Taking A Fresh Look At How To Manage Quality Data

Manufacturers can produce measurable and significant benefits by re-imagining and refreshing how they approach data collection, analysis, and reporting.

Mnet 194940 Manufacturing
Michael LyleMichael Lyle

In the manufacturing world, most companies know how important it is to modernize their plant equipment and manufacturing processes. It helps them keep up with technology advancements and maintain compliance with industry standards. Yet, when it comes to quality data, it’s astounding how many businesses still rely on age-old, manual processes. In an InfinityQS survey of 260 manufacturers, including some of the world’s largest organizations, 75 percent of the respondents stated that they are still collecting data manually. Of these, an alarming 47 percent still use pencil and paper.

Recording quality data manually and storing paper records and reports in filing cabinets or warehouses are time-consuming, frustrating manual processes that are highly vulnerable to human error and inaccuracies. So why do so many manufacturers continue in the same old rut?

Change can be difficult. Employees and executives alike tend to cling to existing systems or ways of doing things because they are familiar with the process. But with today’s solutions and modern techniques, updating the way an organization manages its quality data doesn’t have to be painful. Manufacturers can produce measurable and significant benefits by re-imagining and refreshing how they approach data collection, analysis, and reporting.

Data Collection: Drop the Pencil and Paper

Paper-based data entry is, by nature, more prone to error than automated data collection or even digital data entry by operators. Measurements can be scribbled down, transcribed incorrectly, or overlooked completely. One error, such as a simple math mistake or misread handwriting, can lead to a domino effect of catastrophic results. What’s more, paper records can be easily lost in transport or forgotten in a sea of filing cabinets.

Plus, if data are recorded on paper, a manufacturer has no way of verifying that they have been collected properly and on time. It’s important for operations and quality teams to have access to accurate, real-time data—not data from hours ago that were written on a piece of paper somewhere across the plant floor.

Manufacturers should instead look for a statistical process control (SPC)-enabled quality intelligence solution that supports automated and semi-automated data collection and the use of tablets or mobile devices that support all data collection activities. Also, the system should feature automatic alerts for scheduled data collections, letting operators know if collections are coming up or missed. Additionally, the system should be smart enough to prevent data entry errors from being entered and identify data values that are out-of-specification. Scheduled and automated notifications can help ensure timely and complete data collections, supporting compliance requirements and analysis needs.

Companies that move away from paper-based data collection should immediately begin to see improvements in data integrity. In turn, they can reduce errors, improve compliance, and catch potential issues before they snowball into quality problems.

Data Analysis: Evolve Beyond Spreadsheets

When conducting data analysis, operations and quality managers often spend many hours manually exporting data into spreadsheets from multiple software programs. They could spend days combing through the data to find what’s important before they are able to perform extended data manipulation. They have to juggle multiple spreadsheets, making it impossible to compare multiple products, lines, or sites, and limiting their ability to delve into the types of details and comparisons that can transform a business. In fact, the time needed to perform these tasks can be so arduous that the reporting is often viewed as too expensive to execute and is sometimes ignored altogether.

However, with a quality intelligence solution, manufacturers don’t need to scour for the right data; they can simply query the software to get the information they need in the best format for analysis. Such software should highlight and prioritize the most important and relevant information, according to a specific user’s role.

Of course, a quality intelligence solution should allow users to dig into the details, too. Users should be able to easily compare data across products, lines, processes, lots, shifts, operators, time, departments, sites, and so on—without flipping back and forth between spreadsheets. With software that can automatically provide extensive, easily readable views of the quality data—including multiple chart types, reports, dashboards, and notifications—operations and quality teams can save valuable time while easily identifying the areas that are most in need of attention, and prioritize the opportunities for reducing costs.

The operations team no longer needs to dig through data or travel the shop floor to find the information they need, and the quality team can run analyses and comparisons of any and all data, without hours of manual spreadsheet manipulation.

Data Reporting: Leave Behind the File Cabinets

When manufacturers record quality data on paper and lock them away in filing cabinets, the information becomes difficult–even impossible–to retrieve, especially if they are stored in multiple locations for weeks or months. As a result, organizations can struggle to fulfill audit requirements or even find answers to the simplest customer inquiries.

By automating and standardizing data collection for storage in a unified data repository within a quality intelligence solution, the information becomes easily accessible. Data are no longer lost or difficult to find. This leads to massive time savings when it comes to audits. Instead of taking days to sift through cabinets and retrieve papers from storage, staff can pull up the right data (stored automatically and digitally in the repository) in a matter of minutes and provide exactly what an auditor requests. In turn, with a focused and streamlined information-return process, they can head off costly delays and distractions.

Traceability also improves with the ability to see process data from any point in time, including details on raw materials, shifts and operators, specific lines, and equipment. Everyone can access the same data, so communications become easier and more efficient. Teams also gain the ability to respond more quickly and precisely to customer inquiries, since they can find the right answers with just a few clicks.

People are hesitant to change. But making the switch away from manual processes doesn’t need to be an all-or-nothing scenario. In fact, most SPC experts suggest starting with just one data collection process and a handful of products—something that will have a noticeable impact but doesn’t involve the most complicated line. Starting this way makes it possible to learn the new procedure and explore its limits and possibilities without too much stress. It can be an eye-opening experience and lead to more comprehensive data collection, analysis, and reporting, as well as simplify everyone’s busy workday. Once true insight into production data is attained and the results materialize, it becomes clear how a more comprehensive, global view of quality can offer bigger and greater benefits.

Michael Lyle is President and CEO of InfinityQS International.

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