Data accuracy starts with a clear definition of the information you want captured. What, when, where, and who. It helps to follow the I do - We do - You do learning model when training those who will be capturing the data. Remember, automating bad processes, or in this case bad data collection methodologies, will only get your defects faster.

Data is the base for defining Problem Statement, followed by Goal Statement. Fun Fact, Data Accuracy issue is common across the industries. To overcome the issue, first step is to understand the data source. We need to analyze data collection method and to check first point to data collection. Whether it is manual data collection or digital-we need to mindful with the correct element collection with correct sampling. Second step, to map the flow of the data from the source to the final stakeholder who use./translates data. There should not be any intermediate (which may act as diluters sometimes). I have used Digitalization as tool in some industries for more accurate data, collected directly from the machine itself.

For data accuracy issues in industrial engineering projects, start with implementing Total Quality Management (TQM) principles to promote a culture of continuous improvement and data integrity. Use the DMAIC (Define, Measure, Analyze, Improve, Control) framework from Six Sigma to systematically identify data inaccuracies, analyze root causes, and implement corrective measures. Apply Statistical Process Control (SPC) to monitor data collection processes and identify deviations in real-time. Lean Manufacturing principles can also help streamline data flow, eliminating non-value-added steps that lead to inaccuracies. Finally, train teams on data collection best practices and use regular audits to ensure ongoing accuracy and accountability.