A prescriptive framework to support express delivery supply chain expansions in highly urbanized environments
| DOI | https://doi.org/10.1108/IMDS-02-2022-0076 |
| Published date | 06 June 2022 |
| Date | 06 June 2022 |
| Pages | 1707-1737 |
| 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 | Rafael Diaz,Canh Phan,Daniel Golenbock,Benjamin Sanford |
A prescriptive framework to
support express delivery supply
chain expansions in highly
urbanized environments
Rafael Diaz
VMASC, Old Dominion University, Norfolk, Virginia, USA
Canh Phan
Viec.Co Corp, Ho Chi Minh, Vietnam
Daniel Golenbock
Clariant Corp, Charlotte, North Carolina, USA, and
Benjamin Sanford
Yak Mat, East Columbia, Mississippi, USA
Abstract
Purpose –With the proliferation of e-commerce companies, express delivery companies must increasingly
maintain the efficient expansion of their networks in accordance with growing demands and lower margins in a
highly uncertain environment. This paper provides a framework for leveraging demand data to determine
sustainable network expansion to fulfill the increasing needs of startups in the express delivery industry.
Design/methodology/approach –While the literature points out several hub assignment methods, the
authors propose an alternative spherical-clustering algorithm for densely urbanized population environments
to strengthen the accuracy and robustness of current models. The authors complement this approach with
straightforward mathematical optimization and simulation models to generate and test designs that effectively
align environmentally sustainable solutions.
Findings –To examine the effects of different degrees of demand variability, the authors analyzed this
approach’s performance by solving a real-world case study from an express delivery company’s primary
market. The authors structured a four-stage implementation framework to facilitate practitioners applying the
proposed model.
Originality/value –Previous investigations explored driving distances on a spherical surface for facility
location. The work considers densely urbanized population and traffic data to simultaneously capture demand
patterns and other road dynamics. The inclusion of different population densities and sustainability data in
current models is lacking; this paper bridges this gap by posing a novel framework that increases the accuracy
of spherical-clustering methods.
Keywords Last-mile, Multi-period hub assignment optimization, Spherical-clustering algorithms, Simulation
Paper type Research paper
1. Introduction
As e-commerce continues to grow, the last leg of delivery—e.g. delivery to a consumer’s home
or business—has become more challenging. While in 2016 the online market was expected to
grow by 56% by 2021, traditional markets were only expected to grow by 2% during the
same period (Lindner, 2016). However, this gap between markets has recently deepened as the
worldwide population experiences the COVID-19 pandemic and associated social distancing
Express
delivery
supply chain
expansions
1707
The authors would like to thank the subject matter experts and anonymous reviewers for their insightful
guidance and suggestions. This research was supported in part by ODU-VMASC Internal Research and
Development Project 300770-010: Exploring Additional Applications of Supply Chain Management, AI,
and Cybersecurity.
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 8 February 2022
Revised 10 May 2022
Accepted 16 May 2022
Industrial Management & Data
Systems
Vol. 122 No. 7, 2022
pp. 1707-1737
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-02-2022-0076
(Diaz, 2020). The pandemic has shocked the global economy, primarily affecting purchasing
patterns that drastically accelerated the embracing of e-commerce alternatives (Gupta, 2021;
Susmitha, 2021). As several data sources suggest, online spending in the US has jumped 10–
30% across grocery and non-grocery purchases (Houston et al., 2020). Valasis (2020) surveyed
1,000 US. adults in mid-March of 2020 and found that 42% of consumers were increasingly
shopping online (Sterling, 2020). Meanwhile, large online retailers have hired thousands more
workers on the supply side to help them handle the surge in orders during the pandemic
(Mattioli, 2020;Valinsky, 2020) and explore new market segments (Olson and Dummett, 2020)
that require new network extensions and configurations.
As logistics in e-commerce primarily focuses on fulfillment, online markets and retailers
requirethe best possible way tofill orders and deliverproducts (Grewal et al.,2004). Speed and
accuracy are critical components of package delivery firms, which are dependent on sorting
quality and loading facility locations that strategically serve a target population (Agatz et al.,
2008). Network design (Goetschalcks and Fleischmann, 2008) and transportation mode selection
based on sustainability criteria (Bj€
orklund and Forslund, 2018;Fulzele et al., 2019)are
paramount to accomplishing this strategic goal. Placing distrib ution centers in suboptimal
locations will invariably lead to the inefficient utilization of resources at such sites. Using
accurate forecasting and linking these projections to network and managerial design decisions,
companies can optimize their resources (Pan and Nagi, 2010;Rezaee et al.,2017). Critical to this
connection is the interface between information systems that produce these forecasts and the
deploymentof these systems at the operations management level,as explored in this paper.
In startup firms, operational managers attempt to preserve the fast-growing order volume
and the higher-service levels required by aggressive new e-commerce players (Ferreira, 2019).
However, many firms struggle to develop the processes necessary to attain a sustainable
competitive advantage (Hopp and Spearman, 2011). Because of the effort required to meet
demanding service level agreements (SLA) and the enormous scalability of order volumes,
companies are limited in dedicating efforts to develop a long-term facility network. Thus, an
optimal and flexible supply chain configuration is critical in establishing a competitive
advantage to sustain business growth in this class of industrial systems (Ketchen and
Hult, 2007).
Academics and practitioners have widely recognized the facility location class of problems.
In particular, the hub-and-spoke allocation problem has been studied in the past.An important
part of this type of problem is also known as location analysis or the k-center problem (Han, 2015).
In this sense, the success or failure of both private- and public-sector facilities depend in part on
selected locations (Daskin, 2013). In a densely populated urban environment characterized by
high diversity and constant changes in taste and preferences (Zenk et al.,2009), the complexities
associated with facility locations are distinctive. As urbanized population growth is projected to
increase drastically (Simon, 2019) and online ordering becomes more pervasive, the number of e-
commerce transactions is expected to grow exponentially (Mangiaracina et al.,2015).
Furthermore, the delivery-goods sector that serves this population is subject to forceful
competitive pressures requiring the best resource allocation while maintaining a sustainable
strategy (Porter, 2008). Last-mile logistics networks have thus become vital to efficient
distribution within a progressively shorter time window (Ferreira, 2019).
Recently, last-mile logistics research has gained additional traction as the volume of
e-commerce transactions continues to increase and sustainable options are demanded. For
example, Leunget al. (2020) developed a predictivemethodology for forecastingnear-real-time
e-commerce orderarrivals in distribution centers,whilst Zhang et al. (2019) investigatedorder
consolidationand Liu et al. (2019) evaluated the effectsof collection-delivery points.Similarly,
Cook and Lodree (2017) examined dispatching policies when subject to double-stochastic
behaviors. In many cases, delivery companies make network design decisions based on
accessibilityand intuition(Manuj and Sahin, 2011). By evaluatingthese criticaldecisions with a
IMDS
122,7
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