PrintLogin to download pdfData Analysis & Interpretation [L]

Overview
Data captured should be accurate, easy to understand and visual. Only then would it be useful information to take action on. In most cases there is too much data captured, however little of it is useful in its current format. A lot of time and resource is taken up to sort the data and make any sense of it.
You need to understand the key drivers for your performance improvement and should then align the data required to be captured to affect this. In almost all cases if the data is captured manually,there will be a significant proportion of inaccuracy and so it is beneficial to keep this as simple as possible and really target what is absolutely necessary.
Finally, the data should be presented in an easy to read format for quick analysis. Therefore make it as visual as possible by use of colours, graphs and charts. This pinpoints the areas of focus quickly and you can then use the time valuably by discussing what you need to do to address the concerns.

Benefits
By only capturing the relevant data accurately and presenting it in an easy to read format will ensure that you are focussing on the correct areas of concern. This will also save time and effort in trying to determine where you need to focus as you won’t have to dig through the information and try to make sense of it.

Explanation of each element in detail:

1. Data Collection and Accuracy
You need to ensure that the data you’re using is accurate, because if it’s not, you’re probably not making the best use of your resources. The reality of what’s happening on your production area may differ from what your information is telling you.
In many organisations when a machine stops, the operator logs the stoppage time and reason manually. They may do this every hour, or even after each shift. That means that there’s a lot of guesswork involved.
On a production line, where the flow of product is through various machines, there will be a critical(bottleneck) machine or process present and the production output would be determined by this machine. Whilst it may be useful to capture data at every machine, it is not very useful to know what has affected the line output. To simplify and impact line performance, as a start point it would be beneficial to only capture downtime information that affects the line stoppage and the data should be captured at the point of the critical machine.

To ensure accuracy of information, the data should be captured in real time. This will ensure that the information is as accurate as possible. The best way to capture data accurately is using automated systems. If this is not possible and manual systems are present, it would be useful to install timers on the machines to accurately determine the stoppage time. This stoppage time and reason for the stoppage can then be logged manually.

2. Data Categorisation

You’re bombarded by vast quantities of data every day. It can be overwhelming. Where do you start? Does your data allow you to focus on the right areas?
One way of effectively breaking down your data is into the six big losses. This enables you to focus your effort where you will get the greatest return. Information is easy to deal with and shows you where your losses are coming from – fast. Below is a model that defines how the data should be categorised:

OEE

Once you have the data categorised in this format, you should then drill down to identify the various faults within the categories. By working on the largest fault type will enable you to have the greatest impact on improving the performance. At a tactical level this is fine, however strategically to determine if the greatest loss is getting worse or improving, it is useful to trend the data over a period of 8-12 weeks.

3. Data Visualisation

Endless data is almost impossible to digest. To make a real impact, the information needs to be clear and presented in a way such that at a glance you can see where you need to focus to make real improvements to performance. If viewing numbers, it is beneficial to use coloured text to identify the focus areas. For ease of analysis, graphs are better to view the information. Below is an example ofdata categorised and trended over time in a graphical format for ease of visualisation:

data analysis

Conclusion
By making sure the data is captured accurately, categorised and presented in an easy to read format will enable you to get value out of it. This will ensure that your improvement efforts are focussed on areas that make a real impact. You will be able to make better use of your resources as they will be fixing problems that are impacting your key drivers of performance.

Current State Assessment
1. Do you utilise all the data that you capture?
2. Is the data at a good level of accuracy (at least >80%)?
3. Does the data give good information that allows on the areas that impact your key drivers (e.g. loss to line)?
4. Is the information categorised in a way that easily identifies the greatest loss?
5. Is the information presented in an easy to read format such as coloured text, graphs and charts?
6. Is data such as mean time between failure (MTBF) and mean time to repair (MTTR)captured?

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