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Even though the topic of Industry 4.0 in the last decade has attracted significant and multifarious attention from academics and practitioners, a structured and systematic review of Industry 4.0 in the context of contemporary logistics is currently lacking. This study attempted to address this shortcoming by performing a systematic review of the available literature of Industry 4.0 in the logistics context. To that end, and after a systematic inclusion/exclusion process, 65 carefully selected papers were addressed in the study. The results obtained from this study were illustrated and discussed in order to provide answers to two research questions pre-defined by the authors. In essence, this study identified emerging aspects and present trends in the area, addressed the main technological developments and evolution of Industry 4.0 and their impact for contemporary logistics, and finally pinpointed literature shortcomings and currently under-explored areas with a high potential for impactful future research. Findings of this review can hopefully be used as the basis for future research in the emerging Logistics 4.0 concept and related topics.

During the last decade, digitalization has borne tremendous changes on the way we live and do business. Industry 4.0, the new industrial revolution, is merging the physical, digital and virtual worlds through emerging technologies that collide with each other and create a distinctive paradigm shift. Even though the topic of Industry 4.0, has attracted significant attention during the past few years, literature in this subject area is still limited. The main objective of this paper is to study the current state of the art and identify major trends and research shortcomings. To that end, the authors conducted a methodological literature review based primarily on the SCOPUS bibliographic database. The review returned 49 relative papers dealing with the paper’s subject area. Through a thorough study of the selected papers, four dominant literature categories were recognized and discussed in detail. According to the literature reviewed, it is evident that massive changes are underway for warehouses and intralogistics facilities. Still, despite the intense discussion and appeal of the subject, one of the most important challenges in the scientific area under study, as the literature highlights, is the absence of a matching, to its significance, number of real-life applications. To that end, this paper provides a detailed description of a Cloud-based IoT application drawn from a Distribution Center (DC) that supplies retail home furnishing and sporting goods products to stores in Greece and the Balkan region, with the objective to showcase the feasibility of such an investment, highlight its potential and provide motivation to practitioners to evaluate and proceed in similar technological investments.

AbstractForecasting the demand of network of retail sales is a rather challenging task, especially nowadays where integration of online and physical store orders creates an abundance of data that has to be efficiently stored, analyzed, understood and finally, become ready to be acted upon in a very short time frame. The challenge becomes even bigger for added-value third party logistics (3PL) operators, since in most cases and demand forecasting aside, they are also responsible for receiving, storing and breaking inbound quantities from suppliers, consolidating and picking retail orders and finally plan and organize shipments on a daily basis. This paper argues that data analytics can play a critical role in contemporary logistics and especially in demand data management and forecasting of retail distribution networks. The main objective of the research presented in this paper is to showcase how data analytics can support the 3PL decision making process on replenishing the network stores, thus improving inventory management in both Distribution Centre (DC) and retail outlet levels and the workload planning of human resources and DC automations. To do so, this paper presents the case of a Greek 3PL provider fulfilling physical store and online orders on behalf of a large sporting goods importer operating a network of 129 stores in five different countries. The authors utilize the power of ‘R’, a statistical programming language, which is well-equipped with a multitude of libraries for this purpose, to compare demand forecasting methods and identify the one producing the smallest forecast error.

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