📦 ABS Inventory Analysis – Forecasting & Optimization for Improved Availability 📌 Overview

This project focuses on analyzing and optimizing inventory performance for ABS automotive parts across multiple regions. Using historical sales, replenishment, and lead time data, the analysis identifies demand trends, predicts future requirements, and recommends optimal stock levels to reduce stockouts and overstocking while improving customer satisfaction.

🎯 Key Objectives

Analyze sales & replenishment data to detect seasonal and cyclical demand patterns.

Forecast product demand to improve order fulfillment rates and reduce inventory costs.

Recommend optimal SKU allocation strategies to balance availability and turnover.

🛠 Tech Stack

Languages & Libraries: Python, Pandas, NumPy, Matplotlib, Seaborn, Statsmodels

Forecasting Models: ARIMA, SARIMA

Data Visualization: Matplotlib, Seaborn

Data Source: Proprietary/Simulated ABS inventory dataset

📊 Methodology

Data Cleaning & Preprocessing

Removed duplicates and corrected inconsistent product IDs.

Filled missing sales data using time-series interpolation.

Exploratory Data Analysis (EDA)

Identified top-selling SKUs by region.

Analyzed lead times, replenishment frequency, and seasonal demand spikes.

Forecasting & Optimization

Built SARIMA model for SKU-level demand forecasting.

Recommended reorder points and safety stock levels based on forecast error margins.

Business Impact Analysis

Modeled savings from reduced overstocking.

Estimated customer satisfaction improvements from fewer stockouts.

🚀 Results & Insights

Reduced stockout risk by ~20% with optimized reorder points.

Increased inventory turnover ratio by ~15%, improving cash flow.

Seasonal demand peaks accounted for ~35% of annual sales variation.

High-demand SKUs require shorter replenishment cycles to meet eCommerce fulfillment targets.