The demand consensus meeting is one of the most important steps in sales and operations planning (S&OP), with the resultant demand plan serving as a critical input for all other S&OP process meetings. Without a demand plan as a starting point, not much else can happen in the cycle. There would be no supply and demand balancing exercise, plan valuation, or scenario planning. In fact, when I worked as an S&OP consultant, the demand consensus meeting was the only S&OP process element universally employed at every company I encountered. Some of them also conducted supply review meetings, and a few held formal portfolio review meetings. But every single one had a consensus meeting.
Why is the demand consensus meeting so ubiquitous? First, the demand plan reigns supreme in supply chain planning and is a required input for most planning systems. Therefore, a forecasting process of some sort is essential. The demand plan also is important for financial planning tools and processes, something on which today’s organizations are placing growing value. This need to provide system input— and the understanding that a good demand plan improves downstream planning — creates an implied need to talk about demand, even if the conversation is immature by conventional S&OP standards.
Luckily, talking about demand is a very natural thing for most business leaders. We like to ruminate on, interpret and posit about demand. The concept of consumer behaviors that trigger demand is a lot more intriguing than yawning over the nameplate capacity of some production line during a supply review meeting. Plus, the reality of demand is packed with a ton of data worth leveraging.
In fact, the demand data available to most organizations is pervasive and becoming more so. Planners have long relied on point-of-sale or consumption data, as well as depletions, orders, shipments and econometric data, as inputs into their demand plans. However, recent innovations have spawned a flood of new inputs, such as search engine data, social insights, big data and other buzzworthy trends that are bringing a sea change to the world of planning and demand consensus.
As you might imagine, the challenge is determining which inputs add the most value to the forecasting process. While I would argue that not all — or even many — of these new elements offer meaningful insight into the demand curve, they do serve as evidence of the ongoing quest for better or purer signals, from which planners can weave the demand story. And therein lies the rub: How does one have a quality conversation about demand amidst a jungle of so many inputs and points of reference? What exactly defines a good S&OP demand dialogue?
Before parsing the definition of a good demand dialogue, there are some underlying givens one must consider. First, most demand consensus meetings are driven by history, and more recent historical demand events generally take precedence. Second, nearly all historical demand data is believed to have two underlying parts: a predictable demand element (meaning, we know and can reasonably project at least one component); and an uncertain and often very volatile demand element. This uncertainty usually is the result of large, one-time orders; a market disruption; batch or minimum-order dynamics; or some rare extrinsic factor that alters an otherwise stable pattern. A good demand consensus meeting seeks to understand both input types so that baseline demand is well-defined. Then, and only then, can a consensus team try to determine the best way to proceed.
After many years of planning to anticipate demand, I can assure you that the best and quickest improvements can be obtained by spending time refining the inputs related to the predictable portion of demand. This leaves the uncertain components as either the risk, a potential upside or an area handled by supply planning professionals in the form of buffer inventories or rapid-response methods. Either way, uncertain demand should be a point of interest in the S&OP supply review process.
Successfully managing predictable demand requires the unified effort and energy of many participants. No stone can be left unturned in the aggressive examination of every reasonable input. The outcome of this work is a demand plan forged via a fact-based morphing of statistics, analytics and a feel for the business — all integrated with a well-grounded understanding of consumer behaviors. While others may have a more elegant word for this process, I refer to it as “chewing the demand data.” It is the process of applying critical thought and actively challenging the demand inputs for quality and relevance while constructing a demand plan.
The demand review phase within S&OP is structured to enable such critical thinking because it calls for the right people and right data to be on hand to analyze demand, calculate trends, identify anomalous events, and measure the demand plan and exception process. Most of this effort takes place well before the demand review meeting occurs and is managed via prerequisite assignments allocated across sales, marketing, demand planning and market research. Each group strives to understand all the dynamics that might influence demand, and then these inputs are brought to the meeting.
Recently, I had an extensive conversation with some colleagues here at Combe. I think a representative recap of our discussion might be instructive (of course, all numbers cited here are fictitious). The dialog went something like this:
Demand planner: “The shipment trend for regular-strength Vagisil has been averaging 20,000 units per week and is +3 percent over the last four weeks and +2 percent over the last 13 weeks, indicating some improvement in the near-term trends. At the same time, all point-of-sale trends are flat year-over-year, which suggests that the trade is building inventory. Walmart has added some inventory in the last few weeks, and, after talking with our account rep there, she thinks this may be for an upcoming rollback. This appears to be a one-time event and not a sustaining trend on the shipment side. I suggest we keep the forward forecast flat.”
Marketer: “I agree. Looking at historical point-of-sale data, we do not get much of a lift from rollbacks — just barely enough to pay for the programming — so this definitely is a one-time event and not reflective of trends. There is no seasonality to this item, so I would take the gain associated with the inventory build but leave the forecast trend flat. As an aside, our consumers do not pantry-load on promotion; it is a need-based product. We are hoping a tactical price reduction will steal a little share and keep the private label at bay. It bears noting, however, that we won’t have new creative until the fourth quarter, so don’t expect to see any near-term impact to volume.”
Salesperson: “My team agrees, as well. There have been no major listing changes for the regular-strength item, and there are no unusual promotional events planned for later this year versus last year, so we are fine with holding the forecast flat. As you know, Walmart is by far our biggest customer for regular-strength, so we don’t expect any channel-shifting as a result of the rollback.”
This demand consensus conversation was “chewy,” in my parlance. It reflects depth, thoroughness and a feeling that all inputs have been considered. It addresses the forecast mathematically by asking about the moving average, the trend, any seasonality and whether anything of importance has changed. And it includes insight about consumer and account behaviors. Of course, we could have included other facts — about social media or search engine results — but none of these has been proven to have a significant correlation to either point-of-sale movements or actual shipments.
This example is specific to consumer packaged goods, but the lessons apply to any industry. Conversations must be realistic, honest and transparent. The discussions should entail future plans based on an examination of all inputs, considered in declining order of relative value. In the Vagisil example, shipment trends drove our conversation, but they were quickly followed (in terms of priority) by a discussion about point-of-sales results. One-time events and multiple-time-period trending also were very important. In addition, the focus on demand drivers was detailed, and all work was completed prior to the actual demand consensus meeting. Everyone came to the discussion with opinions based on facts, not gut feelings. The right outcome was driven by the right conversation, with critical thinking as a key element in both demand consensus and the balance of the S&OP process. Now that’s what I call “chewy!”