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More and more companies in the mechanical and plant engineering industry are transforming their business model and evolving from product to solution providers. Subscription business models play a key role in this development. They enable companies to enter long-term collaborative relationships with customers and thus monetize the potential of Industry 4.0. However, this development is not easy for many companies and is associated with numerous hurdles. One of these hurdles is the development of a suitable range of services tailored to customer needs. In this context, the bundling of individual services to service modules plays a key role in realizing new value propositions. In practice, however, companies often lack an understanding of which services need to be combined in what way to be able to realize new value propositions. Accordingly, the goal of this work is to identify relevant services for subscription business models, to cluster them into meaningful value-adding bundles, and to derive new value propositions accordingly. The new value propositions in turn enable mechanical and plant engineering companies to strengthen customer loyalty and thus achieve long-term economic success.
The complexity and volatility of companies’ environment increase the relevance of disruption preparation. Resilience enables companies to deal with disruptions, reduce their impact and ensure competitiveness. Especially in the context of procurement, disruptions can cause major challenges while resilience contributes to ensuring material availability. Even though past disruptions have posed various challenges and companies have recognized the need to increase resilience, resilience is often not designed systematically. One major challenge is the number of potential measures to increase resilience. The systematic design of resilience thus requires a detailed understanding of domain-specific measures. This also includes an understanding of the contribution of these measures to different resilience components and their interdependencies. This paper proposes a systematic approach for configuring resilience in procurement which enables the evaluation and selection of resilience measures. Based on a resilience framework, a resilience configurator is developed. The basis of the configurator are resilience potentials that have been characterized and clustered. Overarching approaches to design resilience and indicators to evaluate resilience are presented. Moreover, a procedure is proposed to ensure practical applicability. To evaluate the results two case studies are conducted. The results enable companies to systematically design their resilience in procurement.
To monetize the potential of digitalization in times of saturated markets, increased machinery and plant engineering companies are starting to transform the transaction-based business model into a customer- and service-oriented subscription business. Even though subscription offerings can create win-win situations for providers and customers, companies encounter significant difficulties in acquiring customers for this innovative business model. Historically linear acquisition processes focused on transactional product sales impede success. To identify key challenges and targeted coping strategies for customer acquisition we conducted in-depth interviews with 18 subscription managers and sales representatives from seven machinery and plant engineering case studies. In our research we uncovered four challenge dimensions: (1) lack of motivation, (2) missing skills and competences, (3) insufficient customer confidence and (4) transaction-oriented sales approach. Beyond that we derived four appropriate coping strategies (1) steering mechanisms, (2) human resource management, (3) trust building instruments and (4) systematic methodology to address them. These insights highlight the key challenges at the management level for customer acquisition that companies face when trying to initiate and sustain the transition from a purely transactional product and service business to subscription-oriented growth. Furthermore, they provide guidance how to cope with these challenges.
The adoption of artificial intelligence (AI) technologies in manufacturing companies is challenging, particularly for SMEs that lack the necessary skills to develop and integrate AI-based applications (AI applications) into their existing IT system landscape. To address this challenge, the research project VoBAKI (IGF-Project No.: 22009 N) aims to enable SMEs to identify and close skill gaps related to AI application development and implementation using proper sourcing strategies. This paper presents the interim results from the second phase of the project, which involves identifying the tasks in the lifecycle of AI applications and determining the specific skills required for executing these tasks. The presented results provide a detailed lifecycle including the phases for the development and usage of AI applications, as well as the specific tasks that SMEs must consider when implementing an AI application. These results serve as the foundation for future research regarding the required skills to execute the presented tasks and provide a roadmap for SMEs to close skill gaps and successfully implement AI applications.
Gap Analysis for CO2 Accounting Tool by Integrating Enterprise Resource Planning System Information
(2023)
Detailed carbon accounting is the foundation for reducing CO2 emissions in manufacturing companies. However, existing accounting approaches are primarily based on manual data preparation, although manufacturing companies already have a variety of IT systems and resulting data available. The gap analysis carried out based on the GHG Protocol and an reference ERP system shows how much of the required information for CO2 accounting can be integrated from an ERP system. The ERP system can cover 20 % of the required information. The information availability can be increased to 49 % through additionally identified modifications of the ERP system. Integrating the CO2 accounting tool with other systems of the IT landscape, e. g. Energy Information System, enables an additional increase.
