LITERATURE REVIEW ON APPLICATION OF QUANTITATIVE METHODS IN DESIGN AND ANALYSIS OF ‘COFFEE BEAN SUPPLY CHAIN’

Introduction

          Coffee beans seem to be one of the major industries in VietNam because VietNam is an agricultural country. The whole system from the production department to customer receives has to be efficient to bring the highest value for the coffee beans companies. The supply chain is one of the most significant factors which decide the competitive cost that impacts the local coffee beans companies. Previously, The local companies have faced the problem with logistics, especially transport and inventory cost. This research will deeply analyze the local coffee bean supply chain to get more perspectives about the whole system. Furthermore, It will figure out the issues that affect the cost of the local coffee bean companies. As a  result, this research is based on two quantitative methods that are time series method and the Economic order quantity (EOQ) method to provide the potential recommendations.

Supply chain design

The coffee bean supply chain in Vietnam has a high level of complexity because of various parties in the whole supply chain. For example, in Buon Me Thuot City, Daklak, Viet nam, the coffee bean supply chain includes government, Farmers, local collectors and coffee bean companies (Figure 1).

Figure 1 : The General supply chain of Vietnam’s coffee (Giang & Tapan 2018)

            According to Figure 1, the flow of the coffee bean supply chain in Vietnam goes through at least 4  sections before the customers. Obviously, The government provides the policy for the whole system. After that, the main department that directly produced the coffee bean is the farmers. It means that the farmers are the main sections that burden the cost of the production such as seed and land. Moreover, The quantity and the quality of the coffee beans are almost figured out in this section. The local collectors play the role of connecting coffee bean companies with the farmers. Finally, The companies play the role of providing the products for the customers. The coffee beans companies are one the main factors that play the role in pricing the coffee bean. They could not directly contract the farmers because farmers included the amount of small households which waste more working time of the companies. So, they have to contract with the local collectors even if the cost could be increased.

Figure 2 : The General system of Vietnam’s coffee bean companies (Giang & Tapan 2018)

Based on figure 2, the entire system to sell the coffee beans is very complicated which includes supply chain management, reverse logistics, Market, Corporate social responsibilities, and Environmental management. The price of the coffee beans is usually based on the purchasing prices that companies and local collectors suggest the farmers. So, The price of the coffee beans almost depends on the operational cost of the companies because they are the main sources that are purchased from the farmers. The representatives of coffee beans companies said that the main cost problems of the companies are from transport and inventory (Giang & Tapan 2018). Furthermore, the problem is that the value of coffee beans in Vietnam is unsteady which could affect the local or exported demand for coffee beans in specific periods. Specifically, The two main problems of the Vietnam coffee bean supply chain are unsteady value and the high cost of inventory and transportation. Coffee beans are seasonal products so that could be the reason for the unsteady value. Because the companies could have more inventory without any more suppliers when it is not the coffee bean season, the price will increase. The problem is that the companies need to figure out the demand of the customers at a specific period of time to have the most efficient inventory. It will improve the shortage inventory condition of the company. On the other hand, The inventory and transport costs could be the reason of the ineffective order quantity. It means the companies have to measure the demand to have the lowest enough inventory. The quantitative method should be applied in the supply of the coffee bean companies to figure out the specific order quantity in the specific period of time. Consequently, the company could improve the inventory cost and even achieve the demand which will stabilize the coffee bean price.

Supply chain analysis

          Times series Forecasting (TSF)

Accuracy forecasting time series could be the key point that impacts personal or organizational decision-making (Martin 2013). The TSF method is based on the previous seasonal dataset to provide the prediction for the next similar periods. It is suitable for forecasting the early plan for production in some specific fields, especially in this case is agriculture (Makridakis, Wheelwright, & Hyndman, 2008). Currently, there are many industries that rely on seasons or trends (Zhang & Min 2005) which means that those industries include some seasonal repeat factors (Hylleberg, 1992) such as the seasons in agriculture. In reality, Agricultural production relies on a number of time factors (Herman, Dominique, Felix, Ferdinando, Carolien & Lieven 2014). In the case of coffee beans, which is an agricultural product, so the seasonal factor is one of the main aspects to affect the price. So, the prediction of the time series could be the direction for the companies to have the preparation in each period. On the other hand, the Accuracy of TSF is the significant factor that improves the inventory management of the companies (Makridakis và Wheelwright, 1987).

