Mohammad Abdus Salam

Anees Fatima, Mohammad Abdus Salam

Demand forecasting in supply chain management (SCM) is critical for optimizing inventory, reducing waste, and improving customer satisfaction. Conventional approaches frequently neglect external influences like weather, festivities, and equipment breakdowns, resulting in inefficiencies. This research investigates the use of machine learning (ML) algorithms to improve demand prediction in retail and vending machine sectors. Four machine learning algorithms. Extreme Gradient Boosting (XGBoost), Autoregressive Integrated Moving Average (ARIMA), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR) were used to forecast inventory requirements. Ex-ternal factors like weekdays, holidays, and sales deviation indicators were methodically incorporated to enhance precision. XGBoost surpassed other models, reaching the lowest Mean Absolute Error (MAE) of 22.7 with the inclusion of external variables. ARIMAX and Fb Prophet demonstrated noteworthy enhancements, whereas SVR fell short in performance. Incorporating external factors greatly improves the precision of demand forecasting models, and XGBoost is identified as the most efficient algorithm. This study offers a strong framework for enhancing inventory management in retail and vending machine systems.

Mohammad Abdus Salam, Alfred Sarkodee-Adoo

Presently, there are not many literatures on the characterization of reputation and trust in wireless sensor networks (WSNs) which can be referenced by scientists, researchers and students. Although some research documents include information on reputation and trust, characterization of these features are not adequately covered. In this paper, reputation and trust are divided into various classes or categories and a method of referencing the information is provided. This method used results in providing researchers with a tool that makes it easier to reference these features on reputation and trust in a much easier way than if referencing has to be directed to several uncoordinated resources. Although the outcome of this work proves beneficial to research in the characterization of reputation and trust in WSNs, more work needs to be done in extending the benefits to other network systems.