Information Sharing in a Supply Chain with Dynamic Consumer Demand Pattern

Transcription

1 Information Sharing in a Supply Chain with Dynamic Consumer Demand Pattern Li Yan Department of Information Systems National University of Singapore liyan@comp.nus.edu.sg Tan Gek Woo Department of Information Systems National University of Singapore tangw@comp.nus.edu.sg Abstract Previous research suggested that applying different information sharing strategy (ISS) to the supply chain under different demand pattern may improve the supply chain performance [14]. But the assumption was made in the scenario that only one kind of ISS was used throughout the entire supply chain when the end consumers demand pattern does not change [14]. In this paper, we set out to explore what will happen if the end consumers demand pattern changes and whether adjusting the ISS accordingly will improve the supply chain performance. Three different demand patterns and three different ISSs will be considered. Hypotheses are developed based on the analysis of the relationship among demand patterns, ISSs and supply chain performance. A simulation model of a two-tier supply chain based on the GPSS platform is built to explore different scenarios and to test the hypotheses. The simulation results show that adjusting ISS according to the changes of end consumers demand pattern will benefit the supply chain performance. Implications for supply chain practitioners are drawn from the results. 1. Introduction Intensive global competition, faster product development, increasingly flexible manufacturing systems, an unprecedented number and variety of products are the characteristics of today s global market [12]. Making supply meet demand in such an uncertain world is becoming a more and more challenging task for supply chain management. Information sharing has long been a suggested solution to problems in supply chain management such as the bullwhip effect [1, 4, 7, 8, 9, 10, 13, 14, 15]. Fisher [3] discussed the relationship among product nature, demand pattern and ISS. Tan and Wang [14] suggested that applying different ISS to the supply chain under different demand patterns may improve the supply chain performance. But the assumption of their research was made in the scenario that only one kind of ISS was used throughout the entire supply chain when the end consumers demand pattern does not change [14]. Up till now, very little research has been done about what will happen if the end consumers demand pattern changes and whether adjusting the ISS accordingly will improve the supply chain performance. In this paper, we set out to explore the behavior of different demand patterns and ISSs. Our main research objective is to find out the impact on the supply chain performance when ISS is adjusted according to the changes of the end consumers demand pattern and how the supply chain practitioner should apply ISS according to the changes of demand pattern to improve the supply chain performance. This paper is organized as follows: Section 2 reviews the basics of supply chain management, demand patterns and ISSs; In Section 3, behaviors of demand patterns and ISSs are discussed in detail. And the research hypotheses are developed. Details of the simulation model and experimental design are described in Section 4. Main research findings are summarized and discussed in Section 5, and managerial implications are drawn. In Section 6, we conclude our paper and point out the future research directions. 2. Background Supply chain management (SCM) is a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses, distributors and retailers, so that goods are produced, distributed and delivered at the right quantities, to the right places, and at the right time [12]. In this part, we introduce an important /04 $17.00 (C) 2004 IEEE 1

2 problem in SCM the bullwhip effect and the suggested solution ISS. We will also introduce the previous research on demand pattern. 2.1 Bullwhip Effect and ISSs One of the most well-known problems of SCM is the Bullwhip effect. It refers to the amplification of demand variability resulted from the information distortion in a supply chain where companies upstream do not have information on the actual consumer demand [7]. The effects of the bullwhip effect include: large safety stock, large inventory costs, poor customer service level and inefficient resource use. Five main causes of it are: (1) demand forecast update; (2) leadtime; (3) order batching; (4) price fluctuation; and (5) shortage gaming [1, 7]. ISS has long been suggested as a method to reduce the bullwhip effect and to help match supply with demand [1, 4, 7, 8, 9, 10, 13, 14, 15], especially in today s world where IT enables the information to be shared among supply chain partners. Commonly used ISSs include: order information, demand information, shipment information, inventory information and forecast information sharing [14]. Each assumes different information policy and the information can flow upstream or downstream in the supply chain. Further discussion on ISS will be carried out in the next section. 