Michelin_Case_Competition_Final_Presentation.pptx

Michelin_Case_Competition_Final_Presentation

1. Michelin Case Competition Proposal Target Industry: Healthcare Imaging Leveraging Color-on-Color Detection Technology

2. Executive Summary - Problem Statement: Existing imaging technologies struggle with low-contrast scenarios (e.g., black-on-black). - Solution: Michelin’s advanced computer vision technology offers unparalleled color-on-color detection. - Target Industry: Healthcare Imaging (Projected USD 80.52 billion by 2034). - Use Case: Early-stage cancer detection, orthopedic imaging, cardiovascular diagnostics. - Strategic Goal: Enter the healthcare market by forming strategic partnerships and launching pilot projects.

3. Market Analysis: Healthcare Imaging - Healthcare Imaging Market Size: USD 49.61 billion in 2024, projected to reach USD 80.52 billion by 2034. - Key Drivers: AI integration, early disease detection, precision medicine. - High Growth Regions: Asia-Pacific (highest CAGR), North America, and Europe.

4. Persona-Based Analysis 1. **Dr. Sarah, Radiologist (45 years old)**: Needs high precision for complex diagnostics, especially in detecting early-stage tumors. 2. **Mr. John, Diagnostic Center Owner (50 years old)**: Concerned about ROI on new technology, prefers reliable solutions. 3. **Lisa, Lab Technician (30 years old)**: Needs easy-to-use tools to reduce manual workload and improve throughput.

5. Competitive Benchmarking - **GE Healthcare**: Leader in MRI and CT scans, leveraging AI for imaging enhancements. - **Siemens Healthineers**: Focuses on portable and connected imaging solutions. - **Philips**: Strong presence in the ultrasound segment, emphasizing compact imaging systems. - **Michelin’s Competitive Edge**: Unique low-contrast detection for hard-to-spot anomalies.

6. Use Case: Oncology Imaging - Problem: Early-stage tumor detection is critical but difficult in low-contrast scenarios. - Solution: Michelin’s color-on-color tech enhances tumor visibility. - Impact: Reduces diagnostic errors and improves early detection rates, leading to better patient outcomes.