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Preventing data entry errors on the shop floor is not only possible—it’s also key to improving operator engagement and efficiency.

But first, manufacturers need to employ the right technology. A modern quality management system streamlines the data collection process, ensuring operators enter accurate data.

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Reducing data entry errors on the front-end of your quality management practice saves you from manual data cleansing on the back end—and enables you to better analyze data, improve efficiency, and maintain high quality standards for finished products.

Collecting accurate data also improves turnaround time on product quality and process efficiency decisions.

Data Errors Are an Avoidable Problem

In manufacturing today, continuous improvement is an imperative. Data errors waste time, cause significant delays, and may result in costly mistakes.

When data is inaccurate, it’s not useful to any of your stakeholders, who rely on accurate information for critical decision making. Unfortunately, data errors tend to show up at the worst times—for example, when a manager urgently needs to track down information or you’re presenting a report in a meeting. That erroneous data needs to be painstakingly sorted and cleansed—and while that’s happening, product quality and productivity suffer.

Doing the Math: Automating Calculations Prevents Critical Errors

When data entry is manual and has no parameters to protect accuracy, problems occur.

For example, take data that must be weight-corrected, like a metric measuring defects per pound (e.g., un-popped kernels in a batch of popcorn) or inclusions per pound (e.g., the number of raisins in a box of cereal). When a data point is either plentiful or rare, the collected sample size must be large enough to be statistically valid.

After recording the result in the sample size, a calculation is needed to report the average value. Sometimes these calculations are simple, and operators can do the math in their head, rather than relying on calculation features built into quality software. But mistakes happen. Automation makes them less likely to occur.

Another common scenario is translating and calculating between metric and standard units. If an operator accidentally marks the wrong type of measurement unit, a product can appear out of specification—which raises serious questions from customers.

Upfront Error-Proofing: Automating Spec-Limit Checks is Proactive Quality Management

Modern SPC-based quality management solutions can help detect data errors like those mentioned above when they are entered in the system. But they can also prevent errors before they enter the system.

Features like specification limits allow users to input reasonable limits to data collection. InfinityQS ProFicient™ software makes it easy to establish specification limits through a simple data entry dialog.

Spec Limit (Click on image to enlarge.)

The system then automatically confirms that operators enter data within reasonable limits. If an operator tries to enter data outside those limits, they will see a warning screen asking them to confirm or correct the suspect data.

IC 1021 InfinityQS Topic 1: Spec Limit Violation

With this double-check, you can gain full confidence in your system’s data; and it relieves any need to clean or manually sort erroneous data later. Instead of checking data line by line to locate potential mistakes, you can quickly identify the root cause of an issue—or spot an opportunity for process improvement.

Eliminate Errors, Unlock Efficiency

Efficient manufacturing relies on quick transitions from data collection to data analysis to process improvement.

Using a modern quality management system, like InfinityQS ProFicient, ensures accuracy in data collection. As a result, manufacturers see reduced errors and can focus on aspects of their job that truly advance efficiency.

Curious to see how leading manufacturers reduce data entry errors? Visit the InfinityQS site and watch videos in the Tales from the Trenches series to see how ProFicient software ensures data accuracy.