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Enabling Win-Win and Sharing in Urban and Multimodal Logistics: Research Challenges and Technology

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Hoong Chuin Lau, Singapore Management University, presents at the 3rd China International Logistics Development Conference in Suining

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Enabling Win-Win and Sharing in Urban and Multimodal Logistics: Research Challenges and Technology

  1. 1. Third China International Logistics Development Conference Enabling Win-Win and Sharing in Urban and Multimodal Logistics: Research Challenges and Technology 城市物流和多式联运之间的双赢和共享: 研究挑战和技术 LAU Hoong Chuin 刘洪泉教授 Singapore Management University (SMU) 新加坡管理大学
  2. 2. • The US$20m Lab (UNiCEN) was established in Oct 2014 • Funded by Fujitsu Ltd and the Singapore National Research Foundation (NRF) • To build capabilities and smart technology to manage urban problems, while constrained by existing manpower, space and transportation infrastructures "Adding Capacity without Building Capacity“ • Research + Real-world Testbedding Urban Computing & Engineering Corp Lab http://unicen.smu.edu.sg
  3. 3. One Belt, One Road The “Belt and Road Initiative” was put forward by President Xi Jinping in 2013, with the purpose of rejuvenating the two ancient trading routes and further opening markets in a mutually beneficial manner. Opportunities for China, for Singapore, for the World
  4. 4. Chongqing Connectivity Initiative • Third Singapore-China government-led project, after Suzhou Industrial Park and Tianjin Eco-City • Connectivity drives business a. Financial connectivity b. System/data connectivity c. Goods connectivity • Focus areas: 1. Financial services 2. ICT 3. Transport and Logistics 4. Aviation
  5. 5. Chongqing Connectivity Initiative • SMU’s collaboration with Chongqing University and Y3 Technologies • Establishment of Big Data Logistics Lab at CQU Common Objectives
  6. 6. Trends in Urban Logistics 6 3. Urban Consolidation Centers 1. Sharing Economy, and Platform 2. AI in Transportation & Logistics
  7. 7. Urban Logistics Platforms • Passengers hate waiting for a cab, but consignees don’t mind some lead time in receiving a package
  8. 8. Mobile Crowdsourcing in Logistics • Last-mile delivery – e.g. Amazon Flex, Walmart, DHL • Inventory Monitoring – e.g., Gigwalk Current Practices Key Research Challenges • Planning and recommending tasks with uncertainty in workers’ movement patterns • Pricing of bundles for workers • Fairness
  9. 9. • Recommend tasks to users that minimizes their detour from their “expected” future paths • Create an experimental platform that helps study behavioral aspects of crowdsourcing  Task Bundling  Pricing  Truthfulness • Investigate the feasibility of crowdsourcing as an enabler of SMU’s “smart campus” vision • Extend to crowdsourcing citizen participation in municipal services The Big Ideas Mobile Crowd-Tasking @ SMU
  10. 10. Urban Consolidation Center (UCC) A facility in which freight flows from outside the city are consolidated with the objective to bundle inner- city transportation activities so as to reduce volume of distribution activities in the city 1. Consolidation 2. Warehousing 3. Cross docking 4. Last-mile delivery
  11. 11. Stakeholders • aims to - reduce costs of goods distribution in urban areas - increase flexibility, speed and service level and supporting adding additional value creation - improve city's social & environ- mental situation through the use of business & decision analytics • addresses city's & industry’s needs • leverages city and government authorities as major supporters • promotes innovative and best- practice solutions across academia and industry Collaborative Urban Logistics Collaborative Urban Logistics …..as enablers • Implement policies to reduce city challenges, e.g. pollution, congestion • Support urban logistics through regulations or incentives, e.g., city toll, delivery restrictions, etc. Authorities ….as customers • Implement products to reduce cost, increase flexibility, speed and service level • Implement solutions that increase the value add for the customer Business owners Service Providers ….as partners • Implement optimized and collaborative services • Design innovative solutions that further increase productivity Challenge: Get stakeholders to collaborate, i.e. need a win-win solution to participate in the UCC Source: DHL
  12. 12. Collaborative Urban Logistics Research Challenges • Data collection and analytics on urban freight flows • Behavioral modeling of shippers, carriers and receivers • Design of mechanisms that enable multiple parties to consolidate loads and coordinate delivery timings to achieve system efficiency and cost effectiveness • Design of an e-marketplace platform
  13. 13. Platform Capacity and Cost Carriers C BA Shippers C B A Ports Demand bid (Consignments, Locations, Timings, Price) UCC Customers: shopping malls, homes Collaborative Urban Logistics Platform
  14. 14. Multi-Modal Logistics • 4 factors driving demand for multi-modal logistics: a) Cross-border e-Commerce b) Speed- to-market product delivery c) Integrated supply chain management d) Establishment of new routes under OBOR • EU Framework Programme Projects – e-Freight: an electronic framework for multimodal transport of goods among all EU freight transport stakeholders – iCargo: support new intermodal logistics services: synchronise vehicle movements and logistics operations; adapt to changes through an intelligent cargo concept and develop an open freight management ecosystem 14
  15. 15. Electronic Logistics Marketplace Cloud computing, IoT, social networking, wireless/mobile technology Source: Wang et al. 2010
  16. 16. Chongqing as Multi-Modal Logistics Hub 16
  17. 17. Multi-Modal Logistics Challenges: From Fragmentation to Coordination • Operational Challenges: – Transportation infrastructure is built by modal agencies that historically did not interact – Transportation companies are still structured around modes – Difficulty to have a single multimodal operator to handle end-to-end delivery 17 • Research Challenges: a) Collaboration and Alliance mechanisms among partners b) Data-driven real-time Decision Support Systems for managing multimodal transport
  18. 18. • Develop sophisticated multi-modal transportation models through the use of data analytics and AI planning & scheduling • Multi-modal logistics challenges: – Strategic – Tactical – Operational 18 SMU-CQU-Y3 Collaboration
  19. 19. Conclusion • Research Challenges and Opportunities for OBOR –Congestion –Collaboration –Customization
  20. 20. Questions/Comments Enabling Win-Win and Sharing in Urban and Multimodal Logistics: Research Challenges and Technology Hoong Chuin LAU (hclau@smu.edu.sg)

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