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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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 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.