With my professional experience starting point as a plant-floor engineer, I’ve spent more than my fair share of time in pharmaceutical, medical device and other regulated manufacturing operations. Prior to forking my career in the direction of quality, regulatory and compliance activities, I was involved with numerous manufacturing operational & efficiency improvement projects, wrote a software application (in dBase II) to optimize and automatically schedule pharmaceutical packaging shift planning, and co-directed a cross-divisional, medical-device supply-chain process harmonization initiative. I’ve also had ownership for operational excellence training at a previous consulting firm.
In short, I’m no stranger to the production mindset, and have been directly involved in the emphasis on manufacturing operational efficiency, the focus on getting product out the door, fixed-cost financial considerations, and a laser-focus on customer fulfillment.
It is from this frame of reference that years ago – when I started really learning about how negative business and operational impacts can result directly from poor quality and compliance outcomes – that I first began to understand the effect of an “unbalanced” operational culture – or what is now commonly referred to as poor “Quality Culture.” As my career focus shifted to risk reduction and minimization of these adverse effects, I began to see common threads that existed in organizations with poor quality culture. After thinking about and reflecting on these many facility experiences, I began to see a pattern that resulted in my creation of a simple measure to quickly determine a rough approximation of a company’s quality culture – at a plant-floor level. I have referred to this measure many times with clients and in informal discussions about Quality Culture in regulated manufacturing settings. The beauty of this measure is that it is easy to understand; easy to measure; and because it just makes sense, difficult to challenge. It costs nothing but time to try it and it is also relatively easy to use the results to affect positive change with. It can also be considered both a LEADING metric and an OUTCOME metric. Further, the basis for the measure is derived in part from established principles of operational excellence; management by walking around (or GEMBA walks for those that like that term); and other plant floor manufacturing management methods.
It is what I call the Two-Data-Point Measure of Manufacturing Quality Culture.
So what is The Two-Data-Point Measure of Manufacturing Quality Culture? It is simply a measure, as established through interviews with actual plant floor personnel, of the average amount of time management, leadership and supervision spends talking about:
- PRODUCTION TOPICS, i.e. discussions about efficiency, cost reduction, units produced to plan, scrap costs, turnover time, focus on speed, etc., etc.
– – COMPARED AGAINST – –
- QUALITY TOPICS, i.e., discussions about quality policy and focus, compliance with procedures and requirements, safety focus, industrial hygiene, environmental improvements, right-first-time quality efforts, training and skills improvement, compliance performance and metrics, etc.
To perform the measurement, during discussions over the years with manufacturing staff, production personnel, quality staff, and logistics/warehousing personnel, I’ve asked them the following question:
“Tell me what a two-data-point pie-chart looks like when the FIRST data point contains [all the types of information in Production Topics above], and the SECOND data point contains [all the types of information in the Quality Topics above.]”
I ask them to give me the results as say, 50% – 50%, 60% – 40%, etc.
What is remarkable is that over multiple years, multiple plants, and within multiple companies, the data has been remarkably consistent. In NO facility was there more time spent on topics in the Quality Topics bucket, than the Production Topics bucket. In general, the charts looked similar to the following chart with the Production Topics data point ranging between just slightly greater than 50% to close to 95%. Based on these discussions it has never surprised me that our industry has a significant problem with Quality Culture.
Example of the Two-Data-Point Quality Culture Measure Chart:
I’m sure there are plenty of individuals that will challenge this measure and approach as lacking an objective data-derived basis; as un-calibrated from individual to individual; and possibly as leading depending on how the questions are phrased. To those critics I would say: those are valid concerns. And in fact, to address those concerns we have developed a much more scientific – much more sophisticated methodology – the Quality Culture Behavioral Assessment Model, that is designed to provide much more calibrated, in-depth measures of organizational culture in multiple assessment categories. For some people and organizations though, such a sophisticated assessment tool may be overkill. It is my experience that this simple two point measure can provide meaningful insight into a company’s culture quickly and easily. After you’ve tried the two-data-point measure a few times, you may find that sometimes simpler is better.
It’s important to remember that regardless of how simple or sophisticated the model, manufacturing quality culture can be measured – even if it is an approximation and “directional” only. I’d challenge readers of this article to try this measure – it doesn’t cost anything. Talk to some people in your manufacturing plant; interview your production operators; check in with your warehouse personnel; chat with your QC staff. Find out what they think. Collect this informal data and do some charting. You may be very surprised with the results!
For further information on the Two-Data-Point Quality Culture Measure or the Quality Culture Behavioral Assessment Model, or for help on how to improve your organization’s quality and compliance culture, please fill in the form below, or, contact Jack Garvey or Teresa Gorecki at firstname.lastname@example.org or at 888-734-9778.
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