Refine
Year of publication
Document Type
- Conference Proceeding (86) (remove)
Is part of the Bibliography
- no (86)
Keywords
- 02 (14)
- 03 (2)
- 3GPP (1)
- 5G (3)
- 5G mobile communication (1)
- 5G use case (1)
- 5G-Technology (1)
- AI (2)
- Additive Fertigung (1)
- Adherence To Delivery Dates (1)
- Agile management Systems (1)
- Agriculture (1)
- Anlaufmanagement (1)
- Artificial intelligence (1)
- Assembly (1)
- Asset Management (1)
- Assistance Systems (1)
- Augmented Reality (1)
- B2B customer service (1)
- Big Data (1)
- BigPro (1)
- Business analytics (2)
- Business ecosystems (1)
- Business model innovation (1)
- CPS (4)
- CPSL (1)
- Capacity Utilization (1)
- Carrier (1)
- Case study research (1)
- Change Management (1)
- Change Request (2)
- Changeability (1)
- Chatbot (1)
- Circular economy (1)
- Cloud (1)
- Co-Creation (1)
- Competencies (1)
- Crisis management (1)
- Cross-Industry Innovation (1)
- Customer Perspective (1)
- Customer Success Management (1)
- Customer success managementl (1)
- Cyber Security (1)
- Data Analytics (1)
- Data Products (1)
- Data Quality (1)
- Data-Centric Platform (1)
- Data-based pricings (1)
- Data-driven services (1)
- Decision Support (2)
- Decision Support System (1)
- Demand Forecasting (1)
- Design of Experiments (1)
- Deviation Detection (1)
- Digital Transformation (1)
- Digital platform design (1)
- Digital transformation (1)
- Digitale Transformation (1)
- Digitalisation (1)
- Digitalisierung (1)
- Digitalization (1)
- Digitization (2)
- Digitization of SMEs (1)
- Discrete Event Simulation (1)
- Disruption Management (1)
- Disruptions (1)
- Distribution management (1)
- Disturbance Management (1)
- Do-it-together (1)
- EPCIS (1)
- ERP (1)
- Efficiency Improvement (1)
- Employee qualification (1)
- Energiemanagement (1)
- Entscheidungsunterstützung (2)
- Ersatzteillogistik (1)
- Evaluation (2)
- Event Data (1)
- Feedback data (1)
- Flexible manufacturing system (1)
- Food Production (1)
- Forecasting capability (1)
- Freight forwarder (1)
- Furniture Production Process (1)
- Human-centered work design (1)
- Humanitarian logistics (1)
- IT-Architektur (1)
- Implementation barriers (1)
- Implementation success factors (1)
- Incorrect Data (1)
- Industrial sustainability (1)
- Industrie 4.0 (10)
- Industry 4.0 (1)
- Information Logistics (1)
- Information Transparency (1)
- Informationslogistik (1)
- Innovative Furniture (1)
- Instandhaltung (1)
- Insufficient Data (1)
- Interface definition (1)
- Internet of Production (4)
- Internet of Things (1)
- IoP (1)
- IoT (1)
- KI (1)
- KMAT (1)
- KMU (2)
- KPI (1)
- Kennzahlen (1)
- Key Performance Indicators (1)
- Knowledge representation (1)
- Konferenz (1)
- Konfigurierbare Materialien (1)
- Künstliche Intelligenz (1)
- Lean Manufacturing (1)
- Learning Game (1)
- Lebensmittelindustrie (1)
- Leistungsfähigkeit (1)
- Logistikdienstleister (1)
- MES (1)
- Machine Learning (1)
- Machine learning (1)
- Manufacturing (2)
- Manufacturing Companies (2)
- Manufacturing Execution System (1)
- Manufacturing firms (1)
- Manufacturing industry (1)
- Matching (2)
- Maturity Index (1)
- Maturity Model, Maturity Index (1)
- Maturity model (1)
- Mixed-Model Assembly (1)
- Modellierung (1)
- Morphology (2)
- Networked (1)
- Online retail (1)
- Onlinehandel (1)
- Ontology (1)
- Operating models (1)
- Operations planning (1)
- Optimized capacity utilization (1)
- Order Quantity (1)
- PPC (1)
- Performance-Management (1)
- Plastics industry (1)
- Platform economy (1)
- Potential Benefits (1)
- Prescriptive Maintenance (2)
- Pricing Models (1)
- Procurement Strategy (1)
- Product Characteristics (1)
- Product-as-a-service business (1)
- Production (1)
- Production Control (3)
- Production Management System (1)
- Production Planning (1)
- Production facilities (1)
- Production management (1)
- Produktentwicklung (1)
- Produktionsmanagement (1)
- Produktionsplanung (1)
- Produktionsplanung und -steuerung (1)
- Produktionssteuerung (1)
- Quarrying Industry (1)
- Quarrying industry (1)
- Reference data model (1)
- Reference model (1)
- Regulatory framework (1)
- Relay traffic (1)
