This review paper delves into the transformative potential of Internet of Things (IoT) and Big Data technologies in revolutionizing inventory management within the Food & Restaurant Industry. With consumption routines in this sector being notably high, the need for efficient inventory control is paramount. We examine the current landscape, revealing a disparity between small and medium -sized businesses and larger enterprises in inventory management practices. Drawing insights from extensive literature analy sis, we explore the trends and opportunities for utilizing IoT and Big Data to address inventory challenges. Our research focuses on high -demand categories, analy sing data from top -selling items in food outlets. Findings underscore the prevalence of ad -hoc inventory management methods, highlighting the necessity for real - time data analytics and forecasting techniques. We propose solutions that involve real -time data gathering, coupled with advanced forecasting algorithms such as Linear Regression and Random Forest. Despite promising outcomes, we also address the challenges and limitations inheren t in implementing these solutions, offering insights for future research and industry adoption. 1. INTRODUCTION Inventory management is the cornerstone for businesses dependent on physical goods, facilitating the effective management of raw materials and products. It serves to reduce supply chain stress by addressing demand fluctuations, supply uncertainties, and pricing stability concerns. Transparency is paramount in inventory control, which bridges the gap between planned and actual operations while promoting supply chain visibility. Leveraging real -time data helps organizations optimize operations and increase supply chain efficiency, as well as benefit from relevant insights into demand forecasting. In today's dynamic market, customer demands change rapidly, requiring agile inventory 2 strategies to maintain competitiveness. Stability in pricing depends on real -time data and prudent decision making by considering cost volatility and delivery timelines. The Internet of Things (IoT) revolutionizes inventory management across industries by connecting physical objects to embedded technology, enabling interactions with their environment on a global scale. Big Data analytics plays a pivotal role in inventory m anagement, addressing challenges like overstocking and understocking while optimizing costs and meeting demand through predictive analytics. In the food industry, IoT integration increases efficiency by providing real -time updates on product conditions, wh ile consumer behavio ur analytics informs responsive inventory adjustments. In cloud kitchens, inventory management is revolutionized through the integration of IoT devices and sensors. These technologies enable real -time tracking of ingredient and supply levels, ensuring efficient operations and minimizing wastage. The key types of sensors and instruments used include RFID tags and readers, barcode scanners, weight scales, temperature sensors, and inventory management software. RFID tags and readers utilize electromagnetic fields to automatically identify and track tagged objects, while barcode scanners read unique barcodes on item packaging. Weight scales measure the weight of perishable goods, and temperature sensors monitor storage unit temperatures to prevent spoilage. Inventory management software serves as the central platform for tracking and managing inventory levels, integrating data from various sensors. It provides real -time updates, alerts, and notifications to kitchen staff, facilitating timely restocking and pr eventing stockouts. When estimating costs for IoT -based inventory management systems, it's important to consider both software and hardware components. Software costs include licensing fees, customization, and ongoing support, while hardware costs encompas s the purchase and installation of sensors, readers, and other IoT devices. Additionally, maintenance and operational expenses should be factored in for a comprehensive cost estimation. This research explores how the Internet of Things (IoT) can revolutionize warehouse management by offering real -time data on inventory through sensor networks. RFID tags and smart shelves are examples of IoT technology that can track and analy se inventory movement, enabling better organization, reduced labour costs, and improved fulfilment of customer demands. The authors propose an IoT framework that integrates machine learning for real -time data acquisition and prediction of inventory fluctuations, potentially optimizing warehouse operations and supply chain efficiency. Traditional warehouse management often grapples with delays in information transfer, resulting in operational inefficiencies exacerbated by suboptimal location choices. Leveraging technologies like RFID tags within the Internet of Things (IoT) framework of fers real -time inventory tracking capabilities, mitigating these challenges. Moreover, integrating machine perception techniques enhances the accuracy of demand and supply fluctuation predictions, aiding in proactive inventory optimization strategies. Addi tionally, addressing class imbalance issues in machine learning models, particularly for backorder prediction, ensures more robust and reliable forecasting, thereby optimizing inventory management practices and streamlining warehouse operations effectively . 