Seeing the Big Picture: Quality Metrics and the Path to Information Maturity

Seeing the Big Picture: Quality Metrics and the Path to Information Maturity

Justin L. Smith, Manager of Product Management

Let’s talk about the elephant in the room. There is an ancient eastern parable that tells the story of several men and an elephant in a dark room. Each man is asked by the king to describe the elephant to him. The man who has his hand on the trunk says the elephant is like a tree branch; the man who feels the tail says the elephant is like a rope; the man who feels a leg says the elephant is like a pillar; and the man who feels the belly says the elephant is like a wall. None of the men describe the elephant as an elephant, and so the king is led astray.

Although there are a number of insights that can be gathered from this ancient lesson, one thing is clear. With a limited picture or partial information, one can easily be misled and establish incorrect conclusions. We see this scenario of people being in the dark play out across the business world all the time.

So how does an organization gain a complete picture of what is going on? The great quality pioneer W. Edwards Deming once said, “In God we trust. All others must bring data.” As an early champion of what has become known as the PDSA (Plan-Do-Study-Act) cycle, Deming pushed this holistic approach as a way to gain valuable knowledge for the continual improvement of a product or process. However, without a complete view of the underlying picture (or data), the continuous improvement cycle will be ineffective, and businesses will continue to be left in the dark.

A Path to Information Maturity

One way to get an organization on track to solving this problem is to use an information maturity model as a roadmap. Although there are a number of different versions of this type of maturity model, most have at their core a pretty simple concept that involves an evolving picture that initially provides hindsight, then insight, and finally foresight. An information maturity model looks something like this:

quality metrics information maturity model

Information maturity model for quality metrics

The idea is to put systems, processes, and quality metrics in place that allow your organization to evolve, or mature, to gain greater and greater knowledge over time. The maturity model begins with simply collecting or capturing data in a system. From a quality perspective, this means having a quality management system (QMS) for your critical quality processes, such as nonconformances, corrective and preventive actions (CAPAs), and complaints. A good QMS will guide users through these quality processes to allow data to be captured in a consistent way and stored in a structured database. As is often the case, the first step is the most important, and organizations that are still using manual or paper processes will struggle to progress through the information maturity model.

By collecting quality data in a system, you now have visibility into what has happened within your organization (hindsight). Visibility into key areas of your quality processes is fundamental to continuous improvement, but visibility on its own isn’t enough. Visibility of your processes and underlying data needs to graduate to the next step in the model, which is to measure the results with well-defined quality metrics.

Establishing Quality Metrics

Lord Kelvin said it best back in the 1800’s: “If you can’t measure it, you can’t improve it.”  Establishing key quality metrics and then quantifying and measuring your performance against those goals is the first step in providing real knowledge across an organization (insight). This initially involves building or leveraging static production reports that help you measure what is going on within your business at a particular time. These types of reports give you a snapshot of information and provide much needed awareness about the performance of your company. In terms of the maturity model, most companies exist somewhere between this phase and the next one.

The next growth step is to analyze your quality metrics over time and across many variables. Trending data over time and across many variables helps you discover patterns that can lead to a better understanding of the health of your business as a whole. Specialized business intelligence tools, such as interactive data exploration and visualization tools, provide a platform to interact with your data in an unguided manner to deliver actionable insights faster. In terms of quality, understanding trends and patterns of various failure modes related to the manufacturing process is at the heart of continuous improvement and leads to a more comprehensive understanding of underlying root causes. The result is more effective processes and higher quality products, which is everybody’s goal.

Moving from Insight to Foresight

The final step in the journey is the most exciting, and it involves being able to model the likelihood of something happening in the future (foresight). Referred to as predictive or advanced analytics, this methodology uses statistics to predict outcomes and involves applying predictive models or algorithms on top of your data to help your organization make better decisions about the future. Predictive analytics is all about being able to anticipate emerging trends and taking proactive steps (preventive actions) to minimize the risk of something occurring. As it relates to quality, this is the holy grail of information maturity.

When evaluating where your business is on the path to information maturity, it is important to know where you are as well as where you are going so you can chart the best course. Keep in mind what Creighton Abrams said: “When eating an elephant, take one bite at a time.”

 

Chart your best course with quality metrics that matter. Learn more about how SmartSolve® Nonconformance (NC) and CAPA Management work better together, especially when viewing the complete picture with quality metrics and reporting tools.




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Justin Smith

Director of Product Management, Pilgrim Quality Solutions

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