Collection And Delivery Points Research Articles

Taxonomy of key practices for a sustainable Last-Mile logistics network in E-Retail: A comprehensive literature review

ObjectiveThe growth of e-commerce has transformed last-mile logistics, with direct-to-consumer deliveries and frequent delivery failures. This has exacerbated pollution and congestion issues, negatively impacting urban life. Given this scenario, the present study aimed to develop a taxonomy that enables the design of a sustainable last-mile logistics network specifically tailored to the context of e-retail. MethodA systematic literature review was employed for collecting robust scientific research, with the PRISMA diagram used to represent the steps followed in this process. Content analysis was applied to categorize the sustainability practices identified in the literature, grouping them according to the last-mile logistics process. RelevanceThis research contributes to understanding the last-mile logistics field in the e-retail context and provides valuable insights for formulating sustainable strategies and practices to mitigate the negative impacts of e-retail growth on the urban environment and society’s quality of life. ResultsThe research presents a structured taxonomy with 31 categorized practices across last-mile logistics processes: fulfillment, transportation, and delivery modalities. The results indicate that a sustainable last-mile network includes consolidation centers, shared micro-depots, shared freight transportation, electric vehicles such as bicycles and tricycles, and the implementation of collection and delivery points (CDPs). Social/Management ContributionsThe taxonomy offers elements that may contribute to operational efficiency, environmental impact reduction, and improving society’s quality of life by providing practical guidelines for the sustainable management of last-mile logistics in the e-retail context.

A spatial agent-based model of e-commerce last-mile logistics towards a delivery-oriented development

The spread of e-commerce has driven a major growth in the parcel delivery market, bringing a negative impact on sustainability, especially due to last-mile deliveries in urban areas. It is crucial to appropriately tackle this issue to foster consolidation of deliveries, possibly by using collection and delivery points (CDPs), where customers might receive their parcels. This paper proposes a new spatial agent-based modelling approach to explore different scenarios of last-mile logistics referred to e-commerce deliveries, comparing fragmented door-to-door deliveries with consolidation-based strategies. The case study is the central urban area of Catania, a medium sized city in Southern Italy. The Agent-Based Model (ABM) reproduces feasible operations considering real-world spatial constraints and demand data, including the possible matching of customers’ systematic trips and parcel delivery via CDPs with small detours from the scheduled trip. Key performance indicators consider both customer and logistics operator perspectives. Main results of the simulation show that the scenario without CDPs is the costliest and least efficient, implying a high number of failed deliveries. Using cargo bikes instead of vans to perform the delivery implies high costs, but much higher benefits in terms of reduced energy consumption. The highest logistics efficiency is achieved in the scenario with a doubled demand, implying a better use of the CDPs. The results suggest that it is advisable to incentive the use of CDPs instead of increasing their number. The ABM can provide useful information to decision-makers on how to manage growing on-demand urban deliveries and plan last-mile logistics using a delivery-oriented development approach.

Locating Collection and Delivery Points Using the p-Median Location Problem

Background: Possible solutions to overcome the many challenges of home delivery are collection and delivery points (CDPs). In addition to commercial facilities, the role of CDPs can also be played by users’ households, providing a crowd storage service. Key decisions regarding CDPs relate to their location, as well as the allocation of users to selected locations, so that the distance of users from CDPs is minimal. Methods: In this paper, the described problem is defined as a p-median problem and solved for the area of the city of Belgrade, using the heuristic “greedy” and the simulated annealing algorithm. Results: Fifty locations of CDPs were selected and the users allocated to them were distributed in over 950 zones. The individual distances between users and the nearest CDPs and the sum of these distances, multiplied by the number of requests, were obtained. An example of modification of the number of CDPs is presented as a way of obtaining solutions that correspond to different preferences of operators and/or users in terms of their distances from the CDPs. Conclusions: User households can be used as CDPs to achieve various benefits. Locating CDPs, i.e., selecting households, can be solved as a p-median problem, using a combination of heuristic and metaheuristic algorithms. In addition, by modifying the number of medians, the total and average distances between users and CDPs can be better managed. The main contributions of the paper are the establishment of users’ households as potential locations of CDPs, the establishment of a framework for analysis of impact of the number of CDPs on the sum and average distances from the customers, as well as the creation of a basis for upgrading and modifying the model for implementation in the business practice.

