THE STUDY OF RETAIL INVENTORY STRATEGIES UNDER UNCERTAINTIES

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1 International Journal of Electronic Business Management, Vol. 2, No. 2, pp (2004) 131 THE STUDY OF RETAIL INVENTORY STRATEGIES UNDER UNCERTAINTIES Peitsang Wu *, Nai-Chieh Wei, Zi-Po Lin and Yung-Yao Hung Department of Industrial Engineering and Management I-Shou University Kaohsiung (840), Taiwan ABSTRACT Supply chain management includes four major elements, namely manufacturers, suppliers, distributors and retailers. control plays a very important role in each of these four modules in the supply chain. There are many uncertain factors that will influence the accuracy of forecasting. Therefore, we need to investigate the uncertainties during the inventory control for these four different strategies. They are Quick Response () strategy model, Vender Management () strategy model, Newsboy strategy model, and Target Weeks Supply strategy model. In each strategy, seven uncertain factors in inventory simulation model are studied. The purpose of this research is to study the retail inventory strategies under uncertainties and the impacts of various uncertainties on these four different strategies and find the most suitable inventory strategy for the apparel retailer. The performances of these four strategy models are also analyzed. Keywords: Management, Supply Chain Management, Quick Response, Vender Management, Newsboy, Target Weeks Supply * 1. INTRODUCTION The economy of Taiwan developed very fast in the past twenty years. The environment of the consumables marketing has been changed hugely. In the past, the supply chain was not well developed. The retailers had to keep inventories as many as possible to meet customer demand. Nowadays, commodities are delivered from manufacturers to local distribution centers. Thereafter, the commodities are delivered from distribution center to retailers directly. The retailers don t need to stock too many anymore. The purpose of supply chain management is to satisfy customer demand in very short time without having too many inventories. Because the retailers are standing at the front line and facing the customers, they play an important role in supply chain. Therefore, how to increase the service level as well as decrease the inventory level is the key to make profits. There are many uncertain factors that will influence the performance of the retail inventory strategies. Our research is focusing on investigating the advantages and disadvantages of the four inventory strategy models: Quick Response (), Vender Management (), Newsboy and Target Weeks Supply. The goal of this study is to * Corresponding author: pwu@isu.edu.tw analyze the impacts of various uncertainties on these four different strategies and find the most suitable one for the apparel retailer. Several researchers have investigated the uncertainties in the supply chain. Most of them thought the uncertainties occurred in three parts of the supply chain. They occurred in the supplier part, manufacturer part and demand part [5,6,9]. In our research, we study the uncertainties occurring in the demand part. Based on researchers studies, seven uncertain factors are investigated in the apparel retail industry. The seven uncertainties are listed as following: 1. Color/Style/Size SKU mix demand error: When the number of stock keeping units (SKUs) in different colors, styles and sizes getting larger, the inventory control is getting more difficult. The prediction of SKU mix will also become difficult. 2. Overall demand volume error: There are always errors in the prediction. The predicting error may cause damage to retailers. For example, the error may cause the lost sale and the increase of inventory level. 3. Markdown policy: When markdown is applied, the sale may grow too fast. Thus, out-of-stock will be caused and service level will decrease. 4. Reorder lead time: Long reorder lead time may lower the inventory turns and service level. 5. Initial inventory: Traditionally, we would like to

