7 Questions from Wednesday's Quality Management Webinar

On November 18, LNS Research hosted a webinar entitled "3 Ways to Instantly Maximize the Value Your Quality Management." The presentation focused the methods and technologies today's forward looking organizations are employing to break down informational silos and better integrate quality data across the value chain, from design through to service.

This includes architecting closed-loop quality processes with Enterprise Quality Management Software (EQMS) as well as emerging trends such as the Industrial Internet of Things (IIoT) and Big Data Analytics.

During the presentation, there were several questions that were asked that we were unable to address within our one-hour event timeframe, which I'll touch upon below.

The full on-demand recording of the webinar can be viewed for free here.

Q&A

Q.) How are you seeing trends like mobile and Big Data Analytics impact quality?

A.) Mobile and Big Data Analytics are changing almost every aspect of quality management and customer engagement. They're changing how customers interact with products, how customers provide feedback, how companies analyze structured and unstructured data, and how companies can make better decisions through the use of leading (predictive) vs. lagging (descriptive) indicators.

Q.) How have you seen manufacturers embrace technology to solve problems today?

A.) Many manufacturers are slow to adopt new technology. There are, however, early adopters in the marketplace; we are seeing just over one third of manufacturers either currently using or planning a pilot project of IIoT technology.

Q.) You mentioned the four buckets of the IIoT platform; which of these do you see as the most critical to advance now to accelerate IIoT viability and adoption?

A.) It is tough to seperate out the different buckets but one of the clear starting points is connectivity, without connectivity as a foundation not much else is possible. With connectivity, many companies move to Cloud and Big Data Analytics; clearly these two can't really be discussed independently. Finally, application development is where the big value add comes in, enabling users and customers with new light-weight applications that combine workflow and analytics.

Q.) How would you define a successful New Product Introduction (NPI)? What are the key criteria involved?

A.) There are many different definitions of a successful New Product Introduction. For survey and benchmarkging purposes LNS Measures the percentage of new products that hit time, volume, and quality goals. Some companies may also add in costing as a metric as well.

Q.) ISA-95 is addressing the new opportunities and challenges of IIoT. What do you think will be biggest challenges that the committee faces?

A.) The biggest challenge the committee faces will likely be creating a model that incorporates a value chain view and is not manufacturing system centric.

Q.) One of your slides referenced a chasm that needs to be crossed before IIoT adoption really trends toward mainstream adoption. Is/are there specific capabilites that you think need to be developed and proven to suddenly push past this tipping point or do you think it's more just a matter of time and education?

A.) Two things need to occur. First these initial pilot project need to prove successful with demonstrable and compelling use cases. Second, the mainstream market needs to be educated about these success and feel comfortable enough with the maturity of the technology to make follow-on investments.

Q.) Is there a particular industry or area where you're seeing in-field product quality data being used for design purposes to really advance quality? This isn't the first I've heard of it but I'm curious to what industries are really using this effectively.

A.) Follow the smart connected assets and the industries they are in. FANUC has a great case study of robots for the automotive industry where they are collecting real time operating data to identify root cause of component failure. DELL has a similar story as well.