An unmanaged supply chain is not inherently stable. Demand variability increases as one moves up the supply chain away from the retail customer, and small changes in consumer demand can result in large variations in orders placed upstream. Eventually, the network can oscillate in very large swings as each organization in the supply chain seeks to solve the problem from its own perspective. This phenomenon is known as the bullwhip effect and has been observed across most industries, resulting in increased cost and poorer service. Causes of the Bullwhip Effect Sources of variability can be demand variability, quality problems, strikes, plant fires, etc. Variability coupled with time delays in the transmission of information up the supply chain and time delays in manufacturing and shipping goods down the supply chain create the bullwhip effect. The following all can contribute to the bullwhip effect: Overreaction to backlogs Neglecting to order in an attempt to reduce inventory No communication up and down the supply chain No coordination up and down the supply chain Delay times for information and material flow Order batching – larger orders result in more variance. Order batching occurs in an effort to reduce ordering costs, to take advantage of…

    To minimize supply and demand imbalances in the supply chain, firms utilize various methods of inventory management. The problem is complicated by the fact that demand is uncertain, and this uncertainty can cause stockouts in which inventory is depleted and orders cannot be filled. Here, we discuss a model in which the inventory level is reviewed periodically, and orders are placed at regular intervals to order up to a certain base stock. This policy is known as a Policy of Periodic Review, Order-Up-To Base Stock. Under this policy, one orders a variable quantity  Q  every fixed period of time  p  in order to maintain an inventory position ( Qty on hand  +  Qty on order ) at a predefined base stock level  S,  also known as the “order-up-to level.” The base stock level  S  is determined by calculating the quantity needed between the time the order is placed and the time that the next period’s order is received, and adding a quantity of safety stock to allow for variation in the demand. The time between the placing of the order and the receiving of the next period’s order is the sum of the review period  p  and the…

  Some firms have successfully improved their supply chain performance by implementing an approach known as Vendor Managed Inventory (VMI). With VMI, the vendor specifies delivery quantities sent to customers through the distribution channel using data obtained from EDI. Vendor Managed Inventory, Just-in-Time Distribution (JITD), and Efficient Consumer Response (ECR) all refer to similar concepts, but applied to different industries. For example, the grocery and apparel industries tend to use ECR, whereas the automobile industry tends to use VMI and JITD. The Vendor Managed Inventory Approach VMI reduces stock-outs and reduces inventory in the supply chain. Some features of VMI include: Shortening of the supply chain Centralized forecasting Frequent communication of inventory, stock-outs, and planned promotions. Electronic Data Interchange (EDI) linkages facilitate this communication. No manufacturer promotions Trucks are filled in a prioritized order. For example, items that are expected to stock out have top priority, then items that are furthest below targeted stock levels, then advance shipments of promotional items (promotions allowed only in transition phase), and finally, items that are least above targeted stock levels. Relationship with downstream distribution channels Result: Inventory reduction and stock-out reduction   VMI Implementation Challenges VMI can be made to work, but the problem…

                                  Traditional production systems produce products and stock them as inventory until they are sold (make-to-stock). In order to reduce inventory and increase the level of customization, some firms have designed their production systems to produce a product only after it is ordered. Such systems are referred to as make-to-order. Make-to-order systems are not appropriate for all types of products, and the make-to-order versus make-to-buy decision must be weighed carefully. The following are some factors to consider when evaluating the prospect of make-to-order:   Value of a custom product:  Are customers willing to pay more for customization? Customer patience:  Are customers willing to wait for a custom product to be manufactured and delivered? If not, the cost of losing the customer to the competition is the margin on the product, plus the value of any future purchases that may be lost as a result of the customer’s switching to the competition. Even if the customer switches to another model from the same firm, a loss of goodwill may result. Cost of stockouts:  Assuming the customer is patient enough to wait the specified delivery time, make-to-order eliminates the problem of…

Linear programming is a quantitative analysis technique for optimizing an objective function given a set of constraints. As the name implies, the functions must be linear in order for linear programming techniques to be used.   Problem Formulation Checklist The objective function and constraints are formulated from information extracted from the problem statement. The following checklist is useful for minimizing the risk of errors in problem formulation. Every number in problem statement should be either implemented in the formulation or rejected as irrelevant, e.g. sunk costs. Don’t forget any initial conditions, e.g. initial staff on hand at beginning of first staffing period. Ensure every variable in the objective function is listed somewhere in the constraints. Ensure that any non-negativity constraints are listed. Ensure that binary integer variables are restricted to 0,1. For example, Y ∈ {0,1} For good form, move all variables to left hand side of equation, writing them in the order of their subscripts.   Sensitivity Analysis While problems may be modeled using deterministic objective functions, in the real world there is variation. A sensitivity analysis can be performed to determine the sensitivity of the solution to changes in parameters. Microsoft Excel can generates a sensitivity report in…