This course focuses on the application of analytical techniques for determining effective solutions to problems associated with supply networks considering the constraints of demand and supply. The objectives of the course are to understand the nature of supply networks, goal of supply networks and explain the impact of analytics based supply chain decisions on the success of a firm. The coverage includes key metrics that track the performance of the supply network in terms of each driver, identification of the key factors to be considered when designing a distribution network and use of analytical techniques for developing a framework for network design. Emphasis is laid on the use of optimization techniques for facility location, capacity allocation and evaluation of supply chain design decisions under uncertainty. Along with these objectives, this course is also aimed at understanding the concepts of multi-criterion decision making in supplier selection and rating, inventory management techniques under uncertain demand and supply environment, mathematical models for design of transportation networks, and the role of soft computing techniques for matching supply with demand.
INTENDED AUDIENCE : Management, Industrial and Systems Engineering, Mechanical Engineering, and related disciplines.
PREREQUISITES : Nil
INDUSTRY SUPPORT : Tata Group of Industries, Multinationals, L&T, and similar such manufacturing and service organizations including IT companies
Week 1 :Introduction to Modeling and Analytics in Supply Networks:
Introduction to Supply Network, Performance Measures for Efficiency and Effectiveness, SCOR model, Strategic Fit and Scope, Types of Distribution Networks, Analytics in Management, Design of Distribution Networks
Week 2 :Supplier Selection Analytics:
Linear Programming, Rating method, Ranking method, Borda Count, Clustering, Goal Programming and related multi-criterion decision making (MCDM) techniques
Week 3 :Transportation Modeling and Analytics:
Transportation models, Route planning, Transshipment, Shipment schedule, Flow path optimization.
Week 4 :Warehousing Modeling and Analytics:
Warehouse location problem, MILP formulation, Location with foreign exchange risks, space calculation for warehouse, Non-linear optimization for warehouse space allocation
Week 5 :Strategic Performance Improvement:
Data Envelopment Analysis for competitive comparisons among multiple warehouses and service units and formulation of strategic action plans for improving the efficiencies of non-performing DMUs, Stochastic Frontier Analysis.
Week 6 :Inventory Analytics - I:
Elementary Concepts related to Inventory Management, Economic Order Quantity (Instantaneous Replenishment), Economic Production Lot Size, Inventory Model with Planned Shortages (Back-Orders), Inventory Management under Uncertainty – Concept of Safety Stock, Continuous Review System, Periodic Review System
Week 7 :Inventory Analytics - II:
Newsvendor Model, Performance Measures: Expected Lost Sales, Expected Sales, Expected Leftover Inventory, Expected Profit, Fill Rate, In-Stock Probability, and Stock-Out Probability
Week 8 :Inventory Analytics - III:
Choosing an Order-up-to Level to Meet a Target Service Level, In-Stock Probability, and Desired Fill-Rate
Week 9 :Inventory Analytics - IV:
Assemble-to-Order, Make-to-Order and Quick Response with Reactive Capacity, Reducing Mismatch Costs with Make-To-Order
Week 10 :Modeling Coordination in Supply Chains:
Information Distortion in Supply Network and Bull-Whip Effect, Coordination and collaboration modeling in supply networks.
Week 11 :Risk Analytics in Supply Network Design:
Mapping the riskiness profile of a country, taxation, Mapping the riskiness profile of possible international routes and Designing the route plan based on riskiness profile
Week 12 :Design and Modeling the global supply chain:
Design and optimization of global supply chain networks, Multi-period supply chain network design