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Project 07AdvancedOperations7-10 focused hoursRetail forecasting workflow
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Demand Forecasting for Retail

Create a structured workflow for forecasting future product demand using historical sales and time-based features.

Time SeriesFeature EngineeringValidationForecasting
FRAME→DATA→ANALYZE→MODEL→EVALUATE→COMMUNICATE

BUSINESS CONTEXT

Why this project matters

A retail operations team needs better forecasts to plan inventory, staffing, and replenishment. Forecasts that ignore seasonality, promotions, or validation timing can cause costly stockouts or overstock decisions.

PROJECT OBJECTIVE

What you are expected to accomplish

Build a forecasting workflow that uses historical demand patterns, time-based features, and realistic validation to estimate future product demand.

DATASET OVERVIEW

Expected dataset structure

Expect historical sales records by date, store, or product, along with calendar effects, promotions, prices, and potentially inventory or stockout indicators.

Main entities / rows

One row per product-date, store-date, or product-store-date combination

date

date

Time index used for trend and seasonality analysis.

store_id

string

Store or fulfillment location identifier.

product_id

string

Product or SKU identifier.

units_sold

integer

Target quantity to forecast.

unit_price

float

Selling price that may influence demand.

promotion_flag

boolean

Whether a promotion was active on that date.

holiday_flag

boolean

Calendar event indicator for seasonality shifts.

stockout_flag

boolean

Signal that observed sales may be capped by inventory availability.

Data quality issues to expect

  • Missing dates may represent zero demand, missing data, or missing product-store coverage.
  • Stockouts can make observed sales lower than true demand.
  • Random train-test splits are inappropriate for time-dependent forecasting tasks.

QUESTIONS TO ANSWER

Focus the work around meaningful decisions

  1. 01

    What trend and seasonality patterns exist in the historical demand series?

  2. 02

    How do promotions, price changes, and holidays affect demand?

  3. 03

    Which lagged or calendar features are likely to improve forecasts?

  4. 04

    How should the validation design reflect future forecasting conditions?

  5. 05

    Where does forecast error matter most from an operational perspective?

PROJECT ROADMAP

Move through the case study in a practical sequence

  1. 01

    Step 01

    Define the forecasting horizon

    Clarify how far ahead the forecast should predict and at what granularity.

  2. 02

    Step 02

    Inspect time coverage

    Check continuity, missing dates, stockout effects, and the target grain.

  3. 03

    Step 03

    Clean the demand series

    Standardize date handling and decide how to treat missing or zero-demand periods.

  4. 04

    Step 04

    Explore trend and seasonality

    Visualize recurring patterns, holidays, promotions, and outliers.

  5. 05

    Step 05

    Engineer time-based features

    Create lags, rolling summaries, and calendar features without leaking future data.

  6. 06

    Step 06

    Validate chronologically

    Use time-aware splits and compare forecast error across windows or segments.

  7. 07

    Step 07

    Communicate planning insights

    Summarize forecast performance, risk areas, and operational use cases.

TASKS / MILESTONES

Concrete work to complete

  • Define the target grain and forecast horizon before modeling.
  • Check for missing dates and determine whether they mean zero demand or incomplete data.
  • Visualize historical demand to identify seasonality, spikes, and structural shifts.
  • Create lag, rolling, and calendar features using only past information.
  • Compare at least one simple baseline forecast against a richer workflow.
  • Evaluate forecast error using time-aware validation.
  • Explain where the forecast is reliable and where operational caution is needed.

SUGGESTED VISUALIZATIONS

Visuals worth creating

  • Historical demand time-series line chart
  • Seasonality comparison by week or month
  • Promotion versus non-promotion demand comparison
  • Forecast versus actual line chart on holdout periods
  • Error summary by product, store, or horizon

DELIVERABLES

What the learner should produce

  • Cleaned time-series dataset or panel
  • Forecasting workflow notebook
  • Time-based feature set
  • Validation and error summary
  • Operational forecast recommendations

SUCCESS CRITERIA

What strong completion looks like

  • The forecast target and horizon are explicit and realistic.
  • Feature engineering avoids future leakage.
  • Validation follows time order rather than random splitting.
  • The final output explains where forecasts are useful and where risk remains.

GUIDANCE

Support when you need a nudge

Reveal practical hints, checkpoints, and framing help without exposing a full finished solution.

Complete this project when the work is yours

Use this only after you have worked through the roadmap, milestones, and your own analysis.

KEY LEARNING OUTCOMES

Skills reinforced by this project

  • Structure a forecasting problem with realistic validation.
  • Engineer time-based features without leaking future information.
  • Interpret trend, seasonality, and promotion effects in retail data.
  • Explain forecast usefulness and limitations to operations stakeholders.
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