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.
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.
Buying health insurance in the U.S. is an odd business. Essentially, you have to balance your monthly premium (i.e. the amount that you pay your insurance provider each month for continued coverage) with your deductible (the amount that you have to pay out of your own funds before the insurance company will contribute to your care, broadly speaking). In general, if one of those two costs is particularly high, the other is likely to be lower, and vice versa. If you’re thinking about your choice in terms of total cost, a high deductible is risky, but has the potential to be cheaper if you can avoid getting sick over the course of the year. A high premium, on the other hand, might put you in a position where you’re essentially paying for medical care that you’re not receiving. The question, then, is how much risk are you willing to take on?
Let us consider the smart fridge. This modern convenience, part of the much-vaunted Internet of Things and a key component of many smart homes, give you the ability to track its contents and see them displayed via smart phone or tablet when you’re away from home. To some, this might seem like somewhat of a frivolous piece of technology, but imagine the following scenario: you’re at the grocery store, doing your weekly stocking up; you have a whole shopping list full of items that you expect to be depleted within the next few days, from eggs and butter to fresh produce. What you’re not planning on buying is milk, because when you left the house you still had most of a gallon left. Then, all of a sudden, you receive an alert from your phone letting you know that you’re out of milk. Unbeknownst to you, your partner has accidentally taken the existing gallon out of the fridge and spilled it. Luckily, she instructed the fridge to send you a real-time update and you were able to add it to your shopping list before you left, saving yourself an extra trip to the store or a week without any milk.
Lots of businesses across disparate corners of the supply chain like to talk about their efforts to go lean, i.e. to drastically reduce their inventory usage by reducing lead times between production and shipping. This is often a logistical high wire act, requiring businesses to improve their production control, their demand forecasting, and their transport logistics. It’s also something that many furniture manufacturers have been doing since long before there was a trendy name for it. In fact, many modern furniture manufacturers rely on workflows that skip the inventory stage altogether, with products going straight from their respective production lines to the delivery vehicles that will bring them to their ultimate destinations.
Topics: Advanced Analytics
Let’s say, hypothetically, that you’re a big fan of baking, and every so often your officemates are happy to act as guinea pigs by trying out whatever inventive confections you dream up. The one problem here is that your business involves a lot of travel and a lot of semi-remote workers, so it can be difficult to estimate how many people are going to be in the office on a given day—i.e. it's tough to know how large a batch of cookies to bring in when you decide to bake for your coworkers. You might simply base your batch sizes on past demand levels, assuming that because the last time you baked there were X number of people in the office, similar numbers are likely to hold true again, but this strategy has the potential to miss the mark drastically.
Topics: Advanced Analytics
For many decades, baseball statistics barely changed. People counted hits, batting average (the number of hits per at bats), and runs batted in and measured the value of their players based on those statistics. In the late twentieth century that all changed. With the advent of Sabermetrics and what would eventually be known as Moneyball, statisticians, baseball executives, scouts, and even casual fans entered a period of statistical renaissance. Old-fashioned stats took a backseat to complex new creations like OPS (on-base percentage plus slugging percentage) and Wins Above Replacement (a complex, GDP-like formula meant to distill value into a single statistic).
Let's say you're an OEM, with a sleek manufacturing space and a sophisticated, technologically cutting edge process for creating a particular automotive part. But you have a problem: At this point, your incredibly sophisticated production techniques aren't not being complemented by an equally sophisticated, multi-level approach to production planning and resource scheduling. This results in a disconnect between the high quality of your products and your ability to maximize capacity and meet customer delivery requirements. How can you build towards a production planning workflow that complements your product and fulfills your business goals?