biancazycao / irs-pm-2023-05-21-replenishnow Goto Github PK

ReplenishNow is a recommendation engine designed for small-to-medium enterprises seeking to improve the accuracy and efficiency of their inventory restocking strategies. The system implements existing industry methods to forecast inbound stock to balance between safeguard against stock-out (no products to fulfill customer orders) whilst minimizing inventory to avoid over stocking (unnecessary overhead and depreciation costs). Under the hood, ReplenishNow uses a combination of machine reasoning and learning to forecast and monitor the supply and demand levels to make daily adjustments to inventory levels in real time.

ReplenishNow

Note: More reference video presentations here

Refer to appendix <Installation & User Guide> in project report at Github Folder: ProjectReport

Go to URL using web browser: ReplenishNow-retool

1. Register account for Retool
2. Check the application components: App, Retool Database, Workflow, etc.
3. Expose the app ready to use

library required: !pip install pycaret

Use the API deployed: POST "https://replenishnowforecast.azurewebsites.net/api/ReplenishNowForecast"

library required: functions-framework==3.* & ortools

Use the API deployed: POST https://function-optimiser-3phvgqtjuq-as.a.run.app

Refer to project report at Github Folder: ProjectReport

Recommended to read the chapters in Project Report:

• Executive Summary
• Market Research
• Project Definition
• System Design (Architecture, data flow, modules design, database schema)
• Demand Forecasting (Theory Study and our methodology)
• Replenishment (Theory Study and our methodology)
• System Implementation & testing
• Project Conclusions: Findings & Recommendation

Refer to Github Folder: Miscellaneous

• /raw_data_DMC: raw data from DMC2022 competition

• inventory.csv, master.csv, sales_order.csv: data being mapped into database tables
• top78Items_aggr_weekly.csv: selected a pool of items from dataset download

• item_20109_moq.csv: result of the naive replenishment strategy applied on item 20109
• replenish_notification.csv: result of automated decision maker exported

• system_design.drawio: including all the diagrams

This project is the practical module of the Analytics and Intelligent Systems and Graduate Certificate in Intelligent Reasoning Systems (IRS) series offered by NUS-ISS.

Lecturer: GU Zhan (Sam)

[email protected]