THE ONE-STOP DIGITIZATION SOLUTION: FASTER ENTRY INTO INDUSTRY 4.0

How can the manufacturing industry find an easy access to the digital world? Nobody can ignore the fact that machinery and equipment need to be digitally connected with each other. No matter how old the systems are and what the origin is, shop floor processes and thus productivity cannot be optimized without digital networking. FactorE. OS is the innovative cloud application. The software can be used independently or in combination with an MES or a Smart-Factory solution. The software acquires data on performance, condition, capacity utilization and energy consumption from every plant by permanently measuring on the power supply line whether it has an IT interface or not. The intelligent pattern recognition recognizes patterns; the modern machine-learning technology identifies and analyses key figures in real-time. FactorE automatically derives recommendations for action.

Plattling, December 14, 2017

Machine Data Acquisition out of the Power Socket

Stategy&, a strategy consulting company belonging to PwC, predicts that manufacturing companies in Germany could increase their productivity by an average of 3.3 percent and reduce their costs by 2.6 percent, e.g. by optimizing the energy consumption of manufacturing plants. The German manufacturing industry however is lagging compared to the international manufacturing industry in terms of digitization and networking of production processes. According to a survey conducted by the Centre of European Economic Research (ZEW) among 4,500 companies in Germany, only four percent of all companies carry out a digital transformation project or are at least in the planning phase.

 

Why is Digitization of Machinery so neglected?

The reason is that machinery is usually very heterogeneous. The fleet of machinery has grown over many years and is made up of machines from a wide variety of manufacturers and of all ages. Older machines do not have an IT interface and are therefore not suitable for industry 4.0. The EU Commission found out in a study that there are around 2.8 million systems operating in machine tools across Europe, compared to only 700,000 machines equipped with corresponding, mostly manufacturer-specific communication interfaces. The progress in digital networking of the non-Industrial 4.0 suitable machines is slow. Machinery must be retrofitted with signal transmitters and a digital I/O interface to link machines with a Manufacturing Execution System (MES). This is expensive and time-consuming and requires considerable effort. It is also expensive if the machine manufacturers need to activate the IT interfaces in modern machines. Bitkom Research conducted a survey which shows that for nearly three quarters of companies, these costs represent the biggest barrier when it comes to the use of industrial 4.0 applications in production.

 

How Production KPIs can be generated on a Power Line

For manufacturing companies and especially for small and mid-sized production enterprises, it is one of the most important things to find a quick, easy and inexpensive way to get started with Industry 4.0 and the Internet of Things (IoT). This is what T.CON GmbH & Co. KG has made its goal together with the strategic partnership with Factor-E Analytics GmbH (FACTOR.E): to provide support to mid-sized companies in this field. With FactorE.OS, the Berlin-based IT company has launched an innovative, scalable cloud application on the market. Machines of different manufacturers and machinery of any age, whether they have network connectivity and can communicate with each other or not, can easily be connected. T.CON integrates the cloud-based Industry 4.0 solution into its own solution portfolio and is responsible for sales and implementations and support. FactorE.OS is a revolutionary solution because it records the process data on the performance, condition, utilization and resource consumption (electricity, compressed air, heat) of a plant directly on the power supply line. Only 30-60 minutes for the installation of the hardware on one machine are necessary. The result is continuous and precise measurement of performance data. The collected raw data is filtered, aggregated and then transferred to the cloud in encrypted form using state-of-the-art IoT protocols such as AMQP or MQTT – via Wi-Fi, 3G, 4G, or Ethernet-based industrial networks.

