A System Dynamics Approach to Study the Sales Forecasting of Perishable Products in a Retail Supply Chain

A System Dynamics Approach to Study the Sales Forecasting of Perishable Products in a Retail Supply Chain Lewlyn L.R. Rodrigues 1, Tanuj Gupta 2, Zameel Akhtar K. 3, Sunith Hebbar 4 1,4 Department of Humanities & Management, MIT- Manipal University, Manipal, Karnataka State, India 2 Student, Department of Industrial & Systems Engineering, University of Southern California, Los Angeles, California, USA 3 Graduate Engineer Trainee, Tata Motors Limited, India 1 rodrigusr@gmail.com, 2 tanujgupta02@gmail.com, 3 zameel123@gmail.com, 4 sunithhebbar@rediffmail.com Abstract - This paper deals with the development of a System Dynamics model to study the impact of forecasting on the performance of the inventory system in one of the units of a leading retail store in India, for the perishable products. Since, efficient inventory management has been considered as one of the important criteria for the reduction of costs in retail supply chains; this paper focuses on this aspect. Simulation and analysis is carried out for a particular perishable product in a retail chain and the effectiveness of the current forecasting and ordering techniques used by the store and an alternate forecasting model using exponential smoothing technique is assessed using a System Dynamics model. From this analysis it was observed that the proposed forecasting techniques showed an improvement in net profit, reduction in the dump and inventory levels. Also, the study was extended to analyze the influence of key factors like promotion costs and discounts on the system in order to predict the maximum value for net profit. Keywords - System Dynamics, Sales Forecasting, Net Profit or Loss, Perishable Products I. INTRODUCTION India with a population of more than 1.2 billion people is one of the fastest growing retail markets in the world. The Indian retail market is estimated to be $500 billion and is one of the top five retail markets in the world by economic value [1]. The inventory management in the Fruits & Vegetables (F&V) section of the retail chain was considered for the study since the focus was on perishable products which are mostly the food items that are subjected to decay, spoilage or destruction depending on the shelf life and this would result in higher costs. The demand and order rate must be carefully managed by proper coordination between vendor and store for perishable products to avoid dump. Timely harvesting and procurement, efficient transportation, and advanced storage, processing and packaging facilities are needed for marketing perishable goods. For the study, the sales forecasting and total dump of Oranges is taken into consideration since it is one of the highest selling perishable products in the F&V section, 104

and has a low shelf life of 3 days. Hence, it is important to manage the inventory efficiently and have a proper control on dump level. Inventory management is the process of efficiently overseeing the constant flow of units into and out of an existing inventory. This process usually involves controlling the inflow of units in order to prevent the inventory from becoming too high, or too low which can affect the sales. Sales forecasting is an important technique and a major function of a supply chain manager whose accuracy will be impacting the inventory management system and also other functions of the supply chains. Some of the important functions of the supply chain which depend on forecasting are, to make timely purchasing requests, develop production plans, evaluate capacity needs, and develop logistical plans. Of the several methods of forecasting, the exponential forecasting method is used in this research, as it is a widely accepted and commonly used method. In this research for sales forecasting, exponential smoothing method is used by considering the previous week sales. A feedback system is extremely important to accurately forecast sales. Accurate forecasts allow the entire supply chain and its scarce resources to be managed more effectively. According to United Nations Development Program, about 40% of food is wasted in India. For a country which has more than 300 million people below the poverty line, such wastage cannot be afforded. The wastage of perishable products once their shelf life is over is inevitable and this is a direct loss to the company as the purchasing cost of the wasted product cannot be recovered. The total dump can be classified as 1st and 2nd level dump. The 1st level dump involves the transportation dump and bad quality product from the vendor. The transportation loss is due the damage to the goods while it is being supplied from the vendor to the store. Products with bad quality are also found because it is not practical to check each product during quality test. The 2nd level dump is the product wasted after its shelf life is over. Good forecasting is necessary to reduce the amount of inventory, reducing the 2nd level dump in the process. Hence, because of the importance of feedback and the efficient management of a complex system, a System Dynamics (SD) approach is used as it is one of the important methodologies which captures the dynamic complexities and predicts its behavior in the long run which is very helpful in good decision making for any system. II. LITERATURE REVIEW SD is a widely used tool for understanding the dynamic behavior of complex systems. It is being used since the inception of the concept in 1961 by Jay W Forrester [2] to understand and evaluate supply chains and inventory management systems. Computer based simulation has proved to be one of the most effective methods of analysis and solving complex problems [3]. This simulation based analysis can be used to develop a model to avoid egregious errors in inventory planning of perishable products. The sale of perishable items is of vastly increasing importance for grocery retailers; around 50% of the total turnover can be accounted to perishable items [4]. Retailers face a formidable challenge of ensuring that they have optimum levels of inventory for goods that are perishable. This is because these goods have a short shelf life without any salvage value and can hurt the profitability of the retailers significantly. A substantial amount of wastage can also be accounted for the same reason [5]. It therefore becomes critical for the retailers to know the accurate forecasts for perishable items such as 105

