Data mistakes in manufacturing | 7 common analytics pitfalls to avoid

data mistakes in manufacturing

data mistakes in manufacturingThe industrial world continues to advance with data-driven technology, but in order to make informed, strategic decisions, it’s important for operators to beware of common data mistakes in manufacturing.

7 common data mistakes in manufacturing

  • Not acting on data. Using data to get a better idea of what is and isn’t working on the manufacturing floor is half the battle. The next step is to make actual improvements. For example, if operators learn that a machine is overheating, it should be serviced immediately.
  • Poor data management. Implement data solutions that fit into the business strategy and promote accountability by assigning people to drive that strategy. Avoid manual data collection and instead focus on automation to reduce human error.
  • Using low-quality data. Bad data can easily lead to costly inefficiencies. It’s important to make sure data is being gathered with the right metrics, giving operators better business insight.
  • Disconnects with leadership. When setting a data strategy, it’s crucial to align with executives to make sure they know data is much more than a simple IT function. It’s important to educate them and other stakeholders to get buy-in.
  • Excluding employees from implementation. Technicians and other employees play a big part in driving daily production efficiency. If they’re included in planning and execution, they’ll more likely be committed to driving process improvement with data.
  • Creating solutions to symptoms. When interpreting data, it’s important to perform a root cause analysis to discover underlying issues that cause inefficiencies.
  • Comparing data-driven results to other operations. This can be a mistake because other plants or machines might have different features. For example, a machine producing small batches and another producing large batches should not be evaluated in the same context.

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