USING DATA TO ESTABLISH TRUST
Revelations about fraud in Kobe Steel production facilities continue to expand the awareness of just how many large customers of Kobe Steel, the third-largest manufacturer of steel in Japan, are affected. The well-deserved finger-pointing is not going to let up any time soon.
About finger-pointing, however, there’s an old trope: when you point one finger at someone, there are also three fingers pointing back… at yourself. For purposes of this article, the “you” is metaphorical. It’s about the supplier quality management practices and systems employed not by Kobe Steel, but by its customers.
What could Kobe Steel’s customers have done to protect their supply chain?
Clearly, you can’t make enough phone calls or visits to your suppliers to adequately drive global quality performance. Is the answer, then, to simply trust the suppliers to do their job?
Given the availability of supplier quality management systems at a reasonable price that can be implemented without disrupting company operations, that approach is disingenuous.
What’s required is an approach that warns you, the customer, when something is going wrong with your suppliers’ processes or materials before they enter your supply chain and are incorporated into your products.
Data Is The Answer
The irony of the Kobe Steel debacle is that it was Japanese industry leaders who absorbed the lessons brought to them by W. Edwards Deming, the famous American scholar and statistician. Many credit Deming with the Japanese ‘economic miracle’ of the 1950s and ‘60s. His business gospel was based on the careful collection and study of data, with an emphasis on Statistical Process Control (SPC), the sole purpose of which is the speedy identification of inconsistent manufacturing, and can be an indicator of fraudulent test results.
The steps that Kobe customers should have taken to avert this breakdown are not hard to understand:
- Collect data on suppliers’ material performance,
- Establish statistical process control (SPC) boundaries,
- Summarize the data into trends diagrams,
- Provide the results to both the supplier and its customers (i.e. you) by material, by plant, by supplier, by material type, by region, by receiving plant(s), etc. so that you can statistically identify supplier consistency issues.
An important corollary to this list is the methodology used to gather and analyze the data. It would not be humanly possible to process all that data manually and make it available in time to avert a breakdown. The data must be mechanized, harmonized, aggregated, analyzed, summarized, reported, and the right professionals alerted if the data indicates a potential threat. The key is speed.
The answer is to use a Software as a Service (Saas) supply chain quality management system that is powered by Statistical Process Control (SPC). SPC has the capability of displaying trends, which pass/fail reports qualification tests can never show. While quality data can be gathered using other means (paper reports or PDFs) the lack of speed results in reports about what has already taken place. SPC, with its early warning about trends at every level of granularity, can show you where a process is heading, not just where it has been.
Had the customers of Kobe Steel been using this approach, problems would have been caught very early in the process. In addition to preventing incidents before they reach your market, the approach also allows your company to reward superior performers. SPC shows consistency and reliability in great detail.
Trust But Verify
The array of customers Kobe Steel has been supplying is not only large, but also long-standing. This kind of relationship has many advantages, but can also create the kinds of problems we see with the company today. The closer, and the more experience-based the relationship is with them, the bigger the buffer of trust that has been built around it. And, the more trust there is, the less inclination there is to look further down the supply chain.
Experience-based familiarity replaces supply chain QA visibility, supported by a robust array of analytics, and supported by Statistical Process Control (SPC). Seen the wrong way, the approach can make the QA team uncomfortable because it implies a lack of trust. If you employ tools that demand detailed data in real time, it might seem there is a lack of trust, and shouldn’t friends trust each other?
Of course, another way of looking at this is that providing data is a way of building even more trust, rather than reducing it. Relationships with suppliers are crucial to your success, especially if you have a complex product and you compete on quality and reliability. The relationship is nurtured over time, with individual people on each side. A key building block of this relationship is trust.
However, a long term organizational (rather than personal) relationship should be built on the principle of “trust, but verify.” It is clear that this arrangement benefits all sides, and especially your customers who are relying on your products.
Meeting the challenges of data analytics
Using analytics is filled with challenges. The most obvious one is the acronym GIGO (garbage in garbage out). The results are only as good as the data that is supplied. That’s why system rigor and workflow discipline are essential to developing a knowledgebase with value.
If you are collecting data from various sources, and it is submitted in different formats, with fields that mean different things to different groups of people, what you see may not be what you think you see. Effective analytics depend on comparing “like” with “like.” The variability of inputs makes this very difficult. For accurate comparison and dependability, it is hard to dispute that all raw data should be entered in the same system, using the same terms, and based on a common understanding of their meaning.
You may simply not have enough information, or be missing an entire section of data. The result can skew your vision of reality.
Business processes have a tendency to become increasingly detailed and complex over time. At some point, an executive or client may have asked for data about an aspect of your quality management system. Perhaps there was an input that required paperwork from a government agency. Circumstances may have changed and this information no longer adds value and is not required. No one really needs it, but there it is, still using resources to be processed.
Each manufacturing process may have its own versions, as well as additional ones, that make Quality Assurance less reliable or more expensive than it needs to be.
A Robust Approach
One of the most cost-effective, affordable and convenient ways your company can approach its supply chain quality management is by partnering with professionals.
EMNS can provide you with a system that helps you avoid a number of pitfalls in managing your supply chain QA. With deep knowledge and experience in working with companies in various sectors, you can be sure that you are tracking what needs to be tracked, and are doing so efficiently, at a level of quality that is necessary to protect your brand and your finances. The biggest threat to this high level of quality is variability in your materials, the kind that is beyond the norms you have established in your specifications. This threat can arise at any link of your supply chain, and only continuous tracking will detect it.