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