Based on the increasingly complex value creation networks, more and more event-based systems are being used for decision support. One example of a category of event-based systems is supply chain event management. The aim is to enable the best possible reaction to critical exceptional events based on event data. The central element is the event, which represents the information basis for mapping and matching the process flows in the event-based systems. However, since the data quality is insufficient in numerous application cases and the identification of incorrect data in supply chain event management is considered in the literature, this paper deals with the theoretical derivation of the necessary data attributes for the identification of incorrect event data. In particular, the types of errors that require complex identification strategies are considered. Accordingly, the relevant existing error types of event data are specified in subtypes in this paper. Subsequently, the necessary information requirements and information available regarding identification are considered using a GAP analysis. Based on this gap, the necessary data attributes can then be derived. Finally, an approach is presented that enables the generation of the complete data set. This serves as a basis for the recognition and filtering out of erroneous events in contrast to standard and exception events.
The mechanical and plant engineering industry faces a stagnation in the new machinery market and is relying on innovative business models such as subscription to overcome these. In this business model, individually customized solution packages are offered. The success of these models depends directly on the future success of the customer, making the selection of the right customers crucial. The aim of this paper is to identify the criteria that indicate the suitability of customers for subscription models. While there are individual descriptions of suitability criteria in the existing literature, there is a lack of comprehensive consideration of customer relationship, customer company, and customer market, as the extensive consideration was not necessary in the transactional sale of machines until now. Therefore, in this study, expert interviews are conducted with companies in mechanical and plant engineering that offer subscription models. The results show criteria that are used to evaluate customers in the six main categories of creditworthiness, market potential, benefit potential, feasibility, relationship, and sales effort. In total, 24 criteria can provide insight into the suitability of the customer for a successful subscription relationship. These criteria are intended to develop target systems that meet the requirements of different stakeholders in the customer and thus support the economic viability of these business models.
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.
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.
Die pandemiebedingt angestiegene Homeofficequote in produzierenden
Unternehmen ist seit Juli 2020 deutlich rückläufig und indiziert ein
geringes Maß an langfristig gestalteten hybriden Arbeitsplatzkonzepten.
Angesichts des Fachkräftemangels besteht Handlungsdruck, eine
attraktive Arbeitsumgebung mit industriellen Tätigkeiten zu vereinbaren.
Um zukunftsorientierte Arbeitsplatzkonzepte zu gestalten, nennt
das vorgestellte Vorgehen systematisch die menschlichen Tätigkeiten
in produzierenden Unternehmen und bewertet deren Remotefähigkeit.
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.
The European Commission set out the goal of carbon neutrality by 2050, which shall be achieved by fostering the twin transition - sustainability through digitalization. A keystone in this transition is the implementation of a prospering Circular Economy (CE). However, product information required to establish a flourishing CE is hardly available or even accessible. The Digital Product Passport (DPP) offers a solution to that problem but in the current discussion, two separate topics are focused on: its architecture and its application on batteries. The content of the DPP has not been an essential part of the discussion, although access to high-quality data about a product's state, composition and ecological footprint is required to enable sustainable decision-making. Therefore, this paper presents a classification of product data for circularity in the manufacturing industry to emphasize the discussion about the DPP's content. Developed through a systematic literature review combined with a case-study-research based on common operational information systems, the classification comprises three levels with 62 data points in four main categories: (1) Product information, (2) Utilization information, (3) Value chain information and (4) Sustainability information. In this paper, the potential content structure of a DPP is demonstrated for a use case in the machinery sector. The contribution to the science and operations community is twofold: Building a guideline for DPP developers that require scientific input from available real-world data points as well as motivating manufacturers to share the presented data points enabling a circular product information management.
Companies in the manufacturing industry are shifting towards a more service-oriented business model. One major challenge of this transformation is the information exchange between the different stages of the product-service-lifecycle.
We extend the existing body of knowledge by conducting an empirical study in the German manufacturing industry, addressing the cause-effect relationship between 1) information gathering over the product-service-lifecycle, 2) data analytics 3) interpretation and use of new information and 4) distribution of new product related information and the impact of these four aspects on performance.
The analysis reveals five different success factors with a significant impact on innovation and operation excellence. The implications from our research can help to develop new and more practical oriented Lifecycle-Product-Service-System approaches on the one hand. On the other hand it enables companies to focus on activities leading to higher service efficiency. Creating new stimuli will transform their existing business model to a more service-oriented one.
