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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
3 | Maxim Integrated
1 About Maxim
2 Machine Learning in Supply Chain –Opportunities
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
4 | Maxim Integrated
* 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
6 | Maxim Integrated
Maxim’s Solutions - Mobile
7 | Maxim Integrated
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
8 | Maxim Integrated
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
9 | Maxim Integrated
Broad portfolio of high-performance analog and mixed-signal solutions
Technology differentiation
Proven track record across industries
Committed to your success
Outline
10 | Maxim Integrated
1 About Maxim
2 Machine Learning in Supply Chain –Opportunities
3 Challenges
Machine Learning – What it Means
11 | Maxim Integrated
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
12 | Maxim Integrated
Some of the Current Use Cases of Machine Learning
13 | Maxim Integrated
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
14 | Maxim Integrated
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
15 | Maxim Integrated
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
16 | Maxim Integrated
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
18 | Maxim Integrated
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
20 | Maxim Integrated
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
21 | Maxim Integrated
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
| Maxim Integrated – Credit: Anthony Niznik
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
| Maxim Integrated – Credit: Anthony Niznik
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
| Maxim Integrated – Credit: Anthony Niznik
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
27 | Maxim Integrated
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
29 | Maxim Integrated
1 About Maxim
2 Machine Learning in Supply Chain –Opportunities
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
30 | Maxim Integrated
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
31 | Maxim Integrated
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
32 | Maxim Integrated
Empowering Design Innovation
| Maxim Integrated33
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