In uncertain market situations, as is currently the case, companies need stability in production combined with flexibility in order and capacity planning. The way to achieve this is through digitally controlled production. The data hidden in shop-floor machines is a treasure that needs to be exploited to achieve cost and resource efficiency.
By digitally transforming production and supply chains, companies can achieve the strategic goals of enterprise-wide resource efficiency: Keep costs under control, increase resilience in processes and supply chains, increase contribution to climate protection, secure innovative strength for the future.
In industry, value creation takes place in the factories. Therefore, the main focus of resource efficiency must be on optimizing all manufacturing processes. In concrete terms, companies achieve resource efficiency through data-driven shop floor management.
Digitization delivers measurable results – plus a 12% OEE increase right from the start
Today, data-driven shop floor management is of vital importance. Big advantage: Smart shop floor management delivers concrete and measurable results.
- If you want to increase the availability of your equipment, you can achieve this by measuring the overall equipment effectiveness (OEE). Our experience: Companies achieve a 12% higher OEE in the pilot phase, after the rollout an increase of more than 20% OEE is possible.
- Those who increase their plant availability achieve this through fewer downtimes and malfunctions. This reduction in turn has a direct positive effect on personnel and energy requirements – with corresponding efficiency and cost benefits.
- Those who want to bring performance and delivery reliability in their subsidiary factories to a target level as quickly as possible will achieve this by measuring OEE in real time via the cloud.
- Those who need a certiﬁcate such as ISO 50001 for regulatory reasons, will achieve this with the results of data-driven energy monitoring.
- Those who want to produce in a more climate-friendly way can rely on digital energy monitoring in conjunction with other smart factory solutions.
For example, an automotive group has digitally connected its most important machines with FORCAM, and the energy data is continuously evaluated. In addition, the customer’s team correlates performance and energy data so that the most energy-efficient machines are used for each order. The result: Our customer has reduced energy consumption by more than 20 percent over the past few years – while maintaining the same processes and increasing production volumes.
Transformation: Better to move forward in small steps than to fail big
The Smart Factory project can only fly with two wings – Technology 4.0 and Transformation Culture 4.0. That is why the following applies to a digital transformation: first motivate the employees, then optimize machines and processes. Even the project management of the project should be organized in a way that conserves resources: It is better to proceed in small steps than to fail big.
A checklist for project management:
- A cross-divisional change team defines tasks, sets deadlines, evaluates suitable technology 4.0
- Plans, procedures, and above all, initial successes are communicated on an ongoing basis to ensure acceptance and motivation.
- The strongest argument for employee motivation: Digitally controlled processes ultimately ensure greater site and job security. Thus, the use of digital technologies is part of the solution for more future security.
- The start is made with a “pilot”, i.e. an area separated from the main production. Initially, it is sufficient to digitally connect 10 to 15 “critical” machines and train new processes in the pilot area.
- All experiences, results and successes are documented – and thus made replicable.
- The rollout is then organized on the basis of all the experience gained.
Technology: Advantage modularity and interoperability
Data-driven shop floor management is best supported by IT solutions that can be modularly composed into individual architectures that enable free exchange with other systems. Modularity and interoperability have two key advantages:
First, companies can flexibly address their goals and measures step by step at their own pace – whether transparency through performance analyses in a pilot, whether higher resource efficiency of an entire production, whether optimized order planning through predictive and AI solutions.
Second, desired solutions can also be seamlessly integrated in the future.
Suitable modular technology copes with three stages:
- CONNECTING: Digitally connecting old and new machines – regardless of age, manufacturer, or type
- HARMONIZING: Big Data becomes Smart Data – bringing signals into a uniform “language”, i.e. into semantically precise information and thus making it usable for further systems
- COMPONING: Enabling seamless interaction between in-house and external IT solutions and systems.
1) Connect machines and create transparency
The vast majority of factories still use machines from different manufacturers of different vintages with different control systems. Therefore, most companies need an end-to-end digital connection of old and new machines (brownfield/greenfield) to protect their investments in existing equipment and at the same time enable innovative applications to achieve sustainable manufacturing. The digital connection of such heterogeneous machine parks is considered the central challenge, especially for global manufacturing companies with international production networks.
The digital connection of such heterogeneous machine parks is considered the central challenge, especially for global manufacturing companies with international production networks.
