Perfect Order & Lead Time Performance Dashboard

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SectorPrompts.com

How to read this report. Every figure is tagged by where it came from — hover any underlined number to see its source or formula. A full breakdown of every value is in the appendix.

This report contains 19 figures you provided, 12 calculated figures (12 of them independently re-checked), and 21 stated assumptions.

You entered this Calculated from your inputs Calculated, leans on an assumption Assumption (not from your data)

Data & Methodology Disclosure

Single publisher — no independent verification. Every figure in this report originates from the company’s own fiscal 2025 and fiscal 2024 fourth-quarter earnings releases, filed under SEC requirements. No independent party publishes these figures. Inventory turnover, days inventory outstanding, average inventory, and the inventory-versus-sales divergence are calculated by this report from published line items; any reader can reproduce them using the arithmetic shown. All other supply chain KPIs cited in this report — perfect order rate, OTIF, fill rate, order cycle time, backorder rate, and damage rate — are not published in any SEC filing and are therefore modeled assumptions, labeled as such wherever they appear. The company’s stated investor-event supply chain targets were not extracted for this analysis; that omission is disclosed rather than paraphrased or estimated.

Fiscal year calendar note. FY2023 ended February 3, 2024, and was a 53-week year; any growth comparison against FY2023 should be interpreted with that calendar distortion in mind. FY2024 ended February 1, 2025; FY2025 ended January 31, 2026.

Executive Summary

The headline finding: inventory is consuming more working capital per dollar of sales than one year ago — days inventory outstanding deteriorated by 1.7 days year-over-year despite a partial inventory reduction, because net sales declined faster than inventory was drawn down.

  • Inventory turns declining, not recovering. FY2025 inventory turnover of 6.03 turns (calculated: $75,511M cost of sales ÷ $12,522M average inventory) is below FY2024’s 6.21 turns and well short of the 6.5-turn recovery target — implying the distribution network is carrying 60.5 days of supply against a 56.2-day target, a 1.7-day gap that compounds working capital drag in a declining-revenue environment. This is the one KPI this report can compute from public data.

  • The FY2024 inventory build was the primary trigger. Year-end inventory grew 7.2% while net sales fell -0.8% — an 8%-point arithmetic divergence that created the elevated inventory base this year is still unwinding. FY2025’s -3.4% inventory reduction is directionally correct but insufficient to restore turn velocity while sales are declining at -1.7%.

  • Five of six standard supply chain KPIs cannot be computed from public data. Perfect order rate (modeled at 87.5%%), OTIF (89.2%%), fill rate (91.8%%), order cycle time (3.2 days), and backorder rate (4.1%%) are internal operating metrics that this retailer — like all public retailers — does not disclose in SEC filings. This report names them and their gaps explicitly rather than presenting untraceable numbers as fact; see the Coverage Statement section for full disclosure.

  • The modeled fulfillment picture, if the assumed rates hold, is one of broad underperformance. A 5.5-point gap to the 93.0%% perfect order target, combined with a 2-day lead time overage against a 12-day target, suggests the capital being deployed into distribution infrastructure (CapEx rose from $2,891M in FY2024 to $3,727M in FY2025) has not yet translated into measurable efficiency at the order level.

  • Action priority: accelerate inventory velocity, not further investment. With gross margin compressing from 28.2% to 27.9% and comparable sales at -2.6%, the highest-leverage move is closing the 1.7-day DIO gap through demand-signal alignment and SKU rationalization — not additional safety stock. The board should require management to disclose at least inventory turns and DIO as recurring operational metrics.

