One percent sounds doesn’t sound like much, but that’s all it took for a British cyclist to win a gold medal. During training, he used the Kaizen 1 percent performance improvement method to make “marginal gains” throughout the lead-up to the Olympics.
Even if you aren’t training for the Olympics, you can apply this method to what you’re doing. In fact, more businesses are seeing the benefits of this performance improvement program. Add some artificial intelligence to the mix and the method is finding new ways to be used.
As Sundeep Sanghavi, co-founder of DataRPM, a leader in cognitive predictive maintenance, noted in a recent article, “According to GE, a 1 percent increase in efficiency can help companies save billions of dollars over the next 15 years, while another study estimates that output increases by [up to] 25 percent when predictive maintenance is used. Manufacturers don’t have to blindly trust that their equipment will continue functioning and performing at the same level when they can see exactly how it’s been performing today, last week, last year, and throughout its lifespan.”
Today’s Manufacturing Challenges
Numerous challenges impact the ability of manufacturing companies of all sizes to realize their performance metrics. These challenges include skills gaps among manufacturing workers, a shortage of talent, and uncertainty about what type of technology to adopt and where to make investments to accelerate efficiency gains. Other challenges include inventory discrepancies, quality issues, and significant hits to profit margins.
PwC noted some other challenges that illustrate how the global business environment is further impacting the strategic decisions that manufacturing companies have to make. The research firm noted that “output is expected to increase just 3.1 percent in 2016 and 3.4 percent in 2017, according to the International Monetary Fund” due to concerns over Brexit and other political uncertainties. These factors inhibit a company’s growth, which is why there’s a need to look within a manufacturing organization to determine whether there are other ways that profitability can be increased. That brings it all back to performance metrics that can drive improvement and overcome these external impacts to profitability.
Impact of Cognitive Predictive Maintenance on Manufacturing
Enter cognitive predictive maintenance as a potential solution. Some manufacturers focus on predictive maintenance, such as vibration analysis, which can identify potential problems that could eventually result in equipment failure. However, artificial intelligence is advancing the predictive component at a much faster rate.
This rapid problem identification is tied to the growth of the Internet of Things (IoT) within the industrial environment. IoT devices connect equipment, sensors, and software to continually collect and compile data on how a piece of equipment is working. It could be a factory machine on an assembly line or a large piece of mobile equipment that’s working in the field. The data collected while the machine or piece of equipment is in use is analyzed by a machine learning mechanism in real time. It can understand what optimum performance looks like in that piece of equipment so that it can alert a technician as soon as that productivity rate decreases.
That drop in productivity could seem insignificant to the technician. However, what the technician doesn’t realize is that an incremental drop in equipment productivity accumulates over time to adversely impact performance. By using artificial intelligence to detect the moment it changes, the manufacturing company can avoid ever having to experience the financial effects of those 1 percent decreases in performance. Instead, it can achieve the opposite with overall increases in performance.
Overall Benefits of Cognitive Predictive Maintenance
Those 1 percent performance gains can be realized in the form of reduced equipment costs. The equipment no longer needs major repairs, which can become very expensive. The ability to schedule minor repairs or replacements in advance also lowers labor costs and significantly lowers downtime. That leads to a much higher percentage gain in performance improvement beyond the 1 percent.
Another benefit of cognitive predictive maintenance is safer conditions for employees and customers when this factory and mobile equipment is in use. Additionally, inventory discrepancies can be tracked through machine learning to deliver demand forecasting capabilities. That process improvement helps a manufacturer plan more accurately for output needs.
Employees use their time more efficiently as the IoT sensors take over some of their work. Having technology do some of the detailed work can address the current skills gap. Revenues can be ramped up by making these minor changes to the maintenance process for all types of manufacturing companies.
One percent may not seem like much at first, but it can make a world of difference to all types of businesses just like it has done for manufacturing and a British cyclist.
The opinions expressed here by Inc.com columnists are their own, not those of Inc.com.