Inventory Replenishment Decision Diagnostic

The decision we assess

Most retailers already have replenishment logic somewhere—in a planning platform, ERP, spreadsheet or planner workflow. The diagnostic evaluates whether that ecosystem consistently surfaces the right decision and where manual effort or overrides indicate a gap.

• Which SKU-location combinations genuinely need attention?
• Where does the current tool recommend replenishment, hold or transfer?
• Where and why are planners overriding the recommendation?
• What quantity is actually executed?
• Which exceptions or override patterns repeat?

What data we use

V1 starts with an agreed extract from the systems the retailer already uses. No new production application is required.

• SKU / product and store / location
• Recent sales history
• Current on-hand inventory
• Incoming / open inventory
• Lead time and pack size
• Target WOS / safety stock / business rules where available
• Current tool recommendation and quantity
• Planner override quantity and reason
• Actual executed quantity
• Historical outcomes where available

What Averin delivers

The output is a consulting decision pack rather than deployed software.

• Replenishment Decision Map
• Priority Exception Workbook
• Current-tool vs planner-override vs Averin comparison
• Data-quality and rule observations
• Historical / policy assessment where data permits
• AI and automation opportunity roadmap
• Executive readout and planner workshop

How the engagement works

• Observe the current planner / buyer workflow
• Agree the data extract and decision scope
• Map retailer policies, thresholds and existing-system logic
• Run independent exception and decision analysis
• Review top differences and overrides with planners
• Refine findings using planner context
• Deliver decision pack and practical improvement roadmap

Why this is different from a platform implementation

Averin does not begin by assuming the retailer needs another application or that its current planning platform is wrong. The diagnostic works across Blue Yonder, o9, ERP, Excel or internal tools and focuses on decision evidence.

• Vendor-independent assessment
• No new application deployment for V1
• Existing-tool recommendations explicitly evaluated
• Planner overrides treated as valuable decision evidence
• Actual execution captured separately
• AI recommended only where it solves a demonstrated decision problem

What happens after the diagnostic

Only if the analysis proves enough value should automation become a technology project.

• Improve existing rules or configuration
• Implement better exception prioritization
• Add recommendations inside an already approved BI or data environment
• Integrate improvements into Blue Yonder, o9 or another existing planning workflow
• Introduce AI explanation / triage with governance
• Move toward assisted or controlled automation only when evidence supports it