Supply Chain Optimization for E-Commerce

Optimize the supply chain operations for a growing e-commerce company facing logistics challenges during peak season.

# Aplly.xyz Case Study Submission

## Title
Supply Chain Optimization for E-Commerce

## Type
Business Strategy & Consulting

## Difficulty
Intermediate

## Estimated Time
60 minutes

## Overview
Optimize the supply chain operations for a growing e-commerce company facing logistics challenges during peak season.

## Case Details

Function Focus: Business Strategy & Consulting — manual reasoning, decomposition, and critical judgment (no spreadsheet or AI tool permitted in Phase 1)

Scenario:
You are the analyst at a fictional consultancy ("Praxis Advisors") tasked with answering: Supply Chain Optimization for E-Commerce. You have a clean, synthetic dataset described below. You must produce a defensible answer using structured reasoning — no tool-assisted shortcut on the first pass.

Dataset Structure:
- facility
- process
- daily_capacity_units
- actual_throughput
- defect_rate_pct
- unit_cost_rs
- setup_time_mins
- downtime_pct
- lead_time_days
- wip_inventory
- labor_efficiency_pct
- automation_pct

Tasks:
1. Frame the strategic question and decompose it into the 2-3 forces/components that matter most.
2. Quantify the market/opportunity using back-of-envelope estimates from the dataset; list every assumption.
3. Evaluate 2-3 strategic options against the quantified picture using a decision matrix.
4. State the single most important risk and the assumption whose failure would change the recommendation.
5. Only after the manual pass, stress-test your numbers with a spreadsheet or tool.

Expected Output:
A one-page strategy memo: decomposition, quantified market picture, option scorecard, recommendation + key risk.

Evaluation Criteria:
Decomposition quality, explicit assumptions, quantified reasoning, balanced risk assessment.

## Data Sources

| facility | process | daily_capacity_units | actual_throughput | defect_rate_pct | unit_cost_rs | setup_time_mins | downtime_pct | lead_time_days | wip_inventory | labor_efficiency_pct | automation_pct |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mumbai Plant | Assembly | 5000 | 4350 | 2.3 | 850 | 45 | 8.5 | 3 | 12000 | 82 | 45 |
| Delhi Plant | Packaging | 8000 | 7200 | 1.8 | 320 | 20 | 5.2 | 1 | 8500 | 88 | 55 |
| Bangalore Plant | Testing | 3000 | 2850 | 0.9 | 1200 | 30 | 3.8 | 2 | 4000 | 92 | 70 |
| Chennai Plant | Assembly | 6000 | 5100 | 3.1 | 780 | 50 | 10.2 | 4 | 15000 | 76 | 35 |
| Pune Warehouse | Storage | 20000 | 18500 | 0.5 | 150 | 15 | 2.1 | 1 | 45000 | 90 | 60 |
| Hyderabad Unit | Fabrication | 3500 | 2900 | 4.2 | 1500 | 60 | 12.5 | 5 | 8000 | 70 | 30 |
| Kolkata Depot | Dispatch | 12000 | 10800 | 1.2 | 200 | 10 | 4.5 | 1 | 6500 | 85 | 50 |
| Ahmedabad Line | Assembly | 4000 | 3600 | 2.8 | 920 | 40 | 7.8 | 3 | 10000 | 78 | 40 |
| Lucknow Plant | Packaging | 6000 | 5400 | 2 | 350 | 25 | 6 | 2 | 9000 | 84 | 48 |
| Nagpur Unit | Fabrication | 2500 | 2100 | 3.8 | 1650 | 55 | 11 | 6 | 7000 | 72 | 28 |


Full dataset: https://github.com/arora200/aplly_case_db/datasets/048-operations-supply-chain-layout (synthetic, 10 records)

## Solution Frameworks
Porter 5-forces / market-entry framing, decomposition tree, scenario & sensitivity analysis, prioritization matrix

## Solver Guidance & Tutorials
_Solver guidance added by the pipeline (tutorial links) — see `solver_guidance` field._

## What You'll Learn
- Decompose fuzzy strategy questions
- Quantify markets with assumptions
- Compare options with a decision matrix

## Tags
E-Commerce, Logistics, Supply Chain

## Registration Links
Register as Solver / Register as Evaluator