Predictive maintenance (PdM) is probably the business use case most responsible for drawing manufacturers to Industry 4.0 adoption.

Preventive maintenance for production line assets (common practice until the advent of Industry 4.0) is a major cost burden for manufacturers. In fact, 40% of all preventive maintenance costs are spent on assets with a negligible effect on actually preventing failures.

It’s no surprise that the ability of predictive maintenance to predict unplanned downtime events and quality deviations is proving to be a game changer.

This is due to the 8 main benefits of predictive maintenance:

  1. Reduction in lost production time – PdM allows for planned downtime which is usually much shorter than what’s needed for reactive repairs, and can be scheduled for times that are convenient and less costly.
  2. Reduced maintenance costs – Repairs are done when needed, instead of routine maintenance which in many cases is redundant.
  3. Lower labor costs – Technicians are called upon for specific and focused tasks.
  4. Reduced equipment costs – Only the problematic components are dealt with, preventing unnecessary replacements and the wear and tear of adjacent parts caused by repair.
  5. Lower chances of secondary damage – PdM identifies problems early on before they escalate and cause more extensive damage to equipment.
  6. Reduced inventory expenses – With PdM, orders can be made only for the parts and materials that are needed.
  7. Longer lasting machinery – Since disassembly is carried out less frequently, equipment lasts longer, increasing remaining useful life (RUL).
  8. Reduced risk-based costs – Fewer unplanned repairs reduce safety risks and the chance of damage being done to other parts or equipment.

 

Preventive Vs. Predictive Maintenance
Preventive Vs. Predictive Maintenance. While preventive maintenance has been common practice for decades, predictive maintenance requires less labour and is significantly better at preventing failures.

What is predictive maintenance software?

Predictive maintenance is a method of preventing machine failure by analyzing machine data to identify patterns and predict issues before they happen.

At the intersection of human-machine interaction within the smart factory, predictive maintenance software gives manufacturers monitoring and control over PdM capabilities.

For this reason, it’s critical that PdM software offer users visual, real-time interaction that’s accurate,  reliable, and can be customized.

What to know about your company’s needs

Every manufacturing facility operates differently. Clearly understanding your operation’s needs, priorities, and economic dynamics is key in implementing the predictive maintenance software that will deliver the highest ROI for your operations.

The opinions and experience of engineers, technicians and management personnel should be included when deciding upon which software to deploy.

Answer these 8 questions to get a better idea of your company’s needs:

  1. What is the monthly cost of unplanned downtime to the business?
  2. What are the common root causes of production disruptions?
  3. What are the common root causes of quality deviations?
  4. What type of problems are you looking to predict – mechanical failures (e.g. motor or bearing failures) or process-centric issues (e.g. deviations from recipes)?
  5. What is the level of in-house data analytics expertise?
  6. Who will be using the software?
  7. Is data from the production lines (OT) accessible to external systems? Is it persisted in a database (e.g. data historian)?
  8. Is data from operational systems integrated with data from business systems (IT) and process flows to enable effective and accurate data analytics?

What to look for in a predictive maintenance software solution

Here are 6 key capabilities to consider when evaluating predictive maintenance software solutions:

Industrial artificial intelligence – AI algorithms integrated into the platform that can be used to drive efficiency through use cases that are relevant to the specific manufacturing type. For example, mechanical failures and process-related issues each require specific types of machine learning.

Simple and intuitive – Predictive maintenance software should be easy-to-use for operators, technicians and management. For example, a digital twin interface can display PdM insights and supporting data in the context of the production line, allowing for quick and intuitive root cause analysis.

Human in the loop – Predictive maintenance software should be able to receive input from production line experts in parallel to data from sensors. In this way, human experience can be leveraged for more accurate predictions as the algorithm learns from expert knowledge.

Actionable and prescriptive – Insights from the software should lead to action, with information on exactly what needs to be done, and how. By pinpointing a predicted failure, technicians perform the prescribed set of corrective actions by accessing and checking off standard operating procedure tasks.

Measurable outcomes – Predictive maintenance software should be able to report on its value and the improved business outcomes using quantifiable metrics.

Compatibility – It’s common for production lines to consist of industrial assets manufactured by a variety of OEMs. PdM software should have the ability to work with different types and brands of assets, seamlessly connecting data historians and PLCs while integrating IT systems (ERP, MES, QMS, etc.)

 

A condition monitoring dashboard of the Seebo platform showing predictive alerts.
The Seebo platform. Predictive maintenance and quality alerts are provided in the context of the production process.

Always check what’s in the box

The predictive maintenance software market is diverse both with regards to the solutions themselves, and the support and accompanied services offered by software vendors.

Make sure you know exactly what the PdM software does – what’s included, and what’s not.
Many software solutions require significant customization to be performed by the vendor’s professional services. Make sure to factor in the extra costs of additional service providers or software applications.

Planning is everything

Once all of the above has been taken into account, there should be one underlying principle guiding you to the right choice – quick and easy deployment.

Consider solutions that can be deployed to deliver value in under 3 months, and that can be continually and easily adapted as the business requires.

Leading vendors will be able to offer you a customized deployment timeline. The timeline should include all the various deployment stages to help you configure the PdM system through prototyping, validation and finally, solution delivery.

For more in-depth information about predictive maintenance, download our free whitepaper.

Get in touch to see how PdM can significantly cut your maintenance costs and improve the performance of your production line.