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Inventory Intelligence

Deadstock Analysis Engine & Trend Comparison Workbook

Inventory health workbook system that classifies stale, dormant, dead, and never-sold stock, layers in pricing exposure, and compares workbook periods to track recovery and deterioration.

PythonPandasOpenPyXLExcelInventory IntelligenceDeadstock ClassificationTrend ComparisonRecovery Tracking

Inventory health command view

Deadstock Analysis Engine

Classification, price review, trend comparison, and workbook delivery for aging inventory

trend-ready
01

Profile

Input QA

02

Classify

Aging logic

03

Price Review

Exposure

04

Compare

Transitions

05

Deliver

Workbook

Classification buckets

Stale
Review slowing movement
Dormant
Low activity and rising risk
Dead
High exposure and action needed
Never Sold
No sales history

Trend comparison preview

period review
BucketPartsExposureΔ
Stale184$38.4K+12
Dormant96$27.8K-8
Dead61$19.6K+6
Recovered23$7.1K-14
Newly DeadRecoveredMarkdown ReviewExposure Summary

Workbook outputs

Deadstock SummaryDormant ReviewTrend ComparisonRecovered ItemsPricing Review
Stale / Dormant / Dead
classification model
Period Comparison
transition tracking
Workbook Outputs
decision-ready review

Business problem

Aging inventory needed better classification, pricing visibility, and period-to-period comparison. Teams could identify that inventory was getting older, but they needed a more structured way to separate stale, dormant, dead, and never-sold parts while also understanding cost exposure and recovery pressure.

The workflow also needed a way to compare periods so leaders could see whether deadstock conditions were improving, worsening, or shifting between buckets over time.

System built

Built a deadstock analysis engine and trend comparison workbook with stale, dormant, dead, and never-sold classification logic, pricing and markdown review, cost exposure visibility, workbook tabs, and two-period comparison outputs.

The result is a structured inventory intelligence system that moves beyond a static aging report and turns inventory cleanup into a clearer operational review process.

Inventory health signals

Signals reviewed

The workbook reviews aging, movement, pricing, classification, and transition signals before presenting recovery-oriented outputs.

Days since last sale
Days since receipt
Quantity on hand
Inventory exposure
Dealer Net
List price
Landed cost
Deadstock flag
Dormant flag
Never-sold flag
Newly dead status
Recovered status

Inventory story

How it works

01

Profile

Normalize inventory inputs and prepare the fields needed for aging, pricing, and exposure review.

The engine starts by validating source columns and profiling inventory so downstream classification rules use consistent inputs.

02

Classify

Assign stale, dormant, dead, and never-sold classifications using aging and movement logic.

This stage turns raw inventory records into business categories that can be reviewed operationally.

03

Price Review

Layer in DNet, landed cost, list price, markdown logic, and exposure calculations.

Pricing context helps show not just which parts are aging, but how much value is tied up and what recovery actions may fit.

04

Compare

Compare current and prior workbook snapshots to track status changes and trend movement.

The comparison layer highlights newly dead inventory, recovered items, and movement between classification states.

05

Deliver

Generate review-ready workbook tabs, transition outputs, and recovery-oriented summaries.

The output layer packages the analysis into decision-ready sheets for pricing review, exposure review, and action planning.

Workbook layers

What the system coordinates

Classification layer

Applies stale, dormant, dead, and never-sold logic so aging inventory can be reviewed in consistent categories.

Pricing review layer

Brings in cost and pricing context so exposure, markdown pressure, and recovery opportunities can be assessed.

Trend comparison layer

Compares current and prior workbook states to surface newly dead, recovered, or reclassified inventory.

Workbook output layer

Produces structured tabs and summaries that support pricing, procurement, and inventory cleanup review.

Impact signals

What the workbook improved

Deadstock and dormant classification at the part level

Markdown exposure logic tied to pricing review

Newly dead and recovered transition tracking

Period-over-period visibility for inventory cleanup review

Workbook outputs that support recovery and disposition decisions

Operational value

Aging inventory review that supports action

Clearer inventory cleanup

Helps teams separate active stock from aging inventory and focus attention on the parts creating the most drag.

Better pricing conversations

Connects classification to pricing pressure so markdown and recovery discussions have more operational context.

Trend visibility

Shows whether deadstock conditions are improving or worsening instead of relying on a single static snapshot.

Decision-ready workbooks

Turns analysis into structured review sheets that are easier to hand to procurement, operations, or leadership.

Why this project matters

Deadstock becomes clearer when inventory is classified, compared, and priced in context.

This project shows how aging inventory can be turned into a clearer decision story. Instead of only listing old parts, the system classifies inventory, layers in pricing exposure, and compares periods so the business can see what is getting worse, what is recovering, and where attention is needed.

The value is not just identifying deadstock. The value is making recovery, markdown, and cleanup discussions more structured and easier to act on.

Confidentiality note

Visuals and descriptions are sanitized conceptual representations. They do not expose private company data, customer records, credentials, raw exports, internal pricing, operational screenshots, or proprietary source files.