Feasibility of Warehouse Drone Adoption and Implementation

While aerial delivery drones capture headlines, the pace of adoption of drones in warehouses has shown the greatest acceleration. Warehousing constitutes 30% of the cost of logistics in the US. The rise of e-commerce, greater customer service demands of retail stores, and a shortage of skilled labor have intensified competition for efficient warehouse operations. This takes place during an era of shortening technology life cycles. This paper integrates several theoretical perspectives on technology diffusion and adoption to propose a framework to inform supply chain decision-makers on when to invest in new robotics technology

Water demand estimation and outlier detection from smart meter data using classification and Big Data methods

Automatic Meter Reading (AMR) systems are being deployed in many cities to obtain insight into the status and the behavior of District Metering Area (DMA) with more granularity. Until now, the water consumption readings of the population were taken one per month or one each two-months. In contrast, AMR systems provide hourly readings for households and more frequent readings for big consumers. On the one hand, this paper aims at predicting water demand and detect suspicious behaviors – e.g. a leak, a smart meter break down or even a fraud – by extracting water consumption patterns. On the other hand, the main contribution of this paper, a software framework, based on Big Data techniques, is presented to tackle the barriers of traditional data storage and data analysis since the volume of AMR data collected by Water Utilities is enormous and it is continuously growing because this technology is expanding .Peer ReviewedPostprint (author’s final draft

Supply Chain 4.0: Autonomous Vehicles and Equipment to Meet Demand

The term Supply chain 4.0 refers to the application of industry 4.0 technologies to the supply chain, aiming to plan with greater efficiency and better to meet the demand. Considering this reality, the study aims to verify which equipment and vehicles are being applied and which one presents the best benefits to each stage of the supply chain demand. To define the vehicles and equipment to be analyzed, were presented a supply chain process model, divided among industry, warehouses and customer. Thus, each ones were characterized and the best equipment could be adopted more precisely. The vehicles and equipment were analyzed, considering as the main aspects the maintenance cost, security, operation, product handling, delivery time and sustainability. The results show that the main vehicles to be applied are automated guided vehicles, autonomous trains and drones, each one being applied in different processes of the supply chain

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201

Smart supply chain management in Industry 4.0

The emerging information and communication technologies (ICT) related to Industry 4.0 play a critical role to enhance supply chain performance. Employing the smart technologies has led to so-called smart supply chains. Understanding how Industry 4.0 and related ICT affect smart supply chains and how smart supply chains evolve with the support of the advanced technologies are vital to practical and academic communities. Existing review works on smart supply chains with ICT mainly rely on the academic literature alone. This paper presents an integrated approach to explore the effects of Industry 4.0 and related ICT on smart supply chains, by combining introduction of the current national strategies in North America, the research status analysis on ICT assisted supply chains from the major North American national research councils, and a systematic literature review of the subject. Besides, we introduce a smart supply chain hierarchical framework with multi-level intelligence. Furthermore, the challenges faced by supply chains under Industry 4.0 and future research directions are discussed as well

Big Data IoT-based Agile-Lean Logistic in Pharmaceutical Industries

Purpose: In today’s world, with the presence of huge volumes of data, although organizations have faced many problems, using big data analysis has been able to significantly improve their efficiency and integrate information in the supply chain through the topic of computing. Cloud and big data achieve coordination between components and improve communication. On the other hand, Internet of Things (IoT) technology tools are one of the most important sources of big data production, and understanding and correct use of this data and their timely analysis using big data analysis techniques and technologies based on artificial intelligence can be effective steps to improve supply chain processes. Also, the use of these technologies can play an important role in process agility and, as a result, supply chain resilience. Methodology: In this study, the dimensions and key components of the use of large data obtained from the Internet of Things (IoT) in an industry's supply chain are investigated as a case study. Finally, a model for implementing an agile and lean supply chain based on IoT data analysis to improve the supply chain performance of these industries during emergency drug distribution during critical conditions is presented. Findings: This study shows that these technologies can be used as a powerful enabler, especially in the distribution of fast-acting pharmaceutical products.                                                                         Originality/Value: In this paper a model for implementing an agile and lean supply chain based on IoT data analysis to improve the supply chain performance of these industries during emergency drug distribution during critical conditions is presented

Reviewing the Challenges of Big Data Use in Smart Industries

Purpose: In recent years, data has been growing on a large scale, and the development of Internet applications, mobile applications, and network-connected sensors has also increased dramatically. These programs and extensive Internet communications continuously generate large volumes of data that are diverse and structurally different, called big data. Methodology: This article first reviews big data and defines its features, and then discusses the challenges it faces in the smart industry. Finally, using fuzzy hierarchical analysis, the most important challenges of using big data in smart industries have been prioritized. Findings: Due to the increasing volume of information transfer in the space of industrial generation, big data problem, import and storage of large volume of data information items and its management, preprocessing and post-processing, speed, accuracy and security of information are very important. It has gained a lot of attention and has attracted the attention of many researchers and experts in the field of information technology and active in the industry. Originality/Value: This article review the challenges of big data use in smart industries