#002demand

Forecasts are consistently too high or too low

Are we repeatedly leaning in one direction?

⚡ 01 · Executive Summary

Why This Decision Matters

Forecast bias is a systematic tendency to over-forecast or under-forecast demand, leading to compounding inventory imbalances.

⚠️ Obvious Failure Mode

Planners often confuse random forecast error (unavoidable noise) with systematic bias (avoidable trend). They try to improve forecast algorithms rather than correcting simple behavioral bias, like sales teams adding pipeline optimism or purchasing managers buying excess safety buffer.

📐 Formulation Framework

Forecast Diagnostics, Time-Series Analysis mathematical optimization with explicit operational constraints.

🎛️ 02 · Interactive Parameter Simulator
Statistical Quality Control

Cumulative Sum (CUSUM) Bias Detector

Proves that positive and negative errors do not just 'average out' in operations. A persistent 5% bias creates compounding upstream supplier distortions via the bullwhip effect.

📐Mathematical Formulation#002 Model
S_t = max(0, S_(t-1) + (y_t - ŷ_t - k))
S_tCumulative Deviation: Running sum of forecast error deviations above reference allowance k
y_tActual Demand: Observed true customer orders in period t
ŷ_tForecast Demand: Predicted volume for period t
kSlack Allowance: Allowable random noise deadband parameter
#002 CUSUM Bias Detection+23 Over-forecast
Actual Demand
Forecast Baseline
CUSUM OFFSET:-0 u
SYSTEM BIAS:+23 Shift
⚖️ 03 · Key Tradeoffs & Constraints

Decisions that Govern Execution

#1Correction Factor: Adjusting raw forecasts based on historical over/under predictions.

#2Planning Level: Determining whether to correct bias at the product category level or individual SKU level.

📋 04 · Step-by-Step Diagnostic Playbook

Execution Sequence for Operators

1

Calculate Mean Forecast Error (MFE) and Tracking Signal over rolling 3-month and 6-month windows.

2

Segment SKUs into over-forecasted, balanced, and under-forecasted categories.

3

Apply a systematic correction factor (bias offset) to forecast outputs before supply planning.

4

Align sales incentives to prevent pipeline over-promising in CRM systems.

🗄️ 05 · Data Requirements & Schema

Required Telemetry Feeds

FieldTypePurpose
Historical ForecastsUnits by SKU & MonthProvides the planning baseline.
Actual Sales / ShipmentsUnits by SKU & MonthUsed as the baseline truth.
📊 06 · Key Performance Indicators

Diagnostic Scoreboard & Formulas

MetricMathematical FormulaInterpretation
Mean Forecast Error (MFE)Sum(Forecast - Actual) / NIndicates the direction and magnitude of the systemic bias.
Tracking SignalCumulative Error / Mean Absolute Deviation (MAD)Triggers an alert when forecast error strays too far from zero.
📚 07 · Canonical References

Foundational Literature

Forecasting: Principles and Practice
Rob J Hyndman and George Athanasopoulos
FIELD NOTEBOOK DISPATCH

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