Reading Lora Cecere’s article “My Take: Let’s Admit Seven Demand Management Mistakes of the Last Decade”* reminded me of several matters of which everyone involved in supply chain management should take note.
The following two excerpts to be refreshingly frank and very much on the mark:
“[T]he words “Demand Planning” stir emotions. Usually, it is not a mild reaction. Instead, it's a series of emotions defined by wild extremes including anger, despair, disillusionment, or hopelessness. Seldom do we find a team excited about demand planning. Supply chain leaders want to improve it, but are not optimistic that they can make improvements.” [Emphasis added.]
And…
“Teams are also confused on the process. What drives excellence in demand planning has changed and well-intentioned consultants give bad advice.” [Emphasis added.]
To say the least, more than 30 years of “supply chain management” efforts appear to have led to some success and then into a cul-de-sac where the path forward is somewhat less than clear.
Cecere confirms this with the following blunt statement:
“Today, most supply chain professionals believe that the supply chain planning solutions have driven steady progress to reduce costs, improve inventories and speed time to market. What we find is that we have actually moved backwards over the course of the last ten years on growth, operating margin and inventory turns.” [Emphasis added.]
One-Number Forecasting is a Hoax
I heartily concur with Cecere’s first demand management mistake: one-number forecasting is a hoax. Of course, I would carry that further. Reliance on forecasts of every kind should be absolutely minimized. Whenever and wherever possible, forecasts should be supplanted with supply chain agility, where flexibility, capacity and rapid replenishment cycles replace reliance on forecasts and large inventories.
The Problem with "Consensus Planning"
Next, Cecere mentions the “consensus planning” approach. She properly identifies the real problem with consensus planning: all too frequently it’s not about consensus, but about company politics. Cecere tells of this episode from her experience:
“I have worked with one company that has redesigned their collaborative demand planning processes three times. Each time it was to improve the user interface to make data collection easier by sales. Not once did they ever question the value and appropriate use of the sales input or apply discipline on the input that was driving a 40% forecast over-bias.”
I cannot tell you how many firms I have worked with over the years where the “sales department” may have been the C-suite. In fact, in many cases, many of the C-level executives had come up through the sales department, so the affinity was to be expected.
Not infrequently I have had owners or CEOs of small to mid-sized business enterprises say to me (in one way or another): “We will do whatever needs to be done. But don’t mess with the sales department’s ‘mojo.’ They’ve warned us that if we mess with their ‘mystical mojo,’ things will go badly for the whole company.” The intimidation of the management structure appeared to be complete.
When it comes to “consensus,” it seems that the “sales department” hold all—or, at least, most—of the cards in a great many cases.
“Wherever there is fear, you will get incorrect numbers.” – W. Edward Deming
There appears to be plenty of “fear” to go around when it comes to anything that “messes with the sales department’s mojo.”
"Collaborative Planning Forecasting and Replenishment (CPFR)"
Number three in the last decade’s mistakes in demand planning, according to Cecere, is “collaborative planning forecasting and replenishment (CPFR).” Here she mentions moving from each participant in the supply chain using their own forecasts and, rather, carefully aligning the manufacturer’s forecast with the retailers’ forecasts.
Here again, I would take her argument further. The problem is not just with forecasting “maturity.” The problem is with forecasts in the nature. They tend to be wrong—all the time.
More importantly, a retailer may be able to tell the supply chain quite accurately how many watches (for example) it will sell over the coming three months. What they cannot tell you accurately is the number of each model of watch it will sell. Yet, it is precisely the model that must be manufactured—and not some generic “watch” to fill a forecast. As a result, when working from forecasts the supply chain will do the following with certainty:
- WASTE resources manufacturing, storing and transporting the WRONG watches
- TIE UP resources manufacturing, storing and transporting the WRONG watches which will, in turn, PREVENT the RIGHT watches from being manufactured and shipped on-time
- LOSE revenues due to out-of-stocks on the most popular models over the forecast period
- LOSE revenues by being forced to liquidate watches that were manufactured, but never sold, because they were NOT the model desired by the end-user
"Data Model Design" and "Rewarding the Urgent"
Number four on Cecere’s list is “data model design: forecasting what to make versus forecasting channel demands,” then on to number five: “rewarding the urgent versus the important.” On this topic she rightly opines:
“Time after time, we see companies implement demand planning technologies and improve forecasting processes, but not improve the overall results of the supply chain.” [Emphasis added.]
Here I would disagree with Cecere’s conclusion as to the cause for the lack of improvement. She blames it on “lack of training on how to ‘use the better forecast signal.’” This may be partially true but, in my opinion, real and lasting improvement in supply chain performance stems from a relentless drive toward improved supply chain agility and a dramatically reduced reliance upon forecasting.
“80% is Good Enough"
The article’s number six reason is stated as “80% is good enough” in which Cecere correctly points out, “the devil is in the details” of the forecast. Some of the details she mentions by name are “seasonality” and “causal factors.”
Here we get to the subtle difference between “demand planning” and “demand management.” Some confound these two—believing them to be the same or interchangeable in meaning. They are not.
Demand Mismanagement Leads to Problems
But one of the major bug-a-boos in the supply chain is the result of what I deem to be “demand mismanagement.” Here’s why I say that:
Forecasting mechanisms are constantly befuddled by demand variability so, it would seem, that supply chain participants would do everything within their power to reduce demand variability. But they don’t.
Many participants in the supply chain are well aware that short-term promotions, end-of-period price breaks or incentives to salespeople, and similar controllable price changes cause demand variability. In fact, they are implemented precisely to cause demand variability (but they don’t call it that when they are trying to create it).
Furthermore, most supply chain managers know that demand variability (in the absence of intimate supply chain collaboration) is a huge contributor to “the bullwhip effect,” and that the bullwhip effect is a major contributor to stock shortages in the supply chain. Yet, with all this knowledge present in the supply chain, managers and executives appear almost unwilling to change policies in ways that would contribute greatly toward the elimination of self-induced demand variability.
What’s up with that?
I concur with Cecere’s error number eight, as well: “focusing on ‘sell-into’ the channel versus ‘sell-through’. She is dead on, but it will take another whole article to address that issue.
Thank you, Lora Cecere, for taking up this topic. I hope many listen to your frank and much-needed discussion on these important matters.
We would like to hear what you have to say on these topics, as well. Feel free to leave your comments here, or contact us directly.
*Cecere, Lora. "Supply Chain Expert Community." My Take: Let's Admit Seven Demand Management Mistakes of the Last Decade. Kinaxis, 29 Jan. 2013. Web. 18 Mar. 2013.
