Necessity never made a good bargain. – Benjamin Franklin (1706-1790)
If you ask someone who thinks they know what a retail demand planner needs from a forecasting system, the response will likely be a list of features and gadgets that they believe will make forecasts “more accurate”. On the surface, this makes some sense – a more accurate forecast has greater planning value than a less accurate one.
Based on this perceived need, the hunt is on to buy a shiny new forecasting system for the demand planners to use. After some evaluations, the list is narrowed down to a couple of front runners. You send them your historical sales data and challenge them to a “bakeoff” – whoever produces the most “accurate” weekly forecast over a few cycles wins (or at least significantly improves their odds of winning).
And what do you learn from this process? You learn how good a bunch of nerds working for the pre-sales team are at fine tuning the inner workings of their system to produce the desired result they’re looking for – a new customer for their solution. How many person hours did they spend trying to win the sale? What exactly did they do to the models? Is any of what they did even remotely close to what a real demand planner can (and should) do on a daily basis to manage a large number of forecasts? You probably won’t learn any of this until the implementation team arrives after you sign on the dotted line.
What you will probably also learn is that each of the front runners produces a more accurate forecast for about 50% of the forecasts – likely with no clear reason as to why one did better than the other for a particular item in a particular location in a particular week. After rolling up all of the results, you find that one software provider’s accuracy is 0.89% higher overall than the other for the sample set used.
That’s when someone creates a fancy spreadsheet to “prove” that this extra 0.89% of “accuracy” actually equates to millions of dollars of additional benefits when you multiply it through all items at all locations and do a 10 year net present value on it. It’s all complete nonsense of course, but because it’s based on a tiny kernel of “truth” from the evaluation, it’s given outsized weight.
Fast forward to 3 years later. All of the real business challenges rear their ugly heads during the implementation and are solved with some compromises. Actual demand planners can’t seem to get the same “accuracy” results that were touted in the bakeoff. They don’t really understand all of the inner workings and don’t have the time that the pre-sales team had to fine tune everything in the same way. All of the press releases say that they now use Software X for demand planning, but in reality, most of the real work is being done in Excel spreadsheets, which the demand planners actually know how to use.
Now what if you asked demand planners directly what they actually need from a forecasting system? It’s really only 2 things: Comprehension and control.
When a demand planner is reviewing a system calculated forecast, they want to be able to say one thing: “Given the same inputs as the model, I would have come up with the same forecast on my own.”
That doesn’t mean that they agree with the forecast, it just means that they understand what the model was “thinking” to come up with the result. They don’t need code level understanding of the algorithms in order to do this, just knowledge of how the model interprets data and how it can be influenced.
Before they move a dial or switch to alter the model, they want to be able to reliably predict the outcome of their actions.
So long as the behaviour of the model can be understood, a demand planner will want to work with it to get the output they want, rather than just give up and work against it with manual overrides they calculated in Excel.
Knowing what the model did and why it did it is important, but demand planners also need to know how to affect changes in the model to make it behave differently, but also predictably so that the system will produce forecasts that they agree with and for which they are willing to be held accountable.
Accuracy is a rearview mirror measure. Demand planners need to be able to live in the future, not the past. In order to support them, a forecasting system needs to be both understandable and directly controllable so that they can fully accept accountability for the outcome.