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One of the central success factors for production in high-wage countries is the solution of the conflict that can be described with the term “planning efficiency”. Planning efficiency describes the relationship between the expenditure of planning and the profit generated by these expenditures. From the viewpoint of a successful business management, the challenge is to dynamically find the optimum between detailed planning and the immediate arrangement of the value stream. Planning-oriented approaches try to model the production system with as many of its characteristics and parameters as possible in order to avoid uncertainties and to allow rational decisions based on these models. The success of a planning-oriented approach depends on the transparency of business and production processes and on the quality of the applied models. Even though planning-oriented approaches are supported by a multitude of systems in industrial practice, an effective realisation is very intricate, so these models with their inherent structures tend to be matched to a current stationary condition of an enterprise. Every change within this enterprise, whether inherently structural or driven by altered input parameters, thus requires continuous updating and adjustment. This process is very cost-intensive and time-consuming; a direct transfer onto other enterprises or even other processes within the same enterprise is often impossible. This is also a result of the fact that planning usually occurs a priori and not in real-time. Therefore it is hard for completely planning-oriented systems to react to spontaneous deviations because the knowledge about those naturally only comes a posteriori.
Long-term production management defines the future production structure and ensures the long-term competitiveness. Companies around the world currently have to deal with the challenge of making decisions in an uncertain and rapidly changing environment. The quality of decision-making suffers from the rapidly changing global market requirements and the uniqueness and infrequency with which decisions are made. Since decisions in long-term production management can rarely be reversed and are associated with high costs, an increase in decision quality is urgently needed. To this end, four different applications are presented in the following, which support the decision process by increasing decision quality and make uncertainty manageable. For each of the applications presented, a separate digital shadow was built with the objective of being able to make better decisions from existing data from production and the environment. In addition, a linking of the applications is being pursued:
The Best Practice Sharing App creates transparency about existing production knowledge through the data-based identification of comparable production processes in the production network and helps to share best practices between sites. With the Supply Chain Cockpit, resilience can be increased through a data-based design of the procurement strategy that enables to manage disruptions. By adapting the procurement strategy for example by choosing suppliers at different locations the impact of disruptions can be reduced. While the Supply Chain Cockpit focuses on the strategy and decisions that affect the external partners (e.g., suppliers), the Data-Driven Site Selection concentrates on determining the sites of the company-internal global production network by creating transparency in the decision process of site selections. Different external data from various sources are analyzed and visualized in an appropriate way to support the decision process. Finally, the issue of sustainability is also crucial for successful long-term production management. Thus, the Sustainable Footprint Design App presents an approach that takes into account key sustainability indicators for network design. [https://link.springer.com/referenceworkentry/10.1007/978-3-030-98062-7_15-1]
Reliability-centered maintenance for production assets is a well-established concept for the most effective and efficient disposition of maintenance resources. Unfortunately, the approach takes a lot of effort and relies heavily on the knowledge of individuals. Reliability data in Computerized Maintenance Management System (CMMS) is scarce and almost never used well. An automated risk assessment system would have the potential to contribute to the dissemination and effective use of risk information and analysis. The individuality of production setting, however, prevents current systems from being practically relevant for most industries. The presented approach combines ontologies to store and link knowledge, an information logistics model displaying the various information streams, and the Internet of production to take the different user systems and infrastructure layers into account. The provided model of a reference digital shadow for risk information and a detailed information logistics model will help software companies to improve reliability software, standardize and enable assets owners to establish a customized digital shadow for their production networks. [https://link.springer.com/chapter/10.1007/978-3-030-57993-7_2]
In this paper, we firstly present a target system which is deduced to assess the economic profitability of reverse supply chains. Considering this, we analyse process reference models to define relevant components of an appropriate target system.
Subsequently, we define applicable business models which are the basis for the manufacturer to offer new services to its customers on the one hand and to manage a goal-oriented return, recovery and resell of used products and components on the other hand. This will be done based on the morphology methodology in order to understand the characteristics and attributes of reverse supply chains.
