How effective is my production? Less downtime for machines through OEE analysis

 How effective is my production? Less downtime for machines through OEE analysis


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  How effective is my production? Achieve Less Machine Downtime Through OEE Analysis

By Martin Grune, Key Account Manager Automation & Patrick Kraus, Head of Marketing Communications, infoteam Software AG

To meet the market's need for speed and speed Schedules while increasing cost efficiency often does not lead to the optimization of OEE (Overall Equipment Effectiveness). For this, the OEE must first be determined and its influencing variables displayed in order to be able to easily identify causes for deviations and optimization possibilities and to initiate measures directly or even automatically. However, what sounds simple in theory often presents companies with challenges in practice because heterogeneous machine landscapes and individual processes make the use of readily available standard software difficult.

How can the production be optimized, where there are possibilities for improvement, which processes are possible to slow down the operative business even indirectly from the background? The main performance indicators for this are GAE (Overall Equipment Effectiveness) or Overall Equipment Effectiveness (OEE). It describes the level of added value of a given asset and can therefore assume the values ​​0 to 1 or 0% to 100%. The OEE is calculated from the product of availability factor, power factor, and quality factor, where the terms include both the time aspect and the quantity aspect of the production (see Figure 1). The goal of an OEE optimization is to find out the reasons for the OEE factor deviation of 100%, ie, why it was not possible to produce [100] [100] the operating time,

  • 100% of the planned time cycle time reached,
  • 100% of the products are manufactured in the defined quality

Fig. 1: Simplified representation of the OEE calculation in production

Determination of the OEE

In order to identify optimization possibilities for increasing the OEE, the OEE is first calculated. A key challenge is to merge all the data from production into digital form in order to calculate and analyze them using software. These include, for example, the planned operating time, unplanned shutdowns, target quantity, actual quantity and yield. The data for this can come from different sources: In many places, existing digital infrastructures such as databases, file systems or Manufacturing Execution Systems (MES) can be used. In addition, additional data is often needed that must first be extracted from the heterogeneous landscape of many different controllers, industrial PCs, and sensors.

Since the individual components within this heterogeneous structure use different protocols for data transmission, the data must first be used standardized with so-called connectors. Depending on the application, this either requires additional hardware such as gateways, which access and standardize the data at defined interfaces, or software connectors installed on the corresponding PCs or controllers and which retrieve the relevant data directly. The harmonized data is then transmitted to the OEE software via a standardized protocol. In order for the software to accurately and meaningfully calculate the OEE, companies must specify in advance which data should be included in the calculation. For example, there may not be data available for the parts that are manufactured correctly, but this value can be obtained from the combination of other production data (eg, not reached the nominal rotation angle of a screwdriver., Screw connection "not OK").

Downtime also This often leads to increased discussion as companies have to define what a planned and what an unplanned outage is to each of their systems. For example, scheduled setup and maintenance times require upper limits, so exceeding these times can be considered unplanned downtime. The software must also be able to distinguish between different downtime. Only then can the availability factor be calculated correctly later.

OEE Software Solutions: Combination of Standard Products and Individual Process Knowledge

As understandable as OEE analysis and optimization are theoretically, they are a challenge for many manufacturing companies in practice. In particular, the aforementioned collection of the necessary data is often the key point between success and failure due to the heterogeneous machine landscapes and parallel software systems. In fact, there is no reason to reinvent the wheel here - it just needs to be rounded up. There are already standard software on the market that offers clear advantages over new developments with short time-to-market and low costs. One example is the Nexeed Production Performance Manager from Bosch Connected Industry. It has standardized interfaces with which different data sources can be standardized and integrated into the overall system. This transfer takes place via a special, production-specific and open protocol. In contrast to the widespread OPC UA, machine-to-machine communication (M2M) is not the focus, but the communication between machines and IoT solutions. In addition, the Nexeed Production Performance Manager visualizes the datasets in the first customizable detail evaluations, which then serve as the basis for discussing anomalies in production.

Common to all standard products is that they provide the required standard options for capturing, standardizing, and evaluating the necessary parameters, but OEE analysis requires company-specific process knowledge that must be mapped in the software. It therefore makes sense to implement custom add-on modules based on extensible standard software that identifies the OEE exactly to the company's requirements and uses the data from the standard software (see Fig. 2). At the same time, the OEE modules can access the functions of the standard software. For example, if the OEE changes, the raw data can be checked down to the smallest detail to determine the cause. Existing ticket systems can also be used to automatically distribute tasks to the appropriate employees based on the defined values ​​from production. This significantly reduces machine downtime by minimizing response time to deviations from standards.

2: Use of extensible standard software for OEE analysis using the example of the Nexeed Production Performance Manager, which consolidates and provides the heterogeneous data from production.

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2018-11-09 21:36:01
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