We Don’t Need a Ferrari

Necessity never made a good bargain. – Benjamin Franklin (1706-1790)

When a retailer seriously embarks on an effort to completely reshape how they plan the flow of goods from supplier to shelf, the discussion inevitably turns to what software they will need to do the job. (And ideally, this isn’t Step 1 of the process).

As the time approaches to evaluate software vendors, someone in company leadership is bound to utter the phrase “We don’t need a Ferrari”. After that, everyone in the room will nod their heads sagely in agreement. You can almost set your watch by it.

The message they’re trying to send is “We don’t need unnecessary sophistication and we don’t want to spend a ridiculous amount of money. We just need to get the basics right.”

I believe the intention is correct. You don’t want the design and implementation team to go off on a wild search for the most sophisticated system they can find – whether or not it’s proven or even necessary. But the advice may not be as useful as you think.

People who are tasked with transforming supply chain planning generally don’t need to be constrained or reined in. They need to be led. By the time you get to this point, you should already have assembled a team with strong convictions and a bias toward pragmatism – they won’t run around chasing shiny objects. Sometimes they will need leadership to be led by them.

Using well-meaning platitudes like “we don’t need a Ferrari” doesn’t really clarify anything and could potentially lead them down the road of picking a simplistic system over a simple one.

The team needs to understand time-tested and proven planning principles, what the true requirements of the organization are (including taking into account future strategic direction) and what results they are expected to deliver.

A system with a simple data structure, easy navigation and limited options that doesn’t adhere to solid planning principles and doesn’t meet the requirements will not deliver results.

Just because some functionality may be more complex or sophisticated than what you have today, that doesn’t make it “too fancy”, unless the mandate is to implement a new system and process that does the same thing you’ve always done (with the same results). Not all sophistication is unnecessary.

Just because people will need to acquire more skills – some of which may be difficult to learn – doesn’t mean that the system or process is “too complicated”.

No, you do not need a Ferrari – because nobody “needs” a Ferrari. 

But there is a wide range of options between a Ferrari and a tricycle. Your requirements need to dictate whether you need a Corolla, a minivan or a pickup truck.

Don’t choose a tricycle just because it’s the farthest option away from a Ferrari. A simplistic system that doesn’t meet requirements makes the implementation just as complicated as an over-engineered system that you don’t need.

Overly Sophistimicated

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?