All the way at the far end of the supply chain, when an automobile reaches its end consumer, it looks like they’re buying one large item. But automotive manufacturers know differently—they know that each car on the road is really comprised of about 20,000 different parts, and all of them had to come from somewhere. After being sourced, they had to be stored, allocated for various production plans, brought to the production plant, and assembled into a road-worthy vehicle that someone could drive off the lot at their local car dealership.

There’s been a big push towards lean manufacturing and logistics in the past few years, with manufacturers doing everything in their power to reduce inventory levels and rely less on their buffer stock. Because there’s a considerable element of risk involved in a truly lean supply chain, virtually all supply chains stop short of completely lean workflows. The one significant exception? The newspaper industry. While newspapers aren’t usually thought of as manufacturers in the traditional sense, they do produce a product in a systematic way in order to be shipped to end-users—with the crucial difference that anything resembling a buffer stock or inventory is rendered useless by the impossibly short lead times, as papers become obsolete just hours after they’re distributed. 

Let’s say that you run a pizza delivery joint. As orders come in by phone or through your website, you have one employee who’s in charge of giving delivery estimates and getting the pizzas to the relevant doorsteps, and another who’s in charge of running back and forth between the storeroom and the kitchen to make sure that the chefs have everything they need to actually make the pizzas. If any of the ingredients in the storeroom get too low, that employee calls the relevant suppliers and arranges to receive and store the delivery. One day, you get a bright idea: what if the delivery person and the employee in charge of restocking the storeroom had direct visibility into one another’s processes?

Ah, the old dilemma: make to order vs. make to stock. The debate has been raging in the world of manufacturing for many years. On the one hand, making to stock (i.e. the process of creating products in anticipation of demand that hasn’t yet materialized) involves a lot of guesswork, with potentially costly results: if demand for a particular product doesn’t meet forecasted levels, you could find yourself in possession of large quantities of unsold stock, which you might have to sell at a loss in order to free up costly warehouse space. Making to order (in which you start your production process only once an order has been placed), on the other hand, presents its own potential pitfalls: you risk meeting demand comparatively slowly, and the relatively lean nature of the typical make-to-order supply chain makes it more susceptible to risk in some ways.

One of the explicit goals of Industry 4.0 in the long run is to empower autonomous machine decision making within production processes. This is a lofty goal—requiring highly visible and highly legible data streams combined with AI or machine learning integration—but it does have the potential to add considerable value to supply chain management processes. How does it do so? By freeing up human decision making capacity for larger-scale choices, and by automating the process by which data is turned into action—i.e. creating an implicit set of procedures for different situations that might emerge on the factory floor. In this way, manufacturers can build new efficiencies into their existing processes and drive towards an increasingly optimized supply chain. 

Effective supply chain management is about getting the right goods to the right place, at the right time, in the right condition. This is easier said than done. Why? Because there are a number of intermediate steps separating a finished product from its final destination, including warehousing and shipping, both of which can be complex logistical problems in their own right. The mark of a successful supply chain is its ability to optimize each element in turn before integrating the different pieces into a cohesive, profitable whole. Today, we’re going to focus on the warehousing side of the equation. For many businesses, this represents the thorniest part of the entire value chain—a touchpoint for which effective tracking and management is particularly difficult. Don’t believe us? We’ve got the stats to prove it. 

Often, when trying to advise their readers on the best ways to avoid supply chain disruptions, experts and other commentators will suggest increasing your buffer stock. No doubt this is effective when it comes to staving off shortages, but it’s still a deeply unsatisfying answer. Why? Because stockpiling goods is frequently costly, and doing so can bog down your operations in the long run. Sure, it can be useful insulation against the unexpected, but it’s also the antithesis of anything resembling an agile or lean supply chain.

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.

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.

Let's say you've got big event coming up—maybe an awards ceremony, or an important anniversary. You and some of your friends are going to the event together, and to make the whole affair a little more special you decide to rent a limousine take you there and back. Though the venue is only an hour’s drive away, your friends’ homes are spread throughout your town in ways that make planning the optimal order in which to pick them up (and drop them off after the party’s over) a challenge. On top of that, not everyone will be ready at exactly the same time, and those who would be picked up later in the process would like to know in advance so that they can spend more time preparing. Where do you begin when it comes to planning out a tour that works for you?