Program 02 · Retail & eCommerce
A decision-intelligence layer for retail and eCommerce brands. We join marketing demand signal with operational reality, so the brand stops driving demand for what it cannot fulfill, and stops sitting on what no one is asking for. Weekly decisions, evidence-backed, delivered inside the data platform your team already uses.
“Are we driving demand for the SKUs we can actually ship?”
Recommendation: Reallocate next month's hero-SKU paid spend toward the higher-margin SKU with production headroom and a stronger recent audience signal. Evidence: capacity utilization, supplier lead time, recent paid-channel cohort performance, channel margin spread.
illustrative · not a real customer
The cost of marketing-ops disconnect
Marketing plans on a fortnightly cadence. Operations plans on a quarterly cadence. They meet each other during escalations and end-of-month reviews. The gap costs real money, silently, every cycle.
The paid campaign performs. The site converts. Checkout fails because the warehouse is out of stock, the supplier has a queue, or the production line is committed elsewhere. The brand pays for traffic it cannot fulfill, and pays again to win that customer back.
Production runs were locked in before the demand signal arrived. By the time the campaign delivers a weak result, the unit is on the warehouse floor. Markdowns absorb what marketing should have caught two cycles earlier.
Marketing spend lives in one stack. Inventory and lead times in another. Margin and supplier capacity in a third. No single tool joins the four, so the decision never gets made on a weekly cadence. Only retroactively, in the variance review.
The shift
The point isn't another dashboard. The point is to change the question the team is answering on Monday morning.
“Why did we miss the quarter?”
A retrospective view. Variance review pulls marketing into one room, ops into another, finance into a third. Each team explains its own numbers. The connection between marketing demand and operational fulfillment is reconstructed after the fact, when nothing more can be done about it.
“What should we ship for, this week?”
A forward-looking view. Every Monday, the team sees a small set of specific reallocations, supported by capacity, supplier, margin, and recent demand-signal evidence. Marketing and operations approve them in one meeting. The cycle stays aligned in flight, not after.
What makes this different
Most retail tooling stops at the first layer. Demand-to-Production Intelligence runs through all four (descriptive, conversational, predictive, recommendation) because that is how decisions actually get made.
A single read across the SKU portfolio that shows where paid-channel spend, projected demand, current inventory, production capacity, and supplier lead times have drifted apart. Built for the joint CMO and COO weekly review. Every chart answers a question, not just displays data.
A natural-language interface across the joined data (spend, inventory, production, supplier capacity, margin). Returns a written answer plus the underlying view, the data sources used, and the time window analyzed. The system declines questions it cannot answer rather than guessing.
Explainable predictions on which SKUs are at fulfillment risk, which channel allocations are about to drift, and which supplier touchpoints carry rising risk. Every prediction is traceable to the underlying evidence: capacity utilization, lead-time variance, recent paid-cohort behavior, channel sell-through.
Weekly recommendations that a senior CMO and COO would take seriously, ranked by expected impact and confidence, traceable to the four joined data sources. Each comes with the constraint logic that produced it and the alternatives it ranked above. Accept, modify, or dismiss; the system captures the feedback.
How it works
The architecture is deliberately decoupled. The program runs inside the brand's existing Microsoft Fabric, Snowflake, or Databricks environment. The intelligence layer above stays stable as the underlying stack evolves.
Cross-system joins across the four sources: marketing-spend telemetry (Meta, Google, TikTok, paid-social attribution), inventory positions (ERP, warehouse, in-transit), production lead times and supplier capacity (ERP production module, PLM, supplier portals), and channel margins. Standard data engineering inside the brand's existing platform, not a new warehouse, not a new SaaS.
A constrained optimization model over the joined data, paired with explainable demand sensing. Surfaces weekly reallocation recommendations with the full constraint logic, the alternatives considered, and the confidence range. Brand-specific calibration is the consulting work. The framework is reusable, the tuning is bespoke.
A Reconciliation Agent that runs the decision layer on a weekly cadence, presents the recommendations to the joint CMO and COO review, and learns from approve, modify, and reject feedback. Human-in-loop by default. No procurement, no supplier-facing action, no spend reallocation auto-executed.
Who this program is built for
This is not for everyone. Below is an honest read on whether Demand-to-Production Intelligence is the right program for your stage.
How an engagement runs
A 30-minute conversation with a senior team member. We confirm category, the shape of the marketing-ops gap, and whether this is the right program, before anything else.
A focused diagnostic on your category, data sources, and current decision cadence. We agree on the specific decisions the program is meant to improve before any build starts.
We stand up the data joins, the decision model, and the Reconciliation Agent inside your existing data platform. The semantic model and intelligence layer are yours at handoff.
The Reconciliation Brief runs in the rhythm of your business. The program is architected to extend into adjacent decisions (marketing and operations execution) when the team is ready.
FAQ
What buyers ask before booking a call about Demand-to-Production Intelligence. Don't see yours? Talk to a senior team member.
Ready when you are
A focused diagnostic on your category, your data sources, and your current decision cadence, surfacing the specific reconciliation gaps that are costing you this quarter. Use it to scope an engagement, or use it on its own to bring clarity to the next leadership conversation.
