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.
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.
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.
The practice of trying to get the right goods to the right place at the right time in the right condition is virtually as old as time—and yet, each year, new trends, ideas, and best practices emerge in the fields of logistics and supply chain management. With the rise of digital technology and increased connectivity across the supply chain, the current rate of change in these fields is unprecedented. The world of logistics even 5 to 10 years from now will probably have undergone even more dramatic evolutions, and it may be virtually unrecognizable compared to the supply chains of the past.
Topics: Logistics 4.0
Let’s picture a hypothetical. You’re a sales and operations planner at a global manufacturer, specializing in a high-end variety of widget that other global companies tend to order in large quantities. Your sales cycle is fairly long, so every time a member of your sales team closes a deal it feels like a major victory. Recently, you’ve closed one of your largest deals yet, meaning that a large quantity of deliverables need to be produced in the immediate future. This will mean leveraging your production facilities at their maximum capacity for some time (potentially resulting in some wear and tear on your machines that will cause slowdowns later), but, like they say, “make hay while the sun shines.”
Imagine for a second that your factory is essentially a black box. Materials go in, and finished products come out, but what happens in between is fundamentally mysterious. What challenges would this present from an advanced planning and scheduling perspective? Sure, in this environment you can get a small sense of the correlation between raw material volumes and finished product volumes—you might even be able to gain a sense of which raw materials loosely correspond to which products. But surely there’s a lot of information you’d really like to have: how do different products differ in resource usage? What are the most common causes of delays and disruptions? How can you more effectively align your capacity with emerging demand levels?
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
Digital supply chains can help manufacturing businesses reduce costs and disruptions in a variety of ways. They make it possible to predict potential breakdowns and bottlenecks far enough in advance that you can take steps to address them, just as they help you to boost operational efficiency through smarter sourcing, inventory management, and capacity management. More than that, going digital makes it easier for your business to integrate with other highly-digitized operations, meaning that especially sophisticated supply chain partners (whether they’re suppliers or logistics providers) will be more excited to partner with your business.
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.