The area of Business Excellence includes all tasks in the fields of customer orienta-tion, employee orientation and result orientation and focuses on the goal of constant-ly improving in those fields. To achieve this goal, one has to anaconstant-lyze in a regular cycle the weaknesses of the organization to be able to eliminate the ascertained weaknesses and to foster a continuous improvement process in the whole company.
As part of Industry 4.0, certain areas are gaining importance and are further ex-plained in the following chapters (Schneider, Geiger, & Scheuring, 2008, p. 239).
This includes Data Analysis, Integration of Real World and Virtual World, Network Communications, Safety and Security and Systems Engineering.
3.6.1 Data Analysis
The motivation behind Data Analysis is the generation of new insights of the pro-cesses generated by big data through CPS. Beyond that, the Data Analysis serves as decision support as well as foundation for autonomous decision making. Especially the early warning of future failure developments are crucial and very important when it comes to Data Analysis. The basic information of Data Analysis is taken from cur-rent data, historical data and unstructured data, for example, from social networks, etc. The data analysis is oriented at the “Data Analytics Maturity Model” shown be-low (Hofmann, 2015, p. 8).
The “Descriptive” area has the goal of revealing unknown coherences. In the “Diag-nostic” area, the model recognizes certain patterns in the received data. Even more important for Industry 4.0 is “Predictive” area, which tries to estimate future system behaviors on the basis of the “Descriptive” and “Diagnostic” analysis. The newly obtained knowledge eventually helps in the “Prescriptive” area, which give a variety of action recommendations and, therefore, a continuous optimization of systems, processes and strategies. Methods of the predictive analysis come from the areas of statistics and data mining. The actual challenge of Data Analysis is the derivation of recommendations and measures on the basis of the analyzed data (Dorst, 2015, p. 36).
Decision making is subject to the area of “Prescriptive” analysis. Examples for the derivation of recommendations are (Hofmann, 2015, p. 8):
• Monitoring of the production in real-time for a better planning and steering of the manufacturing process.
• Derivation and implementation of preventive maintenance scenarios.
• Application of flexible logistics processes.
• Potential for new business models, products and services.
Figure 11: Data Analytics Maturity Model (Puget, 2014)
3.6.2 Integration of Real World and Virtual World
Driven by the internet, the real world and the virtual world are merging together to form an “Internet of Things” (IoT), which enables the communication between phys-ical products and system based products. The chapter “Smart Products” already men-tioned the change in products and the development of physical products to Cyber-Physical-Products, which will communicate autonomously with each other to react to certain environmental influences. The “Integration of the Real World and the Virtual World” will solve unimagined problems of the real world and will advance the way of thinking and solution approach for the future (Dorst, 2015, p. 23).
3.6.3 Safety and Security
With the implementation of Industry 4.0, the connection of products, components, Industry 4.0 plants and companies begins. The topic of Safety and Security is thereby unavoidable for every participating organization. The fear of uncertainty in Data Se-curity entails that Industry 4.0, with all its potential in the overarching network of products, plants and companies, is not pushed forward in the implementation process, because organizations do not want to risk knowledge robbery and data leakage to competing companies (Waidner, Backes, & Müller-Quade, 2013, p. 4).
The launch of Industry 4.0 is delayed because of Safety and Security issues, which restrains the digitalization process of the whole economy. Safety explains the safety and protection of the operator and the environment of the machines and plants. Secu-rity explains the secuSecu-rity of IT systems, which means that IT systems are protected
from external attacks and the sabotage of the IT systems from third parties. When implementing Industry 4.0 technologies, Safety and Security needs to be viewed in a holistic approach, which involves an intensive risk assessment for the implemented Industry 4.0 technologies. One of the leading concepts in terms of Safety and Securi-ty is the “SecuriSecuri-ty by Design” concept (Schneider U. , 2015, p. 10). This concept follows the approach that the potential risks are observed and fixed already in the planning phase of new systems and technologies (Waidner, Backes, & Müller-Quade, 2013, p. 3).
3.6.4 Systems Engineering
Systems Engineering is an interdisciplinary function for the development of technical systems and development activities. Aspects which need to be considered in relation to Systems Engineering are (Dorst, 2015, p. 25):
• The integrative development of products, processes and production systems with the goal that all aspects are developed in close interplay with each other.
• The trial and validation of development decisions in the early stage of the de-velopment process in respect of the future functions (mechanical, electric or system based).
• The assurance of the availability of relevant data and processes across system and company boarders.
• The modularization and the reuse of the plants, machines and systems for the control of the gain in complex processes.
• The reflow of experiences through the application of plants and systems into the development process of the whole company.
• The connection of all systems integrated into the production process for a bet-ter autonomous planning, steering and decision making. This includes cyber-physical-systems, cyber-physical-production-systems, products, plants and machines.