Covid-19 Impact Webinar: Agri-food logistic threats and research opportunities

www.ktn-uk.org @KTNUK
Agenda
14:00 14:05 Welcome Matt Butchers
14:05 14:10 Introduction to V-KEMS David Abrahams
14:10 14:40
Supply Chain & inventory Management
OR for Transportation & Logistics
Network Science
Risk Modelling
Multilevel Optimisation
Statistical Modelling
Alexandra Brintrup, Guven Demirel, Bart McCarthy
Stefano Coniglio, Toni Martinez Sykora, Stephan Onggo
Alexandra Brintrup, Guven Demirel, , Bart McCarthy
Lesley Walls, John Quigley
Stefano Coniglio, Lars Schewe
Martine Barons
14:40 15:00 Q&A Chris Sturman, Alan Champneys
15:00 16:00 Forum remains open Optional

• >150 participants
• All microphones (except speaker/chair) will be muted during presentations
• Please use the chat function to add name/questions for Q&A (this will be saved and
unanswered questions addressed in slower time)
• Please use the chat function (to ICMS Staff) for any meeting arrangement questions
• Recording – Meeting and Q/A Sessions IS being recorded. Any concerns please email ICMS
staff
This event is supported as part of the Virtual Forum for
Knowledge Exchange in the Mathematical Sciences.

Request from
Survey of
threats to agri-
food logistics
Business, Government and
leadership forums
Small group of
mathematical
scientists
Capability
document
Online forum
Wider community
• How can the mathematical sciences support emerging threats to Agri-Food Supply Chain Logistics?
• Where should the priorities be?
• How can UKRI support these relationships?

Introduction to V-KEMS
David Abrahams
Director, Isaac Newton Institute for Mathematical Sciences

Business
Continuity
Providing
“business as
usual” KE
support for
businesses
through virtual
triaging and
problem solving
Reactive
Support for
Emerging
Threats
Creating an
environment for
important and
emerging topics
to be discussed
with business
leaders and
policy makers.
A Resource
for the
Community
Adding value to
existing
initiatives.
Providing
connectivity
within and
beyond the
mathematical
sciences.
Virtual Forum for Knowledge Exchange in the
Mathematical Sciences - Philosophy

Business
Continuity
• Virtual Study
Group (20 – 23
April 2020)
Reactive
Support for
Emerging
Threats
• Weekly seminar
series on
decontamination
of surfaces for
COVID19 (starts
24 April 2020)
• Discussion forum
on Agri-Food
Logistics (28 April
2020)
• Rapid response for
DHSC COVID19
recovery (22 April
2020)
• INI programme as
part of Royal
Society RAMP
(upcoming)
A Resource
for the
Community
• Public lecture –
climate change:
how can
mathematics help
us to respond? (20
April 2020)
Virtual Forum for Knowledge Exchange in the
Mathematical Sciences - Activity

Supply Chain and Inventory Modelling
Dr. Alexandra Brintrup, Manufacturing Analytics Group, IfM, University of Cambridge
Dr. Guven Demirel, School of Business and Management, Queen Mary University of London
Prof. Bart MacCarthy, Business School, University of Nottingham
28/04/2020

Supply Chain and
Inventory Modelling
28/04/2020
Please cite as: MacCarthy B., Demirel G., Brintrup A., Supply Chain and
Inventory Modelling, Covid-19 Impact Forum: Agri-food logistic threats and
research opportunities, 28 April 2020.

Brief Introduction
What is it?
Analytical, mathematical and simulation based tools to
understand and develop effective SC configurations
“Effectiveness” may involve a combination of minimising
cost, lead times, share of value, risks, maximizing
resilience, security of supply
A “Supply Chain” may be viewed from different
perspectives
Global supply ecosystem
Focal company
Particular product

Brief Introduction
How can modelling help?
Uncover how and to what extent a supply chain and actors
within it may be affected by different types of risk
Develop robustness, and recovery strategies and assess
their effectiveness
Buffering
Multi-sourcing
Network reconfiguration
Collaboration in logistics operations
Surveillance and auditing strategies

Example application areas
Dr. Guven Demirel, School of Business and Management, Queen Mary
University of London
Dr. Alexandra Brintrup, Manufacturing Analytics Group, IfM, University of
Cambridge

Typical food/beverage / consumer product
supply chain supply chain

Some dynamic models at a supply
chain level
Bullwhip - distortion
and variance
amplification
transmitted upstream -

Global supply networks – ‘new normal’ post Covid-19
How fragile is my network?
How do I know the network?
M&S Clothing sourcing 2017
34 Countries, 612 Factories,
192,775 Workers
MacCarthy & Jayarathne (IJOPM, 2013)
For policy level questions we need ‘sector’ level models
Clothing
Aerospace

Mapping a supply network
We have developed methods to
capture networks using all
available data sources
Cobalt supply
network for
batteries for
EVs
Six tiers of
supply before
vehicle
assembly

