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Institut / FIR-Bereiche
- FIR e. V. an der RWTH Aachen (34) (entfernen)
Mehrwert der Abschlussarbeit
•Liefert konkretes methodisches Vorgehen zur Beschreibung von intermodalen Mobilitätsketten und deren Aufbau
•Liefert konkretes methodisches Vorgehen zur Beurteilung der Potenziale von intermodalen Mobilitätsketten und Berücksichtigung der spezifischen städtischen Rahmenbedingungen
Herausforderungen der Praxis und ForschungHerausforderungen der Praxis und ForschungHerausforderungen der Praxis und Forschung
•Fähigkeit zur Realisierung von Ende-zu Ende-Mobilitätsketten ist ein wesentlicher Erfolgsfaktor in der Diskussion um zukünftige Mobilitätssysteme
•Aktuelle Indizes und Studien zu global und nicht konkret genug
•Zurzeit existiert keine ausreichende methodische Unterstützung zur Beurteilung des Potenzials und hinsichtlich Realisierbarkeit Reisezeit, Reisekosten und Komfort der Reise Entlang einer Kette
Betrachtete theoretische Grundlagen
•Differenzierte Betrachtung der Definitionen von Mobilität, Mobilitätskonzept, Interodale Mobilität, Smart Cities, Geschäftspotenzial, Mobilitätsbedarf
•Vergleich und Analyse 4 zentraler Mobilitätsindizes und Untersuchung hinsichtlich evtl. Defizite
•Grundlagen im Bereich intermodale Mobilitätsketten (auch Mobilitätsmodi und Mobilitätszweck und -bedarf), Mobilitätsverhaltensforschung Beurteilung Geschäftspotenzial
Vorgehen/Grundstruktur des Modells/Konzepts/Katalogs
•7 Schnittiges Vorgehen, welches ausgehend von einem realen Mobilitätsbedarf Szenarien (intermodale Mobilitätsketten) ableitet und deren Realisierbarkeit untersucht, sie hinsichtlich Zeit, Kosten und Komfort beschreibt und über einen Typologisierungsansatz (Sinus-Milieus) Nutzerakzeptanz ableitet. Diese wird in einer Beurteilung des Potenzials im Unterschied zum Status Quo zusammengefasst.
•Spezifisch ausgearbeitet für die Verbindung zweier Quartiere
•Angewendet am Fallbeispiel Quartier Aachen Innenstadt und Campus Melaten Nord ( täglicher Arbeitsweg)
Mehrwert der Abschlussarbeit
•Liefert konkretes methodisches Vorgehen zur Beschreibung von intermodalen Mobilitätsketten und deren Aufbau
•Liefert konkretes methodisches Vorgehen zur Beurteilung der Potenziale von intermodalen Mobilitätsketten und Berücksichtigung der spezifischen städtischen Rahmenbedingungen von intermodalen Mobilitätsketten und deren Aufbau
•Liefert konkretes methodisches Vorgehen zur Beurteilung der Potenziale von intermodalen Mobilitätsketten und Berücksichtigung der spezifischen städtischen Rahmenbedingungen
Das Projekt ‚DiLinK‘ dient primär dem Ziel, eine ressourceneffiziente, nachhaltige Kreislaufschließung für Unternehmen in der Kunststoffwirtschaft zu realisieren. Durch innovative und an die Problematik angepasste digitale Systemlösungen soll mithilfe von Kooperationen in Forschung und Entwicklung eine Steigerung
der Nutzung von Sekundärkunststoffen ermöglicht werden. Bei den digitalen Systemlösungen handelt es sich insbesondere um die Entwicklung innovativer elektronischer Einrichtungen der Datenaufnahme
durch Sensoren im Bereich der Prozessmesstechnik und der anschließenden Datenverarbeitung und -weitergabe mittels entsprechender Softwarelösungen. Durch den Einsatz in Unternehmenskooperationen sollen diese Lösungen erprobt werden und anschließend Kunststoffverarbeitern, Endverbrauchern und
Recyclern ermöglichen, bislang nicht oder wenig eingesetzte Sekundärkunststoffe in größeren Mengen zu sammeln, aufzubereiten und in den Kreislauf zurückzuführen. Das im Juni 2019 gestartete Projekt wird durch das Bundesministerium für Bildung und Forschung im Rahmen der Fördermaßnahme ‚ReziProK – Ressourceneffiziente Kreislaufwirtschaft – Innovative Produktkreisläufe‘ gefördert und läuft noch bis Mai 2022. Das diesem Bericht zugrundeliegende Vorhaben wurde mit Mitteln des Bundesministeriums für Bildung und Forschung unter dem Förderkennzeichen INNOPRO-098 ‚DiLinK‘ gefördert.
