Refine
Year of publication
Document Type
- Article (3)
- Book (1)
- Part of a Book (9)
- Conference Proceeding (25)
- Contribution to a Periodical (3)
- Internet Paper (1)
- Report (1)
- Working Paper (3)
Is part of the Bibliography
- no (46)
Keywords
- 02 (3)
- 03 (1)
- Aachener PPS-Modell (1)
- Adaptability (1)
- Anlaufmanagement (1)
- Automobilindustrie (1)
- Beratung (1)
- Big Data (1)
- Business analytics (1)
- CPS (2)
- CPSL (1)
- Case study research (1)
- Change Management (1)
- Change Request (2)
- Customer Success Management (1)
- Customer success managementl (1)
- Cyber Security (1)
- Cyber physical production control (1)
- Data Quality (1)
- Data analytics (1)
- Data-based pricings (1)
- Data-driven decision (1)
- Data-driven services (1)
- Datenaustausch (1)
- Datenmanagement (1)
- Datensouveränität (1)
- Decision Support System (1)
- Decision support (1)
- Digital Transformation (1)
- Digitalisation (1)
- Digitalization (1)
- Distribution (1)
- Distribution management (1)
- Distributionsplanung (1)
- EPCIS (1)
- ERP (2)
- ERP-System (1)
- Echtzeitfähige Systeme (1)
- Echtzeitfähigkeit (1)
- Enterprise-Resource-Planning (1)
- Entscheidungsunterstützung (1)
- Evaluation (1)
- Event Data (1)
- Fallstudien (1)
- Finanzkrise (1)
- Footprint design (1)
- Geschäftsmodelle (1)
- Global production (1)
- IT-Unterstützung (1)
- Incorrect Data (1)
- Industrie 4.0 (1)
- Informationslogistik (1)
- Insufficient Data (1)
- Internet of Production (2)
- IoP (1)
- KI (2)
- KPI (2)
- Kennzahlen (1)
- Key-Performance-Indicator (1)
- Konzepte (1)
- Krise (1)
- Kybernetik (1)
- Künstliche Intelligenz (2)
- Leistungsfähigkeit (2)
- Logistik (3)
- MES (1)
- Manufacturing (1)
- Manufacturing Companies (2)
- Manufacturing Execution System (1)
- Manufacturing firms (1)
- Maturity Model, Maturity Index (1)
- Modellierung (1)
- Morphology (1)
- Natural-Language-Processing (1)
- Network configuration (1)
- PPC (1)
- PPS (3)
- PPS-Buch (1)
- Performance-Management (1)
- Product-as-a-service business (1)
- Production Planning (1)
- Production management (2)
- Production networks (1)
- Produktdatenmanagement (1)
- Produktentwicklung (1)
- Produktionsmanagement (1)
- Produktionsplanung (6)
- Produktionsplanung und -steuerung (1)
- Produktionssteuerung (6)
- Produktionssystem (2)
- Reference data model (1)
- Reference model (1)
- Regulatory framework (1)
- Rezepte (1)
- Risikomanagement (1)
- SCEM (1)
- SCM (1)
- SV7113 (1)
- SV7213 (1)
- SV7427 (1)
- SV7459 (1)
- Selbstoptimierung (2)
- Simulation (1)
- Smart product service system (1)
- Studie (2)
- Störungsmanagement (1)
- Subscription (1)
- Subscription Business (1)
- Subscription Business Models (1)
- Subscription business (2)
- Suchmaschine (1)
- Supply Chain Event Management (2)
- Supply-Chain-Management (3)
- Task-oriented Reference Model (1)
- Trends (1)
- Typification (2)
- Unternehmensberatung (1)
- Value-based Pricing (1)
- Value-in-Use (1)
- Value-in-use (2)
- Variantenfließfertigung (1)
- Whitepaper (1)
- Wirtschaftskrise (1)
- acquisition cycle (1)
- asset management (1)
- blockchain (1)
- blockchain-based services (1)
- bullwhip effect (1)
- case study research (1)
- control theory (1)
- criticality analysis (1)
- cyber-physische Systeme (1)
- cybernetics (1)
- data value (1)
- data value assessment (1)
- digital services (1)
- digital shadow (2)
- enterprise resource planning system (1)
- human-robot collaboration (1)
- information logistics model (1)
- intelligent support system (1)
- internet of production (1)
- logistics (1)
- machinery and plant engineering industry (1)
- manufacturing companies (1)
- production management (1)
- production network (1)
- production networks (1)
- production planning and control (1)
- realtime capability (1)
- recursion level (1)
- rev (9)
- risk analysis system (1)
- risk management (1)
- smart product service systems (1)
- subscription business (1)
- subscription business models (1)
- supply chain (1)
- supply chain event management (1)
- supply chain management (1)
- system dynamics (1)
- task model (1)
- viable system model (2)
- Änderungsmanagement (2)
Institute
More and more manufacturing companies are starting to transform the transaction-based business model into a customer value-based subscription business to monetize the potential of digitization in times of saturated markets. However, historically evolved, linear acquisition processes, focusing the transactionoriented product sales, prevent this development substantially. Elemental features of the subscription business such as recurring payments, short-term release cycles, data-driven learning, and a focus on customer success are not considered in this approach. Since existing transactional-driven acquisition approaches are not successfully applicable to the subscription business, a systematic approach to an acquisition cycle of the subscription business in the manufacturing industry is presented, aiming at a long-term participative business. Applying a grounded theory approach, a task-oriented model for themanufacturing industry was developed.