Today, manufacturing companies are facing the influences of a dynamic environment and the continuously increasing planning complexity. Using advanced data analytics methods, processes can be improved by analyzing historical data, detecting patterns and deriving measures to counteract the issues. The basis of such approaches builds a virtual representation of a product – called the digital twin or digital shadow.
Although, applied IT systems provide reliable feedback data of the processes on the shop-floor, they lack on a data structure which represents real-time data series of a product. This paper presents an approach for a data structure for the order processing which overcomes the described issue and provides a virtual representation of a product. Based on the data structure deviations between the production schedule and the real situation on the shop-floor can be identified in real time and measures to reschedule operations can be identified.
One of the major challenges facing today´s manufacturing industry is to differentiate from competition in a highly globalized world. As a consequence to the increasing competitive pressure, many companies transform their product centered business models towards service based business models to differentiate from competition. However, the transformation is often underestimated regarding its complexity and its management challenges to behavioral change.
As a consequence lots of transformation initiatives fail. Besides difficulties in structuring the magnitude of changes in processes and structures, many transformation managers do not perceive the risk of employee resistance against changes, which is one of the key factors causing the failure of transformation. The objective of this paper is to enhance the existing body of research on manufacturer´s organizational transformation towards Product-Service Systems. More detailed, the objective is to develop new knowledge to support the management during the decision-making process in the way how and by means of which instruments the change of behavior can be supported when transforming from a manufacturer to a solution.
We developed a reference framework which structures and defines the relevant dimensions of behavioral change. The identification and validation of the success factors build the second component of our research. We conducted an empirical investigation in the German manufacturing industry and got 79 data sets.
Structural equation modelling was applied for the analyses and the validation of the hypotheses. By this analysis we linked management practice with employee behavior and transformational success variables. On the basis of the gained insights decisions can be made concerning the successful transformation from manufacturer to a solution-oriented service provider.
Today, machine manufacturers generate a significant share of their revenues with the provision of services. At the same time, they are confronted with the challenge of adopting of Industrie 4.0.
One of the most important Industrie 4.0 concepts is the idea of the digital shadow, which contributes to the comprehensive structuring of different kinds of data from different data sources. It can be defined as the sufficiently precise, digital representation of reality in real-time.
Thus, it also functions as a database of the considered area of a company that can be used for numerous applications. It serves as a central platform for the aggregation and distribution of data. Thereby, it helps to open isolated data silos. A system architecture that enables extraction of data from various sources and the aggregation of that data is an important prerequisite for the digital shadow.
In addition, the merger of data from different sources requires a model of the part of the company to be mapped digitally. In this paper, we focus on maintenance, repair and overhaul (MRO) services of machine manufacturers. The scope comprises the whole order processing of a service including the utilized resources and the obtained results.
MRO services and their single elements are mapped and structured using a case study research in a first step. Those elements provide a basis for designing the digital shadow. A second contribution of this paper is a data model for the digital shadow of MRO services that entails a comprehensive representation of that department.
[CIRP Encyclopedia of Production Engineering:]
This high quality reference work has been written and reviewed by members of The International Academy for Production Engineering, also known as CIRP. This Academy is recognized worldwide to represent the highest standards in research on production engineering, which includes design, optimization, control, management of processes, machines, and systems. One key concept behind this Encyclopedia is that apart from covering fundamental concepts in the field of production engineering, it also closely follows recent developments and emerging concepts. In particular this renewed print edition covers a wide range of new topical entries such as Hybrid Processes, High Performance Grinding, Biomimetic Design, Cold Spray, Sheet-bulk Metal Forming, Ecodesign, Cyber Physical System, Nano Technology, or Geometrical Product Specification. The second edition also comprises reviewed entries from the first version, which have been updated to reflect new standards or developments. The target audience primarily comprises researchers, engineers, managers, graduate students, and many others whose day-to-day work gravitates around production engineering technologies in the global market.
One major problem of today’s producing companies is to reach a high adherence to delivery dates while considering the volatile market situation as well as economic aspects. This problem can only be solved by using a production control that is optimally adapted to the processes. A good working, process-oriented production control is essential for being able to control the production situation and to ensure a high adherence to delivery dates. Data generation and processing determine the success of production control. Current processes and IT systems have several shortcomings in meeting these challenges.
