People Analytics Toolbox

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Key statistics

The identity block — every fact is canon, none is generated.

3 of 8

Track

Individual contributor

Family rungs priced

P1 · P2 · P3 · P4 · P5 · P6

13-0000

manufacturing

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Builds and maintains demand forecasts that guide inventory and component planning decisions. At the Mid-Level Professional (P3) level, the role owns features or a sub-system end-to-end, adapts existing approaches, and drives project / team outcomes. Mentors juniors informally.

What this level does

Own the end-to-end demand forecast for a product category or region, planning the monthly forecast cycle independently against accuracy and bias targets
Lead the consensus process with Sales, Marketing and Supply Chain to reconcile bottom-up and statistical forecasts into a single number for the assigned portfolio
Evaluate demand drivers — seasonality, promotions, cannibalization, market trends — and select the appropriate statistical or judgmental method per SKU segment
Run scenario and risk assessments for new product launches and phase-in/phase-out transitions, quantifying inventory and service impact
Present forecast performance and root-cause analysis of accuracy gaps in S&OP reviews and drive corrective actions

Knowledge applied: Deep working knowledge of forecasting methods, segmentation and consensus planning applied independently across a category or region.
Complexity & problem solving: Evaluates diverse, identifiable demand factors — seasonality, promotions, cannibalization — and selects appropriate methods per segment.
Collaboration & interaction: Networks with senior sales, marketing and supply professionals to drive the consensus forecast; presents in S&OP reviews.
Typical experience: 5+ years (BA), 3+ years (MA), or PhD without experience.

Skills at this level

Statistical Demand Forecasting: — Builds baseline demand forecasts by applying statistical models to historical sales data and seasonality, then overlays market trends and commercial signals to project future customer demand at SKU, category and regional levels.
Forecast Accuracy & Bias Management: — Measures forecast performance against actuals using accuracy and bias metrics, diagnoses the root cause of error, and initiates corrective adjustments to improve forecast reliability over successive cycles.
Consensus Demand Planning: — Reconciles statistical, sales and marketing forecasts into a single agreed number by coordinating demand planning activities with Commercial, Finance and Supply Chain teams to drive a single-number mindset.
S&OP Participation & Facilitation: — Prepares demand inputs, exception reports and scenario views for Sales & Operations Planning meetings, and at senior levels transforms S&OP from scheduling into strategic decision-making that connects demand, supply and financial planning.
New Product & Lifecycle Forecasting: — Forecasts demand for new product launches and phase-in/phase-out transitions using scenario planning and risk assessment where little or no history exists, quantifying inventory and service impact.
Inventory & Supply Alignment: — Translates the demand forecast into inventory and replenishment implications, aligning demand and supply to minimize stockouts and excess inventory across the assigned portfolio.
Demand Planning Software Administration: — Configures and maintains demand planning tools and master data, including SAP Integrated Business Planning, to run forecast models and manage the planning hierarchy for the business.
SAP Integrated Business Planning (IBP): — Uses SAP IBP to generate statistical forecasts, manage demand and supply data, run consensus planning workflows and produce the volume forecast that feeds S&OP.
Scenario & Risk Analysis: — Models alternative demand outcomes for launches, market shifts and supply disruptions, quantifying the financial and service-level exposure of each scenario to inform planning decisions.
Cross-Functional Influence & Judgment: — Applies business discernment and combines hard analytical skills with interpersonal and presentation skills to influence decisions across Sales, Marketing, Finance and Supply Chain beyond the planning function.
Strategic Supply Chain Leadership: — Cultivates a strategic mindset, masters risk management and scenario planning, and influences and negotiates with senior stakeholders while shaping the demand-planning and supply chain strategy.
Process Improvement & Automation: — Drives simplification, standardization and automation across demand planning activities to improve forecast accuracy, reduce manual effort and ensure compliance across the function.

Model-authored from 20 retrieved sources, then adversarially reviewed by an independent judge panel and held to a minimum-evidence floor before publication. Distinct from corpus-extracted content, which is labelled as such.

Pay across the ladder

Demand / Forecast Planning — median with , by . Real canon data.

Pay figures are modeled estimates from our market equation — level-differentiated, with honest-wide bands. They refresh as new leveling evidence lands; treat per-role dollars as directional.

Demand — is the field growing, or just turning over?

SOC 13-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

+29.4%Logisticians · United States

182,050 → 235,640 jobs

26,400 openings / year, split into:

growth 4,025

replacement / exits 22,375

Growth — new jobs (15%)Exits — backfill (85%)

15% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +16.7%
Growth share of hiring: 15%
Exit / retention pressure moderate: 85%
Openings / year: 26,400

Two quantities, kept apart: observed = employment growth differenced from two BLS OEWS vintages (what already happened); projected = BLS Employment Projections 2024–34, openings decomposed into growth + exits (growth derived from projected employment change). Neither is a pay figure.

Where this role sits — growth vs. pay

level P3 held

Demand axis is projected growth (not raw openings — that just measures churn). A curated, de-duplicated set of recognizable occupations held at this level; this role is highlighted.