Spatial variability and accessibility of collection and delivery points in Nanjing, China

Collection and delivery points are an alternative to home delivery and represent an important opportunity to reduce delivery failures in urban areas. As online shopping has become increasingly popular, different accessibility modes such as walking, cycling, and driving are considered for the collection of parcels at collection and delivery points (CDPs). The primary objective of the present study was to assess the spatial variability and accessibility of CDPs in Nanjing City, China. The point of interest (POI) data of 1224 CDPs (including 424 China Post Stations and 800 Cainiao Stations), and population and gross domestic product data were employed for the spatial analysis. The results showed that China Post Stations and Cainiao Stations were distributed in Nanjing as clusters at α = 0.01. Both types (51.1% China Post Stations and 63.2% Cainiao Stations) of CDPs were aggregated in the high population density areas. Moreover, 28.0% of China Post Stations and 50.9% of Cainiao Stations were located in high GDP density areas. The overall spatial distribution of China Post Stations in population and GDP density areas was medium, while that of the Cainiao Stations was high. There was a significant correlation between the spatial distribution of the CDPs, population, and GDP. There were significant spatial accessibility differences to CDPs among different accessibility modes like walking, cycling, and driving. Walking and cycling mode accessibility to China Post Stations and Cainiao Stations were 13.8 and 25.3% and 9.2 and 28.9%, respectively while 71.8% of China Post Stations and 71.1% of Cainiao Stations were accessed by driving. The findings of this study would be beneficial for policymakers and practitioners to develop related policies, to assist companies in building up more sustainable urban logistics and a booming CDPs’ network in the future.

Land Efficient Mobility: Evaluation of Autonomous Last Mile Delivery Concepts in London.

Land efficient last mile delivery concepts are key to reducing the traffic in cities and to minimising its environmental impact. This paper proposes a decision support method that evaluates the autonomous delivery concept and applies it to one year’s worth of real parcel delivery data in London. Deliveries to modular and fixed lockers with autonomous delivery vans and road-based autonomous lockers (RAL) and sidewalk autonomous delivery robots (SADRs) have been simulated. Various types of autonomous delivery van fleets, depot locations, customer modes of transport, parcel demand levels, parcel locker network densities and adjustment frequencies of modular lockers are considered. A routing and scheduling algorithm is used to optimise delivery tours and vehicle choice. The optimisation algorithm finds both the optimal number of collection and delivery points (CDPs) and the delivery concept (e.g., modular lockers, sidewalk autonomous delivery robot) depending on the customer mode chosen. The results show that modular lockers which are adjusted weekly are the best option for the current or higher parcel demand levels and road-autonomous parcel lockers (RAL-R) are the best option at the lowest parcel demand level.

Last–Mile Delivery in B2C E-Commerce – Common Practices in Some Countries, But What Do They Mean for Businesses in Vietnam?

E-commerce is a dispensable aspect of modern society, and technology is constantly developing to keep up with the growth of e-commerce. This study synthesizes those innovative solutions and proposes implications aimed at increasing the efficiency of last-mile delivery in business-to-consumer (B2C) e-commerce in Vietnam. It focuses on the analysis of the experience of some countries in terms of employing last-mile delivery models in urban areas, then investigates the situation of adopting last-mile delivery models in Vietnam, and finally, proposes solutions and recommendations to improve last-mile delivery in Vietnam. The research was carried out from May to August 2021 with the techniques of synthesis, comparison, and data descriptive statistics accompanied by short interviews with delivery drivers. The research suggests that delivery companies should develop collection-and-delivery points (CDPs) and parcel lockers (PLs) besides the traditional attended home delivery (AHD). Moreover, to facilitate the unattended delivery models, online shops should not exaggerate goods’ quality to cheat buyers and instead take measures to encourage buyers to make e-payments for their orders. Government and authorities are recommended to continue supervising activities of e-payment, updating legal documents to ensure the information security for customers, advertising digital payment tools in the social network environment to increase buyer awareness of e-payment, and imposing a tough penalty for dishonest or cheating online companies. The research is novel as it suggests means to strengthen alternative, modern models besides AHD, which people are likely to adopt in an ever more busy and modern society.