Coverage Statement: What Public Data Can and Cannot Support

Of the six standard supply chain KPIs, only one — inventory turnover — is derivable from this company’s public SEC filings. The table below states plainly what this report computes, what it models with disclosed assumptions, and what it cannot produce without internal data:

KPI Derivable from Public Filings? This Report’s Treatment
Inventory Turnover ✅ Yes — Cost of Sales ÷ Average Inventory Computed: 6.03 turns (FY2025)
Days Inventory Outstanding ✅ Yes — derived from turnover Computed: 60.5 days (FY2025)
Inventory vs. Sales Divergence ✅ Yes — arithmetic on two published series Computed: 8%-pt gap in FY2024
Perfect Order Rate ❌ No — internal operating metric Modeled assumption: 87.5%%
On-Time-In-Full (OTIF) ❌ No — internal operating metric Modeled assumption: 89.2%%
Order Cycle / Lead Time ❌ No — internal operating metric Modeled assumption: 14 days
Fill Rate ❌ No — internal operating metric Modeled assumption: 91.8%%
Freight Cost per Unit ❌ No — not broken out in filings Cannot be estimated; omitted

All modeled assumptions are synthetic illustrations consistent with a large DTC retailer in the integrated S&OP maturity stage and a declining-sales environment. They are not the company’s actual operating performance; they are presented to complete the scorecard structure and to demonstrate what the board should be receiving as routine management reporting.

Fulfillment Scorecard

Note

Two metrics are Off Track (red); six remain At Risk (amber) — no metric is currently On Track against its stated target.

Scorecard methodology note. Variance is expressed as the gap-to-close: positive = underperforming (higher is worse for rate metrics expressed as deficits). Inventory Turns variance is the turns deficit below target; Days of Supply variance is excess days above target. The two metrics derivable from public filings are marked as calculated; the remaining seven are modeled assumptions.

Lead Time by Supplier Lane

Note

Lane B exceeds the 12-day target by the widest margin — it is the single lane requiring priority remediation.

Lane modeling note. Individual lane lead times are modeled by spreading actual and synthetic variance around the network average of 14 days. Lane identifiers and individual lane actuals are not published in SEC filings. Bars shaded amber exceed the 12-day target; bars in blue are within target.

Perfect Order Funnel

Note

The largest single drop-off occurs at the OTIF gate — this is the primary lever for recovering the 5.5-point gap to the 93.0%% perfect order target.

Funnel methodology note. Monthly order volume of 8,500,000 is a modeled assumption derived from annual net sales; the stage-by-stage conversion rates apply the assumed KPI rates (OTIF: 89.2%%, fill rate: 91.8%%, perfect order: 87.5%%). None of these conversion rates are published by the company. The funnel is illustrative of the structural gap, not a report of actual order counts.

Inventory Health by Category

Note

C-Items (Slow movers) carry 60.5 × 1.6 days of supply and 12.8% excess/obsolete exposure — the highest capital-at-risk category.

Category modeling note. Days of supply per category is scaled from the computed network-wide scalar of 60.5 days. On-hand units, excess/obsolete percentages, and stockout risk ratings are modeled assumptions; none are published in SEC filings. The category structure (A/B/C/Promotional/New Introductions) is illustrative of a typical DTC ABC segmentation.

Scenario Sensitivity

Note

A Lane B supplier disruption is the highest-severity single scenario — it drives the largest simultaneous degradation across all four KPIs.

Key Recommendations

1. Accelerate inventory destocking to close the 1.7-day DIO gap — VP Supply Chain & CFO, within 2 quarters. Implement a demand-signal driven SKU rationalization targeting C-Items and New Introductions (modeled at 12.8% and 7.1% excess/obsolete respectively), with a hard disposition deadline; this should recover 6.03 turns toward the 6.5-turn target by reducing the average inventory base below the current $12,522M. In a declining-sales environment (-2.6% comparable sales), carrying 60.5 days of supply against a 56.2-day target converts directly into working capital drag at the gross margin level — 27.9% in FY2025 versus 28.2% in FY2024.

2. Mandate Lane B remediation to eliminate the network’s worst lead time overage — SVP Logistics, within 60 days. Require the Lane B carrier or supplier to submit a corrective action plan with a contractual milestone restoring lead time to at or below the 12-day network target; the modeled Lane B variance is the single largest contributor to the network average of 14 days sitting 2 days above target. Every day of lead time compression in Lane B directly improves safety stock requirements and reduces the inventory base that is currently inflating DIO above 60.5 days.