Where it pays — by metro

OEWS metro median

San Jose-Sunnyvale-Santa Clara, CA$142k

San Francisco-Oakland-Fremont, CA$127k

Washington-Arlington-Alexandria, DC-VA-MD-WV$118k

Boulder, CO$116k

Seattle-Tacoma-Bellevue, WA$116k

Boston-Cambridge-Newton, MA-NH$111k

Lexington Park, MD$108k

Trenton-Princeton, NJ$108k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $85k; the demand-cube initiative will add growth-by-metro.

What changes across the ladder

Level-distinction canon isn't available for the adjacent levels of this profile yet.

Level differentiators

What separates P3 (P3 — Mid-Level Professional) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Features or a sub-system end-to-end
Autonomy: Works independently on standard work; reviewed on the non-standard
Complexity: Diverse problems; adapts existing approaches
Impact: Project / team outcomes
Decision rights: Owns implementation decisions for own scope
Leadership: Mentors juniors informally

Full level-by-level matrix (side by side across the ladder) lives on the Ladder drill-down — not here.

Promotion

The next rung from P3 is P4.

Performance criteria for this profile render in the assessment kit →

People also watch

Nearest coordinate neighbors — same , same level. Each row's (confident / review / no match) comes from its — computed, not curated.

Profile	Key	Median	Δ structural
Chief Demand Officer	jf_demand_forecast_planning::E3	$363,257	confident0.06
Demand Planning Manager	jf_demand_forecast_planning::M3	$124,177	confident0.06
Senior Demand Planner	jf_demand_forecast_planning::S3	$51,142	confident0.06
Demand Planner	jf_demand_forecast_planning::P2	$74,658	confident0.06

Capability cluster

O*NET 13-1081

Core to this role. Click any element with an ⓘ for its O*NET definition. A job-specific capability model (SkillFrame) will sharpen this and add worked examples.

The Job Swatch

P3

jf_demand_forecast_planning::P3

Demand Planner

Demand / Forecast Planning

The swatch is JobFrame's core primitive: a hueset deterministically by the role's superfunction, the profileKey as its color code, the level ladder, and a content fingerprint. Two swatches that look alike are alike.

Because every role carries computable coordinates, similarity, leveling, and pricing become measurable instead of arguable— the whole point of "Pantone for jobs."

Structural

structural@1

Where it sits— position in the work taxonomy + level (weighted Gower, level > function > focus).

Track: P
Superfunction: manufacturing
Function: supply_chain
Family: demand_forecast_planning
Universal level: 3 / 8

Content

content@2

What it's about— a fingerprint over the role's tasks & capabilities.

Two roles can sit far apart structurally yet be close in content — that gap is itself a signal.

3-state matching

How any two swatches compare — a real distance in coordinate space, resolved to a calibrated verdict (never a fake "94% match").

Distribution Center / Warehouse · P3review0.17

Materials · P3review0.17

Purchasing / Procurement · P3review0.17

Transportation · P3review0.17

Editions — frozen & versioned

structural@1live

Released 2026-06-17

content@2live

Released 2026-07-28

pay@2live

Released 2026-07-02

semantic@1live

Released 2026-07-02

Coordinates belong to a frozen, versioned edition — a coordinate computed today still means the same thing next quarter. Re-fitting issues a new edition, so comparisons are never silently redefined.

How to read this profile

Every number here is traceable — to a named source, a stated method, and an as-of date — and each value is labeled by how we know it: observed from data, computed by a model, projected forward, or governed canon. Nothing is a black box, and nothing is invented to fill a gap.

Where every number comes from

What	Source & method	Edition	State
Median pay + range	JobFrame pay equation (function base × family multiplier × level × focus differential)	pay@2	computed
Pay across the ladder	Same equation applied at each universal level	pay@2	computed
Demand — growth & openings	BLS Employment Projections; openings decomposed into growth + exits	EP 2024–34	projected
Pay by metro	BLS OEWS metropolitan medians	OEWS 2024	observed
Level distinctions	JobFrame universal-level canon (scope · autonomy · complexity · impact · decision rights · leadership)	canon	canon
Capability cluster	O*NET Knowledge / Skills / Abilities	O*NET 13-1081	computed
Coordinates	JobFrame coordinate system — structural (weighted Gower) + content fingerprint	structural@1 · content@2 · pay@2 · semantic@1	computed
Related roles	Nearest neighbors in the structural coordinate space	structural@1	computed

The principles behind the numbers

Ranges, not false precision

Pay is P10 · median · P90, never a single misleading number.

Three honest states, never a fake %

Role matches resolve to confident / review / no-match — a calibrated verdict, not a made-up percentage.

Provenance on every value

Each number carries whether it's observed, computed, projected, or canon — so you always know how much to lean on it.

Gaps shown as gaps

When a figure isn't available at the grain you need, we say so — we don't fabricate a number to fill the cell.

Frozen, versioned editions

Pay and coordinate editions are pinned, so a comparison you make today still means the same thing next quarter.

Privacy by min-N

Any aggregate over people is suppressed below a minimum count, so no individual is identifiable.

Freshness

As of2026-07-30Editioncanon-v2@2.1.0-completeSourcecanon-v2 cell + salary-benchmark (model)