In logistics, data is the foundation for optimizing processes and making informed decisions. But what happens when your data is incomplete? This real-world example highlights a common issue.
This means, for example, you can’t determine how much time passes between a truck’s arrival and the start of its loading. Without that information, your analysis becomes meaningless. Investing in a system that isn’t fed accurate data is like throwing money away. Have you considered how incomplete data might be affecting your analysis and, ultimately, your outcomes?
Data quality is essential for identifying areas for improvement. If you want to optimize your operations, start by ensuring all key steps are properly recorded. Only then can you measure, analyze, and enhance your logistics processes.