Understanding the Choice of Collection & Delivery Point by the E- Consumer via a Machine Learning Model: Moroccan Case Study

Collection and delivery points (CDP) are evolving rapidly, along with consumption and distribution trends. Understanding the e- consumer's choice of such points is essential for parcel transport companies, as it has a strong impact on their continuity in this competitive environment. the results of the statistical study that we did on our sample show that supermarkets, service stations, and post offices are the most used CDP by Moroccan consumers, especially: full-time employees aged between 18 and 40 and well educated. In addition, this paper focuses on machine learning algorithms such as: Decision Tree, Naive Bayes, K-Nearest Neighbors and Random Forest to create a CDP Classifier. The findings indicate that our model (Optimized Random Forest) outperforms the baseline models in terms of precision, recall, F1 measure and accuracy. In terms of accuracy, our model which achieved 87% outperforms the decision tree model by 9%, the Naive Bayes model by 13%, the KNN model by 6% and the original Random Forest model by 2%. This article has many implications, in terms of theory, it helps researchers to have a clear vision on the application of machine learning in the field of pick-up point delivery, since there are no previous studies applying this technique. On the other hand, it allows parcel transport companies to better understand the behavior of e-consumers using collection and delivery points, before choosing the optimal form of such points. For example, supermarkets are potentially suitable places to set up a CDP, as they are densely located in the city and easily accessible, especially in residential areas.

An assessing framework for the proper allocation of collection and delivery points from the residents' perspective

Along with the advancement of information technology, the prevalence of e-commerce has promoted the rapid development of the express delivery industry. In relation to this, collection and delivery points (CDPs) have become important places to facilitate convenience for residents. Thus far, the majority of studies focused on the logistic transportation perspective without considering residents' convenience. Therefore, this study proposes an assessing framework of four CDP features from the perspective of residents' convenience, with a focus on Wuhan City. In particular, descriptive statistics and spatial analysis were conducted to analyze patterns of service target, adjacency, accessibility, and agglomeration. In addition, regression analysis was conducted on the indicators of adjacency and accessibility, combined with policy and socioeconomic factors. Through this framework, the overserved and underserved areas in Wuhan City were identified on a community level based on CDP location. Several results were obtained as follows. 1) CDPs mainly serve residents in residential and commercial land and that attended CDPs are farther from the urban area than unattended CDPs. 2) The levels of delivery comfort and delivery efficiency of suburban residents are generally lower than those of downtown residents, while the level of delivery efficiency near colleges and universities is higher. 3) Overserved areas are found in urban locations, while underserved areas are located in suburban locations. Moreover, residents near the edge of city centers enjoy higher CDP accessibility than those located in the center. In terms of age, the accessibility inequity is pronounced in the over 60 age group. 4) The spatial distribution of CDPs is in aggregation, and unattended CDPs are more compact than attended CDPs. 5) Finally, policies and socioeconomic factors have different effects on CDPs' adjacency and accessibility. Overall, these findings may provide a realistic estimation of residents' situation to help private firms design better CDP layouts and create efficient delivery systems as well as to help public authorities develop equal and effective planning policies and strategies related to CDPs.

Combining Parcel Lockers with Staffed Collection and Delivery Points: An Optimization Case Study Using Real Parcel Delivery Data (London, UK)

