Big Data, Not Big Easy: Overcoming New Challenges in Factory Maintenance Technology
Utilization of big data and the Industrial Internet of Things (IIoT) has the industry’s most forward-thinking companies upgrading not only their facilities, but the technology used in them to get ahead of the competition.
Like many other industries, manufacturing is undergoing a technology revolution. The “big data” buzzword has been tossed around the manufacturing realm for some time now, and IIoT is bringing new possibilities to downtime analysis and prevention. But new technology doesn’t come without new challenges, especially in the area of factory maintenance. So how can organizations overcome these challenges and turn big data into big rewards?
Big Data, Big Benefits
When it comes to industrial maintenance, big data makes predictive analytics faster and easier, allowing you to shift operations from a more diagnostic (or preventative) maintenance approach to a proactive one. This way, you can make more calculated decisions in regards to your machines, leading to improved production outputs and significant cost savings. Along with sophisticated tools like thermography, sonics/ultrasonics, vibration testing and more, your plant operators can see exactly what is going on with a particular machine and plan accordingly. While this technology can build a strong foundation for any modern manufacturer’s predictive maintenance (PdM) program, the challenge lies in pinpointing the right data to use—and how to use it.
More Information, More Challenges
Computerized maintenance management systems (CMMS) can churn out large quantities of information ready to be analyzed, but what good is all this data if you don’t know how to use it? You may face this hurdle in the early stages of implementing your predictive maintenance strategy. To overcome the burden of data-overload in a factory maintenance setting, aim to choose decision-making criteria specific to your industry or production goals up front, and only utilize data relevant to that specific criteria. Implementing systems and programs that make decisions binary, with limits set for specific actions, may also be helpful. Once those details are set, all that’s left for you to do is stick with the program and strategy.
Weighing the Costs
Even with committed employees and a full understanding of PdM benefits, the biggest challenge manufacturers face when implementing these new technologies is their initial cost. In today’s competitive global market, organizations can’t make investments based on an expectation that this technology fixes everything. The best way to work around this is to start small: take the time to identify what systems and processes are most relevant to immediate needs, and implement the necessary changes to manage them. This will help shrink down the data volume to a digestible amount, lower costs, and improve only necessary internal processes. This focus is a great way to test the predictive technology waters before diving headfirst into the investment in data.
The growing use of technology in industrial maintenance will ultimately lead to a shift in processes—and let’s face it, change can be hard to overcome. Along with the regular pains of introducing and learning brand new methods, your long-time workers who are set in their experience may not be as ready to adopt a new advanced, data-centered maintenance strategy. Luckily, most maintenance employees are loyal, willing to adjust to new methods over time… your organization just needs to devote the time to properly train both your existing workforce and your new hires. Once your team sees the benefits of big data first hand, trust in the new process will be earned and maintained.