Quality in the era of Internet of Things (IoT) - Are manufacturers ready?

Couple of years back, an Aircraft manufacturer's fleet was grounded¹ by regulators around the globe. Cause? Alleged manufacturing defects and insufficient testing that led to the failure of a new type of battery. The incident highlighted that unchecked outsourcing also carries risks. Can businesses afford these risks in highly competitive times? Probably not. So what is the way forward?

With the IoT taking every industry by storm, manufacturing is no exception. M2M data - collected and analyzed - can help manufacturers assure quality of their products as well as predict and prevent failures. Data analytics can help manufacturers shape the quality curve at three points - component manufacture, assembly (using in-house factory data) and shipped product (using M2M data).

There can be any number of causes for a dysfunctional product at the point of use - be it poor shipping or installation quality, unsuitable product usage, bad weather etc. While traditionally manufacturers took a reactive approach to quality issues that happened after shipping, customers now expect them to be proactively involved in addressing these problems. This is where sensor data can prove to be a savior. However, analyzing this data and converting it to meaningful, actionable, real time insight still remains a challenge for most manufacturers.

Let's break this challenge in to two parts. First, do manufacturers have the capability to extract and separate useful signals and data from the vast quantities that are collected? And second, do they have the capability to create data structures that can meet new challenges and questions asked of the data collected?

To address these issues, manufacturers need to clearly define the following:

Is all data that's being captured relevant? Or is there a need of smart filters at the point of capture?
How do we determine the key metrics so we know what data to capture and transform to the right level of granularity?
Js our data platform reliable enough to analyze data in motion? Can it work in real time?
Is our architecture future proof? Can it assimilate newer formats as device usage changes?

It's time that manufacturers apply themselves to find answers to these questions or risk falling behind in the quality assurance race. Billions of things will be connected to the Internet by 2020 (estimates vary from 4.9bn to 75bn). By preparing to take advantage of M2M data now, manufacturers can be prepared for the eventuality when something as large as a plane or as small as a heater is connected.

What are your views? How can manufacturers capitalize on data to ensure quality and reassuring their customers? Do share as comments below.

About the Author

Nitesh Kumar Jain has more than 17 years of experience in spearheading different areas of technology led transformations. He has been leading the practice and business portfolios in the areas of analytics, BI, Trade promotions, Foreign exchange trading solutions and corporate performance management practices at Wipro and other leading organizations. He is actively engaged in the field of consulting, business analytics, performance management, data warehousing and implementing strategic analytical solutions for Retail, CPG, Manufacturing, Healthcare, Medical devices and Banking/Financial Services domains. In his career spanning 17 years, he has helped various leading retailers, Manufacturing Companies and Banks implement strategic analytics driven initiatives.

Nitesh holds a Master's in Business Administration from Pune University and has a Bachelor’s degree in Mathematics

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Quality in the era of

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Are manufacturers ready?

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