General Assessment of Industry 4.0 Awareness in South India—A Precondition for Efficient Organization Models?

This chapter provides a questionnaire-based survey on individuals, with an attempt to examine general awareness about I4.0, in the South India region. Results show inadequate future expectations regarding effects on several aspects of respondents’ lives, which mostly stem from a rather poor informational level of I4.0. Conversely, rather positive opinions and expectations of possible future trends come from respondents with prior information about I4.0. Thus, from a broader discussion we generalize, that for sustainable and successful development leading towards the Fourth Industrial Revolution, sufficient information and knowledge base within the potential labour force, especially in a particular region with promising demography, should be achieved. We find obtained information not negligible from SMEs management perspective and successful development of organization and network models.

Investigation of the Potential to Use Real-Time Data in Production Planning and Control of Make to Order (MTO) Manufacturing Companies

The integrated planning and control of logistics processes can be seen as one of the basic prerequisites for the successful implementation of smart production systems and smart and lean supply chains, as well. Therefore, modern Industry 4.0 approaches are mainly focusing on (1) the principles of decentralization and (2) the usage of real-time data to improve the overall logistics performance in terms of promised delivery dates, work in progress, capacity utilization, and lead-times. In this context, this chapter systematically evaluates the application of decentralized production planning and control strategies, e.g., KANBAN and CONWIP, in comparison with traditional approaches, like MRP. Moreover, the impact of real-time data usage in production planning and control systems on lead-times and work in progress is investigated using a discrete event simulation based on primary data from a make to order manufacturer. The results of this industrial case study research confirm the significant potential that lies in smart production systems and smart and lean supply chains and, therefore, in the introduction of Industry 4.0 technologies and technological concepts in production and logistics systems.

Status of the Implementation of Industry 4.0 in SMEs and Framework for Smart Manufacturing

In this chapter, we explore after 10 years of Industry 4.0 the status of the application in manufacturing companies and especially in small- and medium-sized enterprises (SMEs). Based on literature and previously conducted research we present guidelines and a modular framework for implementing smart manufacturing in SMEs. In addition, a stage model is illustrated to support SMEs in breaking down the framework from a design level to an implementation and operational level. Finally, an outlook is given on the future challenges that SMEs will face in the coming years when they want to reach the next level of Industry 4.0 in their own company.

Readiness Model for Integration of ICT and CPS for SMEs Smart Logistics

Although it is an extremely important step to gain sustained competitive advantage in the era of Industry 4.0, the integration of Information and Communication Technology (ICT) and Cyber-Physical Systems (CPS) for smart logistics can be challenging. In order to successfully implement such technologies, it is crucial, particularly for SMEs, to self-evaluate their own readiness for integrating technologies for smart logistics. This research proposes a readiness model which can be used as a guideline before implementing technologies for smart logistics at the SMEs level. It describes essential elements in the integration of ICT and CPS for smart logistics, as well as the potential five stages of implementation, which are initial, trial, organized, automated, and optimized.

Computational Intelligence in the Context of Industry 4.0

Industry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed. Its typical feature is transmission of information in the system environment provided by the Internet of Things. All information should be stored and shared through cloud computing. As a result, access to information should be unrestricted. This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.

AI and ML for Human-Robot Cooperation in Intelligent and Flexible Manufacturing

Human–robot cooperation aims to increase the flexibilization of manufacturing systems. This requires safe human–machine interaction (e.g. with collaborative robots) as well as self and environment awareness capabilities to interact autonomously and smartly between humans and machines. Therefore, the goal of this chapter is to conceptualize and identify the set of real-time information processing and decision-making capabilities required for collaborative robots to be considered as a safe companion in the context of human–robot cooperation (HRC). In particular, the chapter provides an overview of appropriate artificial intelligence (AI) and machine learning (ML) concepts, formally introduces the concept of a safety-aware cyber-physical system and defines a general taxonomy for the perceptive and cognitive problems arising in the context of intelligent and flexible HRC.

Industrial Assistance Systems to Enhance Human–Machine Interaction and Operator’s Capabilities in Assembly

In the context of digitalization and Industry 4.0, the world of work is changing comprehensively. Smaller lot sizes and increasing variability of products in the modern industrial production present new challenges for operators working in manual assembly. Industrial assistance systems help the worker during these production tasks to enhance their capabilities. The development of these systems is not only characterized by questions of the potential feasibility of new technical systems but also by the possibilities of a closer cooperation between humans and machines with the aim to synergize the outstanding abilities of humans with the special features of machines to bring together the best from both worlds. This chapter presents solutions for human–machine interaction and automation and delivers insight into different possibilities to enhance the various types of operator’s skills in industrial assembly. With this knowledge, each worker can be individually equipped with suitable supporting systems in order to be best prepared for future challenges in the daily production.

Transition of SMEs Towards Smart Factories: Business Models and Concepts

When focusing on business models in the context of Industry 4.0 transformation, it is quite obvious that such models will need to adopt new businesses trends, such as mass customization, platform-based businesses, networking manufacturing or creativity-based businesses, respectively. This chapter aims to analyse implementation success factors of Industry 4.0 especially from business models perspective, and also to address some features of human-centred manufacturing in terms of small- and medium-sized enterprises (SMEs). The motivation of this research is awareness of the importance that just a combination of the selected decisive success factors can significantly help businesses to become more competitive and improving their performance.

A Case Study: Industry 4.0 and Human Factors in SMEs

There is a number of barriers for smaller companies when starting the journey toward Industry 4.0. When implementing new technology and processes, there are often strong mental barriers from people that have been doing the work in a certain manner over the years. In addition to technological challenges, organizational adaptations are required, and a change in mindsets. Using the case of one machining services company, the implementation scenario is described, with the steps necessary to ensure an effective implementation of new technology. The management of visibility is critical for the adoption and success of new systems. Neglecting the human factor will inevitably result in failure. Derived from the case, the chapter draws some conclusions for Industry 4.0 implementation in SMEs in a human-centered manner.

Automated Performance Measurement in Internal Logistics Systems

In addition to economic and on-time order fulfillment, the monitoring of the plant performance and its related key performance indicators is a central task of logistics management and control systems. Currently, the determination and calculation of performance figures within the framework of site acceptance tests of automated logistics systems are plant-specific and, therefore, require a lot of manual effort. In this chapter, the authors develop a concept for the automated determination of performance indicators for storage and conveying systems. Based on a comprehensive literature review, structured expert interviews and including various perspectives from industrial applications the approach is designed. Further, the impact of the proposed concept on the logistics performance of the plant and the adequate selection of a maintenance strategy is discussed.