2 132 International Journal of Electronic Business Management, Vol. 2, No. 2 (2004) hold a large proportion of inventory at the beginning to upgrade the service level. However, high initial inventory level may cause low inventory turns and increase the inventory cost. 6. Season length: The number of reorders is related to the season length. The longer the season length is, the more reorders there are. 7. Demand volume and SKU mix: Smaller demand volume and larger SKU mix means that the prediction is getting more difficult. On the other hand, larger demand volume and smaller SKU mix means that the prediction of sale is more accurate. 2. METHODOLOGY 2.1 Apparel Retail Simulation Model In this research, SKUs (Stock-Keeping-Units) is used to trace the inventory of merchandise in selling season. Besides, inventory volume is decided according to initial inventory and expected style/color/size mix ratio. In the apparel retail simulation model, we capture the random character of consumer behavior in the simulated store and the arrival of customers is determined by the Poisson distribution. Before every simulation run, retailer can determine the buyer strategies. However, the actual consumer demand may be different from the expected one in the buyer s plan. Therefore, the allowable error range can be altered in the simulation to correct the prediction error. In the Hunter and King s retail stochastic simulation project [2,4], they have analyzed customer behaviors (see in Figure 1) and suggested the customers entering the retail store should be divided into two groups. The first group is the customers entering the retail store with merchandise wanted to buy in mind. The other group is the customers who don t expect to buy anything and just enter the store to look around. After the customer enters the store, if there is merchandise wanted by the customer, the merchandise will be bought immediately. In the mean time, the store will record a sale and the inventory is reduced. The store will reorder another one from manufacturer if necessary. Out-of-Stock OOS will be recorded, if there is no such merchandise which the customer wanted to buy. Then, the customer may look for another purchase which is the same size but different style or color browse for other items or leave the store. When a selling cycle ends, the reordered merchandise will be delivered into the store at the same time. The selling cycle will keep on recurring until selling season ends. The reader can see Figure 1 for more details. Figure 1: Customer behaviors

3 P. Wu et al.: The Study of Retail Strategies under Uncertainties 133 There are several tangible parameters (such as planed error, manufacture/deliver lead time changes, and demand variation, etc.) and strategy parameters (such as initial inventory, reorder rate and markdown, etc.) that can deal with the uncertainties in our simulation model. They can be set up into system input parameter table. To do so, retailer can modify the uncertain factors easily to get various simulation results. Figure 2 shows the Apparel Retail Sourcing Simulation System. Retailers can enter the inventory strategy parameters through this user-friendly interface easily. Our research applied this sourcing simulation model to collect associated data. The output performances analysis standard in this research includes: gross margin return on inventory, service level % (the percentage of customers enter the store and find what they wanted to buy), lost sales % (the percentage of customers enter the store but don t find what they wanted to buy), inventory turns and average inventory. Figure 2 shows the apparel retail sourcing simulation model interface. Figure 2: Buyer s plan interface There are six input interfaces: the Buyer s Plan, Sourcing, Consumer Demand, Cost Data, Markdowns/Promotion, and Vender Specification. User can enter the specified parameters in the associated interface. For example, user can enter the number of styles, number of colors and number of sizes in the buyer s plan interface; User can also enter number of reorders, week of first reorders, week between reorders, and reorder lead time in the replenishment schedule box of the sourcing strategy interface (see Figure 3). The four inventory strategies are also selectable in the replenishment strategy box of the sourcing strategy interface. Other interfaces such as consumer demand, cost data, markdowns, and vendor specification can be also adjusted by the user. Once the input parameters are entered, the simulation run can be started according to these parameters. Visual C++ simulation software is developed for such simulation run based on the simulation project [4,5]. And performance data associated with these parameters are exported to spread paper-based documents. 2.2 Strategies In order to make a comparison among the four inventory strategies, a basic inventory strategy model is established. Seven uncertain parameters such as Color/Style/Size SKU mix error, overall demand volume error, reorder lead time, season length initial inventory, markdown policy and total demand volume etc., are set to be the same parameter settings. The detailed retail scenarios are described as follows: There are 4000 items to be sold in 20 weeks retailing season from spring to autumn. Overall 48 SKUs are composed of 2 styles in 4 colors and 6 sizes. The initial inventory is set to be 40% of entire sale volume. There have been 14 reorders since first week of selling season came. So long as an order is placed by retailer, the reorder lead time is three weeks. Say, a reorder which is placed in the end of week 1 will arrived at end of week 4. Markdown strategy is very

