Industry 4.0 and the circular economy: Resource melioration in logistics

Logistics is a driver of countries’ and firms’ competitiveness and plays a vital role in economic growth. However, the current logistics industry still faces high costs and low efficiency. The development of smart logistics brings opportunities to solve these problems. As one of the important technologies of the modern information and communication technology (ICT), the Internet of Things (IoT) can create oceans of data and explore the complex relationships between the transactions represented by these data with the help of various mathematical analysis technologies. These features are helpful to promote the development of smart logistics. In this article, we provide a comprehensive survey on the literature involving IoT technologies applied to smart logistics. First, the related work and background knowledge of smart logistics are introduced. Then, we highlight the enabling technologies for IoT in smart logistics. Furthermore, we review how IoT technologies are applied in the realm of smart logistics from the perspectives of logistics transportation, warehousing, loading/unloading, carrying, distribution processing, distribution, and information processing. Finally, some challenges and future directions are discussed.

Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey

Tactile internet and its applications in 5G era: A comprehensive review

The workplace influences the safety, health, and productivity of workers at multiple levels. To protect and promote total worker health, smart hardware, and software tools have emerged for the identification, elimination, substitution, and control of occupational hazards. Wearable devices enable constant monitoring of individual workers and the environment, whereas connected worker solutions provide contextual information and decision support. Here, the recent trends in commercial workplace technologies to monitor and manage occupational risks, injuries, accidents, and diseases are reviewed. Workplace safety wearables for safe lifting, ergonomics, hazard identification, sleep monitoring, fatigue management, and heat and cold stress are discussed. Examples of workplace productivity wearables for asset tracking, augmented reality, gesture and motion control, brain wave sensing, and work stress management are given. Workplace health wearables designed for work‐related musculoskeletal disorders, functional movement disorders, respiratory hazards, cardiovascular health, outdoor sun exposure, and continuous glucose monitoring are shown. Connected worker platforms are discussed with information about the architecture, system modules, intelligent operations, and industry applications. Predictive analytics provide contextual information about occupational safety risks, resource allocation, equipment failure, and predictive maintenance. Altogether, these examples highlight the ground‐level benefits of real‐time visibility about frontline workers, work environment, distributed assets, workforce efficiency, and safety compliance.

Trends in Workplace Wearable Technologies and Connected-Worker Solutions for Next-Generation Occupational Safety, Health, and Productivity

The global collaboration and integration of online and offline channels have brought new challenges to the logistics industry. Thus, smart logistics has become a promising solution for handling the increasing complexity and volume of logistics operations. Technologies, such as the Internet of Things, information communication technology, and artificial intelligence, enable more efficient functions into logistics operations. However, they also change the narrative of logistics management. Scholars in the areas of engineering, logistics, transportation, and management are attracted by this revolution. Operations management research on smart logistics mainly concerns the application of underlying technologies, business logic, operation framework, related management system, and optimization problems under specific scenarios. To explore these studies, the related literature has been systematically reviewed in this work. On the basis of the research gaps and the needs of industrial practices, future research directions in this field are also proposed.

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Operations management of smart logistics: A literature review and future research

Smart Connected Logistics

Nowadays, when high industrial productivity is connected with high quality and low product faults, it is common practice to use 100% product quality control. Since the quantities of products are high in mass production and inspection time must be as low as possible, the solution may be to use visual inspection of finished parts via camera systems and subsequent image processing using artificial intelligence. Recently, deep learning has shown itself to be the most appropriate and effective method for this purpose. The present article deals with the above-mentioned method of deep learning, and especially with its application when recognizing certain objects and elements during the visual product inspection.

Visual Product Inspection Based on Deep Learning Methods

This paper presents an approach for production costs estimation at the stage of machine elements design, based on the variant set of cost drivers and the corrected values of this set. The proposed costing method is based on a formalised description of information about constructional, manufacturing and organisational features related to the designed element, and a model of estimating production costs of machine elements based on Activity Based Costing, and is adjusted to unit and small lot production systems.

Production Costs of Machine Elements Estimated in the Design Phase

Abstract One of the rationalization areas is the material supply process. New tasks of material supply are: integration of supply with the operation of the entire system, especially at the product design stage, effective use of material resources, reduction of the level of supply costs from the point of view of production costs. In the conditions of unit and small batch production the materials requirement needs to be carefully planned and optimized due to the use of many different kinds, types and sizes of materials and dynamic changes of demand over time. In this situation there are needed tools enabling the measurement of production costs for particular tasks on the basis of currently realized processes. One of these tools is activity based costing which is a groundwork for decision making process in the material supply area. Basing on activity based costing, a model of materials requirements planning was developed, which considers minimizing the number of different kinds and sizes of materials by using alternative materials and, consequently, lowering production costs.

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Materials Requirement Planning with the Use of Activity Based Costing

Abstract On the stage of product design there is a problem concerning production cost estimation in the moment when elements are not yet definitely designed. Depending on the amount of the available information, more or less precise cost estimation methods are applied, i.e.: variant methods, generation methods, hybrid methods. The proposed method of cost estimation is based on a formalized description of information related to construction, manufacturing and organizational characteristics concerning the designed element, the automation method of technological processes design using methods of group technology and a model of determining production costs of machine elements based on Activity Based Costing.

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Dariusz Więcek

Papers

Smart Connected Logistics

Visual Product Inspection Based on Deep Learning Methods

Production Costs of Machine Elements Estimated in the Design Phase

Materials Requirement Planning with the Use of Activity Based Costing

Cost Estimation Methods of Machine Elements at the Design Stage in Unit and Small Lot Production Conditions