THE INTEGRATION OF SUPPLY CHAIN MANAGEMENT AND SIMULATION SYSTEM WITH APPLICATION TO RETAILING MODEL. Pei-Chann Chang, Chen-Hao Liu and Chih-Yuan Wang

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1 THE INTEGRATION OF SUPPLY CHAIN MANAGEMENT AND SIMULATION SYSTEM WITH APPLICATION TO RETAILING MODEL Pei-Chann Chang, Chen-Hao Liu and Chih-Yuan Wang Institute of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Dong Rd, Tao-Yuan City 32026, Taiwan ROC. ABSTRACT Supply chain management (SCM) is often incorporated into the business process for its better management from production to retailing. In this research, we propose an inventory-forecasting model and use the simulation models to investigate the efficiency of the SCM by applying three different managing methods: EOQ model, p-model, and combined models. Our objective is to reduce the inventory cost in the process, to increase the service levels, and hence to improve the relationship between the retailers and customers. In our experiment, we use 3C retailer s sales data to test our inventory models. By comparing the results of these three models, we prove that the inventory-forecasting model is better than the traditional methods. In conclusion, we recommend the decision maker a better method to adopt for their organization. Key Words: Supply Chain Management, Inventory Model, Simulation System. 1. INTRODUCTION Manufacturers of upper stream, distribution centers, and even retailers who have sales information, all face the challenge of value depreciation and speed. Providing sales information in a short time, satisfying the customers requirements, and supplying flexible services to fulfill the market demands become an unavoidable trend to deal with the following issues: how to make efficient inventory, computerize storage replenishment, and the rapidly changed customers requirements for retailers. This study utilizes the adaptive forecast to predict customers requirements, which makes the inventory level stable. By this method, the enterprises inventory is minimized, the probability of backorder is reduced, and the service level is increased. The research takes the practical supply-and-demand condition into consideration as the system constraint of the supply chain inventory system. The purpose is to decrease the process time from the manufacturing, distribution and retailers to customer, in order to attain the maximum satisfaction of the customers, reduce the holding costs, and lessen the backorder probability. Furthermore, under the presupposition of minimum holding cost of suppliers, the study attends to decreasing the influence of the uncertainty of market requirements. The research is conducted in two directions: one is through the simulation system constructed by the study and data gathered by questionnaires and investigations, which analyze the customers behaviors and obtain the expected service rate of the customers of 3C retailer. The other is to consider whether the retailers satisfy the customers requirements or not, lower the expenses simultaneously under the prerequisite of promising order quantity for customers, and forecast the market demands through the information technology and the appropriate inventory management. As far as the supply chain is concerned, the information integration, efficient understanding of customers requirements and the production planning and sales strategies are important to overcome the keen competition in the same market

