Optimizing Supply Chains Through Machine Learning

Lalit Wadhwa VP, Global Supply Chain Operations

AVNET, Inc.

@wadhwal https://www.linkedin.com/in/lalitwadhwa

Topics

• About Avnet

• Digital Supply Chains

• Data in Digital Supply Chains (DSC)

• Convergence of DSC & Machine Learning

• Case Studies and Applications

• Drivers for Machine Learning Adoption

• Wrap Up

Our Purpose

We help technology make the world a better place to live, work and play

Who We Are And What We Do

Who

We Are We are one of the world’s largest

global distributors of electronic

components, computer products

and embedded technology

serving customers in

more than 125 countries.

What

We Do

Financial

Scope For the fiscal year ending

July 2, 2016 we generated

revenue of $26.2 billion.

Who

We Are

What

We Do

Financial

Scope We are one of the world’s largest

global distributors of electronic

components, computer products

and embedded technology

serving customers in

more than 125 countries.

We connect the world's leading

technology companies with more

than 100,000 customers by

providing cost-effective, value-

added services and solutions.

For the fiscal year ending

July 2, 2016 we generated

revenue of $26.2 billion.

Company Snapshot

Americas

$10,424B

(40%)

Asia

$7,985B

(30%)

EMEA

$7,811B

(30%)

• Named to the FORTUNE Most

Admired list for technology

distribution, 2007-2016

• Top Business Technology

Innovator on the 2016

InformationWeek Elite 100

• No. 102 on the 2016

FORTUNE 500 (U.S.)

• No. 380 on the 2016

FORTUNE Global 500

• Named a World's Most Ethical

Company by Ethisphere

Institute 2014, 2015 and 2016

An Industry Leader

FY16 Annual Revenue

$26.2B

Avnet, Inc.

$9.7B

Avnet

Technology

Solutions

$16.6B

Avnet

Electronics

Marketing

• Headquartered in

Phoenix, AZ

• Founded in 1921

• AVT listed on

the NYSE in 1960

• 800+ suppliers

• 100,000+ customers

• 100 acquisitions

announced or closed

since FY91

• 17,700 employees

worldwide

Fast Facts

37%

63%

DIGITAL SUPPLY CHAINS – CONNECTED, AGILE, INTELLIGENT

Disruption of the Supply Chain

Traditional Supply Chains

Advanced Analytics &

Machine Learning

Platforms

IoT and M2M Additive

Manufacturing

Advanced Robotics

Drones

Characteristics of Digital Supply Chains

Customer-Centric

Connectivity

Visibility

Traceability

Analytics & Intelligence

Agility

Scalability

Data in Digital Supply Chains

Volume Velocity

Variety Veracity

Data in Digital SC

Data Types in Digital Supply Chains

Source: Big Data Analytics in SCM, Ivan Varela Rozados & Benny Tjahjono

CONVERGENCE OF DIGITAL SUPPLY CHAINS & MACHINE LEARNING

What is Machine-Learning?

Question Data

Collection & Preprocessing

Learning Algorithm

Model

New Data Model Predicted

Output

Types of Machine Learning

Supervised – Evaluate New Data Based on Prior Data

• Regression

• Classification

Unsupervised – Discover Patterns

• Segmentation

• Clustering

• Association

Reinforcement – Autonomous Agents

• Cumulative Reward Maximization

Supply Chains - SCOR Model

Source: APICS SCC, SCOR 11

Structured Data Type - Examples

Process - Source

• Supplier

• Product

• Pricing

• Quantity

• Lead Time

• Inventory

• Shipment

• Quantity Multiples

• Financial Data

• Capacity

• Yield

Process - Deliver

• Customer

• Forecast

• Orders

• Product

• Lead Time

• Scheduling

• Pricing

• Promotions

• Discounts

• Shipping

• Return

• Financial Data

Process - Return

• Customer

• Product

• Return Reason

• Quantity

• Condition

• Product Reviews

• Financial Data

CASE STUDIES

Business Query

• Customer Segmentation Based on 7 Attributes

Data Sources

• ERP, CRM, External Databases

ML Algorithm(s)

• K-means, Hierarchical Cluster Analysis (HCA)

Benefits

• Customized Product, Pricing & Marketing Strategy for Each Segment

Customer Segmentation

Business Query

• Discover Product Association Relationships from Sales Databases

Data Sources

• ERP, CRM

ML Algorithm(s)

• Apriori, Max-Miner

Benefits

• Targeted Promotion, Sales Uplift

Product Association

Business Query

• Optimize Product Pricing to Achieve Predetermined Objectives

Data Sources

• ERP, RFQ, External Databases

ML Algorithm(s)

• Decision Tree, Random Forest

Benefits

• Improve Quote Win-Rate and Profitability Simultaneously

Pricing Optimization

Business Query

• Demand Estimation for New Product Introduction

Data Sources

• CRM, Sales Data, External Databases

ML Algorithm(s)

• Lasso Regression, SVM, Random Forest

Benefits

• Simultaneous Improvement in Inventory Profile & Margin

Demand Estimation

More Examples

Sales Pipeline Win Propensity Prediction

Supply Chain Security & Fraud Detection

Forecasting Obsolescence Risk

Optimizing Warehouse Operations

Manufacturing Process Optimization Through ML

At the Intersection of Supply Chain and Machine Learning

DRIVERS FOR MACHINE LEARNING ADOPTION

Drivers for Machine Learning Adoption

• Alignment with Business Strategy

• Pilot Projects

• Change Management

– Data Driven Decision-Making Culture

• The Role of Human Insight

• Talent

– Convergence of Business, IT and Data Science Experts

Challenges To Be Aware Of

• Data Relevancy

• Data Quality

• Data Quantity

• Carefully Separate Correlation from Causation

• Static Models in Constantly Changing Business Environment

Wrap Up

• Digitization of the Supply Chains

• Transformational Impact of Machine Learning

• Machine Learning in B2B Supply Chains

• Succeeding with Machine Learning Adoption

THANK YOU