- Reliability (1)
- Replenishment Time (1)
- Requirements (1)
- Resource deployment (1)
- Return on Investment (1)
- Risikomanagement (1)
- Risk assessment (1)
- Risks (1)
- Route sectioning algorithm (1)
- SCEM (1)
- SCM (1)
- SME (1)
- SV7126 (1)
- SV7136 (1)
- SV7169 (1)
- SV7190 (1)
- SV7213 (1)
- SV7242 (1)
- SV7266 (1)
- SV7276 (1)
- SV7289 (1)
- SV7427 (1)
- Scenario pattern (1)
- Scenario technique (1)
- Sensors (1)
- Service engineering (1)
- Setup time optimizing sequencing (1)
- Similarity Analysis (1)
- Skills forecasting (1)
- Skills management (1)
- Smart Services (1)
- Smart farming (1)
- Smart product service system (1)
- Smart services (1)
- Social Manufacturing (1)
- Socio-technical analysis (1)
- Sociotechnical (1)
- Softwaretool (1)
- Strategie (1)
- Strategy (1)
- Störungsmanagement (2)
- Subscription (1)
- Subscription Business (1)
- Subscription Business Models (1)
- Subscription business (2)
- Supply Chain Event Management (2)
- Supply Risks (1)
- Supply chain (1)
- Supply-Chain-Design (1)
- Supply-Chain-Management (4)
- Supply-Chain-Networks (1)
- Sustainability (3)
- Swarm robotics (1)
- Task View (1)
- Task-oriented Reference Model (1)
- Taxonomy (1)
- Training (1)
- Transport order (1)
- Twin transition (1)
- Type-specific (1)
- Typification (2)
- Typology (1)
- Value capture (1)
- Value creation (1)
- Value-based Pricing (1)
- Value-in-Use (1)
- Value-in-use (2)
- Viable System Model (2)
- Visual Analytics (1)
- Vorgehensmodell (1)
- Wireless communication (1)
- acatech industrie 4.0 Maturity Index (1)
- acquisition cycle (1)
- agile company (1)
- air mobility (1)
- analysis of potential (1)
- artificial intelligence (1)
- artificial neural networks (1)
- asset management (1)
- attribute and data harmonization (1)
- autonomous technology scouting (1)
- blockchain (1)
- blockchain-based services (1)
- business model (2)
- case study research (3)
- classification systems (1)
- condition monitoring (1)
- counter measures (1)
- criticality analysis (1)
- data valuation framework (2)
- data value (3)
- data value assessment (1)
- decision making (1)
- decision support (2)
- deep learning (1)
- design (1)
- deviation detection (1)
- digital services (1)
- digital shadow (3)
- digitale Technologien (1)
- digitale Transformation (1)
- digitalization (1)
- disruption management (1)
- disturbance management (2)
- energy consumption (1)
- energy efficiency (1)
- flight demand (1)
- hybride Systeme (1)
- hype cycle (1)
- i4.0 (1)
- iIntangible assets (1)
- industry 4.0 (1)
- information logistics model (1)
- information management (1)
- intangible assets (1)
- intelligent maintenance systems (1)
- intelligent support system (1)
- internet of production (1)
- inventory management (2)
- it-architecture (1)
- machine learning (1)
- machine tools (1)
- machinery and plant engineering industry (1)
- maintenance engineering (1)
- maintenance services (1)
- maintenance value contribution (1)
- manufacturing (1)
- manufacturing companies (2)
- microgrids (1)
- milling (1)
- natural language processing (1)
- nonlinear optimization (1)
- order (1)
- order processing (1)
- peak flattening (1)
- predictive maintenance (1)
- process mining (1)
- procurement (1)
- production control (1)
- production networks (1)
- reaction strategy (1)
- real-time systems (1)
- rev (31)
- risk analysis system (1)
- risk management (1)
- service engineering (1)
- smart product service systems (2)
- smart services (1)
- subscription business (1)
- subscription business models (1)
- supply chain (2)
- supply chain event management (1)
- supply chain management (1)
- system dynamics (2)
- task model (1)
- technology management (1)
- technology scouting (1)
- text mining (1)
- thin-haul (1)
- transport demand (1)
- Änderungsmanagement (1)
Institute
Understanding the Organizational Impact of Robotic Process Automation: A Socio-Technical Perspective
(2022)
Interest in AI-driven automation software is growing constantly across
all industries, as these technologies enable companies to almost automate administrative processes completely and significantly increase operational efficiency.
