I recently asked my 7-year-old son and 13-year-old daughter if they wanted to participate in an experiment. My son looked puzzled and my daughter rolled her eyes! But when I revealed a large package of fun size M&M’s, they became more interested. I began by asking what they expected to see when they opened the bags of M&M’s. They expected a variety of colors, smooth round pieces, and the signature “m” on each piece, and a “delicious” taste. Consumers have expectations of all products or services, including M&M’s.

This tasty discussion brings us to an important topic for business managers — quality. Quality is the degree to which a product or service meets customer expectations. For example, if a soft drink label says the can contains 12 ounces of soda, a reasonable customer expects the amount will be very close to 12 ounces. Airline passengers expect their luggage to arrive with their flight. Diners expect restaurant food to taste good and be delivered in a timely manner. When the two young consumers in our household open a package of M&M’s, they expect to see candies in a variety of colors and without blemishes. If you managed the M&M production facility, how would you monitor whether these expectations are being met? I’ll describe our attempt to address this issue with the fun size M&M’s.

We opened the large outer package and counted out 22 small bags of M&M’s. We pretended each of the bags was a random sample of M&M’s produced at hourly time intervals in a factory. To address the quality characteristic of color variety, we calculated the percentage of each color (blue, orange, green, yellow, brown and red) for each sample. We also counted the number of blemishes in each bag — deformities, broken pieces, partially missing letters, etc.

My son observed trends in the data we recorded. Blue and orange pieces were present in all samples, yellow and green were absent in four, red was absent in three, and brown was absent in two. Further analysis revealed that orange pieces made up 25 percent of all pieces sampled, but there were five “hours” in a row where the orange proportion exceeded the overall average. Six samples were defect-free, but the last six “hourly” samples contained defects, with four groups having more than one defect. If these were actually time-ordered data from the production process, the results could be helpful in discovering potential problems that inevitably occur in any production process.

The M&M example demonstrates that monitoring measurements of the product characteristics customers perceive to be critical is an important quality control technique. Observing the percentage of your customers who return a product may reveal important information about the process of understanding and completing an order. Customers also desire responsiveness. Average time to fill orders and percentage of orders filled by a promised date can be measured to monitor this aspect of quality. Similarly, service businesses may choose to measure and monitor the total time to complete a customer’s job. For many businesses, customer satisfaction is more subjective. Direct surveys of customers may be required to measure quality.

As a business owner or manager, identify the characteristics of your product or service that are most critical to meeting customer expectations. Develop measurements related to those characteristics and be aware of trends that occur over time. Importantly, measurements can reveal both good and bad aspects of your business processes. Several hours or days in a row with no poor outcomes observed is a good thing. Can further investigation reveal what made such an occurrence possible? Identifying positive and negative trends leads to continuous improvement.

At the end of our experiment, the three of us addressed the issue of taste. We concluded that there was no problem in the area of taste quality in the package of M&M’s we purchased!

The Excellence in Missouri Foundation (excellenceinmo.org/events) and the American Society for Quality (asq.org/learninginstitute) have live and online training programs that can assist business owners and managers in data use and development measurement that can improve the quality of products and services.

Barry Cobb is an associate professor of supply chain management and logistics in the Department of Marketing at Missouri State University. He holds a Ph.D. from the University of Kansas and conducts research and consulting in the areas of operations and supply chain management.