How Big Data Is Shaking Up the Manufacturing Supply Chain

When companies leverage big data to understand their operational efficiencies, profitability and corporate responsibility improves.

February 26, 2018
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At this point, it’s no surprise to anyone that big data and connected technologies— such as machine learning, predictive analytics, and artificial intelligence—are incredibly disruptive. This is true for nearly every industry in existence, and it’s a good thing.

In July 2014, McKinsey and Company, a prominent analysis firm, published a report titled “How Big Data Can Improve Manufacturing,” and it takes a direct look at how the technology is changing the current landscape. It touches on biopharmaceuticals, chemical and discrete manufacturing, medical, and much more. Though slightly dated, the core touchpoints of the report hold true. Big data is changing the world, and the manufacturing industry is no exception.

More recently, we found out that well over six million developers worldwide are currently working on big data and analytics projects. Spending on big data in 2017 was projected to reach $57 billion by the end of the year. Finally, by the year 2020, 1.7 megabytes of data will be created every second, for each person on the planet. Those are some impressive stats, to back some pretty bold claims. You get the gist, though. Big data and advanced analytics are a big deal, in terms of adoption they are growing at alarming rates, and once again, they are disrupting many industries.

You’d be silly at this point, not to at least consider adopting a big data or advanced analytics game plan. 64% of supply chain execs feel that big data analytics is both important and disruptive and that it will build a foundation for long-term change in their organizations.

What Can This Technology Do for Your Supply Chain?

Looking at statistics, case studies, reports and lots of predictions is interesting. But it doesn’t offer insights as to how you can adopt the technologies into your own systems and processes. For that, you have to understand how it’s being used and deployed in the current landscape, and how that is going to evolve over time.

With that said, let’s take a closer look at how big data, as well as predictive and advanced analytics, is shaking up the manufacturing supply chain today.

Improve Supply Chain Efficiency

To keep the supply chain manageable, administrators and teams must maintain the proper financial efficiencies. In short, this means keeping a lid on spending through the use of predictive data and analytics. By investing in predictive maintenance, your company has the ability to improve its ROI, safety and reputation. From delivery and inventory planning to distribution and fulfillment there are always processes that can be enhanced further to both increase performance and decrease costs.

Analyzing current processes and systems and finding ways to better them, will lead to a positive outcome. But such a thing is only possible by embedding the data analytics systems into operations at a fundamental level.

For instance, you could deploy monitoring tools and sensors that work to measure efficiency and performance along the chain. If you notice a particular step or process is taking longer than it should, you have cause to study further and find out why that is. But the data collected can help with more than just that. It can help you identify trends and patterns that work towards the improvements you so desperately need.

Decrease Risk

61% of leaders consider supply chain risk management “very important,” making it a high priority for most.

The beauty of big data—particularly predictive analytics—is that you can rely on collected and stored information to build a profile of common or previously experienced problems. This, along with the right insights, can decrease the likelihood of future setbacks. Identifying supply chain risk, coming up with alternative solutions or fixes, and then maintaining an efficient process are all necessary to smooth, streamlined operations.

But more importantly, thanks to historical data, scenario planning, risk mapping, and various simulation-based tools you can deploy early warning systems to act before something occurs.

If you’re running into a wall, so to speak, with one of your existing processes due to hardware failures, system defects, or even human negligence, you can use the data collected from such events to prevent them from happening in the future. Notice a machine or system breaks down every time a particular action occurs? Find a new way to carry it out or drop it entirely from your overall process.

Improved Supply Chain Traceability and Response Times

Traceability is often synonymous with risk in the average supply chain, and for this reason it’s absolutely critical that you are able to trace where your products or goods are along the way. In fact, the Ethical Corporation found that 30% of organizations find traceability and environmental concerns the biggest issues they need to watch out for today. In food and beverage distribution, for example, knowing exactly where a product is and where it’s going can help highlight risks and future problems.

The good news is that traceability—alongside recalls—is naturally data-intensive. So, by focusing on it you open up a steady stream of information anyway. When this information is processed, organized, and converted, organizations and leaders can glean enough insights to work towards improved traceability performance. The insights will also cut down on the management and facilitation of your product database, which in turn is meant to capture products that require recalls or retrofits. Big data analytics allows for faster, earlier detection and thus more rapid responses from you.

By knowing when and where your goods are at a given time, and understanding the current status, you can better improve response times to issues, and or eliminate them completely. Imagine finding out that a transportation vehicle is not maintaining the necessary storage temperatures for a type of food or item? Smart and IoT-powered sensors can reveal the kind of information necessary to understand and use these types of insights.

Build Better Relationships

Implementing big data into supply chain operations can also benefit your relationships with partners and consumers, as well. Better customer service comes from the fact that you simply have more information—that is also more accurate—which can be deployed or leveraged along your supply route. If every stop in the supply chain has direct access to customer data, it can be used to fulfill their needs and demands. Your vendors and partners can also benefit from similar use cases.

More information and more knowledge means that you have more to share with your partners and colleagues. Not only can you help them improve the distribution, transfer, and storage of your own goods, but you can help them achieve similar results with their own systems. It’s a win-win situation for everyone. And if every party in your chain adopts this kind of technology, imagine the insights you’ll be able to trade back and forth between one another.