You Can’t Get Buy-In Without Data: Three Tips

Establish a profitable quality program in 2021

Published: Tuesday, February 23, 2021 - 12:01

Quality managers have a problem. The success of their quality program hinges on one thing. It’s not KPIs, and it’s not methodology. It isn’t even employee engagement or customer satisfaction. The one thing a quality manager needs most is leadership buy-in. 

Quality programs fail because they do not have support from the top.

So how can you win executive support? Run a quality program that gets results. Results like increased customer satisfaction, employee engagement, and more efficient processes reflected in the bottom line. Improved profits tied to your quality program will get the attention of your executive team, guaranteed.

Where do you start? First establish a data collection plan. An efficient data collection plan is the bedrock of a strong quality program. The ultimate goal is to use data collected from your processes to understand what is happening now (analysis) and anticipate change in the future (prediction). Statistical tools such as control charts for process stability, t-tests for comparisons, and gauge studies for understanding measurement variation are all key components of analysis. Use designed experiments, regression, and machine learning to learn how process changes will affect results. However, keep in mind that none of this is possible without first collecting data.

Let’s consider three questions to ask before developing a data collection plan:
1. What do we plan to do with the data?
2. What data are currently being collected? (And what data are not being collected?)
3. How is data collection impacting the process?

Think of data collection like a quality improvement initiative. Start by defining the problem (what is our end goal for the data?); measure the current state (what are we doing or not doing now?); analyze the situation (what impact does data collection have?); improve (how can we make data collection more efficient?); and control (what do we have in place to ensure consistent, accurate, data collection going forward?). Six Sigma professionals will recognize this as the DMAIC (define, measure, analyze, improve, control) approach.

Start with the end goal in mind. How will every bit of data collected be used in an analysis? The act of collecting data comes with a cost. Carefully consider the requirements. Seek the root cause of an outcome and take the measurement closest to that point. Tools like process maps and cause-and-effect matrices can be helpful in this step.

Assess the current state of things. Which data are currently being collected on the process? What tools are being used to collect those data? Where are they being stored? Are the data accessible and interpretable? A thorough assessment of the capabilities of your facility’s data collection effort can expose a gold mine of new insights. Typically, the people closest to the process will have the best understanding of how data collection efforts are working... or not working. Process operators are key allies in developing a sustainable data collection plan that can last.

A well-designed data collection plan is virtually transparent. Incorporating measurement capture directly into the production process is possible. Be creative and seek input from stakeholders.

Data collection doesn’t need to be all sensors and automation. Simple tools are available to help collect data. Search for quality control apps online. Or start with paper and pencil! Don’t overcomplicate it. Of course, be mindful of the bottom line. 

Get started on your data collection plan today. A solid foundation of data is key to developing a quality program that moves the needle. Collect the data, analyze the data, make improvements, and get the attention of your executive leadership team. Your quality program is key to your company’s success. It all starts with the data.

About The Author

Tamela Serensits

Tamela Serensits is the founder and CEO of Argolytics, LLC a Pennsylvania start-up developing quality control apps for small and medium-size manufacturers. Prior to entrepreneurship, she worked for 10 years at Minitab, the leader in statistical software for Six Sigma. Tamela holds a Masters in Applied Statistics from Penn State University and is a member of the American Society for Quality and a U.S. Navy veteran.