An extensible manufacturing resource model for process integration

Driven by industrial needs and enabled by process technology and information technology, enterprise integration is rapidly shifting from information integration to process integration to improve overall performance of enterprises. Traditional resource models are established based on the needs of individual applications. They cannot effectively serve process integration which needs resources to be represented in a unified, comprehensive and flexible way to meet the needs of various applications for different business processes. This paper looks into this issue and presents a configurable and extensible resource model which can be rapidly reconfigured and extended to serve for different applications. To achieve generality, the presented resource model is established from macro level and micro level. A semantic representation method is developed to improve the flexibility and extensibility of the model

Measuring the Firm Competitiveness by the Capacity of Operational Flexibility Using Industry 4.0

openL'integrazione delle tecnologie dell'Industria 4.0 nei moderni processi produttivi ha innescato una profonda trasformazione nelle pratiche industriali globali. Questa tesi si concentra su queste trasformazioni nell'illustre panorama tedesco, pioniere nell'innovazione manifatturiera e nell'adozione di Industria 4.0. La tesi esplora il modo in cui la gestione dei dati e la personalizzazione influiscono sulla competitività dell'impresa grazie alla flessibilità operativa che comportano. L'obiettivo fondamentale di questa ricerca è duplice: esplorare l'importanza delle capacità di gestione dei dati nella produzione intelligente e il loro legame con le strategie di personalizzazione e la flessibilità operativa, e mostrare la loro influenza combinata sulla competitività delle aziende manifatturiere tedesche. Analizzando il ruolo della gestione strategica dei dati insieme a un'efficace personalizzazione, questo studio cerca di scoprire i meccanismi che alimentano una migliore competitività nel paradigma dell'industria 4.0. Con la rivoluzione dello stile manifatturiero dalla produzione di massa a quella personalizzata, la determinazione delle richieste e delle preferenze dei clienti è diventata progressivamente importante per i produttori. Verrà discusso l'effetto delle prestazioni della rete di dati di produzione per la personalizzazione di massa e le strategie di adattamento per diventare più flessibili e competitivi.The integration of Industry 4.0 technologies into modern manufacturing processes has ignited a profound transformation in global industrial practices. This thesis focuses on these transformations in Germany's distinguished landscape—an eminent pioneer in manufacturing innovation and Industry 4.0 adoption. It explores how data management and customization affect firm competitiveness by operational flexibility they bring with. The fundamental objective of this research is twofold: to explore the significance of data management capabilities in smart manufacturing and its link to customization strategies and operational flexibility, and to show their combined influence on firm competitiveness among German manufacturing firms. By dissecting the role of strategic data management in tandem with effective customization, this study endeavors to uncover the mechanisms that fuel improved competitiveness in the industry 4.0 paradigm. With the revolution in manufacturing style from mass to customized production, determining customer demands and preferences have grown gradually significant for the manufacturers. The effect of manufacturing data network performance for mass customization and adaptive strategies to become more flexible and competitive will be discussed

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

zfit: scalable pythonic fitting

Statistical modeling is a key element in many scientific fields and especially in High-Energy Physics (HEP) analysis. The standard framework to perform this task in HEP is the C++ ROOT/RooFit toolkit; with Python bindings that are only loosely integrated into the scientific Python ecosystem. In this paper, zfit, a new alternative to RooFit written in pure Python, is presented. Most of all, zfit provides a well defined high-level API and workflow for advanced model building and fitting, together with an implementation on top of TensorFlow, allowing a transparent usage of CPUs and GPUs. It is designed to be extendable in a very simple fashion, allowing the usage of cutting-edge developments from the scientific Python ecosystem in a transparent way. The main features of zfit are introduced, and its extension to data analysis, especially in the context of HEP experiments, is discussed.Comment: 12 pages, 2 figure

New Companies' DNA: the Heritage of the Past Industrial Revolutions in Digital Transformation

AbstractThis paper debates the ongoing processes of digital transformation enhanced by the 4th industrial revolution in successful companies which have a long time business history and an international presence. The aim is analyzing how what they have experienced and learned in the 2nd and 3rd industrial revolutions represents key elements that have shaped their DNA and continue to inspire their transformation to the point that the gradual radical change concept is preferred to the concept of disruption. Such learning processes are primary values to transfer in companies' digital transformation in order to implement it efficiently and effectively creating new opportunities at an international level. The paper leverages on an explorative research based on the empirical setting of three international Italian manufacturing companies that have experienced challenges of the 2nd, 3rd and are now facing an important digital transformation process in a worldwide landscape. A qualitative constructive multiple case research design is conducted to evaluate the conceptual model and some considerations emerged from the analysis. The paper also benefits from 30 years of experience of one of the Authors as Business Development Director of big multinational companies accompanying many enterprises during their digital transformation. This study presents an interpretative framework that provides an exploration of main heritages coming from the past industrial revolutions according to three aspects: business drivers, supply view and entrepreneurial model. The emerged evidences can be taken as lessons by international manufacturing companies in order to implement digital transformation enhancing their international competitiveness

Reconfigurable and transportable container-integrated production system

In this paper, the concept and the prototype realization of a novel reconfigurable small-footprint manufacturing system in a transportable container is presented. The containerized format enables transportation of the system to provide on-site manufacturing, enabling the benefits of localized service delivery without duplication of equipment at multiple locations. Three industrial product use cases with varying manufacturing and performance requirements were analyzed. All of the use cases demanded highly customized products with high quality in low production volumes. Based on their requirements, a general system specification was derived and used to develop a concept for the container-integrated factory. A reconfigurable, modular manufacturing system is integral to the overall container concept. Production equipment was integrated in the form of interchangeable process modules, which can be quickly connected by standard utility supply and control interfaces. A modular and self-configuring control system provides assisted production workflow programming, while a modular process chain combining Additive Manufacturing, milling, precision assembly and cleaning processes has been developed. A prototype of the container-integrated factory with reconfigurable process modules and control system has been established, with full functionality and feasibility of the system demonstrated

The redesign of blue- and white-collar work triggered by digitalization:collar matters

The implementation of digital technologies in the context of Industry 4.0 radically changes methods of production and thereby the jobs of blue-collar workers. Although the work design effects of digitalization on the operator 4.0 have been explored in the existing literature, less is known about the simultaneous effects on white-collar work and the underlying (re)design process of human work including the factors that shape this process. To address this gap, we performed an in-depth industrial case study of an organization in the process of digitalization. Our findings confirm the concurrent impact of digitalization on blue- and white-collar work and suggest that its human implications highly depend on the extent to which, and at what moment, human factors are considered during the design and implementation process. Where work design knowledge lacked, the motivation of system designers turned out to be an important individual factor to realize favorable work design outcomes. At the organizational level, results show the importance of early involvement of system users and incorporating social performance indicators in addition to operational performance indicators in the statement of project goals. Our findings provide important empirical input for the further development of human-centric models and theories that integrate the challenges and opportunities for blue- and white-collar workers that are emerging when adopting digital technologies

1st INCF Workshop on Sustainability of Neuroscience Databases

The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability