Supply Chain Analytics

PRE-REQUISITES: Basic knowledge of Operations management will be desirable.

INTENDED AUDIENCE: It is an elective course for PG students and working professionals.

INDUSTRIES APPLICABLE TO: Supply Chain Analytics is a growing field. Some of the companies already in the business of Supply Chain Analytics and may recognize
the online course can be IBM, Mu-Sigma, Genpact, Accenture, Capgemini, Wipro, Deloitte.

COURSE OUTLINE: In the present time of intense global competition, customers are demanding more and more variety, with better quality and service at the lowest cost. This means that in order to be successful, firms need to develop supply chain strategies and logistical capabilities that serve the needs of their customers whilst maximizing overall profitability. All supply chains, in order to function properly, must focus on the huge opportunity that exists in their analytics.

Specific objectives:

On successfully completing this course you will be able to:
• Understand the importance of the basics of Business Analytics and Optimization
• Understand the importance of the basics of Supply Chain Analytics and Optimization
• Analyze the level of uncertainty associated with the supply of products and services to targeted
customer segments and justify the choice of a supply chain strategy and its fit with competitive strategy.
• Explain the role and applications of Descriptive Analytics in a Supply Chain
• Explain the role and applications of Predictive Analytics in a Supply Chain
• Explain the role and applications of Prescriptive Analytics in a Supply Chain
• Learn the basics of Modeling through R Language

Introduction to Supply Chain Management.
Evolution of Supply Chain Management.
Analytics in Supply Chain Management.
Supply Chain Planning.
Different views of Supply Chain.
Supply Chain Strategy.
Supply Chain Drivers.
Developing Supply Chain Strategy.
Strategic Fit in Supply Chain.
Demand Forecasting in Supply Chain.
Lecture 11.
Lecture 12.
Measures of Forecasting Errors.
Tracking Signal and Seasonality Models.
Forecasting using multiple characteristics in Demand Data and Inventory Management in Supply Chain.
Inventory Management in Supply Chain.
Multi echelon Inventory Management.
Multi echelon Inventory Management (Continued).
Multi echelon Inventory Management for four stations.
Multi echelon Inventory Management for four stations (Numerical Example).
Multi echelon Inventory Management for four stations (Numerical Example continued).
Network Design in Supply Chain.
Network Design of Global Supply Chain.
Alternative channels of Distribution.
Location Decisions in Supply Chain.
Network Optimization Models.
Using Excel Solver for Network Optimization.
Uncertainty in Network Design.
Network Design in Uncertain Environment and Flexibility.
Flexibility in Supply Chain.
Optimal Level of Product Availability in Supply chain.
Time Value of money in Supply Chain.
Different types of Analytics in Supply Chain.
Predictive Modelling in Forecasting in Supply Chain.
Representation on Uncertainty in Supply Chain.
Example of using Decision Tree incorporating Uncertainty in Single Factor.
Using Decision Tree for handling Uncertainty.
Example of using Decision Tree incorporating Uncertainty in two Key Factors.
Modelling Flexibility in Supply Chain.
Trends, Challenges and Future of Supply Chain.