The high cost of failures and repairs and the impacts of asset downtime are issues that keep everyone awake at night. Things break. Parts wear out. Components fail. When they do, it’s a scramble to fix them; in the meantime, progress and productivity grind to a halt.
Preventive maintenance, when machines and equipment are taken off-line during maintenance windows determined by design and operations engineers, has long been the main preventative measure against expensive failures. Yet preventive maintenance wastes resources by calling for costly inspections when repairs aren’t needed; moreover, it doesn’t completely forestall breakdowns.
Descriptive maintenance, which focuses on what happened in the past based on past data aggregation and mining, and predictive maintenance, which seeks to understand the future based on statistical models and forecast techniques, both offer advantages over preventive maintenance alone. A new PAC/CXP Group study, sponsored by Wipro, finds that European manufacturers and transport operators are turning to Internet of Things (IoT) and predictive analytics to improve operations and business outcomes. When data and analytics are combined with connected assets, systems, and platforms, the outcome is a predictive maintenance capability that can significantly impact resource availability and productivity.
But this capability is still not sufficient in the digital world of things. The key to optimizing system performance lies in performing maintenance just in time; that is, predicting failures by measuring and characterizing the environment and conditions of parts, assemblies, and systems in real time and prescribing the appropriate action at precisely the moment it is needed. This is the holy grail of asset maintenance – fixing it just before it’s going to fail.
Enter prescriptive maintenance. Prescriptive maintenance leverages the approaches and capabilities of preventive, descriptive, and predictive maintenance to completely optimize system performance. With prescriptive maintenance, devices – in collaboration with operators – are proactive participants in their own maintenance. Several trends are merging to disrupt manufacturing, especially in regard to maintenance. These include the main forces of digitization (Social, Mobile, and Cloud), IoT, and Big Data analytics.
While in the pre-IoT era, vehicle or equipment maintenance was based mainly on predetermined schedules or milestones linked to the lifespan of the vehicle and equipment, the number of miles driven, or usage, it was not linked to the real-time condition of the equipment. This process sometimes resulted in unnecessary onsite equipment inspections or visits to the service center, potential issues going unnoticed between the schedules, and minimal useful insights for the manufacturers. Making informed decisions was very difficult and taking actions proactively even more so.
Real-time prescriptive maintenance was a pipe-dream, until Wipro and Pega brought together sensor data, event-streaming, in-memory databases, and real-time analytics and combined them with decisioning and workflow orchestration. Now, in one unified environment, Wipro’s IoT-led real-time asset monitoring and remote-management solutions capture and triage sensor data from a variety of manufactured products, plant floor equipment, and automobiles, combining this data with other event or reference information. Wipro’s AutoInsights platform for automotive and Wipro’s LookingGlass IoT asset monitoring and management platform, in combination with Pega’s industry leading Case Management, CRM, and Decision Hub capabilities, recommend the right actions and orchestrate the outcomes, enabling operations managers and engineers to prevent failures at precise times. With Pega, maintenance or manufacturing engineers can proactively dispatch the technician and make the fix — before the end-customer is even aware there is a problem.
Consider the following customer scenario:
Sam, the owner of an SUV, is on the road with his family for a weekend vacation. A few hours into his journey, Sam observes that braking is becoming more difficult and notices the “Check Engine” light has appeared. He worries there is an issue with his vehicle that might spoil his vacation. To continue his journey with minimal disruption, Sam needs real-time assistance and an urgent diagnosis of his vehicle’s issue from an expert service technician.
How do we achieve this? With real-time vehicle health monitoring capability through connected vehicle solution combined with an early warning Prescriptive Service Maintenance approach to detect, diagnose, and rectify such issues. Read below to learn about the joint Digital Prescriptive Maintenance Solution of Wipro and Pega and how it helps customers.
By leveraging IoT Sensor Data, Decision Models, Analytics and Decision rules, we can prescribe required maintenance before disaster strikes. We can extend these capabilities to non-automotive machines too.
A new era is emerging in asset management with the advancement of technology. With current data collection and modeling capabilities, we can compile statistics on age, failure rates, usage pattern, and conditions for any asset. Patterns and trends can guide us in developing an effective maintenance plan based on actual machine conditions rather than on some static metric. The maintenance system thus becomes not only more efficient but also strategic in nature. The future of asset maintenance is clearly prescriptive, and each step towards this goal helps companies reduce costs, ensure equipment availability and up-time, increase service reliability, and, above all, improve both the safety and satisfaction of the end user.