Case Study: Forecasting Supply Chain Disruptions in Manufacturing
What this typically involves
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Understanding disruption drivers (supplier delays, demand surges, etc.)
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Time series data analysis (identify patterns/trends)
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Building forecasting models (e.g., moving average, exponential smoothing)
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Interpreting outputs for decision making
Planned deliverables
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Summary of problem statement, data description
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Visual time series plots
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Model selection and justification
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Forecast results and accuracy metrics (MAPE/MAE)
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Recommendations for supply chain planners
Core tasks
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Review pricing vs. sales quantity data
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Compute price elasticity of demand
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Segment analysis by route/class/date
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Suggest pricing strategies based on elasticity outcomes
Planned deliverables
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Definition and formula for elasticity
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Regression or elasticity estimates
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Interpretation tables
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Strategic insights for airline pricing teams
The exercises are focused on practical forecasts and visualizations in Excel:
A. Time Series Plotting in Excel-
Clean imported data
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Generate line charts with appropriate labels
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Highlight trends and seasonality
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Apply historical revenue data to forecast future revenue
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Use built‑in Excel Forecast Sheet or functions (e.g., FORECAST.ETS)
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Use Excel Data Analysis ToolPak
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Configure smoothing factor and produce forecast
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Combine historical demand with planning forecast
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Compare multiple forecast outputs
Each exercise will include:
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Step‑by‑step Excel instructions
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Screenshots or sample formulas (if requested)
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Interpretation of results