Smart home devices and B2C e-commerce: a way to reduce failed deliveries
| DOI | https://doi.org/10.1108/IMDS-10-2022-0651 |
| Published date | 03 April 2023 |
| Date | 03 April 2023 |
| Pages | 1624-1645 |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
| Author | Arianna Seghezzi,Riccardo Mangiaracina |
Smart home devices and B2C
e-commerce: a way to reduce
failed deliveries
Arianna Seghezzi and Riccardo Mangiaracina
Politecnico di Milano, Milan, Italy
Abstract
Purpose –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.
Design/methodology/approach –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).
Findings –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.
Originality/value –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.
Keywords E-commerce, Last-mile delivery, Innovation
Paper type Research paper
1. Introduction
B2C e-commerce is the online sale of products and services, directly to the final consumer.
In spite of the burst of the “dot com bubble”in the early two thousands, many countries have
first-hand experienced the dramatic rise of the electronic commerce since then. Online sales
have been steadily growing during the last decade, and the number of online shoppers has
been increasing in different industries. This widespread trend is expected to continue in the
future, also due to the changing shopping behaviour of customers (Kandula et al., 2021).
Despite the intangible nature of online transactions, the management of logistics plays a
crucial role in determining the success of companies selling products online (Mangiaracina
et al., 2019). Moreover, the logistics service offered by e-tailers has emerged to be one of the
key factors influencing the customers’decision to shop with them (Ma et al., 2022). Many
works may be found in literature addressing the different logistics issues that B2C
e-commerce opens for companies if compared to traditional commerce. Some authors focus on
the design of the distribution network, to find the right number, type and location of
infrastructures needed to deliver products to the final consumers (Arnold et al., 2018).
IMDS
123,5
1624
© Arianna Seghezzi and Riccardo Mangiaracina. Published by Emerald Publishing Limited. This article
is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce,
distribute, translate and create derivative works of this article (for both commercial and non-commercial
purposes), subject to full attribution to the original publication and authors. The full terms of this licence
may be seen at http://creativecommons.org/licences/by/4.0/legalcode
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 3 November 2022
Revised 30 January 2023
28 February 2023
Accepted 13 March 2023
Industrial Management & Data
Systems
Vol. 123 No. 5, 2023
pp. 1624-1645
Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-10-2022-0651
Other works analyse instead the activities that are performed within the warehouses to
deal with the fulfilment of B2C orders, e.g. picking (H€
ubner et al., 2016). Though, within
the logistics field, it is the last-mile delivery (LMD) that has captured the most the attention
of both academics (whose contributions on the topic have been flourishing) and
practitioners (who have been striving to find strategies to efficiently and effectively
manage it).
LMD represents the “last stretch”of the order fulfilment, aimed at delivering the
products ordered online to the final consumers, either at their home or at a collection point
(Lim et al., 2018). It has a significant impact on both efficiency –sinceitisveryexpensive–
and effectiveness –since it constitutes the interface with the final customers, who directly
perceive the associated service level performance (Pan et al., 2017). In all the major
markets, the dominant B2C delivery mode is represented by the so-called “attended home
delivery”, which requires the customers to be at home to collect the parcel and sign a
delivery receipt before the courier leaves for the next destination (Han et al., 2017). This
being the context, the eventual absence of the customer makes the couriers not able to
accomplish the delivery. This phenomenon –referred to as “failed deliveries”–is
addressed by both academic and managerial efforts, since it has strong negative effects on
LMD performance. On the one hand, it implies high costs for e-commerce players, which
need to re-schedule the missed deliveries in subsequent tours; on the other hand, it
significantly affects the satisfaction of customers, who are typically bothered not to
receive their parcel. The failure rate of deliveries may reach 25% according to different
authors (Edwards et al., 2010;Song et al., 2009;Van Duin et al., 2016). As a result, it often
happens that parcels need to be moved two or even three times before being successfully
delivered.
A possible way to reduce the occurrence of this problem could be scheduling the deliveries
trying to maximise the probability to find the customers at home when parcels arrive, thus
defining the delivery tours based on the probability profiles of the customers being at home.
In order to build these probability profiles, data about their presence at home should be
collected, aggregated and processed. A promising solution for gathering this type of data is
represented by Internet of Things (IoT) smart home devices, whose diffusion has been
significantly growing in recent years, also in less mature markets. The extensive use of these
devices opens fruitful work area, since they allow for the development of innovative and
sustainable logistics solutions in the urban freight logistics context (Al-Turjman et al., 2022;
Pan et al., 2021).
Driven by the continuous growth of e-commerce, the significance of the failed delivery
issues and the ability of smart home objects to easily collect customer data, thisresearch aims
to exploit the potentialities of such devices in improving LMD performance (in terms of both
efficiency and effectiveness). More specifically, it proposes a solution to schedule delivery
tours based on customers’presence data (gathered through IoT devices), which aims to
concurrently minimise the travelled distance and maximise the probability to find customers
at home. A model is developed and applied to Milan (Italy), to compare the performance of the
proposed innovative solution with traditional home deliveries (in terms of both costs and
delivery success rate). On the academic side, this research proposes an innovative data-driven
LMD solution that exploits new IoT-based technological trends and introduces
advancements in literature concerning routing for B2C e-commerce. On the managerial
side, this solution represents an efficient and effective novel option for scheduling last-mile
deliveries relying on the use of smart home devices, which has a significant impact in
reducing costs and increasing the service level.
The remainder of this paper is organised as follows: the second section presents the results
of the literature review; the third section defines the research questions and the adopted
methodologies; the fourth and fifth sections illustrate the model development and its
Smart home
devices and
B2C
e-commerce
1625
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