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Industrie 4.0 is hitting the market. The e.GO Life – an electric car for the city – is developed in under three years and only € 30M investment.
RWTH Campus pairs research and industry partners to pursue innovative ideas and yield cutting-edge products and services to face next-level digitization.
Digital, agile businesses outperform traditional businesses because of lower latencies in the entire reaction chain
acatech Industrie 4.0 Maturity Index:Helping established companies to build the development path to Industrie 4.0
The Maturity Index is offered to the Plattform Industrie 4.0 for transferring the approach to industry and defining Industrie 4.0 performance levels
Failure management in the production area has been intensely analyzed in the research community. Although several efficient methods have been developed and partially successfully implemented, producing companies still face a lot of challenges. The resulting main question is how manufacturers can be assisted by a sustainable approach enabling them to proactively detect and prevent failures before they occur. A high-resolution production system based on analyzed real-time data enables manufacturers to find an answer to the main question. In this context, Big Data technologies have gained importance since the critical success factor is not only to collect real-time data in the production but also to structure the data. Therefore, we present in this paper the implementation of Big Data technologies in the production area using the example of an actual research project. After the literature review, we describe a Big Data based approach to prevent failures in the production area. This approach mainly includes a real-time capable platform including complex event processing algorithms to define appropriate improvement measures.
Thanks to the challenges of the imminent energy turnaround, the power market faces a revolution regarding the energy distribution. In future, energy will not only be distributed from a limited number of large, centralized power plants but also from small, decentralized power generators, e.g. households. This also affects manufacturing companies, which are confronted with developing an energy management strategy. As those companies usually have not set high priorities on their energy management, there is a lack of a structured procedure to build an energy management strategy. Consequently, this creates the need for supporting methods to develop and implement an energy management strategy. This paper tackles the first step in the development of an energy management strategy. For this purpose, a target map is developed and possible use cases are systematized. The next steps of the implementation are presented using the example of load management.
Outsourcing of logistics operations (especially transportation, distribution & warehousing) is one of the most viable options exercised by the customers to excel in their logistic operations. Despite the growing outsourcing of logistics services to 3PL providers, both the service providers & their customers are facing tremendous problems in synchronizing the business processes & analyzing the performance using common key performance indicators. There is a huge demand for an integrated approach to help 3PL and their customers better synchronize their business processes and have common goals & perspectives. Such integrated approaches often take shape of a process oriented reference model covering many diverse aspects related to the operations & controlling of any business. In this paper, an integrated reference model to support 3PL service operations is presented. The Logistics Reference Model (LRM) developed & validated in some 3PL service companies encompasses standard business processes, performance measurement system and best practices.
Electricity generated by wind turbines (WT) is a mainstay of the transition to renewable energy. In order to economically utilize WT is, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WT’s, are a focus of cost reduction activities. Implementing a data-driven prescriptive maintenance approach is one way to achieve this. So far, various approaches for prescriptive maintenance for onshore WT’s have been suggested.
However, little research has addressed the practical implementation considering sociotechnical aspects. The aim of this paper is therefore to identify success factors for the successful implementation of such a maintenance strategy with clear and holistic guidance on how existing knowledge on prescriptive maintenance from science can be transferred to business practice. These recommendations are developed through case study research and classified in the four structural areas of Acatech’s Industry 4.0 Maturity Index: Resources, Information Systems, Organizational Structure and Culture.
Industry 4.0 and smart factories have brought significant advancements in manufacturing processes, particularly in intralogistics. A key factor which forms the basis for creating smart intralogistics systems is data. However, there exist several data-related issues that hamper the efficiency of the intralogistics process such as data unavailability, poor data quality, inconsistent data, or underutilization of available data. The challenge is to identify, categorize, evaluate, and solve these issues. Overcoming this will help organizations understand the most impactful challenges.
