It’s a popularly quoted statistic that supply chain inefficiencies can waste as much as 25% of operating costs, which only goes to show how much an impact you can have on your bottom line by working to reduce waste. This is, of course, easier said than done: supply chain waste comes in myriad forms and is notoriously difficult to root out. Why? Because every decision you make across the entire value stream has the potential to introduce unforeseen costs down the road.
Most prognosticators have pretty much agreed that Industry 4.0 is going to radically change the world of manufacturing through big data, cyber-physical systems, and internet of things (IoT) integration—but not everyone agrees on exactly what this new paradigm is really going to look like. This might seem like disagreement, but in reality it’s part of the point: Industry 4.0 is going to look different at different companies. It’s even going to look different during different seasons, or for different production flows. An easy, consistent definition and an easy set of IT expectations is anathema to the whole idea of the fourth industrial revolution.
We all have different ways of getting a handle on our supply chain activity. Some folks might check a series of KPIs every morning to see what small fluctuations in supply and demand have occurred overnight, while others might be more interested in the big picture, seeking out a comprehensive visualization of the supply chain at the end of every month. However you like to think about and analyze your supply chain data, your routine probably revolves around a dashboard.
If you ever go to Las Vegas, you should be advised that casinos heavily frown upon card counting, and it’s easy to understand why. A game like blackjack is supposed to be more or less random in terms of what cards are dealt when, which puts the house at an advantage. Over the course of several hands (before the entire deck has been reshuffled), however, a careful observer can note the proportion of face cards that have come out in order to come up with a rolling estimate of how likely or unlikely they are to come up in future hands. This puts the player at a real statistical advantage over the house—at least until the casino politely (or not so politely) asks her to leave.
Raise your hand if you’ve heard the story of William James giving a lecture on the structure of the galaxy. After the lecture, an old woman comes up to him and says that his theory (in which the sun is at the center of the solar system) is no good, because the world actually rests on the back of a giant turtle. When James asks his interlocutor what the turtle stands on, she responds: "You're a very clever man, Mr. James, and that's a very good question… but I have an answer to it. And it's this: The first turtle stands on the back of a second, far larger, turtle, who stands directly under him."
From 2018 to 2019, Gartner’s outlook on Industry 4.0 adoption seemingly became a little less sanguine. It’s certainly not the case that their opinion of the potential of this massive industrial paradigm shift has lessened in any way, but the focus of their Industry 4.0 predictions for 2018 was how CIOs could find useful models of successful digitization, while for 2019 their focus was on dealing with the gap between expectations and reality that numerous industrial businesses are encountering with new technology. Again, it’s not that the outlook on Industry 4.0 itself is any less rosy than it was a year ago, but it seems like we’re reaching the point where real implementation hurdles are beginning to show themselves.
They say that those who don’t learn from history are doomed to repeat it—but in point of fact, relying too heavily on historical knowledge can often be just as bad. History tells us a particular new innovation will never work, or a new strategy will never succeed, and as a result we’re often blindsided when something truly innovative or unusual comes around. This is particularly true in the logistics industry, where changes in the global economy and the nature of supply chain technology are causing an exponential increase in the number of paths that any given cargo might take from producer to consumer.
As of a 2017 survey, just 6% of companies felt they had reached supply chain visibility. Elsewhere, nearly a fifth of companies listed visibility as their number one operational challenge (it ranks the third highest priority overall), but more than 60% admitted that they didn’t use any technology for monitoring their supply chains. By the same token, more than 90% of businesses have listed digital transformation as a huge driving force in the evolution of the modern supply chain, but fewer than half of those businesses have an actual plan in place for managing that evolution.
Depending on your background, when you were a child your parents might have told you that your Christmas presents came from Santa Claus. From a supply chain planning perspective, this would have made things difficult for you, since your only source of information was fairly opaque, and you had little insight into the distribution mechanisms for toys and gifts. As a result, you were stuck jumping through whatever holiday hoops were presented to you, whether that was mailing a letter to St. Nick or putting out milk and cookies the night before. Once you realized the truth, however, all bets were off. At that point, you knew that the things that wound up under the tree just came from the toy store, and if you were feeling enterprising you could change your supplier relations to arrive at more favorable terms.
As the era of Industry 4.0 approaches in earnest, production managers will soon have access to more data and information than ever before. Internet of things (IoT) sensors and RFID chips throughout the production chain will offer real-time monitoring for your planned production programs, just as robust software integration will help you to better understand what’s happening at various other touchpoints on the supply chain. This is exciting, but it can also be a bit daunting. After all, what exactly are you supposed to do with all of that data?