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Understanding the Organizational Impact of Robotic Process Automation: A Socio-Technical Perspective
(2022)
Interest in AI-driven automation software is growing constantly across
all industries, as these technologies enable companies to almost automate administrative processes completely and significantly increase operational efficiency.
However, many implementation attempts fail due to a lack of understanding of how these technologies affect the various socio-technical aspects that are intertwined in an organisation. This leads to a widening gap between value propositions of automation software and the ability of companies to exploit them. For long-term
success, collaboration between humans and software robots in the organization must be optimised. Therefore, the social, technical, and organizational impact of Robotic Process Automation was investigated. Following a socio-technical systems approach, a model was developed and validated in a use case of a company in the mechanical engineering sector. Knowing the influencing factors before launching large-scale automation initiatives will help practitioners to better exploit
efficiency potentials and increase the long-term success.
Driven by different trends, such as digitalization, the number of companies aiming for successful business transformation is increasing, while new structures and systems are paving the way. Strategic agile management systems offer significant potential benefits given the increasing speed of the evolving environment in which organizations find themselves these days. To select and implement the appropriate strategic agile management system, companies need to understand the underlying theoretical principles to be able to select the most suitable for the respective company and to introduce it based on individual adaption. Within this paper, a morphology is presented to improve theoretical knowledge about strategic agile management systems. Creating a common understanding of strategic agile management systems and their current areas of application creates a suitable frame of reference for future research projects.
"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.
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.
Europa als erster klimaneutraler Kontinent bis 2050 – unter diesem ambitionierten Ziel treibt die Europäische Union eines der größten Transformationsprogramme dieses Jahrhunderts voran. Das Leben und die Gesellschaft wie sie heute existiert, werden in allen Bereichen signifikanten Musterwechseln unterliegen. Von zentraler Bedeutung bei dieser Transformation wird die Mobilität von Personen und Gütern sein. Eine Reduktion von 90 % der Treibhausgasemissionen soll in weniger als drei Dekaden realisiert werden. Insbesondere im Bereich der Urbanen Logistik ist ein nahtloses Zusammenspiel der verschiedensten Akteure, unterstützt durch neuartige digitale und physische Infrastrukturen, notwendig, um eine nachhaltige Zielerreichung bei mindestens konstantem Serviceniveau sicherzustellen. Cross-industrielle Ansätze, die über das Zusammenspiel von komplementären Lösungsbausteinen Co-Creation ermöglichen, werden zum zentralen Wettbewerbsvorteil für alle Akteure. Die Gestaltung von Business Ecosystems rückt deshalb zunehmend in den Fokus und wird aufgrund des enormen Potenzials für die Urbane Logistik in diesem Beitrag beleuchtet.
While digitization is a strategic advantage in numerous industries such as the automotive industry or mechanical engineering, other industries like the German quarrying industry have not yet established a transformation towards a digitized industry. This leads to inefficient work and inaccurate forecasting capabilities. To address these challenges, digital platforms can incentivize digitization
by supporting the capacity utilization and forecasting capability of these companies. In this paper, the quarrying industry is analyzed by a morphology and different types of companies are identified. Knowing the digital maturity of these companies and by determining the key factors to forecast demands and the capacity utilization, different operating models are derived. Combined with a morphology and the value creation system, different scenarios for the identification of platform services are examined. These scenarios are weighted in a utility analysis to get an operating model blueprint to develop and establish digital platforms in less digitized industries.
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.
The manufacturing industry consumes 54% of global energy and attributes for 20% of global CO2 emissions, demonstrating the industry’s role as global driver of climate change. Therefore, reducing its carbon footprint has become a major challenge as its current energy and resource consumption are not sustainable. Industrie 4.0 presents a chance to transform the prevailing paradigms of industrial value creation and advance sustainable developments. By using information and communication technologies for the intelligent networking of machines and processes, it has the potential to reduce energy and material consumption and is considered a key contributor to sustainable manufacturing as proclaimed by the European Commission in the term “twin transition”. As organizations still struggle to utilize the potential of Industrie 4.0 for a sustainable transformation, this paper presents a framework to successfully align their own twin transition. The framework is built upon three key design principles (micro level: leverage eco-efficient operations, meso level: facilitate circularity and macro level: foster value co-creation) derived using case study research by Eisenhardt, and four structural dimensions (resources, information systems, organizational structure and culture) based on the acatech Industrie 4.0 Maturity Index. Eleven interconnected areas of action are defined within the framework and offer a holistic and practical approach on how to leverage an organization’s twin transition. Within the conducted research, the framework was applied to the challenge of information quality and transparency required for high-value secondary plastics in the manufacturing industry. The result is a digital platform design that enables information transactions for secondary plastics and establishes a circular ecosystem. This shows the applicability of the framework and its potential to facilitate a structured approach for designing twin transitions in the manufacturing industry.
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.