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Considering the last-mile delivery service supply chain as a social-ecological system rather than just a firm-based service system, this research exploit the COVID-19 pandemic disruption to investigate how the supply chain develops resilience from a viewpoint that integrates a social-ecological perspective with the traditional engineering one.

This research adopt a multi-case study approach using qualitative data collected via semi-structured interviews with executive-level managers from nine leading UK last-mile delivery companies. Data analysis is guided by a research framework which is developed by combining the social-ecological perspective with the structure–conduct–performance paradigm. This framework aids the investigation of the impacts of external challenges on companies' resilience strategies and practices, as well as performance, in response to disruptions.

The research identifies three distinct pathways to resilience development: stabilization, focussing on bouncing back to the original normal; adaptation, involving evolutionary changes to a new normal; transformation, involving revolutionary changes in pursuit of a new normal-plus. Three strategic orientations are identified as operating across these pathways: people orientation, digital orientation, and learning orientation.

In contrast to the manufacturing supply chain focus of most current research, this research concentrates on the service supply chain, investigating its resilience with a social-ecological perspective alongside the traditional engineering one.

The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation.

The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation.

Computational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent.

The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics.

This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency.

The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping.

Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research.