Shipment sizing for autonomous trucks of road freight

• Pages: 413-433
• Date: 12 November 2020
• Published date: 12 November 2020
• DOI: https://doi.org/10.1108/IJLM-01-2020-0052
• Subject Matter: Management science & operations,Logistics
• Author: Chun-Miin (Jimmy) Chen,Yajun Lu

Shipment sizing for autonomous

trucks of road freight

Chun-Miin (Jimmy) Chen and Yajun Lu

Freeman College of Management, Bucknell University, Lewisburg,

Pennsylvania, USA

Abstract

Purpose –Unprecedented endeavors have been made to take autonomous trucks to the open road. This study

aims to provide relevant information on autonomous truck technology and to help logistics managers gain

insight into assessing optimal shipment sizes for autonomous trucks.

Design/methodology/approach –Empirical data of estimated autonomoustruck costs are collected to help

revise classic, conceptual models of assessing optimal shipment sizes. Numerical experiments are conducted to

illustrate the optimal shipment size when varying the autonomous truck technology cost and transportation

lead time reduction.

Findings –Autonomous truck technology can cost as much as 70% of the price of a truck. Logistics managers

using classic models that disregard the additional cost could underestimate the optimal shipment size for

autonomous trucks. This study also predicts the possibility of inventory centralization in the supply chain network.

Research limitations/implications –The findings are based on information collected from trade articles

and academic journals in the domain of logistics management. Other technical or engineering discussions on

autonomous trucks are not included in the literature review.

Practical implications –Logistics managers must consider the latest cost information when deciding on

shipment sizes of road freight for autonomous trucks. When the economies of scale in autonomous technology

prevail, the classic economic order quantity solution might again suffice as a good approximation for optimal

shipment size.

Originality/value –This study shows that some models in the literature might no longer be applicable after

the introduction of autonomous trucks. We also develop a new cost expression that is a function of the lead time

reduction by adopting autonomous trucks.

Keywords Economic order quantity, Lead time, Autonomous truck, Shipment size

Paper type Research paper

1. Introduction

Whathappens in a science fiction(sci-fi) movie maynot always stay in a sci-fi movie.In the 2017

American superhero movie Logan, one of the most intense scenes involves Australian actor

Hugh Jackmandriving a car that almost gets hit by a number of speedyself-driving trailers on

the highway. A year later,the Swedish automotive company Volvoreleased a TV commercial

for its autonomoustruck (AT) that lookssimilar to the self-drivingtrailers in the movie. Back in

2016, Anheuser-Busch InBev, a multinational drink and brewing company based inBelgium,

and UberTechnologies Inc.’s autonomoustrucking unit Otto workedtogether and successfully

made the firstcommercial delivery of Budweiserbeer using a self-drivingtruck (Phillips, 2016;

King et al.,2017). While automated vehiclesin controlled areas suchas transportation terminals

or miningsites have been around for many years, onlyin the last five years have we seen major

trucking companies make unprecedented endeavors to take ATs to the open road (Tita and

Ramsey, 2015). In this era of disruptive technologies, we seek to learn how the ATs could

revolutionize theway logistics mangers make decisions on shipment size.

Considering the magnitude of the logistics industry and how driverless technology can

fundamentallydisrupt the industry, governmentagencies have been playing an active role in

Shipment

sizing for

autonomous

trucks

413

This paper forms part of a special section “Decision Making in Logistics Management in the Era of

Disruptive Technologies”, guest edited by Vijay Pereira, Gopalakrishnan Narayanamurthy, Alessio

Ishizaka and Noura Yassine.

