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Field Service Medical 2017: Roundtables Reveal Common Obstacles to Data-Driven Customer Experience Improvements

OnProcess recently chaired several roundtables at Field Service Medical 2017 called, “Prove It: Use Data to Align Customer Expectations with the Way You Do Business.” Thanks to all our great participants, we had lively and very illuminating discussions about how companies are collecting, using and benefiting from customer data, and the obstacles they’ve faced.

OnProcess Technology

Tips for Addressing the Latest Field Service Management Challenges

by Jeff Larson, Director, Account Management, OnProcess Technology Field Service Management pros are facing some pretty substantial challenges thanks to skyrocketing consumer expectations and a delivery ecosystem that is fragmented and largely opaque. If they’re not properly addressed, they could put a significant dent in your business. Here’s a quick look at the challenges we’re […]

Optimize Your Asset Recovery with these Industry-First Benchmarks & Best Practices

Posted by Dan Gettens, Chief Analytics Officer, OnProcess Technology Are you confident your company is achieving the best possible asset recovery results? Do you know whether your return rate could go a few points higher? Or how feasible it would be to slash five, 10 or even 20 percent off your days of inventory? If […]

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OnProcess Technology Awarded CIOReview “20 Most Promising Supply Chain Solution Providers 2016” Ranking

OnProcess Technology Awarded CIOReview “20 Most Promising Supply Chain Solution Providers 2016” Ranking

OnProcess Technology has been named one of CIOReview’s “20 Most Promising Supply Chain Solution Providers” for the second consecutive year. OnProcess was recognized for being the only managed services provider specializing in the post-sale supply chain and leveraging powerful predictive analysts and real-time visibility technology to improve clients’ profitability, efficiencies and customer satisfaction.

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Insights for Using IoT Data to Improve Your Post-Sale Supply Chain

Insights for Using IoT Data to Improve Your Post-Sale Supply Chain

You may not have thought of it this way, but your connected products are talking to you. Through their log files, they tell you every day how to improve your business. We call this Voice of Product, and it provides essential information about what’s happening with each product in the field, pinpointing current and potential issues with software, infrastructure capacity, configuration, hardware and more.

On its own Voice of Product has limited value. But, when analyzed alongside other critical post-sale supply chain data—including Voice of the Customer, Voice of the Process, real-time and historical operational data—VoP can have a significant impact on your ability to predict machine failures so you’re better equipped to handle, and even avert, issues; shift from reactive to proactive modes across traditionally fragmented post-sale supply chain functions; and improve overall supply chain health and outcomes.

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MIT – OnProcess Research Project: Turning IoT into Service Supply Chain Business Value

MIT – OnProcess Research Project: Turning IoT into Service Supply Chain Business Value

Through joint research with OnProcess Technology, Massachusetts Institute of Technology (MIT) recently developed a new model for spare parts forecasting and inventory planning that incorporates machine failure predictability into the equation as part of a student thesis project titled “The Impact of Installed Base and Machine Failure Prediction on Spare Parts Forecasting and Inventory Planning”.

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The Impact of Uptime: Using Machine Analytics to Optimize Supplier, Provider and Patient Outcomes

The Impact of Uptime: Using Machine Analytics to Optimize Supplier, Provider and Patient Outcomes

While many medical technology companies collect machine data, there are clear challenges that need to be overcome to be able to successfully analyse the data and take meaningful actions on predicted outcomes. Relatively low volumes (and high value) of installed products and the delicate nature of the relationship between manufacturers and their customers further slow the change process. However, early adopters are already seeing the benefits of this approach and the next 3-5 years could see a rapid evolution of how the machine data is being managed.

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