Previously, I shared the importance of being clear about the purpose of the forecast and ensuring it aligns with the organizational mission. In this blog, I’m going to spend some time discussing whether the objective of a forecast process should be to arrive at a “one-number forecast”. The big idea is that this approach breaks down functional barriers and promotes collaboration. The benefit of this is a more streamlined, informed and efficient way to anticipate the future, saving time and money. But is this really true and could there be a better way?
Functional silos are bad news for forecasters. Forecasting is an activity that is information dependent, that is to say, it requires not only data, but also the information that puts it in context. For instance, a practitioner may be able to identify a spike in demand, but without context won’t have any idea why it happened. A second fundamental requirement of forecasting is the ability to assess and calibrate process inputs. If those inputs become politicized or subject to functional biases, then the quality of the output can suffer.
What is the root cause of functional silos? There are many ways an organization can choose to organize itself. One of the most common is to organize itself in functional units where work is clustered into smaller groups around “like” work. For instance, all those in finance-related activities may be grouped together in a finance function. This can create operational efficiency through the sharing of resources and also support the development of enhanced capability due to the concentration of specialized expertise. The disadvantage of this approach is that it can result in the creation of a deep-rooted functional ethos and tribe-like culture. This can lead to a suspicion of outsiders who do not share the identity and create a preference to huddle with their own. This functional mindset can result in the poor exchange of information, collaboration and rise of organizational politics as resources and influence is jockeyed for.
The solutions to this have included the rise of less traditional organizational designs such as the divisional or business unit which is organized to include all the necessary resources and functions to support itself. Matrix organizations have also emerged where groups are organized simultaneously by two operational entities. For instance, an associate may be organized by business unit but have a secondary functional reporting relationship
The reality is that despite best efforts to eliminate functional silos, they still exist, even with less traditional organization designs. For instance, in the business unit organizational design, associates can become so focused on their team they may become unaware or unwilling to share information pertinent to others.
Mindful of this, the conventional wisdom 20 years ago was to pursue the goal of “one-number forecasting.” The idea is that if you can’t eliminate the silos then create processes that force collaboration and alignment. Simply put, without your own forecast you are forced to partner and come to a consensus with others. This, however, brings me to Margaret Thatcher’s famous definition of consensus….
“…the process of abandoning all beliefs, principles, values, and policies in search of something in which no-one believes, but to which no-one objects”
Clearly an extreme point-of-view, but with a kernel of truth in it. The pursuit of a single number, facilitated through the often-dreaded consensus meeting, has typically resulted in a compromised outcome. This is often a forecast that is non-objectionable but leaves few with any great sense of ownership or belief in it. In its worst form it can be the outcome of horse-trading, which has little to do with arriving at a more accurate forecast. Significantly, it can also result in groupthink. It can have a distinctly chilling effect on those forecasts that sit outside the range of what conventional wisdom thinks is likely. Diverse opinions can end up being regressed to a more acceptable middle-ground, even though the more extreme view might be a more accurate representation of what might happen.
The other fundamental flaw of “one-number forecasting” is that it glosses over the fact that, more often than not, forecasts have different purposes depending on the functional group that needs them. For instance, a supply chain function is typically interested in the level of detail that is consumed in supply chain planning process (SCP). This is likely to be at a location and SKU level. The finance function is interested in a more aggregate level of detail. They are less likely to be interested in the lowest level detail and more interested in the level at which products are priced or costed.
How the forecast is used is an important driver in how the forecast should be constructed. Many forecast support systems (FSS) lack the capability to simultaneously forecast at multiple levels and then perform a process of optimal reconciliation to resolve the differences between the forecasts at each level. This results in practitioners having to make a fixed decision at what level they intend to forecast at and then, using a rules-based logic, aggregate or disaggregate the values they generate. Typically, a forecast that is consumed at a fine level of detail is created bottom-up and those consumed at a summary level is created top-down. The problem with a “one-number forecast” approach is that a single forecast methodology has to be employed, regardless of how the forecast will be used.
