7 Business Reasons to Develop a Predictive Maintenance Program
Predictive Maintenance allows you to offer new services to customers, where you use realtime data and insights from your deployed base of units to distance yourself from the competition and create enhanced customer value.
Predictive Maintenance (PdM) is the ability to use historical data and analysis at realtime to optimize and predict when a failure is about to occur. It was identified in a recent McKinsey Global Institute report The Internet of Things: Mapping the Value Beyond the Hype calculated that PdM manufacturers savings would total $240 to $630 billion in 2025.
To succeed you need to get access to the data from existing sensors or by adding sensors to the installed base. More data (read: sensors) will increase your ability to succeed with a IoT based Predictive Maintenance Program.
Here we discuss 7 business reasons why you’d want to develop a Predictive Maintenance Program.
7 Business Reasons List
1. Change your Business Model – Innovate with Services
Service as a product (also known as SaaP) is a business model that is globally adopted by businesses in every industry. The era of having customers purchase a product or equipment and then moving on is over. Nowadays, to ensure the customer continues to interact with your business after the product sale installation is more important than ever. The ability to provide customers with service, meet their demands and beat their expectations opens up new doors to opportunities of generating new recurring revenue streams. At the same time, it helps you stay ahead of your competition.
2. Increase your Recurring Revenue
Since focusing on service means creating a stream of ongoing revenue, it also means that being service-centric contributes to business growth. According to the Aberdeen Report, “The Road for Manufacturers Goes Through Service, customer satisfaction is at 87% for leading service companies compared to 83% for manufacturers. Whereas leading service companies saw an 8% improvement in service revenue while only 5% of manufacturers did.
3. Improve your Product Quality and Innovate based on Insights
By collecting data from equipment in the field, you get a deeper understanding of how your products behave during real life operations and at peak times. Those insights can play a key role in your product development in the future, enhance your product quality, and facilitate future product innovation thanks to insights on how your products behave. Be data driven in your product development.
4. Predict the Unpredictable for your Clients – Reduce Downtime
Predictive maintenance enables businesses to perform maintenance at just the right time and minimize unexpected down times and expensive reactive maintenance. Unplanned downtime can result in high costs such as halting the production line, and immediate reactive maintenance is costly for your clients. Therefore, optimizing maintenance cycles which prevent unplanned interruptions for your customers can save them a significant amount of money.
5. Reduce your Own Cost of Support
Predictive maintenance helps to minimize critical and unexpected failures which help your company to be a more reliable business partner. Predictive maintenance helps schedule downtime, optimize spare part supply chain and much more. With the implementation of a Predictive Maintenance Program, you can plan your own supply chain processes better and increase the efficiency in your spare parts depot. Moreover, you can help your clients to eliminate the hidden cost of reactive maintenance such as higher inventories, premium rate for purchasing spare parts and higher stocking levels for critical spares and much more.
6. Enjoy Increased Customer Satisfaction and Loyalty
If your Predictive Maintenance Program enables less downtime, lower total cost of ownership and an overall better service and support you have created values beyond your product. This will increase customer satisfaction and loyalty, and the lifecycle of the customer relationship becomes longer. Services boost margins for businesses and increase customer satisfaction and make them more open to buying additional equipment. Increased customer satisfaction and loyalty will go straight to your own bottom line.
7. Avoid Warranty Claims – No More Finger Pointing
When you collect the operational data, you will know if your customers are operating your equipment correctly, as specified by you. Based on real data, you and your customer will know if a warranty claim has merit or not. This reduces both costs but maybe more important, it will make your customer relationship more friction-less.
These 7 business reasons explain why so many are looking at adopting Predictive Maintenance, a market that is expected to grow tenfold in the coming years according to this report by IoT Analytics.
The benefits of distributed Edge Computing solutions are
- Filtering away often up to >90% of all data which enables huge savings in bandwidth and Cloud/Data center costs
- Allow for local actions in realtime
- Collect all the data but store only the data that is relevant based on anomalies, business rules and algorithms
- Enhanced IoT Security
How to Succeed. The Starting Point – Collecting the Data
In a future blog post, we will go deep into the specific challenges of how to succeed with a Predictive Maintenance Program. However, Predictive Maintenance requires for many use-cases an architecture where Edge Computing plays a central role.
Edge Computing software is specially designed to collect vast amounts of data in realtime, analyze the data in milliseconds, aggregate, filter and take actions on the data in a distributed way (factory floor, supply chain, remote assets or products). Especially in remote or mobile locations where bandwidth is scarce or very costly.
This article was written by Göran Appelquist, currently CTO at Crosser, has 20+ years of experience of different leading positions in high-tech companies. Crosser is a start-up that designs and delivers the industry’s first pure-play real-time software solution for Fog/Edge Computing architectures. Originally the article was published here.