A seven-step journey to prescriptive maintenance

There are several steps engineers can take to move from reactive to prescriptive plant maintenance.

By Marcel Koks October 20, 2022
Courtesy: Cincinnati Incorporated/Steve Rourke, CFE Media and Technology

Prescriptive Maintenance Insights

  • To enable prescriptive maintenance strategies, technology can be brought it to help automate basic tasks, such as scheduling routine inspections, so personnel have more time to focused on advanced tasks.
  • Using algorithms and data science can help identify patterns in data points and project next likely outcomes, such as costs and demand. This can help personnel better plan for the future and make this process more efficient.

Plant engineering and plant maintenance teams face many pressures today. Some are operational and involve keeping assets running. Others have more to do with cashflow strategies and decisions about whether to repair versus replace or upgrade. A new mindset helps companies change the focus from reactive to prescriptive. Technology also helps managers make well-informed decisions. With advanced solutions in place, managers can take a holistic approach to plant maintenance and a long-term view of managing assets.

1. Reliability

Reliable plant operations can become a differentiator. Customers will notice that orders are always on-time, as ordered, and with unwavering product quality. These are unusual characteristics in some industries.

2. Streamlining routine

Technology helps streamline and automate basic tasks, such as scheduling routine inspections and maintenance, tracking parts and materials used so inventory is accurate, and monitoring use of consumables (ink) and replaceable (filters), and parts subject to wear (belts and brake pads). When the basics are covered easily, personnel have time to up to focus on more advanced questions such as diving into analytics.

3. Planning cashflow

Using risk assessments and condition assessments, managers will be able to project future needs and calculate related costs, including replacement parts or any outside special services or contractors that may be needed. With data easily accessible, managers can evaluate replace vs. repair decisions and factor in the cost of down-time.

4. Predicting the future

Today, innovative Business Intelligence (BI) solutions with Artificial Intelligence (AI) contain powerful predictive capabilities, using algorithms and data science to identify patterns in data points and project next likely outcomes. Users can explore “what if” scenarios and obtain forecasts of likely costs and likely demands.

5. Prioritizing investments

This glimpse of future investment needs can be juxtaposed against projected cash cycles also taking into account forecasts for shifting demand. Managers can then prioritize major capital investments when funding and political backing is in place. Plans for stop-gap, bare-minimum fixes may be needed when funds are limited.

6. Providing early warnings

Managers will be able to use predictive analytics to identify early some potential critical issues so that adequate preparations can be made, including having necessary parts or back-up equipment on standby. For example, when a generator nears end-of-life expectancy, back up replacements should be on hand for a seamless switch-over.

7. Meeting compliance

Managers should be alert to such issues as ADA accessibility, building code compliance, OSHA or EPA mandates, or workforce or public safety issues. Non-compliance can be costly. It can also jeopardize safety or hurt brand equity.

Original content can be found at Control Engineering Europe.

Author Bio: Marcel Koks is director of industry and solution strategy at Infor.