A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics

Lēmumu pieņemšanas procesu brieduma ietekme uz lēmumu pieņemšanas efektivitāti

Lēmumu pieņemšanu var uzskatīt par vadības zinātnes un vadības prakses galveno daļu. Tomēr joprojām trūkst izpratnes par to, kuriem lēmumu pieņemšanas procesa panākumu faktoriem ir galvenā loma lēmumu pieņemšanas rezultātu uzlabošanā. Tādēļ šajā promocijas darbā pētīta galveno lēmumu pieņemšanas procesa panākumu faktoru, kurus definē kā lēmumu pieņemšanas procesa briedumu, ietekmi uz lēmumu pieņemšanas rezultātiem, kurus definē kā lēmumu pieņemšanas efektivitāti, pievēršoties stratēģisko piegādātāju atlases procesam ražošanas uzņēmumos kā uzdevuma piemēram biznesa vadības lēmumu pieņemšanā. Turklāt, šajā pētījumā analizēta uzņēmuma iekšējo noteicošo faktoru starpniecības ietekme uz stratēģisko piegādātāju atlases procesuDecision making can be considered as a core part of management science and management practice. However, there still is a lack of understanding as to which major success factors in the decision making process will ultimately lead to better decision making outcomes. In this context, the thesis investigates the impact of the major success factors in the decision making process, defined as the decision making process maturity, on the decision making outcomes, defined as the decision making efficiency, by focusing on the strategic supplier selection process in manufacturing enterprises as an exemplary task of decision making in business management. Moreover, this research analyses the moderating effects of company-internal determinants in the strategic supplier selection process

Lēmumu pieņemšanas procesu brieduma ietekme uz lēmumu pieņemšanas efektivitāti

Lēmumu pieņemšanu var uzskatīt par vadības zinātnes un vadības prakses galveno daļu. Tomēr joprojām trūkst izpratnes par to, kuriem lēmumu pieņemšanas procesa panākumu faktoriem ir galvenā loma lēmumu pieņemšanas rezultātu uzlabošanā. Tādēļ šajā promocijas darbā pētīta galveno lēmumu pieņemšanas procesa panākumu faktoru, kurus definē kā lēmumu pieņemšanas procesa briedumu, ietekmi uz lēmumu pieņemšanas rezultātiem, kurus definē kā lēmumu pieņemšanas efektivitāti, pievēršoties stratēģisko piegādātāju atlases procesam ražošanas uzņēmumos kā uzdevuma piemēram biznesa vadības lēmumu pieņemšanā. Turklāt, šajā pētījumā analizēta uzņēmuma iekšējo noteicošo faktoru starpniecības ietekme uz stratēģisko piegādātāju atlases procesuDecision making can be considered as a core part of management science and management practice. However, there still is a lack of understanding as to which major success factors in the decision making process will ultimately lead to better decision making outcomes. In this context, the thesis investigates the impact of the major success factors in the decision making process, defined as the decision making process maturity, on the decision making outcomes, defined as the decision making efficiency, by focusing on the strategic supplier selection process in manufacturing enterprises as an exemplary task of decision making in business management. Moreover, this research analyses the moderating effects of company-internal determinants in the strategic supplier selection process

The adoption of industrial logistics decarbonization practices: Evidence from Austria

Considering the increasing awareness of customers, shareholders, and employees for climate change, European firms face a changing institutional environment and increased pressures to decarbonize their operations. Although this is already common practice in many areas, the emissions of in- and outbound logistics are still not shrinking. To develop effective novel measures, researchers and practitioners need a clear picture of the state-of-the-art. Currently, a scattered landscape of studies researching the utilization of green logistics practices in Europe exists. By surveying Austrian practitioners, we contribute to this landscape and enhance knowledge on the utilization of green measures, experts’ perceptions of them, and discriminating factors for their implementation. Results indicate that the writing is on the wall for emission reductions in the near future due to several aspects. We elaborate on these, provide benchmarking possibilities for Austrian practitioners and indicate which managerial factors to concentrate on when aiming at decarbonizing logistics. For researchers, the presentation of the state-of-the-art and its discussion highlights future research directions and questions, among them the question of why the perceived potentials of measures differ from scientifically validated potentials and how to align this knowledge gap

State of the Art and Future Directions of Digital Twins for Production Logistics: A Systematic Literature Review