oranges. These products also drive footfall into the retail stores and hence it is important to maintain high levels of service for these products. Supply chain consists of cascades of firms, each receiving orders and adjusting production and production capacity to meet changes in demand. Each link in a supply chain maintains and controls inventories of materials and finished products to keep them within the permissible limits avoiding stock outs and wastage [6]. To adjust the inventory, sales forecasting techniques are used which help in assessing the demand fluctuations. Numerous simulations are carried out testing different forecasting techniques under various ordering patterns. While the use of models for forecasting is widespread, there is reluctance in the SD community to encourage the use of SD models for forecasting. The assumption that the forecast for the future would be like that of the past is naïve [7]. As the focus transfers to demand management, the retail stores start paying attention to the weaknesses in sales forecasting and begins to establish better methods and procedures for order entry; order planning and order management, and supply planning. Since forecasting is an inexact science, forecasts cannot be completely accurate. The challenge becomes careful management of this inaccuracy and balancing the cost of producing a higher accuracy versus the gains in service, processing costs and inventories [8]. Furthermore, to improve the sales of the store, certain marketing policies are used which mainly comprise of advertising and providing special discounts for the customers. Reference [9] in their research noticed that since the 1970s the amount of budget on promotional activities significantly increased. Some authors conclude that sales are mostly affected by price discounts [10]. While others do believe that advertisement and increased inventory affect the promotional sales the most [11]. III. CONSTRUCTION OF THE MODEL The model is developed for understanding the supply chain in one of the biggest retail stores in India. We have limited our study of the inventory management of Oranges in the Udupi outlet of the retail chain. The model is based on the forecasted demand and its influence on other parameters of the system. Analysis is done to understand the current ordering approach used by the store and evaluate the forecasting method being used. The basic components of the model are Ordering Process, Sales and Revenue, and Dump Control. The influencing factors are Promotional Cost, Special Discount, Last Year Order, Ordering time and Inventory which are indigenous to the system. A. Ordering Process 1) Current Model: The shelf life of the product under consideration is 3 days, which allows the store to give purchase orders thrice a week. The last year s purchase order which is being used to forecast this year s purchase is as shown in Table 1. Fig. 1 shows the general structure of the current system. In the current system, the simplest way of forecasting is being done to identify purchase orders, where the store considers the last year order on the same day to make this year s purchase with an addition of an appropriate growth percentage to that value. With the help of this information the values for target inventory is identified and accordingly the inventory correction value is obtained which helps in initiating the purchase orders. 106

TABLE I Last year order values considered for purchase ordering Day Last Year Order Monday 0 Tuesday 400 Wednesday 0 Thursday 300 Friday 0 Saturday 500 Sunday 0 TABLE II Last week's sales value Day Last Week Sales Monday 125 Tuesday 140 Wednesday 400 Thursday 135 Friday 160 Saturday 200 Sunday 280 Fig. 1 General structure of the forecasting and purchasing system B. Sales and Revenue The major factor affecting the product sales is the number of customers who visits the store. The sales also increase when an additional discount is available on the product as the purchasing power of the customer increases proportionately. The revenue of the product is generated through the sales; thereby allowing us to calculate Net Profit or Loss. 2) Developed Model: The new model developed suggests a more accurate method of sales forecasting and also changes the ordering policy which is currently being used. The forecasting technique used in this model is exponential smoothing. Fig. 2 shows the general structure of the modified model with forecasting technique. This method uses three variables to determine the forecast- Last week sales, Smoothing factor and Previous forecast. Purchasing is done on a daily basis for the modified model. The last week s sales value is considered for forecasting the purchase order (as shown in Table 2). Fig. 2 Depiction of Exponential Forecasting in the Developed Model C. Dump control It requires the control of 1st and 2nd level dump. The 1st level comprises of transportation loss and bad quality loss. The 2nd level dump is wastage due to the expiration of the product due to the low shelf life and lack of sales. The Dump Accumulated is calculated by combining both the 1st level and 2nd level dump. 107

