On the first afternoon, it could be summed up as, “Oh father, we have sinned. Please forgive all of us sinners. “
This conference in Dallas was a good time for me to reflect on the history of demand planning. IBF celebrated their 30th Anniversary in Dallas without even a party. I give thanks for IBF and for the vendors like Autobox, John Galt, Logility, SAS Institute, and Terra Technology that support their events. I think that we owe them a debt of thanks for continuing their advancement of demand planning excellence. In my opinion, the greatest sin of all is that we have spent thirty years developing forecasting processes that are largely not used or trusted by the organizations that they serve. Here, in this blog post, I share my reflections on the group’s discussion on sins….
The Seven Sins
The group discussion included these seven deadly sins:
Sin #1. Not Using the Statistical Forecast to Drive Continuous Improvement. I have never worked with a company that could not improve its forecasting through better use of statistics. However, most companies are skeptical. Inherent in the DNA of the firm, there are “experts” that believe that they know the business better than any statistical package ever can. Given that a forecast is always wrong, and the forecasting process is fraught with political issues, companies struggle with how to use and gain acceptance for statistical forecasting.
While benchmarking the forecast is difficult (reference blog post Trading Places), measuring continuous improvement through Forecast Value Added (FVA) analysis is a helpful, and easier method, to drive continuous improvement. In most FVA analysis presentations that I have seen lately, the statistical forecast is improving the naive forecast—forecast made based on prior month’s order history—by 3-5%. Similarly, the lack of control of managerial and discipline in the consensus forecasting process is reducing forecast accuracy by 2-5%. The technique allows companies to measure, improve and better drive forecast accuracy, and gain business alignment and support for the effort by dollarizing the impact of the forecast error. For example, one of the speakers at the conference shared that a 2% improvement in forecast accuracy was worth two headcount in his business. If the forecast could be improved by 2%, he could reduce the time spent on order expediting. Bottom line: Don’t look at forecast accuracy in isolation. (For those of you not familiar with the technique, I think that the white paper written by SAS is very useful. Reference http://www.sas.com/reg/wp/corp/6216).
Sin #2. Only owning part of the forecast. To use a baseball analogy, most demand planning teams are in the “outfield.” They “catch the forecast” from sales and marketing without owning the entire process. They catch and throw the forecast across functions without value-added analysis. Whereas, best in class teams, own the entire forecast. They know the baseline forecast and work on driving root cause analysis to improve demand shaping programs – price, promotions, marketing events, new product launch, and sales incentives. What does the difference look like? For one company that I worked with over the past two years, this change was worth 5 million dollars in the reduction of obsolescence. Bottom line: Move out of the outfield and back to home plate to throw the ball to ensure that the organization can hit homeruns.
Sin #3. Misuse of Downstream Data as an Input. When running out a product—to prevent obsolescence—be careful in the use of downstream data. Realize that you are pushing into the channel and that you do not want to drive replenishment. If you don’t have this discipline, you will recreate the Green Volvo Story. Remember that one? Hau Lee tells the story, “Volvo was awash in chartreuse green cars. Despite trying every option at the distributor to push the cars, but the cars were not selling. So the company decided to price them at a significant price reduction to move them and reduce inventory. However, this strategy was not communicated across the organization to demand-planning. As a result, when the green Volvos sold, the sales orders triggered a forecast and the forecast consumption logic triggered replenishment and the factory cranked back up the production lines to make green Volvos. I was telling this story a couple of years ago to a company that made women’s intimate apparel, and they started laughing incessantly. I finally stopped and asked why? In between uncontrollable laughter, the company shared that their Green Volvos were leopard skin fur thongs. So this sin goes across all industries from cars to lingerie….
When pushing SLOB, turn off the knob to use downstream data, and be careful to not let orders drive replenishment. Likewise, downstream data should be used to trigger the completion of promotional replenishment. Sensing when to end a promotion is also essential to eliminating SLOB (Slow and Obsolete Inventory). Bottom line: Design the forecasting process and the use of the output of the forecasting process from the outside-in. In driving accurate replenishment, there is no substitute for knowing true channel behavior.
Sin #4. A Project not a Program: A frequent question that I am asked is “how can I implement demand planning faster?” I will answer the question, but then I will ask,
“Aren’t you shooting for the wrong goal? Shouldn’t your goal be to implement demand planning well not fast?” One of the companies that I admire, that has proven year over year to be one of the great leaders in the use of SAP APO DP is General Mills. When I wrote a case study of General Mills implementation as an AMR analyst, many companies pushed back and asked why I picked the General Mills case study to showcase. The reason was simple. They did not implement demand planning the fastest, they did it the best. For them, it was a program. It was valued. They wanted to get it right. It was not a project to quickly implement.
Sin #5. Not all Items are Created Equally: In the words of one participant in the workshop, “get to know the DNA of your item.” A few years ago, I was working with a company that made baby formula. Their most important and the lowest volume item was samples sent to the hospitals for new mothers. These samples were distributed on maternity wards at the birth of the baby to promote product trial. A successful trial could drive a couple of years of consumption through the life of the child through their years as a baby. So, a forecast error on these products was worth substantially more than a forecast error on turn volume.
Sin #6. Forecast with the End in Mind: This may sound simple, but it is a sin that is frequently made. While many companies have set up their forecasting systems to forecast what manufacturing needs to make when, the greater opportunity is to model what the channel is going to sell and when. The company then translates these demand requirements to internal and external manufacturing locations. It is not as easy as just modeling the selling unit at the retail chain level. This is usually too low of a level to forecast –insufficient data to be significantly relevant—for the forecasting process. Likewise, with this increased need for transportation forecasting visibility, there is a need to forecast transportation requirements; and, to use channel data to determine distribution requirements. It is a proven fact that forecast consumption logic and one number forecasting is not sufficient. Instead, multiple forecasts need to be translated into a demand visibility signal for the corporation.
Sin #7. Arrogance. Not serving the Organization. At the conference, the SVP of Radio Shack gave a presentation on what makes a great demand planning group. His words of wisdom were “be humble” and “serve the organization.” In his experience, when the demand planning groups become arrogant—a “know it all group” that polices the forecast—everyone looses.
What do you think? Do you have any sins of forecasting that you would like to share with the readers of this blog? Or do you have any insights on the sins outlined and thoughts to share on how others can improve their forecasting? For more on demand management, check out these posts:
Beyond Smoke and Mirrors (https://www.supplychainshaman.com/page/3/)
Trading Places (https://www.supplychainshaman.com/uncategorized/trading-places/)
This week, I am speaking at the Midwest Health Care Exchange. Look for a post next week on new research on How to Heal the Life Sciences Supply Chain.
Yesterday, I had a great day at the Eli Lilly learning center looking at progress in item serialization with 2-D barcodes at their center in Indianapolis. It was great to see their use of some of my 2002 Gartner research note on RFID used to help them formulate a winning strategy.