Demand Forecasting for Meal Delivery: A Data-Driven Approach
- Kaung Myat Linn
- Mar 2
- 2 min read
In the fast-paced world of meal delivery services, accurate demand forecasting is crucial for optimizing operations, reducing waste, and enhancing customer satisfaction. This blog delves into a comprehensive data analysis project aimed at predicting demand for a meal delivery company operating across multiple cities. By leveraging historical data, we explore various factors influencing demand, including promotional strategies, pricing trends, and weekly order patterns.
Understanding the Dataset
The dataset revolves around a meal delivery company with multiple fulfillment centers. The primary goal is to forecast demand for the next 10 weeks to aid in procurement planning and staffing. Key objectives include:
1. Promotional Impact Analysis
2. Pricing Analysis
3. Trend Analysis
4. Center-Wise Performance Analysis
5. Food Demand Forecasting for Each Center and Meal
1.Promotional Impact Analysis
Promotions play a significant role in driving order volumes. Our analysis reveals that both email promotions and featuring meals on the homepage significantly boost orders. The data shows:
No Email & No Featured : 211.42 orders
No Email & Featured : 455.88 orders
Email & No Featured : 431.28 orders
Email & Featured : 816.25 orders
The correlation heatmap further highlights the relationship between promotional activities and order numbers. Email promotions and homepage features have a positive correlation with the number of orders, indicating their effectiveness in driving demand.
2.Weekly Trend Analysis
The weekly trend analysis based on promotional emails shows a clear impact on order volumes. Weeks with email promotions consistently see higher order numbers compared to weeks without. This trend underscores the importance of strategic email marketing in maintaining steady demand.
3.Pricing Analysis
Pricing is another critical factor influencing demand. The analysis of base and checkout prices over weeks reveals fluctuations that correlate with order volumes. Understanding these trends helps in setting optimal pricing strategies to maximize orders without compromising profitability.
4. Center-Wise Performance Analysis
Based on our data set, this visuals show the total orders by meal ID and the performance of each centers.
5.Demand Forecasting
Using historical data, we developed a forecasting model to predict demand for the next 10 weeks using Random Forest Regressor. During the modeling process, we split 80% for training and 20% for testing from training data set and apply some feature engineering processes and train using Random Forest Regressor.
After modeling, we used our 20% predicting data set and resulted with MAE : 77 and RMSE: 176 which mean our model performs really well. Then we can easily apply on the prediction data set of next 10 weeks based on meal_id and center.
Conclusion
Accurate demand forecasting is vital for the efficient operation of meal delivery services. By analyzing promotional impacts, pricing trends, and weekly order patterns, we can make informed decisions that enhance operational efficiency and customer satisfaction. This project not only highlights the importance of data-driven strategies but also showcases the potential of predictive analytics in the food delivery industry.
By integrating these insights, the meal delivery company can better plan its inventory, optimize staffing, and implement effective promotional strategies, ensuring sustained growth and customer delight.
Source Dataset : https://www.kaggle.com/datasets/kannanaikkal/food-demand-forecasting
Comments