 

Real-time Analysis with the help of Machine Learning

The data flows in uninterruptedly and is processed by Factor-E-Cloud in real-time. The software uses intelligent pattern recognition to determine the necessary production key figures: overall equipment effectiveness (OEE), utilization, output volume and also energy consumption. This information provides more insights into the operating status of connected units; the “out of the box” solution helps recognize and classify the machine status without having to train algorithms for this purpose. Reasonable correlation and “on the fly” analysis is made with self-learning machine learning algorithms. All results are visualized in user-friendly and easy-to-handle dashboards at the desired hierarchy level (plant, production line, machine). Actual values are continuously compared with target values so that any discrepancies are immediately noticed, and necessary actions can be taken. Up-to-date OEE values (availability, performance, and quality) are displayed in aggregated form on all hierarchy levels and compared with the average long-term OEE. The target values for OEE, energy consumption or produced quantities originate either from an IT system - MES or ERP - or they are entered manually in FactorE. OS or defined automatically by the software. It is still possible to enter data manually. Manual input is required where automatic data entry or recording is not possible. This is necessary in case that the machine is stopped, if there are no orders to be produced, in case of cleaning or if machine learning reaches technical limits as in the case of incidents for which no pattern can be made.

Alarms give Recommendations for Action

The innovative cloud application wants to go one step further: if actual and target values deviate significantly from each other or a problem or error appear, the worker gets recommendations for problem-solving with the help of machine learning algorithms. A notification or alarm informs the production staff if they need to invent and how to react. In case the application discovers repair work on a tool due to an increased risk of failure, the application recommends the immediate replacement of the tool (Predictive Maintenance). If a machine is in idle operation longer than usual, FactorE.OS may recommend changing the CNC software. Production staff also get the information when the next production order can be loaded and started. To do this, the cloud solution only requires the order number and the target quantity from the MES or ERP system so that it can continuously compare with the produced quantity from the current production run.

 

Increase Productivity, Cut Energy Consumption

A highly efficient energy data management can also be established in this way: it is an essential requirement for an increase of productivity of the entire machinery. If there is a significant difference between actual consumption and target values, the solution immediately sends a message to report the discrepancy. Also, costs for quality losses that can arise due to high power consumption are immediately reported; proposals for optimizing energy consumption are made at the same time. These are the major advantages of FactorE.OS: no matter which company size or industry, the application offers a real added value in optimizing production processes across the entire machinery. What does that mean for daily business? Customers using the cloud solution benefit from higher productivity in their production, a lower power consumption and reduced costs because of less machine downtime thanks to predictive maintenance.

 

Synergy Effects and Added Value by linking MES and ERP

The commercial value increases even further if the real-time KPIs are integrated into an MES and/or a Smart-Factory solution or an ERP system such as SAP ERP and related to other data relevant to production. This is a real added value: with this combination, a comprehensive transparency can be created that enables further optimization of production processes. You can then exactly identify, for example, whether calculated setup times in the SAP scheduling plans or times for staff resources are correct. Energy-efficient production runs can be identified at once for each individual product by analyzing energy data (integrated into the ERP processes). Consumption values from machinery and the energy prices saved in the ERP system are the basis for the controlling department to carry out exact energy cost planning.

 

Flexible Deployment on Multiple Cloud Platforms

With the cloud-based application of FactorE.OS together with an MES or an ERP system via REST APIs a smooth bidirectional data exchange between the applications is ensured - no matter whether on premise or in the cloud. FactorE.OS is platform-independent, i.e. it is extremely flexible, as the application is not tied to a particular cloud platform. It is currently running in the Microsoft Azure cloud, but Amazon Web Services (AWS), SAP Cloud Platform or a private cloud at the customer’s site are also possible. Soon FactorE.OS will also be available on Siemens MindSphere as an application platform and as a platform-as-a-service offering. Configurability and flexibility are constantly being improved and extended. For example, additional functions for the predictive maintenance toolbox or the development of new Smart Grid applications are developed and included.

“With FactorE.OS manufacturing companies benefit from a simple and reasonable application to digitally network all systems of heterogeneous machinery in line with Industry 4.0 and thus make the best possible use of the optimization potential in the production.”
Thomas Blöchl, Head of the MES Division and Member of the Board, T.CON GmbH & Co. KG


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Norbert Kytka, Headquarters Plattling

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