By all accounts, advanced analytics are becoming a more important part of the manufacturing sector than ever—and it’s easy to see why. By McKinsey’s estimates, the combined effects of using advanced predictive algorithms to proactively schedule machine downtime and prescriptive analytics to optimize machine yields can add up to a whopping 10% increase in gross earnings. How is this possible? Well, for starters, predictive analytics trained to predict machine breakdowns can help reduce machine downtime by up to 50%, which not only improves your production plant’s throughput, it also extends the lifespan of each machine, saving additional costs down the road.
For a mid-sized business owner trying to grow his or her business, there’s a lot to get excited about in the paragraph above. At the same time, cost savings and growth, while related, aren’t automatically one and the same—especially in a complex global supply chain. That raises an important question: how, exactly, can advanced supply chain analytics power growth for your manufacturing business? That’s exactly the question we’re hoping to shed some light on below.
Demand-Capacity Planning in the Smart Supply Chain
When businesses talk about growth, there are two elements that come into play: growth in terms of revenue, and growth in terms of your operation itself. It was decades before Amazon turned a profit, but you can bet they experienced a ton of growth during that time anyway. By contrast, it’s easy to imagine a smaller business like a fine dining establishment or a bookseller increasing revenue year over year (up to a point) without ever really expanding. Obviously, these examples are significantly different from the particular complexities of growing a manufacturing business. Why? Because the balancing act between demand and capacity is much more complex in the modern supply chain. To wit, your average carmaker probably has a number of functions in place—from S&OP and S&OE to inventory planning, supply chain planning, and transportation logistics—all designed to help make sure that you have the resources available to meet customer demand when it arises.
If you’re going to boost your revenue, you’ll need to sell more products—which, buffer stock notwithstanding, will mean producing more within your facilities. This, in turn, means finding some way to up your capacity, whether that’s by increasing machine efficiency, adding new machines and new shifts, or opening up more production lines. Likewise, it will mean adjusting your orders from your suppliers and booking more freight for moving the finished goods around—all of which can be risky propositions if you don’t have the data to back them up. Luckily, as the global supply chain is getting smarter and more connected the right data is easier to come by than ever: this can mean anything from IoT sensors on your factory floor to live machine usage data to real-time demand information. All told, these data points will help you make informed decisions regarding growth—while advanced analytics will help you turn that data into unprecedented agility.
Balancing Predictive and Prescriptive Analytics
Okay, but how exactly will advanced analytics do that? Simply put, by feeding the data you’re collecting into predictive and prescriptive algorithms, you can improve your forecasts, your risk management, and your overall efficiency. The only trick here is to make sure you’re balancing these two functions to optimize your results. So, let’s say you want to open up a new production facility in another city, in order to grow your production and transport networks. From an analytics perspective, you’ll want to know whether demand is going to be high enough to justify the expansion, and where the new facility should be positioned for optimal value.
For the first part, advanced predictive analytics can turn all of your previous order information—plus live market data—into improved demand forecasts that give you a sense of your likely future capacity needs. Here, you can adjust your parameters as needed to estimate what changes will result from different conditions in the market and your own production network. Next, you can leverage advanced prescriptive analytics to analyze your existing production network to find areas for increased efficiency and test out potential locations for the new facility. You could even employ a digital twin (i.e. a digital representation of your factory or network that’s designed to run simulations), to get a data-driven account of the result of any network change on a granular, parameter-based level. Thus, you can visualize how a plant in X location would impact your transport routes, how Y location would change your inventory needs, and how location Z might lead to new synergies between plants.
Big Data and Industry 4.0
In the above scenario, advanced analytics made it possible to find the optimal ways to expand in order to maximize flexibility and minimize risk. This means that manufacturers implementing these workflows can better position themselves to meet increasing demand and thus grow their businesses effectively. But this is hardly the final rung on the ladder. As smart technology from IoT sensors to RFID chips continues to bring in ever larger caches of data, advanced analytics processes will only grow more robust—quickly becoming indispensable for growing businesses.
Ultimately, this trend will be one of the most important driving forces behind the rise of Industry 4.0. By combining advanced analytics with Big Data and AI, you’ll be able to gain previously unheard of insights into your supply chain, all in real time. Wondering if your job shop could increase its yield with a different arrangement? You can find out by way of smart simulations. Wondering if you can bundle your shipments more efficiently to reduce costs? Increased software integration and lightning-fast calculations mean that you can find the most efficient possible freight arrangements on the fly, adjusting on a per shipment basis as needed. In this way, you’ll be able to power the increased efficiency, transparency, and end-to-end visibility required for growing a manufacturing business in the context of the modern global supply chain.