By analysing real-world scenarios and interviewing industry experts, the problems present within the intralogistics process that are caused by the previously mentioned data-related factors are identified. The identified issues are clustered, and the clusters are characterized. A literature review explores the existing solutions or approaches to overcome these limitations. Subsequently finding out if the identified problems can be solved with current technologies and approaches or further research and development is needed. Next, a framework is developed which will act as a guide on the classification, evaluation and prioritization of the identified challenges. In the final part, the framework is validated on an industry specific use case and its limitations and future scope are discussed.
This master thesis emphasizes the significance of data in intralogistics processes by identifying and addressing data-related issues. The outcome on one hand is state-of-the-art solutions for the identified problems and on the other hand is a framework which will support businesses in determining how to tackle data-related issues to gain most benefit with respect to efficiency, productivity, flexibility and quality.
The advancements in Industry 4.0 technologies have provided unprecedented opportunities for optimizing material transportation through various use cases that are possible through rapid technological advance. An important driver for the use cases is data. However, the lack of understanding, which
specific data, from which sources and in what frequency, slows down the implementation of use cases or even reduces their potential benefits. Companies lack the ability to prepare themselves correctly for a use case integration, especially from the data perspective (e.g. data availability, quality, integration).
Therefore, the goal of this thesis is to create a framework for evaluation of Industry 4.0 use cases in the materials' transportation with regard to needed data. The scientific approach employed in this research involves research and analysis of existing frameworks for description or assessment of use cases in different fields and industries. Following, specific use cases related to material transportation in the context of Industry 4.0 will be identified in order to find similarities in the structure and requirements
regarding needed data, and thus identifying common characteristics and key parameters. These parameters will then serve as the foundation for developing a framework that enables companies to systematically analyse and assess potential use cases for material transportation, considering the data requirements and its integration challenges.
The expected result of this thesis is the development of a practical framework that empowers organizations to evaluate and implement Industry 4.0 use cases for material transportation effectively. By providing a structured methodology, this framework will facilitate decision-making processes and support companies in identifying the most suitable use cases based on their specific requirements and
data availability.
The global automotive industry is undergoing a major shift from the combustion engines to a wide variety of propulsion technologies. It is further pooled with Industry 4.0, which has lead to a large volatility in technolgical innovations and ambiguity in the product life cycles.
This uncertainty has lead to a rapidly changing demands for the existing products and services. It is causing difficulty in planning yearly demand quantities with suppliers. In many cases, tier-1 suppliers are unable to actually purchase the quantities for which they reserve a particular capacity of its sub-suppliers during annual sourcing agreements. Companies need to improve their flexibility to adapt to such unpredictable market situations by preparing for quantity or product changes.
Before setting a target for a desired flexibility level, the exisiting situation should be assessed. Therefore, this thesis aims to develop a method to assess the flexibility of suppliers in terms of product mix, volume deviations and delivery compliance. A quantification model is derived, which will be applicable for a wide range of suppliers. The model will enable the comparison of different suppliers during new sourcing decisions, as well as the identifcation of the exisiting suppliers that have room for improvement.
Various factors that affect supplier flexibility are identified through literarure research and personal interviews with different employees having supplier specific roles within Rober Bosch GmbH. These factors are analysed through a ‘WHAT-WHY-HOW’ analysis and only those factors are considered which can be coherently quantified. Based on their significance in the overall flexibility, these focus factors are given particular weightages and then quantified for each suppliers using the available data. The resultant of the scored factors will yield a number that indicates the flexibility index for a corresponding supplier. The developed model will be tested using Robert Bosch GmbH as an example.
