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Production in high-wage countries can be made more efficient, cost-effective, and flexible by solving the conflict between planning and value orientation. A promising approach is to focus on planning and decision-making processes (production planning and control, design of production processes and machinery, etc.) and to aim to maximize overall planning efficiency. Planning efficiency can be expressed as the ratio between the benefit generated by preparing detailed process instructions to produce the parts or components and the corresponding planning efforts. Industrial companies wanting to gain a competitive advantage in dynamic global markets have to identify a set of non-dominated solutions with the most favorable effort–benefit ratio rather than a single solution. The optimum between detailed planning and the immediate implementation of value-adding activities (process steps) in the process chain needs to be found dynamically for each product.
In most European countries a structural change from a production dominated towards a service oriented society is progressing. Companies increasingly consider services as means to gain competitive advantages in a global competition. In order to provide holistic, value-adding solutions while simultaneously guaranteeing high quality standards, production companies increasingly join forces with external services‘ providers. Models, methods and tools for service development are rare and in most cases immature. In the context of virtual services‘ development this leads to a dual set of simultaneous chal-lenges: an alignment of systematic services‘ and product development and the coordination of distributed R&D partners. The objective is to provide a meta-process that identifies all steps and decision points necessary to successfully develop innovative services. It is a result of combined service development and virtual enterprises‘/ networks‘ research.
The growth of installed wind capacities generated a market with a huge variety of service offers for operation & maintenance of wind turbines. Different parties like manufacturers, component suppliers as well as independent service providers compete for the attractive after sales market. An innovative service offer which seems to meet the customers’ requirements is the guarantee of availability for wind turbines. However, these service providers are facing new challenges regarding their performance potentials and their financial risks occurring from possible penalties. Service providers have to reconsider their preparedness of performance, their new occurring financials risks, their cooperation and qualification level as well as their localization of service bases. To be able to quantify these new challenges and risks a simulation model has been designed in the context of a German research project named “WinServ”.
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 face the challenge of selecting digitalization measures that fit their strategy. Measures that are initiated and not aligned with the company’s strategy carry the risk of failing due to lack of relevance. This leads to an ineffective use of scarce human and financial resources. This paper presents a target system to help companies select relevant digitalization measures compliant with their strategy for IT-OT-integration projects. The target system was developed based on literature research and expert interviews, and later validated in two use cases. The target system considers the goals of production companies and combines them with digitalization measures. The measures are classified by different maturity levels required for their realization. Thus, the target system enables manufacturing companies to evaluate digitalization measures with regards to their strategic relevance and the required Industrie 4.0 maturity level for their realization. This ensures an effective use of resources.
Industrial companies face tremendous challenges to plan the resources needed to meet future market demands when implementing a PSS based solution portfolio. This paper deals with enhancing the PSS research landscape by presenting an approach to enable better resource-planning in PSS based businesses. In particular, a model is proposed which links resource structures with customer offerings. Linkages are implemented, which connect resources and their use in processes. The model contributes to better understand the complexity in resource structures and elements in the PSS and helps to better understand and describe the structural integration of resources in PSS. This is an important prerequisite for the planning of PSS and allows a qualitative and quantitative description of the service resources allocation enabling companies to build the competence needed to meet customer requirements. A case study based approach was applied for model development.
In an increasingly changing market environment, the long-term survival of companies depends on their ability to reduce latencies in adapting to new market conditions. One strategy to meet this challenge is the anchoring of data-driven decision making, which leads to an increasing use of advanced information technologies and, subsequently, to an increase in the amount of data stored. The complexity of processing these data spurred the demand for advanced statistical methods and functions called Business Analytics. Companies are, despite all promised benefits, overwhelmed with the implementation of Business Analytics as indicated by a failure rate of 65 to 80 %. This paper provides an empirically validated, multi-dimensional model that takes an integrative look at critical success factors for the implementation
of Business Analytics and based on which management recommendations can be generated. For this purpose, constructs of the model are conceptualized, before a structural equation model is developed. This model is then validated with data from 69 industrial partners in the food industry. It is shown amongst others, that the three success factors top management support, IT infrastructure and system quality are pivotal to increase the company performance.
