Running a zero-defect operation is a much desired but unfortunately out-of-reach goal for manufacturers using traditional methods today. That’s because it is impossible for any large or complex manufacturing organization to operate completely without errors using traditional methods. But it’s still desired because to achieve zero defects is to fatten profit margins and win customers.
Indeed, the zero defects concept in practical terms is an aspirational quest—to continuously improve quality in the manufacturing lifecycle, getting better and better by increments. Although perfection might not be achievable, at least you’ll push your quality standards to the point where they are acceptable by even the strictest measures. According to the Six Sigma standard, “zero defects” is 3.4 defects per one million opportunities.
Why make zero defects your goal? A top reason is to reduce waste and thus costs. According to the American Society for Quality, quality-related costs for U.S. manufacturers are 15-20% of sales revenue, although they can be as high as 40% of total operations. For large manufacturers, a single quality defect can add up to many millions of dollars.
The direct costs of poor quality in manufacturing include the costs of notifying affected customers (whether consumers or businesses), repacking and transporting the defective product(s) back to the plant, destroying and disposing of the product(s), and replacing the product(s). And if the defect is due to a design flaw, the cost is much because you must redesign the product or process and retool manufacturing lines. Indirect costs can be loss or reputation and brand value, which are just as serious as direct ones, as most of today’s buyers (53%) rate quality as the most important factor when making purchases (price is just 38%).
How intelligent automation can help
Factories have long used physical robots to regulate as well as speed up factory operations and improve quality. Now they are investing in technologies such as intelligent automation, combining Robotic Process Automation (RPA) and artificial intelligence, to build software robots (“bots”) that can automate many of the other repetitive and mundane tasks that humans were previously required to do. By freeing up human workers—who are, after all, prone to error—for higher-value tasks, you not only gain a more productive workforce, but errors are eliminated, and costs are reduced.
Traditionally, the cost of poor quality (known as COPQ) is divided into four buckets of possible costs: prevention, appraisal (testing), internal failure, and external failure. By doing a traditional COPQ analysis, you see that there is always a cost to quality: it’s just a question of where it happens in the manufacturing lifecycle and how much you ultimately must pay for it. The earlier in the lifecycle—say, during prevention or appraisal—that you eliminate waste and defects, the more cost-effective your quality program will be. Wait until failure occurs in either internal or (worse) external stages, and the costs will mount exponentially.
In this blog, we’re going to see how intelligent automation can be used to reduce costs and improve the efficiency of manufacturing operations.
Helping with four stages of COPQ
1. COPQ Prevention
Whatever you can do to prevent COPQ is a good thing. Yes, it will cost you something to take preventive measures, but it will also provide you with a high return on investment if those measures are smart and effective. One of the best use cases in this category is when intelligent automation is deployed to automate workflows related to quality policies and procedures. These are the instructions for employees on how to maintain high-quality standards. The instructions are different depending on the role of the individual. Typically, to this day, they are still bound in paper files or perhaps PDFs and not particularly easy to refer to. In many cases, quality best practices live in the brains of the most experienced workers at a factory or plant and—if you’re lucky—get passed around using word of mouth.
Now imagine if digital aspects of the workflow were automated with intelligent automation. Intelligent bots can move data from one system—or one Internet of things (IoT) device—to another rather than requiring manual readings and keyboarding in of data. Bots that live on mobile devices can ping employees when they are needed to make decisions within a workflow or if something exceeds quality thresholds. Best of all, by designing processes and workflows that reflect the way things are done—including embedding informal best practices that have been acquired over time in an automated process rather than the theoretical processes in a dated instruction book—you have a better chance of eliminating errors and defects and reducing costs in the early stages.
2. Quality appraisal costs
If you don’t take a preventive stance to design quality into your manufacturing lifecycle, the next-best option is to inspect or test quality in the middle or at the end of the lifecycle before the product ships. This also costs you—the people and equipment time it takes can add up—but it’s better than bleeding money during a later stage of manufacturing.
Again, here you can use intelligent automation to automate workflows that connect IoT devices and shop-floor machinery. Test results from calibrated equipment can be fed into systems that analyze the data for anomalies and inconsistencies and notify a human worker if anything seems amiss. This frees up your human workforce to monitor test equipment, identify when a quality problem may be endemic, and otherwise more intelligently monitor quality so later—and higher—COPQ costs aren’t incurred.
3. COPQ internal failure costs
These are quality problems that occur before a product is shipped to the customer. This includes process as well as material failures.
An internal failure occurs when you must scrap and rework a product, then re-appraise and re-test before shipping it. Not only is this expensive from a materials standpoint, but you can also have data and processes failures due to faulty data entry or missing information or human workers who miss a step in an important process.
You need to think in unconventional ways when considering internal quality problems. It doesn’t just have to be related to the manufacturing line. Just consider sales and marketing: If these departments don’t do their jobs, you can end up with too much or too little raw goods or finished products, which is, of course, a process quality problem that can cost you either way. Marketing can fail to pinpoint the right targets in the right markets or deliver poor prospect lists to salespeople, who then waste their time trying to sell to the wrong people. All these things add up to waste, which means extra cost.
4. COPQ external failure costs
These are the costs of poor quality that occur any time after a product has shipped. These are costs that you don’t want to hear about, as it indicates significant failures within your organization—in the front and back offices as well as on the factory floor. Still, here, too, intelligent automation can be helpful as the technology can accelerate the processing of customer complaints, claims, or returns, as well as certifying and tracking warranty repairs or swaps by automating many of the formerly manually processes involved in taking customer calls, identifying their account, and verifying their purchase. Today, with intelligent automation, all that can be done behind the scenes.
Zero defects: intelligent automation makes the theory tangible
The zero-defects theory drives manufacturers towards a less wasteful and, therefore, efficient and cost-effective entity. Since waste refers to all unproductive processes, tools, and employees, anything that falls into that category needs to be eliminated and the resources used elsewhere for better advantage. As the four COPQ categories of cost show, having an aspirational goal to move to zero defects means trying to do it right the first time. Intelligent automation can help you do that.