Retailers struggle with keeping up with their customers' changing behaviors, but Web-based technology can help them manage these changes in real time.
The inventory game played among members of the retail supply chain is one of educated guesses. But technology has provided tips and strategies for retailers and suppliers to better anticipate what customers will do. This enables them to manage the demand of inventory from week to week. The sharing of information via electronic data interchange (EDI) and real-time inventory management systems over the Web is the reason a study by the Warehousing Education Research Center found inventory turns increasing by 65% over the last five years (October 1999). Retailers need to use this technology to help them forecast demand on a store level, and send that information back to distribution centers, suppliers, and manufacturers. Ken Pikulik, marketing manager for BridgePoint Inc., and Tim Simmons, executive VP of sales and marketing for Stirling Douglas Group Inc., discuss how retailers can improve their bottom lines by improving the way they manage inventory across the supply chain.
What is the biggest problem for companies in the area of forecasting and inventory? How can it be solved?
Ken Pikulik: Companies need to understand how their organizations have performed to date. That is, knowing why they've been successful one time and why they weren't another. The solution starts with global visibility of inventory and orders at the supplier, partner, and end customer points of contact. Companies then need to measure their performance over time and perform comprehensive analysis on the data to understand trends and relationships. This will allow them to make accurate forecasts.
Tim Simmons: Maintaining service levels without incurring excess inventory is the biggest problem. Most retailers are trying to build a strong impression on new customers at a time when funding is hard to come by. The only solution is highly accurate consumer demand forecasting built at the item and location level to drive replenishment.
How can companies overcome the diverse methods of communicating inventory data over the Internet?
Ken Pikulik: Companies need to understand that not every trading partner uses the same EDI, XML, or communications technologies. It is essential that they choose a solution that connects all their resources. Solutions that operate using ASP (application service provider) models often have an advantage because they inherently have the capability to connect different resources, accumulate data in a single location, and make it globally available.
What features should a retailer look for in a forecasting and replenishment system?
Tim Simmons: Forecasting engines are measured by accuracy, which is easy to measure. Any replenishment system can cut an order and send it, but only consistent accuracy in the forecast will drive sales while keeping inventory down. A forecasting system should specialize in item and location levels because that is the lowest level in the merchandising hierarchy. The forecasting engine should contain seasonal profile algorithms because demand varies week to week over a 52-week period for each item. The engine should also be able to run weekly revised forecasts with multiple simultaneous forecast algorithms and automatically pick the best one. This is important because items sell differently based on trends. The engine should also be able to prioritize item reporting and service level management based on sales and gross margin of the item. For retailers, prioritization of item forecasting is critical, and forecasting engines should automatically re-rank items based on their weekly performance. Another important feature is exception management. It is impractical to use an operational tool such as a forecasting engine unless it is automated, learns from itself, and is exception driven. This gives users the ability to input their own rules.
How far in advance can retailers predict the need for merchandise?
Ken Pikulik: Long-term predictions depend on accurate data and a company's ability to understand likely changes to that data. There is value in making long-term predictions, but there is even more value in knowing exactly how much time is needed to implement a decision made today. For example, if the news is predicting an unusually early snowstorm in Wisconsin, a retailer may need to get snow shovels to its stores a month before its original plan. Knowing that it takes five days to produce the shovels, two days to deliver, and one day to stock, enables managers to plan and execute to meet changing demand based on real-time consumer behavior. Add to this equation the fact that the manufacturing facilities are in Asia, and a company has even more variables to include in the process.
How does Web forecasting benefit all ends of the supply chain?
Ken Pikulik: Customers ultimately receive the most value from the forecast because products are available when and where they need them. Using the Web to help create the forecast enables companies to collect detailed information in less time to make or adapt forecasts. If the forecast is wrong, then everyone suffers. If the forecasts continue to miss the mark, customers will find other alternatives. The Internet, by its very nature, is a vehicle for delivery of real-time information. Forecasting and replenishment systems require this type of information to be successful. The Web also helps companies connect diverse partners and suppliers to increase the effectiveness of their predictions.
What are some of your customers' replenishment problems? What are the solutions?
Ken Pikulik: Time is always the biggest replenishment problem. As a global company, being able to introduce new products or replenish existing products from manufacturing or distribution facilities located all over the world requires accurate planning and an extremely efficient supply chain. When demand exceeds expectations, it's critical that new products arrive in time, or customers will go somewhere else.
Tim Simmons: One problem is the recognition that even regularly stocked items have seasonal demand. Retailers need more or less inventory to match sales. Another challenge is promotion forecasting because most retailers want to be out of a line by a specific date and don't know when to run a markdown to maximize margin. Companies are also buying and selling items they have never sold before. Systems can forecast these items at the class level and pay attention to the demographics of customers, matching buying behavior with that of other product lines sold in the past.
How do companies balance the use of automatic replenishment with actually ordering inventory based on changing consumer trends?
Ken Pikulik: Automatic replenishment is fine when you have regular demand for a product. However, you need to have checks in place to manage anomalies that change the immediate demand for specific products. A week of sunshine in Seattle during the month of January increases the demand for sunglasses by 80%. Do you need to increase production to meet this demand for the following week or month? Automatic replenishment would require that you do, but common sense tells you that it was not a normal occurrence and should not be treated as such.
Tim Simmons: Companies should let the forecasting engine take control and input rules to make it so. Automated replenishment systems generate SOQs (suggested order quantities) resulting from the upcoming forecast and visibility of inventory in-house or in-transit. This information is ranked in order of importance by each item's contribution to each category. If the item's SOQ is within the parameters set by the retailer, the system (and buyer) should only review the SOQs that generate an exception.
What is the future of forecasting and replenishment?
Ken Pikulik: I think we are already experiencing the future of forecasting and replenishment. Companies are already shrinking their supply chains to replenish products in real time. To do this, companies are integrating suppliers and trading partners into their planning process and developing better ways of creating, managing, and optimizing these larger supply chains. The final step is to shorten the delivery portion of that process until replenishment correlates directly to buyer behavior and replenishment is done immediately after the product's purchase.
Tim Simmons: The retailing industry is moving from supply chain management (SCM) to demand chain management (DCM). DCM requires routine and operational, item- and location-level forecasting and lets consumer demand drive the supply chain. This translates into what item a company could sell if it were in stock versus what the company did sell. DCM provides demand visibility all the way back to the supplier.Questions about this article? E-mail the author at StephRD@corrypub.com.