Digital Twins (DTs) are widely discussed in the context of the Industry 4.0 paradigm as one of the main opportunities to strengthen the overall competitiveness of manufacturing enterprises. Despite a substantial scientific discussion, there is still no unified understanding regarding the constitution and subsequent usage of DTs within production logistics systems. Therefore, this paper focuses on the application of DTs in production logistics. The authors discuss common definitions, characteristics, and functionalities of DTs and outline current developments and implications from state-of-the-art implementation approaches, by using a systematic literature review. Moreover, based on the research findings, the authors evaluate a set of DT case studies, identify current research gaps, and present potential directions for future research initiatives regarding the field of production logistics in manufacturing enterprises

Identifying a Country’s Freight Transport-Intensive Economic Sectors and Their Logistics Emissions—Method Development and Exemplary Evaluation with Austria

It is unequivocal that global greenhouse gas emissions must be reduced drastically. One opportunity to quickly achieve deep emission reductions is by investigating the largest emitters first. This can be based on countries but also on the underlying sectors of local economies. Focusing on the latter, the transport and industry sectors stand out, as well as their overlap, which is reflected in the emissions from freight transport. To enable legislators and researchers to focus on the major emitters in freight transport and to develop tailored sectoral measures, we present a method to identify the transport-intensive sectors of a country. A two-part approach thereby makes it possible to identify these sectors and their value chains and to analyze the different emission structures of companies between the sectors. This suggests the relevance of decarbonizing transport from a company’s perspective and helps to understand the entrenched situation. Finally, the methodology is applied to the Austrian transport industry as an example to demonstrate its applicability. As applied research in this area has lagged somewhat, our results can provide managers in transport-intensive economic sectors with new motivation to decarbonize logistics, as well as guide policymakers and researchers on which sectors to focus first

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics

Logistics 4.0 measurement model : empirical validation based on an international survey

Funding Information: Funding: This work was supported by the European Union's Horizon 2020 research and innovation program under the Marie SkłodowskaCurie grant agreement No. 734713 (Project title: SME 4.0—Industry 4.0 for SMEs). Publisher Copyright: © 2022, Patrick Dallasega, Manuel Woschank, Joseph Sarkis and Korrakot Yaibuathet Tippayawong.Purpose: This study aims to provide a measurement model, and the underlying constructs and items, for Logistics 4.0 in manufacturing companies. Industry 4.0 technology for logistics processes has been termed Logistics 4.0. Logistics 4.0 and its elements have seen varied conceptualizations in the literature. The literature has mainly focused on conceptual and theoretical studies, which supports the notion that Logistics 4.0 is a relatively young area of research. Refinement of constructs and building consensus perspectives and definitions is necessary for practical and theoretical advances in this area. Design/methodology/approach: Based on a detailed literature review and practitioner focus group interviews, items of Logistics 4.0 for manufacturing enterprises were further validated by using a large-scale survey with practicing experts from organizations located in Central Europe, the Northeastern United States of America and Northern Thailand. Exploratory and confirmatory factor analyses were used to define a measurement model for Logistics 4.0. Findings: Based on 239 responses the exploratory and confirmatory factor analyses resulted in nine items and three factors for the final Logistics 4.0 measurement model. It combines “the leveraging of increased organizational capabilities” (factor 1) with “the rise of interconnection and material flow transparency” (factor 2) and “the setting up of autonomization in logistics processes” (factor 3). Practical implications: Practitioners can use the proposed measurement model to assess their current level of maturity regarding the implementation of Logistics 4.0 practices. They can map the current state and derive appropriate implementation plans as well as benchmark against best practices across or between industries based on these metrics. Originality/value: Logistics 4.0 is a relatively young research area, which necessitates greater development through empirical validation. To the best of the authors knowledge, an empirically validated multidimensional construct to measure Logistics 4.0 in manufacturing companies does not exist.Peer reviewe

Big Data in the Metal Processing Value Chain: A Systematic Digitalization Approach under Special Consideration of Standardization and SMEs

Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition, analysis, and utilization of this key technology can be defined as a driver for decision-making support, process and operation optimization, and therefore increase the efficiency and effectiveness of a complete manufacturing site. Furthermore, if corresponding interfaces within the supply chain can be connected within a reasonable effort, this technology can boost the competitive advantage of all stakeholders involved. These developments face some barriers: especially SMEs have to be able to be connected to typically more evolved IT systems of their bigger counterparts. To support SMEs with the development of such a system, this paper provides an innovative approach for the digitalization of the value chain of an aluminum component, from casting to the end-of-life recycling, by especially taking into account the RAMI 4.0 model as fundament for a standardized development to ensure compatibility within the complete production value chain. Furthermore, the key role of Big Data within digitalized value chains consisting of SMEs is analytically highlighted, demonstrating the importance of associated technologies in the future of metal processing and in general, manufacturing