Sample Decision Report

A decision-ready view of product and inventory evidence.

Illustrative example with fictional data. This sample shows how Elarva structures findings, implications, priorities, and recommendations for management review.

Illustrative ExampleFictional DataFine-Jewelry BusinessManagement Decision Support

Business question

Which categories and SKUs should be protected, reviewed, reduced, or not reordered before the next buying cycle?

The fictional business is carrying meaningful inventory across core earrings, fine necklaces, stackable rings, and gemstone bracelets. Management needs a clear order of attention before committing additional capital.

Priority finding

Inventory investment is concentrated in a category with weak recent sell-through and unit velocity.

Fine necklaces represent approximately 42% of inventory at cost but only 9% of trailing six-month units sold in this illustrative dataset. Core earrings show materially stronger recent movement and lower stock cover.

Decision sequence

Finding → Business implication → Priority → Recommended action

The report connects evidence to a management choice while making assumptions and limitations visible.

Finding

Necklace inventory is high relative to recent unit demand, while core earrings are moving faster and carry substantially lower stock cover.

Business implication

Another broad necklace reorder could increase inventory exposure while capital remains tied up in a slower-moving category. Faster-moving earring SKUs warrant an availability review, but lower relative coverage alone does not establish an immediate shortage.

Priority

Review necklace depth before the next purchase order and identify proven earring SKUs where availability should be protected.

Recommended action

Pause broad necklace replenishment, review SKU-level exceptions, and review faster-moving earring SKUs against lead times, open orders, and expected demand before any selective reorder.

Supporting evidence

SKU and category performance summary

Every figure below comes from one internally consistent fictional dataset. The example does not represent a client or actual business.

Comparative view

Inventory Investment vs. Recent Unit Sales

Fine necklaces account for a substantially larger share of inventory investment than of recent unit movement, making the category a priority for deeper review.

Total inventory: $197,600Six-month units: 828
Core earrings
19.4%
50.7%
Fine necklaces
41.8%
8.9%
Stackable rings
22.7%
28.7%
Gemstone bracelets
16.0%
11.6%
Illustrative six-month category view using fictional data
CategoryUnits soldUnits on handAvg. weekly unit salesSell-throughInventory at costStock coverDecision view
Core earrings42019816.1568%$38,40012.3 weeksProtect proven availability; confirm lead times before reorder
Fine necklaces742782.8521%$82,60097.7 weeksHold broad reorder; review SKU exceptions and exposure
Stackable rings2381959.1555%$44,90021.3 weeksReview depth and variant coverage before reorder
Gemstone bracelets961863.6934%$31,70050.4 weeksReview exposure; test targeted actions before reorder

Illustrative SKU Drill-Down

Illustrative SKU drill-down using fictional data. The six SKUs shown reconcile to the earring and necklace category totals above. All four categories are aggregated from the same ten-SKU fictional dataset; the other four SKUs support the ring and bracelet totals.

SKU detail for the two categories receiving immediate management attention
SKUCategoryUnits soldUnits on handAvg. weekly unit salesSell-throughStock coverInventory at costDecision status
E-101
Signature Hoop
Core earrings188507.2379%6.9 weeks$10,800Protect; check lead time
E-204
Classic Stud Pair
Core earrings142605.4670%11.0 weeks$12,400Review selective reorder
E-317
Linear Drop
Core earrings90883.4651%25.4 weeks$15,200Review depth
N-112
Fine Pendant
Fine necklaces34751.3131%57.4 weeks$22,800Review; hold broad reorder
N-205
Layering Chain
Fine necklaces25910.9622%94.6 weeks$25,400Hold reorder
N-319
Statement Collar
Fine necklaces151120.5812%194.1 weeks$34,400Review exposure reduction

Calculation basis: this fictional six-month reporting window is normalized to 26 weeks. Average weekly unit sales = six-month units sold ÷ 26. Stock cover = closing units on hand ÷ average weekly unit sales. Sell-through = units sold ÷ (units sold + closing units on hand), assuming no returns, shrinkage, transfers, or other stock adjustments. Weekly rates are displayed to two decimals, cover to one decimal, and sell-through to whole percentages; calculations use unrounded values. Category cover is calculated from category totals, never by averaging SKU cover. Historical coverage is not a forecast or proof of a stockout; lead times, open orders, and future demand must be reviewed.

Inventory exposure

Translate concentration into a short list of management priorities.

The objective is not to label every slow-moving item as a problem. It is to separate expected coverage from exposure that deserves a decision.

High-value exposure

Review necklace SKUs with both low recent velocity and high inventory cost before additional commitments are made.

Availability risk

Compare earring SKU coverage with confirmed replenishment lead times and open orders. Prioritize E-101 for this review; no immediate stockout is established by the sample.

Assortment discipline

Review ring and bracelet depth before further buying, alongside necklace exceptions. Their calculated coverage warrants attention, but assortment breadth and stock targets require commercial context.

30 / 60 / 90-day priorities

Sequence the response around the next decision window.

Timing would be adjusted to the client’s buying calendar, lead times, and operating constraints.

30 days

  • Validate open purchase orders
  • Review high-value necklace SKU exceptions
  • Confirm availability of proven earring SKUs
  • Pause unnecessary broad necklace replenishment until exceptions are reviewed

60 days

  • Monitor weekly necklace velocity and sell-through
  • Test targeted actions on selected slow-moving items where commercially appropriate
  • Review category-level inventory exposure
  • Monitor high-performing items for emerging availability pressure

90 days

  • Reassess inventory exposure
  • Compare observed results with the original recommendation
  • Confirm assortment changes for the next buying cycle
  • Update planning assumptions based on actual performance

Assumptions and decision conditions

State what could change the recommendation.

Recommendations are strongest when management can see the evidence boundary and the conditions that justify a different choice.

Relevant assumptions

What the sample assumes

  • Six complete months of comparable sales and inventory history
  • Consistent SKU and category definitions
  • No material unrecorded inventory commitments
Known limitations

What is not established

  • Gross margin is not included in this illustration
  • Planned promotions and future events are not modeled
  • Recent stockouts may distort observed demand
Decision conditions

When to revisit the action

  • Revisit the hold recommendation if necklace sell-through materially improves for four consecutive weeks and stock cover falls below an agreed operating threshold
  • Confirmed events materially increase expected necklace demand
  • Open purchase orders materially change inventory exposure
  • Gross-margin analysis changes the economics of the recommendation

Data requirements

What would be needed for an actual analysis

  • Sales history by SKU
  • Current on-hand inventory
  • Inventory cost
  • Product and category mapping
  • Returns where relevant
  • Open purchase orders if available
  • Promotional or event context where relevant

What this sample does not claim: This illustrative analysis demonstrates Elarva’s decision-support structure. It does not establish profitability, customer motivation, or future demand unless the relevant data are available and analyzed.

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