Digital Twin

MES and MOM – A clarification of terms

Digitalization in manufacturing

Production is one of the most heavily optimized industrial sectors, and for good reason. Avoidable scrap or machine downtimes not only consume time and nerves but, above all, a significant amount of money. To prevent this, companies organize use digital systems to organize and execute their manufacturing processes. For this purpose, they often rely on Manufacturing Execution Systems (MES). Recently, another term has gained increased attention: Manufacturing Operations Management, abbreviated as MOM.

This blog post explains how MES and MOM are related and what to consider when choosing an MES.

What is MES?

MES is software that helps manufacturing companies organize their production. Initially, sales planning is carried out and corresponding production orders are created in the Enterprise Resource Planning (ERP) system. Subsequently, the production department uses the MES to execute these orders.

In the MES, it is determined who will execute which production order and which resources and tools they will use. During production, employees manually enter operational data into the system and therefore supplement the automatically collected data from machine controls and sensors. To ensure product quality, the MES enables planning and documentation of quality inspections.

The MES thus creates transparency within the production department. Finally, employees report completed orders back to the ERP system, triggering logistical and commercial follow-up processes.

What is MOM?

Manufacturing Operations Management (MOM) is a holistic concept with the goal of optimizing the overall value chain process. Companies achieve this by digitally managing their manufacturing processes and transparently providing manufacturing-related information across multiple departments. Production processes are considered an integral part of cross-departmental business processes. To ensure seamless communication from the manufacturing to the management level, information exchange between different IT system domains is essential. This includes, for example:

Product Lifecycle Management (PLM) for product development and planning work steps in production,
Enterprise Resource Planning (ERP) for sales planning and commercial order processing,
Manufacturing Execution Systems (MES) for executing production orders,
Quality Management Software (QMS) to ensure product quality,
Industrial Internet of Things (IIoT) platform to consolidate data from machine controls and sensors and monitor manufacturing processes in real-time.

The interaction of IT systems makes collaborating between different departments and teams more efficient, positively impacting the entire value chain process. Production operates at lower manufacturing costs and can ensure shorter delivery times and high product quality. By integrating production processes into the overall value chain process through the holistic MOM approach, companies can adapt quickly and flexibly to changing market situations.

How do MES and MOM differ?

MES is an important component of the MOM approach. As shopfloor software, it primarily focuses on executing tasks and processes within production. MOM, on the other hand, describes the overarching concept that integrates production processes into the business processes of the overall value chain. The approach aims to optimize the value chain by coordinating information across various departments. The concept includes not only the execution level (MES functions) but also adjacent functions from areas such as ERP, PLM, QMS, and IIoT.

What to consider when choosing an MES?

The challenge in selecting MES software is ensuring that it fits the company’s manufacturing structure and corresponding needs. For example, process manufacturing often requires recipe management, while discrete manufacturing involves working with bills of materials.

Furthermore, it is crucial to focus on the seamless integration of the system into the current IT infrastructure, encompassing elements such as PLM, ERP, QMS, and IIoT platforms. Following the MOM approach, maintaining cross-departmental information consistency significantly improves overall efficiency.

Companies should consider the following aspects:

Expandability
Depending on the project scope, initially rolling out some basic MES functions minimizes project risks. Subsequently, it is possible to gradually add further functional areas until all relevant processes are integrated. For this approach, a modular software that grows step by step with the company’s needs is recommended.
Scalability
In addition to the functional expansion of an MES to cover more areas, it is relevant whether the solution can scale to all manufacturing locations. This requires support for the relevant languages and the ability to centrally consolidate and analyze local information. Ultimately, the MES provider must also be able to conduct implementation projects on a global scale.
Customizability
Production processes are as individual as the manufactured products. The better the MES supports the company’s processes and information needs, the greater the benefit.
Future-proofness
The economic resilience of the MES provider and their affinity to integrating new technologies, such as IIoT and artificial intelligence (AI), are crucial factors for the system’s long-term development.
User Experience (UX)
If the software is intuitive and well-designed, it avoids acceptance issues and the need for extensive training measures. The most feature-rich system might be worthless if end users do not use it correctly.

If you are looking for an MES for discrete manufacturing and want to follow the MOM approach, CONTACT Elements for IoT could be the right solution for you. This holistic manufacturing management system combines traditional MES functions with advanced maintenance management, energy monitoring, and seamless IT integration. The result: cost savings through reduced scrap and downtime and the integration of manufacturing into the overall value chain process. Learn more about CONTACT’s IoT offering.

Numbers, please! Energy efficiency is measurable

Energy is cheapest and most environmentally friendly if we do not consume it in the first place. Therefore, energy efficiency makes a significant contribution to the energy transition, and we have already tapped into savings potential in many areas: LEDs are now standard and energy-intensive devices like old refrigerators or water heaters have either been replaced or switched off. At CONTACT, we have initiated a project to optimize energy efficiency in office buildings. It is surprising how much saving potential still exists, even though employees are already conscientious in their resource usage. By replacing electrical devices and air conditioning in the server room, as well as turning off and merging old servers, energy consumption has been reduced by 50 %. This is not only ecologically sensible but also economically beneficial. A lot can be achieved with little effort, provided consumption data can be logged and visualized.

Energy efficiency is not possible without software

The German government’s report on “Energy Efficiency in Numbers” provides an overview of the final energy consumption in Germany for sectors such as industry, transportation, private households, and commerce/trade/services.

