Calculating costs related to quality problems is not straightforward. Some of the expenses are apparent, but many are not. Quality is a top customer requirement, and lowering costs is key to a profitable business. So, truly understanding the cost of quality warrants the effort. Fortunately, advanced manufacturing technologies can help analyze and understand expenses plus quality problems and escapes.
The cost of poor quality and the cost of good quality are the two components of the metric cost of quality (CoQ). The cost of good quality focuses on getting systems in place to ensure good quality. So, quality problems typically trigger creating a business case to invest in systems that boost good quality.
There are many cost components of low quality. Here are a few cost areas that are typically easy to see:
Yet, these represent only a portion of the actual cost of poor quality. Consider what else may result:
Any subset of those costs can mount up to significant expense. Once you have identified the issues, quantifying them is another step. Often, operations and finance staff must work together to understand the true cost of poor quality.
Calculating the cost of poor quality provides clear ideas on how to improve. First, it identifies the top opportunities, both apparent and hidden. Second, it gives a guideline on how much to budget for projects aimed at lowering those costs. Advanced manufacturing approaches can aim to spot patterns and avoid problems.
Some quality problems arise because of poor communications. Most production processes are complicated, with many interdependent steps. So, improving communication between steps in the process and people can significantly lower the cost of poor quality.
One way to improve communication is with connected workcells. With this technology, each area can see what is happening in the previous or upstream steps in the production process and prepare appropriately. They can also let those in downstream process steps know if there have been anomalies such as out of spec materials, rework, or equipment repair.
Another critical handoff is between employees on different shifts. Today’s technologies allow a complete accounting of what has happened on a shift with minimal effort. Systems can highlight non-conformance issues so those following on the next shift can again see where problems occurred. In many cases, a meeting can focus on solving those before making more product.
Gathering data in manufacturing is easier and more cost-effective than ever. The Internet of Things (IoT) can add new data points to create a more comprehensive picture of the production process and quality issues. Using advanced analytics on the broad set of data from equipment, IoT, and other sources, companies can begin to detect patterns.
There is a pattern for on-track production. Departures from that pattern can predict quality issues. There may also be specific patterns that arise for various quality problems. Seeing how these patterns correlate with quality can dramatically improve a company’s ability to lower certain costs.
Using machine monitoring, a company can keep tabs on these patterns as they arise. Sometimes, the company can prevent the quality problem from happening with timely equipment repair or calibration, employee training, materials management, or other proactive steps.
Manufacturing in these turbulent times demands efficiency and high customer satisfaction. Thus, lowering costs related to quality problems becomes crucial.
Ensuring that everyone on the production floor has good access to information about what’s happened to the products and equipment in prior steps or shifts helps. Fortunately, combining data gathering through IoT into an advanced analytics platform is available to support good quality and lower the cost of poor quality.
Julie Fraser is VP Research, Operations and Manufacturing for Tech-Clarity. She is dedicated to making clear the business value of technology for Industry 4.0, Smart Manufacturing, and Supply Chain. She also leads MESA’s Smart Manufacturing Community. Previously, she was Founder, Iyno Advisors; Senior Analyst MES, AMR (now Gartner); President US, Cambashi; President, Industry Directions; VP Marketing, Berclain (now Infor). She holds a B.A. from Lawrence University with honors, Phi Beta Kappa.