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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.
Es geht um die Entwicklung eines Software-Tools zur Unterstützung bei der Auswahl von geeigneten 3D-Druckdienstleistern im Kontext der additiven Ersatzteillogistik. Im Fokus steht der Logistikdienstleister als potentieller Nutzer des Softwaretools. Das Softwaretool erfüllt zwei zentrale Funktionen: Überprüfung ob ein Ersatzteil additiv gefertigt werden soll und Auswahl eines konkreten Produzenten durch Matchingalgorithmus.
Immer noch ist es um die Zufriedenheit der Kunden mit der Qualität von Diestleistungen hierzulande nicht gut bestellt. An der Verbesserung dieses Zustands müssen wir arbeiten, in den Unternehmen, in den staatlichen Organisationen und in den Bildungseinrichtungen, denn die Frage, ob und wie wir den Wandel zur uneingeschränkten Servicegesellschaft bewältigen - der ohne eine konsequente Digitalisierung der Wertschöpfung unmöglich ist -, hat auch entscheidenden Einfluss auf die Zukunft des Wirtschaftsstandorts Deutschland.
Influenced by the high dynamic of the markets and the steadily increasing demand for short delivery times the importance of supply chain optimization is growing. In particular, the order process plays a central role in achieving short delivery times and constantly needs to evaluate the trade-off between high inventory and the risk of stock-outs. However, analyzing different order strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve processes in the whole value chain. The objective of this research is to evaluate two different order strategies (t, q, t, S) in a four-stage supply chain. In order to measure the performance of the supply chain the quantity of the backlog will be considered. A Design of Experiments approach is supposed to enhance the significance of the simulation results.
The shop floor is a dynamic environment, where deviations to the production plan frequently occur. While there are many tools to support production planning, production control is left unsupported in handling disruptions. The production controller evaluates the deviations and selects the most suitable countermeasures based on his experience. The transparency should be increased in order to improve the decision quality of the production controller by providing meaningful information during his decision process. In this paper, we propose a framework in which an interactive production control system supports the controller in the identification of and reaction to disturbances on the shop floor. At the same time, the system is being improved and updated by the domain knowledge of the controller. The reference architecture consists of three main parts. The first part is the process mining platform, the second part is the machine learning subsystem that consists of a part for the classification of the disturbances and one part for recommending countermeasures to identified disturbances. The third part is the interactive user interface. Integrating the user’s feedback will enable an adaptation to the constantly changing constraints of production control. As an outlook for a technical realization, the design of the user interface and the way of interaction is presented. For the evaluation of our framework, we will use simulated event data of a sample production line. The implementation and test should result in higher production performance by reducing the downtime of the production and increase in its productivity.
"New Service-Work"
(2021)
Durch die Corona-Krise befinden wir uns auf einmal alle in einer völlig neuen Arbeitswelt. Innerhalb kürzester Zeit waren Unternehmen im Service gezwungen, Antworten für – vielleicht auch manchmal schon länger bestehende Fragen – rund um das Thema „New Work“ zu finden. Daher hat sich der KVD in den vergangenen Monaten im Rahmen einer ausgewiesenen Expertenrunde aus Praxis und Forschung mit dem Thema „New Service-Work“ auseinandergesetzt. Vor dem Hintergrund der aktuellen Herausforderungen haben wir die zentralen Handlungsfelder für die Umsetzung dieser neuen Arbeitswelt einmal intensiver beleuchtet. In über 25 digitalen Treffen wurden die Chancen, Risiken oder auch Hemmnisse zu den jeweiligen Handlungsfeldern intensiv diskutiert und aufbereitet. Mit einigen aktuellen, innovativen Lösungsansätzen aus unseren Mitgliedsunternehmen hoffen wir, auch Ihnen erste Hinweise oder Impulse geben zu können, wie auch bald in Ihrem Unternehmen „New Service-Work“ Einzug halten könnte.
In manufacturing, adherence to delivery dates is one of the main logistic goals. The production control department has to cope with short-term deviations from the planned route sheets. Because of unforeseen disruptions, e.g. machine breakdowns or shortage of material or personnel, in some situations, the promised delivery date to the customer is at stake. In practice, a fast and reasonable decision on how to deal with the delayed order is required. This decision process is often based on a qualitative analysis relying on the planner’s subjective assessment of a complex situation. To improve the quality of possible countermeasures this paper presents an application, which supports the decision process through a quantified analysis using real-time data from business application systems in combination with a simulation of the value stream. The developed app is part of the decision process and estimates the effect of selected countermeasures to accelerate a delayed order. Performance indicators illustrate the effect of the countermeasures on the specific order as well as the whole system. This approach empowers the planner to assess unforeseen situations and aims to improve the quality of the decision-making process. This paper describes the architecture of the application, its simulation ecosystem, the relevant data and the decision process to select the most effective countermeasures.
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.
Nowadays, the market for information and communication technologies used for IOT-applications grows daily. Since companies need technologies to transform their business processes corresponding to the digital revolution, they need to know which technologies are available, and fit the best for their use case. Their inertial issue is the lacking overview of technologies suitable to connect their production or logistics. Hence, this paper presents a methodology to select technologies (and combinations) based on their functions. It differentiates between information and communication technologies, digital technologies and connecting technologies by the physical function and its role in a cyber-physical system. Depending on the use case, the applicability of every technology varies. Due to that reason, the paper illustrates a ranked qualification of the technologies for typical use cases, focussing tracking and tracing issues in the intralogistics of producing companies. The evaluation is performed upon a literature research, a market study to identify suitable technologies, and various expert interviews to assess the applicability of the technologies.
