How to Optimize Big Data in Factory Maintenance
The Big Data Revolution
In simple terms, big data can be defined as very large quantities of information that can be analyzed. This analysis, in turn, reveals patterns, trends and relationships among processes and can be invaluable as a basis for strategic decision-making moving forward.
When it comes to maintenance, big data can be highly valuable, thanks to the increasingly sophisticated technology tools available that take advantage of the Internet of Things (IoT) and generate those large amounts of data that are so ripe for analysis. Examples of these tools include oil analysis, thermography, motor current analysis, vibration testing, sonics/ultrasonics and highly sophisticated computerized maintenance management systems (CMMS) that involve failure coding and investigative guidance.
The use of these tools to generate valuable data is the foundation of any modern manufacturing organization’s predictive maintenance program, which, in turn, paves the way for that organization to reap the significant benefits that a modern approach can bring.
From Big Data to Predictive Maintenance to Big Rewards
Big data facilitates predictive analytics, which then make way for a shift from merely diagnostic maintenance work to activities that are more prognostic in nature. That shift is huge. It moves the needle that much closer to working proactively rather than reactively. That, in turn, leads to improved reliability, greater efficiencies, increased productivity and a myriad of other advantages that translate into a significantly healthier bottom line for the entire manufacturing organization.
Big data can do all of that. But, it’s not easy.
Big Data, but Not Big Easy
At first glance, who can argue with having more data to drive more informed decision-making? No one. But in real life, managing all that data can be challenging and overwhelming, especially in the initial stages of implementation. Modern psychology confirms that the more one learns about a subject, the harder it is to make a decision about it. Some ways to overcome this issue in a predictive maintenance setting include setting criteria for decision-making up front and religiously focusing on only utilizing data that is relevant to that particular decision. Also helpful is implementing systems and programs that make decisions very binary, setting limits for specific actions. Then, all that’s left to do is stick to the rules of the program.
Another challenge to getting the most out of big data is that maintenance organizations tend to be resistant to change. It’s a cultural issue and a natural reaction to a relatively new and unknown world: all of this highly sophisticated technology that’s coming from who knows where? Convincing longtime workers with ingrained habits and skills to change practices based solely on data derived from a white-collar desk dweller is tough. The good news is maintenance employees, for the most part, tend to be loyal and trust-driven in nature. They want the company to succeed just as much as the leaders at the top do. Over time, when implementing new programs and procedures related to big data and choosing new hires accordingly while upskilling existing maintenance workers to address cultural and procedural shifts, attitudes will adjust and trust will be earned. This is especially true when maintenance teams start seeing and benefiting from massive benefits firsthand.
One of the biggest hurdles to implementing these types of changes is it can be expensive. This bottom-line sticker shock is reflected in the extra resources and up-front investment required to number crunch data and start to use it to analyze how it might best apply to existing practices, all way before any inkling of cost-saving benefits come into play. In today’s competitive market, it is extremely hard for most companies to make this kind of time and financial investment when facing only the promise of a rosy future where everything’s improved. To combat this challenge, organizations should seriously think about and identify what systems and processes are most relevant to their immediate business and customers, and then implement changes that relate to managing and analyzing only that information relevant to those issues. With this focus, big data can begin to be used on a limited basis to improve internal processes, from production schedules to standard procedures, then eventually into predictive maintenance.
As the amount of data increases, companies with legacy IT systems will be likely challenged along the way. The good news is that highly attractive solutions in the form of advanced IT systems are continuously becoming available and improved upon. It’s just a matter of determining the right time to pull the trigger on implementation to start reaping the rewards.
Focus, Attitude, Patience
There’s no doubt that technology advancements have ushered in a revolution in the manufacturing industry. Big data and the predictive maintenance it drives -- and the big rewards that come from all of that -- is evidence that this revolution is only getting started and the smartest companies will get fully on board as the engine gains steam. With a focus on using the right data for the right decision, companies can combat the overwhelming nature of it all. With a nod to cultural implications of technological change and a little assistance with attitude adjustment, companies can overcome any resistance from their teams. And, with a lot of patience toward realizing solid payoff from a significant investment, companies can systematically modernize, improve and streamline their maintenance processes, thus optimizing big data and making their way to the top of the list of world-class manufacturing organizations already happily humming along today.