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Key statistics

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

4 of 8

Track

Individual contributor

Family rungs priced

P1 · P2 · P3 · P4 · P5 · P6

—

manufacturing

Coordinates

2 / 4 spaces

pay@2 · 2026-07

What this role is

Experienced individual contributor who owns end-to-end demand forecasting for a significant product portfolio or business segment. At the Senior Professional (P4) level, the role owns a system or set of related features, devises new approaches, and drives multi-team / function outcomes. Technical lead for focused efforts; mentors several.

What this level does

Lead the demand planning cycle for a major business segment or multiple categories, integrating commercial, finance and supply inputs into an aligned forecast
Perform in-depth analysis of complex demand variables across interacting product lines and resolve conflicting inputs from Sales, Finance and Supply Chain
Select and standardize forecasting methods, segmentation logic and accuracy metrics across the planning team, improving model design and hierarchy
Mentor and coordinate the work of junior and career-level planners and lead process-improvement and automation initiatives for the segment
Drive the consensus forecast into S&OP as the demand-side owner, influencing inventory and production trade-off decisions

Knowledge applied: Advanced expertise in forecast model design, accuracy metrics and multi-category demand dynamics; standardizes methods for a team.
Complexity & problem solving: Performs in-depth analysis of complex, interacting demand variables and reconciles conflicting cross-functional inputs.
Collaboration & interaction: Coordinates across Sales, Finance and Supply Chain; influences inventory and production decisions and leads/mentors planners.
Typical experience: 8+ years, often with graduate education and demonstrated team leadership.

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?

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 P4 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$188k

San Francisco-Oakland-Fremont, CA$169k

Washington-Arlington-Alexandria, DC-VA-MD-WV$156k

Boulder, CO$154k

Seattle-Tacoma-Bellevue, WA$154k

Boston-Cambridge-Newton, MA-NH$147k

Lexington Park, MD$144k

Trenton-Princeton, NJ$143k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $113k; 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 P4 (P4 — Senior Professional) from the levels around it — this level only. Every word is real canon for this profile.

Scope: A system or set of related features
Autonomy: Self-directed; reviewed at critical decision points
Complexity: Complex, ambiguous problems; devises new approaches
Impact: Multi-team / function outcomes
Decision rights: Owns technical decisions for a system; influences adjacent design
Leadership: Technical lead for focused efforts; mentors several

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

Promotion

The next rung from P4 is P5.

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::E4	$401,582	confident0.06
Senior Manager, Demand Planning	jf_demand_forecast_planning::M4	$173,021	confident0.06
Advanced Demand Planning Analyst	jf_demand_forecast_planning::S4	$58,202	confident0.06
Demand Planner	jf_demand_forecast_planning::P3	$85,090	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

P4

jf_demand_forecast_planning::P4

Senior Demand Planner

Demand / Forecast Planning

no content fingerprint

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: 4 / 8

Content

content@2

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

No content fingerprint for this profile.

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 · P4review0.17

Materials · P4review0.17

Purchasing / Procurement · P4review0.17

Transportation · P4review0.17

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)