Over the last twenty years, the supply chains of manufacturers and retailers have become ever more tightly linked. In many industries, retail sales trigger replenishment orders to manufacturers. Manufacturers with a well-tuned, just-in-time supply chain can automatically restock retail shelves as products are sold. As collaboration has increased, additional data from supply chain partners has allowed companies to use advanced analytic tool to further improve results. Examples include:
Identifying potential problems before they occur. When a customer orders more product than the manufacturer can deliver, the traditional response has been to short the order. This leaves the buyer feeling unimportant and convinced the manufacturer's service is poor. Manufacturers who anticipate the shortage before the buyer is disappointed may be able to offer a substitute product or other incentive to keep the buyer happy.
Optimizing price dynamically. Seasonal products, particularly fashion products, have a limited shelf life. Any that don't sell by the end of the season are scrapped or sold at deep discounts to empty the warehouse. Airlines, hotels, and other companies with a limited, but perishable product, adjust prices dynamically to meet demand. While this is more difficult with clothing and other products where the supply can vary widely, similar forecasting techniques can improve margins.
Improving the allocation of available to promise inventory. Today's tools dynamically allocate resources and schedule work based on the sales forecast, actual orders, and promised delivery of raw materials. Manufacturers are able to confirm a product delivery date when the order is placed, significantly reducing incorrectly filled orders.
Source: cio.com