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.
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.
Imagine a scenario: Your company has contracted a shipper or freight forwarder to complete a delivery of parts to one of your customers. Because of extensive data-collection during your research and development for the parts, you know that high temperatures over a prolonged period of time can increase the part’s failure rate. As a result of a shipping delay, these parts spend too much time in a container that’s not properly temperature controlled.
The rise of Industry 4.0 is already impacting the way that supply chain managers do business. As it continues to promote digitization and interoperability across all touchpoints on the global value chain, it will no doubt bring about significant changes across a variety of different supply stream operations. No doubt one of the most significantly impacted processes will be transport logistics, which might lead one to wonder, “what will transport logistics look like in the Industry 4.0 era?”
Let’s say you’re driving down a winding country road to some remote destination. At first, navigating is easy, but as the sun goes down and your headlights come on it becomes more and more difficult to make sure that you’re driving safely down the correct course. Eventually, night has fallen completely and your headlights provide the only illumination—you have to slow your driving speed, so that if an animal or other unexpected nocturnal being wanders into the path of your headlights you’ll have enough time to stop the car. If there’s stormy weather, the visibility becomes even more limited, and the possibility of an unexpected snafu increases.
Let’s say you’re tasked with designing a factory. You need to decide how to integrate various production lines, where to locate specific resources, how to organize space in a way that maximizes efficiency within and between processes, and how to leave room for potential future process changes. The interplay of a complex series of elements and structures will ultimately determine the success or failure of many planned production programs, so your grasp of the interrelations between these elements must be excellent. Once the factory has been established, things become even trickier. If you want to reposition a piece of machinery, for instance, you should know in great detail what processes involve that machine and how those processes will be affected. In short, these are tasks that you wouldn’t undertake without a carefully devised strategic plans that accounts for a variety of modalities.
Imagine for a moment that you’re planning a camping trip with your friends. There are several of you, and the trip will last a few days, meaning that you’re going to have to take two cars and considerable volume of supplies. How do you decide how each car will be filled? Let’s say your friend already has tent poles and fire starting material, so it might fall to you to procure and transport sleeping bags and food supplies. If one car is more fuel efficient than the other, does that change your plans? How will you go about choosing the right route to your destination in order to find the right balance between toll roads and potentially less direct pathways?
In a recent research report, Business Insider found that when it came to machine learning, 53% of the company executives surveyed were interested in the emerging technology, but unclear as to its exact use cases and applications. Similar figures applied to executive attitudes towards other technological advances, such as artificial intelligence and 3D printing. Although machine learning in particular is already driving new Industry 4.0 workflows and fundamentally changing the way that manufacturers do business, it’s no surprise that many have trouble envisioning specific applications for it. The transformative power of new technological advances comes not from generalities, but from specific tools and methods for integration that must be carefully calibrated to specific business functions.