Supply Chain Bullwhip Effect Simulation Under Different Inventory Stgy WANG Xiaoyan, HUANG Xiaobo Department of foundation, Xuzhou Air Force College, Xuzhou, Jiangsu, China xy_wang1999@126.com Abstract: Bullwhip effect means the magnification of demand fluctuations, which is evident in a supply chain when demand increases and decreases. Different production stgy and stgy can influence the extent of bullwhip effect which is reflected in the change of quantity. This paper discusses the bullwhip effect under different production environment and different policy by virtue of system dynamics Research results show that the longer the leading time, the longer the adjustment, and the longer the smoothing time; Under MTS production environment situation, the smoothing time in (s, S) stgy is shorter than in VMI stgy and fluctuation range of VMI is lower than of (s, S). These researches verify the relation between variables and understand the consequences of decisions for the policy reference in supply chain management. Keywords: Supply chain management; bullwhip effect, system dynamics 1 Introduction Butterfly Effect is an phenomenon that a small change on one side of a system can bring about great influence on the other side of the system which is known as Bullwhip Effect (Bullwhip Effect) in supply chain management in which demand variety adds upstream supply chain. In supply chain management how to reduce the Bullwhip Effect is a research focus. Forrester(1961) put forward reducing the bullwhip phenomenon by a series of adjust stgies such as setting order frequency, order quantity, order security stockpile etc; Lee(1991) thought that reducing the bullwhip phenomenon can be accomplished by using the same sales data in upstream and downstream of supply chain accompanied with VMI stgy; Kelle & Milne(1999) pointed out that suppliers (s, S) policy can restrain the growing demand upstream supply chain. But these studies focused on the solution mode of pure problem-oriented, no observing the relationship among variables and no understanding the variables influence process. This paper wanted to supplement related research by virtue of system dynamics to verify the relation between variables and understand the consequences of decisions for the policy reference in supply chain management. 2 Production Mode and Inventory Stgy 2.1 Inventory Production mode Inventory production mode meet customer needs by producing product in advance. The operation process in MTS production environment is as follows: (1) Upon receiving orders from customers, retailers inspect finished product to satisfy customer demands. When the levels lower to ordering point, retailers order to the local product warehouse by presetting level, safety stock, ordering point (2) According to retailers orders, market product warehouse presets parameters such as level, security stockpile to meet their orders. When inventories are below the default value, the purchase order is subjected to manufacturers, and the manufacturer is responsible for replenishment.the paper title and authors should be exactly in the format as indicated in this template in order to maintain uniformity throughout the conference proceedings. 27
(3) Manufacturers predict demand, plan production and produce in advance according to the history data of local market warehouse. 2.2 Inventory stgy So-called policy means the management model on production or warehouse. This study put forth effort on (s, S) and VMI policy to reduce cost burden in different needs and production mode. The two kinds of policy shows as follows (s,s) stgy This kind of policy belongs to the perpetual system, in which s is ordering point for judging whether actual amount be lower than s point; S is for ordering upper limit deciding the between S point and actual as the ordering amount. In this study, retailers, wholesalers and suppliers have different limit points. VMI (Vendor Manager Inventory) The policy demands suppliers to replenish for manufacturers, and in this study the mode of supply is cycle type, that is, suppliers set target for manufacturers accompanied with more frequent cycle of complement and joint distribution to achieve economic benefits. The program instruction is as follows: (1) Formulate demand forecasting plan according to customer levels and sales status and put forward suggestion on ordering quantity. (2) Issue supplement orders according to the suggestion on ordering quantity, and confirm the order by customers. (3) Master production scheduling and schedule delivery plan according to the order management system. (4) Notify customers replenishment plan and replenish the stock according to the distribution plan. 3 System Dynamics Simulation Model Under MTO production mode, retailers pursue forecasting and ordering on customers' demand; wholesalers pursue forecasting and ordering on