However, many implementation attempts fail due to a lack of understanding of how these technologies affect the various socio-technical aspects that are intertwined in an organisation. This leads to a widening gap between value propositions of automation software and the ability of companies to exploit them. For long-term
success, collaboration between humans and software robots in the organization must be optimised. Therefore, the social, technical, and organizational impact of Robotic Process Automation was investigated. Following a socio-technical systems approach, a model was developed and validated in a use case of a company in the mechanical engineering sector. Knowing the influencing factors before launching large-scale automation initiatives will help practitioners to better exploit
efficiency potentials and increase the long-term success.
Generation of a Data Model For Quotation Costing Of Make To Order Manufacturers From Case Studies
(2022)
For contract or make to order manufacturers, quotation costing is a complex process that is mainly performed based on experience. Due to the high diversity of the product range of these mostly small or medium-sized companies (SMEs) and the poor data situation at the time of quotation preparation, the quality of the calculation is subject to strong variations and uncertainties. The gap between the initial quotation costing and the actual costs to be spent (pre- and post-calculation) is crucial to the existence of SMEs. Digitalization in general can help companies to get a better understanding of processes and to generate data. For improving these processes, an understanding of the important data for that specific process is crucial. Accurate quotation costing for customized products is time-consuming and resource-intensive, as there is a lack of an overview of data to be used within the process. This paper therefore derives a data model for supporting quotation costing in the company, based on literature-based costing procedures and recorded case studies for quotation and calculation. Based on the results, SMEs will have a first overview of the needed data for quotation costing to optimize their calculation process.
The manufacturing industry consumes 54% of global energy and attributes for 20% of global CO2 emissions, demonstrating the industry’s role as global driver of climate change. Therefore, reducing its carbon footprint has become a major challenge as its current energy and resource consumption are not sustainable. Industrie 4.0 presents a chance to transform the prevailing paradigms of industrial value creation and advance sustainable developments. By using information and communication technologies for the intelligent networking of machines and processes, it has the potential to reduce energy and material consumption and is considered a key contributor to sustainable manufacturing as proclaimed by the European Commission in the term “twin transition”. As organizations still struggle to utilize the potential of Industrie 4.0 for a sustainable transformation, this paper presents a framework to successfully align their own twin transition. The framework is built upon three key design principles (micro level: leverage eco-efficient operations, meso level: facilitate circularity and macro level: foster value co-creation) derived using case study research by Eisenhardt, and four structural dimensions (resources, information systems, organizational structure and culture) based on the acatech Industrie 4.0 Maturity Index. Eleven interconnected areas of action are defined within the framework and offer a holistic and practical approach on how to leverage an organization’s twin transition. Within the conducted research, the framework was applied to the challenge of information quality and transparency required for high-value secondary plastics in the manufacturing industry. The result is a digital platform design that enables information transactions for secondary plastics and establishes a circular ecosystem. This shows the applicability of the framework and its potential to facilitate a structured approach for designing twin transitions in the manufacturing industry.
Augmented reality seems to offer great potential benefits in the field of industrial services. However, the question of the exact benefits, both monetary and qualitative, is difficult to evaluate, as is the case with IT investments in gen-eral. Within the framework of the DM4AR research project, an evaluation model was therefore developed. Based on group discussions and interviews on potential AR use cases, a list of monetary and qualitative benefits was compiled to form the basis for selecting suitable evaluation modules in the existing literature. These include an impact chain analysis in the form of a strategy map, a monetary eval-uation as a calculation of the return on investment, based on the assumptions of the use case as well as existing studies, and a qualitative evaluation in the form of a utility analysis. The outcome is an evaluation model in the form of a multi-perspective approach that considers the impact of AR in the four perspectives of the balanced scorecard (financial, customer, internal business processes, learning and growth). The results of the qualitative and monetary evaluation can be sum-marized in a 2D matrix to support decision-making.
