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7 Ways To Use Data To Improve Manufacturing Performance

Continuous improvement projects have never been more important to manufacturers. Companies are getting pressure from their customers to reduce margins and produce more.

Doing this well requires the collection and review of production data to increase the effectiveness of operational equipment and production lines. Unfortunately, these projects fail at a very high rate.

Below are 7 best practices for continuous improvement projects using data that will increase your odds of a successful outcome.

1. Choose the Right Metric

Measure something meaningful that will solve the problem you’re having. Depending on your process you might track OEE, throughput, First Pass Yield, Takt Time, or many other metrics.

You have to measure what you want to improve, and choosing the right metric is the first step. It does not mean you are stuck with this metric forever, change it when you need to improve a different aspect of the process.

2. Measure a Baseline

Once you have your metric, get an accurate and consistent baseline. In reality, consistency is more important than accuracy.

This typically means collecting data automatically. Data can be collected manually, but if someone misses an entry or does not understand how to correctly record the data your results can be skewed and you might try to improve something that does not need improving. The main point is that getting rid of manual reporting by eliminating paper and pen and implementing automatic data collection is necessary.

3. Set a Reasonable Target

Let’s say you want to measure OEE. After you measure the baseline you find out that the OEE for a certain machine is 45%. You may want to set a target of 80% or 85% because you heard that is world-class (which is an urban legend by the way), but the odds of a line improving that much in the short term are low and virtually non-existent.

Set a target you know the team can reach.

When you first start a project like this, you will identify some quick changes you can make that will result in big improvements. Set your targets to a realistic number so the team can celebrate a win.

Then move the target and improve again.

4. Ignore the Number

The number is meaningless; this may seem counterintuitive, but I will explain.

After you set your targets and have your plan to improve, ignore the actual number and focus on the trends. This is especially true when tracking OEE. You want to make sure you are heading towards your goals and not going backward. This is not always clear when looking at an OEE number for one shift or one day.

5. Be Transparent

Show everyone who will be accountable for hitting the numbers what the targets and current values are. This includes displays on the lines, machines, or cells showing the current metrics and targets.

Use the metrics and dashboards in the daily standups and weekly production meetings. That’s just one way you’ll ensure success in implementing manufacturing analytics on the floor.

6. Incentivize the Team

Your teams all need to be on the same page and understand how they are measured. Some companies give financial rewards for consistently hitting their numbers. Others give additional time off or let the teams leave early. While these are just suggestions, it’s a good starting point in exploring the best option for your team.

You will find some shifts and operators do better than others. Encourage information sharing so you can raise the overall performance of the plant and have the operators help each other. It’s a win-win situation when you implement an employee incentive program.

7. Manage Expectations

You want metrics and KPIs to help you better run the business, not to micromanage your employees. Make that clear to them. Make sure they understand it is okay to take breaks, it is okay to have downtime. You are trying to understand how the process is running and make it better.

Make sure everyone looking at these numbers understands what they mean. There is no better way to kill morale or an improvement process than by “freaking out” over the numbers when you don’t understand them.

Manufacturing analytics software is not a hammer; it is a solution to measure where you currently are and devise plans to improve. Once you improve, measure again, set new targets, and repeat.

These projects are about change management across the board, changes in process, and changes in culture.

Bryan Sapot
Bryan Sapot
Bryan Sapot is a lifelong entrepreneur, speaker, CEO, and founder of Mingo. With more than 24 years of experience in manufacturing technology, Bryan is known for his deep manufacturing industry insights. Throughout his career, he’s built products and started companies that leveraged technology to solve problems to make the lives of manufacturers easier. Follow Bryan on LinkedIn here.