The system uses point-of-sale data analysis based on machine learning to improve demand forecasting compared to traditional methods.
Machine Learning Applied to Customer Demand Forecasting
Machine learning plays an integral role in building forecasting models for retail demand. The application of machine learning in demand forecasting improves forecasting accuracy in both stable environments and during crisis management.
Machine Learning Improves Analysis
A system based on machine learning uses data for improving analysis when compared to traditional demand-forecasting methods.
The benefits of using machine learning include:
- Accelerated data processing.
- Improved forecasting accuracy.
- Automatic forecasting updates.
- Analysis of larger data sets.
- Identifying unseen patterns in data.
- Creating a more robust system.
- Increased adaptability.
Improved demand forecasting has a positive impact on many vital processes in the retail environment.
Supplier relationship management improves through better demand forecasting. This improvement comes from calculating the amount of product inventory to order and determining the need for new suppliers or the advantages of reducing the number of suppliers to make improvements in the supply chain.
Customer relationship management improves by having the products wanted by customers immediately available. Demand forecasting predicts the inventory needed for each subsequent product-ordering cycle for each customer location. Improved customer satisfaction increases commitment to a brand.
Order fulfillment and logistics improve as demand forecasting optimizes supply chains. At the time of a customer order, the product will be in stock. Inventory turnover is better managed, and fewer unsold goods take up valuable retail display space.
Marketing campaigns are adjusted by forecasting models to influence sales revenue positively. Forecasting models use marketing data with sophisticated machine learning to discover the most cost-effective marketing methods.
Manufacturing flow management adjusts as part of the enterprise resource planning (ERP) effort. Demand forecasting predicts the production needs for the products that will eventually sell over a certain period.
Steps to Implement Demand Forecasting Based on Machine Learning
- Review the Available Data: In this step, the data used for machine learning is analyzed and tested for its structure, accuracy, and consistency.
- Goals and Success Metrics: Determine the project’s goals and how to measure success.
- Prepare the Data: Clean the data. Check the data for relevance, gaps, and anomalies. Structure the data in the form most useful for machine learning.
- Develop the Machine Learning Model(s): The choice of the machine learning model(s) depends on the data types, data condition, forecasting period, and business goals.
- Training and Deployment: The forecasting model(s) are trained based on historical data. Optimizing the forecasting model(s) includes cross-validation using ten equal data portions to determine which model generates the most accurate forecast. Deployment occurs with the forecasting model(s) integrated into production use.
Venue Management and Point-Of-Sale System for Bars, Restaurants, and Nightclubs
This project included machine learning integration and demand forecasting for the SmartTab® point-of-sale (POS) and venue management system used by high-volume bars, restaurants, and nightclubs.
The project’s goals were to implement demand forecasting using machine learning with minimal action needed to operate the POS user interface and continuous integration for scalability and customization.
Here is the list of the technology, software, and tools used for the project:
- Project Management: UI/UX design, Agile management
- AWS Cloud Services: EC2, EBS, S3, and Route 53
- Continuous Integration: Ansible, Vagrant, and VirtualBox
- Machine Learning Models: Gradient Boosting and Time Series
- Testing: Automated UI, manual testing, stress and performance testing
The project’s goals were met by making all the POS operations of ordering and payments fast (within milliseconds) and precise. Using gradient boosting and time series, as the machine learning models, predicted the number of drinks, meals, and snacks that should sell during a selected future period for each venue.
The system architecture design made it possible to deploy the system at additional venues rapidly. The updating process progressed seamlessly for operating venues. Unique features could automatically be enabled for certain locations.
In total, 20 MobiDev software developers worked on this project. This solution deployed to more than 250 multiple-location, venue chains with 24/7 fail-safe operations using an offline plus cloud architecture.