How Do Big Data and AI Create Value for Manufacturers?

Posted by Brian Hoey on July 21, 2020

businessman hand working with modern technology and digital layer effect as business strategy conceptAccording to Forbes, 99% of firms are investing in AI and Big Data initiatives (including the nearly 2/3rds of those surveyed who are investing at least $50 million this year), but only 14% say that they’ve deployed their AI capabilities in a widespread manner. This puts supply chain managers and manufacturers in an interesting position: You know that AI is a game-changing technology with the power to radically transform the entire value stream, but you don't have a lot of examples of firms that have actually operationalized that transformative power. As such, it’s not always clear how Big Data and AI really add value.

Of course, every operation is different, and every AI deployment is in some way unique. While one manufacturer might be using AI-powered computer vision to detect defects in parts as they make their way through the factory floor, another might be running simulations based on mountains of past logistics data to predict future disruptions. This isn’t to say that every enterprise that invests in this technology has to reinvent the wheel—but it should give you a sense of the variety of different applications that this technology can have, and the equally great variety of value-added propositions that spring from AI deployments.


AI Powers Real-time Planning

In the past—especially before digitization had become commonplace across the value chain—it was the job of the production planner and the personnel on the actual factory floor to bring a modicum of expertise to each new situation. In order to make the right decision in the moment about how to best move parts from inventory to production, or what production ratios to set based on expected order volumes, or how to avoid machine breakdowns. Not only does this present an obvious series of limitations in terms of how much data they can take in and analyze in a given time frame, it also introduces an element of chance. Two planners might be making equally smart decisions, but if they’re not on the same page there’s a good chance that disruptions will crop up anyway.

As supply chains are increasingly going digital, the amount of data that planners and others have to grapple with is greater than ever before—luckily, the tools at their disposal for dealing with that data are smarter and more effective as well. To wit, an AI-powered workflow that’s been trained on giant caches of accumulated supply chain data can both make legible and analyze datastreams even as they’re coming in from various touchpoints on the value chain. In practical fact, this means that you can suddenly introduce real-time planning into your production planning workflows. As data from IoT sensors and other sources of information on your shop floor, for instance, transmit information back to your AI-powered control tower, the algorithm processes that information in real-time in order to give you up-to-the-second information on exactly what’s going on with your production flows. As such, it can automatically alert you when something looks like it could go wrong, and it can provide instantaneous replanning options so that you can act quickly in the face of the unexpected.


Big Data Can Maximize the Value of Digitization

The speed with which humans can analyze information ceases to work as a limiting factor once you introduce advanced analytics algorithms that can create predictions and run simulations in real-time. In this way, the functioning of your production chain simply becomes smarter as a result of fewer dubious in-the-moment decisions. This is, obviously, a huge driver of added value in and of itself. But beyond that, new technologies also have the power to extract value out of digital processes in a way that has never been possible before. Depending on your level of digital maturity and Industry 4.0 readiness, you’re potentially importing data from production planning, inventory planning, and logistics planning—plus data from tracking devices up and down the value stream, information from supplier-side IT integrations, market trends and data, and even customer information. To be sure, the point of digitization is precisely to create this data and make it available—but what do you do with it once it’s collected?

This is where Big Data comes in. Enterprises that implement Big Data-based technology are poised to capitalize on the data that they collect in never-before-seen ways—whether that’s in the form of advanced predictive analytics that create smarter demand forecasts or advanced prescriptive algorithms that offer you new ways to optimize your factory floor, your logistics network, or any other element of your value stream. In this way, Big Data actualizes and operationalizes the latent value that’s lurking under the surface of most modern, digital supply chains. Simply put, this is a way to get value out of assets that would otherwise sit idle.   


The Power of Increased Forecast Accuracy

So far we’ve discussed two ways in which AI and Big Data can add value: the introduction of real-time data and planning flows into the value stream, and the analysis of those datastreams to help optimize plans, networks, forecasts, etc. What we’ve alluded to but haven’t explicitly addressed is the power of data-driven forecasts in particular. Once you’ve introduced analytics technology of the sort we’ve been discussing into your value chain, you can improve the accuracy of your forecasts virtually overnight. This, by itself, can add value to almost every planning process up and down the supply chain. How? By decreasing stockouts, overages, parts shortages, and all manner of other disruptions from end to end.

With data-driven forecasts, all of a sudden you can power smarter S&OE and S&OP processes (which rely on forecasting power to create alignment with mid-term strategic and tactical goals). By the same token, you can use it as fuel for going Lean or adopting an Agile mindset. In this way, you can even further reduce disruptions—plus, you can get more proactive when it comes to meeting your customers’ demands. As things like AI and Big Data power the beginning of “anticipatory logistics,” you could even begin to meet those demands before the customer has placed an order. Obviously, this presents you with the chance to gain a real competitive advantage over the 62% of companies who don’t currently describe themselves as data-driven.

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Topics: Advanced Analytics, Digitization