Let’s says you’re playing chess. Traditionally, a chess player looks at the whole board and comes up with an overarching strategy, which she can then adjust as needed when new conditions (i.e. her opponent's strategies and maneuvers) emerge. For this game, however, you decide to do something different: you have a series of different plans, one for the pawns, one for the bishops, one for the queen, etc. with no obvious connections or interplay between them. As situations arise in which multi-step, cross-functional movements would be helpful, you stay in your lane and stick to the separate plans for each function. At the end of the game, your rooks have performed admirably, and everything went according to plan for your pawns, but you still found yourself in checkmate.
Even manufacturers themselves may sometimes forget how tremendous the global manufacturing sector really is. Manufacturing in the U.S. on its own, for instance, would be in the world’s top 10 economies. Because this sector encompasses so many different businesses with so many different missions and products, it’s easy to prove or disprove almost any prediction. Sure, someone among the incredibly diverse array of global electronics producers is probably using voice activated AI in their plants—just as someone else is probably bucking every emerging trend by continuing to eschew digitization and connectivity. Still, as general trends emerge, it can be helpful to identify and understand them. To that end, here are some predictions for the world of global manufacturing in 2020.
Let’s say it’s the day after Thanksgiving, and you’re trying to see how far you can stretch your leftovers. You take a good hard look at the fridge, where you note that given the amount of turkey, some mashed potatoes, some cranberry sauce, etc., you could probably put together another 6 person-meals for your family of 5. One of your family members, however, is a vegetarian, which means that any meals you put together for them can’t have any turkey or gravy. As such, they’ll need additional yams and mashed potatoes, which affects the proportions of the other plates that have to be assembled.
Demand forecasting plays an important role in manufacturing. That fact isn’t changing; what is changing is how it’s done. Whereas in the past, forecasting had an aura of magic about it, relying heavily on the intuition and experience of the planner doing the forecasting, today it’s largely a data-driven practice. Before Industry 4.0, historical data was combined with gut feelings to produce a sort of crystal ball-like prediction of what the future held for the company in terms of demand and buyer behavior, and by extension what the company should focus on producing. Everyone basically crossed their fingers and hoped for the best. Post-Industry 4.0, this has changed dramatically, with a heavy reliance on advanced predictive analytics being fed data from IoT sensors deployed throughout the supply chain.
We’ve all been there at one point or another: you’re scheduling production for a large, complex item like an automobile in a plant that produces multiple different models. Your materials and capacity are relatively well synced, and your plans for the rest of the week or month seem pretty stable. Then, all of a sudden, a huge rush order comes in from an important client. All of a sudden, you're scrambling to allocate the materials and capacity necessary to slot this order into your existing plans. You pull resources from one order to give this one the resources it needs, but that has a complex ripple effect through your ordering flows; by the time you’re done reworking the schedule, your production plans are confusing and far from optimal.
Let’s say that you’re a manufacturer creating widgets for large scale distribution in your region. Though the widgets are vitally important to your clients, they’re incredibly complex and difficult to produce, meaning that even when everything is going smoothly within your operations, the yield for any given production process has a high degree of variability. No matter how effectively you replicate your process each time, differences in source material quality and production conditions mean that some number of finished goods are going to have flaws that prevent them from going to market.
As the global scope of the modern supply chain continues to increase, there’s going to be more data available to supply chain planners than ever before. For some businesses, this data will likely just sit there collecting dust—but in point of fact it’s increasingly going to be an important source of value for planners. Why? Because modern analytics processes, powered by technologies like AI and machine learning, are making that data exponentially more valuable as a source of usable insights.
Topics: Advanced Analytics
The best laid plans of mice and men often go awry—and nowhere is that more true in the worlds of manufacturing and supply chain management. Sometimes it seems like even the most visible and adaptable supply stream is always one disruption away from chaos. For production planners in particular, you’re constantly battling the risk that new, unexpected orders will come in and you won’t know how to slot them into your existing flows, or that a machine on your production floor will break down and bring your whole operation grinding to a halt. To some extent, occurrences like these are just a fact of life. But that doesn’t mean planners can’t work to prevent them, just as it doesn’t mean that planners can’t work to gain more value from the processes that are already working smoothly.
Let’s talk about Sherlock Holmes for a second. When you think of this famous English detective, a few things probably come to mind: his iconic deerstalker cap, maybe his pipe or his violin, and his magnifying glass—that critical tool for finding clues that Scotland Yard might have missed. Though Holmes was a fictional construct, sprung from the mind of Sir Arthur Conan Doyle, the method he employed in his stories and novels actually had a real-world impact on how detective work and criminal investigations were conducted in the 20th century.
Supply chain management can often be a stressful task, sure, but so can planning a successful potluck. You often don’t know in advance who’s going to bring what dish to your event, which means that any meal-planning you do on your end is essentially guesswork. Though it’s not likely, you could end up with a party where everyone independently decided to bring potato salad, and no one brought any main dishes or desserts. Luckily, in the 21st century, there’s an app for that: party planners can let attendees specify what they plan to bring in advance, and that information can be displayed in real-time for other attendees who are still deciding. In this way, party planners reduce the likelihood of too many repeat items, while putting themselves in a position to fill in any gaps that may arise.