Remote services are services enabled by information and communication components and therefore do not require the physical presence of a service technician at the service object to provide a task. The impact of remote service on the capital goods industry has been increasingly significant over the recent yeas. Still many companies struggle with developing and implemenling successful business model, for remote service. This leads to a lot of unaccomplished benefits for the customer as well as for the companies themselves. A survey throughout companies in Ihe industrial machine and plant production sector was conducted in order to determine what successful companies do differently from those that cannot efficiently implement remote service business models.
The study presented in this chapter identifies key suceess factors of companies that effectively implemented remote services for their products. In order to identify the successful companies a scale for measuring remote service success was developed. Only by the use of this scale further findings regarding the success factors were possible. Key findings include the fact that successful companies actively market their remotle service to their customers. Generally they try to approach their remote service business from the operating company's perspective.
The mechanical and plant engineering industry faces a stagnation in the new machinery market and is relying on innovative business models such as subscription to overcome these. In this business model, individually customized solution packages are offered. The success of these models depends directly on the future success of the customer, making the selection of the right customers crucial. The aim of this paper is to identify the criteria that indicate the suitability of customers for subscription models. While there are individual descriptions of suitability criteria in the existing literature, there is a lack of comprehensive consideration of customer relationship, customer company, and customer market, as the extensive consideration was not necessary in the transactional sale of machines until now. Therefore, in this study, expert interviews are conducted with companies in mechanical and plant engineering that offer subscription models. The results show criteria that are used to evaluate customers in the six main categories of creditworthiness, market potential, benefit potential, feasibility, relationship, and sales effort. In total, 24 criteria can provide insight into the suitability of the customer for a successful subscription relationship. These criteria are intended to develop target systems that meet the requirements of different stakeholders in the customer and thus support the economic viability of these business models.
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.
Management of information and the IT systems it is stored in becomes a crucial capability for the industry. However, companies are struggling with the management of the various requirements and frequent changes of technology. Thus, IT complexity has become a major challenge for companies. At the same time, especially manufacturing companies are striving to implement Industrie 4.0 concepts. Many of these even have developed an Industrie 4.0 roadmap including various projects to change the company. Companies can develop such roadmaps by applying the Industrie 4.0 Maturity Index that gives a broad view on necessary capabilities for Industrie 4.0.
In our research, we analyzed data sets from over 10 manufacturing companies that have performed an Industrie 4.0 maturity assessment. Our hypothesis was that IT complexity challenges are hindering the implementation of Industrie 4.0 roadmaps significantly. We could prove this hypothesis at least for the companies analyzed and give insights on the specific challenges. Based on our analysis, we conclude our article by giving concrete recommendations on how to tackle IT complexity.
Systematisation Approach
(2023)
Current megatrends such as globalisation and digitalisation are increasing complexity, making systems for well-founded and short-term decision support indispensable. A necessary condition for reliable decision-making is high data quality. In practice, it is repeatedly shown that data quality is insufficient, especially in master and transaction data. Moreover, upcoming approaches for data-based decisions consistently raise the required level of data quality. Hence, the importance of handling insufficient data quality is currently and will remain elementary. Since the literature does not systematically consider the possibilities in the case of insufficient data quality, this paper presents a general model and systematic approach for handling those cases in real-world scenarios. The model developed here presents the various possibilities of handling insufficient data quality in a process-based approach as a framework for decision support. The individual aspects of the model are examined in more detail along the process chain from data acquisition to final data processing. Subsequently, the systematic approach is applied and contextualised for production planning and supply chain event management, respectively. Due to their general validity, the results enable companies to manage insufficient data quality systematically.
Systematization models for taylor-made sensor system applications and sensor data fit in production
(2015)
Industrial digitalization to realize smart factories is driven by an informatory base of high-resolution data provided by sensor systems on the shop-floor level. The challenge of technical availability of fitting measurement solutions nowadays turns in a struggle of finding the optimal solution for a specific task in an ever-growing sensor market. This paper analyzes and specifies necessary models to systematically derive and describe organizational, technical and informatory requirements for sensor system applications increasing the technological fit for faster integration and lower misinvestment rates.
Systematization models for taylor-made sensor system applications and sensor data fit in production
(2015)
Industrial digitalization to realize smart factories is driven by an informatory base of high-resolution data provided by sensor systems on the shop-floor level. The challenge of technical availability of fitting measurement solutions nowadays turns in a struggle of finding the optimal solution for a specific task in an ever-growing sensor market. This paper analyzes and specifies necessary models to systematically derive and describe organizational, technical and informatory requirements for sensor system applications increasing the technological fit for faster integration and lower misinvestment rates.
