| Abstract | In this study order variability is investigated in a real supply chain. A supply chain consists of several means to perform various functions from acquiring material, transforming to middle and end products, to conveying to end users. One of recent interest in supply chain management centers around coordination among various members of a supply chain. An important item is the information flows among members that have a direct impact on the production scheduling, inventory control and delivery plans of individual members in the supply chain To study order variability, we need to collect data for each member. With this set of data, we find that demand variabilities in during 30 months are changed largely. This is fluctuating to whole system. We also consider the bullwhip effect for the case. If the information transferred in the form of orders tends to be distorted then upstream members can be misguided in their inventory and production decisions. These information distortions are called the "bullwhip" effect. However, the flow of the product that no bullwhip effect occurs because the company does not rely on the orders placed by the retailers. The company has imported the products depending on supplier's requirement about ordering quantity. To measure and analyze order variability we use the criteria of total relevant cost that consists of ordering, holding, shortage costs, and turnover ratio. Five approaches are considered for an appropriate ordering policy. Pattern 1 is based on (s,Q) policy wherein sand Q are fixed, and Q is calculated by modifying the value of EOQ ( Economic Order Quantity) formula. Pattern 2 is similar to pattern 1 but Q is determined by modifying the value of BRQ (the best replenishment quantity). Pattern 3 is also based on (s,Q) policy but only sis fixed and Q is calculated by modifying the value of EOQ formula over time. This pattern requires us to use better forecast techniques. In pattern 4, we investigate changing both s and Q, in the (s,Q) policy, over time. Q is determined as in pattern 3, s is calculated based on forecast lead time demand and deviation of lead time demand is measured by forecast error. Pattern 5 is similar to pattern 4, but Q is determined as in pattern 2 and monthly updated. The outputs of patterns are fairly dispersed. Two pattern 1 and 2 offer quite low total costs. However, pattern 2 does not cause shortage as in pattern 1. Due to calculation methods, the outputs of three patterns 3,4, and 5 are affected by forecasting errors. Total cost is high and shortage level is severe in highest stage. |