Beyond KPI’s

Working Together or in Silos

Working Together or in Silos

In the asset management world measuring performance through the right KPI’s is important, focusing on the results of the asset management processes and adjusting regularly to the trends seen on the dashboards is vital to continuous improvements.
However it is not enough just to focus on the KPI’s, one always needs to be aware of what is behind the KPI’s, what behaviours KPI’s are driving and what is the ultimate goal for the business as a whole.

Example:
A batch production process breaks down, it is a small component that is relatively easy to change, just takes a few hours to do. However, the spare part is not available in stock and it will take 2 weeks to get that spare part. Luckily, they have two exactly the same types of processes side by side and the production batch cycles allow for the part to be taken from one manufacturing process and installing it on the other process while the batch preparation is done. Thus not affecting the production and delivery of the product is without any production losses noticeable.
So the Maintenance Supervisor adapts to this situation and had a Maintenance Technician perform the switch regularly keeping the production, as well as the customer, happy because production went as planned.
However when focusing on the maintenance KPI parameters this results in an increased Break Down Maintenance over the 2 week period, as they needed to move the part from one process to the next in line with the batch production cycles. This also resulted in non-compliance to part of the PM program, as the maintenance technician did not have the time to do all the planned PM’s because of the added Break Down Maintenance. It can also be assumed that because the PM’s where not done that could result in further unforeseen breakdowns.
The Maintenance Manager comes to the Maintenance Supervisor not happy with his decision, because he monitored the KPI’s and could see that the Maintenance KPI’s, noticeably the break down KPI and PM compliance KPI where not trending in the correct direction.
After weighing the options and understanding the story behind the KPI’s development the Maintenance Manager agreed that out of a bad situation the Maintenance Supervisor selected the best possible path.
Conclusion:
If everyone is focused on a single dominating goal, it is less challenging to adjust to situations as described in the example here above, as long as we understand the underlying attributes that affect the KPI’s developments.
However in a silo situation where there might be tension between the silo’s (e.g. production vs. maintenance). In that case the example might have developed in a different way, i.e. half the production down for 2 weeks because of a failure where the spare part cannot be delivered for 2 weeks, the maintenance KPI’s would suffer a bit (one break down) but the production KPI’s would suffer even more.
Food for thought:
Is there a clear understanding in your organization for the why’s of the decisions that are made, i.e. are there clear governing goals?
Do you sometimes sacrifice your goals for the greater governing goals? And are you recognized for that?
I welcome your feedback and discussions below this Blog post.

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EAM & CMMS Systems, 10 times more data in the system or 10 time less done with the data available?

 

The road to success in using information systems is a difficult one to navigate. When an implementation of a system is started some assumptions are made about what is needed from the system, how it will help with decision-making, how it will help with execution  of maintenance activities and many other factors. The maintenance environment is a complex environment to begin with, then on top of that it is an environment that is constantly presenting itself with new challenges and changing operational conditions. The markets change as well as many other external factors, the knowledge is leaving from the manufacturing environment because of aging workforce as well as more competition for the remaining workforce with knowledge and experience and the list could go on and on.

At the core of information systems is the gathering of data, it needs to be reliable data so that the decisions from that data are based on solid foundation. Without reliable data the decisions made can not be reliable. The way data is collected is extremely important, wherever there is manual input you are introducing an element of risk for mistakes being made. Wherever the data is collected automatically there is an element of failure or calibration error introduced in the collection process. There are also many other factors that are necessary to remain vigilant about.

To answer the question headed in the Blog, today we have the capabilities to store extreme amounts of data in our systems and databases. So too much data is probably not an issue in most cases. Navigating through that data can be challenging but the most important thing about the data should be that it needs to be reliable and accurate! If there is too little done with the data, probably in most cases it is possible to improve how the data is being used. In helping with good quality decision-making it takes time and a lot of thoughts to develop, it is an investment that can be quick to return a profit.

I hope that you have enjoyed the read and if you have any comments or questions please don’t be shy to post them below or contact me directly. Thank you for reading and hopefully sharing.

Data in Maintenance & Reliability

Since my studies in Manchester University, where I studied MSC in Maintenance Engineering & Asset Management, I have always been interested by Data and how we tend to use it to further our Maintenance & Reliability processes.

My MSc thesis titled: “Data collection and its use to advance maintenance management and maintenance practices to support business objectives” discusses it in great details.

So why post a little blog about it? Well, I wanted to get a discussion going about the three main focus areas once you have decided what data to collect.

1. Data collection systems: How can we effectively use them for our benefits? The systems are many and the data is of various natures. There are for example the graphs from our vibration program, the thermal images from our IR program, a lot of statistical data from various systems like ERP, EAM, CMMS, Cost & Profit from accounting data systems… and the list can be quite long. With all of those systems how can we collect all the data and send it to the Data processing systems to be processed effectively and efficiently?

2. Data processing systems: These systems can vary in nature and function. Usually these systems are as many as our techniques to collect data. To effectively process the data it is a key factor to gather the data in a perfectly uniform way. This can be challenging to do and we will need great work processes to be able to get this right.

3. Information output systems: The quality of the results depends greatly first on the quality and amount of the data collected, secondly on the way we process the data and finally on how we interpret the information that comes out of the information output systems. It is crucial for the quality of the decisions made to have the data uniformly collected, processed and put out of the information systems.

In conclusion, it is a three-step process:
1. Collect the data.
2. Process the data.
3. Output information.

After these three steps we will need to make decisions that benefit our Maintenance & Reliability process and the positive effect of those decisions will depend greatly on the quality and uniformity of each step in the process. 

Data collection systems - Data processing systems - Information output systems

Data collection systems - Data processing systems - Information output systems

I would appreciate all of your comments and discussions here below, thank you for your time and interest in my Blog.