Learn about a leading life sciences company's risk-based approach to supplier quality management.
Last September, LNS hosted the Quality Executive Roundtable. During a break at the event, I had a discussion with a quality executive who put today’s disparity between the Supplier Quality Management (SQM) rich and poor in stark contrast. This quality executive relayed how his company digitally transformed SQM. The company expanded upon a robust SQM platform by including it in a Big Data initiative that connected IT systems, Operational Technology (OT) systems, and supplier systems to Machine Learning.
This delivers new and unique insights (e.g. predicting supplier performance issues) as well as unprecedented clarity to classic supplier quality questions (e.g. who are my best and worst suppliers?). When fed into the company’s established SQM system, the combination substantially reduced supplier defects, which in turn improves First Pass Yield, Cost of Quality, customer satisfaction, etc.
Of course, this at a time when many are still relying on people-power to run their SQM engine, using a mix of spreadsheets, documents, email, determination, and dedication. The competitive differentiation is stark, and the executive mentioned above was using it to drive more business with his customers; possibly at a premium.
The Rich Get Richer
You’ve heard a lot from LNS about maturity in the past, and clearly, the concept of maturity is important in SQM. Recently LNS’s quality management maturity model was adjusted so that it would extend beyond quality to Operational Excellence, and the new version is shown below.
Also, LNS characterized the quality maturity of industry, and operational and financial impact of maturity. You’ll find this latest maturity research in the eBook “Supplier Quality Management’s Rightful Role in Your Enterprise: Playbook to Realize the Total Value of Quality.”
The maturity research analyzes the adoption of 46 quality management best practices across the levels of maturity. Of these 46, 16 relate to SQM, which are plotted in the box and whisker chart above. A box and whisker chart is useful in this case because it shows the range of adoption as well as the median adoption for each maturity level.
While the details of the practices and their adoption are in the eBook and are too detailed for this post, the adoption of these SQM Best Practices by Level makes several things clear. Think of SQM best practice adoption as a measure of a company’s SQM capability. The data clearly shows that much of the market has poor SQM capability and that the median performer becomes more capable from level to level. In other words, the rich get richer.
It is also clear that those at L4 and L5 have a substantial advantage in capability versus those that are L1 through L3. In fact, the median practice adoption across all levels is 3.7, which shows that L1 to L3 are lagging median SQM performance.
SQM Maturity is a Journey – Start Yours
SQM maturity is a journey. Consider the example given above – it needed both the traditional SQM system as well as the big data/machine learning system. Digital Transformation would not be nearly as effective if Supplier Corrective Action Report (SCAR) processes or Non-Conformances (NCs) were being issued “by hand” in documents. Machine Learning algorithms are notorious for generating overwhelming volumes of events, and tracking these potential issues without a business system to manage this continual feed of data and associate them with SCARs and NCs is an impossible task. Don’t forget that many early machine learning systems were switched off because humans couldn’t deal with the huge and continuous volumes of data.
The next few years will be interesting. We are beginning to see an increasing number of IIoT pilots in the quality space (remember this?) and some true success stories. While it is still early, it is important for quality leaders to understand advances in technology that can truly transform markets.
In SQM, the rich get richer. Those with immature SQM will be at an even greater disadvantage as more of the market digitally transforms. However, Digital Transformation on its own isn’t sufficient. Leaders should mature their core Operational Excellence (people, process, technology) to realize value and success, and position their companies for the future.