The solution for this problem is the so called “cyber physical production control” (CPPC). It optimally supports the production scheduler in his decision making process based on real-time high-resolution data. With the help of data analytics, the production controller receives decision support over various steps. Due to CPPC, the overall goal of a high adherence to delivery dates can be fundamentally increased.
In short-term production management of the Internet of Production (IoP) the vision of a Production Control Center is pursued, in which interlinked decision-support applications contribute to increasing decision-making quality and speed. The applications developed focus in particular on use cases near the shop floor with an emphasis on the key topics of production planning and control, production system configuration, and quality control loops.
Within the Predictive Quality application, predictive models are used to derive insights from production data and subsequently improve the process- and product-related quality as well as enable automated Root Cause Analysis. The Parameter Prediction application uses invertible neural networks to predict process parameters that can be used to produce components with desired quality properties. The application Production Scheduling investigates the feasibility of applying reinforcement learning to common scheduling tasks in production and compares the performance of trained reinforcement learning agents to traditional methods. In the two applications Deviation Detection and Process Analyzer, the potentials of process mining in the context of production management are investigated. While the Deviation Detection application is designed to identify and mitigate performance and compliance deviations in production systems, the Process Analyzer concept enables the semi-automated detection of weaknesses in business and production processes utilizing event logs.
With regard to the overall vision of the IoP, the developed applications contribute significantly to the intended interdisciplinary of production and information technology. For example, application-specific digital shadows are drafted based on the ongoing research work, and the applications are prototypically embedded in the IoP.
Due to the drastically increasing amount of data, decision making in companies heavily relies on having the right data available. Also because of an increasing complexity of structures and processes, quick and precise flows of information become more important.
This paper introduces a new approach for modelling information flows, creating a basis for an efficient information management. It can be used to structure the information requirements and identify gaps within the information processing.
To display its benefits, the proposed Information Logistics Notation (ILN) is applied to the information logistics of todays and future energy market and grid stability management, both processes of increasing complexity.
The manufacturing industry has to exploit trends like “Industrie 4.0” and digitization not only to design production more efficiently, but also to create and develop new and innovative business models. New business models ensure that even SMEs are able to open up new markets and canvass new customers. This means that in order to stay competitive, SMEs must transform their existing business models.
The creation of new business models require smart products. The required data base for new business models cannot be provided by SMEs alone, whereas smart products are able to provide a foundation, given the creation of smart data and smart services they enable. These services then expand functions and functionality of smart products and define new business models.
However, the development of smart products by small and medium-sized enterprises is still lined with obstacles. Regarding the product development process the inclusion of smart products means that new and SME-unknown domains diffuse during the process. Although there are many models regarding this process there appears to be a substantial lack of taking into account the competencies enabled by the implementation of digital technologies. Hence, several SME-supporting approaches fail to address the two major challenges these enterprises are faced with. This paper generally describes valid objectives containing relevant stakeholders and their allocation to the phases of the product life cycle.
Within each objective the potential benefit for customers and producers is analyzed. The model given in this paper helps SMEs in defining the initiation of a product development project more precisely and hence also eases project scoping and targeting for the smartification of an already existing product.
In order to introduce load management in the manufacturing industry, some obstacles need to be pointed out. This paper presents a feasible approach on how to implement load management measures in companies.
To this end, load management and energy management are explained and distinguished in a first step. Subsequently, the implementation method is introduced. Therefore, by means of this paper, companies will be enabled to use load management measures and significantly reduce their energy costs. In the second part of the paper, the introduced approach will be applied.
Hence, a use case of a manufacturing company is described. Alongside energy analyses with consumption data, specific measures are presented.
Nowadays, cyber physical systems support the improvement of efficiency in intralogistics by controlling and manipulating the production and logistic environment autonomously. Due to the complexity of the individual production processes, designing suitable cyber-physical systems based on their existing production environment is a challenge for companies.
