Enhance your production with Predictive Asset Optimization

IT Specialist, Industrial Sector, Electronics Industry, IBM

The Internet of Things (IoT) presents a whole new level of opportunity for any company involved with production or operational processes. By applying analytics to the data that can be gathered from sensors, these processes can become more efficient. Further, applying predictive analytics to that same data in near-real time enables businesses to be proactive and prevent costly operational delays. Predictive Asset Optimization (PAO) solutions can help optimize the utilization of enterprise assets, as well as help enterprises use IoT data to improve the experience of consumers purchasing smart devices and appliances.

By performing predictive analytics on the data from various sensors, robotics and devices used in the production or operational process, organizations can predict when a problem is likely to occur and perform preventative maintenance before an actual problem arises. These same predictive analytics can be applied to the data coming from connected devices in the consumer environment and can help enhance the relationship between the manufacturer and the consumer. white goods manufacturers (enterprises that make washers, refrigerators and so on) usually don’t have any relationship with the end consumer except warranty services. By monitoring smart devices and analyzing consumer usage patterns, the manufacturer can provide advice or offer additional services. These same patterns can also be used in the continuous engineering process to improve the next round of consumer products.      

IBM has several PAO solutions that focus on ways companies can maximize their production or operational enterprise assets. Many of these solutions are delivered through the IBM Internet of Things Foundation. Plus, these services are available in IBM Bluemix, which is a rapid development platform with an ever-growing list of capabilities such as predictive analytics, cognitive analytics and real-time insights.