Big asset, Big pressure, Big data.
From design to decommissioning, industrial asset managers are challenged to make critical decisions across asset lifecycles to balance safety, regulation, production and cost pressure. Traditionally, companies adopted a combination of two basic approaches to asset maintenance;
- Reactive maintenance – Repair unexpected issues as they arise
- Preventative maintenance – Replace the asset based on empirical time of operation
However, as assets have become more instrumented and connected with the “Internet of things”, there has been an exponential growth of asset data, and a new opportunity for companies to revolutionise their maintenance plans by adding a new Digital lever using data consolidation platforms and advanced analytics.
This is what we call Predictive Maintenance.
By using the data generated throughout the lifecycle of the asset, it is now possible to better plan the returns or to have early identification of performance deviation. It can help to predict consumption, production, maintenance operation, safety risk and as consequence provides critical information other part of the business such as Sales, Supply Chain and Engineering.
Reducing cost by better anticipation
The reduction of whole life cost through the asset lifecycle is the big benefit.
It increases availability through a better knowledge of the asset condition, early warning coupled with timely intervention. Ultimately it means increased uptime of assets reducing power outages, increasing safety, service and asset performance.
It recommends when to maintain through precise condition monitoring and identification of probable issues to allow either delay maintenance or bring it forward to extend the life of the asset. Data analytics can identify when the asset is being operated outside its design limits by using model simulation.
With Predictive Maintenance, asset managers move from reaction to anticipation.
How to implement Predictive maintenance
First, we need an instrumented and networked asset portfolio equipped with sensors that provide data about the condition of the asset (e.g. heat, vibration, stress, strain) sub-systems. Insight is generated from this structured and unstructured data through complex algorithms into digestible information consolidated in one analytics application (often cloud based) where engineers and decision makers can assess this insight remotely. The software uses patterns to compare current and projected asset conditions to normal operating conditions to identify anomalies. This combines with Product Lifecycle Management (PLM) and Maintenance Management system (GMAO) allowing the maintenance team to decide “when”, “where”, and “how” to deliver optimal maintenance. It gives more time for planning interventions, selecting the right maintenance provider to minimise cost and maximise effectiveness.
Opportunities for profitable growth services
The charge towards heavily networked assets will drive new Digital operating models in Utilities, Automotive, Aerospace and Transport industries as companies increasingly exploit their own data as a key strategic asset.
This in turn requires a new technical focus but also adequate operating models such as operations centres to coordinate and to handle the execution of predictive maintenance across multiple dispersed assets fleets. This impacts the whole value chain from manufacturers, operators and service providers. The one who will master and own the data, will be able deliver better and new types of services.
To succeed and find the ‘needle in the haystack’ of big data in the asset intensive industries, the challenge is not only technical and operational but it starts by finding the right practical approach for creating value for the enterprise. Such Digital transformation must help CXOs to define a vision from the defining the value from better exploiting asset, developing asset operation strategy to the delivery of solutions to the front line and the tracking of revenue growth and earnings impact.
Capgemini consulting is helping asset intensive industries to implement Predictive Maintenance and to turn new technologies into profits and growth by levering opportunities offered by Big data and new Digital operating models.
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