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.
20 years ago, your job would have been to organize your sales and demand planning around this fact—building in flexibility and managing customer expectations to be sure that even if you wound up with a fairly low yield you could still meet demand. But in the modern era, technology is increasingly making it possible to stabilize workflows like these and even improve production processes that already appear to be optimal. By using advanced analytics on all of your operational data, for instance, you might uncover a connection between yield and a particular physical property of your raw materials that a human planner never would have found using pen and paper. In this way, you’re able to replicate the success of your high-yield production flows, meaning that you’re able to meet demand more consistently and at a higher level, leading to greater profit margins. All because of a little Big Data integration.
Fueling Advanced Analytics
The example we sketched out above is far from being an outlier. Every day, manufacturers in various industries around the world are using advanced analytics processes to refine their operations. What is it that makes these refinements possible? Big data. Manufacturing processes have always generated large amounts of data, but in the past several years it's finally become possible to utilize all of that data for something more than bookkeeping and benchmarking. Instead of keeping this information on hand for future reference, companies are now feeding it into machine learning-powered analytics flows in order to actively extract value from it in the form of predictive and prescriptive insights.
This takes the form of process improvement like we saw above, yes, but it can also mean smarter demand or pricing forecasts. In this way, you’re able not just to tailor the production of widgets to what you determine to be the ideal circumstances for maximizing throughput, but also a more accurate estimation of emerging demand levels. In this way, you’re able to source materials for your widgets further in advance if necessary, meaning that you can reduce the risk of shortages and lock in the best possible prices. Forecasting is a notoriously difficult business, but the more data you’re able to collect and leverage the more modern technology will provide insights. What can you do to maximize the amount of data you’re providing to these algorithms? Well, for starters, you can reduce information silos across your operation. More than that you can make sure that your IT integrates with that of your suppliers, to ensure that your data collection doesn’t begin and end at the factory floor. If you’re willing to take things one step further, you might install smart sensors or internet of things (IoT) devices throughout your production facility. In this way, you put yourself in a position to gather enough information to really qualify as Big Data.
Insights Towards Lean Manufacturing
Once you have all of these information streams in place—as well as the analytics processes necessary to gain real value out of them—you can begin to use these insights to improve the efficiency, consistency, or overall quality of your operations. What does this look like exactly? Let’s take our widget manufacturer above as an example. Once you’ve uncovered the hidden factors that impact your throughput, you can implement that knowledge in order to create a more stable, cost-effective production flow. This stabilizing effect, however, doesn’t need to stop at the boundaries of your production facility. You could, for instance, take a much more lean approach to inventory management. Previously, because your raw materials didn’t always translate neatly into finished products, you probably relied on a large buffer stock either of finished goods, raw materials, or both. As that variability is stripped away, you can all of a sudden reduce that buffer stock, potentially reducing your storage costs dramatically.
By the same token, this increasingly lean use of inventory could—as lean manufacturing processes tend to do—pave the way for shorter lead times and more efficient use of resources overall. Thus, all of a sudden you’re able to offer your customers more value in the form of shortened delivery estimates, and perhaps even cost savings. This has a ripple effect on your sales cycle, which in turn impacts your broader S&OP (sales and operations planning) workflows. In this way, Big Data helps to reshape entire organizations simply by finding hidden connections in the previously unexplored relationships between disparate factors.
Approaching Industry 4.0
Of course, you can’t talk about Big Data without talking about the fourth industrial revolution, also known as Industry 4.0. Why? Because the kinds of analysis under discussion here presuppose a level of digitization, data integration, and IT cohesion that already align with the idea of the modern, connected smart factory. In a sense, Big Data is one of the cornerstones of Industry 4.0, powering the kinds of cyber-physical processes for which it’s become known.
What does this mean for manufacturers right now? Simply that the more effectively you’re able to collect, store, and analyze data, the more effectively you’ll be able to drive towards the new paradigms that the fourth industrial revolution is expected to help create. In this sense, one of the most significant ways that Big Data is helping manufacturers right now is by helping them pave the way for Industry 4.0 systems. After all, it’s only a few logical leaps from the kind of data collection and insights we’re discussing to the increasingly automated, data-driven world of the smart factory.
