Syncron launched its Syncron Uptime offering that uses Machine Learning and Artificial Intelligence to analyze real-time sensor data, predict failures, prescribe optimized maintenance actions and ultimately maximize product uptime.
Servitization, which is the transformation from selling products to selling products-as-a-service, has ushered in a new generation of customers that prefers access over ownership. This increasingly popular consumption preference is driving original equipment manufacturers (OEMs) to shift from product-centric to service-centric business models.
When implementing an after-sales service strategy that is centered on maximized product uptime, OEMs commonly encounter a small minority of the overall mechanical failures that contribute to unplanned downtime follow a pattern that is correlated to time and/or usage, while the overwhelming majority are random occurrences.
As a result, present approach to preventive maintenance adds to increased total cost of ownership without effectively improving availability and uptime. The only way to improve product uptime without increasing risk, plus improving cost effectiveness, is to look for subtle symptoms and pre-cursors leading up to the point of failure.
To overcome random, symptom-based failures, OEMs have been investing heavily in sensors and IoT, collecting massive amounts of data. However, it is impossible to manually manage or analyze this data in any beneficial way. The only solution is to leverage modern machine earning and artificialiIntelligence technologies – mathematical algorithms that can find the subtle patterns to provide the earliest possible indications of anomalies and failure patterns.
Manufacturers are focused on designing products that are easier and safer to operate. However, this has led to increased complexity when it comes to servicing and maintaining newer equipment. Simultaneously, the baby boomer generation is retiring and taking critical, expert knowledge with them, leaving OEMs struggling to find trained service technicians and engineers who can effectively troubleshoot and diagnose problems based on early symptoms, which can lead to a reduction in first-time fix rates.
Manufacturers’ organizational structures are designed for product-centric approach, treating sales of products, spare parts and service as separate functions, which has led to disparate IT systems and KPIs. Servitization, however, requires a new way of thinking where accountability does not end after a product sale, instead continuing throughout a product’s entire lifecycle. OEMs will be contract-bound for pre-defined performance measures that carry incentives and penalties based on customer outcomes.
Syncron Uptime has been designed to overcome these challenges, enabling OEMs to maximize product uptime cost effectively. With this new, enterprise software solution users can combine OEMs’ investments in IoT and sensor data with machine learning and artificial intelligence to detect anomalies and predict failures, leading to optimized maintenance for every complex machine in the field and improving product uptime and maintenance costs.
It also captures knowledge and best practices for troubleshooting products with the earliest and smallest indications of deterioration in performance, increasing field service productivity and improving first-time fix rates.
The offering breaks down data and organizational siloes to enable coordination between operations, field service management and service parts planning, providing a single source of truth to all stakeholders. It also integrates with predictive maintenance work requests with field service systems and Computerized Maintenance Management System (CMMS) to make the best decisions for both the business and end customers, while maximizing product uptime and improving the overall customer experience.
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