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.
Transparency is at the heart of both Industry 4.0 and the sales & operations planning process. Without visibility into every aspect of a supply chain, planners have no way to know for sure how something they do today will impact another department or team in the months to come. With visibility into those teams’ processes to see where the overlap is, they can see clearly how each move they make will impact the rest of the company and can better ensure that the entire value chain is protected from non-compliance. This may look different for production planners and operations managers, as each has their own priorities and needs. different needs. It can also look different at each stage of the plan from the same department, but the bottom line remains the same—visibility into the planning process is key to successful implementation.
Industry 4.0 is making waves in the manufacturing and supply chain sectors. But what about logistics? How are these same technological advances helping move those products faster and more efficiently? The goal remains unchanged: to use connected workflows and technologies to give people the tools and freedom they need to adapt and pivot with the changing environment and to seek creative solutions to increasingly complex problems. Logistics 4.0 is here, and one of the primary drivers of this revolution is the Internet of Things (IoT). IoT refers to devices of all sorts, be they tablet computers, sensors monitoring machinery or vehicles, or even wearables that track biometrics to ensure the health and well-being of the workforce, that are all connected to the network.
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.