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
In Germany’s transition to a more sustainable industrial landscape, electricity generated by wind turbines (WT) remains a mainstay of the energy mix. Operating and maintenance costs, which account for roughly 25% of electricity generation costs in onshore WTs make improvements of maintenance activities a key lever in the economic operation of WTs. Prescriptive maintenance is a possible approach for improved maintenance activities. It is a concept where asset condition data is used to recommend specific actions and has great potential for the operation of wind parks. However, especially small, but also large wind park operators, and maintenance service providers often struggle with the implementation of such a new maintenance approach. As a part of the research project ReStroK, a learning game has been developed to support the training and familiarization of maintenance technicians with the concepts and underlying principles of this maintenance approach. In this paper, the concept for the development of a learning game will be presented. Multiple scenarios for its usage and their corresponding requirements will be discussed and an overview over the game will be given.
This paper addresses the challenge of a systematic requirement-oriented configuration and selection of cyber physical systems (CPS) for SMEs. As the key technologies of realizing the digitalization and interconnection of production processes, manufacturing companies have realized the potential benefits brought by CPS. However, due to the
complexity and fast development of CPS technology, it is difficult for SMEs, which lack expertise and financial resources, to select the appropriate CPS technologies meeting both functional and financial requirements. To overcome the issue, an online matching platform is developed to let SMEs express their needs and assist them onceptualize
the individual CPS. This paper presents the matching methodology of the matching platform, which can not only match technical characteristics but also evaluate economic potentials. Then, it was demonstrated by a tracking and tracing use case in the end-of-line assembly of a small-sized German electric automobile manufacturer.
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]
Technology management can significantly influence the strategic decisions of a company and thus cause success or failure. Basic templates for technology management are technology radars as well as the determination of the technology readiness level (TRL) to be able to evaluate the maturity of newly deployed technologies (e.g., newcomer vs. established). The radars, as well as the TRL, are identified in time-consuming, manual research by subject matter experts from external consultancies. This process is often repeated due to the further development and new development of technologies so that the necessary research becomes an ongoing task. The TechRad research project, therefore, aims to automate the identification of the TRL as well as technology radars using web crawling and Natural Language Processing (NLP). To commercialize the pre-competitive prototype, the development of a pre-competitive business model is the goal of this paper. Based on customer analyses, a target group definition is created. Based on user interviews, the precompetitive business model will be detailed in a four-step approach using a business model canvas and a value proposition canvas.
The do-it-yourself mentality is particularly widespread in the furniture sector. Homemade furniture is very popular. The individualisation of furniture can be observed in internet forums, such as the online platform Pinterest. These creative ideas of potential customers show a need for individualized sustainable pieces of furniture. The current production structures, however, do not allow individual production according to the end customer's specifications. In addition, information logistics faces a major challenge: making the creative ideas of end consumers available to producers in parametric form. Topics such as customer requirements in relation to sustainable production, material specifications, industrial property rights, fair production conditions and traceability are the focus of this data interchange. An open and innovative European furniture ecosystem must be created to connect all stakeholders in the production process. This is made possible by a platform that channels the creativity of consumers and makes it designable and producible through the professional skills of designers. This requires the involvement of manufacturing specialists who can produce personalised products through sustainable intelligent production technologies. An exchange of information must also take place securely and quickly in order to protect the personal rights of the sources of ideas. This is being developed in the EU research project INEDIT - Open Innovation Ecosystem for do-it-together process. By connecting many different stakeholders along the entire value creation process, a change towards efficient collaborative collaboration is achieved. This paper presents a project insight for the development of an international co-creation platform by presenting the problem and linking it to a potential solution.
Innovation is one of the key drivers of growth, development, and profitability, which increases competitive advantages and has recently been moving towards industry 4.0 technologically. This motivates companies to update their business models (BM) towards industry 4.0. Moreover, there is a technique with the primary characteristics for achieving this motivation called "cross-industry innovation". Cross-industry innovation is a new method of innovation that concerns the creative translation and imitation of existing solutions from other industries for responding to the needs of the current market, sectors, areas, or domains. The challenge is to find out how far managers can rely on that to innovate their BM towards Industry 4.0. The aim of this study was to investigate the application of cross-industry innovation for designing industry 4.0 BM and explore the extent to which companies can rely on it as it has not been used for this purpose previously. This study utilized a database analysis to compare cross-industry innovation practices with industry 4.0 BM's characteristics in terms of value proposition, value creation, and value capture levels. In addition, some interviews were conducted with companies that had previously implemented cross-industry innovation to validate and generalize the results. The results indicated that cross-industry innovation practices can better fulfill flexible and dynamic networks, connected information flows, high efficiency, high scalability, and high availability in terms of value creation as well as variabilization of prices and costs in terms of value capture. Therefore, it demonstrated that cross-industry innovation was a more dependable and applicable strategy for designing the BM of Industry 4.0 than current practices.