retailers' demand; manufacturers pursue forecasting and manufacturing on wholesalers' demand. Because there is a time delay in communication and feedback between every classes of supply chain, which distort terminal customer requirement, and buffet effect in supply chain appears. If time delay factor is considered, the suppliers offer the target stock to downstream vendors according to their lead time for prediction and target, and downstream vendors put forth replenishment regularly against the gap between target quantity and existing quantity, which can remove demand amplification phenomenon. In the (s, S) stock policy, on existing stock being lower than ordering point, the supply chain nodes implement order stgy policy, and the ordering quantity is the difference between target stock and existing stock. In (s, S) policy, existing quantity include traveling quantity. Fig1. shows the (s, S) interactive relation in supply chain MTS circumstance by virtue of VINSIM software, which construct production environment and policy system simulation program. 28
of output Supplier Supplier delivery delivery Retailers Marketing Production demand Supplier Supplier Supplier safety stock ordering safety stock Retailers ordering safety accommodation time Retailers safety stock Retailers Marketing demand Retailers safety Figure1 : MTS circumstance and (s,s) storage stgy Important variables and equations are as follows: Marketing demand =10+STEP(10, 10 ) Marketing = MIN( DELAY1I( Retailers, 1, 0)+DELAY1I( delivery, 1, 0), Marketing demand ) Production demand = DELAY1I( ordering, 1, 0 )+Supplier regulation of output=delay1i(production demand,1, 0 ) Retailers = INTEG (DELAY1I( delivery, 1, 0)-Marketing, 40) Retailers =(Retailers safety stock-retailers )/Retailers safety =1 Retailers ordering =Marketing +Retailers Retailers safety =1 Retailers safety stock=44 Supplier delivery =MIN(DELAY1I(Supplier, 1, 0)+ of output, DELAY1I( ordering, 1,0 ) ) Supplier = INTEG ( of output-supplier delivery, 40) 29
Supplier =1 Supplier =(Supplier safety stock-supplier )/Supplier Supplier safety stock=44 delivery=min( DELAY1I(, 1, 0)+DELAY1I(Supplier delivery, 1, 0 ), DELAY1I(Retailers ordering, 1, 0 ) ) = INTEG (DELAY1I(Supplier delivery, 1,0 )- delivery, 40) =( safety stock- )/ safety ordering = DELAY1I(Retailers ordering, 1, 0 )+ of output Supplier Supplier delivery delivery Retailers Marketing Production demand Supplier Supplier Supplier safety stock ordering safety stock safety accommodation time Retailers ordering Retailers safety stock Retailers Marketing demand Retailers safety safety =1 safety stock=44 In VMI policy, the policy demands suppliers to replenish for manufacturers, and, suppliers set target for manufacturers accompanied with more frequent cycle of complement and joint distribution, that is, firstly retailers formulate demand forecasting plan according to customer levels and sales status and put forward suggestion on ordering quantity; secondly wholesalers and suppliers issue supplement orders according to the suggestion on ordering quantity, and confirm the order by customers; ly manufacturers master 30
production scheduling and schedule delivery plan according to the order management system. Fig2. shows interactive relation under supply chain MTS circumstance and VMI stgy circumstance by virtue of VINSIM software, which construct production environment and policy system simulation program. Figure2 : MTS circumstance and VMI storage stgy Important variables and equations are as follows: Marketing demand =10+STEP(10, 10 ) Marketing =MIN(DELAY1I(Retailers, 1, 0)+DELAY1I( delivery, 1, 0), Marketing demand ) Production demand =Marketing demand +Supplier of output=delay1i(production demand, 1, 0 ) Retailers = INTEG (DELAY1I( delivery, 1, 0)-Marketing, 40) Retailers = safety (Retailers safety stock-retailers )/Retailers Retailers ordering = Marketing demand +Retailers Retailers safety =1 Retailers safety stock=6 Supplier delivery =DELAY1I(Supplier, 1, 0 )+ of output Supplier = INTEG ( of output-supplier delivery,40) Supplier =1 Supplier =(Supplier safety stock-supplier )/Supplier Supplier safety stock=44 delivery =MIN(DELAY1I(, 1,0)+DELAY1I(Supplier delivery, 1, 0 ), DELAY1I(Retailers ordering, 1, 0 ) ) = INTEG (DELAY1I(Supplier delivery, 1,0 )- delivery,40) 4 Simulation and Analysis 31
