CASE STUDY

Improving Manufacturing Process Efficiency with Sensor Data Analytics – A Quantzig Success Story

Aug 21, 2019

The client is a UK based industrial and manufacturing industry player specializing in the manufacturing of auto parts. They wanted to leverage smart sensor technology and sensor data analytics to reduce maintenance costs and delay-causing failures.

The Business Challenge

Owing to the popularity of embedded sensor technology and the availability of embedded sensor devices, manufacturers have access to huge volumes of sensor data which can help them drive significant improvements in their manufacturing processes. With sensors generating large volumes of unstructured data sets, businesses are increasingly being challenged with computational issues that have curtailed their ability to improve business outcomes.

The manufacturer faced a similar challenge that gave rise to several inconsistencies in their manufacturing processes. Having adopted an automated solution based on MEMS technology to replace the manual processes they faced major challenges in managing and maintaining the data. This challenge was further compounded due to their inability to analyze and extract insights from pre-processed sensor data. This is when they approached Quantzig to leverage its sensor data analytics capability and use machine learning algorithms to detect critical wear and tear issues and reasons for machine failures.

The manufacturer faced several challenges as they tried to correlate sensor data from disparate sources, due to-

  • The lack of analytical models to derive actionable insights from data
  • Inability to correlate data from different embedded sensors
  • Lack of analytics tools and expertise to mine unstructured data

Quantzig’s sensor data analytics solutions empowered the manufacturer to address these challenges to generate actionable insights and reduce machine downtime.

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Solution Offered and Value Delivered

To help the client tackle their challenges we put together a team of highly experienced sensor data analytics experts who devised a robust sensor data analytics framework to help the client pre-process, store, and analyze machine-generated sensor data.

Through real-time monitoring and sensor data analytics solutions, the auto parts manufacturer was well-positioned to monitor and track all critical points in real-time. Moreover, the shift towards predictive maintenance radically enhanced their ability to analyze critical components through needs-based maintenance processes.

Quantzig’s sensor data analytics solutions also enable the client to:

  • Correlate sensor data obtained from multiple sensors
  • Improve decision-making with cross-functional insights
  • Minimize downtime and maximize performance
  • Reduce manufacturing costs by 18%

Benefits of Sensor Data Analytics

With the proliferation of ubiquitous, embedded sensors and mobile devices, the scalability challenges of sensor data have reached extraordinary proportions. Most of the devices used today are interconnected and IoT enabled. This has enabled greater possibilities for different kinds of distributed data sharing and analytics. As such, it is predictable that in the coming years, machine-generated data will dominate human-generated data by orders of magnitude, and this gap is only likely to increase with time. However, with sensor data analytics businesses will be well equipped to tackle the challenges arising due to data proliferation.

About Quantzig

Our sensor data analytics solutions integrate technology and smart analytics models to helps businesses looking to transform themselves into analytics-driven establishments. Having helped leading players from across industries our analytics experts can help you leverage cognitive technologies to collect, analyze and visualize real-time and historical machine data from disparate sources such as- operational technology, connected devices, and products. This, in turn, can help manufacturers to improve operations, perform predictive maintenance, and better manage the uptime and availability of industrial assets.

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