MBA: Supply Chain and Operation management (2026)

Explore the four backbone functions of a retail supply chain, procurement, manufacturing, logistics, and fulfillment, and learn how planning and coordination, reverse logistics, and key stakeholders ensure timely delivery.

Understand how planning and coordination drive retail supply chains, covering demand, supply, inventory, and delivery planning to align suppliers, warehouses, logistics, and seasonal demand.

Learn how retail reverse logistics handles returns and repairs, with inspection, restock or refund decisions, and how the fulfillment team coordinates with vendors to manage inventory and disposal.

Explore vendors, distributors, and third party logistics providers (3PL) in the supply chain, and learn how procurement and multi-vendor strategies support regional warehouse on-time stock.

Learn how to measure supply chain performance using key metrics such as on-time in full (OTIF), inventory turn, fill rate, and cost per delivery to drive improvement.

Understand cycle time, lead time, and service level through a grocery example to optimize speed, reliability, and cost in supply chain management.

Analyze the total supply chain cost for a grocery store by breaking down procurement, transportation, inventory holding, operation costs, shrinkage, waste, and safety stock to protect margins, considering just-in-time delivery.

Explore the three main operation systems—project, batch, and continuous—through everyday examples from stores, bakeries, and hospitals, showing how they convert inputs into outputs.

Explore capacity, utilization, and throughput with a bakery example to show maximum output, actual resource use, and how bottlenecks and process mapping affect throughput.

Map every step of a process to identify bottlenecks and the queues that slow throughput. Then boost capacity, rearrange workflow, train staff, or automate tasks to improve flow.

Explore how small demand fluctuations ripple through the supply chain as the bullwhip effect, and learn strategies like ERP, improved inventory planning, and stable pricing to reduce it.

Analyze the differences between efficient and responsive supply chains and when predictability, short product lifecycles, cost, and speed matter most.

Balance efficiency and responsiveness in supply chains by matching functional products with efficient supply chains and innovative products with responsive ones, illustrated by covid-19 bullwhip effects.

Learn how supply chain decisions impact finance, from revenue and cost of goods sold to gross profit and net income, with a focus on procurement and raw material cost reduction.

Explore forecasting supply chain disruptions in manufacturing through a case study, analyzing delays, costs, supplier reliability, and risk scores to inform resilient planning and regional financial impact.

Explore reducing e-commerce returns by analyzing order data, computing return risk scores from discount, delivery time, past returns, and product rating, and using pivot tables to spot high-risk categories.

Explore a weather-driven forecasting case study that shows how weather data and events influence short-term demand across SKUs, guiding inventory planning to prevent stockouts.

Discover supply chain analytics by analyzing data from procurement, manufacturing, and fulfillment to ensure the right item reaches the right place at the right time.

Unlock smarter, quicker decisions by applying supply chain analytics to reduce costs and improve margins, while understanding risk, boosting planning accuracy, and enabling a lean, responsive supply chain.

Master the four types of supply chain analytics: descriptive, diagnostic, predictive, and prescriptive, transforming past data into actionable insights, forecasts, and decisions.

Describe descriptive analytics as a tool that reveals what happened in the supply chain using past reports to provide visibility and a single source of truth, and measure performance.

Diagnostic analytics reveals why events happen in the supply chain by isolating a specific event, identifying root causes, and uncovering anomalies to reduce lead time.

Predictive analytics provides foresight by using past data and descriptive analytics to forecast future sales and guide inventory, pricing, and promotions decisions.

Prescriptive analytics blends descriptive and predictive analytics to provide specific recommendations for reaching desired goals. Use it to optimize inventory, routing, and warehouse sequencing with data-driven software.

Develop a foundation in demand planning and forecasting by analyzing supply and demand, the demand curve, and forecasting techniques such as simple moving average, exponential smoothing, and double exponential smoothing.

Master demand management as a short-term planning approach to balance supply and demand, using forecasting, planning, and moving average or exponential smoothing of historical sales and seasonality.

Explore the goals and objectives of demand management through Amazon's customer-centric approach. See how subscribe and save, dynamic pricing, best seller tag, and fast delivery normalize demand and boost efficiency.