The model consisting of five main tasks and 14 basis tasks serves as best practice to support manufacturing companies in adapting or redesigning acquisition activities for their subscription business models.
Inhaltsangabe Band:
Die vernetzte Digitalisierung hat die produzierende Industrie fundamental verändert. Im Rahmen dessen eröffnen sich produzierenden Unternehmen kontinuierlich neue Chancen, in einem zunehmend dynamischen und durch das Internet geprägten Wettbewerb, wirtschaftliche Erfolge zu erzielen. Durch die veränderten Rahmenbedingungen der vernetzten Digitalisierung müssen produzierende Unternehmen jedoch neue Ansätze für die Organisation der digitalen Transformation verfolgen: Sie müssen die neue Führungsaufgabe Digitalisierungsmanagement gestalten. Dabei muss das Digitalisierungsmanagement eine breite Aufgabenvielfalt abdecken.
Dieses Buch befähigt produzierende Unternehmen die digitale Transformation erfolgreich zu gestalten. Dazu werden Nutzen und Funktionsweisen der wesentlichen Aufgaben des Digitalisierungs- und Informationsmanagements praxisnah dargestellt. Ein spezifisch für produzierende Unternehmen, die eine digitale Transformation anvisieren, entwickeltes Digitalisierungs- und Informationsmanagement-Modell verknüpft schließlich die Inhalte.
Das vorliegende Buch ist als ein Nachschlagewerk für Führungskräfte und Entscheider entwickelt worden, die die Herausforderungen der Realisierung von digitalen Geschäftsmodellen, digitalisierten Produkten und digitalen Geschäftsprozessen angehen wollen. Die Methoden in diesem Buch helfen dabei, die richtigen Managementaufgaben zu verfolgen und diese in der Unternehmensorganisation umzusetzen. Dabei werden auch die Schnittstellen zwischen dem strategischen Digitalisierungsmanagement und dem taktischen bis operativen Informationsmanagement behandelt. Das Buch bietet einen schnellen und einfachen Zugriff auf die wichtigsten Methoden und viele unterstützende Beispiele. Es ist Teil der Reihe „Handbuch Produktion und Management“ und ergänzt dessen Ordnungsrahmen.
(Quelle: https://link.springer.com/book/10.1007/978-3-662-63758-6)
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.
Competitive differentiation in the manufacturing sector is no longer based on product and service innovations alone but on the ability to monetize the usage phase of products and services. To this end, manufacturers are increasingly looking at so-called subscription business models as a way of supplementing the traditional sale of products and services. Since supplier success in the subscription business is directly dependent on customer success, the setup and expansion of a so-called Customer Success Management (CSM) is required. While CSM has already been established in the software industry for several years, companies in the manufacturing sector are often still in the conceptual phase of a CSM, parallel to the setup and expansion of their subscription business. Therefore, this paper aims to support the set-up of a CSM by providing a reference data model, based on case study research, that can be used to support the organizational or daily CSM tasks and to serve as a blueprint for conceptualizing CSM-specific IT systems.
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.
Long-term production management defines the future production structure and ensures the long-term competitiveness. Companies around the world currently have to deal with the challenge of making decisions in an uncertain and rapidly changing environment. The quality of decision-making suffers from the rapidly changing global market requirements and the uniqueness and infrequency with which decisions are made. Since decisions in long-term production management can rarely be reversed and are associated with high costs, an increase in decision quality is urgently needed. To this end, four different applications are presented in the following, which support the decision process by increasing decision quality and make uncertainty manageable. For each of the applications presented, a separate digital shadow was built with the objective of being able to make better decisions from existing data from production and the environment. In addition, a linking of the applications is being pursued:
The Best Practice Sharing App creates transparency about existing production knowledge through the data-based identification of comparable production processes in the production network and helps to share best practices between sites. With the Supply Chain Cockpit, resilience can be increased through a data-based design of the procurement strategy that enables to manage disruptions. By adapting the procurement strategy for example by choosing suppliers at different locations the impact of disruptions can be reduced. While the Supply Chain Cockpit focuses on the strategy and decisions that affect the external partners (e.g., suppliers), the Data-Driven Site Selection concentrates on determining the sites of the company-internal global production network by creating transparency in the decision process of site selections. Different external data from various sources are analyzed and visualized in an appropriate way to support the decision process. Finally, the issue of sustainability is also crucial for successful long-term production management. Thus, the Sustainable Footprint Design App presents an approach that takes into account key sustainability indicators for network design. [https://link.springer.com/referenceworkentry/10.1007/978-3-030-98062-7_15-1]