In baseball, the pitcher and the catcher must communicate via signs in order to implement a strategy to get the batter out. Depending on the strategy, the various fielders may need to position themselves closer to or farther away from home plate (if the pitcher is trying to induce a ground ball out or a fly ball out, for instance), which means that the strategy must be agreed upon beforehand and disseminated amongst the entire squad—not just the pitcher and the catcher. Picture the alternative: the pitcher decides on his own what approach to take, and the catcher is stuck trying to catch whatever is thrown at him without any advanced notice; meanwhile, the fielders don’t know what to expect, so they’re not able to position themselves appropriately. As a result, a batted ball is likely to result in chaos.
Let’s pretend that you and a friend are both mixologists at an upscale cocktail lounge. On weekends, there tends to be a rush of patrons late in the evening who ask for drinks faster than you can produce them. As a supply chain or logistics manager in real life, in this scenario you might be tempted to suggest that you and your fellow bartender start creating a buffer stock of drinks before the big rush, so that people can receive their drinks as soon as they order them. Unfortunately, you can’t really know what drinks people will order in advance (to say nothing of the fact that the ice will melt), so creating a buffer stock is impractical. You can, however, do some prep in advance, like preparing garnishes and simple syrup. When the rush comes, you’re still slammed, but you’re able to create drinks more efficiently.
Baseball may not be the most popular sport in many parts of the world, but when one considers all of the analytical and statistical breakthroughs the game has made in the past two decades, it really deserves to be a favorite of supply chain managers in the Industry 4.0 era. Since the dawn of the “Moneyball” era, scouts, commentators, and prognosticators have developed new, increasingly complex ways of measuring past performance and forecasting future outcomes. Because everything that happens in the game of baseball, from a stolen base to an outfielder dropping the ball, can be represented numerically, entire seasons can be simulated in granular detail, and insights can be gained from those simulations. By integrating these systems with real-time game data, we can now make an ongoing estimate of the win probability of each team in the middle of each contest.
Let’s say you’re an amateur baker, and you’ve just agreed to participate in a pie bakeoff with some of the other bakers in your town. You have a few pie recipes that you like, but because the stakes are suddenly much higher than usual, you want to create a new recipes that improves on your existing ones, in order to better compete with your opponents. Most likely, this is going to mean finding new or old recipes to adjust and adapt, and then taking those adapted recipes and producing test batches of them, trying out the results, and producing new test batches with the tweaked recipes. This process, needless to say, would be incredibly time and resource intensive—not least of all because baking is an extremely fickle business, and it’s often difficult to predict the results of changes in ingredients or cooking time.
It’s still about a month until Thanksgiving, but you may already be deep into the planning process for the big event. Some of your friends and family are flakey, so you won’t have a full list of RSVPs until much closer to the holiday itself, meaning that when you sit down to sketch out what dishes you’ll be cooking and what ingredients they’ll require, you’ll have to use a combination of confirmed and projected attendees. For some dishes, it might be easier to wait until a few days before Thanksgiving to get the necessary ingredients, but you’re worried that your nearby grocery stores might run out of a particular brand you like if you wait too long, and there are some items that need to be purchased well in advance because they need to be stale or overripe before they can be used, like bread for stuffing or bread pudding.
Let’s say you’re moving into a new home. After a long day of loading boxes and furniture into your car and driving them to your new place of residence you’ve finally transported everything into your new house and you’re almost ready to start unpacking and get the place settled. Before you get down to the work of opening up all of your packed boxes, you realize that you haven’t eaten all day, and should probably whip something up before you do any more manual labor. It should be pretty easy to find your kitchen supplies and snack foods, because you made a list of what was in each box before you moved them. One problem: you don’t know which box the list is in.
In order to remain competitive in the world of modern manufacturing, production planners are constantly searching for new ways to derive more value from their operations. This impulse takes many forms, but one of the most common is striving to improve operational capacities, usually by either reducing makespan or improving machine utilization. Though the obvious benefits of increasing your throughput may seem tantalizing, the process of actually doing so is not as simple as ratcheting up production speed or buying new machines. Rather, it is a complex process that requires a high degree of visibility into your value stream. To help you tackle these complexities, here are 5 key strategies for improving operational capacities.
Imagine a world in which trillions of individual pieces of information are gathered each day to create complex predictions about future supply chain disruptions and events. Extremely granular data on trade markets turns information about the movement of goods and services throughout the globe into cognitive systems with the power to illuminate new possibilities and intelligently predict changes in demand before they occur. While this may sound like science fiction, it’s increasingly becoming a reality as supply chains become more and more integrated with machine learning, artificial intelligence, and big data analytics. By 2020, IDC predicts that 50% of supply chains will utilize advanced analytics and artificial intelligence, and the effects on the global supply chain are sure to be widespread.
Additive manufacturing (AM), otherwise referred to as 3D printing, has long been one of those technologies that seems to be just beyond our grasp. By many accounts, this will soon cease to be the case. Gartner estimates that by 2021, 20% of the world’s top consumer goods manufacturers will use 3D printing to produce custom products. Some businesses are already establishing internal start-ups with the intention of refining 3D printing techniques and best practices, and as the process gains speed and production quality it will soon become a viable method for mass production and a disruptive force across the manufacturing sector.
If you had walked onto a factory floor during the second or third industrial revolution, it would have been immediately obvious what was so modern about what you were witnessing. You would have seen raw parts being turned into complex products on a moving assembly line, or newly automated processes making use of modern industrial machinery and early computer networks. In the world of Industry 4.0, the so-called “fourth industrial revolution,” the differences in appearance might be more subtle. You might still see a mix of manual labor and automated, computerized systems carrying out various production tasks, while many of important innovation brought about by Industry 4.0 might remain invisible to you. You might even be prompted to ask, “what’s so modern about modern manufacturing?”