The number of cyber-attacks on small and medium enterprises (SMEs) is constantly increasing. SMEs do not recognize the attacks until the damage has occurred. Only then, they fight with measures to increase IT-security and IT-safety. Many studies come to the point that this refers to a lack of budget, expertise and awareness of the need for IT-security. There are many compendia with recommendations for action, but they are too comprehensive and unspecific to the individual needs of SMEs. In this paper, we present the results of a research activity on the gaps that address the challenges faced by SMEs. In addition, we develop a concept for a serious gaming approach that includes an economic perspective on IT-security measures and shows how SMEs can derive their own IT-seurity target state
Manufacturing companies are constantly increasing their efforts in the subscription business, also known as product-as-a-service business, offering usage and outcome based solutions (value-in-use) instead of transactional services and products (value-in-exchange). Customers are becoming contractual subscribers of the solution in return for recurring, performance-related payments. To address arising, inevitable challenges like (1) reducing customer churn, (2) increasing usage intensity and outcome quality, (3) ensuring the adoption of product and software releases as well as (4) fostering customer loyalty, leading manufacturing companies are setting up a new organizational, customer-facing unit, called Customer Success Management (CSM). This unit has its origins in the software-as-a-service business, operating next to established entities like sales, key account management and customer service. Since there are currently no holistic models for an end-to-end description of CSM-tasks in the manufacturing industry, this paper contributes to a taskoriented reference model, using a grounded theory approach, examining both manufacturing and software companies. Containing a reference framework with 8 main tasks, 17 basic tasks and 76 elementary tasks, the reference model supports manufacturing companies in adapting and customizing a company-specific CSM concept.
Industry 4.0 is driven by Cyber-Physical Systems and Smart Products. Smart Products provide a value to both its users and its manufacturers in terms of a closer connection to the customer and his data as well as the provided smart services. However, many companies, especially SMEs, struggle with the transformation of their existing product portfolio into smart products. In order to facilitate this process, this paper presents a set of smart product use-cases from a manufacturer’s perspective. These use-cases can guide the definition of a smart product and be used during its architecture development and realization. Initially the paper gives an introduction in the field of smart products. After that the research results, based on case-study research, are presented. This includes the methodological approach, the case-study data collection and analysis. Finally, a set of use-cases, their definitions and components are presented and highlighted from the perspective of a smart product manufacturer.
In short-term production management of the Internet of Production (IoP) the vision of a Production Control Center is pursued, in which interlinked decision-support applications contribute to increasing decision-making quality and speed. The applications developed focus in particular on use cases near the shop floor with an emphasis on the key topics of production planning and control, production system configuration, and quality control loops.
Within the Predictive Quality application, predictive models are used to derive insights from production data and subsequently improve the process- and product-related quality as well as enable automated Root Cause Analysis. The Parameter Prediction application uses invertible neural networks to predict process parameters that can be used to produce components with desired quality properties. The application Production Scheduling investigates the feasibility of applying reinforcement learning to common scheduling tasks in production and compares the performance of trained reinforcement learning agents to traditional methods. In the two applications Deviation Detection and Process Analyzer, the potentials of process mining in the context of production management are investigated. While the Deviation Detection application is designed to identify and mitigate performance and compliance deviations in production systems, the Process Analyzer concept enables the semi-automated detection of weaknesses in business and production processes utilizing event logs.
With regard to the overall vision of the IoP, the developed applications contribute significantly to the intended interdisciplinary of production and information technology. For example, application-specific digital shadows are drafted based on the ongoing research work, and the applications are prototypically embedded in the IoP.
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.
The digital transformation is changing the way companies think and design their manufacturing environment. Both due to the increasing number of connections between IoT-Devices, tooling machines, and production lines and the phenomenon of the convergence of IT and OT, systems are becoming more complex than years ago. Organizational and cultural changes within manufacturing companies strengthen this trend and form Industry 4.0 environments and cyber-physical production systems (CPPS). As these systems do not longer stay alone but are connected to each other and the company’s outside, the size of the potential attack surface is increasing as well. Besides that, manufacturing companies, small and medium-sized in particular, are facing complex challenges based on lack of knowledge, budget, and time to understand as well as to interpret their current situation and risk level and therefore to derive necessary counter-measures. Efficient as well as pragmatic tools and methods for these companies do not exist. This paper shows a research approach in which the company-specific set-up of Industry 4.0 environment and CPPS is characterized by its potential vulnerabilities. This enables companies to evaluate their risk potential before setting up this kind of environments and to undJo,erstand the potential consequences more precisely. By doing so, companies can derive and prioritize important counter-measures and so to strengthen their level of cyber-security efficiently. This will decrease the number of cyber-security attacks and increase the company’s competitiveness.