The problems don’t stop there. Different constituent groups have different forecast horizons that they are interested in. A strategic planning group may be more interested in forecasts that represent quarterly or annual buckets. An operations group may be interested in forecasts in daily buckets. If you want an accurate quarterly forecast, you would be well-advised to not aggregate daily level detail to a quarter. The forecast, by virtue of aggregation, would be extremely noisy and unrepresentative. Vice versa you wouldn’t want to take a forecast created in quarterly periods and disaggregate to SKU. This would create a forecast that would be unlikely to represent the underlying trends or seasonality in the data.
One of the final problems is that, by definition, different groups want to see different types of data. Someone in finance typically wants to see a forecast that represents what will be invoiced. This is typically different to the date on which the product was shipped or when payment was received. A supply chain professional doesn’t typically care about when the invoiced is generated but is interested in a projection of when the goods get shipped. This more directly affects labor and supply chain planning processes. In those organizations where goods are being shipped long-distances or there is latency between when the goods are delivered and when the invoice is generated, this is a significant issue.
The solution is to not constrain the creation of forecasts by constituent groups, but instead embrace the diversity each viewpoint brings and develop methods to connect them effectively. The goal, therefore, becomes to create a fully visible set of independent forecasts. This enables process participants to have forecasts that are constructed in a way that is most appropriate relative to how they are used and not be subjugated to a single forecast that they have little buy-in into.
What are the prerequisites to support this approach? Firstly, there is a critical requirement that dimension hierarchies and temporal hierarchies can be reconciled. As discussed previously, there is a great sense in having different methods for forecasting to reflect the level at which the forecast is used, but it must be reconcilable at all levels. For instance, it must be possible to have a finance forecast created at an aggregate level to be disaggregated to a detail level. Vice versa, a SKU level forecast must be able to be aggregated to a summary level. The same applies with temporal hierarchies, a forecast created in daily buckets must be able to be aggregated to a week, month or quarter and so on.
The simplest way to achieve the goal of an aligned set of forecasts is through the use of a fit-for-purpose forecast support system (FSS). This creates the framework and data repository through which independent forecasts can be created and stored for review and retrospective reassessment. The key to this approach is to ensure that the temporal and dimensional hierarchies are created thoughtfully (more of that in a future blog). It is critical they meet the needs of all the participants.
What about the big elephant in the room, what if an organization still insists on a single number forecast? This is still possible and, depending on circumstances, can have some advantages. The recommended approach is to combine forecasts, but not as output of the dreaded consensus meeting; a place where it is all too common for anecdotes and narrative to beat-out empirical evidence. Instead, there should be a scientific method to take the independently generated forecasts and mathematically combine them.
One of the great benefits of a fit-for-purpose FSS is that it can enable this process with the use of rules. Probably the most common is to assess the historical accuracy or bias of each process participant at each dimension level and across each horizon. From this assessment, it is possible to create a primacy rule to determine which forecast is to be used for which dimension level and horizon. Another option is to use a weighting rule that incorporates the inputs of all the participants, but with reference to their historical accuracy or bias. So, a forecast with 20% accuracy is weighted less prominently than a forecast with 80% accuracy. The other great advantage is that this can be an adaptive methodology, in other words, it can self-adjust as accuracy changes.
The organizational benefits of these approaches is that deep domain, siloed knowledge, can be brought to the fore and included (or not) based on merit. All this contributes to engagement as participants are not forced to conform, nor are their inputs discarded. It also increases the speed at which decision making can take place – less time is spent debating and more time spent considering the decision that needs to be made. Meetings become about making decisions on the basis of an aligned set of forecasts, not about influencing others to one point-of-view or other.
Crimson & Co is a global management consultancy that specializes in operations transformation with specific competency in forecasting and supply chain planning.
To see Simon’s first post in this series, Forecasting: future imperfect. Why forecasting is crucial and what we can do to improve
To see Simon’s second post in this series, Forecasting with a Purpose – Context is Crucial
As of September 8, 2020, Crimson & Co (formerly The Progress Group/TPG) has rebranded as Argon & Co following the successful merger with Argon Consulting in April 2018.