The term “Industry 4.0” isn’t new to manufacturers. What is new, for many of these businesses, is the recognition that the next wave of the Industrial Revolution is already breaking. There is no more time for “Let’s wait and see what this means for our business.” No manufacturer can afford to sit on the sidelines and watch as their industry is transformed by major innovations in digital technology — from cloud computing to big data analytics to advanced robotics to the Internet of Things (IoT). They must be in the game. And to be in it, they must transform their operations digitally.
Embracing big data analytics is an important step on the path to smart manufacturing. It has the potential to affect every step of the manufacturing process. Ultimately, advances in big data analytics are expected to augment the interconnectivity of equipment on the factory floor as part of a larger movement toward the IoT and greater manufacturing intelligence.
That’s a pretty big deal. Yet manufacturers, generally, have been slow to adopt big data analytics, especially in manufacturing operations. This is not necessarily due to lack of interest, or worry about costs, privacy, security or even change itself. The real hindrance is a combination of several significant roadblocks that many manufacturers must overcome before they can implement and execute big data analytics successfully.
These common barriers include:
- Unwieldy data and processes — Manufacturers facing this problem can take comfort in knowing it’s an issue that plagues most any company pursuing digital transformation. Certainly, there is no shortage of data being produced by the business. The challenge is figuring out how exactly to bring together that ever-ballooning volume of raw data from different systems and sources so it can be analyzed and turned into actionable insights for the business.
- Disparate systems — This barrier relates to the one above, obviously. Integrating data is complicated by inaccessibility. It is often the case that a business’s legacy technologies have not been designed to facilitate open access to data. The complexity of a typical IT ecosystem makes it very difficult to mine quality data and convert it into a workable format for analysis.
- Expertise shortage — Finding specialized talent to work with big data — especially professionals with knowledge of the manufacturer’s business and industry — can be a tremendous hurdle. Manufacturers are finding that talent is in very short supply, and extremely competitive to recruit and retain. Over time, as the industry becomes more digitized, manufacturers are likely to face talent shortages in even more areas of their business.
Again, these are just some of the roadblocks manufacturers face. They are not trivial, and companies will find that some are quite persistent. But a manufacturer that wants to be a relevant player in Industry 4.0 must address them sooner than later.
Make sure big data projects have a purpose
As manufacturers work to overcome big data analytics obstacles they must not forget an important aspect of their effort: keeping their business strategy in focus. I will come back to this subject and offer a few tips for success in this area in a future post, but the one I want to mention here is extremely important: Identify a specific use case.
Manufacturers should not just “do” big data analytics because they are under pressure to evolve their operations. Any big data initiative should have a clear purpose. Lack of purpose is often the root cause of a company’s struggles to harness its data effectively and turn it into meaningful insights.
Some may consider it an upside that the manufacturing industry has not moved as quickly as other industries to jump on the big data bandwagon. And it is true that manufacturers that have so far taken a “wait and see” approach with big data analytics and similar digital innovations have the benefit of learning from the missteps of early adopters, and can develop a strategy for success based on lessons learned. But they must make their move now, or they risk falling too far behind the digital curve and becoming obsolete in Industry 4.0.