Um langfristig in einem Umfeld zunehmenden Wettbewerbs durch internationale Anbieter erfolgreich zu sein, müssen Unternehmen verstärkt regionale Märkte erschließen. Analog zur Automobilindustrie werden wichtige Wachstumsmärkte zunehmend durch Handelshemmnisse abgeschottet, so dass die Markterschließung durch Exporte vollständig montierter Erzeugnisse häufig ausscheidet. Um dennoch die Handelshemmnisse zu umgehen, hat sich in der Automobilindustrie die Completely Knocked Down (CKD)-Strategie durchgesetzt, bei der Erzeugnisse teilzerlegt in die Märkte exportiert und dort lokal endmontiert werden. Eine grundsätzliche Herausforderung liegt in der situationsgerechten Gestaltung der CKD-Supply Chain. Dazu ist in der Arbeit ein Teil einer simulationsbasierte Gestaltungsunterstützung mit dem Schwerpunkt auf 2D und 3D Simulation erarbeitet worden.
The Aim of this article is to provide a framework which enhances the existing scope of manufacturing asset management by specifically addressing industrial services provided by external suppliers as an integral part of today’s manufacturing structures. Existing research shows that sourcing industrial services from specialized service organizations establishes complex and unique interdependencies and links total production efficiency to the performance of the external service suppliers. Within the context of the EU-Project InCoCo-S - “Innovation, Coordination and Collaboration in Service Driven Manufacturing Supply Chains” a standard business reference model with key focus on operation and integration of business related services (BRS) in the supply chain has been developed. Based on the service type retrofit this paper aims on the one hand to present the modules of the reference model and on the other hand to explain how the model can be used to enhance the retrofit business.
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.
Industrial Service Providers (ISP) are exposed to constantly raising competitive pressures regarding both cost and performance aspects. The massive challenges caused by the current worldwide financial and economic crisis even intensified the need for process optimizations aimed at increasing the productivity of service production. To reach this goal the evaluation and elimination of waste in their production processes becomes a crucial ability for ISPs. This paper proposes a new approach for increasing productivity in service production processes using a generic measurement model for the detection and evaluation of waste. The model is based on established lean management principles, but tailored to the specifics of ISPs by adopting a customers’ perspective to track down and eliminate waste. The evaluation builds on an in-depth-analysis of particular types of waste in the industrial service production processes. Viewed from the customers’ perspective and taking into account the specific characteristics of services (e.g. intangibility, heterogeneity, inseparability, and perishability) and service production (e.g. volatile demand, a tendency to over-capacity, and limits to planning) the approach employs a service blueprint reference model to then determine the different types of waste in the various parts of the service production process.
One of the major tasks of operations managers is to boost uptime while simultaneously keeping budget. To meet this challenge they discover reliability-based management as strategic factor to improve performance. But which parameters are the key to “reliability excellence” and drive a company’s performance? What are the relevant levers to pull in reliability-based management?
To answer these questions McKinsey & Company partnered with Aachen University to launch a global reliability survey in process industries. Objective of the initiative is to provide a statistically proven picture of key factors that drive maintenance and reliability excellence. Furthermore benchmarks and best practices concerning overall operational performance will be identified. The study is based on a questionnaire-based approach which addresses all relevant departments within a company, complemented by best practice analyses.
This paper provides results of the survey. The results demonstrate that reliability pays off. Some unproven beliefs have been confirmed (e.g. a good reliability performance results in a low spare part inventory) but also surprises like a correlation between safety and performance were identified. The analysis also shows that structural differences like company size or geography do not influence reliability performance.
Due to shorter product life cycles the number of production ramp-ups is increasing, while customers have a soaring demand for more variable and individualized products. In the future, optimizing the production ramp-up will become an important differentiation criterion for companies. Considering the whole supply chain in the ramp-up process becomes therefore indispensable. This is what the presented research in this paper concentrates on. The intention of the research project is to develop a model of a supply chain in the production ramp-up stage. Through this model, approaches for optimizing the production ramp-up in the whole supply chain will be derived.
Further the research project concentrates on measuring the production ramp-up performance in the supply chain, showing the impact on economic and financial measures. The result of this research is an approach to align the tasks and objectives of Supply Chain Management with the tasks and objectives of ramp-up management in order to optimize the whole supply chain in the ramp-up stage.
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.