Nearly a third of Germany’s final energy consumption comes from industrial processes. To achieve efficiency improvements here, it is essential to examine them more closely. A significant portion of energy (about two-thirds) is attributed to process heat, which is used, for example, in the production of products. To identify which facilities and machines in the production hall have saving potential, monitoring and controlling are necessary. Our software platform, Elements for IoT, provides companies with the ability to monitor, graphically represent, and analyze their consumption data. Data from metering points, such as those measuring power consumption, can be assigned to individual machines and production processes. Additionally, it is possible to process sensor values and machine control states and merge them into a digital twin of the machine. For the specific requirements of energy management, we have developed a new module that represents a continuous improvement process of energy performance indicators (according to ISO 50001). Starting from energy consumption, broken down by different energy types such as electricity or compressed air for a machine on the shopfloor, consumption values can be calculated down to a manufactured unit of the product. This also provides the opportunity to calculate the CO2 footprint of the manufactured product. The following example of a dashboard for a production facility shows a summary of a shift and provides information on energy consumption for the production process, as well as the average consumption for each manufactured unit from that shift.

Energy efficiency in production

Energy performance indicators can be used in various ways and are particularly important for audits according to ISO 50001. These audits require proof of a continuous improvement process. In addition to implementing sustainability concepts, this simultaneously saves resources such as electricity or gas.

Furthermore, energy information can be used to calculate the CO2 footprint, which can then be exchanged across supply chains. In the context of this data exchange, we implement the concept of the Asset Administration Shell to integrate the submodel for the CO2 footprint into our IoT platform.

Energy consumption data can also be useful in the manufacturing industry to optimize production processes. By assigning energy consumption data to the processes happening simultaneously, analyses show which sections are particularly energy intensive. Often, the usual metering interval of 15 minutes is not sufficient and higher time resolution data is required. Smart meters allow for sampling rates in the minutes or even seconds range, facilitating analyses that help optimize production processes.

AI-based forecasts for energy consumption

Interestingly, machines on the shopfloor are often found in standby mode, waiting for the next production order, even when there are no orders for the next few hours or the upcoming weekend. Optimized machine shutdowns which consider ramp-up times can directly save energy costs. A specific example of this is the implementation of an alarm mechanism that informs machine operators based on planned tool changes, services, or manufacturing orders about when it is advisable to shut down the machine. Additionally, the machine dashboard displays when the next order is due. Historical data studies have shown that for machines equipped this way, electricity cost savings can amount to about 23%. In the dashboard shown below, the shutdown recommendation is visualized by the red traffic light. It also indicates by how many kilowatt-hours the predicted value deviates from the actual measured power consumption.

The forecast of electricity consumption is based on decision trees and directly implemented in the platform. Consumption data is accessed through the digital twin of the machine. The forecast’s inference model uses data from planned manufacturing orders, including time data and information on the material to be produced, to calculate the expected electricity consumption in kilowatt-hours. If the actual measured value deviates from the forecast by a fixed limit, the system informs the responsible person(s) with a red traffic light on the dashboard.

Furthermore, peak management uses forecasts to avoid load peaks. If multiple machines or systems are in operation simultaneously at a production site, this can lead to overlapping peaks in energy demand, resulting in higher fees. Based on forecasts of the electricity consumption, it is often possible to optimize execution times and machine occupancy to evenly distribute energy consumption and prevent expensive penalty payments.

Developer Experience – from intuitive to complex

It sounds like an exciting vision of the future: users from every discipline can use ready-made program modules to quickly and easily create simulations, optimization tasks or analyses using artificial intelligence (AI). This can then also be implemented by departments whose employees do not have knowledge of a high-level programming language. That’s the idea. Of course, developers must first create these program modules so business users can assemble a solution that meets their requirements.

AI-powered analytics for the business department

Together with our partners, we are researching in the AI marketplace project to get closer to this vision. The name-giving goal is to develop AI applications in the field of the product development process and offer them on a central trading platform. The range will also include services such as seminars on selected AI topics or contract development as well as ready-made AI-supported apps and program blocks for very specific tasks. The development and reuse of the apps are currently being tested. The project team is evaluating the benefits and quality of the results at the same time.

Different programming levels for extended use

So that’ s the state of research, but how exactly do we at CONTACT support the development of reusable program modules, the integration of simulation models or AI-supported analysis methods? One example of practical application can be found in the area of predictive maintenance. Predictive maintenance means that maintenance periods do not take place at fixed intervals as before, but are calculated depending on operating data and events at the machine or plant. For such use cases, our Elements for IoT platform provides a solution to analyze operating data directly. The digital twin stores the data of the machine or plant in a unique context. This data can be directly retrieved and easily analyzed using block-based programming. With the no-code functionality of the IoT platform, departments can intuitively create digital twins, define automatic rules and monitor events, and create diagrams and dashboards – without writing a line of code.

In addition, there are applications around the Digital Twin that require more programming expertise. For this, the platform offers analysts the possibility to develop their models themselves in a higher programming language using a Jupyter Notebook or other analysis tools. Especially in the area of prototyping, Python is the language of choice. However, it is also possible to work with a compiler-based programming language such as C++. Continuous calculation of the predictions is then done by automating the models, which are available in a runtime environment. The code is executed either in the company’s own IT infrastructure or directly at the plant or machine in the field (edge).

We call this procedure low-code development, because only the code for developing the models is written. The data connection is made via the Digital Twin and is done configurationally. The piece of program code can then be reused as a program block for various applications, such as digital twins within a fleet.

CONTACT Elements for IoT is thus open to interactions at different levels: from the use of predefined building blocks (no-code), to the possibility of interacting with self-written program code (low-code), to the definition of own business objects and the extension of the platform based on Python.