Challenges and the OpportunitiesDecision Tree Applied for this Use Case 24 | Maxim Integrated –Credit: Anthony Niznik Order Lines Lead Time < 4 weeks >= 4 weeks Demand Upside

JPK

Gro

up2019 Business Forecasting & Analytics Forum

March 25-26, 2019 • San Francisco, CA

Machine Learning in Supply Chain – the Challenges and the Opportunities

March 25, 11:00 am

Madan Chakravarthi – Maxim IntegratedMadan Chakravarthi is an Executive Director of Supply Chain at Maxim Integrated, San Jose CA. An Industrial Engineer and Software Engineer by training, he has decades of international experience in the Semiconductor space in diverse roles. Prior to Maxim

Integrated, he was with GLOBALFOUNDRIES where he managed Manufacturing Automation (CIM) function in Malta NY. Prior to that he was with Silicon Labs in Supply

Chain and IT roles.

View presentation online at: https://jpkgroupsummits.com/sanfranciscoforecasting2019-attendee

Machine Learning in Supply Chain – Challenges and OpportunitiesMadan Chakravarthi

Executive Director - SCM

© 2018 Maxim Integrated

Special thanks to Esther Hammerschmied

Outline

2 | Maxim Integrated

1 About Maxim

2 Machine Learning in Supply Chain –Opportunities

3 Challenges

Outline


1 About Maxim


3 Challenges

About Maxim

• Founded in 1983

• Global footprint with Headquarters in San Jose, California

• Leader in analog and mixed-signal solutions

• $2.5B in revenue*

• 7,000+ employees


* trailing 12 months

Success in a Range of End Markets

5

Revenue by market*

| Maxim Integrated

20%

28%

20%

27%

4%

Communications & Data Center

Industrial

Consumer

Computing

Automotive

* trailing 12 monthsTotal less than 100% due to rounding

Maxim’s Solutions - Automotive


Maxim’s Solutions - Mobile


Power ManagementExtend battery lifeCharge fasterAccurately report batteryShrink solution size

AudioEnhance experience with richer sound

SensorsConnect device with user and environment

Maxim’s World-Class Supply Chain • Advanced Planning Systems

• Customer-centric inventory management

• >90% Ontime-Delivery Performance

• 95% of parts < 6 weeks lead time

• Thousands of die types

• Tens of thousands of parts and orders

• Thousands of customers

• Asset-lite model –outsourced, insourced


F1

F2

F3

F9

F10

F8

A2

A3

A9

A10

A8

A1

S1

S3

S2

S4

T1

T3

T2

T4

Fab Sort Assembly/Bump

Final Test

WaferBank

DieBank

FinishedGoods

TestQueue

C2

D2

C1

D1

Customers &Distributors

In Summary


Broad portfolio of high-performance analog and mixed-signal solutions

Technology differentiation

Proven track record across industries

Committed to your success

Outline


1 About Maxim


3 Challenges

Machine Learning – What it Means


This is a car

This is a 2 door car

This is not a car

Is this a car? Yes it is

Machine LearningModel

MachinesData

What is this? This is a car

How many doors? It has 2 doors

Classification

Quantification

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Artificial Intelligence

Machine Learning

Deep learning

• Artificial Intelligence –Umbrella term for machines that are intelligent or smart

• Machine Learning – Programming machines to automatically learn from data and improve outcome

• Deep Learning –layered/hierarchical neural networks that continuously get better with data


Some of the Current Use Cases of Machine Learning


Technological Evolution in Supply ChainPredictive is the New Buzzword

Big Data/Cloud

Smart Mfg./ Industry 4.0

Internet of Things

Cryptocurrency

Block Chain

Machine Learning

Current

Predictive

Advanced Planning & Scheduling

Software

Available To Promise

Capable to Promise

Simulation

Y2K onwards

Prescriptive

MRP

MRP II

ERP

1980s/90s

Transactional


Why do we need Machine Learning in Supply Chain?

• Data growth in recent years

• Scalability

• Unstructured dataBig Data

• Real-time decisions with real-time data

• Ad-hoc use cases

• Supply Chain collaborationResponsiveness

• Success in complex use cases

• Success in SCM domain – Logistics/eCommerce

• Unlimited opportunities to exploit dataSuccess


SCOR Model – Machine Learning Use Cases

• WIP Arrival Prediction

• Asset Optimization

• Cycle Time Improvement

• Delivery Performance

• Logistics Synchronization

• Inventory Optimization

• Sourcing Optimization

• Make-or-Buy decisions

• Predictive Models

• Demand Forecasting

• Supply Chain Planning, Order Commit

• Optimizing Financials

Plan Source

MakeDeliver


ML Use Cases for Demand Planning / Fulfillment

• Demand Forecasting in a high product-mix environment*

• Demand Forecasting using data in public domain

• Complex cyclical/seasonal patterns

• Demand Duplication

• Social Deep Learning

• Strategic FG Inventory Optimization

• Timing of New Product Introduction

• Delinquency to Customer Request Date

17 | Maxim Integrated* Used by Maxim

ML Use Cases for Supply Chain Planning – Sort/Test

• Predict arrival of WIP

• Dynamic tester allocation/ scheduling > Considering more variables than

a Supply Chain model would

• Strategic tester allocation –balancing Sort and Final Test

• Predict tester downtime and optimize schedules around it

• Real-time dispatching/ scheduling


ML Use Cases for Supply Chain Planning – Assembly

• Die bank inventory optimization

• Predict arrival of WIP to die bank

• Assembly load forecast to vendors*

• Sourcing/cost optimization

• Determine optimal die bank location > Reduce airmiles!

