Kanban India 2023 www.kanbanindia.org

1. Copyright © 2023 Blue Yonder, Inc.
Evolution of supply chain
management through
systems thinking
Vinod Mathur / Umesh Gaur
Blue Yonder

2. Copyright © 2023 Blue Yonder, Inc.
Agenda
Supply Chain and its importance
Evolution of supply chain
management over the decades
Learnings summarized
How does a SC transformation look
like?
Blue Yonder

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Supply Chain – backbone of a business

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Expectations from Supply Chain
Supply chain needs to support business priorities
and meet the expectations:
• Scale up for growth with improved efficiencies
• Maintain and improve customer service while
balancing enterprise costs
• Build a responsive network for new products
• Short life cycles
• Rapidly changing technologies
• Manage disruptions efficiently and proactively
• Adopt structured process-oriented working
Source: https://www.gartner.com/en/supply-chain/topics/supply-chain-organization-
structure-design-and-talent used for educational purposes

5. Copyright © 2023 Blue Yonder, Inc.
Importance of improving Supply Chain performance
Companies that focus on improving their supply chain performance consistently
outperform their peers financially

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1 – Supply Chain Optimization and Analytics over the years
6
OR based models have been central
to the way businesses plan and
optimize their operations
Enormous contribution and that has
resulted in creation of specialized
Supply Chain Planning and
Optimization businesses
Technology has now become the primary driver of innovation. With
ML, AI and Data Science, we now seek to replace more tasks that
require human intelligence
Is it a solution to all problems, can it replace existing concepts?
How can we use AI/ ML in conjunction with OR in the most effective
manner? Especially in Supply Chain Management
1970 1980 1990 2000 2010
Activity based
departments
Integration of
finance into
S&OP/ IBP
Integration into
Material Mgmt/
Distribution
Supply planning
integration into
Logistics
Integration of
Sales planning/
IT into SCM
DataIntegration
Cloud, SaaS
Backroom
Mainframe
Process
automation
Client server
Enterprise
integration
Web based
Planning
Technology focus
2020?
Datascience/
learning driven
planning
Innovation
IoT, Augmented
reality

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2 – Why is there a heightened interest in AI/ ML?
What has changed over the years?
Artificial intelligence/ Machine Learning as an idea has
existed for couple of decades. What is different now?
3 things needed to make it work:
• Algorithms – have been there for a long time;
packaged libraries and programming platforms
• Computing power – Moore’s law is showing its impact
• Real life data for training – basic automation, internet
proliferation and social media has now given huge
amount of real life data
Benefits:
• Ability to search for patterns in huge data volumes
• Removes human bias
• Easy to experiment and test (PoC/ Pilot)
• Benefits in areas unexplored so far through ‘OR’
Computing
power

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Source: Hype Cycle for AI, 2023 https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-cycle used for educational purposes
Where are we on the AI Hype Cycle?
Can technology replace all human
activities?
Technical feasibility is a pre-condition
(necessary) but not sufficient to bring
technology to automate it
Factors include:
• Technical feasibility
• Hardware and software cost
• Cost and availability of labor
• Focus on improving quality/ errors
Can future technologies and algorithms
increase the % of tasks that can be
automated?

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UX UX
DB DB
WFM
OMS
ENGAGE & FULFILL
MOVE & DELIVER
PLAN & SOURCE
WAREHOUSING
RETAIL
PLANNING
SUPPLY CHAIN
PLANNING
UX UX
DB DB
WAREHOUSING
TRANSPORTATION
UX UX
DB DB
ORDER
MANAGEMENT
WORKFORCE
MANAGEMENT
Siloed supply chains cannot address the complex
challenges of today with enough speed or agility
Lack of collaboration
of organizations that say volume
of applications and related
complexity is a barrier to
collaboration
66%
Data challenges
of data analysts’ time is
spent dealing with data
quality issues
40%
With point
solutions…
• Historic-driven decisioning
• Stale, static & disjointed
• High cost & complex
• Lack of coordination across roles

