Impact of industry 4.0 tools in logistics : case analysis in Colombia

Este trabajo presenta un análisis de los escenarios de aplicación de las herramientas de la industria 4.0 (impresión 3D, computación en la nube, realidad aumentada (AR), Internet de las cosas (IoT) y robots autónomos) y su impacto en la logística a escala internacional con énfasis en el contexto colombiano, encontrando beneficios en términos de reducción de costos y tiempos, así como optimización de recursos y el aporte de dichas herramientas en la toma de decisiones dentro de las organizaciones. También se realizó un análisis de 43 empresas a nivel internacional que han implementado herramientas 4.0, señalando sus principales áreas de aplicación. Finalmente, se identificaron las áreas potenciales de aplicación de cada herramienta en Colombia, teniendo en cuenta las diferencias entre su nivel de desarrollo a nivel mundial y en el país.This work presents an analysis of the application scenarios of industry 4.0 tools (3D printing, cloud computing, augmented reality (AR), the internet of things (IoT) and autonomous robots) and their impact on logistics on an international scale with an emphasis in the Colombian context, finding benefits in terms of cost and time reduction as well as resource optimization and the contribution of said tools in decision-making within organizations. An analysis was also carried out of 43 companies at an international level that have implemented 4.0 tools, pointing out their main areas of application. Finally, the potential areas of application for each tool in Colombia were identified whilst keeping in mind the differences between their development level worldwide and in the country

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

Digital strategy implementation in process manufacturing firms: the Sirmax case.

The elaboration aims to investigate how to effectively implement a digital strategy in process manufacturing firms. After having analyzed literature and benchmark cases, the focus is on the digital strategy implementation proposal for Sirmax, a process manufacturing firm.ope

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

Exploring the transition from techno centric industry 4.0 towards value centric industry 5.0: a systematic literature review

This systematic literature review synthesises the literature on human centric IN 4.0 and IN 5.0 while exploring driving forces behind the transition from technocentric IN 4.0 to value centric IN 5.0 using the principles of the multiple level perspective (MLP). Works that discuss contextual, regime and niche level factors which impact on the transition were explored. The Covid- 19 pandemic and Climate change are identified as key contextual, ‘Landscape’, factors impacting the transition while Trust, Mass personalisation and Autonomy are highlighted as key Regime factors. In terms of Niche innovations, Advanced Extended reality technologies, Cobots/ Advanced Robotics, and Advanced AI are often connected with landscape or regime issues. Drawing on MLP theory, the study demonstrates that the transition from IN 4.0 towards IN 5.0 is occurring through a reconfiguration pattern. The paper further emphasises aspects that both practitioners and academics need to be cognisant of in order to affect a transition from IN 4.0 to IN 5.0