While the availability factor and also the quality factor represent relatively easy parameters to measure for the purpose of OEE optimization, the performance factor poses a challenge for many companies. However, the concept of the best-demonstrated cycle time in combination with a granular output measurement solves this problem in a simple way for companies.
The peculiarity of the performance factor of the OEE is that it has to be measured against an expected value. So the question is what output do I expect per unit time of continuous production – i.e. without downtimes and defective products – and what output does the equipment actually generate. Putting these two values in relation describes the performance factor of the OEE.
A simple way of defining the expected output is “nameplate capacity”, which is the equipment manufacturer’s statement of how fast the equipment can produce any given product. For certain scenarios, this information is actually of great importance – for example, if products are manufactured on an equipment for which it was not originally designed. If there are large deviations in this setup between the possible output of the system and the actual output of the product produced, the loss of performance identified in this way is sufficient for a first major optimization step. However, actual product-dependent differences in the cycle times cannot be included in the optimization with this approach.
Challenge of product-dependent cycle times
In operational practice, there is often a need to identify the performance losses depending on the product. In this scenario, a product-specific default speed is needed to calculate the performance factor. In the case of the production of large quantities, this value is determined by the industrial engineering department either from empirical values or through time studies. There are many reasons why values generated in this way do not correspond to shop floor reality:
- The default values were estimates and have never been correct.
- Default values are misused in the course of production planning by using them to correct the planning for production expectations that are not met in reality.
- The default values were measured a long time ago, but the equipment or the environmental conditions have since changed so that they are no longer correct – although these deviations can be both upwards and downwards.
- After determining the target values, employees have found a smarter way of operating the equipment through measures for continuous improvement, so that the original target values are exceeded.
The above list only addresses the issue of default values that are actually incorrect. In addition, it is common in operational practice that employees unknowingly or consciously do not comply with the default values through the selection of the operating parameters – this challenge will not be addressed in this article, however.
Best-demonstrated cycle time
How do you get reliable default values for the planning system on a large scale, i.e. for many products? This is where the best-demonstrated cycle time as an intelligent analytics function of oee.ai comes into play.
Based on a granular data model that endlessly stores the equipment status and equipment performance in minute increments, an algorithm can identify the best-demonstrated cycle time for each material number produced. This is done via a rolling window that moves across the time series and identifies the historically best period. This can be seen in the following animation. The graph represents the number of pieces produced over time:
The width of the window can be defined in order to identify a stable state with certainty. Times for faster cycle sequences may be possible in the short term, but do not represent a stable condition of the equipment. Depending on the production process, windows of 15 to 30 minutes have proven to be sensible.
In the course of using the algorithm, only windows in which no loss of availability has occurred are taken into account. If the algorithm does not find a window of constant production of e.g. 15 minutes, this indicates a very unstable production process in which the optimization priority should actually not be on the performance factor, but on process stability.
The results of the algorithm are made available either via API or csv. In this way they can be fed back into the planning system.
With this technology, the best actually produced cycle times for each individual product from the past can be identified at the push of a button and used in the planning system – and then confirmed again in real time by oee.ai in subsequent operational use or deviations are shown.
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