Das Forschungsvorhaben ‚PROmining‘ , mit Laufzeit vom 01.01.2021 bis 31.12.2022, dient dem Ziel, die Digitalisierung in der deutschen Steine- und Erdenindustrie zu initiieren und auszuweiten. Innerhalb des Projekts soll für KMU durch den Einsatz eines Demonstrators einer Plattformlösung eine datenbasierte Entscheidungsgrundlage geschaffen werden. Branchenweit wurde ein Mangel an datenbasierten Entscheidungsgrundlagen identifiziert, die meisten Entscheidungen basieren auf Erfahrungswerten. Des Weiteren gilt es, die Kapazitätsauslastung der Betriebe mithilfe des Demonstrators zu optimieren. Jene gestaltet sich aufgrund regionaler sowie konjunktur- und saisonbedingter Nachfrageschwankungen sehr volatil. Der Demonstrator soll Unternehmen motivieren, ihre Datenhaltung zu verbessern und den Transformationsprozess hin zu einem digitalen Unternehmen anzustoßen.
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.
Analysis of Strategic Business Ecosystem Role Models for Service-Oriented Value Creation Systems
(2023)
The way companies create service-oriented value is changing as organizational boundaries blur towards value creation in ecosystems. To position themselves strategically, practitioners need to understand the different roles in service-oriented value creation systems (SOVCS). Still, there is no evidence if existing role models can be applied for SOVCS. This paper analyses the adequacy of existing strategic role models for service-oriented business ecosystems. The suitability of the role models is evaluated using central aspects of the Service-Dominant Logic. We demonstrate that the existing central strategic role models cannot be transferred to a SOVCS and outline the research need for an adequate strategic role model. Scholars will find an overview of existing role models and use the conducted evaluation as a foundation for further service science research. Based on the identified inaccessibility, a comprehensive strategic positioning model can be developed.
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.
Objectives and Key Results (OKR) is an approach that focuses on the company's goals through trust-based agreements between leaders and employees. With the OKR framework in its original form, strategic business goals are aligned with the employees' active involvement, which promotes intrinsic motivation, transparency, commitment, and alignment. Inspired by the successes at Google and Intel and shaped by its use in the tech industry, the use of OKR increased across industries. Although companies within all sectors use the OKR framework, numerous implementation efforts fail. The challenges of practitioners are not fully addressed in the development of implementation concepts for OKR. One main reason is that these challenges are not taken into account in scientific publications. The paper aims to investigate to what extent existing OKR frameworks need to be adapted to provide companies with suiting implementation guidance. Firstly, OKR is placed in the context of academically widely discussed Performance Management Systems (PMS).
Secondly, criteria for successful PMS implementation are identified and used as a baseline for analyzing existing OKR implementation concepts. A systematic literature review shows the current state of research, identifying existing OKR implementation concepts from practice and theory. The OKR implementation concepts identified are systematically mapped to the series of identified criteria for PMS implementation. It is shown that the existing OKR frameworks do not address the described criteria necessary for a successful implementation of PMS, thus the adaptation of existing OKR implementation concepts is required.
The quarrying industry, which largely consists of less digitized SMEs, is an integral part of the German economy. More than 95% of the primary raw materials produced are used by the domestic construction industry. Quarrying companies operate demand-oriented with short planning horizons at several locations simultaneously. Due to the low level of digitization and the reluctance to share data, untapped efficiency potential in data-based demand forecasting and capacity planning arises. The situation is aggravated by the fact that SMEs have a heterogeneous mobile machinery so as not to become dependent on individual suppliers, and that transport distances of over 50 kilometers are uneconomical due to high transport costs and low material values. Within the research project PROmining a data-centric platform which improves demand forecast accuracy and multi-site capacity utilization is developed. One of the core functionalities of this platform is an industry-specific demand forecasting model. Against this background, this paper presents a methodology for establishing this forecasting model. To this end, expected demands of secondary industry sectors will be analyzed to improve mid-term volume-forecasting accuracy for the local quarrying industry. The data-centric platform will connect demand forecasting data with relevant key performance indicators of multi-site asset utilization. Following this methodology, operational planning horizons can be extended while significantly improving overall production efficiency. Thus, quarrying businesses are enabled to respond to fluctuating demand volumes effectively and can increase their personnel and machine utilization across multiple quarry sites.
The use of Business Analytics (BA) helps to improve the quality of decisions and reduces reaction latencies, especially in uncertain and volatile market situations. This expectation leads a continuously rising number of companies to make large investments in BA. The successful use of Business Analytics is increasingly becoming a differentiator. At the same time, the use of BA is not trivial, rather, it is subject to high socio-technical requirements. If these are not addressed, high risks arise that stand in the way of successful use. In particular, it is important to consider the risks in relation to the different types of BA in a differentiated way. So far, there is a lack of suitable approaches in the literature to consider these type-specific risks with regard to the socio-technical dimensions: people, technology, and organization. This paper addresses this gap by initially identifying risks in the use of Business Analytics. For this purpose, possible risks are identified using a systematic literature review and verified with a Delphi survey with various partners experienced in dealing with BA. Subsequently, the identified and validated risks are assigned to three different types of Business Analytics (Descriptive, Predictive and Prescriptive Analytics) and assessed in order to systematically address and reduce the risks. The result of this paper is an overview of the interactions between the socio-technically assigned risks, summarized in a risk catalog, and the different types of Business Analytics.