Let's take the simple problem of predicting the outcome of a football game. By investing a small amount of initial effort, we dramatically increase our chances of predicting which team will win. But diminishing returns rapidly take over. No matter how much more effort we put in, the chance of accurately predicting the exact score becomes very small. That is, there is a lot of uncertainty that simply can't be modeled and therefore accurately predicted.

This explains also why supply chain planners struggle to improve their forecasts and end up hitting a ceiling. Uncertainty prevails in supply chain – much more so than in football. This is due to the inherent volatility and randomness of thousands, or even millions, of individual buying decisions and supplier activities. Lumpy “long tail” demand is exacerbated by rapidly changing consumer tastes and demand shaping through our own and through our competitor’s promotional activities. For instance, our dairy customer Granarolo reports that promotions for a specific product (SKU) can increase sales up to 20 times its baseline demand. Even if Granarolo can successfully model and forecast the impact of these promotions, its competition will see them as periods of unpredicted demand decline that increases their demand volatility.

The increase of demand volatility in today’s markets explains why supply chain leaders tend to believe that their primary supply chain problem is forecast accuracy. They look for certainty of future demand from which they can make decisions and act. Yet, they cannot improve the forecast beyond its intrinsic variability. They can only deal with the variability through supply chain responses.

At that critical point of “hitting the ceiling”, understanding uncertainty becomes much more important and plausible than trying to increase forecast precision. That’s when supply chain leaders need to stop avoiding uncertainty and start embracing it!

A fundamental shift

In order to embrace uncertainty, the fundamental shift businesses need to make is to move from a deterministic model to a stochastic (probabilistic) model supported by appropriate tools, processes and people skills. A purely deterministic model is based on the premise of being able to know all the variables that can affect a business and therefore be able to predict the future with absolute certainty. In other words, a pipe dream!  The probabilistic model assumes there will always be a certain percentage of “known unknown” variables and attaches probabilities to them.

Here’s a simple example. Rather than deterministically stating that an average quantity of a given product will be shipped on a specific day, the probabilistic approach models the probabilities that a customer will order a specific quantity on a certain day. So the order of 35 cases for delivery to a retailer’s Dallas distribution center received today had a 64 percent probability. Based on this modeled probability, holding an adequate inventory for the ordered item hedges the risk related to the demand variability and guarantees the planned service level.

The deterministic company waits for plans to blow up and then firefights. This causes all kinds of issues such as overreaction, cost overruns and service shortfalls, not to mention stress and sleepless nights. By contrast, the company that embraces uncertainty is prepared for it.

A first step forward

At this point you might very well be thinking, “Oh no, not more transformation.” But in reality most companies are already partly down the path towards a probabilistic supply chain model. The evolution is happening naturally as the global business landscape becomes more interconnected and complex, along with the effects of multichannel marketing, demand shaping and the internet. If your company has recently launched (or relaunched) an S&OP initiative, or you’ve started to migrate from traditional platforms like APO to advanced analytics tools, or trained your planners on supply chain performance trade-off competencies, you’ve likely already recognized the limits of deterministic planning.

Here is one straightforward opportunity to focus on in the near future: Migrate away from top-down demand forecasting.

Despite the added complexities in today’s supply chains, traditional SCP systems like SAP APO typically apply the traditional "top-down" approach to forecasting based on aggregated data. This approach aggregates demand to smooth out variability, which makes it easier to generate a high-level forecast, but the Item-Location level forecast quality is poor because demand signal details are dismissed along with the “noise”. So this approach of aggregated planning and then applying slicing and dicing rules, only works for simple and highly predictable businesses with few fast-moving commodity items and single-channel distribution. When it comes to long-tail items, forecasting metrics such as WMAPE become almost meaningless or even misleading. When dealing with intermittent demand, they don’t do anything to measure the uncertainty inherent in lumpy demand.

Uncertainty is best managed using a new breed of planning tools that employ adaptive probabilistic algorithms. They deal with the volatility and demanding response times, particularly as required by online and multi-channel markets. These tools use adaptive modeling techniques that allow you to manage the supply chain statistically and with a high level of automation. Tinkering is no longer needed. Planners are called on to intercede only for exceptions that fall outside the boundary limits of statistical uncertainty. 

Supply Chain Insights founder Lora Cecere has repeatedly advised her clients and community members to consider these newer, ‘best-of-breed’ tools to mitigate business risk and future-proof their supply chains. Her blog post, “Three Reasons Why SAP Supply Chain Planning Is a Risk to Your Business” is just one example. Gartner and Nucleus Research have made similar recommendations.

An added benefit

An additional benefit from this approach is improved planner morale. Current approaches to forecasting not only “hit a ceiling” of diminishing returns, they also “hit a wall” due to an inability to cope with increased business complexity. Deterministic top-down systems may have worked in simpler times, but as companies grew, added product lines, acquired other business or went multichannel, those systems required unprecedented amounts of effort. Overwhelmed planners and working weekends ensued.

Planning systems that embrace uncertainty inevitably change the working environment for planners. Because the system understands and is able to handle much of the inherent uncertainty, planners are free from dealing with hundreds of mini-crises that beset a deterministic approach. Instead they can focus on adding business value to the S&OP process and dealing with those few truly unusual events.

When planners are taught to embrace uncertainty using a probabilistic approach, they feel much more in control. They know that over time the known variables provide a level of certainty, and for the rest they can devise controllable contingencies. It eliminates the spirit-crushing defeat of never being able to reach goals. As an automotive industry client of ours said recently: “Nothing affects team morale more than our ability to meet service requirements.” 

Happy planners offer a second benefit for the company. In most regions, there is a distinct shortage of talented demand and supply chain planners. So employing a methodology that improves planner productivity reduces the need for additional planners, and an improved work life increases employee retention.  

Source: ToolsGroup Inc.