The contribution of the TSF method is that it supports the company to measure the demand for the coffee bean in some specific period especially out of a season of the coffee bean. The forecasting will be performed based on the dataset of the specific similar previous season or period. Based on that, link with the Economic order quantity, the company could have a suitable inventory that has achieved the demand with the suitable cost and base on that stabilizing the coffee bean price. By collecting data in the period of time, the TSF method could forecast the future data from the past (Hao & Mao 2010).  On the other hand, the other reason could be the traditional forecasting and inventory management which could not handle long seasonal products (Spedding & Chan 2000). Furthermore, there is no method that works efficiently over a long period of time (Andre, Marwin, Johannes, Norbert, Nikolas & Samuel 2020). So to have an effective application, the coffee bean companies could consider the recommended process that includes 3 sections. It includes pointing out the specific period of time, clarifying the efficient method before doing forecasting (Andre, Marwin, Johannes, Norbert, Nikolas & Samuel 2020). Furthermore, the TSF method is the combination of various parts which are trend factors, seasonal factors, and random changes (Linh & Vilem 2019). The result of the TSF method could be different from the actual. However, the TSF seems to be the efficiency measure that improves the errors in most of the cases (Kartikeya, Nishita, Shriniwas, and Ashoke 2016).

Economic order quantity (EOQ) 

Ordering cost and transporting cost are two main costs of the inventory cost (Kumar 2016) which is the main issue of the Vietnam coffee bean supply chain that we mentioned. The Economic order quantity (EOQ) is one of the oldest approaches used for decision-making that relates to a number of orders, inventory, and reorder with the minimum cost (Kumar 2016). Basically, The EOQ concept figures out the number of ordering products that 100 percent of that is perfectly needed (Abdullah & Gultekin 2007). The traditional cost includes cost per unit, prices, demand, and operating cost are unchanged but the cost of holding per unit is changed depending on the period of inventory time (Howard 1982). The coffee bean companies are not directly producing the coffee bean that they order from the local collectors then the local collectors collect from the farmers. So, in the coffee bean companies aspect, to reduce the cost of transport and inventory, they have to deeply measure the demand that maximizes each order and transportation. Applying the EOQ beside TSF that figures out the best inventory policy relies on the input datasets (Gang 1997). However, to apply the EOQ method most efficiently, there are three-point that the companies have to consider (Tien 2010)

1. The numbers of products should be ordered one time but transport in many times
2. Guarantee 100 percent of the product is not an error, selling for cycle after the checking the last product of each cycle
3. The suppliers have the ability to solve the order

The third function is related to the suppliers which are farmers so that the companies could apply more methods such as linear programming to achieve the function on both sides. On the other hand, the issue of the previous local coffee supply chain could come from the obsolete EOQ. Although it was famous, Its estimation is usually unclear and inaccurate (Kyung 1987). So, the role of the TSF method seems to be more significant which provides more about the seasonal demand for the EOQ method. However, in some cases, the qualities that the EOQ needs are not always perfect. According to Bacel and Mohamad in 2008, their approach shows that their number of ordering products is higher. It could benefit the imperfect consignments.

Briefly Linear Programming (LP)

          As we mentioned, because the EOQ is traditional it did not include the general factors especially mean poor conditions. So, LP could be used as the supportive method of the EOQ. Furthermore, in some cases, It could be the alternative method for the EOQ because of its flexibility. The LP tries to solve the program that includes a number of variables and constraints (Daniela & Benjamin 2004). However the LP is hard to recognize its components, It requires the companies that use the LP have to have a certain knowledge about that. Moreover, the strict request about size and mathematical functions makes the ability to solve random large-scale cases impossible (Farias & Roy 2003). So, as we mentioned, LP should be the supportive method for the TSF and EOQ.

Summary of Findings

          The main issue of the Vietnam coffee bean supply chain is the cost of transport and inventory. The research of Time series forecasting figures out the method to forecast the date for the specific period. Based on the mathematical function, using the dataset from the previous season to predict the future of demand and provide the solutions. There is no perfect method so based on the errors of the time series forecasting the coffee beans companies should be better prepared with a certain ratio. However, Using only the times series forecasting is lacking a solution for an issue that we mentioned. The Economic order quantity would contribute to the early plan that the companies need to prepare when it is out of coffee bean season. It figures out the efficient quantity of the coffee beans that the companies should order. The combination between time series forecasting and the economic order quantity brings better inventory for the companies because they have the pieces of evidence of previous season demand and the order quantity to meet the demand. However, the economic order quantity is the traditional method so it did not provide detailed cases such as poor condition products or suppliers. So, Linear programming seems to be the supportive method in case of a lack of conditions. It solves the problems based on the list of variables and constraints which have to be figured out. It will be efficient for the general case and the clear variables and constraints. However, It is not flexible so that for complicated cases, That will be a challenge for the companies. So, We decide that linear programming plays the role of the supportive method for the time series forecasting and the economic order quantity.

            In conclusion, The coffee bean supply chain of Vietnam has facing operating issues. By using quantitative methods, we generally provide the potential recommendations that could improve the operating cost of the Vietnam coffee bean supply chain.