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The purpose of this paper is to measure and map the potential transportation network impedance to last-mile delivery (LMD) using spatial measures representing attributes of road network and planning controls.

The transport network impedance is estimated as the potential hindrance to LMD as imposed by the characteristics of the built and regulatory environment. A matrix of key transport and planning measures are generated and overlaid in geographical information systems to compute and visualise the levels of transportation network impedance to LMD using a composite indexing method.

The mapped outputs reveal significant spatial variation in transportation network impedance to LMD across different part of the study area. Significant differences were detected along the road segments that connect key industrial hubs or activity centres especially along tram routes and freight corridors, connecting the Port of Melbourne and logistic hub with the airport and the Western Ring Road.

The use of static measures of transport and urban planning restricts the robustness of the impedance index, which can be enhanced through better integration of dynamic and real-time movements of business-to-business LMD of goods. Spatial approach is valuable for broader urban planning at a metropolitan or council level; however, its use is somewhat limited in assisting the daily operational planning and logistics decision making in terms of dynamic routing and vehicle scheduling.

The built and regulatory environment contributes to the severity of LMD problem in urban areas. The use of land use controls as instruments to increase city compactness in strategic nodes/hubs is more likely to deter the movement of urban freight. The mapped outputs would help urban planners and logisticians in mitigating the potential delay in last-mile deliveries through devising localised strategies such as dedicated freight corridors or time-bound deliveries in congested areas of road network.

This is the first study that measured the potential transport network impedance to LMD and improved understanding of the complex interactions between urban planning measures and LMD. Micro-scale mapping of transportation network impedance at the street level adds an innovative urban planning dimension to research in the growing field of city logistics.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

The purpose of this paper is to explore last mile delivery (LMD) to the bottom of the pyramid in Brazilian slums, its challenges and how practitioners overcome them. Urban logistics in precarious circumstances is central to the conceptualization.

A qualitative, grounded theory methodology is developed, gathering data from companies delivering to slums in Sao Paulo and Rio de Janeiro, Brazil. Field notes, documents and interviews led to conceptual categories for LMD to slums.

The study indicates that while some standard urban logistics practices can be effective for LMD to slums, such unusual contexts often call for unusual solutions. A model is developed using grounded theory categorization, resulting in five dimensions for LMD to slums: employing locally, giving back, acknowledging criminals, vehicle and location.

The model is a qualitative proposition representing LMD to slums in two major Brazilian cities. Even though slums in different cities/countries may face similar conditions, additional studies are needed to confirm and replicate the model.

Companies that successfully engage in LMD to slums must adapt and develop idiosyncratic practices.

LMD to slums enables a larger portion of bottom of the pyramid consumers to access a wider range of products and work opportunities, contributing to their social inclusion.

The study provides an understanding of LMD in a new context. The model encourages companies to question their current practices, learning from effective LMD experiences implemented by successful practitioners.

The e-commerce boom presents new challenges for last-mile delivery (LMD), which may be mitigated by new delivery technologies. This paper evaluates the impact of mobile parcel lockers (MPL) on costs and CO₂ equivalent (CO₂e) emissions in existing LMD networks, which include home delivery and shipments to stationary parcel lockers.

To describe customers’ preferences, we design a multinomial logit model based on recipients’ travel distance to pick-up locations and availability at home. Based on route cost estimation, we define the operating costs for MPLs. We devise a mathematical model with binary decision variables to optimize the location of MPLs.

Our study demonstrates that integrating MPLs leads to additional cost savings of 8.7% and extra CO₂e emissions savings of up to 5.4%. Our analysis of several regional clusters suggests that MPLs yield benefits in highly populous cities but may result in additional emissions in more rural areas where recipients drive longer distances to pick-ups.

This paper designs a suitable operating model for MPLs and demonstrates environmental and economic savings. Moreover, it adds recipients’ availability at home to receive parcels improving the accuracy of stochastic demand. In addition, MPLs are evaluated in the context of several regional clusters ranging from large cities to rural areas. Thus, we provide managerial guidance to logistics service providers how and where to deploy MPLs.

Failed deliveries (i.e. deliveries not accomplished due to the absence of customers) represent a critical issue in B2C (Business-to-consumer) e-commerce last-mile deliveries, implying high costs for e-commerce players and negatively affecting customer satisfaction. A promising option to reduce them would be scheduling deliveries based on the probability to find customers at home. This work proposes a solution based on presence data (gathered through Internet of Things [IoT] devices) to organise the delivery tours, which aims to both minimise the travelled distance and maximise the probability to find customers at home.

The adopted methodology is a multi-method approach, based on interviews with practitioners. A model is developed and applied to Milan (Italy) to compare the performance of the proposed innovative solution with traditional home deliveries (both in terms of cost and delivery success rate).

The proposed solution implies a significant reduction of missed deliveries if compared to the traditional operating mode. Accordingly, even if allocating the customers to time windows based on their availability profiles (APs) entails an increase in the total travel time, the average delivery cost per parcel decreases.

On the academic side, this work proposes and evaluates an innovative last-mile delivery (LMD) solution that exploits new AI (Artificial Intelligence)-based technological trends. On the managerial side, it proposes an efficient and effective novel option for scheduling last-mile deliveries based on the use of smart home devices, which has a significant impact in reducing costs and increasing the service level.

An increasing number of last mile deliveries (LMDs) pose many sustainability challenges that retailers and logistics service providers (LSPs) can address. Using cognitive frames (CFs) as a lens, this study explored how retail and LSP managers make sense of sustainable LMDs.

The methodological approach used is a multiple embedded case study. The data were obtained from interviews with retailers and LSPs, supplemented with secondary data for triangulation.

