Business Transformation
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Digital Leadership
(2020)
This article describes digital leadership-specifically character and competency-that differentiate digitally mature organizations from digitally developing organizations. We assess the differentiated actions of leaders of digitally mature organizations and discuss their results. The study is based on Patterns of Digitization survey with insights from 559 decision makers across five geographic regions-America, Europe, Asia, Africa, and Oceania designed to assess how companies are implementing digital transformation, the various strategies they employ, the investments they make, and the actions they take to achieve large-scale institutionalized digital transformations. The insights gleaned from the study should help lagging companies understand what is involved in implementing a digital transformation and what they need to do to catch up.
Patterns of Digitization
(2020)
This article describes the results of Patterns of Digitization survey designed to assess how companies are implementing digital transformation. The survey includes the various strategies companies employ, the technologies they invest in, and, in particular, the actions they take to overcome the organizational resistance that is common to most large-scale transformations. Digital transformation is reshaping entire segments of our society and industries of every type:
communications, retail, and increasingly healthcare, medicine, agriculture, and manufacturing.
While a few companies seem to reach front-runner status, the majority seem to lag. This phenomenon is a top concern of boardrooms worldwide and motivated the development of this study. To help these organizations, we highlight the important actions all companies are taking as well as the differentiated actions digitally mature companies are undertaking to transform their businesses. These insights should help lagging companies understand what is involved in
implementing a digital transformation and what they need to do to catch up.
Patterns of Digitization
(2019)
This article describes the results of a survey designed to assess how companies are implementing digital transformation, including the various strategies they employ and the actions they take to achieve large-scale transformations. While a few companies seem to reach front-runner status, the majority seem to lag behind. This phenomenon is a top concern of boardrooms worldwide and motivated the development of this study. To help these organizations, we highlight differentiated strategic principles and characteristics of the companies' design processes digitally mature companies undertake to transform their businesses. These insights should help lagging companies understand what is involved in implementing a digital transformation and what they need to do to enforce this transformation.
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.
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.
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 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.
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.
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.
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.
Das Forschungs- und Entwicklungsprojekt ‚GALA-Gesundheitsregion Aachen: Innovativ lernen und arbeiten‘, welches vom Bundesministerium für Bildung und Forschung (BMBF) im Rahmen der Programme „Zukunft der Wertschöpfung – Forschung zu Produktion, Dienstleistung und Arbeit" und „Innovation und Strukturwandel" gefördert wird (Laufzeit: 01.04.2021 – 01.03.2024) und aus dem auch der vorliegende Sammelband entstanden ist, zielt darauf ab, die skizzierten Herausforderungen im zentralen Leitmarkt Gesundheitswirtschaft zu adressieren und innovative Arbeits- und Lernwelten in der Gesundheitsregion Aachen zu gestalten. Das Projekt umfasst folgende vier zentrale Leitthemen, wodurch zahlreiche Entwicklungsmöglichkeiten für die Region Aachen eröffnet werden:
- Erhalt und Ausbau von Kompetenzen in neuen Mensch-Maschine-Systemen durch Digitalisierung und Automatisierung
- Erhalt und Förderung der physischen und psychischen Gesundheit bei Arbeitsintensivierung und Transformationsprozessen
- Nutzung der Chancen aus der Digitalisierung für digitale Kollaboration über traditionelle Unternehmens- und Branchengrenzen hinweg
- Förderung von Agilität und Innovation von Unternehmen angesichts zahlreicher inkrementeller und sprunghafter Veränderungen.
Konkret werden im Projekt GALA von den beteiligten Forschungspartnern (FIR e. V. an der RWTH Aachen, Institut für Arbeitswissenschaft (IAW) der RWTH Aachen University, FOM Hochschule, dem Transferpartner (Region Aachen Zweckverband) sowie regionalen kleinen und mittleren Unternehmen, insbesondere aus den Bereichen der stationären und ambulanten Versorgung und der Herstellung medizin(-techn-)ischer Produkte (Gesellschaft für Produktionshygiene und Sterilitätssicherung mbH, St. Gereon Seniorendienste gGmbH, Vostra GmbH, Lebenshilfe Aachen GmbH, Heinen Automation GmbH und Co. KG, Modell Aachen GmbH, AIXTRA/Uniklinikum Aachen und Medaix GmbH), die vier Leitthemen Mensch-Maschine-Interaktion, Gesundes Arbeiten, Digitale Kollaboration sowie Agilität und Innovation fokussiert. Für diese Themen werden in den beiden GALA-Handlungsfeldern Arbeitsgestaltung und Kompetenzmanagement mithilfe von Toolboxen innovative Werkzeuge und Konzepte entwickelt, erprobt und umgesetzt sowie auf diesem Wege die Regionalentwicklung der strukturschwachen Region Aachen nachhaltig gefördert.
