Case Study · Apparel & Fashion

Rebuilding the Commerce Stack for Peak Retail Performance

IndustryApparel: DTC & Wholesale

Company Size~140 employees (SME)

Sales ChannelsDTC storefront, 2 marketplaces, wholesale EDI

Core SystemsERP (order-to-cash), 3PL-managed WMS

SKU Footprint~2,400 styles / 14,000+ size-colour variants

IntegrationMuleSoft Anypoint · Event-Driven

AI EnginesDemand-Sensing + Carrier Rate-Shopping

Peak WindowsHoliday surge, End-of-Season Sale (EOSS)

STRATVALS · CASE STUDY

Rebuilding the Commerce Stack for Peak Retail Performance

COMMERCE · MULESOFT · AI DEMAND & SHIPPING

CASE STUDY·STRATVALS-2026-03

STOREFRONT

MULESOFT

WMS & AI

96.4% DIFOT

3.1× THROUGHPUT

IMPACT MANIFEST12-MONTH POST-LAUNCH

96.4%

DIFOT (delivered in full, on time) - up from 81.3%

−42%

Stockout rate across core SKUs during peak windows

3.1×

Peak order throughput handled without added headcount

68%

Forecast accuracy at SKU-week level, from a 31% baseline

ENGAGEMENT: STRATVALS-2026-03SECTOR: APPAREL & FASHION

Three systems that barely spoke to each other. One rebuilt commerce layer: wired together on MuleSoft, sharpened by AI demand planning and shipping optimization, and tested through the hardest week of the retail year.

This leading apparel brand ran its DTC storefront, wholesale ledger and warehouse off three systems that barely communicated. StratVals rebuilt the commerce layer, wired the ERP and warehouse together on MuleSoft Anypoint, and layered in AI-driven demand planning and shipping optimization: turning End-of-Season Sale week from a fire drill into a routine.

The Client

00 // CLIENT SNAPSHOT

A leading apparel brand selling performance and everyday wear through a direct-to-consumer storefront, two fashion marketplaces, and a regional wholesale channel.

Like most SME apparel sellers, its catalog carries deep size and colour variance across every style, and its order volume swings hard around two annual peaks: a winter holiday surge and an End-of-Season Sale.

Before the engagement, the DTC site ran on a templated storefront with limited control over the product detail experience, while inventory truth lived in three disconnected places: the ERP, the warehouse management system operated by a third-party logistics partner, and a set of shared spreadsheets used to reconcile the two.

Industry

Apparel: DTC & Wholesale

Company Size

~140 employees (SME)

Sales Channels

DTC storefront, 2 marketplaces, wholesale EDI

Core Systems

ERP (order-to-cash), 3PL-managed WMS

SKU Footprint

~2,400 styles / 14,000+ size-colour variants

Integration

MuleSoft Anypoint · Event-Driven

AI Engines

Demand-Sensing + Carrier Rate-Shopping

Peak Windows

Holiday surge, End-of-Season Sale (EOSS)

The Challenge

01 // THE CHALLENGE

Inventory truth drifted by up to 48 hours between the ERP and the warehouse floor, because the two systems only reconciled through a nightly batch job. On drop days and during End-of-Season Sale, that drift turned directly into oversells, backorders and manual cancellations. Demand planning ran on trailing three-month averages with no size-curve or seasonality modelling, and shipping ran on one static carrier contract: moving costs and transit times in exactly the wrong direction at the moment customers were least patient.

SYNC-00124 to 48hr inventory driftERP-to-warehouse sync via nightly batch reconciliation instead of live, event-driven updates.

OOS-014Oversells on peak drop daysAvailable-to-promise shown on the storefront lagged real warehouse stock: manual order cancellations spiked during promotional traffic.

FCST-022Flat trailing-average forecastingNo size-curve or seasonality modelling: reorder points mistimed on core SKUs exactly during peaks.

SHIP-031Single static carrier contractOne carrier at one flat rate, regardless of volume, distance or season: peak cost-to-serve climbed when it should have fallen.

DIFOT-040Delivery-in-full-on-time at 81%DIFOT hovered at 81%, performing worst during the two highest-revenue weeks of the year.

The Solution

02 // THE SOLUTION

PART A

Custom Ecommerce Storefront

A purpose-built, composable storefront replaced the templated site, with the product detail page driven directly by a Product Information Management (PIM) layer so size, colour, fit and fabric attributes stay consistent across DTC and marketplace listings. Available-to-promise (ATP) quantities render live at the variant level rather than the parent SKU, so a customer sees real stock for their exact size and colour, not an aggregate.

