There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot cards or crystal balls. Collectively, these are known as ‘nutty methods’. Or you can put well researched facts into sophisticated computer models, more commonly known as ‘a complete waste of time.’ – Scott Adams
If you have your driver’s license, you can get into virtually any automobile in any country in the world and drive it. Not only that, but you can drive any car made between 1908 and today.
You want to make a left turn? Rotate the steering wheel counter-clockwise.
Right turn? Clockwise.
Speed up? Press your foot down on the accelerator pedal.
Slow down? Remove your foot from the accelerator pedal.
Come to a stop? Press your foot down on the brake pedal.
Think all of the advances in automotive technology – from the Ford Model T in 1908 to the Tesla Model S in 2016… Over 100 years and countless technological leaps, yet the ‘user interface’ has remained the same (and universally applied) for all this time.
This is what makes the skill of driving easy to learn and transferable from one car to the next. And all of the complexities of road design, elevation and traffic can be solved by making the decisions on the part of the driver in any scenario very simple: speed up, slow down, stop or turn. Heck, even the lunar rover used the same user interface to deal with extraterrestrial terrain!
Not only that, but because the interface is simple and control on the part of the driver is absolute, there is built in accountability for the result. If the car is travelling faster than the speed limit, it’s because the driver made it so, manufacturing defects (most often caused by ‘over sophistimication’) notwithstanding.
While supply chain forecasting software hasn’t been around since the early 1900s, it’s been around long enough that it doesn’t seem unreasonable to expect some level of uniformity in the user interface by now.
Yet, while a semi-experienced driver can walk up to an Avis counter and be off cruising in a car model that they’ve never driven before within minutes, it would take weeks (if not months) for an experienced forecaster to become proficient in a software tool that they’ve never used before.
The difference, in my opinion, is that the automobile was designed from the start to be used by any person. Advanced degrees in chemistry, physics and engineering are needed to build a car, not operate it.
While no one expects that ‘any person’ can be a professional forecaster, it should not be necessary (nor is it economically feasible) for every person accountable for predicting demand to have a PhD in statistics to understand how to operate a forecasting system. The less understood the methods are for calculating forecasts, the easier it is for people on the front line of the process to avail themselves of accountability for the results. Police hand out speeding tickets to drivers, not passengers.
Obviously, not all cars are alike. They compete on features, gadgets, styling, horsepower and price. But whatever new gizmos car manufacturers dream up, they can’t escape the simple, intuitive user interface that has been in place for over 100 years.
While I’m sure it’s an enriching intellectual exercise to fill pages with clouds of Greek symbols in the quest to develop the most sophisticated forecasting algorithm, wouldn’t it be nice if managing a demand forecast was as easy as driving a car?