WVU capstone · Inventory analytics

Tractor Beverage Inventory Planning

Remote West Virginia University capstone methods with a project site visit in Columbus, Ohio.

Demo

Project context

Completed remotely through West Virginia University with a project site visit in Columbus, Ohio. The dashboard uses generated SKU demand, lead-time, cost, inventory, and forecast records.

Inventory value

$199.46KSource: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Synthetic on-hand units multiplied by synthetic unit cost.

Synthetic on-hand units multiplied by synthetic unit cost.Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Fictional SKU-level demand, lead-time, cost, and inventory records created solely for a portfolio demonstration.

Service-level attainment

94.2%Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Synthetic fulfilled demand divided by requested demand.

Synthetic fulfilled demand divided by requested demand.Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Fictional SKU-level demand, lead-time, cost, and inventory records created solely for a portfolio demonstration.

Reorder candidates

4Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Synthetic A/B SKUs at or below reorder point.

Synthetic A/B SKUs at or below reorder point.Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Fictional SKU-level demand, lead-time, cost, and inventory records created solely for a portfolio demonstration.

Forecast MAPE

12.6%Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Mean absolute percentage error on the fictional demand forecast.

Mean absolute percentage error on the fictional demand forecast.Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Fictional SKU-level demand, lead-time, cost, and inventory records created solely for a portfolio demonstration.

Monthly demand: actual vs forecast
Monthly demand: actual vs forecast data
MonthUnitsSeries
Jan13,900unitsActual
Jan13,520unitsForecast
Feb14,420unitsActual
Feb14,180unitsForecast
Mar15,110unitsActual
Mar14,740unitsForecast
Apr15,880unitsActual
Apr15,520unitsForecast
May16,640unitsActual
May16,290unitsForecast
Jun17,410unitsActual
Jun17,080unitsForecast
Jul18,120unitsActual
Jul17,940unitsForecast
Aug17,620unitsActual
Aug18,110unitsForecast
Sep16,580unitsActual
Sep17,020unitsForecast
Oct15,640unitsActual
Oct16,010unitsForecast
Nov14,920unitsActual
Nov15,180unitsForecast
Dec16,180unitsActual
Dec15,870unitsForecast
On-hand units by ABC class
On-hand units by ABC class data
ClassOn Hand
A10,920units
B7,400units
C3,740units

Excel-ready synthetic SKU planning table

Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Fictional SKU-level demand, lead-time, cost, and inventory records created solely for a portfolio demonstration.

Excel-ready synthetic SKU planning table
SKUABC classAvg weekly demandLead time (weeks)Unit costOn handSafety stockReorder pointStatus
SKU-A01A820Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset3Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$8.4Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset2,850Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset610Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset3,070Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetReorder
SKU-A02A760Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset3Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$7.9Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset3,310Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset570Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset2,850Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetCovered
SKU-A03A690Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset4Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$8.7Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset2,860Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset690Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset3,450Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetReorder
SKU-A04A640Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset2Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$9.1Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,900Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset420Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,700Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetCovered
SKU-B01B430Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset4Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$8.2Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,720Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset500Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset2,220Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetReorder
SKU-B02B390Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset3Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$7.6Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,960Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset390Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,560Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetCovered
SKU-B03B350Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset5Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$8.9Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,670Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset560Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset2,310Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetReorder
SKU-B04B310Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset4Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$9.4Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset2,050Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset430Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,670Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetCovered
SKU-C01C180Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset6Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$7.3Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,210Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset390Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,470Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetWatch
SKU-C02C150Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset5Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$8.1Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,180Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset320Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,070Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetCovered
SKU-C03C120Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset6Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$9.6Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset730Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset300Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,020Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetWatch
SKU-C04C90Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset8Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset$10.2Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset620Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset290Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset1,010Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES datasetWatch

Model mechanics

The model covers ABC segmentation, service-level targets, safety stock, reorder points, forecast error, and simulation.

Inventory model logic

Source: Synthetic inventory planning datasetTable: Inline synthetic VALUES dataset

Fictional SKU-level demand, lead-time, cost, and inventory records created solely for a portfolio demonstration.

Inventory model logic
MethodLogicDecision use
ABC classificationRank SKUs by annualized demand value; class A receives the tightest service and review cadence.Prioritize working capital and planner attention.
Bootstrap / Monte CarloResample historical-style weekly demand paths to estimate stockout exposure.Stress-test service levels under uncertainty.
Forecast validationRolling actual-versus-forecast comparison summarized with MAPE.Detect bias and recalibrate assumptions.
Reorder pointAverage weekly demand × lead time + safety stock.Trigger replenishment before projected stockout.
Safety stockService factor × weekly demand standard deviation × square root of lead time.Buffer demand and replenishment variability.

Sources

  1. Synthetic inventory planning datasetassets/demos/tractor-synthetic-artifact.json · ANSI SQL · 2026-07-18T16:05:00-04:00

    Fictional SKU-level demand, lead-time, cost, and inventory records created solely for a portfolio demonstration.

    SQL query
    WITH sku(sku, class, avg_weekly_demand, lead_time_weeks, unit_cost, on_hand, safety_stock, reorder_point) AS (VALUES ('SKU-A01','A',820,3,8.40,2850,610,3070),('SKU-A02','A',760,3,7.90,3310,570,2850),('SKU-A03','A',690,4,8.70,2860,690,3450),('SKU-A04','A',640,2,9.10,1900,420,1700),('SKU-B01','B',430,4,8.20,1720,500,2220),('SKU-B02','B',390,3,7.60,1960,390,1560),('SKU-B03','B',350,5,8.90,1670,560,2310),('SKU-B04','B',310,4,9.40,2050,430,1670),('SKU-C01','C',180,6,7.30,1210,390,1470),('SKU-C02','C',150,5,8.10,1180,320,1070),('SKU-C03','C',120,6,9.60,730,300,1020),('SKU-C04','C',90,8,10.20,620,290,1010)) SELECT * FROM sku;