Without any specific effort to coordinate the overall supply chain system, each organization in the network has its own agenda and operates independently from the others. However, such an unmanaged network results in inefficiencies. For example, a plant may have the goal of maximizing throughput in order to lower unit costs. If the end demand seen by the distribution system does not consume this throughput, there will be an accumulation of inventory. Clearly, there is much to be gained by managing the supply chain network to improve its performance and efficiency.
Decision Variables in Supply Chain Management
In managing the supply chain, the following are decision variables:
- Location – of facilities and sourcing points
- Production – what to produce in which facilities
- Inventory – how much to order, when to order, safety stocks
- Transportation – mode of transport, shipment size, routing, and scheduling
The Bullwhip Effect
A problem frequently observed in unmanaged supply chains is the bullwhip effect. This effect is an oscillation in the supply chain caused by demand variability. This problem must be addressed in order to avoid the poorer service and higher costs that stem from it.
Variation in demand increases the challenge of maintaining inventory to avoid stockouts. There exist techniques for inventory management that optimize the performance for a given set of parameters.
Vendor Managed Inventory
An effective way to improve supply chain performance is for the vendor to determine the quantities that should be ordered by its downstream customers, rather than the other way around. This approach is known as Vendor Managed Inventory, abbreviated VMI. While its implementation faces practical challenges, it can be an effective method for reducing inventory and stock-outs.
In the classical news vendor problem, one must decide the best order quantity that maximizes profits given that some money is lost if all of the units do not sell and given the fact that potential profits are lost if the units sell out. In some situations, a second order can be placed once the sales period begins. Such an opportunity helps one to better match supply and demand, since the first order can be a quantity equal to the expected demand minus a selected number of standard deviations ( 2, for example) below that mean. Of course, any minimum order quantities must be taken into account.
In many industries, the variance in demand is proportional to the variance in the forecasts for that demand. This relationship even exists in stock price forecasting. When this relationship holds, it can be used to estimate the mean demand and its variance, and these values can be used in optimization models.
For seasonal goods such as winter sportswear, which has a short selling season and long lead times, a firm can do several things to better match supply and demand:
- Additional events can be held before large trade fairs in order to secure orders further in advance.
- Supplier capacity can be reserved without specifying the exact product mix. This postponement of the final mix has benefits similar to those of postponing product customization until the distribution center.
- Common parts can be used in designs in order to pool some of the variation between individual demands.
Supply Chain Structure
The performance of a supply chain is measured in terms of profit, average product fill rate, response time, and capacity utilization.
Profit projections may improve if another parameter is relaxed, but one must consider the impact of all aspects of the relaxed parameter on profits. For example, if customers are lost because response time is too slow, then the profit projections may be artificially high.
Average fill rate can be improved by carrying more inventory in order to reduce stock-outs. The optimal balance must be achieved between inventory cost and lost profits due to stock-outs.
Response time often can be improved at the expense of higher overall costs. As with fill rate, the optimal trade-off should be found. If response time is sacrificed in order to achieve higher profits, sales forecasts may have to be modified if the elasticity of demand with respect to service is significant at the chosen service levels.
Capacity utilization should be high enough to reduce overhead sufficiently, but not so high that there is no room to grow or to handle fluctuations in demand. Problems often are encountered when capacity utilization exceeds 85%. Lower capacity utilization in effect buys an option for increased output in the future. Higher capacity utilization decreases downside risk since costs are reduced, but also limits the upside gain if future demand should outstrip supply.
To reduce inventory and increase flexibility, some firms have turned to make-to-order production systems. Some companies can reap great benefit from such a system. Make-to-stock is better for other companies, such as those whose customers are not willing to wait for the product.
Carliss Y. Baldwin, Jeffrey H. Dyer, and Donald V. Fites, Harvard Business Review on Managing the Value Chain