime location and monitoring , 2010, Simul. Model. Pract. Theory.

[12]  T. C. Edwin Cheng,et al.  Group scheduling and job-dependent due window assignment based on a common flow allowance , 2014, Comput. Ind. Eng..

[13]  Dean C. Chatfield,et al.  Returns and the bullwhip effect , 2013 .

[14]  Peter Cowling,et al.  Production, Manufacturing and Logistics Using real time information for effective dynamic scheduling , 2002 .

[15]  Christopher S. Tang,et al.  An EOQ model for MRO customers under stochastic price to quantify bullwhip effect for the manufacturer , 2014 .

[16]  Wonjoon Choi,et al.  A real-time sequence control system for the level production of the automobile assembly line , 1997 .

[17]  Peng Zhang.  Industrial control systems , 2010 .

[18]  Richard M. Allen,et al.  Development of the ElarmS methodology for earthquake early warning: Realtime application in California and offline testing in Japan , 2011 .

[19]  J Prince,et al.  Combining lean and agile characteristics: Creation of virtual groups by enhanced production flow analysis , 2003 .

[20]  Na Yin,et al.  Single-machine group scheduling with processing times dependent on position, starting time and allotted resource , 2014 .

[21]  Angappa Gunasekaran,et al.  A real-time warehouse operations planning system for small batch replenishment problems in production environment , 2011, Expert Syst. Appl..

[22]  Yi-Feng Hung,et al.  Real-time capacity requirement planning for make-to-order manufacturing with variable time-window orders , 2013, Comput. Ind. Eng..

[23]  Y. Dallery,et al.  Dynamic re-order point inventory control with lead-time uncertainty: analysis and empirical investigation , 2009 .

[24]  Vineet Padmanabhan,et al.  Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect" , 1997, Manag. Sci..

[25]  Wen-Teng Wu,et al.  Semi-realtime optimization and control of a fed-batch fermentation system , 2000 .

[26]  Ruhul A. Sarker,et al.  Managing disruption in an imperfect production-inventory system , 2015, Comput. Ind. Eng..

[27]  Rita Gamberini,et al.  Dynamic Re-Order Policies for Irregular and Sporadic Demand Profiles , 2014 .

[28]  Patroklos Georgiadis,et al.  Production , Manufacturing and Logistics Real-time production planning and control system for job-shop manufacturing : A system dynamics analysis , 2011 .