As organizations recognize the significance of asset maintenance and its impact on the bottom line, companies are increasingly incorporating asset management in their strategic initiatives. The shift from a reactive culture has asset managers seeking opportunities to proactively contribute to the operational and strategic goals of the business.
Asset Maintenance and Industrial IoT
The maintenance maturity model proposed by the ARC advisory group shows how maintenance strategies can yield higher business benefits as one progresses further along the maintenance continuum. Ralph Rio says that users who moved from preventive to predictive to prescriptive maintenance have reported 50 percent savings in maintenance labor and MRO materials. Better and effective maintenance strategies help improve operational efficiency, prevent equipment downtime, and extend the life of the asset. The ripple effect of such maintenance strategies also has other intangible benefits like improved quality, customer satisfaction and safety. For instance, if the frequency of breakdown in certain machinery is high, then this could also turn out to be a safety issue and would necessitate precautionary measures.
Industrial IoT (IIoT) combined with analytics is the key to maximizing asset productivity and process efficiency. Analytics convert raw machine data into actionable intelligence enabling proactive maintenance execution and increased asset reliability. The number and variety of parameters that is monitored increases dramatically with no compromise on data quality. The IoT sensors monitor asset performance continuously and when this data is combined with today’s advanced analytics, it helps managers make better asset management decisions. You can schedule maintenance only when it is needed and optimize your resources.
But just implementing hundreds of sensors to every asset in the organization will not drive IIoT benefits. Instead, you will need to focus on the specific instances which best fit your industry and your organization. For instance, if your focus is on improving the operating health of the asset, parameters such as vibration and temperature become more critical as compared to other criteria.
Asset Maintenance Maturity Model and IIoT
Use the AMM Model to understand how best to use IIoT effectively for asset maintenance. For instance, reactive maintenance may work for non-critical assets as their preventive maintenance costs might outweigh their asset failure costs.
When asset fail randomly, tracking various trends and parameters is essential to identify root cause. You can use sensor data to set up user-defined rules to monitor and perform asset maintenance activities. Any of the set conditions can trigger work orders requests and assignments. McKinsey says that this type of Condition-based maintenance can not only reduce maintenance costs by up to 25% but also cut unplanned outages by up to 50%, thus contributing to asset longevity. Critical asset failure can have a significant business impact. Using a combination of condition-based maintenance, Advanced Pattern Recognition and machine learning can provide robust analytics which can identify performance issues and predict impending asset failures.
But to reach the top of the pyramid (Prescriptive Maintenance), your asset management strategy needs to incorporate predictive maintenance analytics and machine learning to provide diagnostics and guidance for asset repair. Also, you will need the necessary information to determine the timing and impact of failure to help assess asset priority and urgency.
How we can help
ValuD, together with Motors@Work and IBM Maximo can assist you on your journey to predictive maintenance. Combine the best of what IIoT, Analytics and Cognitive Intelligence has to offer and make informed asset management decisions. Our Maximo experts will assist in upgrading your instance to 7.6 and taking advantage of the benefits. Leverage Motors@Work’s analytics to collect and analyze condition monitoring alerts to improve reliability and asset utilization. You can also:
- Leverage Motors@Work’s patent-pending analytics and proprietary motor catalog to plan, prioritize, initiate, and execute capital replacement recommendations.
- Use IBM Maximo together with Maximo Asset Health Insights (MAHI) and Motors@Work’s pre-configured Energy and Reliability score cards to monitor motor health and to support better asset management decisions.
- Take advantage of Watson IoT’s predictive analytics solution and Motors@Work’s learning algorithms to monitor motor health, build a life-cycle model that predicts asset end-of-life, and trigger maintenance alerts.
For more information, contact us at email@example.com.