1) Professor Chee Yew Wong from Leeds University Business School presented on using machine learning to predict returns of e-commerce fashion products. 2) The project used a Knowledge Transfer Partnership to link and analyze retailer, logistics, and supplier data to generate insights and build machine learning models for return prediction. 3) The models improved return volume predictions from 30-50% accuracy to over 90% accuracy, allowing for better planning and cost reduction.

1. Leeds University Business School
Using machine learning to predict
returns of e-commerce fashion
products
Leeds University Business School, Leeds, UK. (c.y.wong@leeds.ac.uk)
SCL HUB Conference February 6th 2020
Professor Chee Yew Wong

2. Leeds University Business School
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Are these technologies too expansive,
premature, or risky to adopt?
Digital Twins

3. “Digital industrial is the merging of the physical and
digital worlds, and GE is leading the way”…but
https://www.inc.com/alex-moazed/why-ge-digital-didnt-make-it-big.html
https://www.brothers-brick.com/2016/01/21/lego-digital-designer-officially-defunded-and-unsupported-news/
https://www.forbes.com/sites/blakemorgan/2019/09/30/companies-that-failed-at-digital-transformation-and-what-we-can-learn-from-them/
https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/inside-p-and-ampgs-digital-revolution
“The most digital company on the planet”
“Consumer Pulse” “iPad download data from production”
“Control Tower” “Distributor Connect” “GDSN”
“Digitizing innovation” “Virtual Wall” “Data at source”

4. Sometimes we can’t just sit and wait for an
off-the-shelf solution…
Uber uses machine learning, deep learning, and
probabilistic programming to predict supply/demand
A Leeds-based taxi company learns to use
mobile apps and maps for taxi services

5. Leeds University Business School
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Many (e-com) retailers offer
“free returns” to boost sales…
• Can free return policy
massively boost sales?
• Free returns “league board”
357%
Studies show lenient return policy generally increases purchase more than returns.

6. Leeds University Business School
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Consumes are given more time
to return & faster refunds?
https://www.ebay.com/help/buying/returns-refunds/return-item-refund?id=4041https://www.itv.com/news/2019-04-05/asos-launches-new-returns-policy-in-bid-to-block-serial-returners/

7. Leeds University Business School
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Lenient return policies escalate
product return costs
of goods sold are
returned
25% to
50%
of online shoppers
rate easy or free
returns as important
78% 66%
of online shoppers
are put off by
unclear or complicated
return process
• Shop Direct handled 250 million returns annually
• A large online fashion brand owner received 120,000 returns / day

8. Leeds University Business School
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There are genuine returners,
also fraud and abuse

9. Leeds University Business School
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An emerging consumer behaviour:
buying with the intent of returning
4Narvar Consumer Report 2018 — The State of Returns: What Today’s Shoppers Expect
In USA, nearly 2/3 consumers returned at least 1 item during the past holiday (2017), and
23% bought items with the intention to return them later
Wardrobing
Serial returners
Returns cost £60 billions p.a. in the UK

10. Leeds University Business School
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Consumer return rates vary…
4Narvar Consumer Report 2018 — The State of Returns: What Today’s
Shoppers Expect

11. Leeds University Business School
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Reducing the costs of return logistics:
Prediction of returns matters
Customers
“Good” items
• Re-sales at 100% full price
• Re-sales after X days
“Faulty” items
• Charity, donation, etc.
• Staff shop, 2nd markets
Resource, inspect, record,
salvage, rework, pack.
Forecast, IS (OMS), record,
inform, refund, payment.
“Salvage” items (% price)

12. Leeds University Business School
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How AI and machine
learning work?
• Artificial intelligence (AI) = artificial creation of human-like intelligence that
can learn, reason, plan, perceive, or process natural language
• Machine learning is an approach to AI
• Instead of programming the computer to follow a step-by-step process,
machine learning uses learning algorithms that make inferences from data
to learn new tasks…for new, complicated tasks that could not be manually
programmed
Training
Data
Learning
Algorithm
New
Algorithm
New (Data)
Tasks
• Supervised vs. unsupervised
• Reinforcement
• Deep learning

13. Leeds University Business School
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Machine learning for predicting
returns: The KTP project
• Better data
• Increased insights
• Better prediction
• Customer loyalty
• New customers
£££
of cost funded*
33%
Transfer
knowledge
Transfer knowledge
from the University
• Data management
• Excel / BI tools
• Dashboard
• ML algorithm
*KTP associate (data scientist) cost = £24-36k p.a. (after funding)

14. Leeds University Business School
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Better data come from
linking datasets
Retailers
Logistics service
providers
Suppliers Consumers
Order Management Systems (OMS)
Warehouse
Management
Systems (WMS)
ERP
• No primary keys
• No linked tables
• Lack IT/IS knowledge
Invoice
Report
Manual

15. Leeds University Business School
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Insights: From Excel spreadsheet to
Business Intelligence Dashboard
From To
• Simple spreadsheet
• Simple cross-tabulation
• Simple macro
• Simple SQL
• Interactive dashboard
• Visual insights
• Multiple layers of data
• Automated data feed

16. Leeds University Business School
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ML algorithms: Differentiate
months/weeks, time lags
Week
Units
E-com sales
Returns
Monthly seasonal
Units
The algorithms:
• Use test dataset, train dataset
• Use Random Forest. Gradient Boosting Machine, XGBoost regressors
• Train the model on training data
• Assess prediction (next week(s)/months) errors

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Benefits of the machine learning
(KTP) project
• Data & IS design: Understand how different IS create and store
data and why data inaccuracy caused problems
• Data integration: Combine data from fashion brand owners/retailers
and logistics service providers into meaningful dashboards to create
new insights
• BI Insights: Dashboard & ML - how factors including e-com sales
volumes, manufacturers (suppliers), product characteristics (design,
size, gender, colour categories), weather, seasonality (holidays,
events, festival, etc.), consumer demographics
• Improvement: From 30-50% accuracy of the weekly aggregated
return volume to up to 90% accuracy
• Real-time prediction: Use training data and real-time data to
predict future returns

18. Leeds University Business School
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Thank you!
Professor Chee Yew Wong
Leeds University Business School
University of Leeds
Maurice Keyworth Building
Leeds LS2 9JT
T: 0113 3437945
E: c.y.wong@leeds.ac.uk
W: www.greensupplychains.org
W: http://lubswww.leeds.ac.uk/coscr/
ISBN: 9780749473860