Roadmap to Implement AI-Based Inventory Forecasting
🔧 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄
Backend / AI: Python (FastAPI, Pandas, Prophet or XGBoost)
Frontend: Angular
Database: PostgreSQL / MySQL / MongoDB
Deployment: Docker + AWS/GCP/Azure
✅ 𝟭. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 & 𝗖𝗹𝗲𝗮𝗻 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮
Data Required:
Sales (item ID, timestamp, quantity)
Inventory levels (daily/weekly)
External factors: weather data, holidays
Store metadata (location, hours)
Tools:
Store in PostgreSQL / MongoDB
Use APIs (e.g., OpenWeatherMap) for weather data
Example schema:
CREATE TABLE sales (
id SERIAL PRIMARY KEY,
item_id INT,
quantity_sold INT,
sale_date DATE
);
CREATE TABLE inventory (
item_id INT,
date DATE,
quantity_in_stock INT
);
✅ 𝟮. 𝗧𝗿𝗮𝗶𝗻 𝗮 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹
Option 1: Prophet (Facebook's open-source lib)
Option 2: XGBoost + Features (day-of-week, weather, etc.)
📦 Install dependencies:
pip install pandas scikit-learn prophet fastapi uvicorn
Sample Forecasting Code with Prophet:
import pandas as pd
from prophet import Prophet
# Load sales data
df = pd.read_csv('sales.csv')
df = df.groupby('sale_date')['quantity_sold'].sum().reset_index()
df.columns = ['ds', 'y'] # Prophet needs ds (date) and y (value)
# Train model
model = Prophet()
model.fit(df)
# Forecast next 30 days
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)
forecast[['ds', 'yhat']].tail(10)
✅ 𝟯. 𝗦𝗲𝗿𝘃𝗲 𝗔𝗜 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝘃𝗶𝗮 𝗔𝗣𝗜
Use FastAPI to build a REST API.
from fastapi import FastAPI
import pandas as pd
from prophet import Prophet
app = FastAPI()
model = Prophet()
df = pd.read_csv('sales.csv')
df.columns = ['ds', 'y']
model.fit(df)
@app.get("/forecast")
def get_forecast(days: int = 7):
future = model.make_future_dataframe(periods=days)
forecast = model.predict(future)
return forecast[['ds', 'yhat']].tail(days).to_dict(orient='records')
✅ 𝟰. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗔𝗻𝗴𝘂𝗹𝗮𝗿 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱
Make HTTP calls from Angular service:
// forecast.service.ts
import { HttpClient } from '@angular/common/http';
import { Injectable } from '@angular/core';
@Injectable({ providedIn: 'root' })
export class ForecastService {
constructor(private http: HttpClient) {}
getForecast(days: number) {
return this.http.get(`http://localhost:8000/forecast?days=${days}`);
}
}
Bind to a component to display forecast:
// dashboard.component.ts
this.forecastService.getForecast(7).subscribe(data => {
this.forecastData = data;
});
✅ 𝟱. 𝗘𝗻𝗵𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗠𝗼𝗿𝗲 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀
Add weather and holidays as features
Use LSTM for more advanced forecasts
Add alerts for predicted low stock
Create visual charts with Chart.js or D3.js
✅ 𝟲. 𝗗𝗲𝗽𝗹𝗼𝘆 𝗮𝗻𝗱 𝗠𝗼𝗻𝗶𝘁𝗼𝗿
Dockerize FastAPI backend
Host Angular on Firebase/Netlify
Deploy model API on AWS/GCP (EC2, Lambda, or App Engine)
Add logging and alerting (e.g., Sentry, Prometheus)
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