Digitalizing your Shop Floor

The key acronyms and their definitions. Getting to the bottom of OFE, OEE, OMP, and other such terms.

The biggest hurdle to stepping into a new field of knowledge is the technical jargon. It goes without saying that you will come across strange new terms at every turn in this new world. Worse still are the acronyms. The field of shop floor digitalization is littered with three-letter acronyms, such as OFE, OEE, and OMP. We have compiled a list of the main acronyms and created a glossary to provide a clearer overview. Here is Part 3 of our disentanglement of terms – for a language barrier-free journey around the world of Industry 4.0.


OFE

Overall Factory Effectiveness (OFE)

 

Every manufacturing process has always been a complex interaction of machines, people, materials, departments, companies, and processes. If we view these intertwined activities predominantly in isolation from one another, we lose sight of the complexity. One hand (department, level, ...) does not know what the other hand is doing/needing/causing.

 

Efficiency improvements in one area of the shop floor can lead directly to a decline in many other areas of the company. The solution, of course, is to focus not only on the performance of the individual equipment, but on the performance of the company as a whole. This approach underlies the overall factory effectiveness KPI, or OFE for short. Caution, beware of confusing terms! The concept of a KPI for an enterprise-wide view is far less common (and supported by reference material) than the omnipresent OEE for measuring equipment effectiveness. As a result, the term OFE has been far less widely adopted.

 

There are many other terms, such as OPE – Overall Plant Effectiveness or Overall Production Effectiveness, or even TEEP – Total Effective Equipment Performance. However, these always refer to indicators that take into account the interaction of many different operational activities and express the connections between machines and processes as well as the network of information, decisions, and activities of interdependent systems and subsystems. This "holistic" approach is regarded as an extension or modification of OEE, the reigning queen of shop floor KPIs.


OEE

Overall Equipment Effectiveness (OEE)

 

The road to success on the shop floor consists of continuous, step-by-step improvement of the production processes. The tool of choice for enabling and supporting this optimization are relevant KPIs. OEE – Overall Equipment Effectiveness – is the queen of the complex world of KPIs. It is considered to be THE controlling instrument for identifying and combating wasted resources in highly mechanized production processes. Examples of causes of waste may include:

 

  • Excessive waiting times
  • Unnecessary transport routes and material movements
  • Incorrect work processes during processing
  • Excessively long gripping and walking distances
  • Necessary rework or corrections

 

The effects of events such as these are immense – but they often go undetected (without proper control) or are misjudged. To counteract this, data is used, which is (should be) available to the relevant people as quickly as possible. This data comprises three factors: Data on (1) availability, (2) performance, (3) quality. This data is collected based on the concept of the "Six Big Losses".

 

Six Big Losses

Event Cause
Availability loss: Planned Stops Maintenance, cleaning, planned breaks/training, etc.
Availability loss: Planned Stops Malfunction, power failure, sick leave, lack of material, etc.
Performance Loss: Small Stops Minor stops due to temporary malfunctions, search for material, defects < 5 minutes
Performance Loss: Speed Loss Speed loss is everything that prevents the (theoretical) maximum speed
Quality Loss: Startup Rejects Startup rejects are rejected parts caused by heating, starting, or other prior manufacturing stages
Quality Loss: Production Rejects Production rejects are rejected parts produced during normal operation

 

We have highlighted the real-world challenges that shop floor management must face in order for productivity to actually benefit from OEE in the blog post entitled The OEE Paradox.

 

OMP

Open Manufacturing Platform (OPM)

 

Data acquisition, data exchange, data analysis – data is the raw material and basis of shop floor digitalization. The prerequisite for fully exploiting the possibilities inherent in machine data: it must be available in a standardized form – it must "speak the same language". The fact that this is often not the case has been a stumbling block on the road to Industry 4.0 for decades.

 

Although standards have been established – e.g., I/O-Link, RAMI, OPC UA – none of the systems has so far managed to emerge victorious in the battle for dominance in the world of industrial manufacturing. This has brought big players onto the scene: BMW and Microsoft. Their joint baby, which they named in 2019, is OMP, Open Manufacturing Platform. Since then, the founders have been working as an "alliance" to develop OMP together with their partners Bosch, automotive supplier ZF Friedrichshafen, and brewery Anheuser-Busch. The goal is to deliver solutions that enable industrial manufacturing companies to collaborate extensively, break down data silos, and overcome the disadvantages of complex, proprietary IT systems that have so far held back the optimization of production processes.

