Representative mock-ups of reporting, apps, automations, and documentation I build. All based on real deliverables — sanitized of confidential data.
| Category | Turns |
|---|---|
| Licensed Apparel | 8.4 |
| Accessories | 6.1 |
| Headwear | 4.7 |
| Drinkware | 3.9 |
| Seasonal / Novelty | 2.2 |
| POD Blanks | 9.1 |
Leadership had no consistent view of how fast inventory moved across channels and categories. Turns were calculated manually in spreadsheets once a quarter.
Power BI report connected to BC via SQL Server gateway. Semantic model calculates turns automatically with scheduled daily refresh. YoY comparisons and category drill-downs.
Replaced quarterly manual process with a live dashboard. Enabled category-level buying decisions. Now presented weekly in state-of-the-building leadership meetings.
No single view of what was on the dock, what was in transit, or which containers had holds. Receiving leads tracked status via group text and a whiteboard.
Container-centric tracking app with live carrier ETA data, dock door scheduling, hold code workflows, and a wall-mounted TV board route. Zero licensing cost.
Eliminated status-chasing. Supervisors and leadership see real-time dock state. Hold code resolution time dropped — buyers get structured templates instead of email threads.
Daily @ 2:00 AM
WarehouseActivity
Lines dataset
Filter, reshape,
CSV format
Overwrite staged
CSV file
Scheduled refresh
picks up new data
Power BI Pro licensing blocked paginated report exports. Warehouse activity data was locked inside BC with no automated path to the reporting layer.
Power Automate scheduled flow that queries BC's WarehouseMovements dataset, transforms to CSV, and overwrites a staged file in SharePoint. Power BI refreshes from SharePoint.
Fully automated daily data pipeline. Zero manual export steps. Solved a licensing constraint without upgrading to Premium capacity.
Replenishment is initiated when a pick bin's on-hand quantity falls below its minimum threshold, calculated from the SKU's 30-day rolling sales velocity. The Calculate DSV Orders replenishment report in the Movement Worksheet evaluates all active pick locations against their configured minimums and generates proposed warehouse movements for any bin below threshold. Movements pull from the nearest available reserve location using the bin ranking hierarchy.
Replenishment logic lived in one person's head. New team members had no reference for how the system worked, when it triggered, or what the fallback process was.
Comprehensive process document with flow diagrams, trigger logic, risk analysis, and exception handling. Versioned and maintained alongside the actual BC configuration.
Onboarding time for new inventory leads cut significantly. Process survived a team transition without disruption. Document became the reference for a system configuration audit.
| Wave | Wave Desc | SKU | Location | Qty Needed | Active Inv | To Be Picked | To Be Filled | Shortage Type | Status | Replen Task | Case | Task Status | Age (hrs) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | WV-8841 | DTC Priority | BLK-TEE-LG-001 | A03B012L02P01 | 24 | 0 | 24 | 0 | No Replen | Held | Not Found | — | — | 14.2 |
| 2 | WV-8841 | DTC Priority | NVY-HOOD-XL-003 | A04B088L01P01 | 48 | 6 | 48 | 36 | Replen insufficient | Held | PLN-441028 | CSN-992104 | 08 | 14.2 |
| 3 | WV-8839 | Retail Wave 3 | GRY-JOG-MD-007 | A02B044L03P01 | 12 | 12 | 12 | 0 | Covered in Active | Clear | — | — | — | 18.7 |
| 4 | WV-8841 | DTC Priority | RED-CAP-OS-012 | A06B112L02P01 | 36 | 0 | 36 | 36 | Covered on Replen | Held | PLN-441031 | CSN-992118 | 00 | 14.2 |
| 5 | WV-8838 | Retail Wave 2 | WHT-TANK-SM-019 | A07B066L01P01 | 72 | 8 | 72 | 48 | Replen insufficient | Held | PLN-441019 | CSN-991987 | 15 | 22.4 |
| 6 | WV-8838 | Retail Wave 2 | BLU-SHORT-LG-024 | A01B008L02P01 | 18 | 4 | 18 | 24 | Covered by Active + Replen | Held | PLN-441022 | CSN-992056 | 08 | 22.4 |
Replenishment tasks were holding pick waves from releasing in Manhattan Active® Warehouse Management, but clerks had no consolidated view of which locations were short, whether replen tasks existed, or what status those tasks were in. Troubleshooting meant checking multiple WMS screens manually.
Built a real-time query tool with direct SQL against Manhattan's WES layer. Joined task detail, wave data, and active location inventory to produce a single operational view with automated shortage classification — five categories from "Covered in Active" through "No Replen" — plus cross-referenced replen task and case status. Designed for clerks and shift leads to refresh on demand.
Enabled a structured "fire truck" priority workflow that resolved held waves oldest-first with exact case and location targeting. Replaced manual multi-screen troubleshooting with a single refreshable view. Built solo, still in production today at a major retail e-commerce fulfillment center.
Traditional ABC slotting breaks down in high-diversity catalogues with extreme seasonal swings — 20K+ active SKUs where the top seller represents less than 0.4% of total volume, and monthly units swing from 115K to 395K between seasons. Four distinct distribution channels (DTC, large/small B2B, FBA) each drive different pick patterns against the same bin structure.
Browser-based slotting engine pulling directly from the ERP's OData API. Channel-weighted velocity scoring with configurable weight sliders — DTC, B2B, and small-retailer channels independently weighted, FBA excluded from velocity but included for stock level consideration. Seasonality-adjusted scoring on a rolling window. Five views: dashboard, transfer candidates, new item placement, slotting group analysis, and a printable move worksheet.
Replaced static spreadsheet-based slotting with a live, tunable system that adapts to seasonal shifts and channel mix changes. Surfaces misslotted SKUs by score, recommends transfers with estimated travel reduction, and handles new item placement. Locally deployed with zero licensing cost — designed from Oracle OROMS slotting documentation principles, adapted for a multi-channel fulfillment operation.
These are representative examples. Yours would be built
around your operation, your data, and your team's workflow.