12 Manufacturing Tips for a Brilliant 2017 (Part 4)

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12 Manufacturing Tips for a Brilliant 2017 (Part 4)

To get the most out of your manufacturing potential, you need to share data and make sure that tweaking one part of the chain won't negatively impact another.

· IoT Zone ·
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We're picking up where we left off from parts one, two, and three. As we look at how connectivity is changing the landscape of manufacturing and industry, let's see how data availability and sharing will play a role.

Tip 7: Unexpected Plant Bottlenecks

GE is executing its own strategy for developing brilliant factories that are digitally optimized to drive greater efficiency, productivity, and lower costs. In this brief series, I will share some stories of how analytics and data intelligence have been applied to discover inefficiencies or problems in manufacturing plants—unlocking new levels of performance and factory optimization.

Let’s start with a common manufacturing challenge: non-intuitive reasons for plant bottlenecks

As you may know, GE is on a journey to become a digital industrial business. This involves digitizing as many processes we can, and using the information gathered to make the best decisions and discover inefficiencies that can be improved.

Take GE Transportation, for example. We partnered with this business unit to help unlock insights into tracking equipment performance across the operation and use that information to understand where the bottlenecks were and where quality improvements could be implemented. This operation involves the equipment reconditioning and MRO activities for diesel locomotive engines. 

Anybody who works around diesel equipment knows that you can't even look at an engine without getting your hands full of soot. No engines could begin remanufacturing until they were cleaned. When analyzing data in the plant, it quickly became apparent that the washer that was used for cleaning the engine frames “blocks” was the bottleneck for the plant. Why was this?

There were dedicated operators stationed at the frame washer during the day. Was the frame washer breaking down? A quick look at the downtime data indicated that that maintenance issues were not the problem, but unexpected downtime was definitely occurring. So, what was causing the slowdown in work through this critical part of the production process? The next step was to go out and look at exactly what was happening and observe firsthand what could be causing the problem. 

We observed the operator. He was working very hard at this job. We also noticed that he was responsible for loading the cleaned frames onto a flatbed truck to be removed from the plant. Occasionally, this meant that the frame washer would accumulate downtime while he finished loading the truck. He indicated that last year, as part of a Six Sigma project, they had eliminated the job specifically responsible for loading trucks and had given this additional responsibility to the frame wash personnel because they were very close to the same work location. So, an improvement in one area was having a direct negative improvement on another aspect of the plant, ultimately decreasing efficiency. The remedy was simply to reinforce to the operator and supervisor that the frame washer should be running at all times, even if it meant stopping the loading of the truck and finishing it later. It was a simple conversation that greatly improved throughput.

Think about this as you start to make improvements in your plants. You can only lean your plant based on how much information you have. Sometimes, the things that are causing issues can come from activities not directly associated with the problem, and sometimes the problems are not as apparent to you if you don't have the right data.

bottlenecks, connected data, industrial iot, iot

Published at DZone with permission of Steve Garbrecht . See the original article here.

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