The applications of remote sensing technologies to precision agriculture were reviewed. The different uses of conventional satellites and unmanned aerial vehicles (UAVs) for data collection were also examined. The ways in which the collected data have been used in terms of precision agriculture are laid out in further detail. Modern UAVs provide a range of advantages over conventional satellites in data collection and analysis. In addition, precision agriculture itself is a wide topic; and practitioners, researchers, and policy planners need to account for a complex value chain with diverse stakeholders. Thus, in order to further the discussion in a meaningful manner, the later sections of this article provide additional analysis on topics deemed crucial for the development of precision agriculture: a discussion on key technological limitations for scaling, a corresponding list of suggestions for the development of solutions, and a brief overview on the current social-economical landscape of the agriculture industry.

This study examined the association between digital transformation (DT), supply chain integration (SCI), and overall sustainable supply chain performance (OSSCP). The current literature has preliminarily explored the concepts of DT and SCI and their relationship with sustainable supply chain performance. However, real empirical evidence of the direct impact of DT and SCI on OSSCP has been missing so far. To fill this gap, data were collected from 134 professionals working in international manufacturing companies operating in Morocco through a questionnaire-based survey from August 2022 to November 2022. A conceptual framework was developed based on DT, SCI, and OSSCP and analyzed by partial least squares structural equation modeling (PLS-SEM) with the assistance of SmartPLS 4.0 software. The findings revealed that DT has a significant positive influence on SCI and OSSCP. Furthermore, SCI directly and positively impacts OSSCP with a partial mediation effect on the relationship between DT and OSSCP. Further, this research provides insights for practitioners into enhancing sustainable supply chain performance by adopting digital technologies and integrating SC functions. In particular, this study revealed that DT adoption drives a higher ethical supply chain level from the perspective of sustainability and efficiency in operations. This study is the first to analyze the influence of digital transformation and supply chain integration on sustainable supply chain performance in a manufacturing context.

This paper explores how Artificial Intelligence (AI) techniques integration into circular economy (CE) in product design phase. Designing products for circularity is a phenomenon, which is rising in manufacturing industries to develop and improve product sustainability as well as to drive extra cost prevention mechanisms in production. Circular product design strategies and similarly some guides have been developed by academia to help designers in industries to design products which match best with characteristics of circular products. In addition, implementation of AI technologies in CE solutions increases productivity through higher optimization and real-time data analysis. This research used qualitative research methodology as two stage process. First a literature review on concepts of circular material design and integration of AI in CE along with smart production. Secondly, framework on the key roles of AI in circular product design and the key enablers are proposed based on the findings of qualitative research and theoretical part of the study. The outcomes of the study suggest a positive influence on implementing AI techniques in CE solutions in manufacturing industries.

Promoting and applying circular strategies in the product planning stage by industrial designers have significant environmental impacts. Product design has an enormous influence on sustainable ecology. Huge amounts of data analysis in designing circular products as well as reducing human biases in testing and prototyping are the main reasons for urging digital technologies in industries. Digitalization assets in ecodesign in collaboration with humans and as a complement for human skills. This study found the circular design tools and strategies which can help organizations in their product designs and the way artificial intelligence enhances product circularity. Real-time data transformation and analysis ability can help in massive data analysis which is less time consuming and less energy consumption is needed. In addition, rapid prototyping and fast testing will reduce the waste in design process. Furthermore, AI transfers precise data and information on materials and products’ availability, condition, and accessibility which makes easy monitoring and enables remote maintenance as well as reuse, remanufacturing and repair opportunities.

Laser additive manufacturing (LAM) is a layer wise fabrication method in which a laser beam melts metallic powder to form solid objects. Although 3D printing has been invented 30 years ago, the industrial use is quite limited whereas the introduction of cheap consumer 3D printers, in recent years, has familiarized the 3D printing. Interest is focused more and more in manufacturing of functional parts. Aim of this study is to define and discuss the current economic opportunities and restrictions of LAM process. Manufacturing costs were studied with different build scenarios each with estimated cost structure by calculated build time and calculating the costs of the machine, material and energy with optimized machine utilization. All manufacturing and time simulations in this study were carried out with a research machine equal to commercial EOS M series equipment. The study shows that the main expense in LAM is the investment cost of the LAM machine, compared to which the relative proportions of the energy andmaterial costs are very low. The manufacturing time per part is the key factor to optimize costs of LAM.Peer reviewe

Continuous technological development, digitalization, Industry 4.0, robotization, virtualization, and related investments in new types of physical assets are imposing increasing financial and intellectual demands on micro, small, and medium-sized enterprises (MSMEs). While fast technological development and rapid societal change make maintenance of a successful competitive edge ever more challenging, they also offer considerable potential for differentiation. In the area of networking and outside resources, MSMEs can utilize external resources and cooperate and collaborate with higher educational institutions (HEI) to boost their innovations pipeline and develop new technologies and processes to generate commercial products/services and improve their service offering. This research explores existing highly effective university–industry collaboration (UIC) models and seeks explanations for their success by examining the literature from the point of view of establishing successful relationships, emphasizing the importance of critical drivers for success. Our work synthesizes current knowledge of best practices based on a comparative analysis of practical collaboration. In the work, we identify eight popular and successful collaboration models: research and development partnerships, internships and co-op programs, knowledge transfer programs, entrepreneurship, and incubation programs, sponsored projects and grants, joint ventures and licensing agreements, executive education, professional and student career development. Based on analysis of globally reviewed successful models, a concept for robust, productive, and extended collaboration between companies and universities is produced suitable for the Finnish context. Several practical experiences are given for robust collaboration in the current post-COVID transition and energy crisis.