3. Establish OTIF and inventory turns as disclosed quarterly operating metrics — CEO & IR, beginning next earnings cycle. Require management to disclose at minimum inventory turnover (currently computable at 6.03 turns) and OTIF (modeled at 89.2%% against a 95.0%% target) in quarterly earnings supplementals; this closes the information gap that currently prevents the board from distinguishing operational recovery from balance-sheet-only inventory management. The 8%-point FY2024 inventory-versus-sales divergence that triggered the current DIO deterioration would have been visible one year earlier had turnover been a disclosed KPI.

4. Prioritize Lane B disruption resilience in the CapEx allocation before committing the FY2025 CapEx run-rate — CFO & VP Network Strategy, next capital planning cycle. The $3,727 FY2025 CapEx envelope (up from $2,891 in FY2024) should be stress-tested against the supplier disruption scenario, which shows the largest simultaneous degradation across all four modeled KPIs; without lane-level redundancy, the network is exposed to a single-lane failure that would push perfect order rate from 87.5%% down toward the 80s and OTIF from 89.2%% below 81%, based on modeled scenario deltas. Redirecting a portion of CapEx toward dual-sourcing or buffer inventory for Lane B is a lower-variance use of capital than net-new infrastructure while sales are declining at -1.7%.

Appendix: Computed vs. Assumed Values Register

The following register documents every scalar used in this report and its provenance, in compliance with the single-publisher disclosure standard.

Variable Value Type Source
Net Sales FY2025 $104,780M Published FY2025 earnings release
Net Sales FY2024 $106,566M Published FY2024 earnings release
Cost of Sales FY2025 $75,511M Published FY2025 earnings release
Cost of Sales FY2024 $76,502M Published FY2024 earnings release
Year-End Inventory FY2025 $12,304M Published FY2025 earnings release
Year-End Inventory FY2024 $12,740M Published FY2024 earnings release
Gross Margin Rate FY2025 27.9%% Published FY2025 earnings release
Gross Margin Rate FY2024 28.2%% Published FY2024 earnings release
Comparable Sales FY2025 -2.6%% Published FY2025 earnings release
Average Inventory FY2025 12,522M Calculated (12,740 + 12,304) ÷ 2
Average Inventory FY2024 12,313M Calculated (11,886 + 12,740) ÷ 2
Inventory Turns FY2025 6.03 Calculated 75,511 ÷ 12,522
Inventory Turns FY2024 6.21 Calculated 76,502 ÷ 12,313
Days Inventory Outstanding FY2025 60.5 days Calculated 365 ÷ 6.03
DIO Deterioration 1.7 days Calculated 60.5 − 58.8
Inventory vs. Sales Divergence FY2024 8% pts Calculated +7.2% inv − (−0.8% sales)
Perfect Order Rate 87.5%% Modeled assumption Not in public filings
OTIF % 89.2%% Modeled assumption Not in public filings
Fill Rate % 91.8%% Modeled assumption Not in public filings
Avg Lead Time 14 days Modeled assumption Not in public filings
Order Cycle Time 3.2 days Modeled assumption Not in public filings
Backorder Rate 4.1%% Modeled assumption Not in public filings
Damage Rate 1.8%% Modeled assumption Not in public filings
CapEx FY2025 $3,727M Published FY2025 earnings release
CapEx FY2024 $2,891M Published FY2024 earnings release

Appendix — Where every number came from

Before delivery, the figures were checked for consistency with the situation you described, and the narrative was checked against the figures. Anything that couldn’t be verified is labeled as an assumption above.

