Carnival of Quality Management Articles and Blogs – April 2019

Welcome to April 2019 edition of Carnival of Quality Management Articles and Blogs.

Our core subject of Quality Management – Road Ahead to Digital Transformation during the year 2019, we have covered:

  • The Basics of Digitization, Digitalization and Digital Transformation in January 2019;
  • The foundation of the Digital Quality February 2019.
  • Quality 4.0 in March 2019

Presently we will take up first of the nine disruptive technologies of Industry 4.0 – Big Data Analytics, wherein we will first look at Big Data and Analytics separately, then take a collective look and then connect it up with its use in the manufacturing.

Gartner defines (circa 2001) Big Data as data that contains greater variety arriving in increasing volumes and with ever-higher velocity.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.[1]

But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

This is where the Big Data Analytics comes into play.

Big Data analytics refers to the use of powerful tools and techniques to leverage data insights, trends and patterns from huge – often unstructured and disparate – data sets and make them easily and quickly accessible to business leaders, managers and other key stakeholders. These insights are used to inform and develop business strategies and plans (Bertolucci, 2013a; Zakir et al., 2015).[2]

Even in the 1950s, decades before anyone uttered the term “big data,” businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends. [3]

The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.

4 Big Data Use Cases in the Manufacturing Industry [4]are:

  1. Improving Manufacturing Processes
  2. Custom Product Design
  3. Better Quality Assurance
  4. Managing Supply Chain Risk

There are dozens of others. If you can narrowly define the problem and assemble the right data you can harness big data to address almost any manufacturing problem.

By incorporating robust analytics and visualization tools, you can build a more granular understanding of how your production line operates, and how you can streamline it further.[5]

At both strategic and tactical levels, only a small percentage of organizations’ data is actually converted to useful information in time to leverage it for better insight and decisions. Much of this gap can be explained by the fundamental disconnect in goals, objectives, priorities, and methods between IT professionals and the business users they should ideally serve. [6]

The other challenge facing leadership is the rapid evolution of the data platform (see below.)  How do you create strategies that adapt to a changing landscape?

The figure below from Data Management Association ( captures the data management foundational elements and the overarching management elements that need to be in place to pull it together.

Given the understanding of data as a strategic resource for the digital economy, the structure of the data management framework builds on the principles of performance management and the logic of management cycles.

Given the understanding of data as a strategic resource for the digital economy, the reference model specifies design areas of data management in three categories: goals, enablers, and results, which are interlinked in a continuous improvement cycle.[7]

Data Excellence Model

5 Ways Big Data will Impact Quality Management

  1. Correlating performance metrics across multiple plants
  2. Perform predictive modeling of manufacturing data
  3. Better understanding of supplier network performance
  4. Faster customer service and support
  5. Real-time alerts based on manufacturing data

LNS Research’s new paper discusses  “Big Data: Driving Quality Intelligence at the Speed of Manufacturing.” Click here to get the paper

We may sum up our discussion on the subject by noting that you get realistic and attainable results when you look more closely at the data you’re already collecting.

Fully leveraging data requires a comprehensive model

We will now turn to our regular sections:

For the present episode we have picked up article, Finding Insight in a Digital Sea of Information, by Josh Steimle, …. As we embrace the power of data-driven decision-making, we move into an age of limitless connection, that will inevitably alter the way we think about the world for all time….Today’s generation of children are born into the digital age….Tomorrow’s generation will be born into the age of big data.

We now watch ASQ TV, wherein we look at videos related to Big Data Analytics:

  • Big Data looks at Big data, data analytics, and predictive modeling, and how organizations and quality professionals can use all three.
    Additional reference: The Deal With Big Data
  • New Era of Quality: Big Data and Predictive Analytics – Nicole Radziwill, Quality Practice Leader, Intelex Technologies Inc., discusses big data and predictive analytics, and the opportunity to augment human intelligence to help people become more capable in their own jobs.

Jim L. Smith’s Jim’s Gems posting for March 2019 is:

  • Pursuit of Quality – Beware of pitfalls, disguises and misconceptions – Many organizations continue to pursue improvement using traditional approaches. Some of those approaches might be based on concepts that surfaced decades ago…Many organizations become short-sighted. They often repackage old beliefs focusing on quality improvement…Among the most challenging hindrance to quality improvement is cost reduction in pursuit of short-term profit. More recently cost reduction is known as productivity improvement… Let it be understood by one and every body that Improvement endeavors have their greatest potential when they are understood and accepted by everyone…In order to properly convey this seemingly simple rationale for improvement, managers must first understand why, and when, to communicate the rationale. This is much more than trying to achieve buy-in.
  • Imagination – Take a few minutes to stretch your imagination to see what you can discover. Perhaps share what you see with others as you must be able to visualize this future world before it can ever be created, but it’ll take change…Change, however, can be intimidating; but using your imagination can present all sorts of possibilities. William Arthur Ward, American author and educator, said “if you can imagine it, you can achieve it. If you can dream it, you can become it.” The challenge is to fine tune your imagination. The sooner you begin, the greater your possibilities.
  • Say Bye to Negativity – Successful people, however, have learned how to quickly get rid of their negative thoughts when they do surface.
      1. identify the thought that is bothering you.
      2. remind yourself that a very high percent of the time, things that we dread (fear), never materialize.
      3. interrupt the worry by a visualization technique.
      4. reject the negativity.
      5. replace the negativity. Instead of negativity, put a positive affirmation in its place and repeat it several times.

I look forward to receive your inputs / suggestions that can further enrich our discussions on the subject of Digitalization in the Quality Management

Note: The images depicted here above are through courtesy of respective websites who have the copyrights for the respective images.

[1] What is Bid Data?

[2] Big Data Analytics

[3] Big Data Analytics – What it is and why it matters?

[4] 4 Big Data Use Cases in the Manufacturing Industry

[5] The Future of Manufacturing and Big Data By Mark Samuels

[6] BI, Analytics, Reporting Center of Excellence (CoE) by Ravi Kalakota

[7] Data Excellence Model



In July 2011, I opted to retire from my active career as a practicing management professional. In the 38 years that I pursued this career, I had opportunity to work in diverse capacities, in small-to-medium-to-large engineering companies. Whether I was setting up Greenfield projects or Brownfield projects, nurturing the new start-ups or accelerating the stabilized unit to a next phase growth, I had many more occasions to take the paths uncharted. The life then was so challenging! One of the biggest casualty in that phase was my disregards towards my hobbies - Be with The Family, Enjoy Music form Films of 1940s to mid-1970s period, write on whatever I liked to read, pursue amateur photography and indulge in solving the chess problems. So I commenced my Second Innings to focus on this area of my life as the primary occupation. At the end of four years, I am now quite a regular blogger. I have been able to build a few very strong pen-relationships. I maintain contact with 38-years of my First Innings as freelance trainer and process facilitator. And yet, The woods are lovely, dark and deep. But I have promises to keep, And miles to go before I sleep, And miles to go before I sleep.

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