2.2 Demand Patterns Previous research normally categorizes demand patterns into two major categories: (1) classification of demand pattern of single product with only demand volume in consideration [5, 6, 11]; (2) classification of demand pattern of multiple products with both demand volume and demand mix in consideration [14]. The demand pattern of single product is further categorized as statistically unpredictable and statistically predictable demand. The statistically predictable demand pattern has relatively smooth and repetitive fluctuations and can be analyzed by statistical and forecasting methods. These patterns can be further divided into two categories; one is that with trend, the other is that without trend [10]. The demands with trend can be demand with simple trend or demand with cyclic (seasonal) changes. The demands without trend can be time independent demand or time dependent demand [10]. Time independent demand patterns are the constant demand patterns or the stochastic demand patterns. The constant demand may be strict constant or quasi constant, and it is also called the Stable demand in Tan and Wang s study [14] and the stochastic demand is named Unknown demand by them [14]. Time dependent demand patterns are those with seasonal changes, but without trend which is also known as Cyclic demand In Tan and Wang s study [14]. 3. Development of Research Hypotheses Based on the background introduced in the previous section, we will discuss the behavior of demand patterns and ISSs respectively in details in order to develop the hypotheses of impact on supply chain performance of adjusting ISS according to the changes of end consumers demand pattern (Figure3.1). Behavior of Demand Patterns Behavior of ISSs H1 H2 Adjusting ISS according to the changes of Demand Patterns Figure 3.1 Deduction of Hypotheses 3.1 Hypotheses of Performances of Demand Patterns In section2.2, we have introduced previous research on different demand patterns. Most of them assumed that demand pattern is unchanged. But actually, in real life, demand will change. In our research, we will consider the three most common demand patterns: Stationary, Unknown and Cyclic Demand. The demand changes among these three can be easily found in real life. For example, when a new product is first introduced to the market, there is no demand history of it. The demand pattern may be unknown at the very beginning. Later, when the product reaches its maturity in its life cycle, the demand may turn into stationary pattern. If there are seasonal or cyclic reasons, like the changes of climates or the festival seasons, the demand may change to cyclic pattern. In order to understand the behavior of each demand pattern, we introduce the way the stock level, which is used to buffer the demand uncertainty in the supply chain, is calculated. This equation will help us in analyzing the impact of demand variation on stock level. StockLeve =AVG*AVGL+z*STD* sqrt(avgl) (3.1) Where AVG is the average demand during the average leadtime H /04 $17.00 (C) 2004 IEEE 2

3 AVGL is the average replenishment lead-time from upper tier to the present tier z is the safety factor which is determined by the expected service level STD is the standard deviation of demand during the average lead-time Stationary Demand (SD) When the demand pattern is stationary, the variation of the demand volume is small and STD in equation 3.1 is small. Thus the safety stock which is used to buffer the demand fluctuation in each forecasting period will be low. Also, it is easier for each tier to do the demand forecasting since the demand pattern is stationary and there will be less forecasting error Cyclic Demand (CD) Cyclic demand pattern magnifies the changes of demand volume greatly. The demand drops and increases cyclically. The variation of demand volume is bigger. Then the STD in equation 3.1 is bigger, so that the stock level takes a relatively longer time to reach a proper value to correctly reflect the customer s order. During the peak season when a product goes out-of-stock, it is harder to fulfill the backorder as demand remains high during the period. Similarly, it is harder to reduce excess stock during low demand season [13] Unknown Demand (UD) In the case of unknown demand pattern, the demand volume fluctuates randomly. But the high and low demand fluctuations have a better chance of evening out over a shorter period compared to the cyclical demand. The STD in equation 3.1 should be bigger than that of stationary demand, but smaller than the cyclic demand. Although compared to stationary demand pattern, it needs higher stock level to buffer for this uncertainty, it is easier to clear the excess inventory and fulfill the backorder compared to the cyclic demand pattern Changes of Demand Pattern When demand changes