This is the second part of the two part blog series on last-mile distribution in the pandemic. Here, we further explore the ideas of sustainability and resilience in the context of e-commerce delivery. If you haven’t read the first one already, I’d recommend you to do so.
In the years prior to the pandemic, consumer shopping trends had seen a steady and significant shift towards online retail. Despite the prevalence of e-commerce platforms with lucrative shopping offers for consumers, traditional in-store shopping still dominated daily consumer purchases. Nonetheless, more and more consumers had been engaging in omnichannel behavior, with product search, trial, and final purchase occurring in different channels. However, the COVID-19 pandemic significantly inhibited public movement, and an unprecedented number of consumers, including many first-time users, took to e-commerce platforms for the purchase of critical goods, daily essentials, groceries, medications, and health-care products. The figure below showcases this shift in consumer shopping behavior due to the COVID-19 pandemic in the US in the form of increase in e-commerce transactions for the first half of 2020. Beyond the typical business-to-consumer (B2C) services, some e-retailers also delivered personal protective equipment, including gowns, masks, and gloves to frontline healthcare services. Typically, these e-retailers account for only minor day-to-day and seasonal disruptions and thereby design their distribution structures for low-cost just-in-time deliveries, thus leaving the supply-chain vulnerable to such severe and unforeseen disruptions. Given the role of e-retailers in supply of essential goods not only to the typical customer but also to frontline services, in this blog we assess last-mile distribution resilience in terms of an e-retailer’s ability to maintain and efficiently restore level of service in the event of such a low-probability high-severity disruption.
To cope with low-probability high-severity disruptions, we assumed that the e-retailer outsources part of its operations via one of the many outsourcing channels available, namely, with crowdsourced fleet of light-duty trucks, or collection-points for customer pickup (lockers), or via a logistics service provider operating from micro-hubs using a fleet of electric cargo-bikes (see figure below). The crowdsourced operations take their inspiration from the Amazon Flex program, and thus, much like the e-retailer’s delivery trucks, the crowdsourced drivers collect packages at the e-commerce fulfillment facility before embarking on e-retailer designed delivery tours. However, for collection-point pickup, the e-retailer must fulfill the collection-points using its fleet of delivery trucks before customers can travel to one of the collection-points to collect their packages. Similarly, when the e-retailer outsources part of its distribution via a logistics service provider, the e-retailer must fulfill the logistics service provider’s micro-hubs using its fleet of delivery trucks before the cargo-bikes from these micro-hubs can embark for last-mile deliveries.
Considering the opportunities and challenges we explored with the different outsourcing channels, we found that it could be useful to establish crowdsourced deliveries to cope with low severity disruptions, deploy backup distribution for moderately severe disruptions, and encourage customers to self-collect packages to cope with high severity disruptions. Nonetheless, the e-retailer must carry out appropriate pre-disruption planning to create suitable platforms and incentives to ensure reliable crowdsourced deliveries, position sufficient number of lockers near residential areas to ensure customer willingness to self-collect packages, and negotiate contracts with several logistics service providers to ensure backup last-mile distribution. Moreover, as the disruption evolves, the e-retailer must gauge the availability of crowdsourced drivers, the willingness of customers to self-collect packages, and the capability of the logistics service providers to ensure functionality of its distribution channel, so that the e-retailer can deploy the appropriate outsourcing channel(s) during the different phases of the disruption. And finally, as the disruption recedes, the e-retailer must re-engage strategic and tactical decision-making process not only to restore the level of service efficiently and in a timely manner, but also to plan ahead for a changed post-disruption landscape. Moreover, consistent with other studies in the resilience literature, this work highlights the need for organizational, social, economic, and engineering units of last-mile distribution to consistently perform pre-disruption mitigation, appropriately respond during the disruption, and efficiently carry out post-disruption analysis and recovery for last-mile distribution to be resilient to disruption. Continuing the discussion from the previous blog, our work highlights the need to develop not only sustainable last-mile distribution structure that can render economically viable, environmentally friendly, and socially equitable operations capable to cope with high-probability low-severity fluctuations in the delivery environment, but also resilient last-mile distribution structure that is robust, redundant, resourceful, and rapid against low-probability high-severity disruptions.
Notes for nerds:
Research on low-probability high-severity disruptions in the context of transportation is limited to disaster management, humanitarian logistics, and relief operations for earthquakes, tsunamis, hurricanes, terrorist attacks, etc. However, the total breakdown of global supply-chains and the consequent surge in e-commerce demand witnessed for months after the initial SARSCoV2 outbreak was unlike any other low-probability high-severity disruption, and therefore warrants dedicated research. To this end, we integrated the R4 Resilience Framework and the Resilience Triangle Framework, thus developing the R4 Resilience Triangle Framework to assess the resilience of an e-retailer’s last mile distribution operations developed using Continuous Approximation (CA) techniques. This novel resilience framework quantifies the qualitative properties of resilience, i.e., robustness, redundancy, resourcefulness, and rapidity using the resilience triangle thereby characterizing the drop in performance of the system due to the disruption. Considering the simplistic use of triangles to quantify loss in level of service, this framework is well capable of assessing impact of varying types and severity of disruption. Moreover, the domain-agnostic nature of this resilience framework enables assessment of a system’s response to disruption not only in the context of transportation systems, but across varying domains.
R4 Resilience Framework is based on Bruneau et al., 2003 work – A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities.
Resilience Triangle Framework is based on Tierney and Bruneau, 2007 work – Conceptualizing and Measuring Resilience: A Key to Disaster Loss Reduction
This blog is based on our work, Assessing E-retailer’s Resilience During the COVID-19 Pandemic (Jaller and Pahwa, 2022), and Assessing last-mile distribution resilience against COVID-19 instigated market disruptions (Pahwa and Jaller, In Review).