• Lot size optimization

19 | Maxim Integrated* Used by Maxim

ML Use Cases for Supply Chain Planning – Wafer Fab

• Raw wafer inventory optimization

• Wafer starts optimization

• Make or Buy decisions

• Postponement

• Lot size optimization


Machine Learning Use Cases – Supply Chain Systems

Input

• Targeted adjustments of input data

• Data cleansing/ Automated Master Data Management

• Predictive Analytics as part of input (e.g. downtime)

Supply Chain Engine

• Digital simulation of parts of Supply Chain using ML models

• Hybrid models –Machine Learning + Advanced Planning Systems

Output

• Root cause analytics –Why we are doing well (or not)

• Using historical data of planning engine output for prediction models


Case Study: Predicting Delinquent Orders

• Goal: Predict which Open delinquent orders will ship as delinquent

• Benefit: Improved delivery metrics through targeted actions

> Expedite order lines in dispatching systems and processes

> Elevate constraints and update planning system, e.g. allocate extra tool

22

Qualitative date

Open Orders Shipped Orders

Ontime Delinquent

Delinquent Orders

| Maxim Integrated – Credit: Anthony Niznik

Case Study: Predicting Delinquent Orders

23

• Algorithm utilizes dozens of order characteristics or variables

• Historical data is used for testing

• Model is then applied to current data to predict which open delinquent orders ship late as well

• Supply chain levers are applied on predicted shipped delinquent orders to improve delivery performance

Predict

Test

Train

Delinquent Orders


Decision Tree Applied for this Use Case

24 | Maxim Integrated – Credit: Anthony Niznik

Order Lines

Lead Time

< 4 weeks

>= 4 weeks

Demand Upside

<= 20%

> 20%

Assembly Location

Americas

Asia

Europe

Partial List of Variables used in Model:

• Order Lead Time

• Demand Upside

• WIP location*

• Revenue

• Tardiness

• Throughput Rate

• Historical Delinquency

• Assembly Location*

• Shipment Region*

• Customer Group*

• Cycle Time* Text variablesHigher probability of a late order

Delinquent Orders

Predicting Which Open Delinquent Orders Will Ship LateRetrain model periodically

25

Train on Historic data

Test on Last Qtr data

Predict using Current Data

Delinquent Orders


Case Study: Predicting Delinquent OrdersAlgorithm Results – Initial Accuracy

26Data for illustration only

Delinquent Orders

306

61%

73

14%

47

10%

74

15%

Actual Status of Shipment:

Not Shipped Late81%

Shipped Late61%

Not Shipped Late87%

Shipped Late50%

379

353 147

121

Predicted Status by Algorithm:

Finetune Advanced Planning / Dispatching


Expedite / Elevate Constraints

Case Study: Assembly Forecasting

Current System

• High variability – Tends to over-forecast

• Risks of Under-Forecasting – Challenge to secure capacity/procure materials

• Risks of Over-Forecasting – Excess raw materials, unused capacity (credibility?)

• Opportunity to improve SCM Metrics

Machine Learning Model

• Predict shipment dates and convert to assembly loads


Assy FC

Sample Assembly Forecast from ML Model22% Improvement with Deep Learning (Neural Network) Model

28

Assy FC

| Maxim Integrated – Credits: Sai Anurag Modalavalasa, George Koikaramparambil

Outline


1 About Maxim


3 Challenges

Challenges

• “If I don’t understand it, I won’t use it”

• Spaghetti ML models

• FOMO

• Should ML Org reside with IT or End-User Team?

• How far can one go without big data on cloud?

• Is a major upgrade from your Software vendor going to disrupt everything?

> Develop in-house or buy COTS

• How to ramp up experience during initial phase—need to go beyond just using “toolboxes”

• Data quality and Data cleansing


Next Steps for Maxim

Articulate SCM ML vision and roadmap

Utilize the current use cases fully

Enhance ML competency within SCM team

Increase university engagement

Deploy ~6 use cases by end 2019


Conclusion – Quotes

“In Algorithms we trust”— The Economist

“Karma of humans is AI” – Raghu Venkatesh

“Neither Man nor Machine can replace its creator” – Tapan Ghosh

“What AI and Machine Learning allows you to do is to find the

needle in the haystack” – Bob Work


Empowering Design Innovation

| Maxim Integrated33

Documents

Challenges and the OpportunitiesDecision Tree Applied for this Use Case 24 | Maxim Integrated –Credit: Anthony Niznik Order Lines Lead Time < 4 weeks >= 4 weeks Demand Upside