This document discusses artificial intelligence (AI) trends for 2020 and beyond in supply chain decision making. It notes that AI represents a significant business opportunity for many companies according to a recent survey. The document outlines how AI-powered decisioning can provide value in supply chains through techniques like optimization, advanced analytics, machine learning, and deep learning. It provides examples of AI solution techniques that can help with problems like demand forecasting, root cause analysis, and supplier risk segmentation. Finally, it offers tips for organizations looking to apply AI, such as identifying early adopters and building applications iteratively.

1. Digital Decisioning for the New Decade:
2020 and Beyond
6th February, 2020
Andy Fox
Senior Director: Opex Analytics, a LLamasoft company

2. Enormous Value of AI in Supply Chain
Source: McKinsey
Economist.com
Economic-value creation
from AI in the next 20 years
(in $US trillion)

3. October 2019 Survey from MIT Sloan & BCG:
90% companies agree that AI represents a significant business
opportunity for their company.
70% of companies report minimal
or no impact from AI so far.
Source: https://www.bcg.com/d/press/15october2019-MIT-BCG-winning-with-ai-231852

4. AI-powered decisioning for the supply chain

5. Data science &
operations research
experts
Twenty-Three of
Gartner’s Supply
Chain Top 25
Make their decisions with
LLamasoft technology
25%
of Revenue spent
annually on R&D
>50% of FORTUNE 100
Rely on LLamasoft for technology
enabled decisions
Opex Analytics
Global leader in
AI-based solutions and
deployment services
Global team with over
750+
Employees
200+
Headquartered in
Ann Arbor, Michigan,
USA
20 locations
around the world
Supported more than 3,000
Supply Chain Analytics Initiatives
750
Global Clients
for over
Over $15 Billion Dollars
In value identified by LLamasoft customers

6. AI: How We Got Here
2006/7

7. How Analytics Evolved
Predictive
Machine Learning
Simulation
Forecasting
Alerts
Descriptive
Reports
Drilldown
Machine Learning
Visualization
Prescriptive
Optimization
Stochastic Opt
Predictive
Supervised learning
Simulation
Forecasting
Alerts
Descriptive
Reports
Drilldown
Unsupervised learning
Visualization
Prescriptive
Optimization
Stochastic Optimization

8. AI is an Umbrella Term
for a collection of algorithmic techniques
OPTIMIZATION
ADVANCED ANALYTICS
MACHINE LEARNING
DEEP LEARNING
REINFORCEMENT LEARNING
SIMULATION …

9. New Cognitive Capabilities Enable New Value
TRADITIONAL IDEAS
Predict Solve Puzzles
SEE
THE NEW IDEAS
(COGNITIVE)
HEAR READ
ALGORITHMS DATA COMPUTING
ESSENCE OF AI
+ +
Analytics Beyond Reporting

10. 2020
AI TRENDS

11. AGI
ANI
Social
AI
Enterprise
AI
Flavors of AI are evolving

12. Organizations are understanding what AI is not
AI DIGITAL AI RPA

13. Macro
Optimization, Heuristics
Micro
Machine Learning
Feature Driven | Very Specific | Market Driven
Test
Simulation, Reinforcement Learning
AI Algorithms support Micro & Macro decisions
Hierarchical optimization on
cost vs. service
Novel inventory policies
Forecasting demand,
delivery agents,
transportation rates

14. Students with R
or Python
experience
Executives
realizing the need
for tailored AI
Platforms vs.
“off the
shelf” tools
Algorithm
Libraries
More data:
internal &
external
Rise in development of custom AI apps
Supply chains are not starting from scratch with AI

15. Applied AI
Use Cases

16. 16
AI Solution Techniques for Troublesome Problems
Point vs. Range Forecasting
How to avoid service issues?
Increasing complexity with a growing tail Unpredictable demand

17. 17
Outcome-based Action vs. “Looking Backwards”
Point vs. Range Forecasting
PREDICTION
• 100s of features, mostly from Retailer data
➢ Consumption forecasts & actuals
➢ Historic orders & fulfillment
➢ Glance views & pageview out-of-stocks
➢ Customer inventory on-hand
• Random Forest Quantile Regressor models
potential order patterns
“Feature engineering”: the art of the science

18. 18
Outcome-based Action vs. “Looking Backwards”
Point vs. Range Forecasting
OPTIMIZATION
• Generates probability of not meeting
demand at current inventory levels
• Calculates inventory gaps
• Recommends actions
➢ Stock transfers
➢ Production timelines
➢ Production quantities

19. ML to Derive Input Parameters for Inventory Models
Point vs. Range Forecasting
MACHINE LEARNING
POWERED
PARAMETERS
OPTIMIZATION
POWERED
MODEL

20. Root Cause Detection Algorithm for Service Losses
Next Generation Root Cause Analysis: Intelligent and Automated
GOAL
Replace manual cause coding of service
losses with algorithmic approach,
increasing standardization and objectivity
of root cause assignment while
eliminating manual planner effort
APPROACH
Non-linear Root Cause Analysis –
Feature Creation, Scoring, &
Unsupervised Learning
Quality
Supplier
Delays
National
Forecasts
Regional
Deployment
Production
Attainment
Production
Capacity

21. On-Time Delivery and Chargeback Root Cause Analytics
Next Generation Root Cause Analysis: Intelligent and Automated
ORDER ATTRIBUTES ORDER FLOW EVENTS
BUSINESS PROCEDURES
CARRIER PROCEDURES TRANSIT TIMES
CARRIER EVENTS
FORECASTS
HOURLY DAILY QUARTERLY
Predicting and
expediting late
orders
Measuring root
cause of late
orders
Quantifying
investment
opportunity
ANALYTICS
ENVIRONMENTAL FEATURES
DATA

22. On-Time Delivery and Chargeback Root Cause Analytics
Next Generation Root Cause Analysis: Intelligent and Automated

23. Status quo: actions to mitigate supplier risks provide data
Supplier Risk Segmentation
Perform supplier
audits and periodic
tests
Create contingency
plans
Collect third-party
information
› Supplier news stories
› Geopolitical events
› Natural disasters

24. AI Use Case: Clustering to create Supplier Segments
Supplier Risk Segmentation

25. Applying AI
to your Organization

26. Three use cases for data science
Generate Insights
One-time insight
Engines
Feeds enterprise
systems
Decision Products
Business user
consumer

27. Supply chain professionals need applications

28. AI departments should be “App Building Factories”

29. Get started with AI: our Top Five tips
1
AI Discovery and Immersion
What problem areas in your organization may have new and/or different data to make better decisions?
2
Identify Early Adopters
Which teams are data savvy today? Who is “off-roading” with your systems to achieve their goals?
3
Be Agile, Build Iteratively
View AI initiatives as “products”, not “projects”. Start small and leverage the Early Adopters for feedback.
4
Invest in Talent and Platforms
Ensure that investments in AI and data scientists are tailored to the problems you’re trying to solve
5
Keep Learning
LLamasoft seminars and Analytics Academy

30. Continue the discussion
FREE SEMINAR:
Make Smarter Business Decisions
with a Supply Chain Digital Twin
27th February 2020, LLamasoft Offices, Birmingham
Discover how companies including AB InBev, Heinz, Tesco and Unilever are harnessing the
power of a digital twin to make smarter decisions, better predict outcomes and prescribe actions,
improve efficiency, performance and responsiveness, and democratise supply chain intelligence.
Visit the LLamasoft booth today to collect your exclusive invitation!

31. Thank you