from stationary to cyclic demand, inventory begins to pile up when it is in the low demand period of the cyclic pattern; or backorder begins to build up when it is in the high demand period of the cyclic pattern. When demand changes from cyclic to stationary demand, things will happen in the opposite direction. When demand changes between stationary and unknown demand, the similar thing will happen like what will happen when demand changes between stationary and cyclic demand. But since the high and low demand fluctuations of the unknown demand have a better chance of evening out over a shorter period compared to the cyclic demand, the inventory s piling up or back order s building up at the demand changing point will be less serious compared to what will happen when demand changes from stationary to cyclic. When demand changes between unknown and cyclic demand, it will be the hardest to cope with among all pairs of changes. These two demand patterns are both involved with high degree of uncertainty, changes from one to the other normally lead to poorest supply chain performance. H1a. The pair of demand changes between stationary and unknown demand will yield best supply chain performance with lowest inventory, lowest backorder and highest fill rate. H1b. The pair of demand changes between cyclic and unknown demand will yield worst supply chain performance with highest inventory, highest backorder and lowest fill rate. 3.2 Hypotheses of Performances of ISSs In this paper, we will consider three ISSs: order information, demand information and shipment information sharing and play with the changes among them. There are also other ISSs like inventory information and partial information sharing. We limit our scope within these three ISSs because they are most commonly used and our present focus is on a two tier supply chain model. From Wang s result [15], it is known that ISS influence the supplier more than it does to the retailer. So at this point, our hypotheses will mainly focus on the supplier s tier. Later, when the impact of adjusting ISS is analyzed, influence on retailer will be discussed Order Information Sharing Strategy () Under, each tier bases its demand forecast only on the order from the lower tier but does not know other tiers inventory, shipment, or delivery [7]. This gives rise to the bullwhip effect and each tier is facing more serious distortion of demand variation, and has relatively bigger forecast error. As the demand variation is amplified along the supply chain, the stock level of each tier is also getting higher and higher. The more demand variability, the more inventory is required at each tier to buffer this uncertainty and to keep an acceptable fill rate. When demand is fairly stationary, this strategy tends to keep an overly high inventory level in the supply chain. However, when the demand becomes volatile, this strategy has more chance to even out the demand changing, so that it is comparatively less sensitive to demand fluctuation. Its inventory naturally goes up because it requires a larger inventory to buffer against unpredictable demands. But this strategy also /04 $17.00 (C) 2004 IEEE 3

4 gets higher fill rate and lower backorder in return because it keeps higher inventory to buffer the uncertainty. H2a. will get the highest fill rate, lowest backorder at the supplier s tier at the cost of a higher inventory Demand Information Sharing Strategy () Under, each tier is provided with the real end consumers demand [7]. The inventory management system used is the echelon inventory system. It includes the inventory position of all tiers downstream in its calculation of inventory position. It calculates its desired stock level by using the end consumer s demand, which is not amplified. tends to keep less inventory, even when upper tier knows the order is changing, it still uses first tier s demand to update its forecast, so that it keeps less inventory than other information sharing strategies. As a trade-off, lower level of inventory causes an increase in backorder and a drop in fill rate. When demand is stationary, the desired inventory is fairly stationary which results in lowest inventory; when demand is fluctuant, it ends up with high back order and low fill rate. Compared to other strategies, is the slowest to replenish backorder although it can reduce the inventory and minimize the distortion along these tiers. This situation is especially serious as the demand pattern is cyclic, since a high inventory is required to buffer against the rapid changing demand. H2b. will get the lowest inventory at the supplier s tier at the cost of a higher backorder and lower fill rate Shipment Information Sharing Strategy () Under, upper tiers sharing their shipment information to their downstream customers can help them make their production/inventory decision [16]. Since the transportation time between partners is usually constant, the lower tier can easily calculate the