The circular economy (CE) has become one of the prominent topics in both natural science and management literature over the last few decades. CE is a dual-loop regenerative system that focuses on the effective and efficient utilization of resources in the ecosystem, which is beneficial to environmental and economic performance optimization. Dual CE initiatives allow firms to increased resource eco-efficiency, as well as resource effectiveness. CE has profound consequences for economic and operational advantage. This reinforces the need for reflection on the definition that may provide guidelines to assess and advance the depth and diversity of the field. We aim to provide a definitional analysis of the CE and suggest future research streams to advance the existing literature. For this purpose, we employed a systematic literature review to collect related publications in the CE. As a result of this, a total of 91 papers were selected, studied, and analyzed. We proposed a sound definition of a circular economy that includes the main identified elements, organizational planning processes, customers and society, utilization of the ecosystem, and economic resource flows. Moreover, future direction agenda, in CE research, is suggested considering three research streams: (1) circular design as value creation and capture, (2) antecedents of key activities, and (3) consequences of key processes. There is limited empirical research conducted on CE, and much of the existing research focuses on theoretical, conceptual, and normative. A few empirical research studies are mainly cross-sectional in their focus and are confined to developing and emerging economies. We hope this study’s findings will extend the field of CE, in which some of the most influential information regarding CE literature is provided. This study suggests that the development of CE initiatives plays an important role in the growing digital transformation in the value chain. There have been limited research studies in the interface of circular economy and Industry 4.0. Future research studies may investigate the extent to which digital transformation can increase the implementation of CE, and their influence on digital performance management.

In proposing an integrated production–distribution model for multinational corporations (MNC), the current research undertakes a novel, simultaneous consideration of facility location, capacity acquisition/disposal, labor, manufacturing, purchasing, exchange rates, tariffs, and container and/or individual shipping factors. Unlike prior studies that examined some such factors. Based on real-world data from a window manufacturing MNC, a mixed integer linear programming model is developed and solved using CPLEX Solver. It reveals how the different features affect the base solution. Specifically, increased demand pushes more manufacturing to US and UK facilities rather than (low-cost) Chinese facilities; it helps meets some of China’s demand and leads to reduced total tariff costs. Decreased demand instead favors the Chinese facility, and the increase in tariff costs can be offset by savings in manufacturing and transportation costs. Changes to the factors have insignificant effects on the production and distribution mix, with little to no impact on total profits, because these costs are lower than capacity costs. The comprehensive proposed model provides firms with a framework for obtaining meaningful insights into how they can maintain their operations in sustainable, profitable ways. This research also should assist policymakers in making informed decisions regarding regulations on production and distribution strategies.

The Internet of Things (IoT) is changing how organizations forecast product demand by enabling real-time data collection through connected sensors. Despite growing interest in this area, research on IoT-enabled forecasting remains scattered across multiple disciplines, making it difficult to identify key contributions and overarching trends. To address this gap, a bibliometric analysis was conducted using Biblioshiny software to answer two research questions: (1) Which contributions have been most influential in IoT-based demand forecasting for supply chains? and (2) What emerging topics are likely to shape future developments in the field? The PRISMA methodology was applied to gather and screen articles from the Scopus database. The analysis includes publication trends, leading authors, top journals, highly cited documents, contributing institutions, and countries. Keyword analyses are presented through word clouds, trend-topic maps, conceptual structure diagrams, and thematic evolution plots. The findings offer a structured overview of the field’s development and propose future directions, including sustainable forecasting practices and greater interdisciplinary collaboration. Keywords Internet of Things, Supply Chain Management, PRISMA, Bibliometric Analysis, Demand Forecasting.