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.
Zusammenfassung: Die intelligente Steuerung von Supply Chains ist eine wesentliche Voraussetzung für die Erschließung der Potenziale neuer digitaler Technologien. Im folgenden Beitrag wird Smart Logistics als Komponente von Industrie 4.0 und in ihren Zusammenhängen zu anderen Komponenten dargestellt. Es werden Technologiekonzepte der Smart Logistics beschrieben. Darauf aufbauend werden Nutzungspotenziale und mögliche weitere Entwicklungen analysiert. Als Ergebnis eines internationalen Projektes werden weiters die Voraussetzungen dargestellt, die in Unternehmen für die Einführung von Smart Logistics gegeben sein müssen.
Cloud Computing is one of the most significant trends in IT. With every organization in this competitive world looking for more efficient supply chain management, cloud computing contributes to making processes more efficient. This paper outlines the benefits of cloud solutions for logistics and supply chain management and also the key factors influencing the cloud adoption by enterprises. Implementation is not an easy task, so procedures and tools are required for successful introduction with minimal risks to organizations. Enterprises have to use efficient decision models, risk assessment models, and risk optimization models which will help decision makers to determine the optimal safety investments to minimize the cost of failures.
Industry 4.0 approaches have gained increasing relevance and impact on logistics research and practical applications. However, logistics research often focuses on the investigation of isolated concepts, which leads to a systematic neglect of more holistic research frameworks. Therefore, this paper conceptualises Smart Logistics as an important element within the context of Industry 4.0 approaches. Furthermore, a set of technological concepts for Smart Logistics is identified and potential applications are outlined and discussed. Moreover, the paper presents recent developments in the area of Smart Logistics based on both primary and secondary data analyses and recommends further directions for future research efforts.
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