exact date and quantity of coming goods and adjust its future order quantity with such information. So, usually gives the lower tier higher fill rate and lower inventory. It actually transfers the pressure to the upper tier. So the upper tier either chooses to hold more inventory to buffer the uncertainty of demand volume from the retailer and keeps a high fill rate, or chooses to hold less inventory and accepts an unsatisfied fill rate. H2c. will benefit the retailer more at the cost of poor performance at the supplier s tier. It will get the highest inventory, highest backorder and lowest fill rate at the supplier s tier. 3.3 Hypotheses of Performance of Adjusting ISS According to the Changes of the Demand Pattern In our paper, we will test the impact on supply chain performance of adjusting ISS (nine combinations of three ISSs:,,,,,,,, ) under each of the six combinations of the three demand patterns changing from one to another (SD-CD, SD-UD, CD-SD, CD- UD, UD-SD, UD-CD). Scenarios to be tested will be shown in the experimental design in the next section Demand Pattern changes between Stationary Demand and Cyclic Demand When demand changes from stationary to cyclic demand, two things might happen: 1. inventory begins to pile up, when it is in the low demand period of the cyclic pattern; 2. backorder begins to build up, when it is in the high demand period of the cyclic pattern. When the change is in the opposite direction, that is from cyclic to stationary demand, the similar scenarios will also appear in the opposite direction. In this pair of changes, will have the lowest inventory, will have best fill rate for supplier. As for retailer, actually transfers the inventory pressure to the supplier, so may give the lowest inventory for the retailer. As for the retailer s backorder and demand fill rate, either the or the combination of and will yield the best performance. H3a. Hypotheses about the impact on supply chain performance of adjusting ISS when the demand pattern changes between Stationary pattern and Cyclic pattern: H3a.1 will have the lowest inventory at the supplier s tier; H3a.2 will have the lowest backorder and the highest fill rate at the supplier s tier; H3a.3 will have the lowest inventory at the retailer s tier; H3a.4 Either the or the combination of and will have the lowest backorder and the highest fill rate at the retailer s tier; Demand Pattern Changes between Stationary Demand and Unknown Demand When demand changes from stationary to unknown demand, small amount of backorder or small amount of excess inventory will appear since the demand fluctuation has a better chance to even out in a shorter period compared to that in cyclic demand. When demand changes in the opposite direction, that is from unknown to stationary demand, similar thing will also happen in /04 $17.00 (C) 2004 IEEE 4

5 the opposite direction. At the supplier s tier, will still have the lowest inventory, will have better fill rate. Since the change between these two demand patterns is not so drastic, it may not be necessary to change the ISS. As for the retailer, will have the lowest inventory, either the or the combination of and will have the lowest backorder and highest fill rate. H3b. Hypotheses about the impact on supply chain performance of adjusting ISS when the demand pattern changes between Stationary pattern and Unknown pattern: H3b.1 will have the lowest inventory at the supplier s tier; H3b.2 will have the lowest backorder and the highest fill rate at the supplier s tier; H3b.3 will have the lowest inventory at the retailer s tier; H3b.4 Either the or the combination of and will have the lowest backorder and the highest fill rate at the retailer s tier Demand Pattern Changes between Unknown Demand and Cyclic Demand When demand changes between unknown and cyclic demand, it will be the hardest to cope with since these two demand patterns are both involved with high degree of uncertainty. Changes from one to the other normally lead to highest inventory and lowest fill rate. In this pair of change, will have the lowest inventory and will have best fill rate for the supplier. While for the retailer, will have the lowest inventory and either or the combination of and will give the highest fill rate. H3c. Hypotheses about the impact on supply chain performance of adjusting ISS when the demand pattern changes between Unknown pattern and Cyclic pattern: H3c.1 will have the lowest inventory at the supplier s tier; H3c.2 will have the lowest backorder and the highest fill rate at the supplier s tier; H3c.3 will have the lowest inventory at the retailer s tier; H3c.4 Either the or the combination of and will have the lowest backorder and the highest fill rate at the retailer s tier; 4. Simulation Model and Experimental Design Simulation is the methodology usually used to measure the performance of a given system or to predict how a new, or altered system will behave. Simulation is dynamic because it can emulate the behaviors of the system under what if scenarios and may often come out with some emerging behaviors which are not within the pre-design of the model. Our aim is to understand the behaviors of the supply chain and to find out the impact on supply chain performance of adjusting ISS according to the changes of end consumers demand pattern. The performance of the system under a number of different scenarios needs to be measured, which falls into the specific field of computer simulation. GPSS/PC (General-Purpose Simulation System PC version), maintained by Minuteman Software, is used to build up the simulation model. 