3 2. METHODOLOGY The solution employs ML algorithms to forecast perishable food demand for grocery delivery services, ensuring optimal inventory levels and preventing wastage or stockouts. It leverages ML's ability to learn patterns from noisy supply chain data, enabling a ccurate predictions and proactive decision -making. Robust tracing techniques are integrated for food safety compliance, enhancing supply chain resilience. By predicting demand well in advance, it facilitates efficient inventory management across warehouses , stores, and distribution centr es. Effective communication between suppliers, warehouses, and clients is prioritized, minimizing disruptions and maximizing customer satisfaction. 2.1 Understanding Business Problem: o Grocery delivery services need accurate daily and weekly forecasts for perishable items to prevent wastage and stockouts, ensuring customer satisfaction. o Effective communication among suppliers, warehouses, and clients is crucial for a smooth supply chain. o Implementing robust tracing techniques is necessary for food safety. o Accurate forecasts aid in planning, decision -making, and minimizing inventory costs. o Predicting demand for each product in advance ensures optimal inventory levels. o Supply chain time series data is noisy; it's essential to extract true patterns and ignore noise. o ML techniques offer the ability to learn complex patterns and control the learning process, aiding in supply chain forecasting. 2.2 Data Collection and Preprocessing: o Utilized Kaggle datasets for demand forecasting, including: o Weekly demand data (train.csv): Historical demand with features like center ID, meal ID, prices, promotions, and orders count. o Test data (test.csv): Similar structure to train.csv for model testing. o Meal information (meal_info.csv): Details about meal types, categories, and cuisines. o Fulfillment center information (fulfillment_center_info.csv): Data about centers, including city code, region code, center type, and operational area. o Preprocessed data by merging all datasets into one, ensuring proper format for analysis. 4 2.3 Exploratory Data Analysis (EDA): o Examined data for quality issues and insights. o Analy sed categorical variables, such as center type, food category, and cuisine type. o Visualized correlations among variables. o Conducted analysis of orders count (num_orders) with categorical variables, including center type, food category, and cuisine type. 3. RESULTS AND DISCUSSION In the predictive model ling phase, various machine learning models are employed to analy se data and make predictions. The models discussed include: I. Linear Regression: A model used for predicting continuous outputs by fitting a linear curve to the data based on feature weights. II. K Nearest Neighbo urs (KNN): A non -parametric method for classification and regression that predicts values based on the similarity of neighbo uring data points. III. Decision Tree regressor: A tree -based algorithm used for regression and classification, where decision nodes are based on conditions over features. IV. Random Forest Regressor: An ensemble model that combines multiple decision trees to create a robust and accurate model, which outperforms individual trees. V. XGBoost: A gradient boosting method that sequentially adds decision trees to the ensemble to minimize the loss function, resulting in high model performance and computational efficiency. Each model has its own characteristics and is suitable for different types of data and prediction tasks. The choice of model depends on factors such as the nature of the data, the problem at hand, and the desired performance metrics. 5 In the model ling phase, various machine learning models are employed after thorough research and evaluation. Linear Regression utilizes features to predict continuous outputs by fitting a linear curve to minimize mean squared error through gradient descent. K Nearest N eighbo urs utilizes feature similarity among neighbo uring data points for prediction, employing distance metrics like Euclidean, Manhattan, and Hamming. Decision Tree Regressor constructs a tree -based model by recursively splitting data based o n feature conditions, using the Gini index for node selection. Random Forest Regressor combines multiple decision trees through bootstrapping and majority voting to create a robust ensemble model. XGBoost, a gradient boosting technique, sequentially adds d ecision trees to minimize loss, offering computational efficiency and high performance through its objective function and regularization. The model performance is assessed by plotting actual versus predicted values and evaluating using the R2 score, indicating predictive capability where 1.0 signifies perfect prediction. Scores for each model are as follows: Linear Regression (0.24), K Neare st Neighbo urs (0.075), Decision Tree Regressor (0.70), Random Forest Regressor (0.85), and XGBoost Regression (0.83). 6 7 4. CONCLUSION Random Forest outperformed other models due to its ensemble nature, which reduces bias and moderates variance, effectively handling overfitting. It also conducts implicit feature selection, performs well on medium -sized datasets, handles outliers adeptly, and is suitable for non -linear data. In contrast, Linear Regression assumes linearity and independence among features, while KNN struggles with outliers and is more suited for classification tasks. Decision Trees can lose information in time series data an d are prone to overfitting, especially with many categorical variables. XGBoost, although comparable to Random Forest, is sensitive to outliers and performs better on smaller datasets. Overall, leveraging IoT and Big Data for inventory management fosters p roactive decision -making, efficiency, and cost savings, crucial in navigating dynamic market demands and competition.