Making AI Real With

Size Pack OptimizationExcel Can’t Touch This!

Tim Carney – 9/18/2019

Dumbest Man Alive? or Do We Have a Tendency to Overlook Things?

Before 2014, Belk Used Excel To Write Orders by Size

Applying average size ratios across stores

• Increases stock outs / lost sales

• Causes overstocks by location

• Increases markdowns

• Lowers profitability

• Causes customer dissatisfaction

2

Upgraded Merchandising & Planning Systems

• Automated Processes that used Artificial Intelligence

• Used Machine Learning to synthesize data & make better decisions

• Integrated SAS Size Pack Optimization into order process

• Some brand margins grew as much as 230 basis points over 4 years

Project Smart 2012 – 2014 – Process Automation

3

Two Distinct SAS Solutions – Size Pack Optimization

Pack OptimizationDetermines best packs to satisfy need while

minimizing costs

Size ProfilingCreates library of size ratios by

product by door

1 2

50% / 26% / 24%

S M L

4

How Belk uses SAS SPO?

Business Use SAS Pack Optimization Additional Benefit

Assortment Planning

(Initial Order

Placement)

• Optimized packs by

product / store /

time of placement

• Auto PO generation

Replenishment

Forecasting

Create An Optimized

Allocation

right sizes

right packs

right locations

right time

5

6

How Belk is Using Artificial Intelligence

and Machine Learning to Capture Size

Demand By Store

Size Profiling

• Uses historical data to determine size ratio demand

based on :

– Merchandise

– Location (Brick & Mortar Stores vs. Ecom)

– Time boundaries

Size Profiling – Uses AI & Machine Learning to Capture Size Demand

S M L

S M L

S M L

Store 458

Crabtree

Store 452

Southpark

Store 678

Flowood

17% / 63% / 20%

37% / 45% / 18%

12% / 42% / 46%

7

Size Profiling – Our Virtual Assistant Does The Heavy Lifting

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• Lowest Profile Level - defined by

user

• System creates profiles for every

level in the hierarchy for the specified

Dept. down to Lowest Profile Level

• User reviews exceptions from their

Virtual Assistant

S M L

S M L

S M L

Branded

Knits Wovens

Polo

S M L

Ven/Class

S M . L

Vendor

S M L

WovensKnits

Lowest Profiling Level

Dept. 387

Collections

S M L

S M L

Machine Learning – Imputation Process

9

SouthPark ran out of

Medium Red Polos,

but sold 3 Smalls and

4 Larges

S M L

Sold 3Sold 0

due to 0

in stock

Sold 4

S M L

Sold

30%

Sold

30%

Sold

40%

Mediums represented

30% of sales for

similar stores over the

same time period

SouthPark could have

sold 3 Mediums if they

were “in stock”

S M L

3 4

Actual Sales

Actual Sales at

Similar Stores Imputed Sales

?3

Flowood CrabtreeSouthPark SouthPark

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How Belk is Using Artificial Intelligence

and Machine Learning to Satisfy Size

Demand By Store

Pack Optimization

• To convert assortment plans into optimized packs at the style/ color/ door

level

• Tool can also provide the exact quantity to order by size for vendors that

can ship and allocate by size (“eaches”)

SAS Pack Optimization – Objective

Store 458

Crabtree

Store 452

Southpark

Store 678

Flowood

S M L

S M L

S M L

17% / 63% / 20%

37% / 45% / 18%

12% / 42% / 46%

X

X

X

108 Pcs

120 Pcs

96 Pcs

Op

timal P

acks!

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Human Interaction – Choose from 3 Pack Options

• Before an order can be optimized, the user must define the type of pack:

1

Used for Private

Brands

System

recommended packs

Vendor

Predefined

packs

Selected – packs

2

ONLY includes a single

style / color / size

3Bulk packs

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• Use constrains pack creation with pack settings:

– Define number of units per pack

– Maximum number of packs to recommend – puts a cap

on the total number of packs

– Share pack configurations across products – forces

same configurations across all colors

– Recommend multi colors into a single pack, e. g.

“Rainbow packs” or “Fun Packs”

Example

Inner Pack Units 6, 12, 18

Max # of

Packs 3

Share Pack?

yes

Multi Style Color? No

13

User Guides Virtual Assistant With Pack Settings

13

SPO Uses Machine Learning, Optimzation And Automation To Determine

Best Pack Combinations While Minimizing Operational Costs

Pack Optimization logic will minimize the mismatch across an entire order

• Calculates Mismatch – difference between target (optimal units) & Actual units

ExampleSketchers Athletic

Shoe

Machine Learns - Minimizes the Mismatch

17

Summary - Artificial Intelligence – Accenture Labs

“Explainable AI won’t replace

people, but will complement

and support them so they

can make better, faster, more

accurate and more

consistent decisions”

"The future of AI lies in

enabling people to

collaborate with machines to

solve complex problems.

Like any efficient

collaboration, this requires

good communication, trust

and understanding."

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APPENDIX

Class 1: Knits

S LM

Using Partitions based

on a Attribute Class

Partition

Dept. : Moderate Tops

Partition

Class 2: Sweaters

XLS LM S LMS LM XLXS

Van Heusen

S/S

Knit

Saddlebred

L/S

Knit

Van Heusen

Merino

Sweater

WH Belk

Cashmere

Sweater

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Key Concept - Profile Creation - Partitioning Using Attributes

1. Static Hierarchy 2. Attribute Partitioning

FOB

Demand Center

Dept

Vendor

Ven/Class

Class*

Label

Company

Style

Style/Color

shorts

Dept

jeans caprisskirts Attribute based

on CLASS

slim

Vendor

skinny relaxedboot Attribute based

on FIT

* Class is not in static hierarchy

Key Concept - Profile Creation Flexibility – Hierarchy vs. Attribute

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Advanced attribute partitioning (in FY15)

• Store Groups - Collection of stores that

have similar size distributions for a group of

products

• Considers three situations:

1. “Comp” stores have been open “long enough” to

have sufficient data

2. “Non-comp” stores have not been open “long

enough” to have sufficient data

3. “New” stores – have no data

• Systemic activities

1. “Comp” Stores are grouped together

2. “New” or “Non Comp” Store are classified into

the overall average store group

New

Non-Comp

Store Group # 0

All Stores

Key Concept - Store Groups

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Key Concept - Imputation With Sparse Data

Tool imputes the “lost sales” that were caused by a lack of inventory by analyzing the

demand of the same item across a group of stores during a similar time period

• When there is not enough data to impute sales, SAS will omit the entire week of

sales data for that Style / Color combination

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