4.1 Model validation The model presented in this paper has been verified and validated by a series of tests. The first group of tests are verification tests. These tests are intended to determine whether the computer simulation works as it is expected to work. After the verification of the model, validation tests are done. Validation tests are grouped into two: (a) structure validation tests, which are done in order to determine whether the model has an adequate structure, by testing the behavior of the model under extreme conditions; (b) behavior validation tests, which are done in order to determine whether the behavior of the model resembles the behavior exhibited by the real system that was modeled. To test the structural validation of the presented model, extreme condition and sensitivity tests have been applied. (It is impossible to present those results here due to lack of space, see). Behavior validity was tested by comparing the behavior of the model to judge whether the result be satisfy the actual situation. On the whole all the tests passed through. 4.2 Simulation experiment Bullwhip Effect is an phenomenon that the demand amplification and fluctuation from supply chain origin to supply chain terminal. 100 Graph in MTO-VMI 72.5 45 17.5-10 Retailers : MTO-VMI Supplier : MTO-VMI : MTO-VMI Figure 3: Graph in MTO circumstance and (s, S) stgy 200 Graph in MTO-(s,S) 140 80 20-40 Retailers : MTO-(s,S) Supplier : MTO-(s,S) : MTO-(s,S) Figure 4: Graph in MTO circumstance and (s, S) stgy 32
60 Retailers 45 30 15 0 Retailers : MTO-(s,S) Retailers : MTO-VMI Figure 5: Contrast Graph on Retailers 80 57.5 35 12.5-10 : MTO-(s,S) : MTO-VMI Figure 6 :Contrast Graph on 200 Supplier 140 80 20-40 Supplier : MTO-(s,S) Supplier : MTO-VMI Figure7 :Contrast Graph on Suppliers Figure2 ~ Figure7 show the experimental results of the bullwhip effect, in which the customers demand rise from 10 units up to 20 units, and the demand change induce the fluctuations of retailer s, wholesaler s and supplier s. In this section, we suppose that customer demand Customer demand rises from 10 units to 20 units in 33
10th cycle, and we will observe the fluctuation and smoothing time in MTS circumstance on (s, S) stgy and VMI stgy. From the above we can draw up some findings on the bullwhip effect. (1) The smoothing time in supply chain nodes takes on the phenomenon of increasing in that there is an accumulation of leading time from the supply chain upstream. The longer the leading time, the longer the adjustment, the longer the smoothing time. (2) Under MTS production environment situation, the smoothing time in (s, S) stgy is shorter than in VMI stgy in that under (s, S) policy deadline changes accompanied with demand change and the deadline is supposed as the criterion for setting the ordering quantity; on the other hand, under VMI policy terminal demand forecasting is supposed as the criterion for setting the ordering quantity along with fixed period distribution and gradual adjustment in an forecast period. (3) Under MTS production environment, fluctuation range of VMI is lower than of (s, S) in that VMI stgy is based on fixed delivery cycle and by dividing leading time, on meeting surprise rise the replenishment correction action can be done fast to reduce the fluctuation. 5 Conclusion Bullwhip effect means the magnification of demand fluctuations, which is evident in a supply chain when demand increases and decreases. Different production stgy and stgy can influence the extent of bullwhip effect which is reflected in the change of quantity. This paper discusses the bullwhip effect under different production environment and different policy by virtue of system dynamics Research results show that the longer the leading time, the longer the adjustment, and the longer the smoothing time; Under MTS production environment situation, the smoothing time in (s, S) stgy is shorter than in VMI stgy and fluctuation range of VMI is lower than of (s, S). These researches verify the relation between variables and understand the consequences of decisions for the policy reference in supply chain management. References [1]. Fogarty, D.W., J.H. Blackstone, and T.R. Hoffmann, Production and Inventory Management, South-Western, 1991. [2]. Forrester, J.W., Industrial Dynamics, MIT Press, Cambridge, MA, 1961. [3]. Kelle, P., and A. Milne, The Effect of (s, S) Ordering Policy on the Supply Chain, Int. J. Production Economics, Vol.59, pp.113-122,1999. [4]. Lee,H. L., and V. Padamanabhan, The Bullwhip Effect in Supply Chains, Sloan Management Review, pp93-101, 1997 Spring. [5]. Lee, H. L., V. Padamanabhan, and Seungjin Whang, Information Distortion in a Supply Chain: The Bullwhip Effect, Management Science, Vol.43, No.4, pp.546-565, April 1997. [6]. Metters, R., Quantifying the Bullwhip Effect in Supply Chains, Journal of Operations Management, Vol.15, pp89-100, 1997. [7]. Naish, H.F., Production Smoothing in the Linear Quadratic Inventory Model, Quarterly Journal of Economics, 104, 1994,pp864-875. [8]. Patrovic, D., Rajat Roy, and Radivoj Petrovic, Modeling and Simulation of a Supply Chain in an Uncertain Environment, European Journal of Operational Research Vol.109, pp299-309, 1998. [9]. Senge, P.M. and J.D Sterman, System Thinking and Organizational Leaning: Acting Locally and Thinking Globally in the Organization of the Future. European J. of Operations Research, 59, 3, 1992, pp. 137-145. 34