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.
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]
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.
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)
A large number of product-accompanying services in the machinery and plant engineering industry is based on the cross-company exchange of data and information. By providing services, additional sales potential on the manufacturer side as well as far-reaching product and process advantages for appliers can be reached. However, the necessary cross-company exchange of information is nowadays limited due to a lack of trust in the interacting partner and the applicable existing technologies, which results in significant losses in the terms of business potential. The uncovering of this potential now seems to be made possible by the use of the Blockchain technology. Through the key factors security, immutability, transparency and decentralisation, it serves as an enabler for cross-company communication and product-accompanying services. The technological implementation of a Blockchain can take on a broad spectrum of attributes, which can lead to decisive restrictions for the execution of services. This justifies the necessity for a qualified and context-related assessment of service-types-individual specifications and the resulting requirements on the system. Within the scope of this paper, different types of product-accompanying services are identified and analysed regarding their requirements for a Blockchain-based machinery and plant connection. This can serve as a basis for a qualified and goal-oriented configuration of the Blockchain.
Pricing for Smart-Product-Service-Systems in Subscription Business Models for Production Industries
(2021)
In the production industry, subscription business models have the potential to create long-term relationships where a supplier provides a continuous value-oriented service to a customer based on digitalisation. Monetising this increase in value through pricing represents a central challenge for suppliers in subscription business. Unlike the current dominant transactional business, the focus of pricing is on the value-in-use of the customer (e.g. on the increase in output for the customer). In this regard, there is so far no pricing approach for practice that allows the linking of the performance data of the customer with the periodically charged price. However, in subscription businesses, such an approach is required to create win-win situations for the customer and supplier through continuous performance improvement. Therefore, this paper develops a novel process model for pricing of smart-product-service-systems in subscription business for production industries. This process can serve as basis for suppliers of subscriptions in the production industry to align pricing with the created value-in-use. In the long term, this allows companies to systematically develop their pricing to monetise the potential of digitalisation.
Digital technologies have gained significant importance in the course of the 4th Industrial Revolution and these technologies are widely implemented, nowadays. However, it is necessary to bear in mind that an ill-considered use can quickly have a negative impact on the environment in which the technology is used. For more responsible and sustainable use, the regulation of digital technologies is therefore necessary today. Since the government is taking a very slow response, as the example of the AI Act shows, companies need to take action themselves today. In this context, one of the central questions for companies is: "Which digital technologies are relevant for manufacturing companies in terms of regulation? This paper conducted a quantitative Delphi study to answer this question. The results of the Delphi study are presented and evaluated within the framework of a data analysis. In addition, it will be discussed how to proceed with the results so that manufacturing companies can benefit from them. Furthermore, the paper contributes to the development of an AI platform in the German research project PAIRS by investigating the compliance relevance of artificial intelligence applications.
The Impact Of Manufacturing Execution Systems On The Digital Transformation Of Production Systems
(2021)
With the focus of manufacturing companies on the digital transformation, Manufacturing Execution Systems are market-ready, modular software solutions for manufacturing companies to integrate the value-adding and supporting processes horizontal and vertical in the company. Companies, especially small and mediumsized companies, face high internal and external costs for the implementation of the MES modules. An advantage of MES is the possibility to implement the systems in a continually, module-by-module approach, with the benefit of timely distributed investments. By realizing fast improvements, companies can use the benefits for further module implementations. This paper proposes a maturity model to measure the impact of an MES on the digital transformation of the company’s production systems. The model fulfils two purposes. The first, companies can measure the impact based on the difference between its current maturity index and the potential index of an implemented MES. The second is, the user can identify what impact an MES has in general on the digital transformation since the developed maturity model is derived from an established industry 4.0 maturity model. The development of the maturity model is based on the methodologies of AKKASOGLU and focuses on the further development of an established model. As an outlook, the application of the model will be described briefly. The proposed maturity model can directly be used by practitioners and offers implications for further development of MES functionalities.