2 2. LITERATURE REVIEW 2.1. Supply Chain Management (SCM) Supply Chain Management (SCM) has been studied by several researchers. What exactly is SCM? Bowersox (1987) defined that SCM is a set of approaches utilized to manage the whole process. Bechtel and Jayaram (1997) defined that SCM is strategic alliance and corporative relationship between partners. Ellram (1991) emphasized that SCM is a strategy pursued to partners mutual benefits. According to these definitions, we concluded that SCM utilizes the current resources to make the most effective and efficient integration of the material and information flow which are produced from the process of purchasing and procuring raw materials, producing items, shipping to retailers or customers, and the after service between corporations and related partners Forecasting Forecasting plays an important part in the inventory management field, and the result always influences the inventory strategies. In the uncertainty environment, an accurate forecasting result can reduce the inventory, decrease the short supply, improve the service levels, and advance enterprise competitiveness. There are two general approaches to quantitative and qualitative forecasting. Quantitative forecasting applies a set of mathematical rules to a series of past data in order to predict outcomes. These techniques are preferred when managers have sufficient hard data that can be used. Averaging Techniques, Exponential Smoothing, Time Serious and Regression are common techniques of quantitative forecasting. Qualitative forecasting uses the judgments and opinions of knowledgeable individuals to predict outcomes. Delphi Technique, Jury of Executive Opinion and Historical Analogy are common techniques of qualitative forecasting. Different forecasting approaches may apply to different fields, so the prediction accuracy varies. According to the past research, we find that combining method is a quite effective forecasting approach. Chan (1999) proposed that one traditional forecasting approach is only one of many aspects to achieve one inventory performance. The improved forecasting approach must go hand in hand with changes to other parts of the inventory management and control policies. So, combining different forecasts will lead to significant improvements in demand forecasting performance. Neural network, Regression analysis, Exponential smoothing and combining method are the most common approaches to forecast. In this research, we first utilize some of these approaches to forecast the market demand in the lead-time, and then determine the best approach to build our inventory model. We hope this inventory model can help to balance the product flow, minimize the stock, decrease short supply, and improve service levels Inventory System Good inventory management is essential to the successful operation of most suppliers, manufacturers, distributors and retailers. Especially to the product with high value; good inventory management can help to economize a large number of expenses. For this reason, several researchers have studied the inventory fields, and have developed many inventory management models and systems. Chaudhry et al.(1996) used the traditional Economic Order Quantity(EOQ) model to develop their inventory decision support system in the small business. Wang et al. (1996) utilized EOQ model to develop an inventory control system to determine the Q unit size and reorder point model with uncertain product capacity and production rate consideration. Hung (1999) considered two uncertain factors: flow time and production rate in the inventory control system. And he also used a simply method to calculate the standard deviation of flow time in order to estimate the safety stock demand level. Hill (2001) proposed that hybrid using backorder model and lost sales model outperformed using unique model. Much research has been done on the inventory model and most of them were effective, but a few studies have been considered with uncertainty. Furthermore, general inventory model did not include the forecast model and develop the inventory decision support system because of its complexity. However, a good forecasting system is an important step in the development

3 of such a model and can be used within the current inventory control system to advantage until a better system has been developed. So, we will utilize Neural Network and Combining Method to forecast the market demand, and then determine the best approach to build our inventory decision support system. 3. METHODOLOGY 3.1. Consumers Behavior Model The simulation software Arena and Excel are used to simulate the relationship between costs and service level in the retail business. The simulation model including the consumers behavior model and retailer s administrative model is established in order to find out the best model for retail business. The main idea of our consumers behavior model is distinguished by what consumers will do after they enter the shopping mall. This can be divided into two types: type 1, consumers have already decided what to buy before entering the shopping mall. Type 2, consumers just ramble around the shopping mall. For type 1 behavior, if customers find something they really like, they will buy immediately and proceed to the next activity. If they can t find the products they want, they will ask the attendant about the stocks. After that they may leave right away, buy the products, keep looking for another product or ramble around. But for type 2 behavior, if consumers enter the shopping mall for rambling around, they will leave right away after browsing, or they will decide to buy something or not when they are attracted. Then they may keep rambling around or leave. Besides, the consumption process will be automatically recorded into the system for calculating the service level Supply Chain Model The main propose of this research is to discuss the relationship between consumers and the retailer. And the inventory management model is developed after comparing each model. The following is some basic hypotheses of the supply chain model we develop: 1. There is only one retailer in the supply chain system. 2. Product type: electronic related product. 3. Supplier has to disburse the shipping cost. 4. No alternative is considered. 5. Mass purchase is not considered Inventory Model The inventory model used in this research is introduced as follows: 1. Economic Order Quantity (EOQ) Model The basic EOQ model is the simplest model. It is used to identify the order size that will minimize the sum of the annual costs holding inventory and ordering inventory. The optimal order quantity can be determined as: 2 DS Q opt = (1) H Where Q is optimal order quantity, D is annual demand, S is the ordering cost and H is the holding cost. And the reorder point (R) can be determined as: Or R=Expected demand during lead time + Safety stock R d L + SS = d L + Z Lσ (2) = d Where L = average lead-time, d = average or expected demand, Z = number of standard deviations and σ = the standard deviation of lead time demand. d

