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I think it was Yogi Berra who said, “In theory there is no difference between theory and practice. But, in practice, there is”. Just another example of the brilliant and sage advice from the legendary Berra.
Theories are wonderful things. They help us to explain things and sometimes help understand an entire discipline. In math and science, theories need proof. Rigorous, thorough and defensible arguments and facts that confirm the hypothesis.
Theories in business processes are a lot like math and science. Instead of needing proofs, they require practice – to be more
specific, putting the theory into practice. Flowcasting is essentially a theory. And a number of years ago it was just that. A pipe dream. An idea that a hockey-loving French Canadian, an American MRP guru and a couple of process designers from a large Canadian retailer had. At the time a theory that the retail supply chain could be seamlessly connected by a single forecast only at product store level.
Through perseverance, hard work, luck, many pilots and attempts, Flowcasting has moved beyond the realm of theory and is now being put into practice. As we speak, a number of early pioneers are implementing the Flowcasting process and reaping the benefits of a completely integrated supply chain.
All because a few people concocted a theory and turned the theory into practice.
You see in business, until something is implemented and working, it’s just theory. How many conferences, speeches and youtube videos have you watched, then scratched your head and muttered…”nice theory, how would that work?”
In supply chain planning there are many theories. Consider Forecasting, or Demand Planning as an example. I don’t know about you but I’ve been awfully impressed with the theories I hear a variety of experts talking about when it comes to demand planning. Here’s a few of the “theories” I’ve heard or read about, that maybe you’ve seen to:
- You should factor in the latest weather forecast and that will improve your demand plans
- You should design planning processes that take into account the latest available information from social media to automatically sense and adjust your demand plans
- You need to build cognitive learning capabilities into your demand planning process
- You need to incorporate Big Data into your demand planning process to recognize early patterns to improve your forecasts
Now, I have to admit, these all sound wonderful but they are all just theories. I’ve yet to see any proof. And if you find any proof for the above can you do me a favour? Send me the details.
One of the problems with most of these theories is that people either don’t seem to realize or are missing a simple fact – that people are accountable for producing decent forecasts. And the problem with most of these theories is that they tend to overlook the human element in demand planning.
People need to completely understand the demand planning process, including how any sytems and theories arrived at the predictions. I’ve got a little theory of my own about demand planning. “The more complicated a theory in forecasting, the less practical it is”.
The good news is I’m not alone in my thinking. Scott Armstrong is pretty knowledgeable about forecasting processes. In fact, he might be one of the world’s leading authorities. Armstrong is a professor at the Wharton School of Business at the University of Pennsylvania. He’s dedicated his whole life (and he’s 78) to studying forecasting and his book, “Principles of Forecasting”, should be considered a bible to anyone interested in the field.
Us supply chain planners could learn a lot from old Scotty. One of his gems of wisdom is about complexity of the forecasting process/model. He states, “there’s been no case in history where we’ve had a complex thing with lots of variables and lots of uncertainty, where people have been able to make forecasting models or any complex model work. The more complex you make the forecasting process the worse the forecast gets”.
Hmmm…makes you wonder, eh?
We have lots of theories about how to factor in Big Data, the weather, and a whole host of other variables, all with complex models aimed at sensing and translating and responding the short and long term forecast, yet according to Armstrong, that’s never worked in practice. But practice, unfortunately, is where we work and where the rubber hits the road.
Of course the Flowcasting disciples understand something fundamentally critical to demand planning processes. Any supply chain really should only have a forecast at the point of consumption and all other demand plans can and should be calculated. The farther away from the consumer/point of consumption you’re forecasting, the more variables and constraints you’ll need to try to factor in. Exactly Armstrong’s point!
In theory, you can make your demand planning process as complicated as you want.
In practice, you can’t.