machine learning in supply chain challenges and …...machine learning use cases –supply chain...
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Machine Learning in Supply Chain – Challenges and OpportunitiesMadan ChakravarthiExecutive Director - SCM
© 2018 Maxim Integrated
For registered summit participants of American SCMS Summit. Please do not distribute to unauthorized users.
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
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* trailing 12 months
Success in a Range of End Markets
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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
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Maxim’s Solutions - Mobile
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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
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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
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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
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Some of the Current Use Cases of Machine Learning
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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
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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
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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
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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
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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
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Qualitative date
Open Orders Shipped Orders
Ontime Delinquent
Delinquent Orders
| Maxim Integrated – Credit: Anthony Niznik
Case Study: Predicting Delinquent Orders
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• 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
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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
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Assy FC
Sample Assembly Forecast from ML Model22% Improvement with Deep Learning (Neural Network) Model
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Assy FC
| Maxim Integrated – Credits: Sai Anurag Modalavalasa, George Koikaramparambil
Outline
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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
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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
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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
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Empowering Design Innovation
| Maxim Integrated33