4 2. Fixed-Order-Period Model (P-model) The fixed-order-period model is used when orders must be placed at fixed time intervals (weekly, twice a month, etc.). If demand is variable, the order size will tend to vary from cycle to cycle. This is quite different from an EOQ model in which the order size generally remains fixed. Order size in the fixed-interval model is determined by the following computation: Or Amount to order = Expected demand during protection interval + Safety stock Amount on hand at reorder time Q = ( OI + L)+ d z OI + L A σ (3) d Where OI = order interval and A = amount on hand at order time. If order interval is T* and order size is Q*, then T* can be determined as T* = D Q * 2S T* = DH (because Q* = 2DS H ). 3. Combining Models This new combining model is developed in this research. The characteristic of this combining model is adding the forecasting function while it is close to the reorder time. The market demand in the lead-time will be forecasted by the way of the forecast approach. Thus, the reorder period and reorder quantity can be adjusted to conform the variation of market demand. Multiple regression analysis and Neural Network is used to forecast in this combining model. The main ideal of this combining model is to adjust the inventory strategy by forecasting so as to reduce the inventory and holding cost. Two models are included in this research; one is EOQ model that the order quantity is fixed, and the other model is the combining method that the reorder quantity and reorder intervals are not fixed. The reorder point is the same between these two models. For the second model, the related computations are as follows, or Reorder point Order quantity R = R d f = D ' (4) Q = d( T + LT ) D t 1 f ' (5) f Where, R is the reorder point, D f is the demand in the forecast lead-time, d f is the demand before the forecast lead-time, LT is the Lead Time interval and T t 1 is the previous order period. 4. THE SIMULATION MODEL OF RETAIL BUSINESS 4.1. Basic Model Establishing We discuss the supply chain process of retailers and the subjects as follows: 1. The optimal ordering quantity and the optimal reordering interval in the inventory model. 2. The influence of safety stock with retail goods. 3. Reducing the inventory cost while keeping the same service level. 4. Selecting the best service level for supply chain. 5. Simulating the supply chain model to analyze the relation between service level and its costs Simulating the Customers Behavior The customers behaviors are simulated by using Arena software. The input data is collected from 100 people who were entering the shopping mall. The visiting place is the entrance of the shopping mall. After analyzing, we obtained the following information: 1. 55% customers intend to buy some products before entering the shopping mall and 45%

5 customers just look around. 2. Customers spend 26.6 minutes rambling around a 3C mall in average. 3. Customers who have decided to buy some products before entering the mall will buy about 2.9 items a round in average. 4. Customers hope they can terminate the paying procedure within 2.6 minutes. 5. If customers enter the shopping mall to buy something, they hope they can leave in 16.5 minutes. 6. If the customer enter the shopping mall and is attracted by some products, 43.9% customers will ask the attendant about the products. 7. If the product is out of stock, then 35% customers will ramble around in the mall, 55% customers will buy other products, and 10% customers will leave right away. We simulate the customers behaviors and the result shows that if we want to satisfy all customers needs, our service level must be at least 96.4%. According to this simulating system, we can gather further information about the customers entering the shopping mall, such as the average spending time, the percentage of busy attendants, average queue numbers, and average waiting time. 5. ANALYSIS OF THE EXPERIMENTAL RESULTS We first use different forecasting methods to find the accuracy and suitability of each testing case, and then apply the most precise forecasting method to develop the forecasting inventory model. Finally we compare and analyze the forecasted inventory model we propose with a general inventory model (EOQ, P-model) The Comparison of Forecasting Methods The forecasting methods we use are Back Propagation Network (BPN) within neural network, Multiple Regression Analysis, Exponential Smoothing Method, and Combining Method. Among those methods, the Combining Method is combined with two more precise forecasting methods. The demand data of our test case is I type product of some company. We tested five products with different demand conditions. The testing data is as follows: Table1. Testing data Items Demand (year) Average demand (week) Standard deviation of demand (week) I II III IV V The Forecasting Results of BPN Neural Works Professional II is used to establish neural network of BPN. Through five different products, we obtain 52 numerical data based on week of demand quantities. Among above, 40 from 52 are training samples, and the others are testing ones. The basic BPN framework is showed in figure1. First, we enter history data in the input layer. Second, calculate those data in the hidden layer. Third, we can get the forecast data in the output layer. Last, we calculate the error between forecast and real data. Then return it to adjust the parameters in BPN, and keep on recurring

