Developing Digitalization Strategies for SMEs: A Lightweight Architecture-based Method

Like larger companies, small and medium-sized enterprises (SMEs) need to develop and implement digitalization strategies. These help to address necessary organizational- and technology-related changes in order to create competitive advantages. However, SMEs often face specific challenges, including a lack of IT know-how, relevant market information and appropriate methods for developing a strategy. In this paper, we present a lightweight, architecture-based method including its underlying model for the development and implementation of digitalization strategies in SMEs. It was developed by following the Action Design Research (ADR) method and in cooperation with two medium-sized companies. Rather than adopting highly abstract and complex enterprise architecture frameworks, we suggest creating easy-to-use visualizations of the enterprise architecture, the business ecosystem and related cross-layer dependencies. While transferring the discipline of enterprise architecture (EA) into the context of digital entrepreneurship, we derived four design principles helping to enrich the theoretical body of knowledge in this research area

Quantified vehicles: data, services, ecosystems

Advancing digitalization has shown the potential of so-called Quantified Vehicles for gathering valuable sensor data about the vehicle itself and its environment. Consequently, (vehicle) Data has become an important resource, which can pave the way to (Data-driven) Services. The (Data-driven Service) Ecosystem of actors that collaborate to ultimately generate services, has only shaped up in recent years. This cumulative dissertation summarizes the author's contributions and includes a synopsis as well as 14 peer-reviewed publications, which contribute to answer the three research questions.Die Digitalisierung hat das Potenzial für Quantified Vehicles aufgezeigt, um Sensordaten über das Fahrzeug selbst und seine Umgebung zu sammeln. Folglich sind (Fahrzeug-)Daten zu einer wichtigen Ressource der Automobilindustrie geworden, da sie auch (datengetriebene) Services ermöglichen. Es bilden sich Ökosysteme von Akteuren, die zusammenarbeiten, um letztlich Services zu generieren. Diese kumulative Dissertation fasst die Beiträge des Autors zusammen und enthält eine Synopsis sowie 14 begutachtete Veröffentlichungen, die zur Beantwortung der drei Forschungsfragen beitragen

Skills Requirements for the European Machine Tool Sector Emerging from Its Digitalization

Abstract The machine tool industry, which is the starting point of all the metal producing activities, is presently undergoing rapid and continuous changes as a result of the fourth industrial revolution Industry 4.0. Manufacturing models are profoundly transforming with emerging digitalization. Smart technologies like artificial intelligence (AI), big data, the Internet of Things (IoT), digital twin, allow the machine tool companies to optimize processes, increase efficiency and reduce waste through a new phase of automation. These technologies, as well, enable the machine tool producers to reach the aim of creating products with improved performance, extended life, high reliability that are eco-efficient. Therefore, Industry 4.0 could be perceived as an invaluable opportunity for the machine tool sector, only if the sector has a competent workforce capable of handling the implementation of new business models and technological developments. The main condition to create this highly qualified workforce is reskilling and upskilling of the current workforce. Once we define the expected evolution of skills requirements, we can clarify the skills mismatch between the workers and job profiles. Only then, we can reduce them by delivering well-developed trainings. For this purpose, this article identifies the current and foreseen skills requirements demanded by the machine tool industry workforce. To this end, we generated an integrated database for the sector with the present and prospective skills needs of the metal processing sector professionals. The presented sectoral database is a fundamental structure that will make the sector acquire targeted industrial reforms. It can also be an essential instrument for machine tool companies, policymakers, academics and education or training centers to build well-designed and effective training programs to enhance the skills of the labor forceThis research was partly funded by (a) the European Union through the Erasmus Plus Programme (Grant Agreement No. 2018-3019/001-001, Project No. 600886-1-2018-1-DE-EPPKA2-SSA-B). (b) the HAZITEK call of the Basque Government, project acronym Adit4All and (c) Accenture, Inzu Group, Fundación Telefónica and Fundación BBK, partners of the Deusto Digital Industry Chair

Defining a Digital Strategy in a BIM Environment to Manage Existing Reinforced Concrete Bridges in the Context of Italian Regulation

Regulatory activity concerning the management of existing bridges has recently been affected by updates, for instance, in Italy, which calls for a speedy and pragmatic approach based on new technologies such as building information modeling (BIM), when dealing with the survey and risk classification as well as the evaluation and monitoring of structural safety. This paper focuses on the development and integration of a digital solution, based principally on the specific framework developed by the authors, which supports BIM modeling and information management activities, in the structural setting under investigation, through the use of several technologies and tools, namely BIM-authoring, CDE platform and visual programming, in addition to programming in Python. Starting from the organization of a specific BIM object library and the initial data, inserted by means of a custom-made input environment, it was possible to reproduce digital models of bridges in accordance with specific information requirements following the new Level of Information Need setting. The applicability of the proposal is tested on two judiciously chosen real-life cases with different characteristics. Through this implementation, a series of advantages emerge, including expediting traditional procedures for BIM modeling, accessibility and traceability of information— which are constantly updated to support the monitoring of structural safety over time—and the decision-making process related to the bridge management context

Digital Strategy Formulation: An Investigation with Design Sprints and Deep Learning