Delivering parcels to collection and delivery points (CDPs) is often seen as a better option compared with home delivery. However, if the demand is inhomogeneous, either the parcel locker utilization or the service level (i.e., the number of parcels that can be delivered) is low. Either situation would reduce the financial viability. This paper compares two options to increase the utilization, namely: (i) modular lockers (i.e., numbers of lockers adjusted periodically depending on demand) and (ii) combining parcel lockers with staffed CDPs. The latter has the advantage of a low investment cost of staffed CDPs and a low cost per parcel of parcel lockers. Secondly, the paper calculates the optimal number of lockers at a staffed CDP, assuming that all parcels are placed in the staffed CDP if the parcel locker is full. This method was applied to data collected by a parcel delivery company in London. The advantage of using real world data over one year is that it includes seasonal and daily changes in the parcel demand. The decision support method accounts for parcels not being picked up by customers on the delivery day, returned deliveries, and the net present value (NPV) of the investment. This paper shows that having enough lockers for 100% of all parcels compared with 80% doubles the number of required parcel lockers because of the inhomogeneity of the demand. In addition, combining fixed lockers with staffed CDPs offers greater financial benefits compared with modular lockers in this case study.

Why do some sustainable urban logistics innovations fail? The case of collection and delivery points

Combined with current trends in e-commerce, demand for urban logistic services are putting significant pressure on the environment. While some European examples show that collection-and-delivery points (CDPs) offer a sustainable solution, this is not always the case. This paper explores the mechanisms that support CDPs as sustainable urban logistics innovations while providing viable market offerings. To do so, it analyses a failure case using multiple data sources, such as a consumer survey, interviews and secondary data. Using diffusion of innovations (DoI) theory, the study explains how CDP failed in a developing market setting. Sustainable logistics innovations fail due to both supply chain-related and market-related factors. Significant factors on the supply chain side include network structures, IT integration and diverse value propositions while the market side includes consumer market characteristics, regulations, security issues and convenience of existing alternatives. Important factors for success include looking for horizontal collaboration opportunities, building strong network partnerships with customers and distribution channel actors. CDPs should be positioned as sustainable solutions and complemented with other urban logistics services to diversify the value proposition.

A Scheduling Strategy of Mobile Parcel Lockers for the Last Mile Delivery Problem

In the form of unattended Collection-and-Delivery Points (CDP), the fixed parcel lockers can save courier miles and improve the delivery efficiency. However, due to the fixed location and combination, the fixed parcel locker cannot accommodate the change of demands effectively. In this paper, an approach to supplementing fixed lockers by mobile parcel lockers to meet the demands of the last mile delivery has been proposed. With the goal of minimizing the operating cost, the location and route optimization problems of mobile parcel lockers are integrated into a non-linear integer programming model. An embedded GA has been developed to optimally determine the locations of distribution points, the number of mobile parcel lockers needed by each distribution point and the schedules and routes of mobile parcel lockers, simultaneously. Finally, a numerical example is given to compare the optimization results of the schemes with and without the aggregation problem. The results show that the scheme with the aggregation problem can greatly save the delivery time. However, for the scheme without the aggregation problem, time windows are more continuous, so it saves the number of vehicles.

Data-driven decision and analytics of collection and delivery point location problems for online retailers

The location of collection and delivery points (CDPs), impacted by online customers’ demand data, plays an important role for online retailers. While previous delivery points optimization researches do not use customer behavior data, we propose new models, integrating with customer behavior data analysis, to optimize collection and delivery points for online retailers. We explore a real customer behavior data and use totally 257,685 users’ records (212,062 records for training set and 45,623 records test set). We first estimate the customer purchase probability by five data mining models. Based on the estimation results, we establish two facility location models to respectively optimize the attended and unattended CDPs locations with the objective of cost minimization. Our numerical experiments make a quantitative analysis of customer service level and location cost. Our results can further help online retailers to decide the suitable CDPs with trading off the consumer service level and the total logistics cost. We make interesting contributions: (i) we analyze real customer behavior data and find that gradient boosting trees algorithm outperform other four algorithms when estimating customers’ purchase probabilities; (ii) We propose a new data-driven method integrating data mining models and facility location models to determine CDP locations for online retailers.