6 B 1 Y 0... Y k Output layer Bias1 B 00 Z 0 Z 1 Z j Hidden layer Bias0 X 0 X 1 X i Input layer Figure1. The basic framework of BPN The Forecasting Results of Multiple Regression Analysis MINITAB 13 is used to run the multiple regression analysis. The variables are semi-finished goods, finished goods and season factors The Forecasting Results of Exponential Smoothing Method EXCEL is used to run the Exponential Smoothing Method. With the varieties of demand, the α -value in Exponential Smoothing Method would change The Forecasting Results of Combining Method By the MAPE-values of different forecasting methods, we found that the BPN and Multiple Regression Analysis were the better methods. We recalculated a new forecasting value by combining these two methods. We use Trial-Error method to find the best weights ( w1, w2 ) of these two methods. We simply format it as: w (Result from BPN) The Comparison of Accuracy w 2 (Result from Multiple Regression Analysis) First, we compare the accuracy of four forecasting methods in different demand conditions. And then we establish the forecasting inventory model by the forecasting method with higher accuracy. We find that the Combining Method is the best. The Combining Method is more precise and objective than the other ones. Accuracy% Table2. The accuracy of forecasting Items I II III IV V BPN Multiple Regression Analysis Exponential Smoothing Method Combining Method

7 From the table shown above we can conclude that, the combining method is the most accurate forecasting method for all forecasting methods, and the exponential smoothing method is the worst one. Therefore, the combining method is used to forecast the market demand in our inventory system Inventory Model This research is based on the project material supplying model and simulates the project material requirement situation by EXCEL. We analyze the inventory method (Fixed Order Quantity (EOQ); Fixed Order Period (P-model)). In the same situation, we use different management tools and enter different material requirement data to calculate and record the inventory quantity. After that, we analyze the inventory control results produced by different inventory management tools. This simulation process is simulated about 1 year s situation including the material requirement quantity and tests 5 different required types. The following table shows the descriptions of materials. Table3. Relation data of productions Items Relative data A B C D E Price Lead time (week) Order cost Holding cost 22% 14% 15% 22% 17% Table 4 is the compared inventory model about Fixed Order Quantity (s, S) and Fixed Order Period (R, S). According to the results, we find that the inventory level of (R, S) model is higher than (s, S) model. In other words, (R, S) model needs to spend more expenses. Table4. Compare with fixed order quantity & period Inventory model Fixed order quantity Fixed order period Items I II III IV V I II III IV V Average Order period Average Order quantity Reorder point Average Inventory Safety Stock Service level By observing table 3, we find the inventory model, (s, S) gets better performance in each case. Based on the result, we suggest the organization to use the (s, S) model with higher priority. After testing the inventory management method of this organization, the following section will discuss our inventory forecasting model Inventory Forecasting Model When we add the forecasting function, the compared result between fix and unfix quantity is shown in table 5. Even if the average inventory quantity and order quantity solved by unfix

8 model is lower, when we consider purchase cost at the same time, the total cost just differ by a few. Therefore, we may assume that having fixed order quantity or not causes very few influences in the inventory cost. Table5. Compare with different forecasting inventory model (fixed order quantity & period) Inventory model Fixed order quantity Fixed order period Items I II III IV V I II III IV V Average Order period Average Order quantity Reorder point Average Inventory Safety Stock Service level By studying table 5, we know the average inventory of (R, S) model is higher than the other, but the service level is also higher than (s, S) model. Because of this viewpoint, the (R, S) model is adopted at key parts inventory control, which has higher influence. The compared result of inventory forecasting model and the general one that are using (R, S) inventory model is shown in table 6. The result shows that if we forecast the requirements during the purchasing lead-time and also consider the standard deviation of forecasting, we may reduce the order point, average inventory quantity and inventory cost. Furthermore, we find the service level of our inventory forecasting model is better than the general one. Therefore, if the organization desires to use (R, S) inventory model, we will strongly suggest them to use our inventory forecasting model first. Table6. Compare with forecasting inventory model and general model (fixed order period) With forecasting inventory Inventory model General model model Items I II III IV V I II III IV V Average Order period Average Order quantity Reorder point Average Inventory Safety Stock Service level The table 7 shows the inventory forecasting and original model, which are using (s, S) inventory model. The result shows that if we forecast the requirement during the purchasing lead-time and also consider the standard deviation of forecasting, we may reduce the order point, average inventory quantity and inventory cost. Also, we find the service level of our inventory forecasting l is higher than 96.2%. According to table 7, we find that the results of inventory forecasting model are better than the traditional one in each test. Therefore, if the company wants to use (s, S) model, we will strongly suggest them to use our inventory forecasting model with higher priority

