Manufacturing analytics is well beyond the pilot stage at this point, that much is clear. It’s no longer a want, but a need for manufacturers. To collect and contextualize data from the plant floor to make data-driven decisions, manufacturing analytics is required. There is a proven, worthwhile strategy that ensures success. That strategy includes 5 key steps. Manufacturing analytics is your next big win. Here’s why:
- Manufacturing analytics fills a gap created by other “solutions”
- Contextualized data is more important than ever
- Manufacturers fully embrace the cloud
- Manufacturing analytics is a small investment with a great return
- The start small, think big, grow fast strategy gives manufacturers the power to scale
- Use of analytics provides a huge competitive advantage
Why is Data in Manufacturing Important?
We’ve shown time and time again that manufacturing analytics actually works and will improve the efficiency of your plant floor. So, why haven’t all manufacturers jumped on board? The answer is that, before now, there were preconceived notions that manufacturing analytics wouldn’t provide many benefits. That, our friends, is simply not true. Quite the opposite, actually. For you specifically, it’s your next big win; manufacturing analytics can provide increased productivity on your floor and a strategic continuous improvement goal. There are a number of factors that have resulted in manufacturing analytics becoming a need, rather than a want.
We used to think comparing IoT or analytics solutions associated with an ERP or MES to a true manufacturing analytics solution was, all things considered, equal, but found that’s just not the case. Manufacturing analytics fills a gap created by other “solutions” promising to deliver insight into the efficiency of the factory floor.
Manufacturers are also now starting to realize that data without context or in isolation is not useful. Sure, asset condition monitoring is important; knowing temperature, vibration, or flow rate can be beneficial, but only with context. Trying to predict where a problem is or will occur is impossible without context. What line was the manufacturer running that day? What was happening in the plant that could potentially cause a problem? The data on its own doesn’t actually give the manufacturer that information. Contextualized data is key.
Data storage in the cloud is more popular than ever. 65% of global and 93% of U.S. companies use cloud storage services. Manufacturers are becoming more comfortable with connecting machines to external networks to gather data. Developing solutions that use the cloud is now easier than ever because of the lack of security concerns in storing data in the cloud.
Manufacturing analytics is a relatively small investment in comparison to the significant return manufacturers gain. We understand that an ERP system is the plumping and manufacturers have to have it, but if a change in software or upgrade is needed, a return on investment may or may not happen. Implementing manufacturing analytics and getting visibility into a process where there was otherwise no visibility not only increases efficiency but also reduces waste and the costs of people doing manual reporting. (Oh, and by the way, manufacturing analytics integrates with most ERP systems.) Considering the ROI, manufacturing analytics is worth the investment.
The start small, grow big vision has grown to define the manufacturing analytics industry. Manufacturers are now planning to roll out manufacturing analytics to a few key machines or lines, and as efficiency and productivity improve, roll out analytics to the entire floor or to multiple plants. The concept gives manufacturers the power to grow over time.
Does Manufacturing Analytics Give Manufacturers a Competitive Advantage?
Absolutely. Think about it, if you have two competitive manufacturers who make the same thing, for the same customers, and one of those manufacturers is able to eliminate all weekend overtime, produce more, increase growth, and reduce costs because of manufacturing analytics, who is the clear winner in the end? There’s no question as to which manufacturer would dominate the competition.
Why is Context More Important than Regular ‘Ole Data?
We’ve said it before and we’ll say it again – context is everything. Without context, data is meaningless. Think in terms of “The 5 Why’s”.
- If vibration on a motor is trending towards critical – Why?
- You go look, but initial glance doesn’t suggest anything abnormal. Why?
How will Contextualized Data Play More of a Role in Strategy Improvement?
As we’ve come to realize, most manufacturers want to start using manufacturing analytics to understand utilization. That’s it. It’s really simple – are the machines running or not? It’s a single data point that answers if a machine is down for a portion of a shift. Then, as manufacturers see a machine is down, the natural next step would be to ask why the machine is down? Is the reason planned downtime that was otherwise miscategorized? Or a changeover that wasn’t scheduled. Why? This is where manufacturers naturally progress to expanding the data solution to include contextualized data. The lack of context prevents manufacturers from obtaining the supporting information to go through that “5 Why’s” process to really determine the root cause of a machine going down. (While this still can be done manually, it’s nowhere near as efficient as if the data was collected and contextualized, automatically, for you.)
How Do You Get Started with Manufacturing Analytics?
The best advice we can give for someone that wanted to implement manufacturing analytics – start small and start smaller than you think you would need to. Start with one data point or a couple (maybe 3, but that depends on the machine you’re wanting to monitor). The reason for this is to focus your efforts. Instead of collecting all different data points to understand what you’re doing and why, you’re focusing on critical factors or numbers for your particular business. Focus and improve on critical efforts, first. This strategy is overall less expensive and time-consuming and easier to add additional data points throughout time. A win, win. You will ensure your success with the reiterative process, rather than trying to do it all once, because if you try to do it all, you’ll inevitably bury yourself in data with no end in sight.