Fig. 3 Stock and Flow diagram of the inventory system for the current system Fig. 4 Stock and Flow diagram of the inventory system for the proposed system 108

D. The Stock and Flow Diagram With respect to the structure of the system as explained in the section 3.1 to 3.3 a Stock and Flow diagram has been built for the current system and the proposed new system which is as shown in Fig. 3 and Fig. 4 respectively. The two models are very similar with the only difference in formulation for ordering process as explained, which represents the forecasting techniques used by the model. Some of the key variables are Inventory which is dependent on the purchase and sales. Dump Accumulated which again depends on the inventory levels of the products and transportation loss and is represented by a flow variable dump collection rate. The other important variable is the Net Profit or Loss which is dependent on Total costs and Revenue. Some of the costs which are being considered in the model are Wastage costs, Inventory holding costs, Purchase costs, and Promotion costs, the sum of which represents the Total cost. IV. SIMULATION AND ANALYSIS Both the current and suggested models were simulated over a period of a month for analysis of dump, inventory levels and net profit or loss. The average weekly values for Number of customers, Last year order, Last week sales and pricing factors were taken from the store manager according to the policy followed by the store. The values for Net profit, Inventory and Dump accumulated were observed over a period of 30 days to develop an understanding of the working of the store. The model was simulated to observe the effects on the major parameters of the system by varying the following factors- Promotion cost Special discount A. Simulations and Analysis for the current model 1) By varying the Promotion Cost: The amount spent by the company on advertising is identified as the promotion cost. It is initially taken as $1.67 per day as an average estimation ($1= INR 60). On increasing the amount spent on advertising, it is observed that the net profit increases up to a certain point and then it starts to decrease. The optimum point is found to be at $3.17 per day which gives an increased net profit of $232 by the end of the month, compared to $152 for the initial. When the value is further increased beyond the optimum value to $4.17 per kg the profit will start decreasing (Fig. 5). 24,000 24,000 24,000 8,500 8,500 8,500-7,000-7,000-7,000 Net Profit or Loss Net Profit or Loss for Existing Model : Promotion cost 1 Net Profit or Loss for Existing Model : Promotion cost 3 Net Profit or Loss for Existing Model : Promotion cost 4 Fig. 5 Influence of promotion cost on Net Profit/ Loss 2) By Varying the Special discount: The store has a policy to give an additional discount on oranges on Wednesday. This discount policy forces the store to sell oranges at a lower price than the normal selling price of the product thereby incurring a loss on that particular day but reduces the wastage. The store balances this loss by selling other products thereby gaining a net profit across the floor. This is an important marketing strategy employed by the store leading to net profit and an increased customer base. The cost price for oranges is $53 per 100 kg for the month under consideration and the 109

