Most businesses in the manufacturing sphere have some form of sales and operations planning (S&OP) workflow that covers the monthly or quarterly timetable that’s often left unplanned in longer term business goals. In the Industry 4.0 era, a newer, even more granular level of planning has emerged to supplement S&OP by covering the daily, weekly, and monthly supply chain activities that might otherwise go without any cohesive planning structure. The name of this new level of planning? Sales and operations execution, or S&OE.
Let’s talk for a second about pattern recognition. The human brain is constantly searching the perceptible world for patterns, sometimes in order to make better decisions (in the case of, say, emerging traffic patterns while driving) and sometimes simply in order to pass the time (in the case of constellations). The thing is, while the search for patterns is an innately human pastime, it’s not something that we as a species are necessarily all that good at. Think about it: how often do we read or hear about people making the same mistakes over and over again without identifying the common factor? How often do we see businesses rolling out the same strategies over and over again without ever noticing the ways in which those strategies could be improved?
Often, when trying to advise their readers on the best ways to avoid supply chain disruptions, experts and other commentators will suggest increasing your buffer stock. No doubt this is effective when it comes to staving off shortages, but it’s still a deeply unsatisfying answer. Why? Because stockpiling goods is frequently costly, and doing so can bog down your operations in the long run. Sure, it can be useful insulation against the unexpected, but it’s also the antithesis of anything resembling an agile or lean supply chain.
Pop quiz: how many of you reading this are wearing a Fitbit right now? We’re willing to bet that at least a handful of you answered in the affirmative, maybe even a large percentage of you—and on some level that makes sense, because step-counters and other pieces of wearable technology give us insight into and control over our health in ways that simply weren’t available to previous generations. A mere couple of decades ago, most people presumed themselves healthy until they received some evidence to the contrary, whether that came in the form or new pain and discomfort or a stern talking to from a primary care physician. Now, with just a wristband and a smartphone you can monitor your sleep habits, your heart rate, and your physical activity in real time, meaning that if something changes in your health status you’ll notice early and take immediate action.
In 2015, Greek Finance Minister Yanis Varoufakis made the bold claim that The Matrix (the 2000 science fiction film in which all humans were being held captive in an elaborate computer simulation) was not so much a science fiction film as a documentary about modern capitalism. We’ll leave aside for now any quibbles we have about the use of the word “documentary” in this context, but it’s worth thinking about how much the world has changed in the 18 years since the movie originally came out. Why? Because computer simulations have actually become a meaningful fact of life for many businesses across the world, particularly in the manufacturing sector.
At this point, if you’ve heard of digital twins, it’s likely that you’ve also heard them discussed in relation to the NASA’s Apollo 13 mission. For those of you who haven’t, the modern conception of a digital twin owes a lot to the structures that NASA put in place in case of exactly the sort of malfunctions that almost doomed the astronauts aboard Apollo 13. To wit, once John Swigert communicated to NASA that the spacecraft was experiencing an issue (in this case, an oxygen tank explosion had caused a cascade of system malfunctions), engineers and planners on earth were able to replicate the problems using a full-scale, physical model of the entire craft. Using this live, physical simulation of the systems operating in space, they were able to identify the issue and communicate a plan for repairs to the crew.
In no particular order, the top supply chain disruptions include climate and weather events, forecasting errors, new trade regulations, oil and freight price fluctuations, machine and fleet breakdowns, and poor IT and technology integration, among others. As you peruse the list above, you might notice each of these disruptions can be put into one of two categories: fast or slow. Things like machine breakdowns and catastrophic weather can happen in the blink of an eye, and supply chain managers have to be prepared to preserve value via a backup plan. But other issues, like poor forecasts or integration issues, compound slowly over time—sometimes so slowly that it can be hard to identify the root cause of whatever difficulty your company is experiencing.
Historically, we tend to think of Henry Ford’s adoption of the assembly line as one of the most important moments in the history of automotive manufacturing—a moment when, all of a sudden, automobiles could be produced in an efficient, cost effective way. While it’s certainly true that this was a watershed moment for the industry, this emphasis on the assembly line can have the effect of obscuring that other production scheme that’s so integral to lives of many modern auto makers: the job shop. Indeed, job shop environments are crucial in many production workflows for the creation of parts and even whole cars. As a result, the ability to create efficient production schedules in such an environment can be a key value added propositions for manufacturers.
Let’s say that you’re in charge of the omelet bar for a boutique hotel breakfast service. Guests line up, plates in hand, and when they reach the front of the line they let you know what type of omelet they’d like (egg vs. egg-white) and with what ingredients. On a slow day, you can cook the eggs as you go, but when the line starts to get longer it becomes incumbent upon you to start cooking the eggs in advance and add the extra ingredients (cheese, onions, peppers, etc.) as they’re ordered. When the line gets really long, you start adding cheese to a number of the pre-omelets, on the assumption that most people will want cheese. When the line gets even longer, you have a set of common omelet orders on the griddle ready to go.
Topics: Advanced Analytics
Let’s take a second to compare two hypothetical World Cup forecasts. Both forecasts are trying to determine who the likely winner of the contest will be, but their methods differ fairly radically. The first forecast starts out with team rosters, facts and figures, and all manner of statistics pertaining to the various players and teams. Based on those facts and figures, a statistician begins to derive and weight a set of probable outcomes. Those outcomes are sent on to a human prognosticator (an expert in the sport, perhaps a former player or coach or a newspaper commentator) who uses his experience and judgment to tweak the probabilities handed down to him by the statistician. The stats think that a particular player on the French team will age poorly, but the prognosticator thinks otherwise, and changes the predictions accordingly. After this first round of edits, the predictions are passed on to the next editor, who brings her own experience to bear, changing the projected outcomes yet again.