Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

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

Smart lighting systems : state-of-the-art and potential applications in warehouse order picking

Artificial lighting is a constant companion in everyday private and working life, influencing visibility in interior spaces as well as outdoors. In recent years, new technical solutions have extended traditional lighting systems to become ‘smart’. Different types of smart lighting systems are available on the market today, and researchers have concentrated on analysing their usability and efficiency, especially for private households, office buildings and public streets. This paper presents a systematic literature review to analyse the state-of-knowledge of technologies and applications for smart lighting systems. The results of the review show that smart lighting systems have been frequently discussed in the literature, but that their potentials in industrial environments, such as production and logistics, has rarely been addressed in the literature so far. Lighting systems for industrial environments often have very different requirements depending on the working environment and operating conditions. Based on the results of the literature review, this paper contributes to closing this research gap by discussing the usage potential of smart lighting systems to improve the efficiency of warehouse order picking, which is an application that may benefit from various functions smart lighting systems provide. Several propositions are developed that emphasise research opportunities and managerial implications in this context

Smart Trolley Apps: A Solution To Reduce Picking Error

An order picking activities refers to an act of retrieving any items from the storage locations in the warehouses. In common situation, these activities is often performed by human. Due to that condition, high human error and high cost impact were spotted on a manual order picking activities. Thus, previous studies have developed various methods to support the practitioners especially in creating a more efficient order picking process. In spite of the vast discussion and evidence that shows an order pickers tend to deviate from its optimal routes and putting the efficiency of these routing approaches at stake, very little discussion were focus on the implementation of smart application through IT usage and device to reduce the problem faced in the warehouse. Thus, it is the main intention of this this paper to presents a detailed analysis on the relative factors affecting the efficiency of order picking activities in the warehouse and suggest the smart trolley as a solution to overcome the problem. The smart trolley apps is then proposed to increase the picking process in warehouse. The results of this paper indicate that extensive use of smart trolley apps as a solution to a more effective ways of order picking

Smart and efficient: Learning curves in manual and human-robot order picking systems

Order picking has been identified as the most labour-intensive, as well as costly activity within warehouse logistics and is experiencing significant changes due to new technologies in the forms of artificial intelligence (AI) and automation. One fundamental question concerns the employees learning progress in human-robot picking systems compared to existing manual technologies. Therefore, this paper presents an empirical analysis of learning curves in manual pick-by-voice (n=30 pickers) and semiautomated (n=20 pickers) order picking. Aspiring to measure the individual learning progress without a priori assumptions, this publication is the first to apply Data Envelopment Analysis and examine order pickers learning curves in real application scenarios. The findings indicate that automating human work accelerates the individual learning progress in human-robot picking systems

real time assistance to manual assembly through depth camera and visual feedback

Abstract The current fourth industrial revolution significantly impacts on production processes. The personalized production paradigm enables customers to order unique products. The operators assemble an enormous component variety adapting their process from product to product with limited learning opportunities. Digital technologies are increasingly adopted in production processes to improve performance and quality. Considering this framework, this research proposes a hardware/software architecture to assist in real-time operators involved in manual assembly processes. A depth camera captures human motions in relation with the workstation environment whereas a visual feedback guides the operator through consecutive assembly tasks. An industrial case study validates the architecture

The Clean Energy Economy: Repowering Jobs, Businesses and Investments Across America

Examines U.S. and state-by-state clean energy policies and trends in and potential for growth in jobs, businesses, and investments. Explores policy options for a comprehensive plan, including financial incentives and energy efficiency standards

Case Study of Intralogistics in the Framework of Logistics 4.0

Industry 4.0 has led to changes that have reduced the labor force and created production environments where machines that bring together information technology and industry communicate with each other. Logistics 4.0, which emerged with Industry 4.0, paved the way for improvement in logistics processes. Using information technologies in logistics reduces the labor force costs of enterprises by leading all stages of activities to digitalization. It can be possible to increase customer satisfaction and product quality by reducing human failures with digitalization. This study was performed by planning intralogistics using Logistics 4.0 technological tools, and also the problem of a manufacturing company was elaborated as a case study. This study was carried out by quantitative data analysis in the case study and a large-scale production company in the automotive industry in Turkey providing the intralogistics of the materials from the supplier in the entrance warehouse with RFID (Radio Frequency Identification) technologies. This paper presents the research, development, and application of logistics 4.0 in the intralogistics process from the entrance warehouse to the production lines. The aim of the case study was provided to information about the technologies available within the scope of Logistics 4.0 and contribute to the literature and industry with solution suggestions depending on the result of the application study within the logistics operations. As a result, depending on the case study, it was determined that Logistics 4.0 improved intralogistics operations costs by 13.37%

How can we make sense of smart technologies for sustainable agriculture? - A discussion paper

This paper discusses the challenges of assessing the benefits and risks of new digital technologies, so-called ‘smart technologies’ for sustainable agri-food systems. It builds on the results of a literature review that was embedded in a wider study on future options for (sustainable) farming systems in Germany. Following the concepts of Actor-Network-Theory, we can conceive of smart technologies in agriculture as networks that can only be understood in their entirety when considering the relationships with all actors involved: technology developers, users (farmers, consumers and others), data analysts, legal regulators, policy makers, and potential others. Furthermore, interaction of the technology and its implementers with nature, such as plants, entire landscapes, and animals, need to be taken into consideration. As a consequence, we have to deal with a highly complex system when assessing the technology – at a time where many of the relevant questions have not been sufficiently researched yet. Building on the FAO’s SAFA guidelines, the paper outlines criteria against which smart technologies could be assessed for their potential to contribute to a sustainable development of agri-food systems. These include aspects of governance, ecology, economy and social issues. We draw some tentative conclusions on the required framework conditions for implementation of digital technology, in particular from the perspective of sustainable agriculture. These are aimed at fuelling further discussion about the potentials and risks of the technology