4.1 A Two-tier Supply Chain Model Consumers Retailer Source Order C Order R Order S Figure 4.1 A two-tier Supply Chain Model In order to keep the simulation model both simple and realistic enough for the observation of the supply chain s behaviors, we simulated a linear supply chain of two tiers with one supplier and one retailer (Figure 4.1). To simplify the analysis, we assume that there is only one entity per tier. This is a common practice in the study of supply chain management. Wang [15], Lee et al. [8], Tan and Wang [14] also based their research on a two-tier supply chain for the same purpose. It can be extended later if needed. The selling channel manages a single product. Retailer comes to supplier and makes its purchase regularly, based on its forecast of the end consumer s demand and its own inventory, to ensure a continuous selling. The supplier fulfills the retailer s order, and the upper tier of supplier is supposed to be the very initial outside source of the product and has full capacity to supply whatever the supplier orders. We assume an (s, S) inventory policy. The calculation of stock level (S) has been introduced in section 3 as shown in equation 3.1. The calculation of reorder quantity (s) will be discussed in next sub section. 4.2 Order Decision Supplier Since there is no manufacturing involved, the reorder quantity is calculated as: Reorder Quantity = max {StockLevel InvPos, 0} /04 $17.00 (C) 2004 IEEE 5

6 : InvPos = curinv + transitship-backorder; : InvPos = curinv + outstanding order + downstream InvPos retailer backorder; : InvPos = curinv + outstanding order - upstream shipment- backorder; InvPos: inventory position of present tier CurInv: current inventory on hand of present tier Backlog: current backorder at the present tier TransitShip: shipment in transit from the upper tier to the present tier 4.3 Design of Demand Patterns The mean of the demand is approximately 5000 units. 1. For stationary demand (SD), we use normal distribution, with standard deviation of Cyclic demand (CD) is generated by changing the mean value of demand periodically. In the first 30 cycles of cyclic demand, the mean is set to 8000, later it goes down to a low level with mean value 4000, and remains for next 90 cycles. Then it will go up again for the next 30 cycles and fall down for the following 90 cycles. This pattern will repeat itself like this. 3. Unknown demand (UD) is generated randomly. Given an average demand 5000, we use a uniform distribution to generate a demand in the range [0, 10000] ([0, 2µ]). 4.4 Experimental Design Impact on the supply chain performance of nine combinations of ISSs under each of the six combinations of the three demand patterns changing from one to another will be tested. Each scenario will be simulated for 4000 successive cycles and replicated for 10 times, each of which uses different random seed and reset initial condition. In the 4000 successive cycles, demand pattern will be changed from one pattern to another for only once (6 combinations). When demand pattern changes, the ISS will be adjusted (changed or remain unchanged) accordingly (9 combinations). Altogether fifty-four scenarios will be tested. 4.5 Performance Measurements Fill Rate the percentage of order quantity the present tier is able to ship to its downstream customer. Inventory Unit It is calculated as the current inventory on hand of present tier. Backorder Unit It is calculated as the current backlog of present tier. 5. Result Analysis In this section, the general discoveries from the simulation will firstly be presented. Then the result will be analyzed from two different dimensions: one is the analysis of supply chain performance under the changes of demand pattern (six different combinations of demand pattern changes); the other is the analysis of supply chain performance under different ISSs. Through the interpretation of the results, it can be seen that when the demand pattern changes from one to another, how the ISS should be adjusted to improve the supply chain performance. 