6 Industry Challenges addressed by Predictive Maintenance
14 September 2021

6 Industry Challenges addressed by Predictive Maintenance

industry challenges addressed by predictive maintenance

It is not a big surprise that the first benefits people mention when thinking of Predictive Maintenance (PdM) are directly related to maintenance. However, not everybody realises that the business case for implementation of PdM is based on a lot more and supported by not only the Maintenance Department, but many stakeholders within an organization.

Industry challenges faced by most companies

To best explain these benefits and stakeholders, let’s start with first describing the challenges most companies face.

  • Increasing Maintenance costs. The first challenge is perfectly in line with what people expect Predictive Maintenance can address: For a typical company, maintenance costs are estimated to range between 15% and 40% of their total production costs and are increasing year over year. The biggest contributors are salaries and spare parts, combined representing approximately 70% of the maintenance costs.
  • Rise of Downtime costs. However, it’s not only the direct maintenance costs that are increasing, also the costs of downtime rise rapidly. There are several sources that report on the cost of downtime per industry. One of the more conservative studies shows that the average cost of downtime across all industries is EUR 225,000 per hour (in other words: EUR 3,750 per minute!), a 60% increase from only 2 years earlier. Even though these numbers are already quite high, it is not difficult to find reports that show significantly higher costs.
  • Unexpected Resource allocation. The problem with maintenance for most companies is that it’s either too early or too late. 70% of companies are reported to not have full sight of when equipment is due for maintenance or an upgrade. An effect of this is that, on average, Maintenance personnel can only spend less than 30% of their working hours on productive tasks.
  • Not achieving Production goals. On average, factories lose between 5% – 20% of their productivity due to downtime and equipment failure is calculated to cause 42 % of the unplanned downtime.
  • Loss of Knowledge. With an increasing number of our customers, Knowledge retention within their organisation is one of the key topics we discuss. A survey among industrial companies actually found that 69% of employers struggle to fill Maintenance personnel positions and therefore risk losing valuable insight of their more experienced employees. We have had conversations with a Maintenance College that stated that the request from the industry for Maintenance graduates is 10(!) times higher than what the college could actually deliver.
  • Safety Risk. Unfortunately, Maintenance personnel fatigue regularly causes maintenance accidents and serious incidents, and it is reported that Maintenance activities cause up to 30% of all manufacturing fatalities.

How does Predictive Maintenance help with industry challenges?

Market Research company IoT Analytics provides an overview of how deploying Predictive Maintenance will help companies to address these challenges.

  • Cost reduction is achieved by a reduction in maintenance costs and unplanned downtime.
  • PdM can improve Overall Equipment Effectiveness (OEE) as it addresses asset availability, performance and quality.
  • Safety is improved by avoiding hazards, evacuations and possible regulatory fines.
  • Less failures lead to an extended asset life span.
  • Companies can better allocate their resources since predicting failures allows for optimized allocation of capital, labour and spare parts.
  • AI & PdM can, for instance, use logs, instructions and e-mails that were generated by experienced employees, ensuring their knowledge is maintained within the company.
  • Compliance is ensured by being able to better model and prepare for environmental impacts and privacy & security issues.
  • By supporting the whole production process with failure data, PdM has the ability to improve the overall data processes by leading to an “informed culture” throughout the organization.
  • After an initial PoC it is relatively easy to scale the solution to other assets of the same type.
  • Manufacturers can optimize their product development by using their maintenance data and e.g. using a digital twin, performance modeling, etc.

Industry challenges and Predictive Maintenance – summary

These challenges and examples of how PdM helps to address them clearly have benefits that are broader than just for the Maintenance Department.

Although we have listed only the 6 biggest challenges for enterprises today, we know that each company can face its specific difficulties. We will be happy to advise you on how ReliaSol’s RSIMS platform can support your business – sign up for a session with us here!

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