This paper presents a new methodology on how to design cyber-physical systems conceptually to suit an individual production environment. Compared to existing design approaches, this methodology matches immediately the required functions to existing information and communication technology’s components insisting on the neutral assimilation of requirements.
Therefore, the requirement specification asks for needed functions in relating to offered functions of information and communication technology (ICT) components. The paper focusses the use case of implementing a cutting-edge mobile network technology into an existing tracking and tracing process.
Real-time data analytics methods are key elements to overcome the currently rigid planning and improve manufacturing processes by analysing historical data, detecting patterns and deriving measures to counteract the issues.
The key element to improve, assist and optimize the process flow builds a virtual representation of a product on the shop-floor - called the digital twin or digital shadow. Using the collected data requires a high data quality, therefore measures to verify the correctness of the data are needed. Based on the described issues the paper presents a real-time reference architecture for the order processing.
This reference architecture consists of different layers and integrates real-time data from different sources as well as measures to improve the data quality. Based on this reference architecture, deviations between plan data and feedback data can be measured in real-time and countermeasures to reschedule operations can be applied.
The design of data-driven industrial services in the context of industry 4.0 represents a major challenge for industrial service providers and manufacturing companies for investment goods. Data-driven services require technological and strategic components that most companies have not build up yet and that differ from current configurations. That is why many companies lack a systematic approach and implementation competence for the use of data in the context of industrial services and therefore face the challenge of not being able to expand their market position in an ever-growing competition for data.
The present paper addresses this research deficit with the aim of describing strategic features and characteristics of data-driven industrial services by identifying the related crucial features and characteristics through a morphological approach. This will enable industrial service providers to improve strategic and operative management decisions in order to define a specific strategy and to configure data-driven services.
The importance of social networks and, in particular, enterprise social networks in business contexts is increasing significantly. Regarding the prerequisites for a successful implementation of an enterprise social network, exclusively providing the technical infrastructure is insufficient. A holistic view that considers and integrates different perspectives is crucial for success. This includes technological, organisational and human aspects as equally important parts of the network. This paper identifies prerequisites for a successful launch of enterprise social networks and groups them along these three dimensions.
In order to introduce load management in the manufacturing industry, some obstacles need to be pointed out. This paper presents a feasible approach on how to implement load management measures in companies. To do so, load management and energy management are explained and distinguished in a first step. Subsequently, the implementation method is introduced. Therefore, by using this paper, companies will be enabled to use load management measure and reduce their energy costs significantly.
Industry 4.0 is driven by Cyber-Physical Systems and Smart Products. Smart Products provide a value to both its users and its manufacturers in terms of a closer connection to the customer and his data as well as the provided smart services. However, many companies, especially SMEs, struggle with the transformation of their existing product portfolio into smart products. In order to facilitate this process, this paper presents a set of smart product use-cases from a manufacturer’s perspective. These use-cases can guide the definition of a smart product and be used during its architecture development and realization. Initially the paper gives an introduction in the field of smart products. After that the research results, based on case-study research, are presented. This includes the methodological approach, the case-study data collection and analysis. Finally, a set of use-cases, their definitions and components are presented and highlighted from the perspective of a smart product manufacturer.
Manufacturing companies are constantly increasing their efforts in the subscription business, also known as product-as-a-service business, offering usage and outcome based solutions (value-in-use) instead of transactional services and products (value-in-exchange). Customers are becoming contractual subscribers of the solution in return for recurring, performance-related payments. To address arising, inevitable challenges like (1) reducing customer churn, (2) increasing usage intensity and outcome quality, (3) ensuring the adoption of product and software releases as well as (4) fostering customer loyalty, leading manufacturing companies are setting up a new organizational, customer-facing unit, called Customer Success Management (CSM). This unit has its origins in the software-as-a-service business, operating next to established entities like sales, key account management and customer service. Since there are currently no holistic models for an end-to-end description of CSM-tasks in the manufacturing industry, this paper contributes to a taskoriented reference model, using a grounded theory approach, examining both manufacturing and software companies. Containing a reference framework with 8 main tasks, 17 basic tasks and 76 elementary tasks, the reference model supports manufacturing companies in adapting and customizing a company-specific CSM concept.