Background: The US retail sector grapples with persistent challenges related to supply chain waste, including inefficiencies, overstocking, and logistical barriers, necessitating targeted reduction strategies to mitigate escalating costs, environmental impacts, and diminished profitability. Methods: This study adopts a qualitative research method that draws on secondary data sources such as books, journals, articles, and websites to explore supply chain waste reduction strategies within the US retail industry. The study delineates various supply chain waste types, examines associated challenges and drivers, and proposes a simplified model tailored to the US retail landscape to enhance efficiency and sustainability through waste reduction and control. Results: The findings of this paper underscore the necessity for proactive measures within the US retail sector to minimize supply chain waste, optimize operations, and bolster environmental stewardship. Conclusions: By offering a comprehensive overview actionable insights and proposed reverse logistics model, this study aims to equip US retailers with strategies conducive to sustainable growth and heightened competitiveness while advancing the broader discourse on supply chain efficiency and waste reduction.

This study explores the application of blockchain technology in optimizing supply chain and logistics management within North America. Through qualitative thematic analysis, three key objectives were addressed: identifying benefits, examining implementation challenges, and assessing barriers to adoption. The analysis highlights significant benefits of blockchain adoption, including enhanced trust, transparency, automation, traceability, and data security, supported by findings from various studies. However, implementation challenges such as system design complexity, resistance to change, and inadequate IT staff pose obstacles to adoption. Furthermore, barriers to widespread adoption, such as the absence of a proper legal framework and the capital-intensive nature of blockchain adoption, were identified. These barriers underscore the need for collaborative efforts to address regulatory uncertainties and financial constraints. It was suggested that further studies could explore the long-term impacts of blockchain technology on supply chain resilience and sustainability, as well as investigate strategies to overcome implementation challenges and maximize the technology's potential across diverse industries and regions.

In the rapidly evolving landscape of retail analytics, the accurate prediction of sales figures holds paramount importance for informed decision-making and operational optimization. Leveraging diverse machine learning methodologies, this study aims to enhance the precision of Walmart sales forecasting, utilizing a comprehensive dataset sourced from Kaggle. Exploratory data analysis reveals intricate patterns and temporal dependencies within the data, prompting the adoption of advanced predictive modeling techniques. Through the implementation of linear regression, ensemble methods such as Random Forest, Gradient Boosting Machines (GBM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), this research endeavors to identify the most effective approach for predicting Walmart sales. Comparative analysis of model performance showcases the superiority of advanced machine learning algorithms over traditional linear models. The results indicate that XGBoost emerges as the optimal predictor for sales forecasting, boasting the lowest Mean Absolute Error (MAE) of 1226.471, Root Mean Squared Error (RMSE) of 1700.981, and an exceptionally high R-squared value of 0.9999900, indicating near-perfect predictive accuracy. This model's performance significantly surpasses that of simpler models such as linear regression, which yielded an MAE of 35632.510 and an RMSE of 80153.858. Insights from bias and fairness measurements underscore the effectiveness of advanced models in mitigating bias and delivering equitable predictions across temporal segments. Our analysis revealed varying levels of bias across different models. Linear Regression, Multiple Regression, and GLM exhibited moderate bias, suggesting some systematic errors in predictions. Decision Tree showed slightly higher bias, while Random Forest demonstrated a unique scenario of negative bias, implying systematic underestimation of predictions. However, models like GBM, XGBoost, and LGB displayed biases closer to zero, indicating more accurate predictions with minimal systematic errors. Notably, the XGBoost model demonstrated the lowest bias, with an MAE of -7.548432 (Table 4), reflecting its superior ability to minimize prediction errors across different conditions. Additionally, fairness analysis revealed that XGBoost maintained robust performance in both holiday and non-holiday periods, with an MAE of 84273.385 for holidays and 1757.721 for non-holidays. Insights from the fairness measurements revealed that Linear Regression, Multiple Regression, and GLM showed consistent predictive performance across both subgroups. Meanwhile, Decision Tree performed similarly for holiday predictions but exhibited better accuracy for non-holiday sales, whereas, Random Forest, XGBoost, GBM, and LGB models displayed lower MAE values for the non-holiday subgroup, indicating potential fairness issues in predicting holiday sales. The study also highlights the importance of model selection and the impact of advanced machine learning techniques on achieving high predictive accuracy and fairness. Ensemble methods like Random Forest and GBM also showed strong performance, with Random Forest achieving an MAE of 12238.782 and an RMSE of 19814.965, and GBM achieving an MAE of 10839.822 and an RMSE of 1700.981. This research emphasizes the significance of leveraging sophisticated analytics tools to navigate the complexities of retail operations and drive strategic decision-making. By utilizing advanced machine learning models, retailers can achieve more accurate sales forecasts, ultimately leading to better inventory management and enhanced operational efficiency. The study reaffirms the transformative potential of data-driven approaches in driving business growth and innovation in the retail sector.