normal selling price is $67 per 100 kg. After discount the store sells it for $60 per 100 kg giving a discount of $7 per 100 kg. This discount is varied to assess the optimum discount so that the store ends the month without any profit or loss for the particular product which will lead to a greater net profit on the shop floor.the optimum value obtained on simulation is $14.88 per 100 kg which can be approximated to $15 per 100 kg for the discount amount (Fig. 6). 5,000 5,000 5,000-10,000-10,000-10,000 Net Profit or Loss Net Profit or Loss for Existing Model : Discount 7 Net Profit or Loss for Existing Model : Discount 10 Net Profit or Loss for Existing Model : DIscount 15 Fig. 6 Influence of special discount on Net Profit/ Loss B. Comparison of Current System with Proposed Model After analysis of the current store model, another model was prepared introducing exponential smoothing technique to forecast the demand and the impact was studied on important parameters like total dump, inventory and net profit or loss. 1) Net Profit or Loss: The graph in Fig. 7 shows a comparison of the net profit of the two models. It can be observed that the suggested model which uses exponential smoothing for forecasting demand provides a higher net profit than the current model used by the store. The net profit at the end of the month for the current model is $152 and for the new model is $199 showing an increase of 30.5% which is a considerable jump from the current values. The current store model incurs higher cost due to their ordering policy as ordering thrice a week increases the inventory holding cost. This increase in profit is seen even though the purchasing price for oranges in the original model is less than the new model due to bulk purchasing. 0 0 Net Profit or Loss - - Net Profit or Loss for Existing Model : Comparison Net Profit or Loss for Suggested Model : Comparison Figure 7: Comparison of Net Profit/ Loss for the two models 2) Inventory: Fig. 8 shows a comparison of the inventory levels throughout the time period. We can easily identify that the new model shows a considerable reduction in inventory levels. At the end of the time period it can be observed that there is about 29.52% reduction in inventory level and hence the holding cost. This is mainly because of the daily sales forecasting used in the new model to continually predict everyday sales more accurately. Lower inventory levels are beneficial for cost reduction and the percentage of product going to waste also decreases. The high peaks in the current model graph show the ordering days which clearly indicate the cause of higher inventory holding cost. 600 Kilos 600 Kilos 300 Kilos 300 Kilos Inventory 0 Kilos 0 Kilos Inventory of Existing Model : Run Inventory of Proposed Model : Run Fig. 8 Comparison of Inventory levels of the two models Kilos Kilos 110

3) Dump Accumulated: The reduction in dump shown by the new model establishes the need for the sales forecasting using the proposed model. A reduction of 28.06% is achieved in the suggested model as compared to the current model. It can be observed that even though the dump level is lower during the first day, for the current model there is drastic increase in this value as compared to the proposed model whose increase in dump level is much lesser than that of the existing model (Fig. 9). 350 350 175 175 0 0 Dump Accumulated Dump Accumulated for Existing Model : Run Dump Accumulated for Proposed Model : Run Fig. 9 Comparison of Total Dump Accumulated V. IMPLICATIONS It is imperative from the simulation and analysis that the modified model with forecasting technique has a significant effect on the key parameters of the system. The comparison has shown a considerable increase in the net profit when the ordering policy and forecasting method were changed. The new model also shows a better control of inventory and leads to reduction in 2nd level dump. The optimum promotional cost for the existing store model indicates that it can be increased up to value of $3.17 per day above which it is not feasible and would result in the reduction of profit. The model also reveals the flexibility in fixing a discount to increase the customer base negating the loss incurred for the particular product. VI. CONCLUSION The SD methodology used in the paper shows the effectiveness of simulation analysis to demonstrate the use of sales forecasting in inventory management. By varying the parameters, we can analyze the capability of the model in finding the optimum values of these factors for the goals set by the company. The model brings better understanding of the factors which effect the operations and provide a way for decision making in short and long term aspect. The new model uses exponential smoothing method for demand forecast. This forecast is used to obtain the target inventory. Good forecasting technique is necessary to control the amount of inventory, issue timely purchasing orders, and avoid excess wastage of the product. The wastage of the product is prior factor in the inventory management of perishable product. In the suggested model, apart from the increase in net revenue, decrease in the total inventory and dump has been observed. This helps in reducing wastage cost and inventory holding cost. The model gives an insight into the current forecast scenario in retail chains in India and how better forecasting techniques can be implemented to improve the operational efficiency of the system. VII. REFERENCES [1] S. Majumder, (2011), Changing the way Indians shop, BBC News, http://www.bbc.com/news/worldasia-india-15885055. [2] J. W. Forrester, Industrial Dynamics, New York: MIT Press and Jhon Wiley & Sons Inc, 1961. [3] L. L. R. Rodrigues, S. Hebbar, S. Rao, & F.G. Motlagh, Identifying the Optimum Warehouse Capacity Requirement: A System Dynamics Approach, UKSim 5th European Symposium on Computer Modeling and Simulation, 2011, pp. 220-224. 111

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