 

OMP supports other alliances such as the OPC industry consortium and the Industry 4.0 network platform. It uses existing industry standards, open-source architectures, and data models to drive scalable IoT infrastructures forward in the manufacturing industry. Initially, OMP working groups will focus on solutions that enable IoT devices and equipment to be connected to "the cloud". The cloud is seen as a key technology for digitalization. This is because cloud computing is the (only) way to analyze and harness the huge volumes of data upon which everything "smart" is based. In parallel, OMP working groups are tweaking the "language" to enable and facilitate the management of data in a unified format and across multiple sources with ever-evolving semantics.


OPC UA

Open Platform Communications Unified Architecture (OPC UA)

 

Machines, servers, clients, devices, etc. do not operate in isolation. Thus, if you use different software programs, programmable logic controllers (PLCs), and human-machine interfaces (HMIs), each creating their data in their own formats, this data needs to be interpreted. A "translator" is required. This translator must be able to understand data from different sources and send it back and forth in the format that each source needs and understands.

 

That’s where standards come in. OPC UA – Open Platform Communications Unified Architecture – is one of these such standards. It enables the exchange of data between a wide range of PLCs, HMIs, servers, clients, and machines, effectively providing them with a common language. OPC UA is the most important successor of the classic OPC (Open Platform Communications) – also developed by the OPC Foundation. One of the biggest advantages of OPC UA is its platform independence: OPC UA can be integrated into Windows, Linux, Mac, Android, and other platforms. This is important in all manufacturing industries in which machines and systems operate on different platforms – in other words, in practically all of them.

 

In addition, OPC UA (unlike its predecessors) not only transmits machine data (controlled variables, measured values, parameters, etc.), but can also describe it semantically in a machine-readable form. This semantic description is key, because the degree of interaction and cooperation between machines and equipment components is constantly increasing. By the same token, even though humans have the ability to interpret vague information correctly, to some extent at least (because they have the benefit of experience and knowledge), machines can only act correctly if information is unambiguous. This semantic unambiguity is provided by the OPC UA standard.


MQTT

Message Queue Telemetry Transport (MQTT)

 

The Message Queue Telemetry Transport (MQTT) messaging protocol was first developed by IBM and Cirrus Link (formerly Arcom Control Systems). Although it has been around for more than two decades, it is considered to be a protocol that has actually gained in popularity in recent years (1) – and one that is also best suited for modern Industrial Internet of Things applications. In this context, MQTT is considered the solution of choice especially where IIoT applications rely on active notifications (i.e., the device provides data only when needed and not on a regular basis).

 

MQTT uses a publish-subscribe pattern to exchange messages. An MQTT system includes a broker and multiple clients, where clients can be either publishers or subscribers. Publishers send data to the broker as MQTT packets, which consist of a "topic" and a "payload." The broker then distributes the data to subscribers based on the topics in which they are interested. The MQTT protocol does not specify a standard format for data transmission, although applications commonly use the JSON protocol or plain text.

 

One of the recognized advantages of MQTT for IIoT applications is that the publish-subscribe pattern used allows IoT developers to solve certain common connectivity issues: For example, request-response patterns require the client and server to be online simultaneously to ensure that data is successfully transmitted and received. For IIoT applications in particular, however, it may be impossible for devices to maintain a strong enough connection to the network to receive the required data. Accordingly, the request-response pattern is not suitable for these types of applications. The publish-subscribe pattern of MQTT, on the other hand, is tailored to situations where there is no guarantee that devices will be connected to the network at the same time.(2)

 

(1) Source: Bernhard Osterheimer, Axel C. Schwickert, Thorsten Rühl: Overall Equipment Effectiveness: Grundlagen, Konzepte, Methoden, Werkzeuge; Justus-Liebig-Universität Giessen 2012

(2) See: Mario Sallat, Das Anwendungsprotokoll MQTT im Internet of Things, Hochschule Offenburg, 2018

(3) Source: IT-Production online magazine, Die Erfolgsgeschichte eines Protokolls - MQTT im Industrial Internet of Things; https://www.it-production.com/industrie-4-0-iot/erfolgsgeschichte-eines-protokolls, 16 September 2019

 

How we approach these issues: We make sure that machines are capable of using the standards and protocols that enable them to communicate across the board – so that you get the KPIs that drive you forward. In short, we solve connectivity issues on the shop floor. If you need us to connect your machines to your digitalization software – you can count on us.

 

View Smart-Factory-Glossary | Part 1

View Smart-Factory-Glossary | Part 2

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Tirol | Austria

 

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Peter Baumgartner

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