Since the invention of transistors, digital technologies have continued to have a profound impact on the global economy. Relentless performance improvements combined with convergence of digital technologies such as artificial intelligence, internet of things, and cloud computing has led to a surge in scale and importance as a source for competitive advantage. However, in 2019, only around 16% of companies managed to realize a significant improvement in business performance from digital transformation (DT). The challenges that organizations face in succeeding at DT can be traced back to strategy formulation and execution. Therefore, the aim of this research is to develop insights and tools to enhance the understanding and practice of digital strategy formulation. A comprehensive review of the literature demonstrated that DT, as an emerging body of knowledge, is lacking an in-depth and applied investigation of digital strategy formulation. The main knowledge gaps are: (1) a lack of guidance on digital strategy formulation process activities and outcomes; (2) limited consideration of the iterative nature of digital strategy formulation and validation; and (3) limited empirical investigation of digital strategy archetypes to guide the formulation process. Addressing this research gap was accomplished over three stages. First, an in-depth exploratory case study was conducted by investigating digital strategy formulation process with active participation research over six months. This investigation identified key process activities and highlighted the role of roadmapping in integrating the outcomes. Second, the findings were supplemented with literature review to design a conceptual framework for agile roadmapping to facilitate the digital strategy formulation process. This framework was then tested and calibrated over three pilot studies with companies across Europe attempting to start their DT journey. Finally, deep learning and natural language processing techniques were employed to empirically investigate the digital strategy of Fortune 500 companies from earnings call transcripts. This empirical investigation identified four digital strategy archetypes that are being employed by companies across various sectors. The findings from this research contribute to a better understanding of digital strategy formulation. It was identified that digital strategy formulation is an ongoing search process for an adequate strategic response to the DT of the economy. Specifically, incorporating agility into the formulation process is an effective way of managing the associated uncertainty of DT. Moreover, the findings demonstrated that proactively iterating between strategy formulation and validation can accelerate the realization of the emergent digital strategy. The proposed framework and the digital strategy archetypes provide a baseline for DT professionals toward a more robust digital strategy formulation.Ministry of Education - United Arab Emirate

Template-based reverse engineering of parametric CAD models from point clouds

openEven if many Reverse Engineering techniques exist to reconstruct real objects in 3D, very few are able to deal directly and efficiently with the reconstruction of editable CAD models of assemblies of mechanical parts that can be used in the stages of Product Development Processes (PDP). In the absence of suitable segmentation tools, these approaches struggle to identify and reconstruct model the different parts that make up the assembly. The thesis aims to develop a new Reverse Engineering technique for the reconstruction of editable CAD models of mechanical parts’ assemblies. The originality lies in the use of a Simulated Annealing-based fitting technique optimization process that leverages a two-level filtering able to capture and manage the boundaries of the parts’ geometries inside the overall point cloud to allow for interface detection and local fitting of a part template to the point cloud. The proposed method uses various types of data (e.g. clouds of points, CAD models possibly stored in database together with the associated best parameter configurations for the fitting process). The approach is modular and integrates a sensitivity analysis to characterize the impact of the variations of the parameters of a CAD model on the evolution of the deviation between the CAD model itself and the point cloud to be fitted. The evaluation of the proposed approach is performed using both real scanned point clouds and as-scanned virtually generated point clouds which incorporate several artifacts that could appear with a real scanner. Results cover several Industry 4.0 related application scenarios, ranging from the global fitting of a single part to the update of a complete Digital Mock-Up embedding assembly constraints. The proposed approach presents good capacities to help maintaining the coherence between a product/system and its digital twin.openXXXIII CICLO - INGEGNERIA MECCANICA, ENERGETICA E GESTIONALE - Meccanica, misure e robotica01/A3 - ANALISI MATEMATICA, PROBABILITA' E STATISTICA MATEMATICA01/B1 - INFORMATICA09/B2 - IMPIANTI INDUSTRIALI MECCANICIShah, GHAZANFAR AL

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

This paper introduces a novel reverse engineering (RE) technique for the reconstruction of editable computer-aided design (CAD) models of mechanical parts’ assemblies. The input is a point cloud of a mechanical parts’ assembly that has been acquired as a whole, i.e., without disassembling it prior to its digitization. The proposed framework allows for the reconstruction of the parametric CAD assembly model through a multi-step reconstruction and fitting approach. It is modular and it supports various exploitation scenarios depending on the available data and starting point. It also handles incomplete datasets. The reconstruction process starts from roughly sketched and parameterized CAD geometries (i.e., 2D sketches, 3D parts, or assemblies) that are then used as input of a simulated annealing-based fitting algorithm, which minimizes the deviation between the point cloud and the adapted geometries. The coherence of the CAD models is maintained by a CAD modeler that performs the geometries’ updates while guaranteeing the possibly imposed constraints and model coherence. The optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow local fitting and interfaces detection. It is a user-driven approach where the user decides what are the most suitable steps and sequence to operate. It has been tested and validated on both real scanned point clouds and as-scanned virtually generated point clouds incorporating several artifacts that would appear with real acquisition devices

Developing the Digital Task Analysis (DTA) framework to enable the assessment and redesign of digital resources in mathematics education

Digital task design is an important issue when integrating technology into mathematics education. However, existing frameworks often are not fine-grained enough for supporting teachers in designing tasks or they only focus on geometric topics. In this paper, we share a case study as the first cycle of our design-based research study that aims to extend and adapt the well-known Dynamic Geometry Task Analysis framework for analyzing further digital materials. The adapted framework is named Digital Task Analysis (DTA) model and can be utilized to analyze, modify, and design digital materials from other mathematical topics. The model focuses on supporting teachers in integrating two essential aspects within digital materials, namely creating cognitively stimulating tasks and exploiting added value of technology. In this paper, we present the first analyses of three cases representing digital materials including visualizations addressing lower secondary mathematics following the DTA model. The results show that the presented DTA model is suitable to analyze such digital materials and has the potential to support teachers in designing, assessing, and modifying digital tasks that support learners in focusing their attention on mathematically relevant aspects of digital resources, and in deepening their awareness of how to formulate targeted tasks for learners