A Holistic view of Product Quality


While managing quality within the four walls of your own operation is all well and good and totally necessary, both the market and your bottom line are demanding a more holistic, quality lifecycle approach, and in support of that aim there is a treasure trove of downstream data waiting to be tapped and exploited to improve product quality and customer satisfaction.

qualitycheck(1)The impact of this downstream customer quality data can be badly described by this less-than-perfect analogy about having a baby.  It goes like this:  With little or no incoming inspection of supplier materiel, the baby is conceived and spends the next nine months in production.  Typically born in a hospital, the baby will undergo a number of invasive outgoing inspection procedures before being released for shipment.  Depending on the manufacturer’s health care plan, the baby will come with an 18 to 24 month warranty, during which time period the proud parents will make regular pediatric dealer visits for routine inoculation maintenance.  The warranty tends to expire before the (now) toddler begins to operate in less forgiving environments, with many of the post-warranty malfunctions being handled by the nearest urgent care or emergency room  ( I have three children, and have been to the emergency room four times during their childhood – all four times with the same child.  It now would appear, however, that the involuntary software upgrades (i.e. learning, experience) that accompanied these hardware failures have at last had their intended effect in ameliorating the culpable risk taking behavior).

Then they grow up, go off to college, move out of the house, and live for ANOTHER seventy years; seventy more years of additional hardware (and occasionally, software) breakdowns.  From this article, “The Eleven Most Implanted Medical Devices in America”, the top five are:  lens implants to replace cataracts, ear tubes, stents, artificial knees, and metal screws, pins, plates, and rods.  My son, the other one, the one who never went to the emergency room once despite playing lacrosse through both all of high school and college, is in grad school studying biomechanical engineering – it looks like there’s a bright future for him in either joints (knees and hips) or cardio (stents, pacemakers and defibrillators) should he so choose.

The poorly illustrated point here is, of course, that with humans, as with manufactured products, there are numerous downstream quality issues that never get reported back to original hospital / manufacturing plant.  Just as you will likely never return to the hospital of your birth for any kind of treatment, but will be treated in a variety of specialized care centers across the country or the globe, your malfunctioning manufactured products are going to be repaired at a host of dealers, retailers and repair shops both in and out of your distribution network.  Formally, back at the ranch, you are typically only going to see a fraction of all customer returns, warranty claims and product problems. This gives a false indication of customer return rates and reasons. Failure rates can differ greatly between manufacturing and customer returns. It would be like a medical / public health system ignoring the last seventy years of a person’s life after they left the care of their pediatrician.

You can see this effect especially with consumer electronics as they get more mobile and are used in environments and for purposes that were never foreseen back in the design lab.  While some types of failures, attributed to things like poor shipping or packaging, might surface quickly and consistently enough for a reliable root cause analysis and fix, other problems with the user interface might only show up after years and years of cycles operating in previously untested environments.

As you begin to tackle this, one data management issue that will become readily apparent is the need for common failure symptom descriptions across all stages of data collection. You won’t be able to diagnose the problem if everyone is describing the same thing in five different ways.

Collecting, processing and acting on this downstream data will become easier as the Internet of Things evolves into the Connected Consumer with every product communicating continuously with the mothership throughout its life, but awareness now, along with making the best of the data you currently have or can get to, can have a substantial impact on your total quality program.  Making the best of what’s available would most certainly include social media, sentiment and text analytics, where you can assess what’s being said about the quality of your product behind your back.

While it might be difficult today for a single business to justify providing financial inducements to downstream players to incentivize them to report their findings back to the manufacturer, depending on how the Internet gatekeepers of the future structure themselves, we might see the evolution of syndicated warranty / repair information service providers similar to those that operate on the POS side.  And if it’s not you taking advantage of this information, perhaps it will be one of your competitors.


(My special thanks to Jeff Pink, Director of Operations at ViaSat, whose presentation at the IE Group’s Manufacturing Analytics Summit this past May provided the inspiration for this topic)


About Author

Leo Sadovy

Marketing Director

Leo Sadovy currently manages the Analytics Thought Leadership Program at SAS, enabling SAS’ thought leaders in being a catalyst for conversation and in sharing a vision and opinions that matter via excellence in storytelling that address our clients’ business issues. Previously at SAS Leo handled marketing for Analytic Business Solutions such as performance management, manufacturing and supply chain. Before joining SAS, he spent seven years as Vice-President of Finance for a North American division of Fujitsu, managing a team focused on commercial operations, alliance partnerships, and strategic planning. Prior to Fujitsu, Leo was with Digital Equipment Corporation for eight years in financial management and sales. He started his management career in laser optics fabrication for Spectra-Physics and later moved into a finance position at the General Dynamics F-16 fighter plant in Fort Worth, Texas. He has a Masters in Analytics, an MBA in Finance, a Bachelor’s in Marketing, and is a SAS Certified Data Scientist and Certified AI and Machine Learning Professional. He and his wife Ellen live in North Carolina with their engineering graduate children, and among his unique life experiences he can count a singing performance at Carnegie Hall.


  1. Markos Glykofrydes on

    I was just linked to the SAS blogs when I looked for SAS Software on Twitter. Thank you for this intelligent and entertaining statement. Your approach is very suitable especially for the automation world illustrating the complex cycles of production using the family as point of common understanding.

  2. Pingback: A Holistic View of Product Quality | EPM Channel

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