Rebound Logistics
(2009)
Today, the flow of product returns is becoming a significant concern for many manufacturing companies. In this research area, three fundamental aspects of product returns need to be taken into consideration: First, companies become increasingly aware of the fact that product returns may offer an opportunity for enormous profit generation and for improving the competitive advantage of a manufacturing company when taking into account the accretive value of the products and technology. Second, the impact of green laws, legislative provisions and the increasing impact of a sustainable production management due to marketing aspects force companies to design and manage the reverse supply chain actively. Third, the importance of managing the reverse supply chains effectively will be enforced by the currently volatile economic climate. This paper outlines first results of designing a methodological framework for implementing an integrative reverse supply chain for manufacturing companies based on a type-specific Reverse Supply Chain Reference Model.
In the near future, tooling companies will offer their customers not just maintenance services, but complex remote service packages for their engineering asset management, which is the total management of physical – not financial – assets. The overall goal is to enhance the efficiency of the engineering asset, e.g. to reduce TCO, on the customers´ site by means of value creating partnerships. These partnerships may be, e.g. the classical output or reliability partnership, but also process optimizing partnerships or lifecycle partnerships. The process optimizing partnership offers, e.g. the optimization of the system’s performance or the output quality, an optimized ramp-up and restart procedure or optimization of the production process parameters. The lifecycle partnership, on the other hand, accompanies the intelligent tool-machine-system throughout the whole lifecycle, which includes, e.g. provision of spare parts during the entire usage phase, storing, refurbishment, recycling and even the support of relocation of production facilities. Intelligent remote services have great potential for realizing all these partnerships.
To realize such engineering asset-related partnerships, two major tasks have to be done. First, there has to be the intelligent tool-machine system, which delivers the information that is required for these services. And furthermore, this information has to be integrated into the maintenance processes, so that it is delivered at the right place and time and in the required form. Second, the activities and processes that are combined to the engineering asset-related partnerships have to be configured out of standardized service and process modules. Therefore configuration logic is essential.
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.
Holistic PLM- Model
(2010)
Product Lifecycle Management (PLM) is a widely discussed topic concerning the increase of efficiency of product development in terms of time to market as well as customizing products to the different needs of customers worldwide adequately. Historically PLM focuses the early phases of the product’s lifecycle, namely the product development phase. Therein the roots of PLM are based in supporting the information logistics of product data: Consistent data sets should be available to all stakeholders in the different departments at all times. Due to the increasing product complexity PLM has to be extended in terms of the temporal dimension (not limited to product development phase) and systemic dimension (not limited to the information logistic aspect). In this paper the authors derive a holistic framework for Product Lifecycle Management by analysing existing integrated management approaches. The framework consists of four dimensions: PLM strategy, PLM process, Product structure and PLM IT-Architecture. The sustainability and benefits of the framework is demonstrated by applying the framework to the communication service provider industry (CSP).
Within each of the three design fields numerous design elements exist (e.g. degree of centralization, number of warehouses etc. in the field network design). Hence, the interdependencies of all design elements have to be analyzed to allow optimal decisions for the design of an efficient and effective spare parts logistics. Nevertheless, the complexity among all interdependencies can hardly be understood. Therefore it is necessary to reduce the complexity of design decisions by focusing on the most important design elements according to the logistical requirements of different spare part categories. In order to achieve this goal, a classification of spare parts in terms of their key characteristics has been developed. For different spare part categories only a smaller set of design elements and their interdependencies has to be taken into account. The reduced number of key design elements per spare part category can be analyzed and understood in depth. Thus a Systems Dynamics approach is used to allow a better configuration of network design, cooperation concepts and inventory management in spare parts Supply Chains on the basis of specific logistics requirements of different spare part categories.