Locating collection and delivery points for goods’ last-mile travel: A case study in New Zealand

Collection and Delivery Points (CDPs) have been proved to be efficient in terms of decreasing the occurrence of first-time failed deliveries and delivery costs of goods bought online in European countries. However, CDPs have only recently been introduced in New Zealand, and service providers are trialling with a few CDPs established in larger cities, such as Auckland, Wellington and Christchurch. Selection of CDP locations on an ad-hoc basis is likely to be expensive for service providers and the adopted location configuration may not be able to attract the desired consumer patronage. Thus, selection of locations and the density of CDPs needs to be done on the basis of considering factors, such as consumers’ distribution pattern and accessibility to potential CDP locations. This study aimed to identify the optimal density and locations for establishing CDPs, using Christchurch as a case study. A set-covering problem approach to locating facilities was adopted, taking city demographics and travel distances between population centres and potential facility locations into consideration. Non-conventional CDPs, such as dairies and supermarkets were found to be more accessible, and thus may well prove to be efficient for locating CDPs than traditional post shops. The results also suggest the spatial location of facilities forming an optimal configuration of CDPs.

Establishing Collection and Delivery Points to Encourage the Use of Active Transport: A Case Study in New Zealand Using a Consumer-Centric Approach

The current and projected trends of growth in online shopping might change the activity and travel patterns in Christchurch, one of the largest cities in New Zealand. Online shopping might reduce consumers’ shopping trips, but it has substantially increased courier companies’ trips to deliver parcels to the end-consumers because a considerable proportion of parcels are often required to be redelivered due to consumers not being at home during the first delivery attempt. This also adds to the operational cost of courier companies and adverse traffic impacts. To mitigate these issues, collection-and-delivery points (CDPs) have recently been introduced in New Zealand on a trial basis. This study aims to identify the optimal density and locations for establishing CDPs in Christchurch using a modified p-median location-allocation (LA) model. A consumer-centric approach to locating CDPs has been adopted by considering the socio-demographic characteristics of Christchurch’s residents and the distances to/from CDPs. Non-traditional CDP locations (e.g., supermarkets and dairies) were considered as potential candidate facilities and were found to be more suitable as CDPs than traditional post shops. Based on consumers’ shopping pattern, supermarkets appeared to be the most frequently visited and preferred type of facility to be used as CDPs. However, the results of the LA analyses show that dairies are the most accessible locations, and CDPs at dairies located within two kilometres will encourage consumers to walk and cycle to receive their parcels from CDPs. The results suggest the optimal location configuration for each type of facility considered, based on their spatial distribution in the city.

Integrating collection-and-delivery points in the strategic design of urban last-mile e-commerce distribution networks

The use of collection-and-delivery points (CDPs) within multi-echelon distribution networks is becoming an increasingly popular last-mile distribution strategy. In this work, we provide a method to integrate CDPs in the design of multi-echelon distribution networks. We formulate a non-linear optimization model that includes CDP location decisions and accounts for changes in demand patters occurring with their placement. To enable its application to large-scale problem instances, we formulate extended routing cost approximation formulae. We then propose a heuristic solution method and apply the model and solution approach to a real-world case study of last-mile distribution by a major Brazilian e-commerce player.

Analysis of accessibility from collection and delivery points: towards the sustainability of the e-commerce delivery

Abstract The collection and delivery points (CDP) are an alternative to home deliveries and an important opportunity to reduce failed deliveries within urban areas. In this study, we propose to analyze the accessibility from CDPs located in commercial establishments (drugstores, gas stations, post offices, supermarkets, and malls) considered most attractive by e-consumers to receive this delivery solution. The methodological approach uses spatial analysis to identify the coverage area of the CDPs considering socioeconomic data and e-consumer preferences. Also, spatial tools to analyze the accessibility of CDPs were used, considering the transport infrastructure, the population location, and job opportunities location through different transport modes. The results indicated differences in levels of accessibility between the CDPs, highlighting the important associations among economic activities, land use patterns, and transport to plan for sustainable cities. The use of cluster analysis methods as a methodological approach is one contribution and can be replicated to support decision-making in other cities.

The Collection-And-Delivery Points Implementation Process from the Courier, Express and Parcel Operator’s Perspective

Collection-and-Delivery Points (CDP) have become increasingly important for couriers operating in the e-commerce sector. Through this way of delivery, it is possible to mitigate many issues related to the traditional home delivery (e.g. missing delivery, complex routing planning, and traffic delays). Despite this, there are still obstacles that prevent the CDP to become a dominant mode of parcel delivery. Through semi-structured interviews to companies working in the courier, express and parcel sector, this work aims to understand the enablers and barriers to the spread of the CDP mode of delivery, with a focus on the Italian market.

