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

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KTN hosted a forum to discuss the overlap of mathematical science topics, agri-food supply logistics and emerging threats associated with Covid-19.

Find out more: https://ktn-uk.co.uk/news/ktn-forum-on-agri-food-logistic-threats-and-research-opportunities-outcome-and-slides-now-available

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

  1. 1. www.ktn-uk.org @KTNUK
  2. 2. 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
  3. 3. www.ktn-uk.org @KTNUK • >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.
  4. 4. www.ktn-uk.org @KTNUK 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?
  5. 5. www.ktn-uk.org @KTNUK Introduction to V-KEMS David Abrahams Director, Isaac Newton Institute for Mathematical Sciences
  6. 6. www.ktn-uk.org @KTNUK 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
  7. 7. www.ktn-uk.org @KTNUK 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
  8. 8. www.ktn-uk.org @KTNUK 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
  9. 9. 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 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.
  10. 10. 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
  11. 11. 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
  12. 12. Example application areas Prof. Bart MacCarthy, Business School, University of Nottingham Dr. Guven Demirel, School of Business and Management, Queen Mary University of London Dr. Alexandra Brintrup, Manufacturing Analytics Group, IfM, University of Cambridge
  13. 13. Typical food/beverage / consumer product supply chain supply chain
  14. 14. Some dynamic models at a supply chain level Bullwhip - distortion and variance amplification transmitted upstream -
  15. 15. 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
  16. 16. 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
  17. 17. Was anyone looking? Who should be looking? Where should they look? Who pays? Supply network surveillance - Horsemeat
  18. 18. Supply network surveillance Map Model, measure and analyse Audit
  19. 19. Example application areas Prof. Bart MacCarthy, Business School, University of Nottingham Dr. Guven Demirel, School of Business and Management, Queen Mary University of London Dr. Alexandra Brintrup, Manufacturing Analytics Group, IfM, University of Cambridge
  20. 20. 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
  21. 21. Supplier risk segmentation
  22. 22. Example application areas Prof. Bart MacCarthy, Business School, University of Nottingham Dr. Guven Demirel, School of Business and Management, Queen Mary University of London Dr. Alexandra Brintrup, Manufacturing Analytics Group, IfM, University of Cambridge
  23. 23. 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
  24. 24. 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)
  25. 25. 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)
  26. 26. 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
  27. 27. www.ktn-uk.org @KTNUK Operational Research for Transportation & Logistics Stephano Coniglio University of Southampton Toni Martinez Sykora University of Southampton Stephan Onggo University of Southampton
  28. 28. Operational Research for Transportation & Logistics Stefano Coniglio Toni Martinez Sykora Stephan Onggo Center for Operational Research, Management Science and Information Systems
  29. 29. Operational Research for Transportation & Logistics 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)
  30. 30. Operational Research for Transportation & Logistics 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)
  31. 31. Operational Research for Transportation & Logistics 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
  32. 32. www.ktn-uk.org @KTNUK Network Science Alexandra Brintrup University of Cambridge Guven Demirel Queen Mary University London Bart McCarthy University of Nottingham
  33. 33. Network Science 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
  34. 34. 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 + +
  35. 35. Networks are everywhere! mobile communication networks human disease network internet
  36. 36. 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
  37. 37. Why is network science relevant for supply chain management? Network effect on bullwhip effect (demand amplification) > +
  38. 38. Example application areas Prof. Bart MacCarthy, Business School, University of Nottingham Dr. Guven Demirel, School of Business and Management, Queen Mary University of London Dr. Alexandra Brintrup, Manufacturing Analytics Group, IfM, University of Cambridge
  39. 39. 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
  40. 40. 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
  41. 41. Example application areas Prof. Bart MacCarthy, Business School, University of Nottingham Dr. Guven Demirel, School of Business and Management, Queen Mary University of London Dr. Alexandra Brintrup, Manufacturing Analytics Group, IfM, University of Cambridge
  42. 42. Supply network mapping and visualization
  43. 43. Stability of supply networks What are the effects of different types of material flow elasticities on stability of supply networks?
  44. 44. Influence and sensitivity of firms in supply networks influence sensitivity
  45. 45. 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
  46. 46. Early warning signals in supply networks
  47. 47. Example application areas Prof. Bart MacCarthy, Business School, University of Nottingham Dr. Guven Demirel, School of Business and Management, Queen Mary University of London Dr. Alexandra Brintrup, Manufacturing Analytics Group, IfM, University of Cambridge
  48. 48. Emergent patterns in supply networks and relation to robustness FORD TOYOTA supplierfirms Plants Pollinators 21% Brintrup et al (2015, 2016)
  49. 49. Injection of inventory at strategic network positions to maximise resilience OEM 1 TIER 2 TIER 3 TIER Ledwoch, Brintrup, Yasarcan (2018)
  50. 50. 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)
  51. 51. 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
  52. 52. 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.
  53. 53. www.ktn-uk.org @KTNUK Risk Modelling Lesley Walls Strathclyde University John Quigley Strathclyde University
  54. 54. Risk? RISK PROBLEMS Simple Complex Uncertain Ambiguous e.g. frequency order fulfilment e.g. disasters e.g. multiple stakeholders e.g. interconnected infrastructure
  55. 55. Managing Risk involves Making Decisions under Uncertainty NATURE OF UNCERTAINTY Aleatory Epistemic Strategic randomness state of knowledge intentional
  56. 56. Modelling Risk using Bayesian Networks Simple Complex Uncertain Ambiguous Aleatoric Epistemic Strategic RISK PROBLEM NATURE OF UNCERTAINTIES IN MODELLING
  57. 57. availability of material production An Example
  58. 58. weather availability of material absenteeism production
  59. 59. weather availability of material absenteeism production staffing inventory
  60. 60. weather availability of material absenteeism production staffing inventory profit customer experience
  61. 61. weather availability of material absenteeism production staffing inventory profit ability of supplier to deliver Quality of supply customer experience
  62. 62. weather availability of material absenteeism production staffing inventory profit ability of supplier to deliver Quality of supply customer experience Test data
  63. 63. 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
  64. 64. 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
  65. 65. www.ktn-uk.org @KTNUK Multilevel Optimisation Lars Schewe University of Edinburgh Stefano Coniglio University of Southampton
  66. 66. Multilevel Optimisation: team Lars Schewe Stefano Coniglio
  67. 67. Multilevel Optimisation Government Private investors Competitive market Model for multiple decision makers taking decisions in sequence anticipating the later decisions Example: Investments within competitive markets
  68. 68. 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?
  69. 69. 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
  70. 70. Multilevel Optimisation
  71. 71. www.ktn-uk.org @KTNUK Q&A Alan Champneys University of Bristol Chris Sturman Chartered Institute of Logistics and Transport
  72. 72. www.ktn-uk.org @KTNUK 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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