In dynamic markets flexible and efficient production systems are the main success factor for companies. The production system in this context includes all five phases of the SCOR-Model: Source, Make, Deliver, Plan and Return. In a subproject of the cluster of excellence "Integrative Production Technology for High-Wage Countries" at RWTH Aachen University, a configuration logic is being developed that enables companies to configure their production system according to the dynamic requirements of the market. As a major intermediate result, a holistic description model for production systems has been defined. In combination with numerous attributes in the sub-models, a detailed characterization of the production system is possible.
The sub-model for the design of the Supply Chain (mainly Deliver) will be depicted in detail in this paper. Representative for the design of a Supply Chain, spare parts logistics - as one of the most challenging tasks in logistics planning - is analyzed in depth. For this purpose spare parts logistics is divided into three design fields: network design, cooperation concepts (e.g. with logistics providers, customers, suppliers) and inventory management. Decisions in the design fields are highly interdependent, any spare parts logistics configuration has to take these interdependencies into account.
With big data-technologies on the rise, new fields of application appear in terms of analyzing data to find new relationships for improving process under-standing and stability. Manufacturing companies oftentimes cope with a high number of deviations but struggle to solve them with less effort. The research project BigPro aims to develop a methodology for implementing counter measures to disturbances and deviations derived from big data. This paper proposes a methodology for practitioners to assess predefined counter measures. It consists of a morphology with several criterions that can have a certain characteristic. Those are then combined with a weighting factor to assess the feasibility of the counter measure for prioritization.
Manufacturing companies are facing an increasingly turbulent market – a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.
Digital Servitization is one of the significant trends affecting the manufacturing industry. Companies try to tackle challenges regarding their differentiation and profitability using digital services. One specific type of digital services are smart services, which are digital services built on data from smart products. Introducing these kinds of offerings into the portfolio of manufacturing companies is not trivial. Moreover, they require conscious action to align all relevant capabilities to realize the respective business goals. However, what capabilities are generally relevant for smart services remains opaque. We conducted a systematic literature review to identify them and extended the results through an interview study. Our analysis results in 78 capabilities clustered among 12 principles and six dimensions. These results provide significant support for the smart service transformation of manufacturing companies and for structuring the research field of smart services.
Die Herausforderungen der Zukunft werden geprägt durch digital veredelte Produkte von höchster Qualität und hoher Variantenvielfalt bei gleichzeitig kleiner werdenden Losgrößen. Konventionelle Entwicklungsmethoden stoßen aufgrund zunehmender Komplexität und kürzer werdender Lebenszyklen auf Produktebene an ihre Grenzen. Dadurch werden bei kundenindividueller Produktion die Aufwände in der Arbeitsplanung und -vorbereitung überproportional größer. Eine mögliche Lösung stellt die generative Erstellung der Produktionsstückliste während der Montage dar. Durch das eventbasierte „Mitschreiben der Montage“ werden administrative und planungsintensive Prozesse in der Arbeitsvorbereitung überproportional reduziert und die Erstellung der Stückliste in die manuelle Montage transferiert.
Reinforced through the pandemic and shaped by digitalization, today's professional working environment is in a state of transformation. Working remotely has become a vital component of many professions' regular routines. The design of remote work environments presents challenges to organizations of all sizes. By providing a classification, this paper reveals a comprehensive understanding of the fields of design to be considered to establish lasting remote work concepts in organizations. A hierarchical classification with four dimensions consisting of human, technology, organization, and culture, seven design elements and, twenty design parameters indicates to organizations the fields of design that need to be examined. To satisfy both the theoretical foundation and the practical application, design elements are derived by implementing a systematic review of the literature that represents key areas of interest for remote work. Additionally, these are verified and complemented by a dedicated case study research to incorporate practice-oriented design parameters.
Pricing is one of the most important, but underestimated tools, to enhance a company's profitability. Especially value-based pricing has a high potential to reach higher levels of satisfaction because it equates the needs of providers and customers. Even though, it is a well-known price model and promises higher satisfaction, many companies struggle to implement it. Especially the manufacturing industry is characterized by cost-plus pricing and competition-based pricing. However, especially for digital products these pricing strategies are insufficient. Therefore, this paper aims at exploring the design fields for value-based pricing of digital products in the manufacturing industry. To achieve this, the basics of digital products and value-based pricing are explored. Furthermore, an expert workshop is conducted that follows a framework for value-based pricing consisting of four consecutive steps analysis, price strategy, pricing, and market launch to capture the design fields. This paper concludes with limitations, and practical and research implications.