4 134 International Journal of Electronic Business Management, Vol. 2, No. 2 (2004) common for retailers when it s close to the end of selling season. Because they want to sell out all merchandise as soon as possible to cut down inventory cost after season ends, a markdown is set at the week of 16th with 25% discount. And the mix error percentages of color, style and size SKU are 20%, 30%, and 10%, respectively. Overall demand prediction error is not considered in this strategy, i.e., demand volume of whole season is fixed at 4000 SKU units. The above settings are the base for the four inventory strategies. However, the differences between these four inventory strategies are the characteristics and different objectives in recalculating scenarios. For example, the purpose of Target Weeks Supply is to keep inventory volume fixed for each week; allows suppliers to do additional replenishment to keep supply always available; and Newsboy uses service level as the target to decide next reorder volume. Figure 3: Sourcing strategy interface 3. RESULTS AND ANALYSES The performances of this research are investigated with seven critical uncertain factors. How the inventory affects the model, model, Newsboy model and Target Weeks Supply model are analyzed. The details of analysis are described as follows: 3.1 Impacts of Color/Style/Size SKU Mix Error In SKU mix error analysis, three different SKU mix combinations are applied to be the inputs parameters. They are 0%/0%/0%, 20%/20%/10% and 40%/40%/20%. For example, 20%/20%/10% means the allowable prediction error for demand of style is 20%, the allowable prediction error for demand of color is also 20% and the allowable prediction error for demand of size is 10%. Table 1 shows the impacts of SKU mix error for different strategies. The results are analyzed as follows: 1. In 0%/0%/0% SKU mix error condition, the model, model and Target Weeks Supply model don t show many differences. The Newsboy model earns higher service level, but gets higher average inventory. However, the service level is just 5% higher than that of model. 2. In comparison with the service level of the model and Target Weeks Supply model, the service level of the model drops dramatically from 92% to 77.7% when SKU mix errors are increasing from 0%/0%/0% to 40%/40%/20%. Obviously, the response of the model is worse than those of the model and Target Weeks Supply model. 3.2 Impacts of Overall Demand Volume Error The retail scenario did not consider demand volume error in the basic model defined before. The expected volume is set to be 4000 SKUs for whole season. In this experiment, we assume there are volume errors. Therefore, we set two kinds of volume errors: one is underestimated error and the other is overestimated error. For the underestimated error, the actual demand volume is set to be 4800 SKUs. For the overestimated error, the actual demand volume is set to be 3200 SKUs. Based on above assumptions,

5 P. Wu et al.: The Study of Retail Strategies under Uncertainties 135 the simulation result is shown in Table 2 and the analyses are as follows: 1. The model is the most reliable one under demand volume error. There re only slightly influences on the performances of average inventory, service level % and lost sales %. On the other hand, the Target Weeks Supply model obviously influences on the performance of average inventory. 2. When the demand volume is underestimated, the performances of the service level for both the model and Newsboy model drop rapidly. Therefore, we should avoid using these two models when demand volume is underestimated. Table 1: Comparison of color/style/size SKU mix error % Error Lost Sales % Style/Color/Size %/0%/0% %/20%/10% %/40%/20% PS Newsboy is shown as and Target weeks supply is shown as. Demand Volume N=4000 =Plan N=4800 (Plan+20%) N=3200 (Plan-20%) Table 2: Comparison of demand volume error Lost Sales % Impacts of Markdown Policy In this experiment, there are two markdown strategies applied to compare with the situation of no markdown. The first markdown strategy is to start one markdown in the beginning of the 16th week with 25% discount and continue the discount till the end of the selling season. Second markdown strategy is to place two markdowns in the selling season. The first markdown is started in the beginning of the 10th week with 25% discount and ended in the end of the 13th week. The second markdown is started in the 14th week with 35% discount and continued till the end of the selling season. Table 3 shows the impacts of markdown policy for different strategies. The results are analyzed as follows: 1. Markdown policy can cause negative effects on the retail performances. The only performance will be positively affected is the inventory turns, that is because of the increasing of demand volume. 2. Besides the bad performance in the service level for the model, the model and Target Weeks Supply model preserve good performances. 3. The Newsboy model is the most stable model among all. But it also accompanies higher average inventory cost. 3.4 Impacts of Reorder Lead Time In this analysis, four different reorder lead time combinations are used to be the input parameters. They are 2 weeks, 3 weeks, 4 weeks and 4 weeks with 50% initial inventory. Based on above scenarios