The impacts of final delivery solutions on e-shopping usage behaviour

PurposeThe purpose of this paper is to examine the effects of final delivery solutions on e-shopping usage behaviour by modelling their interaction across residents living in different neighourhoods with availabilities of different facilities, including automated parcel stations (APSs), collection and delivery points (CDPs), and the direct-to-home delivery stations of parcel express firms (PEFs).Design/methodology/approachThe study is based on a survey on e-shopping behaviour and delivery awareness. A mixed structural equation model is used to predict the interactions among availability of final delivery facilities (AFDF), level of satisfaction with delivery services and e-shopping usage after controlling individual socioeconomic attributes and retail environment.FindingsCompared with AFDF, individual socioeconomic attributes are the most influential factors contributing to e-shopping spending and frequency. Improving AFDF has only a slight effect on e-shopping spending, while a larger impact on e-shopping frequency and perceived satisfaction to delivery services is observed. The quantity of PEF delivery stations has a relatively large influence on e-shopping usage but the effects of APSs and CDPs are not as strong as expected.Research limitations/implicationsThe causality between final delivery solutions and e-shopping behaviour can be further tested by using social experiments or longitudinal data.Practical implicationsAll findings will help business and public policy decision makers to derive a balanced and effective deployment of final delivery solutions, which is also referential for other emerging markets similar to China.Originality/valueThis study theoretically contributes to the international literature by examining the heterogeneous effects of final delivery solutions on different aspects of e-shopping engagement.

Acceptability of collection and delivery points from consumers’ perspective: A qualitative case study of Christchurch city

The growth in global online retail sales during the last decade has led to an enormous increase in the number of parcels to be delivered. Although vehicle kilometres travelled associated with shopping trips might decrease as a result of online shopping, an increased number of failed home deliveries might out-weigh this reduction, worsening urban transport problems caused by high car dependency. In many European countries, such as France, UK and the Netherlands, Collection and Delivery Points (CDPs) have been implemented and observed to increase delivery efficiency by reducing the number of failed home deliveries.Similar to the worldwide trend of online shopping, a major proportion of New Zealanders also shop online. However, CDPs were only recently introduced in limited locations within three of New Zealand’s biggest cities, i.e. Auckland, Wellington and Christchurch. Thus, little is understood about New Zealanders’ acceptance of CDPs and their online shopping behaviour. This study aimed to address these issues by means of focus groups, using Christchurch as a case study. The results show that the factors influencing the Christchurch’s residents’ acceptance of CDPs, can be categorised into several themes, such as CDP network density, parking availability at CDPs, spatial location of CDPs, proximity to consumers’ home or office, safe and secure operation, and hours of operation of CDPs. Such knowledge is important for policy makers to help formulate policies that can lead to more sustainable urban environments, and for the companies that want to establish successful CDP networks in Christchurch.

Urban end distribution optimization under e-commerce environment

With the rapid development of e-commerce, urban end distribution plays more and more important role in e-commerce logistics. The collection and delivery points (CDPs), between online retailers and customers, provide a way to improve the service quality of urban end distribution. But it will be more difficult to obtain an optimal solution of urban end delivery plan when many CDPs joint a complicated delivery network, since the solution space is always too large for many traditional heuristic algorithms to search. In this paper, a two-stage optimization method based on geographic information system (GIS) and improved cooperative particle swarm optimization (CPSO) is proposed. This method takes full advantage of powerful network analysis of GIS and strong global search of CPSO. A new cooperative learning mechanism, global sub-swarm, local sub-swarm and normal sub-swarm (GS-LS-NS), is used to improve the search mode of CPSO. Finally, several experiments are conducted to show the better performance of GIS-CPSO, compared with single PSO, GIS-CPSO and ArcGIS (software of GIS) separately. The conclusion of this research is much useful and applicable for logistics service providers.