5.1 General Results The simulation results show that the performances of the supply chain are different when the end consumers demand pattern changes and when the ISS is adjusted accordingly. Several general discoveries are as follow: First, it is found that the performance of retailer is normally better than the performance of the supplier under the same scenario. That is to say, when the changes of demand pattern and ISS are the same, retailer normally gets lower inventory, lower backorder and higher fill rate than the supplier. This can be explained as the result of the bullwhip effect. This can be seen from Figure 5.1, Figure 5.2, and Figure 5.3 comparing the inventory, backorder and fill rate between retailer and supplier respectively when the demand changes from stationary to cyclic demand (we use this scenario as an example) and when the ISS is adjusted accordingly. Secondly, the adjustment of ISS has less influence on the performance of retailer than on the supplier under the same scenario. This can also be seen from Figure 5.1, Figure 5.2, and Figure 5.3. There is less difference among the performances of retailer than among the performances of the supplier under different combinations of ISS. Unit Inventory Comparison between Retailer and Supplier (SD->CD) Changes of Information Sharing Strategies Retailer Supplier Figure 5.1 Inventory comparison between retailer and supplier /04 $17.00 (C) 2004 IEEE 6

7 600.0 Backorder Comparison between Retailer and Supplier (SD->CD) 5.2 Supply Chain Performance under Changing Demand Pattern Unit Changes of Infor m ation S har ing S tr ategies Retailer Supplier Figure 5.2 Backorder comparison between retailer and supplier. Percentage Demand Fillrate Comparison between Retailer and Supplier (SD->CD) Changes of Information Sharing Strategies Figure 5.3 Fill rate comparison between retailer and supplier Retailer Supplier Third, among the six combinations of demand pattern changes, when the demand changes between stationary and unknown demand, the performance of both retailer and supplier will be much better compared to their performances under other combinations of demand changes. Statistical test is carried out later to show that this difference is significant. As is shown in Figure 5.4, the demand fill rate for supplier is the highest when the demand pattern changes between stationary demand and unknown demand. This is just an example. In other scenarios, this result will also hold true. Demand Fillrate of Supplier We summarize the retailer and supplier s performances under six combinations of demand pattern changes (SD-CD, SD-UD, CD-SD, CD-UD, UD-SD, UD-CD) in Table 5.1. The performances under different combinations of demand changes are arranged from the best to the worst (from the left to the right). From Table 5.1, it can be seen that among the six different combinations of demand pattern changes, when demand changes between stationary and unknown demand (either SD-UD or UD-SD), the supply chain performances of both retailer and supplier are the best. That is to say, this combination of demand changes will get the best supply chain performance with the lowest inventory and backorder, and the highest fill rate at both tiers. H1a is supported. Table 5.1 also shows that although the worst performance at both supplier and retailer always happen when demand changes between unknown and cyclic demand (either CD-UD or UD-CD), in some cases, because of the impact of the ISS, either CD-UD or UD-CD will have better performance than CD-SD or SD-CD although the supply chain performance under the demand changes between Stationary and Cyclic demand normally falls between that under the changes between Stationary and Unknown demand and that under the changes between Unknown and Cyclic demand. H1b is partially supported. Result of T-test in Table 5.1 shows that the difference between the best and worst performances is significant. For example, retailer s inventory performance under SD-UD is significantly better than that under CD-UD. Table 5.1 Comparison of supply chain performance under different demand pattern changes ( > represents better in this table. T-test at the confidence interval of 95%) Percentage Changes of Information Sharing Strategies SD - C D SD - U D CD-SD CD-UD UD-SD UD-CD Significant Difference Between the Best and Worst Performances: t Stat P value SD-UD > UD-SD > SD-CD > UD-CD > CD-SD > CD-UD R Inventory S UD-SD > SD-UD > CD-SD > SD-CD > UD-CD > CD-UD R UD-SD > SD-UD > CD-SD > SD-CD > CD-UD > UD-CD Backorder S UD-SD > SD-UD > CD-SD > CD-UD > SD-CD > UD-CD Demand R UD-SD > SD-UD > CD-SD > CD-UD > SD-CD > UD-CD Fill Rate S UD-SD > SD-UD > CD-SD > CD-UD > SD-CD > UD-CD Result of T-test in Table 5.2 shows that the difference between retailer and supplier s performances under the same combination of demand pattern changes is significant. For example, retailer and supplier s inventory performances under SD-UD are significantly different. Figure 5.4 Demand fill rate for supplier /04 $17.00 (C) 2004 IEEE 7