It is crucial today that economies harness renewable energies and integrate them into the existing grid. Conventionally, energy has been generated based on forecasts of peak and low demands. Renewable energy can neither be produced on demand nor stored efficiently. Thus, the aim of this paper is to evaluate Deep Learning-based forecasts of energy consumption to align energy consumption with renewable energy production. Using a dataset from a use-case related to landfill leachate management, multiple prediction models were used to forecast energy demand.The results were validated based on the same dataset from the recycling industry. Shallow models showed the lowest Mean Absolute Percentage Error (MAPE), significantly outperforming a persistence baseline for both, long-term (30 days), mid-term (7 days) and short-term (1 day) forecasts. A potential decrease of up to 23% in peak energy demand was found that could lead to a reduction of 3,091 kg in CO2-emissions per year. Our approach requires low finanacial investments for energy-management hardware, making it suitable for usage in Small and Medium sized Enterprises (SMEs).
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.
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]
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]
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.
High Resolution Supply Chain Management aims to counteract the trend towards more and more centralised and rigid enterprises. Today, most companies strive to increase efficiency of business processes applying highly sophisticated, centralised planning approaches. These centralised approaches limit the companies’ ability to react flexibly and act adaptively due to external and internal turbulences. In today’s buyer’s markets companies usually try to bypass these turbulences keeping high levels of inventory resulting in a low overall efficiency. High Resolution Supply Chain Management tries to solve the problem at its root from a holistic perspective. Based on the Viable System Model developed by Stafford Beer a four-dimensional holistic production management system model, embedding an organisational structure view, an cause and action view, a control loop perspective and a decision making level has been elaborated. The basis of this model is the integration of all four perspectives into an interacting framework.
High Resolution Supply Chain Management (HRSCM) aims to stop the trend of continuously increasing planning complexity. Today, companies in high-wage countries mostly strive for further optimization of their processes with sophisticated, capital-intensive planning approaches. The capability to adapt flexibly to dynamically changing conditions is limited by the inflexible and centralized planning logic. Thus, flexibility is reached currently by expensive inventory stocks and overcapacities in order to cope with rescheduling of supply or delivery. HRSCM describes the establishment of a complete information transparency in supply chains with the goal of assuring the availability of goods through decentralized, self-optimizing control loops for Production Planning and Control (PPC). HRSCM pursues the idea of enabling organization structures and processes to adapt to dynamic conditions. The approach includes the strengths of the existing planning models as well as the process of decision making in organizations. A precondition for this decentralized adaptation is the synchronization of the objectives of the several units or process owners. The basis for this new PPC Model are information transparency, stable processes, consistent customer orientation, increased capacity flexibility and the understanding of the production system as a viable, socio-technical system.
In the age of digitalization, manufacturing companies are under increased pressure to change due to product complexity, growing customer requirements and digital business models. The increasing digitization of processes and products is opening up numerous opportunities for mechanical engineering companies to exploit the resulting potential for value creation. Subscription business is a new form of business model in the mechanical engineering industry, which aims to continuously increase customer benefit to align the interests of both companies and customers. Characterized by a permanent data exchange, databased learning about customer behavior, and the transfer into continuous innovations to increase customer value, subscription business helps to make Industry 4.0 profitable. The fact that machines and plants are connected to the internet and exchange large amounts of data results in critical information security risks. In addition, the loss of knowledge and control, data misuse and espionage, as well as the manipulation of transaction or production data in the context of subscription transactions are particularly high risks. Complementary to direct and obvious consequences such as loss of production, the attacks are increasingly shifting to non-transparent and creeping impairments of production or product quality, which are only apparent at a late stage, or the influencing of payment flows. A transparent presentation of possible risks and their scope, as well as their interrelationships, does not exist. This paper shows a research approach in which the structure of subscription models and their different manifestations based on their risks and vulnerabilities are characterized. This allows suitable cyber security measures to be taken at an early stage. From this basis, companies can secure existing or planned subscription business models and thus strengthen the trust of business partners and customers.
