Demand Planning Software RFP Template + Questions

A demand planning RFP template is only useful if it forces vendors to say no. Most RFPs are written so every vendor can answer "yes, we support that" to every line, and you end up scoring marketing language instead of capability. I've run this process from the buyer's side at a $250M manufacturer and sat on the other side helping a vendor respond. The questions below are the ones that separate a real demand planning engine from a slide deck. Use this template to build a scored, defensible shortlist your CFO will sign off on.

This is structured for a $100M-1B manufacturer or retailer evaluating modern platforms, including AI demand forecasting tools like Pigment, against legacy planning suites. Lift the sections directly into your RFP document.

How to weight the scoring (do this before you send it)

The biggest mistake in any demand planning RFP template is equal-weighting every requirement. Forecast accuracy and data integration should dominate. UI polish should not. Here's the weighting I'd defend to a steering committee.

Force each evaluator to score 1-5 with a written justification. "Feels modern" is not a justification.

Section 1: Forecasting engine and accuracy questions

This is where vendors hide. Make them be specific.

• Which forecasting methods do you support, and do you select best-fit per SKU automatically or apply one method globally?
• How do you handle intermittent and lumpy demand (Croston's, bootstrapping, ML)? What share of a typical 20,000-SKU portfolio routes there?
• Can you ingest external/causal drivers (price, promotions, weather, macro indices) and show which ones improved accuracy for which segments?
• Do you report forecast value-add (FVA) natively, comparing the statistical baseline to human overrides?
• How do you treat new product introductions with no history, and end-of-life transitions?
• How are models retrained, how often, and is it automatic or a services engagement?
• Prove it: run our anonymized 24-month history through your engine and report SKU-level MAPE and bias by ABC-XYZ segment.

That last line is the whole RFP. A vendor that won't run a proof-of-concept on your data is selling you a demo.

Section 2: Data integration and ERP fit

A demand planning implementation lives or dies on data. Ask:

• Pre-built connectors for our ERP (SAP, Oracle, NetSuite, Microsoft D365, Infor)? Native or middleware?
• Can you handle shipment data censored by stockouts without training the model to under-forecast best sellers?
• How do you manage product/location hierarchy changes, SKU rationalization, and reorg of the item master?
• Refresh frequency: batch nightly, or near-real-time? What's the latency from ERP change to updated forecast?
• How is outlier and one-time-event tagging handled, and can planners curate it?

Section 3: S&OP, consensus, and collaboration

• Does the platform support a structured consensus process with workflow, not just a shared spreadsheet?
• Can you enforce override governance (require a reason and owner above a threshold) and track whether overrides beat baseline?
• Scenario planning: how fast can we model a demand swing, a price change, or a supplier disruption and see the inventory and revenue impact?
• Audit trail on every forecast change, by user?

Scenario speed is where Pigment-class platforms tend to separate from legacy suites. A real demo should let you build a downside scenario live, in under five minutes, not "we'll get back to you."

Section 4: Inventory optimization linkage

Forecast accuracy only converts to cash through inventory. The demand plan must drive the inventory plan.

• Do you compute safety stock from forecast error distribution per SKU, or apply a flat days-of-supply rule?
• Can you set service-level targets by segment (98% on A items, 90% on C) and optimize stock against them?
• Do you surface stranded and excess inventory and tie it back to forecast bias?
• Multi-echelon optimization, or single-stage only?

Section 5: Cost, implementation, and the questions vendors hate

• 3-year TCO: license, implementation services, data engineering, and internal FTE time. Make them itemize services separately.
• Who does the implementation: your team, a partner, or ours? What's the realistic time-to-first-value?
• Reference customers in our size band and vertical running for 18+ months. Talk to a churned customer if you can find one.
• What's your typical post-go-live accuracy lift, measured, with a customer name attached?

The three demos that expose the truth

Don't watch the canned demo. Run these scripted exercises with your data:

1. The cold-start POC. Hand over anonymized history, ask for SKU-level MAPE by segment. Compare against your current manual baseline.
2. The live scenario. Have them model a 20% demand drop on your top category and show inventory and margin impact in the room.
3. The messy-data test. Give them a file with stockout gaps, a one-time bulk order, and a new SKU. Watch how the model handles each.

A vendor that aces all three is real. One that deflects on the POC is not.

Don't write the RFP before you know what you're buying

The sharpest move isn't filling out this demand planning RFP template faster. It's knowing which 20% of these questions matter most for your specific portfolio before you send it, so the scoring reflects where your money actually leaks.

We'll run a free planning-maturity assessment and a stranded-inventory teardown on your real SKU data, then help you weight this RFP around the segments costing you the most. Book a 30-minute call and we'll build your scoring model together before a single vendor sees the document.