6 136 International Journal of Electronic Business Management, Vol. 2, No. 2 (2004) assumptions, the simulation result is shown in Table 4 and detailed analyses are as follows: 1. In the model and Target Weeks Supply model, the number of inventory turns increase when the reorder lead time increase. But the service levels for both models are decreased. However, the whole system still keeps in stable situation. The service level % is still kept over 90% and the average inventory is lower than 1000 units. 2. In order to increase the performance for the whole system, the initial inventory should be set higher if the reorder lead time is longer. Numbers of Markdown Table 3: Comparison of markdown policy Lost Sales % Reorder Lead time 2 Weeks 3 Weeks 4 Weeks 4 Weeks & 50% Init. Table 4: Comparison of reorder lead time Lost Sales % Impacts of Initial In this experiment, three different initial inventory combinations are used to be the input parameters. The initial inventory is set to be 30%, 40% and 50% of overall demand volume. Table 5 shows the impacts of initial inventory for different strategies. The results are analyzed as follows: 1. The level of the model and Target Weeks Supply model is increased when the initial inventory level is increased. However, there are negative effects on the performances of the average inventory and inventory turns. Therefore, we should be careful in increasing initial inventory when applying the model or the Target Weeks Supply model. 2. The model gets higher average inventory level and lower service level when the initial inventory is increased. However, the Newsboy model is not affected by increasing initial inventory. 3.6 Impacts of Selling Season Length In this experiment, three different season length combinations are used to be the input parameters. Because the season length is different, the parameter settings of basic model have to be modified according to Table 6. Other parameters are kept the same as the basic model. Table 7 shows the impacts of season

7 P. Wu et al.: The Study of Retail Strategies under Uncertainties 137 length for different strategies. The results are analyzed as follows: 1. When season length becomes shorter, the demand volume of overall season decreases, too. Besides, the inventory turns are decreasing dramatically. 2. Because the season length is decreased, the number of reorders is also reduced. The performances of the service level, lost sale, and drop off obviously in the model with 10 weeks season length. Moreover, season length is too short to make an accurate reorder calculation. Accurate prediction of orders can t be improved and performances are declined. Initial % 30% 40% 50% Table 5: Comparison of initial inventory level Lost Sales % Scenario Table 6: Changes of season length Initial Demand Number of Volume Reorders Markdown Policy 15 Weeks 50% times 12 th week 25% 10 Weeks 60% times 8 th week 5% Season Length 20 Weeks 15 Weeks Table 7: Comparison of season length uncertainty Lost Sales % Impacts of Demand Volume and SKUs In this experiment, fifteen combinations of demand volume and SKUs are adopted. Five levels of demand volume: 4000, 2000, 1000 and 500 units and three levels of SKU s: 18 SKUs (1 style/6 colors/3size), 48 SKUs (2 styles/4 colors/6 sizes) and 80 SKUs (2 styles/5 colors/ 8 sizes) are used. Table 8 shows the impacts of demand volume and SKUs for different strategies. The results are analyzed as follows: 1. When the demand volume is fixed and SKUs is getting higher, the service level and inventory turns are getting lower. It is because the number of styles which customer can purchase is reduced. 2. When the demand volume is getting higher and SKUs is fixed, our research found that the service level is getting higher, too. It is because the initial inventory is also increased and the choices of customers are also increased.

