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Commentary: Manufacturing quality reimagined: The hidden power of your data

Manufacturers that only use quality data in a reactive manner at the local plant level are missing out on opportunities to generate large-scale, enterprisewide savings.
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For many manufacturers, quality management means using data to help them respond to on-site alarms after a process or machine fails or when quality checks indicate products are outside of specification limits. This data may include part or component measurements, process parameters, and traceability fields such as lot codes, shifts, and work orders. But, manufacturers that only use their data in a reactive manner at the local level are missing out on realizing the full power of their quality data. This data also has the potential to provide strategic insights that can be used to proactively prevent problems from occurring and quickly uncover opportunities to make significant enterprisewide improvements.

These improvements can be attained by aggregating data from all of an organization's plants and suppliers to provide visibility into the performance of the entire organization, rather than just one plant or production line. When data is aggregated and delivered to the corporate level for analysis using statistical charts and visualizations, it can generate actionable insights that can be used to streamline global operations, improve overall product quality, and save companies millions of dollars.

Enterprisewide visibility
However, despite advancements in data collection and storage technology, many manufacturers still manually collect quality data and store it in discrete databases or keep paper records in filing cabinets at the local plant level. A recent InfinityQS survey of 260 manufacturers found that 75 percent of respondents still manually collect their data-with 47 percent of those relying on pencil and paper. Such an approach creates data silos and inconsistencies, which prevent executives and decision makers from seeing what's happening across multiple sites and extending improvements beyond a single plant.

A better approach is to unify quality data from all sources, including global suppliers, incoming inspection, shop floor operators, the quality lab, and packaging. With such enterprisewide visibility, manufacturing companies can discover insights that were previously hidden by data silos or locked away in filing cabinets. This unification of data can best be achieved by first centralizing manufacturing data from across the enterprise in a single repository. Then a statistical process control (SPC) engine can be used to analyze the data and compare operations from line to line, product to product, and site to site. For many companies, a cloud-based, software-as-a-service (SaaS) quality intelligence system is an appropriate way to achieve that.

For instance, a bottled water company previously employed a paper-based system for collecting and analyzing its quality data. When plant-floor issues arose, its quality engineers had to retrieve all of the necessary data, and then stop operations to sort and decipher hand-written notes before they could fix the problems. By automating data collection and moving to a cloud-based quality intelligence solution, the company now has real-time visibility over its processes-both within each individual site and from the corporate level across more than 25 facilities. The company can now track trends in its quality data, at both the corporate level and within individual plants, to make intelligent, timely decisions. For instance, at regular intervals, operators can pull products off the production line and gather data on key performance indicators (KPIs), such as height, diameter, and thickness. By reviewing and analyzing the collected data, the company is able to ensure consistency in the shapes and sizes of its water bottles, and highlight the greatest opportunities for improvement in the blow molding process. This approach enables it to catch issues early in the process and reduce, if not eliminate scrap, recalls, and potential delays in production.

Notably, a quality intelligence solution enables quality and process improvement teams and plant floor operators to identify issues in real time and catch problems before they occur. Manufacturers can then shift from reacting to quality issues to actually preventing them.

For example, one global tire manufacturer wanted to better leverage its production data for proactive process improvement. By utilizing a quality intelligence solution, the company can keep an eye on its processes at all times, including recordkeeping for ISO 9001 compliance and overall product quality. The quality intelligence solution issues automated alerts that notify key personnel when manufacturing processes begin to drift, ensuring that each tire is consistently produced to the highest quality standards. With this approach, the company was able to drive operational process improvements that resulted in significant cost savings and increased productivity. In one plant, it realized US$400,000 in annual savings on a single belt line. It did this by analyzing dimensional data and uncovering previously unknown quality information that revealed opportunities for reducing waste and raw material usage. Similar savings were realized on other production lines and throughout other facilities.

Providing operational insights
Once a company has consolidated the quality data from all its sites, that data can then be analyzed by quality professionals, vice presidents, operations managers, or C-level executives. Aggregated data can be sorted and viewed in different ways to compare plant-to-plant, product-to-product, and line-to-line performance, enabling quality personnel to proactively pinpoint opportunities for improvement that can significantly increase output and efficiency across the enterprise.

Such comparative analyses can show which sites need help and where the biggest gains and cost savings opportunities are located. With these operational insights, quality professionals transform from reactive "firefighters" into quality and process improvement strategists. They can use these operational insights to streamline, optimize, and transform processes and operations across the enterprise, thereby elevating product quality, improving efficiency, and creating significant cost savings.

Global transformation
Quality professionals and executives at the corporate level can also identify which lines and plants are their top-performing ones. They can take those best practices and standardize them to all facilities to achieve substantially improved results. Organizations can then begin to see real, measurable impacts to their bottom lines.

In some cases, those impacts can be substantial. One North American consumer packaged goods company uses the data in its secure, centralized repository to perform predictive analyses and respond quickly to variances across plants. Because the data are centralized in the cloud, the company can monitor if different facilities are producing above target and quickly make adjustments to prevent waste, reduce excess product or "giveaway," promote standardization, and minimize plant-to-plant variations. Furthermore, the manufacturer is able to analyze the aggregated data to identify areas for continuous improvement. The company has reported a staggering US$2.1 million in savings due to waste reduction alone.

Many organizations are exhausted from reacting to crises on the plant floor and constantly looking for opportunities to squeeze more profitability from the production line. Reimagining how they manage quality can help break that cycle. When the same data that alert manufacturers to plant-floor issues also provide critical insight into how to optimize global operations, quality becomes a competitive advantage. With the proper technology in place-automated data collection, cloud computing, a centralized data repository, and quality intelligence-manufacturers can unveil the hidden power within their quality data.

Doug Fair is Chief Operating Officer of InfinityQS International Inc., an industrial statistician, and a Six Sigma Black Belt.

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