This article presents a comprehensive study of sales predictions using time series analysis, focusing on a case study of Walmart sales data. The aim of this study is to evaluate the effectiveness of various time series forecasting techniques in predicting weekly sales data for Walmart stores. Leveraging a dataset from Kaggle comprising weekly sales data from various Walmart stores around the United States, this study explores the effectiveness of time series analysis in forecasting future sales trends. Various time series analysis techniques, including Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), Prophet, Exponential Smoothing, and Gaussian Processes, are applied to model and forecast Walmart sales data. By comparing the performance of these models, the study seeks to identify the most accurate and reliable methods for forecasting retail sales, thereby providing valuable insights for improving sales predictions in the retail sector. The study includes an extensive exploratory data analysis (EDA) phase to preprocess the data, detect outliers, and visualize sales trends over time. Additionally, the article discusses the partitioning of data into training and testing sets for model evaluation. Performance metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are utilized to compare the accuracy of different time series models. The results indicate that Gaussian Processes outperform other models in terms of accuracy, with an RMSE of 34,116.09 and an MAE of 25,495.72, significantly lower than the other models evaluated. For comparison, ARIMA and SARIMA models both yielded an RMSE of 555,502.2 and an MAE of 462,767.3, while the Prophet model showed an RMSE of 567,509.2 and an MAE of 474,990.8. Exponential Smoothing also performed well with an RMSE of 555,081.7 and an MAE of 464,110.5. These findings suggest the potential of Gaussian Processes for accurate sales forecasting. However, the study also highlights the strengths and weaknesses of each forecasting methodology, emphasizing the need for further research to refine existing techniques and explore novel modeling approaches. Overall, this study contributes to the understanding of time series analysis in retail sales forecasting and provides insights for improving future forecasting endeavors.

This study examines the intricate interplay between electricity consumption and economic growth, specifically Gross Domestic Product (GDP), in Nigeria. Despite the acknowledged influence of electricity consumption on GDP, the research takes a comprehensive approach by scrutinizing various economic factors that may contribute to Nigeria's remarkable GDP performance within the African context. Utilizing a meticulous methodology, the investigation explores the relationships between GDP and variables such as unemployment, population, lending interest rates, importation costs, and inflation rates across a 50-case observational dataset. The findings highlight the substantial impact of electric power consumption, population, and lending interest rates on Nigeria's GDP, while revealing the relatively minor effects of other predictors.

The study examined the critical role of technology in addressing supply chain challenges in the United States, with a focus on identifying gaps and exploring opportunities for improvement. Through a comprehensive analysis of existing literature and empirical studies, the research uncovered key supply chain challenges faced by firms in the U.S., including issues related to counterfeit goods, logistics inefficiencies, environmental concerns, inaccurate demand forecasting, and cyber-security threats. Also, the study examined the role of emerging technologies such as blockchain, artificial intelligence, Internet of Things, big data analytics, and machine learning in enhancing supply chain resilience and agility. This study highlighted the contributions of these technologies in promoting transparency, traceability, sustainability, and efficiency across various aspects of supply chain management. By synthesizing insights from diverse sources and datasets, the study provided valuable recommendations and strategies for leveraging technology to address supply chain challenges effectively. These recommendations covers areas such as investment in advanced technologies, collaborative data sharing platforms, integration of sustainability metrics, predictive analytics for risk management, and supply chain resilience planning.