In today’s rapidly evolving industrial landscape, maintenance strategies have undergone a profound transformation. The integration of cutting-edge technologies makes it possible to develop a successful predictive strategy. However, it’s imperative to establish the appropriate foundations of strategies, tools, and data collection.
For digitally mature manufacturers, AI-driven predictive maintenance and prescriptive maintenance are both powerful strategies for enhancing operational efficiency, minimizing downtime, and cutting costs. These tech-driven approaches revolutionize the way organizations manage their assets and make critical decisions, ushering in a new era of data-driven maintenance practices.
Bringing AI to your predictive maintenance strategy can improve overall operations by reducing costs and risk. Ultimately, AI-driven maintenance empowers teams to work toward a production schedule with zero unplanned downtime.
In this white paper, we’ll explore how innovative AI and prescriptive technologies can help digitally advanced organizations further advance their maintenance strategies to save time, money, and resources.
Under an AI-driven maintenance strategy, teams only perform maintenance when it is necessary by leveraging AI predictions to minimize production loss. Ideally, teams can work towards zero downtime and optimized root cause analysis.
There is no one-size-fits-all for maintenance practices, teams, or technology. To adequately perform an AI-driven maintenance strategy, manufacturers still need a foundation of historical data in place. However, today’s advanced AI technologies do not need nearly as much data as previously required. As technology continues to evolve and adapt, databases aren’t required to be as large as before.
Choosing which assets to include in your strategy is key to any stage of PdM (predictive maintenance). Still, it is particularly vital when deciding which assets to tackle first under an AI-driven maintenance approach.
AI-driven maintenance works best on equipment that has consistent failures, whether they are predicted or anomaly failures. Identifying these technologies early in the process is an important step for building a strong AI-focused strategy.
For example, at Kimberly-Clark, cross-functional teams are focused on building predictive models to optimize their capabilities by better asset performance. Braincube’s tools enable them to make better predictions—with smaller chunks of their historical data—than ever before.
“Data diversity is often more important than quantity because diversity gives you what you are looking for in the near term,” said Vivek Krakindikar, an Engineering Technical Strategist at Kimberly-Clark.
“Being able to generate a lot of these very high-quality predictions using just a minimal sample size [from Braincube] is quite extraordinary,” confirmed Tyler Shirley, an Electrical Engineer at Kimberly-Clark.
Kimberly-Clark also leverages the Braincube API to pull data and custom models (including offline models) directly from the cloud. For lines or plants with similar equipment, this may even evolve into an asset fleet PdM strategy.
This is a key benefit of AI-driven maintenance. Service can be scheduled in groups to ensure fleets of related assets are serviced on similar schedules. With this approach, manufacturers are using data from different assets, systems, and other sources to identify collective maintenance that can reduce company-wide downtime.
AI-driven maintenance enables teams to reduce overall downtime, schedule maintenance activities at more opportune times for production, and leverage key AI/ML findings to work towards a zero unplanned downtime production schedule.
A prescriptive maintenance strategy builds upon an AI-driven strategy. This strategy leverages AI discoveries and recommendations to provide teams with information to make strategic decisions when they matter most.
Remember that Machine Learning models improve themselves over time. As a result, your organization will be fed new insights from AI/ML tools as maintenance events occur. These self-improving models can help your company go beyond simply improving maintenance scheduling: they can make it easier to drive decisions based on business value.
Rather than just always choosing to accept or react to AI-driven recommendations, teams start to use data more holistically. They can increase their situational awareness to make decisions that fit the current environment or business demands. In turn, they can decide what’s best to do based on current production conditions.
For example, there are some cases when achieving zero downtime is not the leading objective: there may be more pressing priorities given specific circumstances at your facility.
Let’s say that your team is receiving proactive alerts that a machine part is getting close to failure. The situation isn’t yet critical, but it will need to be repaired soon. Still, the early notice gave the team time to check out the situation and order a replacement part.
At the same time, your production team is working hard to meet a customer quota before the end of the week. The part required for repairing the machine won’t be in until next week: after the customer deadline has passed.
Predictive tools—like those used in an AI-driven maintenance strategy—can give the team valuable information on how to proceed. They can now decide to keep running as normal and risk a breakdown, or take the equipment down and risk missing the deadline and potentially upsetting your customer. Long-term damage to your customer relationship can be as detrimental as a poorly timed downtime, so it’s not an easy choice.
Of course, these choices are binary, with only a “do” or “do not” outcome. Prescriptive analytics goes beyond these kinds of polarizing choices and offers a wider range of options, giving teams more flexibility in how to proceed.
In this same scenario, a prescriptive model may have also provided you with a solution to reduce the speed of your equipment. Identifying speed reduction is a viable option to both hit your customer’s delivery timeframe and keep the asset running is an optimal business value strategy.
In other words, it’s not just about predicting maintenance needs ahead of unplanned downtime events. Prescriptive analytics provides you with knowledgeable insights so you can make data-based decisions by considering current performance and business needs.
Like many of the other stages that build towards predictive maintenance, technology is a key enabler in executing prescriptive maintenance strategies. Tools like Braincube’s Machines’ Performance Tracker (MPT) App can be instrumental in time to value. The MPT App was designed to help shop floor teams continuously monitor groups of machines running on the same production step. With automated monitoring and alerting, operators can focus efforts on the equipment that matters most at any given time. They can use data across a fleet of machines to drive better predictions and value-oriented optimizations.
Braincube’s Machines’ Performance Tracker app monitors groups of machines and sends real-time alerts.
With the right data, prescriptive maintenance makes it possible to simulate business impacts. Teams can better understand the financial payoff of continuously fixing assets versus replacing them. They can also better understand the key levers that make it possible to optimize business performance. It can reduce the doubt that accompanies intuition or previous habits by using data-driven, factual decisions.
As digitally mature manufacturers look to advance their maintenance strategy, utilizing AI and prescriptive technologies can help take the guesswork out of the next steps. These robust tools put valuable data in the hands of your teams so they can make decisions that move your entire organization forward.
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