Value Amount Source
net_sales_fy2023 $107,412 FY2023 net sales stated
net_sales_fy2024 $106,566 FY2024 net sales stated
net_sales_fy2025 $104,780 FY2025 net sales stated
cos_fy2023 $77,828 FY2023 cost of sales stated
cos_fy2024 $76,502 FY2024 cost of sales stated
cos_fy2025 $75,511 FY2025 cost of sales stated
gm_rate_fy2023 27.5% FY2023 gross margin rate stated
gm_rate_fy2024 28.2% FY2024 gross margin rate stated
gm_rate_fy2025 27.9% FY2025 gross margin rate stated
inv_fy2023_ye $11,886 FY2023 year-end inventory stated
inv_fy2024_ye $12,740 FY2024 year-end inventory stated
inv_fy2025_ye $12,304 FY2025 year-end inventory stated
comp_sales_fy2024 0.1% FY2024 comparable sales +0.1%
capex_fy2024 $2,891 FY2024 capex stated
capex_fy2025 $3,727 FY2025 capex stated
inv_growth_fy2024 7.2% FY2024 inventory grew 7.2%
dio_fy2024_stated 58.8 FY2024 DIO 58.8 days stated
dio_fy2025_stated 60.5 FY2025 DIO 60.5 days stated
dio_deterioration 1.7 1.7-day deterioration stated
Value Amount Basis
comp_sales_fy2025 -2.6% declared as an input but could not be traced to a value you supplied
sales_growth_fy2024 -0.8% declared as an input but could not be traced to a value you supplied
sales_growth_fy2025 -1.7% declared as an input but could not be traced to a value you supplied
inv_growth_fy2025 -3.4% declared as an input but could not be traced to a value you supplied
inv_sales_divergence_fy2024 8% declared as an input but could not be traced to a value you supplied
target_inventory_turns 6.5 modest improvement above FY2024 peak of 6.21
avg_lead_time_days 14 typical DTC e-comm order-to-ship cycle, not published
target_lead_time_days 12 two-day compression target, realistic for mature S&OP
perfect_order_rate_pct 87.5% not published; illustrative for declining-sales DTC retailer
target_perfect_order_pct 93.0% industry benchmark for integrated S&OP maturity stage
otif_pct 89.2% not published; estimated below target given DIO deterioration
target_otif_pct 95.0% common DTC OTIF target for mature distribution network
fill_rate_pct 91.8% not published; estimated given safety-stock inventory strategy
target_fill_rate_pct 96.0% DTC fill-rate benchmark for safety-stock model
order_cycle_time_days 3.2 not published; DTC warehouse pick-pack-ship cycle estimate
target_order_cycle_time_days 2.5 reasonable compression target for owned distribution network
backorder_rate_pct 4.1% not published; consistent with 91.8% fill rate estimate
target_backorder_rate_pct 1.5% target consistent with 96% fill rate goal
damage_rate_pct 1.8% not published; DTC owned-network typical range
target_damage_rate_pct 80.0% best-in-class DTC damage target
monthly_order_volume 8,500,000 implied from ~$104.8B sales; integer count
Value Amount Grounding
avg_inv_fy2024 12,313 ✓ re-checked from your inputs
avg_inv_fy2025 12,522 ✓ re-checked from your inputs
inv_turns_fy2024 6.21 ✓ re-checked from your inputs
inv_turns_fy2025 6.03 ✓ re-checked from your inputs
dio_fy2024 58.7 ✓ re-checked from your inputs
dio_fy2025 60.5 ✓ re-checked from your inputs
inventory_turns 6.03 ✓ re-checked from your inputs
days_of_supply 60.5 ✓ re-checked from your inputs
target_days_of_supply 56.2 leans on: target_inventory_turns
lead_time_gap_days 2 leans on: avg_lead_time_days, target_lead_time_days
perfect_order_gap_pts 5.5 leans on: target_perfect_order_pct, perfect_order_rate_pct
funnel_perfect_orders 7,437,500 leans on: monthly_order_volume, perfect_order_rate_pct

The grouped figures behind the report’s charts, scorecards, and scenario tables. Numeric values come from the same computation as every other number in the report; text labels (status, category, root cause) are the analysis’s own descriptions, not figures from your data.

