Logistics

The Post Graduate Diploma in Data Science for Logistics is a one-year, full-time postgraduate diploma programme offered by the School of Skill Education, TISS in collaboration with the Logistics Sector Skill Council, Chennai. In today’s fast-paced business environment, data science is essential for uncovering insights, optimizing processes, and supporting strategic decision-making across industries.

This programme focuses on the powerful role of data science in logistics and supply chain management, where its integration can greatly enhance efficiency, lower costs, and minimize risks. The curriculum is designed to provide students with specialized skills and expertise at the intersection of data science and logistics, preparing them for high-impact roles within the industry. 

1. Data Analyst: Analyses transportation data and optimises supply chain efficiency.

2. Supply Chain Analyst: Optimises inventory management and procurement processes.

3. Project Manager: Leads logistics projects using data-driven insights.

4. Operations Analyst: Improves port and airport operations efficiency.

5. Sustainability Specialist: Develops green supply chain strategies.

6. Blockchain Logistics Specialist: Implements blockchain for transparency and security.

7. IoT Analyst: Monitors and optimises logistics processes in real-time.

8. Documentation Specialist: Streamlines logistic documentation processes.

9. Supply Chain Manager: Optimises supply chain networks and decision-making.

10. Machine Learning Engineer: Develops AI solutions for logistics optimisation.

11. Revenue Manager: Analyse data to optimise revenue and distribution strategies.

12. Quality Assurance Analyst: Utilises data analytics to ensure product quality and compliance with industry standards throughout the supply chain.

13. Customer Experience Manager: Analyses customer data to enhance satisfaction and loyalty, optimising delivery processes and personalised services based on customer preferences.

14. Regulatory Compliance Manager: Ensures compliance with regulatory requirements and industry standards, leveraging data analytics to monitor and enforce compliance across logistics processes.

15. Forecasting Analyst: Uses predictive modelling techniques to forecast demand, inventory levels, and transportation needs, optimising resource allocation and reducing stockouts.

16. Vendor Relations Manager: Manages relationships with suppliers and vendors, using data analytics to evaluate performance, negotiates contracts, and optimises procurement processes for cost savings and efficiency gains.

17. Global Trade Compliance Specialist: Ensure compliance with international trade regulations and customs requirements, using data analytics to manage risks and streamline cross-border logistics processes.

Knowledge Partner for Skill Training

 Logistics Sector Skill Council and relevant industries

Updates in Data Science for Logistics:

1. Data-Driven Logistics Market Expected to Reach INR 4,000 Crores by 2026

The integration of data science in logistics is revolutionising the sector, with the global data-driven logistics market projected to reach INR 4,000 Crores by 2026. The use of predictive analytics, machine learning, and data visualization tools is enhancing supply chain efficiency and decision-making. Companies are now seeking data science professionals who can analyze logistics data to optimize routes, manage inventory, and forecast demand.

2. AI and Data Science to Help Reduce Logistics Costs by 20%

The adoption of AI and data science in logistics can reduce operational costs by up to 20% through route optimization, demand forecasting, and automation. Data science professionals are critical in applying these technologies to streamline processes, leading to faster deliveries and better customer satisfaction. This trend is driving employment growth for data scientists specializing in logistics analytics.

3. India’s Logistics Sector Sees Increased Investment in Data Analytics

Indian logistics companies are ramping up their investment in data analytics to enhance supply chain transparency and performance. This trend has led to a 30% increase in job openings for data science roles within the logistics sector. Graduates with expertise in data-driven insights, predictive modeling, and supply chain management are in high demand to fill these positions.

4. Data Science Key in E-commerce Logistics

With the rapid growth of e-commerce, logistics companies are adopting data science to manage large volumes of data, forecast demand, and optimize delivery networks. The trend is driving demand for data analysts and scientists who can implement machine learning models to predict customer behavior and improve last-mile delivery processes.