More production output: Red becomes colorful!

More production output: Red becomes colorful!

More production output: Red becomes colorful!

Digital twin of improvement measures with the PROXIA OEE/CIP method

More output, more profit. As simple as the formula may sound, it is difficult to identify the driving factors behind it. The PROXIA OEE/CIP method, based on operational data acquisition enhanced by messenger services and PROXIA measure management, shows where the potential can really be found on the shop floor.

OEE (Overall Equipment Effectiveness) is the key figure in production that all eyes are on. Put simply, OEE is the product of availability, performance and quality of a machine network. However, the contributions to OEE - typically between 75% and 100% - have different influence. Especially when it comes to tirelessly increasing the OEE in the spirit of MES and Kaizen.

The driving force behind the OEE: Availability

If we have a look at the detailed key figures that make up the OEE, we come up with the following conclusions:
Quality: Dedicated quality management has been in place in many manufacturing companies for many years, with the nice result that the relative values for quality are already close to the 100 percent mark (grade 1.0). Significant improvements are therefore not to be expected here. And that's a good thing after all, behind this is our claim to "Made in Germany".
Performance: Performance is without a doubt an essential parameter for large-scale manufacturers, but it offers few starting points for classic and special machine engineering sectors with their manageable quantities. This is due to the fact that the machine performance essentially determines the overall performance, which is a result of the applied technology. So a milling machine with a certain cutting performance was deliberately bought. And the machine is as it is.
Availability: It remains a driving force when we consider performance and quality as described above. The machine availability is the central element in OEE optimization. Following the assumption that quality and performance in relation to availability show only a very small variance, an increased system availability is the key to an improved OEE. The PROXIA OEE/CIP method therefore focuses on availability

100% availability in sight with Measure Manager

PROXIA has now introduced a Measure Manager onto the market in order to validate and verify the results reliably as part of a continuous improvement process (CIP). With the PROXIA Measure Manager, the user can track down precisely those contributions that have thwarted the 100 percent goal of availability so far.
The background: The ERP system is only interested in the "green area" of availability ("machine on the move"), because this information is relevant for calculation purposes. Red, however, stands for all the "unproductive" times such as breaks, setting up, staff absence, missing material or crane. However, these times cannot initially be charged to anyone. But minimizing them gradually brings us to 100% availability.
Very important: This "red" is not set in stone - it can be converted into production revenues! PROXIA technology makes it possible to record the individual faulty contributions with great meticulousness in record time and to work on their elimination - to put it casually: "Red becomes colorful".

Eyes and ears as sensors for the CIP

The person responsible for CIP must repeatedly check every improvement measure to see whether it is still useful or whether it has already lost its effectiveness due to other measures (CIP circle). The messenger services of the PROXIA MDE/BDE software help to adjust the processes in a targeted manner in their design space. This requires the measures to be fully digitized. PROXIA speaks of the creation of the "digital twin of a measure". This term, which at first sounds strange, includes the person responsible, the objective of the measure, the deadline for the resubmission, and the start and end times. Various measures can be grouped with PROXIA Measure Manager, for example in order to "optimize the machine park" or "introduce a CAD/CAM system". Target definitions are formulated in free texts, e.g .: "Machines often stand still during breaks, although parts with a long running time could go around them." For visualization purposes, a kind of information bubble is formed around each measure which is broken down into daily shifts as an example. Documents can be assigned and measures can be located in the timeline. The digital twin of the measures can be compared directly with the results of monitoring tools (example: how many production hours have actually gone by without personnel). Such multi-moment recordings are particularly valued by the management. 


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