A Manufacturer’s Guide To Successful Prescriptive Maintenance

Gone are the days when plants were forced to shut down in response to surprise equipment failures and emergencies. Thoughtful companies can now use technology and data to predict, prevent or plan for potential failures in their systems. What are the positive outcomes of this? Companies can increase sales, decrease risk, and expand their market share. 

This post will explain the need-to-know information about prescriptive maintenance so you can set up your business for maximum outputs with minimal downtime and risk. Let’s dig in.

What is Prescriptive Maintenance?

To understand Prescriptive Maintenance (abbreviated as RxM), we must first understand its predecessor, Predictive Maintenance. The science of predicting maintenance needs came about in highly technical environments where equipment was under heavy use day in and day out. Predictive maintenance uses a series of sensors to collect ongoing data about how well a piece of equipment is functioning, and whether or not it is wearing out. That way, it can be replaced prior to the part failing unexpectedly. Or, a team can at least know how much work-time remains prior to it failing.

In this model, a production floor still needs to be shut down temporarily for a proactive fix, and companies must account for downtime and needed labor. Nonetheless, the proactive time spent is likely faster than dealing with an emergency, and many parts can be proactively maintained at once, in a planned shutdown. 

So what then, is prescriptive maintenance? Prescriptive maintenance pulls in an analytics component so companies are not only able to know the approximate lifespan remaining in a piece of equipment, but they can also explore possible outcomes of repairing or not repairing that part. These hypothetical results help inform strategic decision making around productivity, budget, and alternative solutions. 

Today, companies around the world rely on RxM to give insight into tasks such as:

  • real time product performance tracking
  • collecting maintenance logs
  • conducting assessments of security risks and vulnerabilities

Which tools are used for prescriptive maintenance?

Prescriptive maintenance is possible thanks to artificial intelligence and machine learning. The foundation of this intelligence uses digital twins, which are real-time interactive digital replicas of buildings. Digital twins utilize asset mapping software to go beyond just a simple architectural map. They are capable of anticipating the flow of humans moving throughout a specific space, highlighting efficiencies or lack thereof when it comes to equipment, furniture, employees and more. The digital twins employ prescriptive analytics to not only predict future machine failures, but also analyze possible solutions or alternatives to current workflows. This revolutionary approach takes efficiency to unprecedented levels. 

In layman’s terms, artificial intelligence looks at a digital twin of a plant and all its individual parts, down to the screws and hardware holding pieces together. From there, it creates algorithms and patterns that model how the machinery operates on a daily basis. Machine learning incorporates the oversight of skilled engineers helping point the algorithm in the right direction. Other times, in unsupervised machine learning, the AI creates its own algorithm based on patterns it observes. 

How is prescriptive maintenance implemented?

Smart maintenance can be streamlined and versatile if you use appropriate software. But how exactly would digital prescriptive maintenance be implemented in your plant?

The first step is creating a comprehensive digital twin of your production space. This can include everything from the equipment and machinery on your production floor to the location of your bathrooms and break rooms. It boils down to what efficiencies and workflows you are seeking to investigate and improve upon. 

To hone in on what processes you are interested in examining, we recommend working with experts in indoor mapping technology. Engineers with experience managing or designing the AI for machine learning will be most helpful in creating custom programming for your plant’s goals. 

What are the benefits of prescriptive maintenance?

benefits of prescriptive maintenance

Getting down to brass tacks, there are a number of undeniable benefits to implementing RxM. Here are just a few:

  • Effective algorithms can help predict capital expenditures needed, months before they are a reality. For the obvious reason of financial planning, this can help companies make sound fiscal decisions. 
  • Prescriptive maintenance can point out inefficiencies that may not be apparent to the naked eye. For example: the amount of staffing needed at different times of day during a production cycle. 
  • Using AI to predict when equipment will fail can help employees schedule planned maintenance. But even more importantly, RxM can let workers know the exact % of power needed for a piece of equipment to last a desired amount of time.  For example, knowing that a piece of equipment has about 100 working hours remaining and that a new part for another piece of equipment is scheduled to arrive in X number of days can inform the capacity at which to run the waning equipment so its failure aligns with an already planned shutdown to install the other equipment. 
  • When a team is dealing with a lot of moving parts, relying on a software that provides a digital picture of the RxM needed is crucial. Maptelligent has created easy-to-use web applications that create actionable items for teams to follow, in one organized platform. 

Successful plants are in constant pursuit of optimal reliability with their equipment. Maptelligent has the technology and experts to ensure plants are operating at the highest efficiency possible. 

Are you ready to reduce downtime at your production facility? Request a demo today! 

Rich Ziccardi

About Rich Ziccardi

Mr. Richard Ziccardi is a financial professional with over thirty years of experience in Banking, Insurance and Investments with a business focus on financial products. During his working career, Mr. Ziccardi held various roles including, but not limited to: LOB Controller, Product Manager, Chief of Staff, CAO, and Global Head of Revenue and RFP Pricing. Most recently, Mr. Ziccardi spent 19 years at Bank of New York Mellon in Asset Servicing.