Many ERP systems support configurable materials. Due to an ever increasing number of product variants the benefits of this approach are well understood. However, these implementations are not standardized. In this article we propose a new standard interface for the exchange of configuration data. This would lead to further benefits as systems as Advanced Planning systems could better use manufacturing flexibility while web shops as Amazon could easily integrate manufacturers of complex products with much reduced implementation effort.
Towards the Generation of Setup Matrices from Route Sheets and Feedback Data with Data Analytics
(2018)
The function or department of production control in manufacturing companies deals with short-term scheduling of orders and the management of deviations during order execution. Depending on the equipment and characteristics of orders, sequence dependent setup times might occur. In these cases for companies that focus on high utilization of their assets due to long phases of ramp up and high energy costs, it might be optimal to choose sequences with minimal setup time times between orders. Identifying such sequences requires detailed and correct information regarding the specific setup times. With increasing product variety and shorter lot sizes, it becomes more difficult and rather time intense to determine these values manually. One approach is to analyse the relevant features of the orders described in the route sheets or recipes to find similarities in materials and required tools. This paper presents a methodology, which supports setup optimized sequencing for sequence dependent setup times through constructing the setup matrix from such route sheets with the use of data analytics.
Manufacturing companies worldwide recognized the high potential of Industrie 4.0 in order to increasing production efficiency. Key benefits include creation of integrated systems, networked products and improvement of service portfolios. However, for many companies deriving and evaluating necessary measures to use Industrie 4.0 potentials represents a major challenge. This paper introduces the "acatech Industrie 4.0 Maturity Index" as an approach to meet this challenge. The development of multidimensional maturity model intents to provide companies an assessment methodology. The aim is to capture the status quo in companies in order to be able to develop individual roadmaps for the successful introduction of Industrie 4.0 and manage the transformation progressively.
Service Engineering Models
(2019)
Since the field of service engineering emerged in the late 20th century, the service industry has undergone drastic changes. Among the reasons for these changes is the increasing digitalization, which has made it difficult for companies to successfully develop new service offerings. While numerous service engineering models are available to provide guidance during the design of new services, many of them cannot keep up with the requirements of today’s economic environment. The present paper examines the requirements that service engineering models need to meet in order to be suitable guidelines for the digital age. To this end, the introduction illustrates how digitalization has changed the service industry. Afterwards, selected service engineering models and related norms are presented. Finally, a set of requirements for modern service engineering models derived from best practices from recent years is introduced.
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.
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.
Forecasting-based skills management, which is oriented to the respective corporate goals, is gaining enormous importance as a central management tool. The aim is to predict future skills requirements and match them with existing interorganizational skills. Companies are required to anticipate changes in markets, industries, and technologies at an early stage as well as to identify changes in job profiles within an occupational profile by tapping into and evaluating various data sources. Based on these findings, they can then make informed decisions regarding skill gaps, for example, to implement targeted further training measures. Forecasting-based skills management offers the opportunity to optimally qualify employees for constantly changing tasks. At the same time, however, the targeted development of such skills requires a high level of time, financial and personnel resources, which small and medium-sized enterprises (SMEs) generally do not have at their disposal. In addition, many SMEs are not yet aware of the importance of this issue. Within the framework of research and industrial projects of the Smart Work department at the FIR (Institute for Industrial Management) at the RWTH Aachen University, an AI-based skills forecasting tool will be developed. The goal of the paper is to conceptualize the future machine learning method, that is able to generate individualized skills forecasts and recommendations for SMEs. This is achieved by linking societal forecasts and sector trends with company-specific conditions and skills. In order to generate a corresponding database, the derivation system is made available to various companies (large companies and SMEs) in order to obtain as many data sets as possible. The data sets obtained via the derivation system are then used as training data sets for the machine learning method, with the help of which an automatic derivation of competencies depending on new trends is to be made possible.
Digitization is constantly affecting the working world and is of enormous interest in many fields of science. But to what extent are innovative technologies actually being applied in regional SMEs and what are the obstacles to their introduction? From a psychological point of view, it is essential to consider the employee's health and the effects of innovative technologies on their everyday work. The aim of using innovative technologies should not be to completely replace human labor or to dequalify employees, but to relieve the workforce and free up working time for more meaningful activities. One concept that should be included in the human-centered design of human-machine interaction in artificial intelligence is the HAI-MMI concept (Huchler, 2020), which offers starting points for high-quality collaboration at various levels. To reduce the gap between science and industry, this paper focuses on the actual demands of SME in the Aachen region in Germany referring to a requirements analysis within the research project AKzentE4.0 (N = 50 SME) and discusses how appropriate innovative technologies of the Industry 4.0 and AI can be implemented and deployed in a human-centred way. Moreover, the establishment of a Human Factors Competence Center for Employment in Industry 4.0 is outlined, which is meant to be used for the dissemination of research results from the project and should narrow the gap between science and industry in the long run.
For developing a European industrial cooperation and involvement in the furniture industry, the international research project INEDIT conducted a survey for furniture customers. By finding out the needs and wishes of the customer regarding innovative products and the production process the project will establish a new way for designing and producing furniture. Within INEDIT a platform is built on which customized, technologically innovative and sustainable furniture can be created and produced in a co-creation process. The furniture industry should thus become significantly more flexible, transparent and sustainable. Following the "do-it-together" approach, a business ecosystem will be generated which creates added value not only for customers but also for designers, suppliers and manufacturing companies. In order to involve the customer even more actively in the design process and the production, the platform will provide access to a mix of digital and physical services and is linked to all other stakeholders in the value chain. To match the platform and the process to the needs, wishes and demands of the customer an anonymous survey with 300 participants was developed and conducted. By analyzing the survey, important factors were found for buying and for using furniture considering new technological inventions (e.g. 3D-printing or smart objects), sustainability of the products and the production process. Furthermore, the potential customer-group and their usage of the do-it-together process and additional activities can be tightened.