New technologies—including the rise of the Internet of Things (IoT)—and market pressures to reduce costs are pushing companies to move from reactive, condition-based maintenance and analytics to predictive maintenance. MarketsandMarkets forecasts the global predictive maintenance market size to grow from USD $3.0 billion in 2019 to USD $10.7 billion by 2024.
Predictive maintenance is generally thought to be most applicable to the manufacturing industry. While manufacturing certainly benefits from proactive maintenance, which encompasses predictive and preventative efforts, predictive maintenance can be applied to and benefit a wide range of industry sectors.
Predictive maintenance: Five client case studies from five industries
IBM is helping companies across industries apply predictive maintenance to improve business performance. Below are five IBM client examples demonstrating how predictive maintenance in the cloud is helping businesses from five different industries excel.
Government of Jersey cleans waste management. The Government of Jersey is moving from reactive to proactive maintenance to better serve the approximately 100,000 residents of Jersey, the largest of the Channel Islands located off the coast of France. Maintenance had previously been done largely reactively and documentation sometimes took hours to find. Now, the Government of Jersey solid waste department is deploying solutions from IBM Business Partners Ennovia and Crazylog on the IBM Cloud to address these challenges. The CrazyLog Quickbrain solution provides modules for maintenance management, including preventative maintenance scheduling, inventory management and a record of reactive maintenance. The government-run waste department now has greater visibility into its equipment and can more easily access and find relevant information from its 5,000 pieces of documentation.
Read the case study for additional details.
EcoPlant helps Israeli food companies improve efficiencies. Air compression systems are used by the food and beverage sector to package food, cut and shape food products and clean machinery. However, they’re also quite expensive to run, using as much as 30 percent of plant electricity, according to the US Department of Energy (DOE). Israeli startup EcoPlant is changing the landscape by helping the food and agricultural manufacturing plants cut energy use, reduce costs and improve maintenance and visibility, all with predictive maintenance on IBM Cloud.
KONE keeps elevators running smoothly. KONE is in the business of keeping people in motion. Traditionally, elevators and escalators have been maintained on a calendar basis or when a problem occurred, but KONE recently launched its 24/7 Connected Services offering on IBM Cloud to provide predictive maintenance for its elevators. The Connected Services offering uses IBM Watson IoT and analytics to help reduce equipment downtime, minimize faults and provide more detailed information about equipment performance and usage.
Performance for Assets increases windfarm efficiencies and output. Wind energy is on the rise globally, according to data from Wind Energy International, but windfarm owners have typically had limited or no insight into the condition of their machines. To address this gap, Performance for Assets (P4A) teamed with the IBM Garage to develop an advanced monitoring system for wind turbines in the IBM Cloud. Their solution is designed to help windfarm owners gain insights that’ll help them maintain wind turbines, thereby increasing energy output and profits.
Sandvik Mining and Rock Technology improves mining output and safety. Sandvik Mining and Rock Technology is bringing advanced predictive analytics the mining industry. A common industry challenge is maintaining equipment; without properly functioning machinery, mining operations will slow drastically or cease altogether. Sandvik worked with IBM to enhance Optimine, its information and process management solution. Running on IBM Cloud, the solution uses IBM Watson IoT and IBM Maximo Asset Management to analyze vast amounts of data and predict maintenance needs. Now, mining operators can better act on insights to improve production efficiency.
Read the blog post to learn more about the solution that’s helping mining companies reduce mine production downtime by as much as 30 percent.
Learn more about predictive maintenance
See the following resources for more information about predictive maintenance services:
- Blog post: A predictive maintenance breakdown
- Solution page: Enterprise asset management and preventative maintenance
- Guide: A business guide to modern predictive analytics
- Interview: Electronic Design Editor Bill Wong talks with Greg Knowles, Program Director for the Watson IoT Portfolio Strategy, about predictive maintenance and artificial intelligence.
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