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.
Der Betrieb lokaler privater Mobilfunknetze in lizenzierten Frequenzbändern ist eine der Kerninnovationen aktueller 5G- und zukünftiger 6G-Netze. Die prognostizierte Leistungsfähigkeit privat verfügbarer und störungsfreier Frequenzbereiche, wie z. B. privater 5G-Netze, sogenannter Campusnetze, ist für Industrieunternehmen oft von großem Interesse. Die Integration der 5G-Netzwerkinfrastruktur in bestehende Brownfield-Umgebungen muss jedoch erhebliche technische und Management-Herausforderungen überwinden. Im Vergleich zu selten anzutreffenden Greenfield-Szenarien kann das Potenzial von 5G nur gezeigt werden, wenn signifikante Leistungsvorteile gegenüber bestehender drahtloser Netzwerkinfrastruktur (z. B. Wi-Fi) nachgewiesen werden können und gleichzeitig eine nahtlose Integration in die Prozessumgebung in der Praxis gewährleistet werden kann.
Vor diesem Hintergrund stellt das Competence Center 5G.NRW in diesem Beitrag ein agiles System zur kontinuierlichen, netzwerkübergreifenden Überwachung von Ende-zu-Ende-Leistungsgarantien in Bezug auf Durchsatz, Latenz und Zuverlässigkeit vor. Während einzelne punktuelle Messungen während der Netzwerk-Inbetriebnahme und -Erprobung oft die erwarteten Leistungsspitzen anzeigen, untersucht dieser Beitrag speziell das Potenzial eines räumlich verteilten Stresstests, der die Netzwerkqualität aktiv und kontinuierlich während des Produktivbetriebs überwacht. Anhand einer umfangreichen Fallstudie wird die Leistungsfähigkeit des verteilten Ansatzes für die Leistungsbewertung von Mehrbenutzer- und Zellenrandumgebungen demonstriert.
Metropolitan Cities
(2019)
Durch die Vernetzung und Mobilisierung von geografisch verteilten Orten soll eine europäische Modellmetropole mit einzigartigem Charakter entstehen. Dies haben sich zahlreiche Unternehmen, Forschungseinrichtungen und die öffentliche Hand für die Entwicklung der fünftgrößten Metropolregion Europas zum Ziel gesetzt.
The acquisition, processing and analysis of internal and external data is one of the key competitive factors for corporate innovation and competitive advantage. Many firms invest a significant amount of resources to take advantage of advanced analytics methods. Machine learning methods are used to identify patterns in structured and unstructured data and increase predictive capabilities. The related methods are of particular interest when previously undiscovered and unknown structures are discovered in comprehensive data sets in order to more accurately predict the outcome of manufacturing or production processes based on a multitude of parameter settings. So far, this knowledge is often part of the individual or collective knowledge of experts and expert teams, but rarely explicit and therefore not replicable for future applications. On the one hand, it is demonstrated in this paper how different machine learning algorithms have been applied to better predict the output quality in the process industry. On the other hand, it is explained how the application of machine learning methods could contribute to making previously not accessible process knowledge explicit. In order to increase the prognostic accuracy of the model diferrent methods were combined, later on compared and evaluated within an industrial case. In this paper a comprehensive approach to knowledge-based process engineering is being presented.
Die Blockchain-Technologie ist in aller Munde. Spätestens seit dem rasanten Aufstieg der unterschiedlichen Kryptowährungen beschäftigen sich auch Unternehmen abseits der Finanzbranche mit dieser. Um das volle Potenzial bewerten zu können, müssen Entscheider sich an der aufkommenden Start-up-Szene orientieren. Diese muss jedoch entsprechend klassifiziert werden, um aufzuzeigen, welche Anwendungsfälle sich ergeben und wie sich diese unterscheiden.