In a subproject of the cluster of excellence “Integrative Production Technology for High-Wage Countries” at RWTH Aachen University a configuration logic is under development that enables companies to configure their production system according to the dynamic requirements of the market. As a result of this project, a holistic description model for production systems has been defined. With numerous attributes in the sub-models a detailed characterization of the production system is possible.
The sub-model for the design of the supply chain will be depicted in detail in this paper. Representative for the design of a supply chain, the spare parts logistics of the wind energy industry is analyzed in depth. Designing this supply chain is not only one of the most challenging tasks in logistics. Only a responsive but also cost efficient design of the spare parts supply chain guarantees high productivity, extended life spans of the wind turbines as well as the expected profit for all companies in the supply chain.
Assets of integrated production systems, especially in the heavy industry, are facing high requirements in terms of reliability and availability. In case of component breakdown, the operating firm is confronted with high costs due to downtime and loss of production. Modern maintenance concepts in combination with advanced technologies can help to improve the plant availability and reduce the downtime costs caused by unplanned breakdowns. Against this background, the research institutes FIR and IMR from RWTH Aachen University, Germany, are collaborating within the research project “SiZu”. This project deals with the integration of condition monitoring system and real time simulation to assess the condition of components and to support failure cause analysis.
In the last decade, enterprises realized the high value of data and learned to successfully utilize it for internal processes and business models, and they are trying to find more ways to acquire relevant data. Since enterprises are part of complex networks, the data from their partners and customers can also be beneficial: from adjusting the demand and supply to planning production and aligning capacities. One such example is adaptive process control: detailed material data from a supplier can be used to adjust process parameters in their production. This approach may be especially beneficial for the steel industry, as there is a possibility to adjust the material properties by changing the speed, force, or temperature in their own production processes. However, such an approach requires tight collaboration, e.g., regarding improving IT infrastructure, ensuring data acquisition and transfer and most importantly, the utilization of such data.
With the development of publicly accessible broker systems within the last decade, the complexity of data-driven ecosystems is expected to become manageable for self-managed digitalisation. Having identified event-driven IT-architectures as a suitable solution for the architectural requirements of Industry 4.0, the producing industry is now offered a relevant alternative to prominent third-party ecosystems. Although the technical components are readily available, the realisation of an event-driven IT-architecture in production is often hindered by a lack of reference projects, and hence uncertainty about its success and risks. The research institute FIR and IT-expert synyx are thus developing an event-driven IT-architecture in the Center Smart Logistics' producing factory, which is designed to be a multi-agent testbed for members of the cluster. With the experience gained in industrial projects, a target IT-architecture was conceptualised that proposes a solution for a self-managed data-ecosystem based on open-source technologies. With the iterative integration of factory-relevant Industry 4.0 use cases, the target is continuously realised and validated. The paper presents the developed solution for a self-managed event-driven IT-architecture and presents the implications of the decisions made. Furthermore, the progress of two use cases, namely an IT-OT-integration and a smart product demonstrator for the research project BlueSAM, are presented to highlight the iterative technical implementability and merits, enabled by the architecture.
Manufacturing companies face the challenge of managing vast amounts of unstructured data generated by various sources such as social media, customer feedback, product reviews, and supplier data. Text-mining technology, a branch of data mining and natural language processing, provides a solution to extract valuable insights from unstructured data, enabling manufacturing companies to make informed decisions and improve their processes. Despite the potential benefits of text mining technology, many manufacturing companies struggle to implement use cases due to various reasons. Therefore, the project VoBAKI (IGF-Project No.: 22009 N) aims to enable manufacturing companies to identify and implement text mining use cases in their processes and decision-making processes. The paper presents an analysis of text mining use cases in manufacturing companies using Mayring's content analysis and case study research. The study aims to explore how text mining technology can be effectively used in improving production processes and decision-making in manufacturing companies.
