The R&D output and global commercialization of intellectual properties (IPs), especially patents filed in many countries, have increased dramatically over the past decade. The overwhelming growth in research and IP activities has led to a major challenge to understand and forecast technology development insights and trends. Evidence-based data analytics is essential for technology mining. The assessment of patent values is a critical aspect of technology mining, which remains a highly subjective task performed by domain experts. As businesses become globalized, subjectivity in underlying assessments of large volumes of patent documents leads to overpriced or undervalued IP sales or licensing that exposes stakeholders to legal and financial risks. Thus, the development of intelligent methods for patent valuation requires new research emphasis. This article applies a deep learning analytical method for automatic and intelligent patent value estimation. Principal component analysis (PCA) is used to identify significant patent value indicators from the given patent dataset. Then, deep neural networks (DNN) for value prediction are modeled and trained using the training set. A detailed case study of 6466 manufacturing Internet of Things (IoT) patents is analyzed to demonstrate the improved results of building PCA-preprocessed DNN models to perform patent valuations. Finally, selected higher value IoT patents owned by leading Taiwan assignees are identified and analyzed to verify the technological competitive intelligence.

Purpose — This research aims to evaluate the current adoption of Industry 4.0, enabling technologies (I4.0-ET) in the manufacturing and supply chain management (SCM) context. The evaluation provides insights on critical concepts, current status, and adoption challenges. Further, a summation of underexplored research areas in the I4.0-ET context is presented. Methodology — This study applies a systematic literature review (SLR) approach to present essential literature across multiple databases. The search scope includes Scopus, Web of Science (WoS), Science Direct, EBSCO, and ProQuest. Research publications have systematically been selected with a focus on the adoption of I4.0-ET to provide collective insights through theoretical synthesis into fields and sub-fields. The selected publications are further classified into five groups depending on relevance to manufacturing and SCM to aid additional visualizations. Findings — The study reveals 65% of the articles published between the year 2015 to 2019. An indepth analysis of these publications shows the adoption of I4.0-ET in the manufacturing and supply chain sector gained attention in recent years and is still at a nascent stage. There are substantial challenges that need to be addressed to accelerate adoption. These challenges are discussed in detail as avenues for future research. Practical Implications — This study facilitates practicing managers towards enabling technologies concepts, challenges, and risks linked with its adoption in manufacturing and SCM. The discussions help frame strategies to prioritize efforts for I4.0-ET incorporation. Originality — This study is among the first to provide theoretical contributions by performing indepth analysis and classification of existing literature on the adoption of I4.0-ET in manufacturing and SCM context.

Immersive technology for human‐centric cyberphysical systems includes broad concepts that enable users in the physical world to connect with the cyberworld with a sense of immersion. Complex systems such as virtual reality, augmented reality, brain‐computer interfaces, and brain‐machine interfaces are emerging as immersive technologies that have the potential for improving manufacturing systems. Industry 4.0 includes all technologies, standards, and frameworks for the fourth industrial revolution to facilitate intelligent manufacturing. Industrial immersive technologies will be used for smart manufacturing innovation in the context of Industry 4.0’s human machine interfaces. This research provides a thorough review of the literature, construction of a domain ontology, presentation of patent metatrend statistical analysis, and data mining analysis using a technology function matrix and highlights technical and functional development trends using latent Dirichlet allocation (LDA) models. A total of 179 references from the IEEE and IET databases and 2,672 patents are systematically analyzed to identify current trends. The paper establishes an essential foundation for the development of advanced human‐centric cyberphysical systems in complex manufacturing processes.

Purpose: The research article's primary purpose is to understand the advancements in urban logistics and allied fields over time along with a consideration of its enabling technologies.Design/methodology/Approach: An initial review is used to build a keyword vocabulary, combinations of which were then applied to the Scopus, ScienceDirect, Emerald Insights, the Web of Science (WOS), Elsevier, Taylor and Francis, Wiley, Inderscience, Springer, Google Scholar and IEEE Xplore for extracting academic publication collection. The first part includes bibliometric analysis; network analysis is done based on the finally selected 645 papers (only those articles include either of the keywords mentioned above in title, abstract, and keywords). The second part conducts a review of the existing literature review studies (only 21 literature review studies out of 645 articles). The last one discusses the advancement in the topics based on the selected research articles.Findings: This research discussed the advancement of the urban logistics and allied field, key academic forums and key researchers. It is evident from the analysis that the research related to key emerging themes like implementing innovative concepts and sustainability; application of green technologies; data collection, visualization, monitoring and sharing; and automatic logistic systems are still in the nascent stage. However, these research areas gained momentum in the recent past.Research limitations: Urban logistics are essential and play a crucial role for such rapidly growing cities to function. Despite playing a vital role, urban ecosystem logistics is often neglected in formal urban planning. Hence, as a response to customer and business demand, private entities regularly invest in new technologies and solutions. Since such investments are toward profits, various environmental, social and economic challenges arise.Originality/value: This research investigates the advancements in urban logistics toward smart, sustainable reforms in developing enabling technologies and markets. The obtained research articles are subjected to bibliometric, descriptive, network and content analysis to present a rundown of advancements, relationships and trends in emerging research gaps.