Inventory risk should surface before it becomes an operational fire.
Inventory risk should surface before it becomes an operational fire.
Inventory risk should surface before it becomes an operational fire.
Inventory risk should surface before it becomes an operational fire.
I redesigned a fragmented reporting workflow into one exception-first investigation system—helping inventory planners see what changed, understand why, and decide what to do next.
I redesigned a fragmented reporting workflow into one exception-first investigation system—helping inventory planners see what changed, understand why, and decide what to do next.
I redesigned a fragmented reporting workflow into one exception-first investigation system—helping inventory planners see what changed, understand why, and decide what to do next.
I redesigned a fragmented reporting workflow into one exception-first investigation system—helping inventory planners see what changed, understand why, and decide what to do next.
Risk lived across spreadsheets, legacy dashboards, and disconnected tables. The work was not reading a chart — it was assembling enough context to trust the next move.
01
Stockouts
Reconstructed / estimated$50K–$200K cost
Empty shelves = lost sales
Outages cascaded across DCs before anyone owned the SKU exception.
02
Overstock
Operational riskWarehouse gridlock
Capital tied up + operational waste
Buffer inventory rose because planners couldn’t see true weeks of supply.
03
Delays
Operational riskRipple effects
Supply chain disruptions
Late insight turned local misses into multi-DC recovery work — the cost lived outside any one report.
Planner spreadsheetRough WOS priority list planners rebuilt outside the weekly review
Legacy PrecimaOlder reports — density without a clear exception path
“
I spend 4 hours a day just trying to find which SKUs need attention. By the time I figure it out, it's often too late. I need the system to tell me what matters and why it matters—not show me 10,000 rows of data.
Pain points
What the daily ritual cost
4+
Hours
Average daily time hunting critical SKUs across 3 systems.
6 Sources
6
Sources
Manual joins across OTIF, WOS, forecast, inbound — context rebuilt each cycle.
45% Adoption
45%
Adoption
Most planners still trusted Excel over the weekly platform.
24+ Hours lag
24+
Hours
Lag between risk becoming visible and a coordinated response.
[03]Who needed what decision
The Inventory Planner
The real job: finding the right problems to solve first.
Responsibilities
Prevent stockouts across 12+ DCs without bloating on-hand
Cut excess where unit turns fall into the red
Coordinate transfers when one DC is long and another is short
Metrics tracked
Stockout RateRisk monitor
Weeks of SupplyBalance signal
Unit TurnsEfficiency
Aged InventoryCapital drag
A typical day
Four calendar moments from Monday triage to the actions that stick overnight — framed in Inventory Efficiency report language.
[04]Success criteria
From reporting data to directing decisions.
Speed, confidence, and daily adoption — the bar that had to beat spreadsheet ritual.
01
Speed to Insight
Reduce the time it takes to identify a critical stock risk from hours to minutes.
Time-to-Detect
< 5 mins
02
Decision Confidence
Enable planners to trust system insights without needing to manually validate raw data.
Trust
4.5 / 5
03
Workflow Adoption
Make the tool the planner's primary workspace, replacing the daily spreadsheet ritual.
Daily Active
> 85%
[05]Core problem
Four modes that made investigation expensive.
01
Fragmented
3 separate legacy systems + Excel reports requiring manual data reconciliation. Planners opened multiple tools just to answer one question: which SKUs need attention today?
02
Reactive
Issues surfaced days after they became critical. By the time a stockout appeared in reports, it had already cost the business thousands in lost sales.
03
Opaque
No explanation for why a SKU was flagged. Planners didn't trust automated alerts because the system never showed its reasoning — it was a black box.
04
Overwhelming
Thousands of rows with no prioritization logic. Everything looked equally urgent, so nothing felt urgent — more time finding problems than solving them.
[06]Understand the system
Map the ecosystem before the interface
Before designing interfaces, we mapped the entire supply-chain ecosystem — people, feeds, and handoffs that decide what gets fixed first.
What I did
Stakeholder Mapping
Mapped planners, buyers, DC ops, and leadership to see who owned each decision handoff.
4 User Groups
Data Dependency
Traced WOS, forecast, transfers, and assortment feeds that fragmented every investigation.
6 Data Sources
User Shadowing
Sat with planners through morning triage — spreadsheet ritual, tools, and exception loops.
40+ Hours
Log Analysis
Reviewed audit trails to find where context dropped between heatmap, queue, and transfer.
Audit Logs
Report architecture
Core decisions that shaped the board
Core decisions
A
Default view = exceptions only
Open on SKUs at High/Medium risk — the full catalog waits one intentional drill away.
B
Top 10 prioritization
Surface highest revenue-impact and exposure items first so triage beats alphabetical hunting.
C
Risk scoring on the board
Explainable Red / Amber / Green classification keeps severity readable without a rebuild.
D
Progressive disclosure
Scan → understand → act stays one path — context (SKU · DC · period) never drops between views.
[07]Concept exploration
Pressure-test paths that kept spreadsheet habits
Three catalogue-led concepts failed for investigation depth, guardrails, or cognitive load — convergence locked the exception-first path.
