AI for Fashion Supply Chain

The fifth international workshop on fashion and KDD
in conjunction with
The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020)
San Diego, CA, USA, August 23 - 27, 2020

Workshop webpage: https://kddfashion2020.mybluemix.net/

Paper submission deadline: June 1, 2020

Workshop date: 24th August 2020

Background
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Fashion is a multi-billion-dollar industry with social and economic implications worldwide. One of the biggest problems confounding several fashion houses is the problem of unsold dead inventory. Despite having vast amounts of historical data pertaining to product, sales and inventory, only about 50-60% of the products sell well and rest go through severe mark downs. This is primarily due to volatile nature of fashion trends and fashion brands producing several new products to catch up with the trend. Fashion, i.e., “prevailing style during a particular time”, by design and definition changes by season, and so any unsold inventory at the end of each season is generally liquidated. While smaller designers and retailers generally move the merchandise to discount stores, some top design houses resort to recycling or even destroying the merchandise to avoid dilution of their brand. The environmental impact of the industry is even worse with the US EPA estimating 16 million tons of textile waste generated in 2016 in just the US.


Behind many fashion brands is a highly complex supply chain. Unsold merchandise/inventory is mainly due to mismatch between supply and demand. It could be that the inventory has been over-produced or not distributed properly at the right location and at the right time, mainly due to inaccurate demand forecasts and inefficiencies in supply chain planning. With the advent of modern AI technologies and vast amounts of (structured and unstructured) fashion data the impact on the fashion industry could be transformational and eventually enable intelligent self-correcting sustainable supply chains so that excess inventory is minimized. The stakes are high for fast-fashion retailers and the insights provided by data can help facilitate earlier trend detection and more accurate demand forecasts to help build a more flexible and faster supply chain that reacts to market trends, manage assortment and inventory tuned to the local tastes and set prices and markdown optimally.

The fifth international workshop on fashion and KDD will be hosted at KDD 2020 in San Diego, CA, USA on August 24th, 2019. The goal of this workshop is to gather people from academia, industry, and startups working at the intersection of fashion and data mining and knowledge discovery to further fashion technology and its adoption. The first four installments of this workshop were also held at KDD, in 2016, 2017, 2018, and 2019. It has become the premier venue for presenting works that are solving problems related to fashion using artificial intelligence, machine learning and data mining. This year, responding to attendee feedback and increasing focus on responsible and sustainable supply chains, we are pivoting the workshop more towards the entire fashion supply/value chain rather than just the consumer facing use-cases.

Topics of Interest
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This is a new emerging area for the KDD community, and we hope this workshop will bring together all the researchers, practitioners, and interested audiences to explore the open problems, applications, and future directions in this field. We believe that the fashion industry introduces a number of interesting data analytics problems that are either not studied or scarcely studied in the past and can attract great interest in the general KDD community given their practical implications. Suggested topics include (but are not limited to):

AI for fashion supply/value chain
Deep learning for fashion
Sales and demand forecasting
Improved forecasting incorporating external events like weather, events etc.
Attribute based demand forecasting
New product demand forecasting
Spatial-temporal hyper-local demand forecasting
New stores sales forecasting
Demand transference models
Discrete choice models
Trend analysis and forecasting
Sentiment analysis
Hyper-local assortment planning
Inventory allocation
Store clustering
Inventory policies and dynamic auto-replenishment
Markdown optimization
Stock re-allocation and inter store transfer
Algorithmic product design
Omni-channel order fulfillment and inventory planning
Visual search for fashion e-commerce
Fashion image understanding and auto-tagging of apparel
Virtual personal fashion assistants
Recommendation engines for fashion
AI tools for fashion designers, buyers, merchandisers, and consumers
Sustainable supply chains
Improving provenance and trust in the fashion supply chain
Circular economy and methods and policies to encourage reuse and recycling of materials
Handling disruptions in fashion supply chain
Ideas from other industries with rapid obsolescence


Submission Guidelines
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https://easychair.org/my/conference?conf=ai4fashionsc20#

We solicit submission of papers of 4 to 10 pages representing reports of original research, preliminary research results, case studies, proposals for new work and position papers. We also seek poster submissions based on recently published work (please indicate the conference published). All papers will be peer reviewed, single blind (i.e. author names and affiliations should be listed). If accepted, at least one of the authors must attend the workshop to present the work. The submitted papers must be written in English and formatted in the double column standard according to the ACM Proceedings Template, Tighter Alternate style (http://www.acm.org/publications/proceedings-template). The papers should be in PDF format and submitted via the EasyChair submission site (https://easychair.org/my/conference?conf=ai4fashionsc20#). The workshop website will archive the published papers.

For more information or any clarifications please email aiforfa...@gmail.com

- Paper Submission Deadline: June 1, 2020
- Acceptance Notifications: June 15, 2020
- Workshop date: 24 August, 2020

All deadlines are at 11:59 PM Pacific Standard Time.

Organizers
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1. Vikas C. Raykar, Manager, AI for Supply Chain, IBM Research
2. Pavithra Harsha, Research Staff Member, IBM Research
3. Nupur Aggarwal, IBM Research
4. Surya Shravan Kumar Sajja, IBM Research