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/0957-4093.htm

Received 30 January 2020

Revised 29 July 2020

Accepted 20 October 2020

The International Journal of

Logistics Management

Vol. 32 No. 2, 2021

pp. 413-433

© Emerald Publishing Limited

0957-4093

DOI 10.1108/IJLM-01-2020-0052

guiding theprivate sector and steeringthe development of automatedvehicles. In 2013, the US

National Highway Traffic Safety Administration (NHTSA) published a description of

developmentsin automated drivingand explained the automationlevels (NHTSA, 2013).In the

spirit of continuously encouraging innovations to self-driving technology, NHTSA published

federal guidance for automated vehicles in recent years (NHTSA, 2017;US Department of

Transportation,2018). In 2016, the UK chancellor pledgedto remove impediments to adopting

the technologyand announced plans in the budgetto roll out driverless haulagetechnology by

the end of the decade (Cambell, 2016). In 2017, Germany permitted the automotiveindustry to

develop and test self-driving cars with much moreflexible ways to road-test vehicles(Wa cket

et al., 2017).It is commonly believed thatthe biggest barriers to technologydeployment usually

are not technical but rather inadequate or unclear government regulations. Thankfully,

governments haveproposed legislation and issued licenses that are crucialand necessary for

improving the development of automated vehicles (Spector and Ramsey,2016).

Automated vehicles entail many modern technologies costs as well as viable social and

commercial benefits. ATs need hardware and software to be able to better sense and judge the

surrounding environment (e.g. traffic, pedestrians, objects, lane markings, weather)

(Anderson et al., 2014). Among all kinds of vehicles, freight-carrying commercial motor

vehicles (e.g. trucks and tractor trailers) are commonly believed to be more commercially

viable than passenger-carrying consumer vehicles for the following reasons (Kilcarr, 2016a;

Markoff, 2016;Cheng, 2019). First, the substantial hardware and software investments

necessary for enabling automation are relatively less costly for a truck than a car (Waters,

2019). Second, labor costs account for a substantial percentage of road-freight operating

costs; ATs can, theoretically, cut labor costs to as little as zero (O’Brien, 2017;Kilcarr, 2017).

Third, the ongoing truck driver shortage is a worldwide phenomenon, with trucking

companies having a hard time recruiting and retaining drivers (Schouten, 2016;Ramsey,

2016;O’Marah, 2016;Millett, 2017;Meahl, 2017;Woodward, 2017;Cambell, 2018;Meyer,

2018). Last, in view of the federal Hours of Service (HOS) rules, ATs promise great economic

benefits as they allow freights to arrive at their destinations sooner because no human driver

needs to take a break during the trip (FMCSA, 2013). In many ways, ATs are poised to

promote benefits and alleviate imminent issues facing the logistics industry.

Anticipating promising advantages, trucking companies have tried to bring automated

vehicles out of controlled settings (e.g. mining sites, construction sites, transportation terminals,

distribution centers, production plants and agricultural fields) and into uncertain environments

(e.g. streets and highways) (Van Meldert and De Boeck, 2016). In 2016, a fleet of self-driving trucks

from major companies, including Volvo, Daimler and Volkswagen, completed a cross-continent

journey as an effort to not only take ATs onto real roads but also demonstrate the tangible cost-

saving benefits of ATs (Vincent, 2016). More recently, Ford and Volkswagen have collaborated

on developing self-driving cars to share the cost of new technologies (Boudette and Ewing, 2019).

Other collaboration examples among companies can be found in Wilmot (2019) and Vaish (2019).

United Parcel service (UPS), a multinational package delivery and supply chain management

company, has invested in a self-driving trucking start-up company with a goal of testing the

capabilities and limitations offered by a fully autonomous delivery fleet (Vartabedian, 2019).

Despite many successful cases of ATs to date, exact costs for enabling ATs remain elusive due to

thelackofcommerciallyavailablesystems(Kilcarr, 2015;Woodward, 2017;Banks, 2017;

Chottani et al., 2018). As a result, studies of the implications of AT hardware or software costs on

the inventory decision are scarce. It is necessary to gain a better understanding of the AT

enabling costs because many major companies have shown determination to move forward with

AT in ways that will capitalize the savings of this disruptive technology.

Putting aside AT costs for a moment, we believe the logistical benefits of ATs are

undeniable.One of the major competitiveadvantages introducedby this disruptive technology

is transportationlead time reduction. This does not mean that ATs can violate speeding laws

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