9 Table7. Compare with forecasting inventory model and general model (fixed order quantity) With forecasting inventory Inventory model General model model Items I II III IV V I II III IV V Average Order period Average Order quantity Reorder point Average Inventory Safety Stock Service level Figure 2 is the inventory cost s improving rate between our inventory forecasting model and the traditional one. Improving Rate % I II III IV V Items Figure2. The inventory cost improving rate of each item. According to the result shown in Figure 2, we test 5 different material types in this study. When using our inventory-forecasting model to control inventory, we may reduce inventory management cost by about 10%-17%. By adopting the model to simulate and analyze, the organization will know when to order so as to lower the inventory quantity and reduce the inventory cost. This model can also support the company on a decision-making basis, and choose the appropriate managing model. 6. CONCLUSIONS AND FUTURE RESEARCH The system constructed in this study can be applied into practical management in retailing. Based on the simulation model, the results of this study may not go well with every kind of retailing operation. However, they can provide some directions to SCM running of retailer and the blueprint for continuous improvements. The following instructions can be considered by the retailer. 1. By empirical results, the combined forecasting method is pretty effective, which is superior to single forecasting method and is stable without being effected by the variation of environment

10 2. The forecasting model built in this study can be applied to situations with steady demands, vibrating demands, cyclic demands or those with seasonal factors. 3. The service level condition should be considered when setting the ordering quantity. The laciness of goods leads to the loss of sales whenever it happens, so the service level should be enhanced. 4. By comparative analysis, the forecasting inventory management model to add forecasting ordering quantity and period when approaching the ordering point is superior to both of fixed ordering quantity or fixed ordering period. 5. The results of this study also display that fixed ordering quantity is superior to fixed ordering period if Point Of Sale system (POS) has derived into the retailer s business. 6. The demands and characteristics of customers consuming in 3C retailing stores can be easily found by simulation model. Through these characteristics, the retailer can adopt and take reactions. In the end, this study also provides some directions for future works for those researchers in relative issues. 1. The competitive behavior of the same retailing business can be considered, that is, to discuss about the competitive relation while increasing the number of retailers. 2. The substitution characters of merchandise can be considered. 3. The comparison of the theoretical results between simulation model and real model. REFERENCES Bates, J. M. (1969), The combination of forecasts, Operational Research Quarterly 20, Bowersox, et al. (1987), Emerging Patterns of Logistics Organizations, Journal of Business Logistics, 8(1), Bechtel, C. and J. Jayaram (1997), Supply Chain Management:A Strategic Perspective, The International Journal of Logistics Management, Chan, C. K., Brian, G., H. Wong. (1999), The value of combining forecasts in inventory management a case study in banking, European Journal of Operational Research, 117, Chaudhry, S. S., Salchenberger, L., and Beheshtian, M. (1996), A small business inventory DSS: design, development, and implementation issues, Computer & Operational Research, 23, Clemen, R. T. (1989), Combining forecasts: Areview and annotated bibliography, Interational Journal of Forecasting, 5, Ellram (1991), Supply Chain Management: The Industrial Organisation Rerspective, International Journal of Physical Distribution & Logistics Management, 21, Hill, R. M., and Dominey, M. J. (2001), Inventory policies for all-or-nothing demand process, International Journal of Production Economics, 71, Hung, Y. F., and Chang, C. B. (1999), Determining safety stocks for production planning in uncertain manufacturing, International Journal of Production Economics, 58, Jacek, M. Z. (1992), Introduction to Artificial Neural Systems, West Publishing Company. Kalpakam, S., and Sapna, K. P. (1997), A lost sale inventory system with supply uncertainly, Computers & Mathematics with Applications, 33, Korpela, J., and Tuominen, M. (1996), Inventory forecasting with a multiple criteria decision tool, International Journal of Production Economics, 45, Lee, H. L., and Billington, C. (1993), Material management in de-centralized supply chain uncertainly, Operations Research, 41, Petrovic, D., Roy, R., and Petrovic, R. (1998), Modeling and simulation of a supply chain in an uncertain environment, European Journal of Operational Research, 109, Wang, Y., and Gerchak, Y. (1996), Continuous review inventory control when capacity is variable, International Journal of Production Economics, 45,