Exploring the potential of Internet of Things
Gartner says that there will be 4.9 billion connected devices, embedded with sensors, in 2015, a huge 30% increase from 2014. Additionally analytics will help manufacturers take IoT to the next level by turning sensor data into actionable insights that will eventually allow manufacturing organizations to achieve operational excellence and avoid high costs of equipment failures.
The Importance of parts failure prediction in driving product success
Original Equipment Manufacturers (OEMs) and parts suppliers are constantly looking for ways to achieve superior product performance for greater market success through parts failure analysis. Few ways in which parts failure analysis influences product success are:
- Improve design
- Warranties Management
- Hassle-free warranty claims
Analytical models for predicting parts failure
Statistical tools and techniques are critical components of these analytical models and can be applied depending on the context of parts failure prediction. Two major categories of these are:
- Conventional analytical models
- Composite analytical models
To choose an analytical model: top three criteria
The choice of the right analytical model determines the predictive accuracy of parts failure. It is therefore critical to diagnose critical factors before zeroing in on the right analytical model to predict parts failure.
Building a smarter manufacturing businessManufacturing organizations are at the cusp of creating a data-driven business to improve results. And implementing analytics across the IoT enabled installed base is the most crucial piece of this data puzzle. As the IoT goes mainstream, analytics will be the key to building smarter, more efficient, and profitable manufacturing businesses.