Feasibility Analysis of Entity Recognition as a Means to Create an Autonomous Technology Radar
(2021)
Mit den neuesten Technologietrends auf dem Laufenden zu bleiben, ist für Fertigungsunternehmen eine entscheidende Aufgabe, um auf einem global wettbewerbsfähigen Markt erfolgreich zu bleiben. Die Erstellung eines Technologieradars ist ein etablierter, jedoch meist manueller Prozess zur Visualisierung der neuesten Technologietrends.
Der Herausforderung, Technologien zu identifizieren und zu visualisieren, widmet sich das Projekt TechRad, das maschinelles Lernen einsetzt, um ein autonomes Technologie-Scouting-Radar zu realisieren. Eine der Kernfunktionen ist die Identifizierung von Technologien in Textdokumenten. Dies wird durch natürliche Sprachverarbeitung (NLP) realisiert.
Dieser Beitrag fasst die Herausforderungen und möglichen Lösungen für den Einsatz von Entity Recognition zur Identifikation relevanter Technologien in Textdokumenten zusammen. Die Autoren stellen eine frühe Phase der Implementierung des Entity Recognition Modells vor. Dies beinhaltet die Auswahl von Transfer Learning als geeignete Methode, die Erstellung eines Datensatzes, der aus verschiedenen Datenquellen besteht, sowie den angewandten Modell-Trainings-Prozess. Abschließend wird die Leistungsfähigkeit der gewählten Methode in einer Reihe von Tests überprüft und bewertet.
Industrial practice shows a strong trend towards digitalization. It is not only economic crises, such as those triggered by Covid-19, that are reinforcing this trend. It is also the entrepreneurial urge to fulfill customer wishes in the best possible way and to adapt to new requirements as quickly as possible. Due to the advancing digitalization, the role of business application systems in manufacturing companies is therefore becoming increasingly important. The data processed in IT-Systems represent a great potential, especially for the evaluation of change requests in production. Through efficient change management, companies can record and process changes quickly. However, the necessary data basis to decide on existing change requests is still hardly used. Existing IT-Systems for change management coordinate the processing of change requests, but do not relate to data of operational application systems such as Enterprise-Resource-Planning. Therefore, a conceptual approach is required for the evaluation of change requests. This approach is based on an objective recording system that enables the transformation from the change description to an evaluation space. The paper presents an approach for the systematic transfer of requirement characteristics into the world of operational IT-Systems.
Digital technologies such as 5G, augmented reality, and artificial intelligence (AI) are currently being used in various ways by manufacturing companies. As the fourth industrial revolution progresses, it has become apparent that reckless use and inadequate regulation of these technologies have a detrimental effect on the environment in which they are utilized. Therefore, regulation of digital technologies is imperative today to ensure more responsible and sustainable use. While governments usually establish regulations, progress is not keeping pace with the demands and hazards of employing digital technologies. The European AI law serves as an example of the considerable distance yet to be covered before binding guidelines are established. Consequently, companies must take proactive measures today to ensure that they use digital technologies responsibly in their environments. In this context, identifying which digital technologies are pertinent to manufacturing companies in terms of regulation is crucial. Furthermore, a comprehensive approach is required to design compliance holistically for digital technologies and to systematically derive the corresponding guidelines. This paper introduces a set of models that not only determine the importance of
compliance in the application of different technologies but also present a framework for methodically designing compliance. Furthermore, the paper contributes to the development of an AI platform in the German research project PAIRS by investigating the compliance relevance of applications such as artificial intelligence.
Industrial companies are moving to a solution driven business by offering smart product service systems (Smart PSS). In addition to an existing portfolio of physical goods and technical services, companies develop new digital services and combine all three offerings to an integrated digital solution business. While the development of new digital services does not pose any major challenges for companies, the successful sale of Smart PSS does. Due to changing customer requirements and value propositions of a solution, the sale of Smart PSS requires new design principles for the sales organization compared to the simple sale of physical goods or technical services. While there are already many publications on the topic of industrial sales in research, the description of Smart PSS in particular represents a new field of research. The combination of both topics is therefore not only interesting from a theoretical point of view, but also has a particularly high practical relevance and impact for industrial companies. This paper therefore describes on the one hand, which characteristics can be used to derive customer requirements for Smart PSS and on the other hand, which effects these requirements have on the sales organization of the industrial company. The design principles give recommendations for the organizational structure, the resources, the information systems and the culture of the company depending on the targeted customer type. In order to identify and describe both the customer requirements and the design principles, two morphological boxes were developed based on a literature research and semi-structured interviews with industrial companies. The paper gives an outlook on the different characteristics of the design recommendations and describes first best practices for the successful transformation of the sales organization.
