Operational Performance Review of Chocolate Factory Inc.

This repository contains a comprehensive data-driven analysis of Chocolate Factory Inc.'s operational performance, with a focus on uncovering actionable insights in sales, customer behavior, cost structure, and supply chain efficiency.

Overview

This project involved reviewing operational data extracted from Chocolate Factory Inc.'s order management system using SQL, modeling and feature engineering in BigQuery, and developing interactive dashboards in Looker Studio. The objective was to evaluate current performance, identify inefficiencies, and propose strategic improvements.

Repository Contents

• Operational Performance Review of Chocolate Factory Inc.pdf
  The full final report containing methodology, analysis, insights, and strategic recommendations.

• Chocolate_Factory_Report.pdf
  A PDF snapshot of the Looker Studio dashboard used to visualize performance metrics.

• Chocolate Factory.pptx
  Executive presentation slide deck summarizing key findings and recommendations for stakeholders.

• SQL Queries.docx
  The SQL scripts used for data cleaning, transformation, and analysis within Google BigQuery.

Executive Summary

The analysis highlights the following core insights:

• Sales Performance: Strong seasonal spikes in December and February suggest an opportunity to align inventory planning with peak demand cycles.
• Customer Behavior: A significant proportion of customers are inactive, revealing opportunities for retention strategies through segmentation and loyalty incentives.
• Cost and Profitability: Margins are under pressure due to rising operational costs and inefficient pricing structures. Product-level pricing optimization is recommended.
• Supply Chain: Disparities in regional factory performance and inconsistent shipping lead times drive inefficiencies. Operational standardization and improved logistics coordination are key focus areas.

SKU & Product Strategy

A detailed SKU-level performance review was conducted using ABC classification, sales-per-unit analysis, and cost benchmarking. Findings revealed that:

• Category A SKUs (top 20%) contributed over 75% of total sales, while nearly 50% of the catalog contributed less than 10%.
• Many underperforming SKUs exhibited high unit costs and low demand, inflating holding costs and operational complexity.
• Seasonal and novelty items—though creatively branded—showed inconsistent performance and added strain to fulfillment and changeover operations.

These insights support the need for SKU rationalization, demand-driven inventory policies, and lean product portfolio management to drive margin efficiency and reduce waste.

Tools & Technologies

• Google BigQuery SQL: Data extraction, cleaning, and advanced transformations
• Looker Studio: Interactive dashboarding and visualization
• Python (Geopy): Reverse geocoding for deriving factory city names
• Google Slides & Docs: Reporting and stakeholder communication

Key Recommendations

• Implement dynamic inventory forecasting aligned with seasonal demand
• Introduce customer segmentation and lifecycle marketing strategies
• Reassess factory cost structures and product pricing
• Standardize logistics operations and optimize factory-to-region alignment

License & Confidentiality

This project was conducted using proprietary business data. As per client confidentiality requirements:

• The company’s actual name, operational details, and sensitive information have been anonymized.
• All datasets and identifiers have been altered or withheld to protect business confidentiality.