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The concepts behind benchmarking research for industrial operations have been applied successfully by many leading organization in the world. However, many companies still struggle with the basics. Benchmarking, in the simplest terms, involve comparing performance to peers, understanding gaps in operations, and taking steps to close the gap and improve performance.
Unfortunately, it is not as simple as it sounds. Below are the top 3 challenges we have seen industrial organizations face while benchmarking their operations.
Granularity: Benchmarking Research is Too General or Too Specific
The first area of challenge we have seen at LNS Research is around the business processes that need to be benchmarked. Often the topic that needs to be benchmarked is too general or too high level, which makes it difficult to take specific steps for improvement.
A good example of this is benchmarking “operational excellence” this means many things to many different companies and it is difficult to garner real actionable steps for improvement when companies look to benchmark such a topic.
On the other extreme, there is a similar issue if the business process that needs to be benchmarked is very specific. For any benchmarking process to be successful, it is critical to understand how the outcome of the process impacts the key goals of the division or plant or even the overall organization. This in turn means specific is good but that can make the quest for data very challenging.
A good example of this would be trying to benchmark the throughput and mean time to failure metrics for a very specialized piece of machinery in a very specialized industry. All we can say is good luck getting a statistically relevant sample in such a case.
Data: Availability, Quality, Statistical Relevance, and more.
The second key challenge with benchmarking industrial operations is data. If all data had the following characteristics benchmarking projects would almost always run smoothly:
- Easily available
- In one central location
- Using common definitions
- Having a statistically relevant sample size
Unfortunately, this is just not the case and it takes a lot of hard work to get there.
For example, if you are planning to benchmark the quality processes of five plants in North America and there is no consistency in the way data is collected across these plants or how metrics are defined it will be an uphill battle. In such a case, it becomes very challenging to effectively execute the benchmarking process even internally between plants; never mind against external organizations.
Getting Value from the Results
The final challenge has two parts and focuses on the way the results of the benchmarking process are utilized. This stage is often more important than the benchmarking process itself. Because, if what you learn from the results of the benchmarking process isn’t applied to the business, the entire exercise was one in futility.
First is the challenge in achieving the right actions from the results of the process and effectively executing these actions. To accomplish this effectively there needs to be buy-in from all levels of the organization as well as the right culture in place to accept the change due to the new actions.
The second part of this challenge lies in answering the question: What happens next?
Organizations that think about the benchmarking process as a one-time exercise are likely to fail. The key to the success of any benchmarking process is in setting up a culture and process of continuous improvement.
The real value of a benchmarking exercise is delivered when you learn from the results of the program, apply those recommendations, track the success of the actions and continuously improve based on the results of those actions.
Has your organization faced similar or different challenges? We are sure there are many other challenges that companies have faced while benchmarking industrial operations that are not covered here.
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