Was anyone looking?
Who should be looking?
Where should they look?
Who pays?
Supply network surveillance - Horsemeat

Supply network surveillance
Map
Model, measure and analyse
Audit

Optimal supplier development
improvement
investment
non-
conformances
profit -x
learning
about
supplier
supplier
capability
improvement
site visits, further
part tests, audits
spend on
more info
How much to
invest in
improvement?existing info

Supply chain AI & Analytics
Discover hidden patterns
in data that yield useful
insights for improving
supply chain operations
Use patterns to predict
current/ future state of
the system and companies
embedded within
Use autonomous
algorithms to control daily
low-level operations to
nudge supply chain
systems to a more desired
state
Descriptive
analytics
Predictive
analytics
Prescriptive
analytics
Aerospace
Automotive
FMCG

Supply link and disruption prediction
Supply Chain Miner
with Natural Language
Processing
Link Prediction Algorithms
Fake event: during Hurricane Sandy 2012
(Hill 2012)
Real event
Disruption prediction
algorithms
Event monitoring
Wichmann et al (2019), Brintrup et al (2019)

Network reliability optimisation
Each supplier can produce a number of different
products; has a historical reliability score and varying
costs for production and delivery
What is the best (min cost, max reliable) combination
of supply paths to bring together a product?
suppliers
products Bill of materials
unreliable configuration ->more reliable configuration
Brintrup and Puchkova (2019)

11001?
(share?)
01101111
01101011
(ok!)
Won’t allow orchestration
via lock down
Limited span of
visibility
Lack of incentives
to orchestrate
Lack of scalable
optimisation
approaches
fetch.ai and Value Chain Lab
Collaborative logistics
Connected Everything Network Plus II

Operational Research for Transportation &
Logistics
Stephano Coniglio
University of Southampton
Toni Martinez Sykora
Stephan Onggo

Operational Research for Transportation & Logistics
Stefano Coniglio
Toni Martinez Sykora
Stephan Onggo
Center for Operational Research, Management
Science and Information Systems

Transporting agri-food products from the production
centres (farms) to the places of consumption at the right
time, right quantity, right quality and the right cost.
Cost is typically a trade-off between: economic, social,
environment, resilience etc.
Operational Research techniques: development of
algorithms for supporting decision making via:
● Optimisation techniques: find good solutions subject
to constraints under uncertainty (robust optimisation)
● Simulation techniques: estimate performance of
different policies under uncertainty (e.g. sudden
increase in demand, supply disruption)

Current projects at CORMSIS (Southampton) 1/2
● To determine the optimum location of warehouses for
food and their replenishment policy for better
preparedness in responding to natural disasters
(location inventory problem)
● To determine the optimum replenishment policy of
perishable food products and their distribution to
retailers (perishable inventory routing problem)

Current projects at CORMSIS (Southampton) 2/2
● Pallet loading/packing
● Multimodal transportation/gig-economy
● Thailand’s mango supply chain
● Warehouse location and maintenance scheduling for
the Royal National Lifeboat Institute RNLI
Nam Dok Mai Golden mango

Network Science
Alexandra Brintrup
University of Cambridge
Guven Demirel
Queen Mary University London
Bart McCarthy
University of Nottingham

Network Science
28/04/2020

Complex networks
“More is different”
Anderson, P. W. More is different: Broken symmetry and the nature of the
hierarchical structure of science. Science, 177: 393–396, 1992.
node
link
+
+

Networks are everywhere!
mobile communication
networks
human disease
network
internet

Supply networks
Dyadic focus in supplier relationship management
Importance of larger motifs, at least triads
Supply chain networks
Importance of larger motifs, at least triads

Why is network science relevant for supply chain
management?
Network effect on bullwhip effect (demand amplification)
> +

Uk Food supply networks
Many UK food producers/suppliers, big employer
Global supply inbound, local outbound
Highly competitive, changing retail market –
omni-channel delivery
Packaging supply important
Many intermediaries and small logistics players –
nationally/internationally
Human resource issues at all levels

Uk Food supply networks
Many UK food producers/suppliers, big employer
Global supply inbound, local outbound
Highly competitive, changing retail market –
omni-channel delivery
Packaging supply important
Many intermediaries and small logistics players –
nationally/internationally
Human resource issues at all levels
How can models help?
Collaboration – benefits, where, who?
Digital – benefits, where, how?
Emerging/new configurations - impact on supply/
availability?
Surveillance and early warning signal on global food supply
networks

Supply network mapping and visualization

Stability of supply networks
What are the effects of different types of
material flow elasticities on stability of
supply networks?