The findings present the operational aspects of LMDs that managers associate with sustainability and indicate that retail and LSP managers frame sustainability primarily as emission reduction. Managers indicate an externalization of responsibility and a compartmentalization of the supply chain, in which social sustainability is not associated with the last mile. Most managers indicate hierarchical CFs regarding sustainability, in which sustainability is an important topic but is subordinate to economic interests.

Collaboration between retailers, LSPs and other stakeholders is viewed as challenging but could alleviate some of the sustainability shortcomings and aid in the paradoxical framing and inclusion of social issues.

A conceptualization of managerial CFs for sustainable LMDs, together with empirical frame indicators and three propositions, is presented, providing novel insights into how paradoxical CFs could make LMDs more sustainable. This approach illuminates the possibilities for how to untangle the operational manifestations of managerial framing and adds to the empirical exploration of CFs in supply chain management.

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.

This study aimed two objectives: The first objective was to explore carriers' intentions to use cargo electric vehicles (EVs) and the factors influencing these intentions in last-mile delivery (LMD). The other objective was to provide recommendations for policymakers and manufacturers to promote and customize cargo EVs to meet the requirements of carriers in the LMD sector.

In this study the authors constructed a research framework that adjusted and extended the original technology acceptance model (TAM). The proposed model combines eight psychological factors, including attitude, perceived usefulness, perceived ease of use, perceived risk, public engagement, face consciousness, financial incentive policy and carrier intention, in which four factors, namely attitude, perceived usefulness, perceived ease of use and intention, were obtained from the original TAM and the four remaining factors, namely perceived risk, public engagement, face consciousness and financial incentive policy, were added.

The results showed that psychological factors such as attitude, perceived ease of use, perceived risk, public engagement and face consciousness might affect carriers' intentions to use electric cargo vehicles in LMD service in Vietnamese cities. These results agree with the previous studies and confirm that attitude, perceived ease of use, perceived risk, public engagement and face consciousness might be important for shaping intention to use electric cargo vehicles in emerging markets.

This study has several limitations, first, the data were collected in Vietnam, a country with weak academic contributions. Therefore, these findings might not be generalizable to other areas. The authors expect to apply the same research framework to other countries to explore the similarities and differences across the countries. Second, the authors conducted the surveys in three cities; except for Hanoi, the other two cities are not really large markets in the LMD service sector. Third, the authors ignored the relationship between the demographic characteristics and electric cargo vehicles. Further studies should address this gap.

Based on the findings, manufacturers should ensure the high-quality performance of electric cargo vehicles in terms of extending driving range and shortening recharging time. Policymakers should develop the roadmaps for electric cargo vehicles, starting from switching from conventional cargo motorcycles to electric motorcycles. In addition, developing public charging infrastructure should be prioritized, which is the fundamental basis for operating electric cargo vehicles. Finally, manufacturers should research and develop a product that would improve the reputation of carriers because carriers with higher face consciousness are more likely to pursue brand-name and high-priced products in order to enhance their reputation.

This study contributes to the literature in two aspects: First, the authors investigated intentions to use electric cargo vehicles in LMD service, which is rare from other studies, and they further identified the psychological determinants of carriers' intentions. Second, the findings increase the knowledge of carriers' intentions and suggest implications for policymakers and manufacturers to promote the adoption of electric cargo vehicles in last-mile deliver service.

Last-mile delivery is associated with a negative environmental impact and high costs. The purpose of this paper is to develop an approach to designing stationary parcel locker (SPL) networks while minimizing both CO₂ equivalent (CO₂e) emissions and costs during delivery and pick-up.

This study uses a multinomial logit model to evaluate recipients' willingness to use SPLs based on their availability at home and travel distance. To determine optimal SPL locations, this study formulates a mixed-integer linear programming model.

The empirical study of different regional clusters reveals that optimal SPL locations can generate cost savings of up to 11.0%. SPLs have a positive impact on total CO₂e emission savings in urban areas (i.e. up to 2.5%), but give rise to additional emissions (i.e. 4.6%) in less populated areas due to longer travel distances during the pick-up process.

This paper optimizes SPL locations and the ecological effect of SPLs by minimizing emissions and costs simultaneously. Furthermore, it extends existing discrete choice models by also including recipients' availability at home, increasing the accuracy of recipients' preferences. So far, the effect of SPLs has been studied for metropolitan areas only. A global logistics service provider shared a real dataset which allows us to study seven different regional clusters ranging from rural areas to large cities. Thus, this study contributes to the field of sustainable urban logistics.

This paper focusses on on-demand food delivery (ODFD), i.e. the delivery of freshly prepared meals to customers' homes, enabled by the use of online platforms. In ODFD, a key process is represented by last-mile deliveries (LMDs): they directly affect customers (the delivery price influences their purchase intention), riders (the compensation drives their willingness to perform deliveries) and platforms (deliveries are very expensive). In this context, this work aims to investigate the economic performances of ODFD LMDs.

This study adopts a multi-method threefold process. First, it develops a model that – after the generation of customers' demand and the assignment of deliveries to available riders – identifies incomes and costs faced by an ODFD operator. Second, the model is applied to a base case in Milan (Italy). Third, sensitivity analyses are performed (on daily demand and riders' salary).

The analyses allow – besides the identification of significant values associated to ODFD profitability – to draw general insights about delivery price (e.g. free delivery is not economically sustainable), daily demand (e.g. greater demand values do not only improve positive results but also worsen negative ones) and fixed/variable wage mix (e.g. increasing the variable wage enhances the profitability for platforms).

On the academic side, this word enhances extant literature about ODFD, proposing a model – with multidisciplinary implications – to strategically investigate profitability conditions of LMDs. On the managerial side, it provides support for (logistics/marketing) ODFD practitioners since it allows to evaluate the potential impact of significant decisions on profitability.