Course Description: Fundamental optimization tools for quantitative analysis to develop modeling and decision-making skills in management sciences; Linear programming; Integer programming; Nonlinear programming; Goal programming; Game theory; Markov chains; Queuing theory and decision analysis techniques; Advanced topics in optimization.

Understanding the role of modeling and simulation in the development and improvement of logistics and supply chain operations; Methodology and modeling; Conducting of a simulation study; Hands-on exercise of a particular software package and its application in a practical context.

Overview of the procurement and purchasing activities in a supply chain; Supplier evaluation and selection; Pricing, negotiation, contracts; Outsourcing; Multiple sourcing; Just-in-time procurement; Inventory management; Buying decisions and plans; Cost analysis; Purchase agreements; E-procurement; Real-time internet-based e-supply chains; Reverse logistics and customer services; Supply chain for financing; Purchasing analysis of capital equipment; Institutional and government purchases.

Characteristics of various modes of domestic and international transportations; Vehicle types; Urban, air, ocean, highway, pick-up and delivery systems; Scheduling; Factors that influence transport demand; Costs; Market structures; Carrier pricing; Carrier operating and service characteristics and their influence on other supply chain costs and supply chain performance, such as routes, labor, competition.

Fundamental operations in warehousing including roles of warehousing, layout and facility design, warehouse technology such as bar codes, radio frequency identification (RFID) for inventory control systems, modern warehouse operations, classifying products, materials handling, racking and shelving, automated storage and retrieval systems (AS/RS), aisle width decision; Information technology for warehouse operations; Health and safety issues.

Fundamental of Design of Experiment; Simple experiment design, factorial, fractional factorial experiments; ANOVA analysis, model adequacy analysis, mixed level designs, response surface methodology and Taguchi design; Review of successful experimentation in Supply Chain Management practices.

Profitability, liquidity; Analysis and interpretation of published financial statements; Cost behavior analysis; Profit, volume analyses; Budget preparation and control; Standard costing; Divisional, segmental performance measurement; Capital investment; Risk and uncertainty analysis; Effects of inflation and taxation; Introduction to computer based financial modeling; Good corporate governance.

Logistics case study analysis to develop master students’ problem solving skill; Applying analytical tools, quantitative and/or qualitative tools, and mathematical and/or simulation models to analyze logistics problems; Real problems from industry and research articles with supporting data in transportation management, inventory management, distribution network design, and other logistics, supply chain related problems; Visits to industrial sites, factories, distribution centers to learn problem context and for data collection.

Introduction to physical network design modelling; Advanced modelling techniques and their application in network design and logistics; Emerging trends in supply chain network operations and the impact on their design.

Advanced topics in integrated logistics and supply chain operations which are not offered in other Logistics and Supply Chain Systems courses. Topics may vary from semester to semester.

Advanced topics in integrated logistics and supply chain operations which are not offered in other Logistics and Supply Chain Systems courses. Topics may vary from semester to semester, but are different from SE630.

A study on current interests in the field of logistics and supply chain systems and operations.

A study on current interests in the field of logistics and supply chain systems and operations that are different from SE632.

Concepts, tools and techniques of artificial intelligence (AI); data science; machine learnings; decision tree, artificial neural network, support vector machine, ensemble method, convolutional neural network, and deep learning; standard process of data mining; RapidMiner Studio; Google COLAB; data-driven decisions; AI prototype development; best practices; case studies.

Concepts, tools and techniques of business intelligence; data visualization; data exploration; business analytics in descriptive, predictive, and prescriptive; data cleansing, verification, validation, and integration; data mining; data-driven business decisions; design of analytics dashboard prototype; best practices and case studies.