Unilever Using Data Analytics in Supply Chain Course at Coursera 🚛📦

By : Muhammad Fatih Idlan (faiti.alfaqar@gmail.com)

This project fulfills the Module 5 assignment (Prosacco activity presentation) for Unilever's Using Data Analytics in Supply Chain course on Coursera.

⚠️ The dataset used in this project is sourced from a fictitious company (which provided by this course) and was created for educational purposes to demonstrate my data analysis skills related to supply chain activity.

📌 Report Outline

• Introduction
  • Problem Statement
  • Objectives
• Analysis
  • Affected Customer Pareto Analysis
  • Historical SKUs Inventory Analysis
• Key Takeaways

📢 Introduction

🚧 Problem Statement

As we know, according to the dataset, there are several customer complaint about delayed distribution on these country:

• Canada
• United States
• Mexico

🎯 Objectives

• Determine the most affected customer
• Determine the root cause of delayed distribution
• Planning further strategy

📋 Analysis

📊 Affected Customer Pareto Analysis

As we can see, Customer 1 and 2 are the major affected customer during this issue. Both of them contributed around 95% of total uncovered sales.

📆 Historical SKUs Inventory Analysis

• Final inventory on week 41 & 42 are at negative level
• At the same week, there are sudden spike of uncovered demand due to increasing demand at week 41 and production pause from week 39 to 41

🔑 Key Takeaways

• Customer 1 & 2 contribute to 95% sales of delayed distribution
• The causing issues are increasing demand at week 41 & production pause on week 39-41
• Re-design production planning to tackle delayed production
• Integrating production schedule with demand forecasting to enhance production accuracy