The Complete Operations Management Course - In Excel

In the normal capacity model, account for cycle-time losses from size changes, material changes, downtime, and assignment, compute real capacity, and assess demand against 95% capacity.

Learn textile capacity by computing a weighted average speed from fabric weight (gsm) and product mix to derive average time per shirt and cycle time.

Explore ramp up capacity in manufacturing, adjusting machines, preventative maintenance, and efficiency losses through dynamic scheduling and size changes to meet demand.

Explore the case for optimization, define linear programming, and use Excel Solver to model objectives, variables, and constraints across archetypes like travel time, mixing, and packing problems.

Explore solving a traveling salesman problem in Excel with linear optimization: minimize total distance by defining objective, constraints, and using solver to reveal the optimal city sequence.

Explore the backpack problem, a linear optimization archetype, minimizing weight while meeting 1800–2200 calories, at least one serving from each food group, and at most two servings per group.

Learn the mixing problem, a linear optimization in gasoline blends using additives a, b, and c. Define variables, constraints, and a profit objective, then maximize profit with simplex lp.

Learn how to read a sensitivity report in Excel's simplex LP solver, interpret shadow prices, allowable increases/decreases, and how constraints on additives A, B, C affect profit.

Install and enable the solver add-in in Excel, then use it alongside other optimization tools in module three and add it to the quick access toolbar for easy access.

Compare deterministic and stochastic models to understand uncertainty, and learn how statistics and probability provide tools to quantify confidence, plan for extremes, and diagnose complex systems.

Explore probability and statistics, from descriptive to inferential, using sampling, margin of error, and tests like linear regression, t-tests, and ANOVA in operations modeling.

Explore the normal distribution and central limit theorem, examining symmetry, bell curve shape, mean, median, and mode equality, and using the data analysis tool pack to analyze skewness.

Explore the uniform and exponential distributions, highlighting equal probability and short vs long-tailed behavior, and apply them to operations modeling, including wait times, downtime, and simulations.

Explore a nine-tab statistics reference card that lets you adjust mu and standard deviation and compare distributions such as gamma and exponential.

Learn how inferential statistics reveal which operational factors drive capacity and efficiency, enabling improvements without capital investment by optimizing downtime and capacity models.

Learn to formulate null and alternative hypotheses and decide to reject or fail to reject using data, illustrated by heart rate differences between horror and family movies and normal distribution.

Examine how to perform one-way and two-way ANOVA in Excel to compare multiple groups, assess whether differences exist, and interpret interaction effects.

Explore the simple regression test to determine if two continuous variables have a linear relationship. Interpret the regression output, r-squared, and p-values, and distinguish it from correlation and causality.

Explore statistical process control by understanding process variation, distinguishing common and special variation, and seeing how reducing variation lowers defects and costs while boosting profits.

Define process capability as producing within specification by reducing variation; apply normal distribution to targets, and explain defects per million, yield, sigma level, and Cpk with real-world examples.

Explore a hands-on capability analysis in Excel, using length and width data to compare production against specification limits, compute yield, defects per million, and CPK.

Learn how process control charts relate to capability analyses, using real-time data, upper and lower control limits, and detection of special versus common cause variation to improve quality.

Explore process control charts to identify deviations in cycle time in manufacturing; learn to handle downtime events by excluding them to maintain accurate control limits and improve efficiency.

Learn to apply process control charting to binary pass/fail data by tracking proportions within subgroups, building p-charts, and adjusting control limits using Poisson-based reasoning.

Learn to perform control chart signaling directly in Excel worksheets using cycle time data, defining high and low signals with worksheet logic for real-time monitoring and easy root cause analysis.

Master simulation fundamentals, including Monte Carlo in Excel, to imitate process dynamics, assess risk and capacity, forecast demand, and answer critical questions through multiple trials with random events and distributions.

Explore a Monte Carlo simulation in Excel by modeling a manufacturing process with 10,000 trials, using normal and gamma processes, and interpreting results to plan staffing.

Use Monte Carlo simulations to optimize movie theater concessions inventory, forecasting weekday and weekend demand, cup sizes, and kernels, and assess costs with a projected histogram.

Use a Monte Carlo simulation on a call center process map with gamma-distributed wait times and routing logic to estimate happiness, discouragement, and the average time through the system.

Explore simulating with historic data in Excel, replacing distributions with real records. Use randbetween and vlookup to sample production, machines down, and downtime from historical data.