Different machine signals translated into one standard
A high-performance edge solution integrates a wide variety of machine controllers and standardizes the signals into a uniform machine data model (Machine Twin), which is available in downstream systems – for example, for real-time analyses in SAP DMC (Digital Manufacturing Cloud), for forecasts, and for AI applications.
The solution must be able to obtain a wide variety of measured values and key figures from the collected signals. Of fundamental importance, for example, is the information regarding a machine’s operating status i.e., in production or at a standstill – measured objectively, as well as the reasons for the standstill.
Templates accelerate the machine connectivity
The connection of the same plants should be easy to scale. Templates in a machine library, the “Machine Repository”, ensure such global management. Once a plant type has been recorded and successfully connected, the template concept must make it possible to connect further identical plants, whether in the same plant or distributed worldwide, simply and quickly.
Such a machine library works in our solution FORCE EDGE CONNECT with an innovative plug-in concept. In addition to the most important industry standards such as OPC, MTConnect, modbus, MQTT, etc., manufacturer-specific protocols such as Siemens, Heidenhain, Fanuc, etc. are also available. To support NC machines, the solution also includes extensive file exchange functions. Here, too, both standards and manufacturer-specific protocols are available.
2) Harmonize signals into smart data and increase efficiency
From signal to meaning: Data is the oil of the digital age when a wide variety of signals are “refined” into data. This means that they are given the right meaning by software. In operational terms, this means that once the foundation has been laid for the digital networking of all factory systems, the collected machine and sensor signals must be converted into relevant and usable information – Big Data into Smart Data.
The goal is the digital twin of production, which maps all processes in real time in all desired systems. It provides all IT systems and apps with the smart fodder they need for a wide variety of real-time analyses. With this precise information, analyses of all kinds can be run in specialized programs – for example
- Efficiency and quality analyses (overall equipment effectiveness OEE)
- Traceability (Track & Trace)
- Resource efficiency and CO2-reduced production (energy monitoring)
High-performance, cloud-based computing solutions for data modeling and validation are necessary for the digital twin of production.
Real-time analyses are the first step towards higher efficiency. For sustainable success, comprehensive historical analyses should also be possible. Key questions for sustainable optimization include:
- In which production area was the highest quality achieved in the past month?
- Which machines had the highest energy consumption in the last six months?
- Which machines are likely to have increased maintenance needs in the next month?
Typically, the solution delivers the data directly to the consuming system. However, signal values can also be stored locally and queried later.
This is provided by the so-called data lake. It provides both the raw values of the signals, such as consumption, temperature, counter, feed rate, and automatic mode, as well as business-related summarizations, such as production status or good/bad counters, provided these can be derived from signal combinations.
In addition, a data lake should be able to store configuration changes, write operations and log transferred NC files. Among other things, this makes dispatch logs possible:
- When was which signal sent and how?
- Who changed something in the configuration of the machine connection and when? (Revision security)
- Which file with which version number was sent to which system (NC module) or retrieved at which time?
3) Composing IT solutions – and ensuring flexibility in the future as well
In terms of technology, the goal is to have all the desired systems working in real time with uniform data – both production and planning. Shop floor and top floor run synchronously.
To achieve this, the digital twin of production must be available to the company’s own or external systems for current and historical real-time analyses – from performance analyses (OEE) to traceability and energy monitoring.
Open web interfaces and common communication protocols ensure free data exchange. The free composition and collaboration of own and partner solutions becomes possible.
Four common protocols are available to deliver data upwards:
- OPC/UA (Open Platform Communications/Unified Architecture),
- MQTT (Message Queuing Telemetry Transport)
Via the Restful API/HTTP, all configurations and master data can be read out and even written by higher-level systems. This enables seamless and automated configuration in complex architectures.
Data-driven shop floor management with modular IT solutions and a step-by-step transformation process make production more flexible, enable short-term redirection and provide concrete support to make a company more resilient overall to faltering supply chains and rapidly changing market requirements.
Über den Autor
Oliver Hoffmann ist Geschäftsführer bei FORCAM, zuständig für den weltweiten Vertrieb, das Marketing, das Partnergeschäft sowie die Digitalisierung ausgewählter Geschäftsprozesse. Der studierte Wirtschaftsinformatiker hat über 25 Jahre Erfahrung im Vertrieb von beratungsintensiven Software-Lösungen.