Depending on your background, when you were a child your parents might have told you that your Christmas presents came from Santa Claus. From a supply chain planning perspective, this would have made things difficult for you, since your only source of information was fairly opaque, and you had little insight into the distribution mechanisms for toys and gifts. As a result, you were stuck jumping through whatever holiday hoops were presented to you, whether that was mailing a letter to St. Nick or putting out milk and cookies the night before. Once you realized the truth, however, all bets were off. At that point, you knew that the things that wound up under the tree just came from the toy store, and if you were feeling enterprising you could change your supplier relations to arrive at more favorable terms.

Of all the stages of the supply chain, logistics often gets a bad rap. This appears to be largely due to a combination of the seeming unpredictability of the unknowns like weather patterns and fuel costs; and the skyrocketing costs associated with last-mile delivery in recent years. This potent combo makes it all the more unexpected that logistics is also quite often overlooked when it comes to applying learnings from demand forecasting. The predictive analytics used by demand forecasting solutions takes historical data, runs it through advanced AI algorithms and generates predictions for demand in a specified upcoming time period. That sounds pretty useful for cutting logistics costs and leveling out some of the uncertainty that’s endemic to this sector, doesn’t it?

How are you using your ERP software? Strictly for resource planning, as intended? Or are you stretching that definition to include aspects of your supply chain management needs as well? ERP solutions are an offshoot of financial software, and most of it functions as such and can be clunky when pressed into alternative uses. A dedicated SCM software solution, on the other hand, is as flexible and multifunctional as your supply chain itself. Think of it like this: would you rather build your personal daily calendar out of an Excel spreadsheet, which is totally doable, or just use a ready-made calendar tool like Google Calendar? Yes, both are workable solutions, but only one is actually made to help you keep track of lunch dates and offer reminders for those important meetings you just can’t miss.

Imagine you’re hosting the family reunion this year. 75 people are going to be expecting a great venue, amazing food, and some planned activities. But you’re just one person, with some help from your girlfriend. Are you going to cook all the food, decorate the community hall, schedule and set up for the band, AND be there to greet everyone as they arrive? I certainly hope not. You’re going to hire a caterer for the food, be sure the band brings their own crew to set up and tear down the stage, and rope your girlfriend into being the greeter so you can still supervise the proceedings. On the other hand, if you’ve got a wife, 3 grown kids with their own significant others, and a circle of close friends, maybe you can do it all in-house. This is the power of outsourcing, it keeps the playing field even for even the smaller players. The question is—how do you maintain control over the cooks, musicians, crew, and cousins?

Why does your company exist? This isn’t a metaphysical question, like “why are we here,” it’s purely practical. We hope the answer you thought of was, “to keep our customers happy,” because if not, the rest of what we have to say today might not make as much sense. Customers are the reason manufacturers make things. If there were no customers, you would have no reason to make products, and therefore you’d have nothing to ship, right? As a production planner, you’re likely already relying on S&OP workflows and software solutions as a key piece of your strategy to keep your customers happy. Especially in today’s world of next-day shipping, S&OP remains necessary to keep up with these demands and delivery expectations.

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.