Inventory Replenishment & Exception Decision Sprint

The decision we improve

Most retailers already have reports. The harder problem is deciding what actually needs attention and what action to take. The sprint focuses on the recurring weekly decision: replenish, hold, transfer or investigate.

• Which SKU-location combinations are at genuine stockout risk?
• Where is inventory building faster than demand?
• What should be replenished now versus monitored?
• Which exceptions deserve planner attention?
• What decision rules are planners already using implicitly?

What data we use

The sprint is designed to start with practical data already available to the retailer rather than waiting for a large data transformation.

• SKU / product identifier
• Store, channel or location
• Sales history
• Current on-hand inventory
• Incoming / open inventory where available
• Price and cost where useful
• Lead time or replenishment cadence
• Existing target-stock, WOS or business-rule fields where available

What Averin builds

The output is a decision prototype, not a generic dashboard or chatbot.

• Replenishment Decision Map
• Explicit thresholds, constraints and exception logic
• Prioritized exception list
• Explainable recommended actions
• Historical back-test on prior periods
• 90-day AI adoption and operating-model plan

How the two weeks work

• Days 1–2 — observe the current planner / buyer workflow and select one decision
• Days 3–5 — map the decision logic, data and exceptions
• Days 6–8 — build the working decision prototype on retailer data
• Days 9–10 — back-test, review with the planner and refine recommendations
• Final readout — findings, prototype, adoption path and next-step recommendation

Why this is not replaceable by a generic AI prompt

A prompt can explain replenishment principles. It cannot know how your team actually decides, which data is trusted, what constraints apply, what exceptions matter or whether the resulting recommendations would have worked on your historical data.

• Retailer-specific decision logic
• Retailer-specific data context
• Planner workflow and judgment
• Explicit constraints and controls
• Historical evidence through back-testing
• Reusable decision IP rather than one-time analysis

Commercial model

The standard sprint is deliberately small enough to approve and execute without a large transformation program.

• CAD $5,000 fixed professional fee
• Approximately two weeks
• One replenishment / exception decision
• One working prototype
• One historical back-test
• One 90-day adoption plan
• Limited design-partner pilot may be available for a selected retailer in exchange for feedback and permission to use anonymized learnings