Die Verschärfung des Wettbewerbsumfelds produzierender Unternehmen und die als Antwort hierauf in den Fokus rückenden agilen Methoden vergrößern die Bedeutung einer effizienten Handhabung von Änderungsprozessen. Am Beispiel des Maschinen- und Anlagenbauers Ortlinghaus zeigt der Beitrag, dass eine Kombination aus ungeeigneten Änderungsprozessen und mangelhaftem IT-Support in der Praxis oft die schnelle und gleichzeitig qualitätsgesicherte Durchführung von Änderungsprozessen verhindert. Der Zielkonflikt aus geringem Zeitbedarf und hoher Prozessqualität lässt sich durch Anpassungen in der IT-Unterstützung reduzieren. Hierdurch können Erfolgsfaktoren für ein effizientes Änderungsmanagement gehoben und die Problemfelder der Workflowunterstützung, Informationsverteilung und Datenhandhabung verbessert werden. Zentrales Hindernis zur Adressierung der Erfolgsfaktoren stellt die aktuell zur Abwicklung von Change Requests genutzte Arbeitsumgebung dar. Der Beitrag präsentiert hierfür als zentralen Lösungsansatz die Internet of Production Infrastruktur. Das Potenzial der Internet of Production Infrastruktur im Kontext des Änderungsmanagements wird anhand von drei Anwendungsbeispielen verdeutlicht. Abschließend wird der Migrationspfad für Unternehmen bei der Einführung eines effizienten Änderungsmanagements aufgezeigt.
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]
Industrie 4.0 is said to have major positive effects on productivity in manufacturing companies. However, these effects are not visible yet. One reason for this is the lack of understanding of maintenance services as a crucial value contributing partner in production processes, although scientific literature already highlighted the importance of indirect maintenance costs. In order to retrieve the unused potential of maintenance services, a digital shadow in form of a sufficiently precise digital representation is required, providing a data model for the value of maintenance actions so that asset and maintenance strategies can be optimized later on. Using case study research for process manufacturers, the first research contribution of this paper consists of 21 value contributing elements being identified. The second contribution is a reference processes model, showing seven major process steps as well as the required intra-organization interaction on an information technology system level. Therefore, it provides the base for the missing data model shaping the targeted digital shadow of maintenance services’ value contribution. [https://link.springer.com/chapter/10.1007/978-3-030-57993-7_69]
Due to Digital Transformation, also called Industry 4.0 or the Industrial Internet of Things, the barrier for implementing data collecting technology on the shop floor has decreased dramatically in the past years – leading to an increasingly growing amount of data from a multitude of IT systems in production companies worldwide. Despite that, the production controller still relies heavily on intrinsic knowledge and intuition for the management of disruptions in production. Thanks to advances in the fields of production control and artificial intelligence, potentials for the collected data for disruption management arise. However, in order to transform data into usable information and allow drawing conclusions for disruption management in production, the relevant data-objects, disturbances and alternative actions must be known. Thus, the decision-making can be supported, reducing the decision latency and increasing benefit of alternative actions. Therefore, the goal of this paper is to discuss the prerequisites necessary to perform a data based disruption management and the methodology itself, serving as an approach to allow companies to build a data basis, classify disruptions and alternative actions in order to improve decision making in the future. [https://link.springer.com/chapter/10.1007/978-3-030-28464-0_13]
Industrie 4.0 is changing the industrial landscape in an unanticipated way. The vision for manufacturing industries is to transform to an agile company, in order to react on occurring events in real-time and make data based decisions. The realization requires also new capabilities for the information management. To achieve this goal agile companies require taking measured data, analyzing it, deriving knowledge out of this and support with the knowledge their employees. This is crucial for a successful Industrie 4.0 implementation, but many manufacturing companies struggling with these requirements. This paper identifies the required capabilities for the information management to achieve a successful Industrie 4.0 implementation. [https://link.springer.com/chapter/10.1007/978-3-319-65151-4_3]
Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consistently and comprehensively by manufacturing companies. Currently, attempts on data valuation are primarily taking place on internal company level and qualitative scale. This leads to inconclusive results and unused opportunities in data monetization. Existing approaches in theory to determine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to transfer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper contributes to data value assessment by presenting a four-part valuation framework that specifies how to transfer internal, qualitative to external, quantitative data valuation. The proposed framework builds on insights derived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Placing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue. [https://link.springer.com/chapter/10.1007/978-3-030-85902-2_19]