Measuring The Impact: Quality Metrics And Manufacturing Intelligence

Because quality affects every level of an organization — from the plant floor, to the C-suite, to the customer — it is far more than a cost of doing business; it is a game changer. However, not everyone in your company may understand the true value of quality.

 

What if you could report to your board room that reducing scrap has increased your company’s profit by 10 percent? Or, what if you could show plant managers that exactly 15 defects occurred within a particular shift? Because quality affects every level of an organization — from the plant floor, to the C-suite, to the customer — it is far more than a cost of doing business; it is a game changer. However, not everyone in your company may understand the true value of quality, which may hinder your ability to justify investments in improvement projects.

To prove the value of quality, you need to increase the visibility of quality by providing the right quality metrics, to the right people, in the right format. When done properly, it is possible to turn manufacturing data into actionable information, or Manufacturing Intelligence, which can both demonstrate the value of quality improvement projects to upstream management, and result in ROI. Given today’s manufacturing challenges, quality metrics are more important than ever. For one, production is rarely a single-site operation and manufacturers rely on suppliers all over the globe to provide quality products. Other complexities, such as compliance with government regulations, siloed data, and the focus on customer satisfaction, lead manufacturers to rely on quality metrics to control and improve their products.

The first step in proving the value of quality is understanding what each department or staff member sees as valuable. To identify what data to collect, get input directly from each level of your organization, from the top down, and find out what metrics are important to them. For example, executives will want high-level summaries of data, such as percentages and raw data values, which help them determine areas of improvement, like waste reduction. On the other hand, plant managers require metrics such as Overall Equipment Effectiveness (OEE) or number of defects in a particular timeframe, which help identify costs and plant performance efficiencies. Lastly, operators also rely on OEE, as well as other more targeted metrics, like product-specific features and alarms for out-of-spec events.

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