One of the major challenges facing today´s manufacturing industry is to differentiate from competition in a highly globalized world. As a consequence to the increasing competitive pressure, many companies transform their product centered business models towards service based business models to differentiate from competition. However, the transformation is often underestimated regarding its complexity and its management challenges to behavioral change.
As a consequence lots of transformation initiatives fail. Besides difficulties in structuring the magnitude of changes in processes and structures, many transformation managers do not perceive the risk of employee resistance against changes, which is one of the key factors causing the failure of transformation. The objective of this paper is to enhance the existing body of research on manufacturer´s organizational transformation towards Product-Service Systems. More detailed, the objective is to develop new knowledge to support the management during the decision-making process in the way how and by means of which instruments the change of behavior can be supported when transforming from a manufacturer to a solution.
We developed a reference framework which structures and defines the relevant dimensions of behavioral change. The identification and validation of the success factors build the second component of our research. We conducted an empirical investigation in the German manufacturing industry and got 79 data sets.
Structural equation modelling was applied for the analyses and the validation of the hypotheses. By this analysis we linked management practice with employee behavior and transformational success variables. On the basis of the gained insights decisions can be made concerning the successful transformation from manufacturer to a solution-oriented service provider.
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.
Volatile electricity prices caused by an increase of renewable energy sources push producing companies towards taking in an active role in balancing the electricity grid. Possible actions at the customer side to actively adapt to volatile energy prices are called demand response actions. In production logistics such actions can be the modification of production schedules motivated by possible economic benefits. So far, the focus in scheduling problems has been the optimization in the dimensions of quality, time and costs. This paper presents the results of a simulation study on the economic benefits of demand response actions for a generic production system.
Today’s manufacturers are facing numerous challenges such as highly entangled and interconnected supply chains, shortening product lifecycles and growing product complexity. They thus feel the need to adjust and adapt faster on all levels of value creation. Self-optimization as a basic principle appears a promising approach to handle complexity and unforeseen disturbances within supply chains, machines and processes. Therefore it will improve the resilience and competitiveness of manufacturing companies.
This paper gives an introduction to the concept of self-optimizing production systems. After a short historical review, the different levels of value creation from supply chain design and management to manufacturing and assembly are analyzed considering their specific demands and needs for self-optimization. Examples from each of these levels are used to illustrate the concept of self-optimization as well as to outline its potential for flexibility and productivity. This paper closes with an outlook on the current scientific work and promising new fields of action.
Pricing is one of the most important, but underestimated tools, to enhance a company's profitability. Especially in the furniture sector, customers place a special interest in cost-efficient products and easy processes. Individualised and sustainable furniture can help to create a unique selling point and deliver real value to the customers. Therefore, a platform to create designs together is needed and can involve several stakeholders in the design and production phase. However, in order to include several stakeholders, the pricing and revenue model need to reflect individual needs and be a benefit to all. In this paper, the initial situation and potential revenue model options will be presented. Furthermore, multiple scenarios for practical use will be discovered and an overview given.
The steady increasing of supply chain complexity due to a rising global cross-linking of production and sales regions leads to an increasing sensitivity to disturbances while in the meantime the requirements of the availability, the time of delivery and the security of supplies within the supply chain increases. To meet this challenges the security of the supply chain infrastructure and the feasibility of supply chain processes need to be ensured, despite of the high specialization within the supply chain partners, the low stock and time buffers, and the information shortcoming between supply chain partners.
In this research, a System Dynamics simulation model, based on the manufacturing supply chain model of Sterman, has been developed for representing the actual complexity and dynamic in manufacturing supply chains. Therefore, the modeled manufacturing supply chain shows the processes of a four level supply chain focusing the processes and interactions of the mid-positioned two supply chain participants. The main contribution of the work described in this paper, is the description and implementation of necessary additional modules and parameters to Sterman’s basic model for the diagnosis of disturbance impacts as well as for the realization of supply chain adjustments. Finally, the model has been simulated and examined for realistic values.