Influence and sensitivity of firms in
supply networks
influence sensitivity

Source Puma et al. (2015) "Assessing the evolving fragility of
the global food system." Environmental Research Letters
10(2): 024007.
How resilient are the global
food supply networks
against disruptions?
Which countries are most
critical / vulnerable?
What are the best long-term
import / export and
agriculture policies?
Global food supply networks

Early warning signals in supply networks

Emergent patterns in supply networks and
relation to robustness FORD TOYOTA
supplierfirms
Plants
Pollinators
21%
Brintrup et al (2015, 2016)

Injection of inventory at strategic network
positions to maximise resilience
OEM
1 TIER
2 TIER
3 TIER
Ledwoch, Brintrup, Yasarcan (2018)

Detecting emergence of criticality in complex
networks
Criticality measures how risky a node is to the
operation of a network
Measuring criticality enables nodes to proactively respond
to anomalies
In telecommunications, data packets are sent around
the network. If they can’t be sent, they are queued.
The more queued packets, the worse the network
functionality. If key nodes have long queues, major
sections of the network cannot communicate.
Design distributed measures of criticality so for each
node, information for a local subset surrounding the
node is used to compute it:
scalable
when there is a cost to communication
When whole network is not visible (e.g. in supply nets)
Size indicates queue size. One can see that the red
node have long queues, blocking two parts of the
network from each other
Proselkov, Parlikad, Brintrup(2020)

Possible applications to Agri-Food
Logistics and Supply Chain Threats
Firm/supply chain level Business ecosystem level
Short term Detect criticality
Inject inventory based on
network topology
Within-day network
optimization
Estimate ripple effects

Possible applications to Agri-Food
Logistics and Supply Chain Threats
Firm/supply chain level Business ecosystem level
Medium/
long term
Deciding on capacity
investments and supplier
development based on
network topology
Analyze emergent patterns
Mitigating systemic risks in
global food supply networks.

Risk Modelling
Lesley Walls
Strathclyde University
John Quigley
Strathclyde University

Risk?
RISK PROBLEMS
Simple
Complex Uncertain
Ambiguous
e.g. frequency
order
fulfilment
e.g. disasters
e.g. multiple
stakeholders
e.g. interconnected
infrastructure

Managing Risk involves Making Decisions under Uncertainty
NATURE OF UNCERTAINTY
Aleatory Epistemic Strategic
randomness state of knowledge intentional

Modelling Risk using Bayesian Networks
Simple
Complex Uncertain
Ambiguous
Aleatoric
Epistemic Strategic
RISK PROBLEM
NATURE OF
UNCERTAINTIES
IN MODELLING

availability of
material
production
An Example

weather
availability of
material
absenteeism
production

weather
availability of
material
absenteeism
production
staffing
inventory

weather
availability of
material
absenteeism
production
staffing
inventory
profit
customer
experience

weather
availability of
material
absenteeism
production
staffing
inventory
profit
ability of
supplier
to deliver
Quality of
supply
customer
experience

weather
availability of
material
absenteeism
production
staffing
inventory
profit
ability of
supplier
to deliver
Quality of
supply
customer
experience
Test
data

weather
availability of
material
absenteeism
production
staffing
inventory
profit
relationship
with
supplier
ability of
supplier
to deliver Quality of
supply
preferences
of
supplier
customer
experience
willingness
to provide value
TestTest
data

Risk Modelling for AgriFood Challenges
Firm/Supply Chain Level Business Ecosystem Level
Short term o Predicting delays
o Rescheduling resources, staff for
production and deliveries under disruption
o Resource pooling
o Deregulation, reconfiguration of
storage facilities
o Prediction of regional/UK wide
chokepoints
Medium/Long Term o De-risking strategies, eg redundant
facilities, mode switching
o Prediction of system dependencies
o Multi-sourcing
o Mitigating systemic risks in global
food supply networks
o Policy-making
Simple risk with
aleatory uncertainty
Ambiguous risk with
strategic uncertainty
Complex risk with
epistemic uncertainty
Uncertain risk with
epistemic uncertainty

Lars Schewe
University of Edinburgh
Stefano Coniglio

Multilevel Optimisation: team
Lars Schewe
Stefano Coniglio

Government
Private
investors
Competitive
market
Model for
multiple decision makers taking decisions in sequence
anticipating the later decisions
Example: Investments within competitive markets

Setting an electricity tariff for smart grids
● Retailer sets the tariff
● Consumers adapt their consumption to the tariff
Multilevel Optimisation: examples
How to design an optimal tariff anticipating the actions of the consumers?
But what if the consumers install batteries storage?

Multilevel Optimisation: examples
Investments within competitive markets
Power markets
Airline market
● Power market
● Airline ticket market
Government:
incentives for
renewables
Private investors:
new renewable
power plants
Day-ahead
electricity
market
Government:
taxation or
incentives
Airport
companies:
investments in
runway capacity
Airline ticket
market
Airline
companies:
investments in
new aircraft

Q&A
Alan Champneys
University of Bristol
Chris Sturman
Chartered Institute of Logistics and Transport

As Professor Abrahams has indicated, there is support available through the:
• Individual research institutions you have heard from today
• Knowledge Transfer Network
• International Centre for Mathematical Sciences
• Isaac Newton Institute, and the
• Newton Gateway to Mathematics
If you wish to follow up on anything, please contact matt.butchers@ktn-uk.org

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