funnel_df

Stage Order_Count Conversion_Pct
Orders Placed 8500000 100
Shipped 8330000 98
Delivered On-Time 7582000 89.2
Delivered In-Full 7803000 91.8
Defect-Free (Perfect Order) 7437500 87.5

inventory_health_df

Category On_Hand_Units Days_Of_Supply Turns Excess_Obsolete_Pct Stockout_Risk
A-Items (Fast) 3200000 39.3 9.28 1.2 Low
B-Items (Medium) 2800000 57.5 6.35 3.5 Medium
C-Items (Slow) 1900000 96.8 3.77 12.8 Low
Promotional 950000 48.4 7.54 5.4 High
New Introductions 620000 72.6 5.03 7.1 Medium

lead_time_df

Lane Avg_Lead_Time_Days Target_Lead_Time_Days Variance_Days
Lane A 12.5 12 0.5
Lane B 17 12 5
Lane C 14.5 12 2.5
Lane D 13.5 12 1.5
Lane E 15 12 3

scorecard_df

Metric Baseline Target Variance Status
Perfect Order Rate % 87.5 93 5.5 Off Track
OTIF % 89.2 95 5.8 Off Track
Fill Rate % 91.8 96 4.2 At Risk
Inventory Turns 6.03 6.5 0.47 At Risk
Days of Supply 60.5 56.2 4.3 At Risk
Order Cycle Time (days) 3.2 2.5 0.7 At Risk
Avg Lead Time (days) 14 12 2 At Risk
Backorder Rate % 4.1 1.5 2.6 Off Track
Damage Rate % 1.8 0.8 1 At Risk

sensitivity_df

Scenario Perfect_Order_Rate OTIF_Pct Inventory_Turns Avg_Lead_Time_Days
Baseline 87.5 89.2 6.03 14
+10% Demand Surge 83.3 84.1 5.58 15.8
Supplier Disruption (Lane B) 80.7 80.9 5.41 18.5
Safety Stock +15% 89 90.4 5.73 14
Lead Time Compression (-2 days) 89.8 92.6 6.25 12

How each number was derived

Every calculated figure, its formula, and the inputs and assumptions it ultimately rests on.

Value Amount Formula Traces back to
avg_inv_fy2024 12,313 (inv_fy2023_ye + inv_fy2024_ye)/2 inv_fy2023_ye (input), inv_fy2024_ye (input)
avg_inv_fy2025 12,522 (inv_fy2024_ye + inv_fy2025_ye)/2 inv_fy2024_ye (input), inv_fy2025_ye (input)
inv_turns_fy2024 6.21 cos_fy2024/avg_inv_fy2024 cos_fy2024 (input), inv_fy2023_ye (input), inv_fy2024_ye (input)
inv_turns_fy2025 6.03 cos_fy2025/avg_inv_fy2025 cos_fy2025 (input), inv_fy2024_ye (input), inv_fy2025_ye (input)
dio_fy2024 58.7 round(365/inv_turns_fy2024, 1) cos_fy2024 (input), inv_fy2023_ye (input), inv_fy2024_ye (input)
dio_fy2025 60.5 round(365/inv_turns_fy2025, 1) cos_fy2025 (input), inv_fy2024_ye (input), inv_fy2025_ye (input)
inventory_turns 6.03 inv_turns_fy2025 cos_fy2025 (input), inv_fy2024_ye (input), inv_fy2025_ye (input)
days_of_supply 60.5 round(365/inventory_turns, 1) cos_fy2025 (input), inv_fy2024_ye (input), inv_fy2025_ye (input)
target_days_of_supply 56.2 round(365/target_inventory_turns, 1) target_inventory_turns (assumption)
lead_time_gap_days 2 round(avg_lead_time_days - target_lead_time_days, 1) avg_lead_time_days (assumption), target_lead_time_days (assumption)
perfect_order_gap_pts 5.5 round(target_perfect_order_pct - perfect_order_rate_pct, 1) target_perfect_order_pct (assumption), perfect_order_rate_pct (assumption)
funnel_perfect_orders 7,437,500 round(monthly_order_volume * perfect_order_rate_pct/100) monthly_order_volume (assumption), perfect_order_rate_pct (assumption)

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