The Impact of Uptime: Using Machine Analytics to Optimize Supplier, Provider and Patient Outcomes
By Bhaskar Banerjee, Chief Solutions Officer, OnProcess Technology
I recently hosted a roundtable at the Field Service Medical conference in San Diego on February 17th 2016. The topic was on the impact of machine analytics on supplier, provider and patient outcomes. It was well attended by senior executives of major global medical devices companies that are manufacturers of diagnostics systems and medication management systems.
The roundtable was focused on how machine data can be utilized to drive outcomes in service supply chain – reducing remote and field support costs and inventory and logistics costs while improving uptime and customer satisfaction. The discussion started with the current state of the participants and how they’re currently using machine data and their plans for using it in the future to improve uptime. Here’s a summary of what we heard:
- Close to 100% of new medical devices generate machine data – mostly as log files.
- Most of the participants have already made significant progress in collecting the machine data – some do it manually on an as needed basis and some have completed the “plumbing” necessary to continuously get machine data from their devices.
- However, after the data is collected, very few companies utilize the data for service management in a comprehensive manner.
- There was unanimous agreement that analyzing machine data and acting on it promptly will help improve service delivery, service accuracy, first time fix and will reduce cost-to-serve.
Machine analytics can efficiently diagnose a device issue, and when backed by a diagnostics knowledge base, make the process of remote support efficient. Navigating through machine logs, identifying issues and taking the right action is time consuming and difficult. Service engineers in the medical industry are very experienced and expensive. It takes years to train a new person into the service engineer role. Tribal knowledge from experience enables medical devices engineers to be effective at their jobs. However, when machine analytics is combined with a robust knowledge base, it can enable junior engineers to be as productive as experienced engineers. Performing a machine data based diagnosis prior to visit will enable field engineers to reduce time onsite – an increase in efficiency and improvement in customer experience. Self-help by customers can be made more effective if machine data is made available to the self-diagnosis process.
The roundtable ended with an agreement that there is tremendous value to be unlocked in service management by utilizing machine analytics and it will emerge as a key differentiator in service management over the coming years.
What is your strategy for utilizing machine analytics to improve your service performance and how do you plan to execute them? Let us know in the comments below.