8 Table 5.2 Statistical significance test on the difference between supplier and retailer s performance (T-test at the confidence interval of 95%) Inventory comparison Between supplier and retailer Backorder comparison Between supplier and retailer Fill Rate comparison Between supplier and retailer SD-CD SD-UD CD-SD CD-UD UD-SD UD-CD t P t P t P Supply Chain Performance under Three Different ISSs The behavior of the three different ISSs at the supplier s tier is summarized in Table 5.3. As seen from the upper part of Table 5.3, ( ) usually gets the highest fill rate and lowest backorder at the supplier s tier, but its inventory is also higher. ( ) usually gets the lowest inventory at the supplier s tier, but it also has a lower fill rate and a higher backorder as the tradeoff. ( ) usually gives the supplier highest inventory and backorder, and lowest fill rate. H2a, b, c are all supported. 5.4 Supply Chain Performance under Adjusted ISS according to the Changes of Demand Pattern After analyzing the performances of demand patterns and ISSs respectively, we summarize the best and worst performance at both the retailer and supplier tier of adjusting ISS according to the changes of end consumers demand pattern in Table 5.3. Table 5.3 Best and worst performances of adjusted ISS at retailer and supplier tier Supplier SD-CD SD-UD CD-SD CD-UD UD-SD UD-CD Retailer SD-CD SD-UD CD-SD CD-UD UD-SD UD-CD Inventory Backorder Demand Fill Rate Best Worst Best Worst Best Worst - > > > > > > - > > > > > > - > > > > > > - > > > > > > - > > > > > > - > > > > > > Inventory Backorder Demand Fill Rate Best Worst Best Worst Best Worst - - > > > > > > - - > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > When demand changes between Stationary and Cyclic pattern, for the supplier, has the lowest inventory, while has the lowest backorder and highest demand fill rate; for the retailer, the has the lowest inventory and has lowest backorder and best demand fill rate. H3a1, 2, 3, 4 are supported. When demand changes between Stationary and Unknown pattern, for the supplier, has the lowest inventory, has the lowest backorder and highest fill rate; for the retailer, has the lowest inventory and has the lowest backorder and best demand fill rate. H3b1, 2, 3, 4 are supported. When demand changes between Cyclic and Unknown pattern, for the supplier has the lowest inventory, has the lowest backorder and highest fill rate; for the retailer, has the lowest inventory and has the lowest backorder and best demand fill rate. H3c1, 2, 3, 4 are supported. We summarize the result of hypotheses testing in Table5.4: Table 5.4 Summary of hypotheses H1a H1b H2a H2b H2c supported Partially supported supported supported supported H3a.1 H3a.2 H3a.3 H3a.4 supported supported supported supported H3b.1 H3b.2 H3b.3 H3b.4 supported supported supported supported H3c.1 H3c.2 H3c.3 H3c.4 supported supported supported supported 5.5 Discussion and Implication In this paper, we analyze the impact of adjusting ISS according to the changes of end consumer s demand. The research is based on the previous discoveries that different ISS should be applied to different demand pattern [14]. But our simulation results show that in some cases, ISS should be changed according to the changes of demand pattern; in other cases, it is not necessary to change it to gain better supply chain performance. Like in supplier s tier, regardless of the changes of demand pattern, always has the lowest inventory, always has the lowest backorder and highest fill rate, and yields the worst result in every performance measurement. But in retailer s tier, gives the lowest inventory. While in order to get a lower backorder and higher fill rate, the ISS should be changed between and when the demand changes between stationary and cyclic /04 $17.00 (C) 2004 IEEE 8

9 demand or when the demand changes between stationary and unknown demand. The first implication we get from the simulation result is that different changes of end consumers demand pattern will have different effect on the supply chain performance and even the same change of end consumers demand pattern will have different effect on supplier and retailer. While some of the changes are easy to cope with like the changes between stationary and unknown demand patterns, enterprises should be extremely alerted to other changes like those between unknown and cyclic demand patterns which are involved with great uncertainty both. This may happen when a new product (unknown demand) is introduced into a market with cyclically or seasonally fluctuated demand (Cyclic demand) like that of swimming suits or ski equipment. In case like this, enterprises should be quick in the detection of the changing point of demand pattern [10] and should be agile in adjusting ISS accordingly to cope with the change and gain better supply chain performance. Second implication is that adjusting ISS according to the changes of end consumers demand pattern will improve the supply chain performance. But the combinations of ISSs do not perform equally well on all performance measurements under the same changes of demand pattern. For example, in order to get lowest inventory at