Hardly any other term has become as much known in recent years as the term "industry 4.0". New topics such as "Big Data" and "Artificial Intelligence", or "AI" for short, are being added and are focusing on the comprehensive and value-adding use of the comprehensively available data worlds.
The road to industry 4.0
The starting point of every industrial revolution was the use of new technologies and the associated improved organization and management of entire value chains. Today, the focus is on the intelligent networking of all value-added processes.
The 4th industrial revolution
The term "Cyber Physical Systems" is being introduced and encompasses the integrated networking of the entire value-added process and product life cycle. Cyber Physical Systems refers to the integration of embedded information technologies in objects, materials, devices and logistics, coordination and management processes as well as their networking. In the context of production, one often speaks of the Smart Factory, in which all data is used comprehensively. In this context, a manufacturing execution system (MES) has the special role of acting like a hub in a wheel with many spokes. The MES provides a home for the de-centrally recorded and partly also stored data and its use for the planning, control and monitoring of the entire manufacturing process underlines the value of MES.
Development trends in production and industry 4.0 toolbox
The toolbox shows the status quo and future development paths via the "performance classes" that are relevant for production, the consistently successive characteristics of the "application layer", the production-related applications from "no system support" to "automatic, highly integrated processes".
Where machines are still working without coupling today, they will interact in the future with each other with regard to their status and work progress. The company-wide networking of production will enable integrated and lean processes. The production, today still mostly a black box which is decoupled from the corporate environment, becomes transparent from the supplier to the customer. Man and machine move closer together. The human-machine interface will evolve from the PC to mobile devices and virtual realities and assistants.
Status quo in industry
Based on a study conducted by Porsche Consulting in 2016, it is clear that major changes will occur in the coming years. The benefit is known to 75 % of respondents. What is remarkable, however, is that according to the estimates of around 70 % of the companies surveyed, the necessary competence for digital change is lacking.
83 % of respondents expect potential benefits from improved responsiveness to customers (93 %), more flexible production
(75 %), cost reductions (70 %) and improved internal responsiveness (58 %). Only 15 % hope for a unique selling proposition. Digitalization becomes apparent understood as a general development trend, as a "me-too strategy".
75 % of managers fear that they will not be able to maintain their market share without a digitalization strategy. It is therefore all the more surprising that only 35 % of the respondents are concerned with this issue. More than half of the respondents were of the opinion that production would be optimized on the basis of real-time data and orders automatically scheduled in just 2 years. This is far from being the case today. Rather, one encounters a conglomerate of isolated solutions everywhere that are hardly connected with each other and generate significant friction losses. Numerous resulting errors and sources of waste could already be eliminated with the MES solutions available today.
Ernst & Young stated in 2016 that productivity had not increased significantly in recent years and confirmed that SMEs that actively and comprehensively rely on digitalization are growing much faster. This study warns insistently: "All companies that wait too long are at risk of second-class status". "Smaller companies in particular are sticking to their old business models and missing the need for artificial intelligence".
Industry 4.0 is perceived as the first step in a comprehensive digital transformation. The generation and monitoring of data alone does not lead to significant competitive advantages, but the intelligent use of data promises success. It is becoming increasingly important to analyze the large amounts of data and use powerful algorithms to recognize the patterns that lead to real competitive advantages. Based on this knowledge, machines are to be enabled to learn independently in networked processes and to display automated intelligent behavior. So it is not surprising that with Big Data, Machine Learning and M2M, the next development steps are already in full swing.
Virtual process engineer
Would it not be nice if you could tell right now how quality is developing? To do this, it is necessary to extract from innumerable dependencies those that have a significant impact on quality in order to be able to issue warnings at an early stage that lead to an avoidance of scrap.
First of all, a comprehensive sensor system and data acquisition system is required, which is usually available in modern machines. This data is already available today and is being evaluated. They must be compared with the valid specifications to generate warnings for process deviations. Any affected roles must be locked. In addition, the right correlations are required in order to make predictions and access the set of successful measures from the past in order to actively avoid errors. This results in virtual assistance systems that initiate adjustments before an error occurs. The virtual process engineer.
Fully integrated roll inspection
Optical control systems for roll inspection have been used for a long time. The defect map generated in this way is made available to the MES. Here, the defects are valuated and the defect items are marked on the role. In the subsequent or the same work phase, these defects can be processed into a cutting plan for the roll by cutting planning. The defective rolls are automatically blocked and reported to QS or immediately declared as rejects. Each roll gets a unique ID number and is unmistakably marked. Integrated packing robots pack the rolls on the pallets and the integrated transport control system transports the rolls depending on their status (error-free, blocked, rejects) to the corresponding storage area and storage location for goods or to a blocked warehouse. The integration of the entire manufacturing process across all production stages and with all data relevant to production (machine, process, quality and operating data) allows conclusions to be drawn about errors during post-assessment and forms the basis for sustainable improvements.
Fig. 2: OCS Integration
Roadmap of change
History has taught us – the only constant is change. As little as there is the only promising development path to the digital transformation, the option of remaining in old patterns will promise success. The roll manufacturing industry is on the move and many players are already very active in dealing with the issues surrounding industry 4.0. It is therefore highly recommended that the existing challenges are consciously addressed and the necessary skills are built up. The company's own individual approach, which is tailored to its goals and benefit expectations, will lead to the efficiency and productivity advantages that will lead to a sustained strengthening of its competitive position. In most cases the evolutionary and not the revolutionary approach will be chosen in order to lead changes to success with measure and goal.
Grass GmbH, Bad Kreuznach/Germany, accompanies leading players on their way to Industry 4.0 and offers both strategic competence and industry-specific software solutions. The solution portfolio ranges from an easy to implement and cost-effective Line Data Management System – a first step to Industry 4.0 – to a comprehensive Manufacturing Execution System for roll manufacturers and converters. The industry-specific portfolio is rounded off by tailor-made lean consulting and specific technology consulting.