Bachelor of Business
Administration (BBA)
in Logistics and Supply
Chain Management

Prerequisite: MNS 205 and MTH 210

An introduction to the fundamentals of business analytics. Focuses on the management science approach for problem solving, the application of linear programming, the use of decision analysis techniques, as well as project management tools. It is practical and students gain advanced skills in Excel. This course is a prerequisite for MGT 451.

Prerequisite: MGT 451

An introduction to supply chain management, an essential discipline for competitiveness in the global marketplace. This course explores the eight key supply chain components, focusing on how demand management is used to balance consumer needs with supply chain capabilities. You’ll learn how advanced forecasting techniques and rapid product development can combine to create a responsive supply and competitive value to the consumer.

This course examines procurement and inventory management through an integrated management and analytics lens. Emphasis is placed on category strategy development, e-sourcing, and supplier performance evaluation using Python-based spend analytics, including price variance analysis, should-cost models, and total cost of ownership. Students analyze inventory decisions across single- and multi-echelon systems, applying news-vendor logic, service-level optimization, intermittent-demand modeling, and (s, S) policy design to support managerial decision-making. Applied labs use pandas and statistical models for ABC-XYZ segmentation and risk-adjusted safety stock calculations, alongside Pyomo optimization modeling to determine reorder parameters. The course also introduces supplier risk scoring using basic machine learning techniques and includes a focused module on ESG considerations and Scope 3 supply chain data to support responsible and resilient sourcing strategies.

This course examines how modern fulfillment and transportation operations function in environments where customers purchase through multiple channels and expect fast, accurate delivery and efficient returns. Students study how order management, warehouse operations, and transportation processes connect to support order promising, carrier selection, shipment visibility, exception management, and last-mile execution across both consumer and business settings. The course emphasizes applied decision-making by evaluating routing and delivery planning under real operational constraints and by analyzing cost, service, and capacity trade-offs through scenario-based problem-solving. Additional topics include distribution and fulfillment strategies such as warehouse layout and picking approaches, alternative delivery and pickup models, and returns policies designed to reduce cost to serve while protecting customer experience. The course concludes with a short applied module on how analytics and emerging digital tools can improve reliability and operational performance.

This course examines the design and management of international sourcing and logistics strategies in the context of evolving trade rules, disruption risk, and sustainability expectations. Emphasis is placed on trade compliance fundamentals that shape logistics decisions, including HS classification, Incoterms, duties and free trade agreements, and basic sanctions awareness. Students evaluate global transportation and routing options across ocean, air, and ground modes and connect these choices to broader questions of supply network design, cost, service, and risk.

Applied labs develop quantitative decision skills used in global logistics and trade strategy roles. Students complete scenario-based facility location modeling, simulate lead time variability and duty exposure using Monte Carlo methods, and calculate shipment-level emissions to evaluate carbon and ESG trade-offs alongside financial and service outcomes. The course culminates in an executive-style recommendation that compares nearshoring and multi-regional network strategies and communicates a defensible path forward supported by data, risk logic, and implementation considerations.

This course focuses on measuring end-to-end supply chain cost and managing risk using quantitative, decision-oriented methods. Students develop an activity-based Cost-to-Serve model to understand where cost and time are generated across sourcing, fulfillment, and service, and then evaluate how variability and disruption change performance outcomes. Emphasis is placed on modeling uncertainty using practical risk tools, including Monte Carlo simulation and stress testing, to assess exposure to demand volatility, lead-time variation, and supplier outages.

The course also examines resilience strategy design and implementation, including redundancy, postponement, and dual-sourcing, with attention to the trade-offs among cost, service levels, and risk reduction. A focused module addresses traceability and serialization and evaluates when advanced approaches, including blockchain-based solutions, are appropriate compared to conventional data systems. The course culminates in an applied control-tower case in which students combine alerts, thresholds, and decision rules to propose a disciplined response plan that improves recovery performance and supports resilient operations.

Prerequisite: MNS 407

This course develops the ability to model, solve, and communicate supply chain network decisions using optimization, simulation, and introductory digital twin methods. Students formulate and analyze core logistics and operations problems, including transportation planning, assignment decisions, and facility location, and implement these models in Python to generate defensible, data-supported solutions. The course also introduces practical routing heuristics and solution evaluation approaches used when real-world problems exceed the limits of exact optimization.

To capture operational realities that optimization alone may not represent, students build discrete-event simulation models to analyze variability, congestion, and capacity constraints and to quantify impacts on throughput, service, and cost. The course culminates in an applied project in which students integrate an optimization model with a simulation model to create a basic digital twin that supports a capacity planning recommendation, reported through clearly defined KPIs and sensitivity analysis.