Explore supply and demand mechanics, identifying equilibrium where supply and demand meet, and analyze curves and elasticity—from insulin and gasoline to bread—to understand perfectly inelastic, relatively inelastic, and elastic demand.

Examine how demand curves shift right or left, why price hikes may not reduce demand for luxury or popular products, and how supply responds to costs, technology, and sellers.

Identify the determinants of demand—price, income, tastes, related goods, consumer expectations, number of buyers, and other factors—and how they shape elasticity and quantity demanded.

Explore how seasonality, fashion, income changes, and global change drive demand variation, then map functional vs innovative products with corresponding demand and supply uncertainties and supply chains.

Collect and feed customer, sales, and social data into models for demand management, then forecast demand using quantitative and qualitative methods and plan inventory to balance service levels and costs.

Explore price elasticity modeling for airline ticket pricing to optimize revenue across routes, fare classes, and seasons using booking history and competitor prices to inform pricing experiments.

Master Microsoft Excel basics for data analysis in supply chain and operations management, using essential formulas like if and sumif, pivot tables, vlookup and hlookup, plus practical exercises.

Learn to use pivot tables and Excel functions like vlookup, navigate data quickly with keyboard shortcuts, and generate insights from retail dataset through practical problems.

Master pivot tables in Excel and Google Sheets to analyze sales data, calculate sums and percentages, and build dashboards with slicers.

Learn advanced pivot table techniques to analyze Nikon and Nykaa product sales, convert dates to day names, apply vlookup, create monthly breakdowns, assess seasonality, and measure promotion effects using charts.

Analyze a dataset similar to Amazon Fresh to compute total revenue by product and category, assess country-level product sales, and visualize results with pivot tables and pivot charts.

Explore how to create and use line, bar, pie, donut, and stacked charts, including secondary axis and pivot table integration, with a primer on related functions, distributions, and relationship charts.

Learn to visualize performance data in Excel by using appropriate chart types to compare actual and target sales with bar charts, and add a secondary axis for revenue and units.

Forecast using past data, blending qualitative and quantitative insights to predict outcomes across weather, stock prices, Uber fares, and Amazon recommendations, using multiple linear regression and exponential moving averages.

Identify the problem, gather and clean data, explore time series patterns, and select and evaluate a model (moving average, exponential moving average, regression, ARIMA, SARIMA) for forecasting.

Explore qualitative and quantitative forecasting methods, including expert opinion, Delphi method, market research, moving average, exponential smoothing, and regression, for supply chain and operations planning.

Explore how to choose forecasting techniques based on data type, from time series to regression analysis, and apply simple, weighted, and exponential smoothing to handle stationary, trend, and seasonal data.

Explore time series forecasting by examining time series data, its components like trend and seasonality, and methods such as linear, double and triple exponential smoothing, plus regression.

Plot time series data with a scatterplot and an overlaid mean line to show how runs scored fluctuate above and below the average.

Explore time series components: trend, seasonality, and noise, and how granularity, frequency, and horizon rules shape forecast accuracy, using stock prices and smoothing methods like exponential smoothing.

Explore the components of time series data—trend, seasonality, and noise (white noise or residual)—using stationary, upward, and downward examples with scatter plots, for mba supply chain and operations management.

Explore how time series data reveals seasonality and a trend in e-commerce revenue, with a fourth-quarter spike due to Black Friday and Christmas, and a moving-average trend line.

Practice creating a time series and moving average plot of airline miles data, analyze seasonality, and explain the 2000–2001 decline, using the provided assignment and solution.

Explore rollover (naive) forecasting, moving averages, weighted moving averages, and exponential smoothing to forecast sales and inventory, and compare accuracy using absolute error with Excel ABS.

Explore how mad, sse, and mape assess time series forecast accuracy, showing how mape accounts for base differences and reveals the most reliable forecast.

Learn why time series must be stationary by removing trend and seasonality, keeping mean, variance, and autocorrelation constant, and converting non-stationary data into a stationary form.

Explore exponential smoothing in Excel to forecast univariate time series, using alpha and past forecasts to smooth level, trend, and seasonality, improving forecast accuracy and managing inventory.

Learn to perform simple exponential smoothing in Excel using the Data Analysis Toolpak, specify the input range, set damping factor as 1 minus alpha, and reinforce fundamentals with a chart.