Auf Basis einer systematischen Literaturanalyse wurden insgesamt 11 Kennzahlen identifiziert, welche die Grundlage zur Beschreibung der operativen Leistungsfähigkeit von Unternehmen bilden. Die Kennzahlen wurden in die vier Leistungsdimensionen Effizienz, Qualität, Zeit und Flexibilität eingeteilt.
The use of chatbots has hardly been established in B2B companies to date and involves various challenges. The goal of this paper is to identify the biggest barriers to the successful implementation of chatbots in B2B customer service and to develop measures to overcome them. The barriers are identified by conducting expert interviews within the framework of Eisenhardt's case study research. These are examined through a socio-technical analysis focusing on people, technology, and organization. By means of systematic literature research and in-depth interviews with German chatbot providers and customers of chatbots, measures for overcoming the barriers are identified. Using interviews with experts from German chatbot providers, the responsible stakeholders of each measure according to the RASCI Responsibility Matrix are determined. A total of 46 implementation barriers and 100 measures to overcome these barriers are identified. The study shows that there are major barriers in the areas of people, technology, and organization of a socio-technical system that can cause the implementation of a chatbot to fail. A holistic view is therefore essential. The results provide firms with a guideline on how to overcome potential barriers during chatbot implementation in B2B customer service.
While digitization is a strategic advantage in numerous industries such as the automotive industry or mechanical engineering, other industries like the German quarrying industry have not yet established a transformation towards a digitized industry. This leads to inefficient work and inaccurate forecasting capabilities. To address these challenges, digital platforms can incentivize digitization
by supporting the capacity utilization and forecasting capability of these companies. In this paper, the quarrying industry is analyzed by a morphology and different types of companies are identified. Knowing the digital maturity of these companies and by determining the key factors to forecast demands and the capacity utilization, different operating models are derived. Combined with a morphology and the value creation system, different scenarios for the identification of platform services are examined. These scenarios are weighted in a utility analysis to get an operating model blueprint to develop and establish digital platforms in less digitized industries.
Manufacturing companies (MFRs) are increasingly extending their
portfolios with services and data-driven services (DDS) to differentiate themselves from competitors, tap new revenue potential, and gain competitive advantages through digitization and the subsequently generated data. Nonetheless, DDS fail more often than traditional industrial services and products within the first year on the market. Particularly, companies are failing to sell DDS successfully and efficiently with their existing (multi-level) distribution structures. Surprisingly, there is a lack of scientific research addressing this issue. Since there are currently no holistic models for an end-to-end description of distribution-tasks for DDS in the manufacturing industry, this paper contributes to a task-oriented reference model for mapping interactions in the multi-level distribution management. Therefore, a case study research approach is used, to identify and describe the interactions in the multi-level distribution management of DDS, as well as to develop a regulatory framework for MFRs and their multi-level distribution management. This research uses the established theoretical framework of Service-Dominant-Logic to address the co-creation in multi-level distribution management of DDS. As a result, this paper identifies different interaction variants as well as the need for a new management function with 4 main and 14 basic tasks.
Aufgrund kürzer werdender Produktzyklen und steigender Produktvielfalt werden produzierende Unternehmen mit einer zunehmenden Anzahl von Produktanläufen konfrontiert. Ziel aktueller Forschungsaktivitäten ist es daher, anlaufintensive Unternehmen zu befähigen, verlässliche Produktionsprogramme in kurzer Zeit zu erstellen. Lerneffekte sollen genutzt werden können ohne Diversifikationseffekte zu vernachlässigen. Zur Erreichung dieser Zielsetzung wird ein Modell für eine kybernetische PPP bei Produktanläufen entwickelt.
Changing customer demands lead to increasing product varieties and decreasing delivery times, which in turn pose great challenges for production companies. Combined with high market volatility, they lead to increasingly complex and diverse production processes. Thus, the susceptibility to disruptions in manufacturing rises, turning the task of Production Planning and Control (PPC) into a complex, dynamic and multidimensional problem. Addressing PPC challenges such as disruption management in an efficient and timely manner requires a high level of manual human intervention. In times of digitization and Industry 4.0, companies strive to find ways to guide their workers in this process of disruption management or automate it to eliminate human intervention altogether. This paper presents one possible application of Machine Learning (ML) in disruption management on a real-life use case in mixed model continuous production, specifically in the final assembly. The aim is to ensure high-quality online decision support for PPC tasks. This paper will therefore discuss the use of ML to anticipate production disruptions, solutions to efficiently highlight and convey the relevant information, as well as the generation of possible reaction strategies. Additionally, the necessary preparatory work and fundamentals are covered in the discussion, providing guidelines for production companies towards consistent and efficient disruption management.