Concept exploration
Supply Chain / Reports / Catalogue
Report Page
01
Catalogue → table & chart report
From the reports catalogue into a chart-and-table report — fast scan from a familiar export mental model.
Report Page with Geo Map
Report Page with Heatmap
02
Geo map ↔ heatmap investigation
Same report spine with geo map or heatmap mid-board so planners navigate risk by place and time.
Supply Chain / Reports / Catalogue
Report Page
03
Catalogue → KPI strip report
Catalogue opens into a KPI-led report so planners orient on health before opening inventory risk.
Concept 01 of 03: Catalogue → table & chart report
[08]Convergence
Heatmap → queue → SKU detail
One drill path replaces hunting across disconnected reports.
Supply Chain / Reports / Catalogue1 / 6
Layer 01
Heatmap risk surface
See concentration of High/Medium risk across store–DC cells before opening any row.
Layer 02
At-risk queue
Top SKUs ranked by exposure — triage list replaces spreadsheet hunting.
Layer 03
SKU detail table
Full inventory evidence waits one drill down — available when validating, not when scanning.
[09]Define the workflow truth
The redesigned path
Current-state sprawl collapses into an exception-first path — triage, localize, validate, act — without leaving the platform.
One exception-first path · Triage → Localize → Root cause → Validate → Act · context preserved end-to-end
What changed
Workflow friction → guided path. These are section proof points — not hero impact metrics.
6 · 8 — Fragmented before6 · 8
Fragmented before
Reports · systems — context rebuilt on every hop
0 — No saved state0
No saved state
Decision happened outside the platform
1st — Exception-first1st
Exception-first
See risk before the table — drill instead of hunt
~8m — Closed loop, same session~8m
Closed loop, same session
Root cause → validate → act in one workflow
[10]Core Framework
3CP — Probability · Explainability · Adaptability
01
Probability
Rank exceptions by likelihood and business exposure so planners start on the risks most worth their time.
Exposure distribution
Crit
92%
Watch
64%
Healthy
38%
Queue biasTop 10 first
02
Explainability
Show the drivers behind every flag — velocity, DC concentration, forecast drift — without a raw-data rebuild.
Why · Because
FlagVelocityWOS gapAct
Driver panelWhy flagged
03
Adaptability
Progressive disclosure lets roles scan, investigate, and act without losing product, DC, or period context.
Depth path
HeatQueueSKUAct
Depth pathHeat → Act
Intelligence layer
See risk · understand cause · take action
01
ScanHeatmap
02
InvestigateTooltip + drilldown
03
ExplainRoot cause
04
ActAlerts + actions
05
ValidateTable
High-fidelity product screens
Exception-first heatmap
See risk instantly
The workflow begins with an exception-first heatmap that surfaces inventory anomalies across distribution centers and time periods. Planners identify unusual patterns, inspect cells, and move into deeper investigation — shifting from manual data hunting to rapid anomaly detection.
Root-cause panel
Understand why it’s happening
Once an anomaly is identified, planners open the root-cause panel without leaving the view. OTIF, forecast variance, sales velocity, and inventory levels explain the drivers — so diagnosis happens inside the workflow instead of across reporting tools.
Item opportunity + handoff
Take action without losing context
Rather than exporting data or switching tools, planners evaluate item performance, prioritize corrective actions, and keep SKU · DC · period context attached from analysis to action.
Screen 01 of 03: See risk instantly
[11]Validation
What five moderated sessions changed
Prototype task: identify and fix three risks. Findings drove label, filter, and approval revisions before pilot.
Sessions
5 moderated
Prototype
Figma hi-fi
Task
Identify & Fix 3 Risks
Task success
100%
Time on task
−60%
FindingEvidenceChange made
Terminology
Evidence
4/5 planners stalled on “EOP” and “velocity anomaly” before scanning the risk queue.
Change made
Renamed labels to plain language + inline glossary tooltips on first hover.
Filter Blindness
Evidence
3/5 scrolled the full inventory table without applying Region or Category filters.
Change made
Pinned filter bar above the heatmap and added empty-state guidance when no lens is set.
Action Anxiety
Evidence
Users hesitated on transfer approve — “what if I’m wrong?” blocked completion.
Change made
Added impact preview + confidence state before confirm; soft default to Review.
[12]Impact
What the pilot unlocked for planners.
The pilot launched in Q4 and changed how the inventory team ran their week.
“
I used to dread Monday mornings. Now I can clear my risk queue before my first coffee. It's not just faster; it's less stressful.
Sarah R. — Sr. Inventory Planner
Issue detection
48 hrs< 1 hr
over 90% faster
Stockout risk
$3.5M proj$1.4M
$2.1M saved
Tool sprawl
4–5 tools1 tool
Consolidated
Trust score
2.1 / 54.8 / 5
+128% lift
[13]Next step
Same decision craft for your next operational fire.
Inventory, supply, or exception systems — a focused sprint for an investigation spine, explainable risk, and proof stakeholders can trust.
Sarah R. — Sr. Inventory Planner