Maintenance has always been a strategic concern for Industrial Equipment Manufacturers and Manufacturing Enterprises. For Manufacturing Enterprises maintenance and asset repairs consume vast amounts of resources, eat deeply into operational costs, and present a serious impediment to efficient operations. A single hour of downtime alone can cost a large enterprise over $200,000 in productivity losses. For equipment manufacturers, this means higher field service costs, higher customer service center costs, lower customer satisfaction, and a distinct disadvantage over competition.

For years, Manufacturing Enterprises and Industrial Equipment Manufacturers have followed the reactive or time-based maintenance approach but with the advent of Industry 4.0, also known as the Industrial Internet of Things, there is a new trend i.e. Predictive Maintenance.

Now, let’s take a look at the disadvantages of reactive or time-based maintenance approach before delving into why Predictive Maintenance is the need of the hour.

Time-based Maintenance or Reactive Maintenance
In this approach, the age of the machinery defines the maintenance schedule plans because older equipment needs more frequent maintenance. There are several problems with this approach. For instance, past data has shown that 18% of equipment failures are due to age-related reasons while 82% of equipment failures have a more random pattern. Another report suggests that 30% of maintenance activities are carried out too frequently. Here are some scenarios of the problems of this approach in Repair, Field Service, and Customer Satisfaction:

1. Reactive Repairs
   • Technician gets a call from a customer whose machine is malfunctioning.
   • The technician proceeds to make repairs and the problem is resolved.
   • No further analysis is conducted on trending issue data or insight into contributing factors that could further impact the machine or other equipment.
2. Reactive, Expensive Field Service
   • Technicians need to visit the customer’s site in order to accurately diagnose and address problems.
   • However, when the technician arrives onsite they do not have the exact parts they need to carry out repairs because they were given partial or incorrect information about the issue. Attaining complete resolution of the malfunction can rake up repair costs because it will require multiple visits from the technician(s).
3. Volatile Customer Satisfaction
   • Incorrect diagnoses and slow resolution times can reduce customers’ trust in the product.
   • Moreover, low FTFR can lead to poor overall customer satisfaction.

Therefore, it is quite clear that Predictive Maintenance is the way forward for the Manufacturing industry. Let’s explore the benefits and types of Predictive Maintenance.

Predictive Maintenance
Predictive maintenance refers to a maintenance process that is based on the evaluation of real time equipment data attained from sensors to gain visibility into the current condition of the equipment. This information can be used to deliver needs-based maintenance to reduce downtimes, increase equipment uptime, and improve overall customer satisfaction. The field service agents are given work according to data from sensors that indicate an asset’s health with complete information about parts and tools required to perform repairs and only acting based on the probability of an outage.

Benefits of Predictive Maintenance

1. Proactive Prevention
   • Technicians monitor equipment based on data sent directly from machines and can address malfunctions before they happen
   • After maintenance, technicians continue to collect data for trend analysis and gain insight into a wider picture of general maintenance and service needs
2. Extended Machine Life
   • Catching issues early – or preventing them altogether – means less overall wear and tear from breakage
   • Equipment runs smoother and lasts longer which increases ROI
3. Proactive, prepared technicians
   • Technicians can identify the correct issue, part, and solution before they get onsite thus saving the customer time and money
   • Improved first-time fix rates (FTFR can be improved to around 90%
4. Proven value and increased satisfaction
   • Improved FTFR and mean time to repair (MTTR) can have an impact on improving overall customer satisfaction
   • Manufacturers’ uptime rates increase in turn, adding value to service reputation and securing contract renewals
5. Upsell opportunities
   • Equipment manufacturers can use IoT data to sell replacement parts before the unit breaks and the customer starts looking for cheaper and inferior alternatives offered by competitors.
6. Reduced Cost of Poor Quality
   • The quality of the manufacturing output will meet the required specifications since the equipment will work without any defects leading to fewer quality rejects, thus resulting in reduced Cost of Poor Quality (CoPQ).

Types of Predictive Maintenance

• Rule-based Predictive Maintenance: Also known as ‘condition monitoring’ where sensors continuously collect data about assets and send alerts according to predefined rules
• Machine Learning-based Predictive Maintenance: This relies on large sets of historical or test data, combines with Machine Learning algorithms to run different scenarios and predict what will go wrong and when

Enterprises start with Rule-based Predictive Maintenance and then switch to Machine Learning-based Predictive Maintenance.

Impact of Predictive Maintenance on Business Models
Predictive Maintenance will allow companies to start selling Industrial Equipment on a subscription basis, like software-as-a-service (SaaS). This is referred to as the servitization model where the user pays for the uptime of the equipment and the equipment manufacturer’s job involves servicing and replacing the equipment with minimal to zero disruption.

Service contracts, too, will become less about the frequency of maintenance and more about equipment uptime. Basically, the industry is moving towards a system where businesses are fixing problems before the customer notices or points out that something might be wrong.

IIoT enabled manufacturing equipment is the key to enabling Predictive Maintenance. Industrial Equipment manufacturers should make IIoT enablement part of their new product development process but when it comes to legacy non-IIoT products, which have already been installed, there is a problem. To solve this problem, equipment manufacturers can offer aftermarket IIoT enablement solution to their customers where the existing equipment can be retrofitted to enable Predictive Maintenance.

Sasken with its deep expertise in communication technologies and multiple solution accelerators across the IIoT value chain such as IIoT Sensor Kit, IIoT Gateway reference architecture, IIoT Analytics platform and a use case solution accelerator for Predictive Maintenance can help equipment manufacturers with:

• Re-engineering their current equipment to make them IIoT enabled
• Launching next-gen IIoT enabled equipment to enable Predictive Maintenance