Task-Specific Decision Support Systems in Multi-Level Production Systems based on the digital shadow
(2019)
Due to the increasing spread of Information and Communication Technologies (ICT) suitable for shop floors, the production environment can more easily be digitally connected to the various decision making levels of a production system. This connectivity as well as an increasing availability of high-resolution feedback data, can be used for decision support for all levels of the company and supply chain. To enable data driven decision support, different data sources were structured and linked. The data was combined in task-specific digital shadows, selecting clustering and aggregation rules to gain information. Visual interfaces for task-specific decision support systems (DSS) were developed and evaluated positively by domain experts. The complexity of decision making on different levels was successfully reduced as an effect of the processed amounts of data. These interfaces support decision making, but can additionally be improved if DSS are extended with smart agents as proposed in the Internet of Production.
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.
The House of Maintenance
(2009)
In order to guarantee an efficient and effective employment of production equipment, it is essential to identify any possible potential for improving performance, not only in the production process, but also in supporting areas such as maintenance. One of the major tasks in increasing maintenance performance consists of systematically identifying the company’s most significant weaknesses in maintenance organisation and thus being able to implement improvements there where they are most needed.
But how is a company to tackle this important task? To answer this question, this paper describes an assessment and improvement approach, based on a capability maturity model (CMM). By means of this approach, the status-quo of a maintenance organisation can be analysed and its individual improvement opportunities identified.
The Impact Of Manufacturing Execution Systems On The Digital Transformation Of Production Systems
(2021)
With the focus of manufacturing companies on the digital transformation, Manufacturing Execution Systems are market-ready, modular software solutions for manufacturing companies to integrate the value-adding and supporting processes horizontal and vertical in the company. Companies, especially small and mediumsized companies, face high internal and external costs for the implementation of the MES modules. An advantage of MES is the possibility to implement the systems in a continually, module-by-module approach, with the benefit of timely distributed investments. By realizing fast improvements, companies can use the benefits for further module implementations. This paper proposes a maturity model to measure the impact of an MES on the digital transformation of the company’s production systems. The model fulfils two purposes. The first, companies can measure the impact based on the difference between its current maturity index and the potential index of an implemented MES. The second is, the user can identify what impact an MES has in general on the digital transformation since the developed maturity model is derived from an established industry 4.0 maturity model. The development of the maturity model is based on the methodologies of AKKASOGLU and focuses on the further development of an established model. As an outlook, the application of the model will be described briefly. The proposed maturity model can directly be used by practitioners and offers implications for further development of MES functionalities.
Increasing productivity in product-service systems is a vital success factor for industrialized economies and individual businesses. The service production is typically described as an integrated value chain setting, in which the provider and the customer are co-creators.
This paper embraces a characteristic curve model in order to illustrate the influence of the customer on the productivity of service production. The characteristic curves are derived from a system dynamics simulation model for a synchronized takt-based service production. In conclusion this research leads to designs recommendations for service production systems in order to reduce lead times and increase adherence to delivery dates.
Digital technologies have gained significant importance in the course of the 4th Industrial Revolution and these technologies are widely implemented, nowadays. However, it is necessary to bear in mind that an ill-considered use can quickly have a negative impact on the environment in which the technology is used. For more responsible and sustainable use, the regulation of digital technologies is therefore necessary today. Since the government is taking a very slow response, as the example of the AI Act shows, companies need to take action themselves today. In this context, one of the central questions for companies is: "Which digital technologies are relevant for manufacturing companies in terms of regulation? This paper conducted a quantitative Delphi study to answer this question. The results of the Delphi study are presented and evaluated within the framework of a data analysis. In addition, it will be discussed how to proceed with the results so that manufacturing companies can benefit from them. Furthermore, the paper contributes to the development of an AI platform in the German research project PAIRS by investigating the compliance relevance of artificial intelligence applications.
This paper contributes to an assessment framework for valuing data as an asset. Particularly industrial manufacturers developing and delivering Smart Product Service Systems (Smart PSS) are comprehensively depended on the business value derived by processing data. However, there is a lack in a framework for capturing and comparing the Smart PSS data value with the purpose of increasing the accountability of data initiatives. Therefore a qualitative data value assessment approach was developed and specified on Smart PSS, based on an industrial case study research. [https://link.springer.com/chapter/10.1007/978-3-030-57997-5_39]
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.