The agricultural industry is facing unprecedented challenges in meeting the growing demand for food while minimizing its impact on the environment. To address these challenges, the industry is embracing technological advancements such as 5G networks to improve efficiency and productivity. However, the benefits of 5G technology must be weighed against the costs of implementing a suitable network. This paper presents cost-benefit dimensions that are needed to assess the economic feasibility of implementing 5G networks for several agricultural applications. The paper describes the costs of deploying and maintaining a 5G network and the benefits of several 5G-specific use cases, including precision agriculture, livestock monitoring, and swarm robotics. Using industry reports and case studies, the model quantifies the benefits of 5G networks, such as enabling new digital agricultural processes, increased productivity, and improved sustainability. It also considers the costs associated with equipment and infrastructure, as well as the challenges of deploying a network in rural areas. The results demonstrate that 5G networks can provide significant benefits to agricultural businesses and provide an overview about the cost factors. Both benefit and cost dimensions are analyzed for the 5G-specific agricultural use cases.
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)
Two major trends are driving many companies in the manufacturing industry to rethink and reconfigure their business logic: the trends towards applying a service dominant business logic, and the trends towards collecting and using information about the market life cycle of products. The pursuit of market lifecycle information has lately been one that is driven mostly by tremendous developments in the area of the Internet of Things and information system integration. Companies in the manufacturing industry are reconfiguring their value chains, tending towards a higher degree of service orientation. This transformation requires an understanding of the principles behind offering additional value through industrial product service systems. The design of an adequate information architecture and the subsequent management model are the key factors for a successful implementation. This article focuses on how information gathering, analysis, and the meaningful use of information have been linked to the success of those companies within the German manufacturing industry which have made the transformation towards service-orientation. On the basis of an empirical study, five success factors with a significant impact on either innovation performance and/or operational performance are identified. These findings are enhanced to derive guidelines for an adequate information architecture. The guidelines are underpinned by best practices of prosperous companies with a strong product-service-orientation. Links between best practice application and performance are analyzed, and significant relations are identified.
Influenced by the high dynamic of the markets the optimization of supply chains gains more importance. However, analyzing different procurement strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve the production process. The objective of this research is to evaluate different procurement strategies in a four-stage supply chain. Besides, this research aims to identify main influencing factors on the supply chain’s performance. The performance of the supply chain is measured by means of back orders (backlog). A scenario analysis of different customer demands and a Design of Experiments analysis enhance the significance of the simulation results.
Big data are collected along the entire food industry value chain, but remain mostly unused. Data sharing in data ecosystems could lead to efficiency gains and new revenue streams. We investigate data sharing within food industry and derive challenges and opportunities for data sharing in this context. We conducted interviews with ten qualified experts from the German food industry. The results reveal that mainly trust, usefulness and value influence users’ attitude towards data sharing. Our results confirm social exchange theory in conjunction with technology acceptance model as relevant underlying IS theories of data sharing.
Towards a Methodology to Determine Intersubjective Data Values in Industrial Business Activities
(2021)
This paper contributes to a valuation framework for valuing data as an intangible asset. Especially those industrial manufacturers developing and delivering holistic digital solutions are limited in calculating the true business value of data initiatives. Since the value of data is strongly dependent on the respective use case, a completely objective valuation is not possible. This complicates decision-making on the internal side regarding investments in digital transformation, and on the external side to communicate existing benefits to third parties via financial reporting. Therefore, the target is to design a valuation framework that allows industrial manufacturers to determine an intersubjective, i.e., traceable and transparent, data value. In order to develop a framework that can be applied in practice, the approach is based on industrial case study research.
Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010–2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points.