Manufacturing servitization using Makoro™
October 6, 2019
October 6, 2019 |
Manufacturing servitization using Makoro™
Servitization is not actually a dictionary word – it is a concept that has come about recently while innovating on business models for manufacturers to engage more proactively and meaningfully with their end customers. It has been defined as “a process of building revenue streams for manufacturers from services”.
Why is servitization important?
Traditionally manufacturers have sold products and then sold services separately to maintain those products in the field. Parts and repairs would be charged to customers. In such a model customers may end up paying for poor product quality that results in more repairs. Rolls Royce turned this model on its head when it started offering customers an outcome-based service model for its engines as early as 2010 where customers pay by the hour according to the amount of time an engine is in flight. So if an engine is down, Rolls Royce absorbs the cost. This is a great example of servitization.
Few other companies have followed suit, while several other companies are playing the concept and evaluating how this model affects their business. This is not an easy shift for manufacturers because it involves adding data capabilities to their products and taking actions in real-time based on triggers received from this data.
How does Makoro™ make servitization easy for customers?
Inside the plant, Makoro™ is already monitoring the assets that make the products. It understands the asset parameters, maintenance history, fixes and failures, and product quality. Taking Makoro™ to your end customers to deliver proactive services will be the next logical step. Makoro™ will gather data from your product’s onboard sensors, analyze this data continuously together with the product service history and data from other enterprise systems to make maintenance recommendations for the product.
For industrial equipment manufacturers, using Makoro™ to manage their equipment in the field based on the condition of these equipment represents:
- A significant reduction in service times
- Enhanced diagnostics
- Efficient planning of parts inventory
- Service workforce optimization
Ongoing services are used to make future predictive maintenance recommendations more accurate. However, the most significant value you derive is a better understanding of your end customer, which can open up a slew of new opportunities.
For Original Equipment Manufacturers (OEMs) of industrial vehicles, Makoro™ opens up the opportunity of additional revenue stream and direct customer relationship through fleet management. And if you already run a fleet, connecting to MAKORO™ will improve overall fleet planning and performance. In either case, adopting Makoro™ for your fleet will allow you to:
- Improve ETA predictions for your customers with combined predictions from Makoro™ Care and Force
- Gain real-time visibility into health and status of your fleet and monitor vehicle usage through Makoro™ Line. Recommendations will start appearing automatically when there is enough data about your fleet
- Reduce Vehicle Downtime through predictive maintenance recommendations from Makoro™ Care
- Enhance overall fleet safety with recommendations from Makoro™ Force
Makoro™ as a solution is well suited because it has the following features:
- Advanced analytics, including machine learning and deep learning, which learns from data in the field and makes field experience continuously better for your customers
- An open ecosystem for incorporating third-party tools and devices, which allows us to partner with your device providers or bring our own
- Automated and continuous delivery of insights and recommendations so no manual intervention is needed
- Comprehensive data governance so your data is secure in transit, and in our cloud or yours
- Rapid data ingestion so you can be up and running in minutes
- Support for different data types so we can understand and interpret data from a broad range of devices and enterprise systems
- Role-based access so recommendations are delivered to users who can interpret and act on them quickly