8 138 International Journal of Electronic Business Management, Vol. 2, No. 2 (2004) Number of SKUs Table 8: Comparison of demand volume and SKUs Lost Sales % Demand Volume CONCLUSION In this paper, four inventory strategy models: the, the the Newsboy, and the Target Weeks Supply have being studied and analyzed. The effects of different retail inventory strategies under uncertainties are discussed. Through the whole research process, the apparel retail simulating system has been built effectively to compare the,, Newsboy and Target Weeks Supply inventory

9 P. Wu et al.: The Study of Retail Strategies under Uncertainties 139 strategies. And based on the results of the simulation models, the characteristics and rules between different inventory models have been analyzed. Several suggestions have been provided when retailers deal with uncertainties in actual supply chain environment: 1. strategy has high ability to handle the uncertain impacts of Color/Style/Size SKU mix error. Therefore, model will be a good choice when it is needed to deal with this uncertain factor. 2. Markdown policy should be avoided in apparel retail store, because the overall performance is dropping off obviously. If it is necessary to use markdown policy, repeated using should be avoided. 3. If reorder lead time is longer than 4 weeks, we should use higher initial inventory level to avoid the slow response for handling demand changes. 4. The performance of each strategy has dropped off quickly when season length is 10 weeks or shorter. This is because the selling season becomes shorter, and reorders become fewer, too. i.e. the number of correcting demand volume is getting smaller. Therefore, we suggest retailers should try to shorten reorder lead time and increase reorders. 5. The Newsboy strategy seems to give higher service level. But it also brings higher inventory cost. From the above analyses, the conclusions and suggestions can be applied to actual supply chain environment. The various analyses for the effects of uncertainties can be the consulting basis for decision maker in the future. Future study will focus on the decision support system for such analyses. Another research can be considered is the decision surface interactive interface modeling for the four strategies. REFERENCES 1. Ellram, L. W., 1991, A managerial guideline for the development and implement of purchasing partnership, International Journal of Purchasing and Materials Management, Vol. 27, No. 3, pp Hunter, H. L. W., King, R. E. and Hunter, N. A., 1991, A stochastic model of the apparelretailing process for seasonal apparel, Journal of the Textiles Institute, Vol. 82, No. 2, pp Hunter, N. A., King, R. E. and Nuttle, H. L. W., 1992, An apparel-supply system for retailing, Journal of the Textiles Institute, Vol. 83, No. 3, pp King, R. E. and Poindexter, M. L., 1990, A simulation model for retailing apparel buying, Raleigh, NC, Department of Industrial Engineering, North Carolina State University NCSU-IE Technical Report, pp Lee, H. L. and Billington, C., 1993, Material management in de-centralized supply chain uncertainly, Operations Research, Vol. 41, pp Lo, K. C., 2000, The research on the uncertainty factors and their adaptive strategies for the implementation of supply chain management-using information & electronic industry in Taiwan as the study object, National Cheng-Chi University Master Thesis. 7. Matthew, A. W., Eric, J. and Davis, S. T., 1999, Vendor managed inventory in the retail supply chain, Journal of Business Logistic, Vol. 20, pp Peitsang, W., Fang, S. C., Nuttle, H. L. W. and King, R. E., 1995, Decision surface modeling of apparel retail operations using neural network technology, International Journal of Operations and Quantitative Management, Vol. 1, No. 1, pp Petrovic, D., Roy, R. and Petrovic, R., 1998, Modeling and simulation of a supply chain in an uncertain environment, European Journal of Operational Research, Vol. 109, No. 2, pp ABOUT THE AUTHORS Peitsang Wu received his Ph.D. degree in Operations Research from North Carolina State University (NCSU) and is currently an associate professor of Industrial Engineering and Management at I-Shou University. His research interests are soft computing, decision support system and operations research. He is currently serving the department chair of Industrial Engineering and Management at I-Shou University. Nai-Chieh Wei received his Ph.D. degree in Industrial Engineering from Wayne State University and is currently an associate professor of Industrial Engineering and Management at I-Shou University. His research interests are logistics, simulation, and supply chain management. Tzu-Po Lin is a graduate student of Industrial Engineering and Management at I-Shou University. His research interests are neural network and supply chain management. Yung-Yao Hung is a graduate student of Industrial Engineering and Management at I-Shou University. His research interests are neural network, genetic algorithm, and supply chain management. (Received July 2003, revised September 2003, accepted November 2003)