HEADLESS COMMERCEPIM-DRIVEN PDPVARIANT-LEVEL ATPMOBILE-FIRST CHECKOUT

PART B

MuleSoft ERP to Warehouse Integration

An API-led integration built on MuleSoft's Anypoint Platform connects the ERP, the 3PL's warehouse management system, the storefront, and the marketplace and wholesale EDI channels. System APIs expose the ERP and WMS; process APIs orchestrate order, inventory and fulfilment logic; experience APIs feed the storefront and marketplace listings. DataWeave transformations map SKU and variant taxonomies across systems, and inventory and order events move through an event-driven pub-sub layer instead of a nightly batch: cutting sync latency from days to seconds.

ANYPOINT PLATFORMAPI-LED CONNECTIVITYEDI 850/856/940/945EVENT-DRIVEN SYNC

PART C

AI-Driven Demand Planning

A demand-sensing model forecasts at SKU-week granularity, blending historical sell-through, promotional calendars, and size-curve and pack-ratio patterns specific to apparel. It recommends reorder points and safety stock per distribution node, flags SKU rationalization candidates, and rebalances slow-moving colourways toward the channels most likely to sell them before markdown is the only option.

SKU-WEEK DEMAND SENSINGSIZE-CURVE FORECASTINGSAFETY STOCK / ROP TUNINGMARKDOWN REBALANCING

PART D

AI-Driven Shipping Optimization

A shipping-decision model rate-shops across contracted carriers in real time and switches logic between peak and non-peak modes: consolidating freight and batching pick-waves during normal volume, and shifting to zone-skipping and expedited lanes only where an on-time promise is at risk during EOSS and holiday surges. The result is a lower average cost to serve without sacrificing delivery promises when volume triples.

DYNAMIC CARRIER SELECTIONPEAK / NON-PEAK ROUTINGFREIGHT CONSOLIDATIONPICK-WAVE OPTIMIZATION

Integration Architecture

03 // ARCHITECTURE

Inventory and order events travel as a continuous stream rather than a nightly file drop. The AI demand and shipping engines sit as consumers of the same event bus: a forecast update or a peak-mode routing switch reaches the storefront and the warehouse floor within the same integration cycle, not the next overnight batch.

STOREFRONT & CHANNELSDTC Composable Web · Wholesale EDI · Marketplace Connectors

MULESOFT INTEGRATION LAYERSystem, Process & Experience APIs · DataWeave SKU Mapping · Event Bus

CORE SYSTEMS & AI ENGINESERP System of Record · 3PL WMS · AI Demand Sensing · AI Shipping Router

Business Impact

04 // BUSINESS IMPACT

81.3% DIFOT

96.4%

Delivery-in-full-on-time rate, including peak weeks

31% forecast accuracy

68%

SKU-week forecast accuracy at variant level

48hr inventory drift

<30sec

ERP-to-warehouse inventory sync latency

1.0x baseline

3.1x

Peak-season order throughput with same headcount

Core SKU stockouts

-42%

Stockout rate on top-selling styles during peak

Peak cost-to-serve

-18%

Average shipping cost per order during EOSS

Manual reconciliation

-35 hrs/wk

Inventory admin time reclaimed by the ops team

Checkout conversion

+22%

Lift after variant-level ATP and faster PDP load

Rows of high-bay warehouse racking in modern fulfillment center — FIG. 01: DISTRIBUTION NODE, POST-CUTOVER

Shipping container terminal and freight logistics node — FIG. 02: INBOUND FREIGHT, PEAK REPLENISHMENT CYCLE

Engagement Timeline

05 // ROLLOUT

WK 01-03

Discovery & systems audit

Mapped ERP, WMS and storefront data models; audited SKU/variant taxonomy and EDI document flows with the wholesale channel.

WK 04-12

API-led integration build

Stood up System, Process and Experience API layers on MuleSoft Anypoint; migrated inventory and order sync from nightly batch to event-driven.

WK 08-16

Storefront & AI model build (parallel track)

Built the PIM-driven storefront and trained the demand-sensing and shipping-optimization models against 3 years of historical order and fulfilment data.

WK 17-19

EOSS pilot

Ran the full stack live through that season's End-of-Season Sale as a controlled pilot before holiday peak.

WK 20

Full cutover

Retired the legacy templated storefront and batch reconciliation process; ops team fully transitioned to the new stack ahead of the holiday surge.

“We used to plan peak season around what the warehouse spreadsheet told us on a Tuesday. Now the storefront, the ERP and the floor agree in real time, and the shipping engine handles the cost-versus-promise trade-off on its own.”

Supply Chain & Operations Lead, Leading Apparel Brand

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