By Darryl Landvater
In recent months, many analysts and business administrators have noticed a change in how companies are forecasting demand for their products and necessary supplies. For many, this has led to a question of whether distribution resource planning (DRP) is an outdated or near obsolete form of predicting how much product will be needed and when.
It is a bit premature to be asking that question. The "death" of DRP, as some have seen it, isn't occurring, even with current advances in both computer hardware and software. However, DRP is evolving, and new approaches are taking hold, creating substantial improvements in demand planning accuracy. There are some lessons to be learned from current implementations that will help companies bring demand planning into the 21st century. The impact on financial performance is huge, so it's worth taking a closer look.
The Data Problem
Going back to the 1970s, suppliers and retailers have calculated demand for products based on boxes leaving the warehouse — or in other words, the people managing supply chains typically figured out how much inventory they would need based on how much of that inventory was shipped. But anticipating demand at the distribution center turned out to be less than ideal. For example, if a company's stores had more inventory in a current year than they did the previous year, the demand on that company's DC would be significantly less, even though the product may have been selling similarly in stores. Or, the demand for the product may have increased in stores, but that increase might not have become visible at manufacturing facilities for weeks — one of the many characteristics of what's referred to as the "bullwhip effect." So, for many years, people have realized that it would be better to forecast demand at the store level, and then calculate demand back through the supply chain.
Unfortunately, this vision simply wasn't technically feasible — until recently. Two main problems traditionally kept forecasters out of the store. The first is the incredibly high volume of data involved with planning products at the point of sale. When a business forecasts at the warehouse level, that business is dealing with potentially several thousand SKUs — pallets of product moving in and out. At the store level, however, especially with larger, global chain stores, those businesses need to consider those SKUs multiplied by the number of stores. This means the system needs to deal with tens or hundreds of millions of store-item combinations over the entire organization. In the past, the software used to plan distribution centers was not able to handle the store-level data volumes with then-current computer hardware.
A second problem has been the functional differences between retailers and suppliers, which operate their DRP systems at the store level and at the DC-level, respectively. Slow-moving items are a good example: There are relatively few slow-moving items at the DC level, so those items can essentially be ignored while they're in the warehouse. However, at the store level, there are a significant percentage of these items, and so they cannot be ignored. Instead, there must be logic built right into the system to handle them appropriately.
Solutions to both of these problems have come recently in the form of better technology and software that has been rewritten from scratch to use the fundamental elements of DRP in a new and different way. Collaboration is one area where the fundamental principles of DRP have been adapted to a new set of competitive realities, though it has not always worked well. Agreeing on a plan is different than agreeing on a realistic plan, after all. Retailers are often quick to demand a certain amount of product at a certain time, but actually executing that plan is a lot more difficult than may initially meet the eye. Or that plan might be fundamentally flawed, unbeknownst to those drafting it at the outset. Companies have gradually learned that the supplier-reseller relationship must be broken down into granular tasks to be productive, elements like how much product, transportation, labor and equipment are needed and when. This is a variation on the observation from Peter Drucker that "all great ideas degenerate into work."
Death of DRP?
Automating a process like this has not been easy, but pushing DRP all the way to the store level is an adaptation that has provided much more effective collaboration in managing the supply chain. DRP can now also be used to build loads appropriate to a given channel — businesses no longer have to use fixed order quantities at the DC or the store level. Rather, companies can look at the demand picture and build loads that work from the viewpoint of the entire supply chain.
But does all of this mean we're moving beyond DRP to a new form of demand planning altogether? The answer is no. Let's take a look at the practice of double-entry bookkeeping. While new tools have been created to make the process easier, more efficient and more automated, the fact remains that double-entry bookkeeping is a fundamental element in the financial universe. No matter how many technological breakthroughs come about, the basic process will remain unchanged. Like double-entry bookkeeping, DRP is a fundamental element in the supply chain universe — it doesn't go in and out of fashion, but is simply adapted to meet the changing needs of modern business.
In other words, DRP is a basic building block of any supply chain strategy. Nothing comes after DRP, since it is so fundamental. History shows that DRP has been and will continue to be adapted to whatever competitive realities exist. A better question would be to ask how new forecasting models will adapt to the basic truths that DRP exposes.
At the root, collaboration becomes key. While organizations might throw hundreds of thousands of dollars into sophisticated demand forecasting solutions, the most powerful computing system on the planet can't magically assess how much product a supplier has on hand or how many employees that supplier might have to manufacture those products. Unless, that is, the supplier is willing to divulge that information to their customers and raw materials providers. Without collaboration, a business might have the ability to know how much inventory they will need in the coming months, but they won't know how much of that inventory they will actually be able to get.
Beyond "Private" Forecasts
A problem many companies run into is the fact that each organization along the supply chain has its own "private" forecast of both production and necessity. For example, if a large retail chain cuts its order volume of product by 8 percent, then its main supplier might subsequently slash orders from its own suppliers at a rate of 10 percent, since that supplier doesn't have visibility into its customer's actual forecast. What's more, the component supplier might decrease orders by 12 percent, the raw materials supplier by 16 percent, and so on, all because each company is basing orders off of its own forecasts rather than a common one. For complex, global chains with multiple distributors and manufacturers, these inaccuracies have a cumulative effect as they work themselves through the supply chain. The result is a raw materials supplier that receives too few orders to stay in business.
This chain of events becomes a real problem for retailers when an upswing in demand is met by a lack of supply. Suppose the retailer that made an 8 percent cut to order volume experiences a rush on its stores six months later, requiring a 12 percent boost in supply. By this time, raw materials providers have scaled their businesses to a level that cannot provide for the new round of demand, resulting in millions of dollars in lost sales.
We should note here that the woes felt on the part of retailers have arisen because they have not shared their own demand data with suppliers to provide a single source of the truth. This is why many institutions have sought a better solution, namely, pulling data from point-of-sale transactions rather than from those at the distribution level. On paper, the idea makes sense. In the real world, however, retailers still have to take the final step by bringing store-level demand forecasting to the next level by giving their suppliers — and their suppliers' suppliers — visibility into their point-of-sale data.
Toward More Sophisticated DRP
Will those companies that share data see success? To answer that question, I helped conduct a study to examine how accurately store-level collaborative data could predict manufacturing requirements. In a trial that lasted over a year, we surveyed 63 stores and six different SKUs for a Fortune 100 retailer, while its supplier used new technologies to analyze point-of-sale data provided by that retailer. Ultimately, 378 different forecasts (i.e., 63 x 6) were produced. The company later compared this information with what the retailer actually ordered over the course of the trial. (For more information, please visit http://bit.ly/cbQL3R, pp. 11-51.)Results showed forecast accuracy between 83 percent and 97 percent. In addition, the retailer divulged that it had ordered more from the manufacturer during certain months in anticipation of higher holiday sales volumes — meaning that forecasting accuracy would have been higher had the manufacturer been provided with additional data from past years.
The model we used was simply a more technologically driven, sophisticated enactment of DRP. So the key takeaway is that while new tools exist to help collaborative companies manage their forecasts, the process of putting those forecasts together has not changed, nor will it ever. Smart businesses will do well to remember their fundamentals and use advancing technologies to make the hard work easier.
About the Author: Darryl Landvater is co-founder of the RedPrairie Collaborative Flowcasting Group, a joint venture with RedPrairie, which delivers productivity solutions to help companies around the world with inventory, transportation and workforce management. Landvater can be reached at email@example.com. More information on RedPrairie at www.RedPrairie.com.