This study seeks to examine the various attributes that impact health and safety in construction (HSIC) across different companies, stakeholders, and nations. The objective is to identify these attributes and organize them within a framework to facilitate a clearer understanding. The research identified common characteristics that promote the adoption of HSIC, yielding advantages for governmental, private, and public entities. The United Kingdom, the United States, Canada, Australia, and Hong Kong are considered the leading countries in terms of conducting research on HSIC attributes. There exists significant potential for enhancing the contributions of developing countries. The proposed framework acknowledges a comprehensive set of 61 attributes, which are categorized into four distinct groups: Corporate regulatory, Employee's self-supportive, Workplace regulatory, and Federal regulatory attributes. These attributes function as a framework for clients and policymakers to enhance the quality of HSIC. In forthcoming periods, it is recommended to prioritize the utilization of empirical surveys conducted across diverse locations in order to ascertain the attributes that are deemed of utmost importance and necessitate significant attention.

In the ever-evolving landscape of supply chain management, the quest for efficiency has become paramount. This abstract explores a groundbreaking solution that combines the power of permissioned blockchain technology with innovative data reduction strategies to redefine how supply chains operate. Traditional supply chain systems often grapple with data overload, causing delays, inaccuracies, and operational inefficiencies. However, this abstract presents a promising approach that unleashes efficiency by harnessing the capabilities of a permissioned blockchain. Through data reduction techniques tailored to the needs of supply chain management, this approach streamlines the flow of information while maintaining security and trust among participants. This paper seeks into the technical foundations of permissioned blockchains, highlighting their suitability for supply applications where confidentiality and controlled access are imperative. Furthermore, it examines various data reduction methodologies, emphasizing their role in minimizing redundant data, optimizing communication, and enabling real-time decision-making. The impact of this innovative approach on supply chain stakeholders is profound. It reduces data related bottlenecks, enhances transparencies, and fosters collaboration among participants. Additionally, it provides a scalable framework adaptable to diverse supply chain ecosystems. As supply chain efficiency becomes increasingly important in our interconnected world, this permissioned blockchain-driven data reduction strategy offers a compelling vision for the future. It promises to unlock a new era of streamlined operations, cost savings, and improved customer satisfaction, ultimately shaping the next generation of supply chain management.

Fashion and Apparel Supply Chains work in a very fast-changing environment and always demand better quality, higher availability of products, broader assortments and shorter delivery times. An efficient Supply Chain Management can make a difference between success and failure in the market. In this context, the main purposes of the presented work are: (i) to define the physical and informative flows, together with connected cost and revenue items, which characterize a Fashion Supply Chain working with a wide network of direct-operated or franchising mono-brand stores and (ii) to optimize Supply Chain performances through a responsive approach which, during the sales season, analyses actual market demand and adjusts operations plans accordingly. The framework aims at becoming a decision support system for the optimization of the performances of a process that starts from the development of the collection by the Styling Office and ends with the withdrawal of unsold items from the stores. In order to analyze the performances under different scenarios, a set of Key Performance indicators, partially selected from the SCOR Model, is defined.