Companies in the manufacturing sector are confronted with an increasingly dynamic environment. Thus, corporate processes and, consequently, the supporting IT landscape must change. This need is not yet fully met in the development of information systems. While best-of-breed approaches are available, monolithic systems that no longer meet the manufacturing industry's requirements are still prevalent in practical use. A modular structure of IT landscapes could combine the advantages of individual and standard information systems and meet the need for adaptability. At present, however, there is no established standard for the modular design of IT landscapes in the field of manufacturing companies' information systems. This paper presents different ways of the modular design of IT landscapes and information systems and analyzes their objects of modularization. For this purpose, a systematic literature research is carried out in the subject area of software and modularization. Starting from the V-model as a reference model, a framework for different levels of modularization was developed by identifying that most scientific approaches carry out modularization at the data structure-based and source code-based levels. Only a few sources address the consideration of modularization at the level of the software environment-based and software function-based level. In particular, no domain-specific application of these levels of modularization, e.g., for manufacturing, was identified. (Literature base: https://epub.fir.de/frontdoor/index/index/docId/2704)
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]
Industrial practice shows a strong trend towards digitalization. It is not only economic crises, such as those triggered by Covid-19, that are reinforcing this trend. It is also the entrepreneurial urge to fulfill customer wishes in the best possible way and to adapt to new requirements as quickly as possible. Due to the advancing digitalization, the role of business application systems in manufacturing companies is therefore becoming increasingly important. The data processed in IT-Systems represent a great potential, especially for the evaluation of change requests in production. Through efficient change management, companies can record and process changes quickly. However, the necessary data basis to decide on existing change requests is still hardly used. Existing IT-Systems for change management coordinate the processing of change requests, but do not relate to data of operational application systems such as Enterprise-Resource-Planning. Therefore, a conceptual approach is required for the evaluation of change requests. This approach is based on an objective recording system that enables the transformation from the change description to an evaluation space. The paper presents an approach for the systematic transfer of requirement characteristics into the world of operational IT-Systems.
Due to shorter product life cycles and the increasing internationalization of competition, companies are confronted with increasing complexity in supply chain management. Event-based systems are used to reduce this complexity and to support employees' decisions. Such event-based systems include tracking & tracing systems on the one hand and supply chain event management on the other. Tracking & tracing systems only have the functions of monitoring and reporting deviations, whereas supply chain event management systems also function as simulation, control, and measurement. The central element connecting these systems is the event. It forms the information basis for mapping and matching the process sequences in the event-based systems. The events received from the supply chain partner form the basis for all downstream steps and must, therefore, contain the correct data. Since the data quality is insufficient in numerous use cases and incorrect data in supply chain event management is not considered in the literature, this paper deals with the description and typification of incorrect event data. Based on a systematic literature review, typical sources of errors in the acquisition and transmission of event data are discussed. The results are then applied to event data so that a typification of incorrect event types is possible. The results help to significantly improve event-based systems for use in practice by preventing incorrect reactions through the detection of incorrect event data.
The successful use of Business Analytics is increasingly becoming a differentiating competitive factor. The ability to extract data-driven insights and integrate them into decision-making is becoming growingly important. The underlying technologies are evolving exponentially, the value proposition differs from simple descriptive applications to automated decision-making. Existing approaches found in literature and practice to classify those levels only insufficiently mark down the boundaries between the different technology levels. As a consequence, it is often unclear which characteristics of the technology interact with the working environment, which can be described as a socio-technical system. Using a systematic literature review, this paper identifies the characteristics of Business Analytics and delineates three types of Business Analytics based on case studies. Thus, a starting point for the socio-technical system design and optimization for the use of Business Analytics is created.
The number of available technologies is constantly rising. Be it additive manufacturing, artificial intelligence (AI) or distributed ledger technologies. The choice of the right technologies may decide the fate of a company. Due to the overwhelming amount of information sources, regular technology market research becomes increasingly challenging, especially for SMEs. In order to assist the technology management process, the authors will introduce the architecture of an automated, AI-based technology radar. The architecture will automatically collect data from relevant sources, assess the relevance of the respective technology (i.e. their maturity level) and then visualize it on the radar map.