Die Projekte „ServiceAnalytics" und „Analytics for Innovation" unterstützen insbesondere KMU bei der aufwandsarmen und praxisnahen Umsetzung von Business-Analytics im Service, um zum einen die Serviceprofitabilität zu erhöhen und zum anderen die Entwicklung innovativer After-Sales-Services voranzutreiben. Durch die beiden diametralen Zielsetzungen (Profitabilität steigern und Innovationen stimulieren) ergänzen sich die Projekte ideal, um aufzuzeigen, welche Mehrwerte durch den Einsatz von Business-Analytics im Service geschaffen werden.
The almost boundless possibilities of realizing saving potentials and innovations drive manufacturing companies to implement Business Analytics as part of the digitalization roadmap. The increasing research within the field of algorithm design and the wide range of user-friendly tools simplify generating first insights from data also for non-professionals. However, small and medium sized companies struggle implementing Business Analytics company-wide due to the lack of competencies. Especially the customization of a multitude of analytic methods in order to match a superordinate, business-relevant question is not done easily. This paper enables researchers as well as practitioners to close the gap between business relevant questions and algorithms. From a practical point of view, this paper helps shortening the search time for a suitable algorithm. Out of a research perspective, it aims to help positioning new algorithms within a structured framework in order to enhance the communication of algorithms’ capabilities.
Im Forschungsprojekt „Legitimise IT“ wurde ein einheitlicher Ansatz zur Nutzung von Schatten-IT für produzierende kleine und mittlere Unternehmen (KMU) entwickelt. Dadurch sollen KMU zur kontrollierten Legitimierung nutzenstiftender Schatten-IT unter Berücksichtigung vorhandener Risiken befähigt werden.
Schatten-IT ist in den meisten Unternehmen vorhanden. Durch den unkontrollierten Einsatz von Schatten-IT im Unternehmen entstehen zahlreiche Risiken, welche zu Ineffizienzen und Fehleranfälligkeiten bei den Betriebsabläufen führen können. Dabei wird die Entstehung von Schatten-IT nicht zuletzt durch die Schnelllebigkeit und Vielfalt der technologischen Entwicklungen weiter beschleunigt. Der Ansatz, durch eine strikte Vorgabe der Unternehmensführung lediglich auf genehmigte und zentral verwaltete IT-Anwendungen zurückzugreifen, um Schatten-IT zu unterbinden, hat sich in der unternehmerischen Praxis nicht bewährt. Bisherige Ansätze adressieren nicht die Gründe für die Notwendigkeit von Schatten-IT und bieten keinen organisatorischen und insbesondere technologischen Rahmen, um deren Vorteile unternehmerisch zu nutzen.
Daher wurde im Projekt ein Ansatz entwickelt, der einerseits die aufgezeigten Risiken minimiert und andererseits Mitarbeitenden die notwendigen Freiheiten für eigene, kreative Lösungen bietet. Damit Unternehmen ihre großen Herausforderungen bei der Abschätzung der Risiken- und Nutzenaspekte wie auch beim strikten Verzicht auf die eingesetzten Schatten-IT-Anwendungen bewältigen können, wird eine entsprechende Methodik gefordert.
Das Paradigma von Zweckgebäuden als Immobilie, deren Wert sich lediglich über die Lage, die Ausstattung, das Alter und die verfügbare Nutzungsfläche bemisst, wird zunehmend aufgelöst. Neubauten wie der cube in Berlin, The Edge oder auch Bestandsimmobilien wie das Spark, beide in Amsterdam, zeigen, dass der Wert einer Immobilie sich zunehmend über den Grad der Vernetzung, die Integration neuer datenbasierter Dienstleistungen und somit über eine deutlich gesteigerte Effizienz im Gebäudebetrieb definiert. Der folgende Beitrag systematisiert aus der Forschungsperspektive datenbasierte Dienstleistungen für das Smart Commercial Building (SCB) und liefert eine Übersicht existenter sowie zukünftiger datenbasierter Dienstleistungen. Die Ergebnisse dienen sowohl den Herstellern technischer Gebäudeausrüstung, Gebäudebetreibern, Objektbetreuern und Investoren gleichermaßen als Orientierung und Anhaltspunkt für zukünftige datenbasierte Dienstleistungen im SCB.