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Supply Chains Are
Shifting From Linear to
Composable, Connected & Intelligent
SILOED
LINEAR
MANUAL
COMMON
DATA CLOUD
UNIFIED
EXPERIENCES
HIGH COST,
HIGH COMPLEXITY
COGNITIVE
ORCHESTRATION
COMPOSABLE
MICROSERVICES
LOWER COST &
COMPLEXITY
TRADITIONAL
PROCESSES
MODERN
SUPPLY CHAIN

11. Copyright © 2023 Blue Yonder, Inc.
How do we leapfrog?
1. Choosing the right Technology platform
● Production Grade – not just for POCs
● Mix of AI/ML, OR, Descriptive Capabilities
● More Flexibility – access to API’s, enterprise data lakes, etc.
● Reliable data layer – better decision integrity
● Extensible framework – support new business process with
easy workflows with new generation UI
● Packaged Industry workflows as accelerators
Data Layer & API
User Experience
Modular services
Depth & Breadth
Workflow / Orchestration
Best in Class Platform

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How do we leapfrog?
2. Reimagining the business processes/ not just automating
AI/ ML to improve
forecast accuracy
Inventory buffers based
on higher errors
Production pre-builds and
schedules not based on
demand plan
Procurement based on
historical average
consumption
Value leakage in the planning processes will not allow us to realize benefits of advanced analytics
Need to assess and fix planning process gaps
Procurement plan Production plan Distribution plan Demand forecast Can I get the supply
constraints as I make
changes to demand
numbers?
Can the system suggest the
right mix of demand to
target based on the
constraints?

13. Copyright © 2023 Blue Yonder, Inc.
Choosing the right use cases
Fundamental guidelines for supply chain transformation
You cannot act if you
do not know
- When did you know?
When were you able to
evaluate options?
- near term and long
term impact?
When did you act?
- agility of translating
decision to action
Did you learn from
previous incidents and
actions?

14. Copyright © 2023 Blue Yonder, Inc.
How do we leapfrog?
3. Using the right data
Unstructured Big data
Structured data
Volume
&
Variety
Consumer data on
Internet
Low
High
Enterprise Systems,
ERP, Planning Data
Data Analytics Domain
Complimentary nature of structured and unstructured data
analytics; initial set of projects can start with focus on
structured planning data
GDPR implications also need to be looked into for consumer
data usage

15. Copyright © 2023 Blue Yonder, Inc.
How do we leapfrog?
4. People – the main pillar of any transformation
Project Program Management
Manage client and program relationships
Visionary/ Industry Expert
Envision analytics output; provide domain expertise; translate
business issues into solvable problems; assesses the processes for
sustainability of benefits
Supply Chain Analyst
Convert point business problems into mathematical constructs;
Analyze output; look for patterns; communicate with client
Data Scientists
Runs mathematical models; runs applications/ solutions; provides
output to the analyst
Technical Consultant
If needed, build BI framework to report back analysis output; port to
production systems
Talent:
• Bridging the gap between Data Scientists and
Business Analysts
• Supply chain as a core discipline in the ecosystem
Leadership and Performance Metrics:
• Top level sponsorship
• Redesign of performance metrics

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What can we abstract?
Talent:
• Top level sponsorship, business case is necessary not sufficient to get the funding
• Right use case is essential: Build what you can sell; sell what you can deliver – reduce inventory pile up; align across
the value chain
• Change management – hand holding in the initial phase
• Redesign of performance metrics to align with the new objectives
• Inclusion of financials in your journey; IBP
• AI/ ML/ Tech is a means, not the goal
• Visibility is most important – Operate-ability in SaaS world

17. Copyright © 2023 Blue Yonder, Inc.
Who is Blue Yonder?
Supply Chain Company
Autonomous Supply Chain
& SaaS Company
Supply Chain Operating System
for the World
1985

18. Copyright © 2023 Blue Yonder, Inc.
The Blue Yonder Advantage
18
Semantic Platform
Data & Experience
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Factory to Consumer Predict & Automate
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550+
Customers
99.97%
Average
Uptime
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Predictions
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Tasks
Automated
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Planning
Customers
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Data
Feeds
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