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.

In a recent poll, PwC found that while 60% of respondents were “dabbling” with Industry 4.0 technology, only 3% had truly achieved a working Industry 4.0 paradigm. To some of you, this might come as a big surprise. After all, Industry 4.0 has been the subject of countless news stories, opinion pieces, blog posts, and whitepapers in the last several years—almost all of them pointing out its unprecedented potential for changing the face of manufacturing. Some readers, on the other hand, probably aren’t surprised by this statistic in the slightest. Why? Because they know how difficult it can be to find and implement the kinds of technology solutions that make Industry 4.0 possible. Businesses often have to wade through jargon to understand what’s on offer, and a solution, once selected, might require large-scale operational changes that can be difficult to implement. To help mitigate some of these challenges, here are a few questions to ask yourself as you evaluate Industry 4.0 technology solutions for your manufacturing outfit.

We tend to think of the (first) industrial revolution as a moment where the world changed in the blink of an eye. One morning, the world was dominated by cottage industries, and the next, steam power had completely transformed the nature of commerce, manufacturing, and modern life. What we sometimes forget is that the period we think of as the industrial revolution actually lasted more than 60 years—more than the length of a human life span during that era. Sure, things move a lot more quickly now, but it’s still a nice reminder that for all the talk about Industry 4.0 (aka the fourth industrial revolution), nothing happens overnight. The process of factories getting more connected and supply chains going digital might not take 60 years, but in the meantime it still remains a work in progress. That said, it's increasingly likely that the rewards will be worth the challenges.

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).

In a recent Seattle Times Article, readers got an intimate account of how Cloudburst Brewing creates its seasonal fresh hop beers. While most hops used in beer production are dried before they’re shipped from the farm, fresh hop beers utilize fresh-picked, “wet” hops that haven’t been dried yet. As such, this popular style can only be brewed during and immediately after the annual hop harvest, and only under ideal conditions. A traffic jam, a flat tire, or a power outage at the brewing facility could jeopardize brewers’ efforts, owing to the extremely short shelf-life that these “wet” hops have.

Let’s talk about Amazon Go for a moment. The incredibly successful online retailer has recently made headlines with its latest foray into brick and mortar shopping, a series of convenience stores that, notably, don’t feature any human cashiers. Instead, shoppers (all of whom need an Amazon Prime account) use an app on their phones to scan each item they put into their (physical) shopping cart. This is noteworthy for a host of reasons, but let’s look at it from an inventory management perspective. The store is stocked with a considerable number of items, which all need to be replenished as they are sold, and Amazon is able to track the flow of goods out of their stores with no human intervention. Not only that, but they’re able to link each piece of inventory that leaves the store to a particular user account, and then make recommendations to that user based on analytics processes designed to predict future buying behavior.

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.

Logistics 4.0, digital logistics, modern transport logistics: whatever you want to call it, the new paradigm emerging in the world of transporting goods from production plants to consumers is gaining steam rapidly. While, in the past, logistics was frequently a matter for pen-and-ink planning, relying on a set of well-trodden trade routes, the industry is becoming more sophisticated, more complex, and more connected than ever before. As the industry evolves, the utility of this new level of connectivity will become more and more apparent, resulting in exciting transformations in the way that goods are moved from place to place. Don’t believe us? Just take a look at some of these statistics.

Imagine for a second that you’re entering a friendly betting pool for the 2018 World Cup. Germany won the contest in 2014 (the most recent tournament), so you decide that it stands to reason that Germany will win again this time around. Hindsight being 20-20, we now know that you would have lost your bet, as France won the tournament and Germany didn’t advance out of the first round. Your betting strategy of assuming that past results would continue to hold ultimately wouldn’t prove to be the best approach.