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 economics and game theory, writers have traditionally used the term “widgets” to refer to objects of variable characteristics in production and, to a certain extent, transport. A widget can be of any shape, size, or make, and can have any other characteristics that suit the question that’s being posed or the point that’s being made. Since the advent of personal computing, the other definition of widget (an application or interface) has in many circles become more widespread, supplanting the original meaning.
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.
Raise your hand if you’ve ever played a game of telephone. For those of you who are unfamiliar, the game starts with a number of players sitting in a circle; one player whispers a word or phrase to the person next to them. That player, in turn, whispers the word or phrase to the next adjacent player, until the word or phrase has cycled back to the original player, who tells everyone else what the original word or phrase was. Usually, players find that the phrase has morphed into something else entirely over a series of mis-hearings and miscommunications—which is the entire point of the game.
In discussions around supply chain logistics in the past few years, some people have been describing the systematic increase in customer delivery expectations as the “Amazon Effect.” And it's certainly true that Amazon’s push towards faster and faster delivery turnarounds has had a huge impact not just on how (and how quickly) customers expect their items to be shipped, but on the way supply chains are administered around the globe. Where traditional shipping workflows might have required a few touches to get a given shipment from the manufacturer to the final destination Increased delivery speeds have increased the average number of touches, i.e. the number of legs in each journey.
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.
When you play chess, you’re supposed to think several moves ahead. This means that whenever you move one of your pieces, you should be anticipating the possible moves that your opponent will make in response, and what you’ll do in response to your opponent’s next moves. Since at each stage there are multiple possibilities, the possible scenarios you need to keep in your head at any given time begin to multiply pretty quickly. And yet, for each scenario it’s imperative to be able to look at the entire board in your mind and consider all of the hazards and opportunities that present themselves. In this way, it’s a little bit like logistics planning.
Plenty has been written on the perils and best practices that come with selecting the right technology for your business. Usually, businesses will be told to look at online reviews, to do their due-diligence on the provider to make sure that they deserve the trust that’s being placed in them, and to be conscious of what the typical pricing structures are within the relevant industry. This is all excellent advice, but it might not directly speak to the most important questions being considered by businesses. Why? Because while evaluating an IT solution is, in some ways, just like evaluating any other product, it’s also markedly different in others. Specifically, it requires businesses to think not just practically but conceptually, considering the long-term, transformative implications of a given piece of software.
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.