the supplier s tier, should be applied, but in order to achieve lowest backorder and highest fill rate under the same scenario, should be adopted. That is to say, when the signal of demand pattern changes is detected in the supply chain, the supply chain manager should adopt different ISS according to their own needs. Enterprises emphasizing on customer service level should have different choice from enterprises emphasizing on cutting down inventory and saving cost. Another interesting observation is that the same combination of ISSs will have different impact on the performance of supplier and retailer. For example, when demand pattern changes between stationary and unknown demand, will give the supplier the lowest backorder and highest fill rate, while it will not give these to the retailer, but the lowest inventory instead. That is to say when facing the same change of demand pattern, different tiers may act differently according to their own condition and own interest. To achieve the best performance of the whole chain, negotiation should be carried out among the tiers to reach an agreement about what ISS to adopt and how should the benefit of the information sharing practice be redistributed among each tier in the supply chain. 6. Conclusion and Future Work In this paper, we examine the impact on supply chain performance of adjusting ISS according to the changes of end consumers demand pattern. In order to do so, the behavior of three demand patterns and three ISSs in the supply chain are discussed first. The results of the simulation have shown that the changes of end consumers demand pattern will affect the supply chain performance and adjusting ISS according to the changes of demand pattern can improve the supply chain performance. In this paper, the three demand patterns are classified by the demand volume. In future work, demand mix can be included. Our research is carried out in a two-tier supply chain. In the future, the number of tiers can be extended and the impact of this extension on the supply chain can be studied. Real demand data can also be applied in the simulation to get more realistic results. Information sharing is an important issue in supply chain management. We believe our research and the future work will contribute a lot to both the supply chain research and real life applications. 7. References [1] Chen, F., Drezner, Z., Ryan, J.K., and Simchi-Levi, D. Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Leadtimes and Information, Management Sciences (46:3), 2000, pp [2] Choi, R. H., Malstrom, P. E., and Tsai, R. D. Evaluating Lot-Sizing Methods in Multilevel Inventory system by simulation, Production And Inventory Management Journal (29:4), 1988, pp [3] Fisher, M.L. What is the Right Supply Chain for Your Product? Harvard Business Review, Vol.75, 1997, pp [4] Gavirneni, S., Kapuscinski, R., and Tayur, S. Value of Information in Capacitated Supply Chains, Management Science (45:1), 1999, pp [5] Kobbacy, A. H., and Liang, Y.S. Towards the Development of an Intelligent Inventory Management System, Integrated Manufacturing Systems (10:6), 1999, pp [6] Langabeer, J., and Rose, J. Creating Demand Driven Supply Chains: How to Profit from Demand Chain Management, Chandos Publishing, Oxford, [7] Lee, H. L., Padmanabhan, P., and Whang, S. Information Distortion in a Supply Chain: The Bullwhip Effect, Management Science (43:4), 1997, pp [8] Lee, H. L., Kut C. S., and Tang, C.S. The Value of Information Sharing in Two-level Supply Chain, Management Science (46:5), 2000, pp /04 $17.00 (C) 2004 IEEE 9

10 [9] Li, J., Shaw, M.J. and Tan, G.W. Evaluating Information Sharing Strategies in Supply Chains, Proceedings of 8th ECIS, Vol. 1, Vienna, Austria, 3-5 July 2000, pp [10] Li Y. Information Sharing in a Dynamic Supply Chain with Changing Demand Patterns, Master Thesis, National University of Singapore, [11] Muir, J. W. Forecasting items with irregular demand, American Production and Inventory Society Conference Proceedings, 23 rd Annual International Conference, 1980, pp [12] Simichi, L.D., Kaminsky, P. and Simichi L.E. (2000). Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies, Irwin/McGraw-Hill, Boston, [13] Tan, G.W. The Impact of Demand Information Sharing on Supply Chain Network, PHD Thesis in Business Administration in the Graduate College of the University of Illinois at Urbana-Champaign,1999 [14] Tan, G.W., and Wang, B. The Relationship between Product Nature, Demand Patterns and Information Sharing Strategies, Proceedings of 22 nd ICIS, New Orleans, USA, December 2001 pp [15] Wang, B. Relationship between Product Nature, Demand Patterns and Information Sharing Strategies, Master Thesis, National University of Singapore, [16] Zhang, C., Zheng, X., Robb, D.J., and Tan, G. W. "Sharing the Information of Shipping Quantity In a Two-Tier Supply Chain," Proceedings of International Conference on Global Supply Chain Management, China, 2002, pp /04 $17.00 (C) 2004 IEEE 10