Accenture Consumer Goods United States

SXD

Supply Chain Optimization

SXD needed a way to reduce material waste in fashion design so discarded textiles could become commercially useful products instead of creating more landfill and emissions.

up to 69% in material consumption
~10x greater material savings
~80% reduction in CO2 emissions

Reported impact

Business outcome

SXD improved material efficiency and reduced waste, giving fashion producers a repeatable way to reuse textile leftovers while lowering the environmental cost of creating new garments.

  • Cost Reduction

Solution

SXD created an AI-driven design approach that turns leftover textiles into zero-waste fashion outputs, and Accenture helped bring the work forward through the H&M Foundation's Changemaker ecosystem. The core technology, SXD AI, analyzes available leftover or waste material and generates designs that make use of those constraints instead of treating them as scrap. That means the system does not simply optimize a traditional pattern workflow; it starts from the available material and produces new wearable-art concepts intended to avoid offcuts and reduce resource waste. By using prior material inputs to shape the next design, SXD turns circularity into a design parameter embedded in the AI process itself. The result is a practical AI workflow for fashion and textile reuse that lowers material consumption, helps reduce emissions and water use, and gives discarded material a structured path back into production rather than landfill.

Data flow

Leftover or wasted textile material is used as the design input for SXD AI, which generates zero-waste wearable designs based on the available material constraints. The resulting outputs can then be used for production without creating the same level of scrap as conventional design approaches.

Solution architecture

1 components · 1 layer
  1. Compute
    • SXD AI Generates zero-waste designs from leftover and discarded textile material so outputs fit available inputs rather than creating new scrap.

Architecture clues

  • The AI workflow is intended to eliminate scraps in downstream garment design.
  • The design process starts from leftover or wasted material instead of a blank pattern.

Evidence from the source

"SXD AI delivers ~10x greater material savings compared to any existing solution."
"SXD can use leftover or wasted material from the past to create zero waste designs without scraps wasted for the future."
"save up to 69% in material consumption"
"~80% reduction in CO2 emissions"
Transparency Evidence & methodology 90/100 reference value · 89% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability19/25
    9 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Accenture

    AI Crafts Art from Textile Waste | SXD Case | | Accenture

    First-party
    https://www.accenture.com/en/case-studies/sustainability/sxd-turning-textile-waste-into-wearable-art-with-ai
    Source type
    Vendor-published customer story
    Published
    Feb 3, 2026
    Supports
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. SXD

    SXD | Zero-Waste Design Powered by Patented AI

    First-party
    https://sxd-ai.com/

    The Organization JSON-LD lists a PostalAddress with addressLocality New York, addressRegion NY, and addressCountry US.

    Source type
    Official company website
    Supports
    • Region

Date evidence

  • Published on source page Feb 3, 2026 · Accenture · First-party
    Open date source
Not separately sourced Customer, Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.