In the first part of this three-part series, I discuss quality management tools for identifying a problem, the root cause and its impact. Next I’ll dive into the quality management tool selection process.
In today’s business environment, it can be almost impossible to sift through the multitude of emails and alerts that demand our attention. Frequent meetings and changing or unclear priorities further dilute a person’s ability to concentrate on the most impactful areas, and many companies publish hundreds of KPIs that may or may not have an impact on strategic initiatives, product costs or market position.
The Quality Assurance (QA) professional stands in the middle of the chaos, charged with addressing an infinite number of potential quality management problems with decidedly finite resources. Most QA teams have more projects in their portfolios than they can possibly handle.
The QA Manager’s Tool Bag
Fortunately, most QA teams have tools for addressing these challenges, but many of the tools bring their own, additional challenges to the fray, making them a two-edged sword. When it comes to prioritizing problems, what is really needed is a way to identify the most pressing problems. Here’s a look at a few of the best tools.
KPIs
KPIs (Key Performance Indicators) are often the first line of defense in identifying quality issues, but they may come with complications of their own. Many companies adopt so many KPIs that it becomes impossible for employees to understand them all.
With KPIs, it’s better to focus on a few high impact metrics rather than using every one of them included with the company’s business systems. Does labor efficiency matter to an asset intensive manufacturer? Maybe, but perhaps not as much as OEE (overall equipment effectiveness) or equipment utilization do.
On the other hand, labor efficiency may be crucial to a job shop or a manufacturer with many hands-on processes.
In many environments, each of these metrics may eventually lead to uncovering the same issues — so it’s a matter of deciding which one makes the most sense based on the company’s strategic goals. Choose, then forget about the others. Remember those finite resources.
The other problem with KPIs is that they can be labor intensive. If the company’s business systems collect the data and deliver the graphs, that company is ahead of the game.
In most cases, QA teams gather the data, load it into spreadsheets, analyze the data and then create the graphs. This can be a drain on resources, which is another reason to limit the number of KPIs in use.
Root Cause Analysis
Oftentimes people believe they know the fault behind a problem’s manifestation, but they may not be following the problem all the way to its root cause. In the first article in this series, we talked about the problem with the bulbs in car headlamps burning out quickly. The engineers jumped on the problem by extensively redesigning the car itself, ultimately adding to the cost and complexity of the product, and frustrating and annoying customers in the process.
The real root cause of the problem was a bulb technology with a short MTBF (mean time between failure). A change in the bulb spec could have solved the problem faster and without adding product complexity or wasting engineering resources.
Confusing Cause and Effect
Today, QA pros have tools such as the Five Whys, or Kaizen teams to help with root cause analysis. The Five Whys, part of the Lean Manufacturing toolkit, involves stating the problem and then asking why.
When that question is answered, ask why again. Repeat the process until there are no more statements to apply the ‘Why’ question to.
Had this been used in the bulb example, it might have gone like this:
“Our customers are getting into accidents because they’re driving without headlights.”
“Why are they driving without headlights?”
“Because they burn out and are difficult and costly to replace.”
“Why are they burning out?”
Do you see how this one “why” shortens the path to the real root cause?
Much of the ensuing problem could have been avoided by ensuring the team was addressing the real issue, which was the bulbs’ short lifespan, instead of the perceived hassle of changing the bulbs. This example makes it obvious why driving to the real root cause is critical. The team is unlikely to come up with the best solution if they’re trying to solve the wrong problem.
Choosing the Right Team
The above example highlights one of the most important parts of root cause analysis — choosing the right team. Identifying a process problem won’t be easy if the team consists of desk jockeys and managers, with nobody from the line included and considered an equal member of the team.
Customer use issues need real customer input, not more ideas from the team that created the original design.
An effective team must include members from every group of stakeholders, and all input must be considered equally valuable and legitimate, regardless of its source.
Data Analytics is the Key to Effective Quality Efforts
The most effective way to implement and use KPIs is with an automated system that gathers data from multiple sources and uses it to calculate results and present them in an actionable format.
This can be nearly impossible for a QA or operational excellence team to do manually, especially when the company uses data from its ERP, SCM, CRM, Service Management and other business systems. It becomes even more difficult with IoT data included, unless there’s a tool that can aggregate the data from multiple sources, as an EQMS solution can.
How an EQMS Can Help
The best EQMS solutions include tools to help the team identify the root cause of an issue, and it should include a repository to aggregate and analyze quality improvement suggestions, ongoing or potential future projects, issue management techniques and notes about continuous improvement meetings.
In addition, a complete EQMS should include the ability to connect to machinery and equipment (IoT) and to other business systems using simple REST or SOAP protocols.
Non-conformance and CAPA solutions are a must-have to track issues, in conjunction with a customer and user complaint repository. Advanced product planning and design control helps identify potential problems early in the product life cycle, where it is easier and more cost effective to resolve issues.
Metrics to analyze the accumulated data and present it in a format that provides insight and enables rapid action help by providing information about the effectiveness of changes. Document and training management modules help to round out the list of tools, modules and functionality necessary to operate effectively.
Stay tuned for the third and final part of this three-part series, which will cover a key to quality management success: Communication.
