Delivering the Future: Examining the Impact of Autonomous Vehicles on Supply Chain Management

A Capstone Project Submitted in Partial Fulfillment of theRequirements of the Renée Crown University Honors Program at

Syracuse University

Benjamin J. Houle

Candidate for Bachelor of Science Degreeand Renée Crown University Honors

Fall 2019

Honors Thesis Project in Supply Chain Management

Capstone Project Advisor: _______________________Zhengping Wu, Associate Professor of Supply Chain Management

Capstone Project Reader: _______________________ Gary La Point, Professor of Supply Chain Practice

Honors Director: _______________________ Danielle Taana Smith, Director

Abstract

Supply chain management consists of the activities required to plan, control and execute the flow of a product as efficiently and effectively as possible. Managing transportation and logistics are critical to a supply chain’s success. Collectively, firms spend over $3 trillion a year on transportation and it represents a key focus area for firms to reduce costs. The introduction of autonomous vehicles will have a significant impact on how supply chain managers work and the strategies they use. Autonomous trucks are expected to help reduce lead times and decrease transportation costs. This will ultimately reduce a firm’s inventory levels and minimize the influence of the bullwhip effect. Given decreased costs and faster resupply, it will be more economical for firms to make smaller orders, more often. Automatic guided vehicles in warehouse settings reduce labor costs, improve worker safety, and decrease damaged inventory. Despite these benefits, there are several areas of concern regarding the implementation of autonomous trucks and vehicles. Infrastructure to support them needs to be developed and could prove to be expensive. Autonomous guided vehicles supplement labor in warehouses, and pay themselves off in less than a year, on average. They also increase worker safety and reduce damaged goods by limiting process variation and eliminating elements of human error. Autonomous trucks and autonomous guided vehicles would also alter the way many people in the transportation and warehousing sectors work, which may cause some delay in adoption by those hesitant to change. Because autonomous trucks are so early in their lifecycle, data are relatively limited. Further research will need to be done in the future when the results of their implementation are documented.

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Executive Summary:

Supply chain management (SCM) is the broad range of activities required to plan, control

and execute a product's flow, from acquiring raw materials and production through distribution

to the final customer, in the most streamlined and cost-effective way possible (Rouse, 2018).

Transportation and logistics are a critical part of SCM. Collectively, firms spend over $3 trillion

a year on them, making them a prime target for cost minimization. Autonomous trucks and

automated guided vehicles present an opportunity for managers to cut their costs.

Autonomous trucks (ATs) and automated guided vehicles (AGVs) use technologies such

as GPS, radar, sonar, lasers and cameras to gather information on the vehicle’s surroundings.

That data is then used as inputs for an artificially intelligent “brain,” which uses the information

to make decisions on speed and direction, while staying within the rules of the road. Artificial

Intelligence (AI) is most successful when performing long, repetitive tasks with a strict ruleset,

making long-haul driving an opportune application.

The benefits that come alongside transitioning to ATs and AGVs are impressive.

Replacing a human driver with an autonomous truck may decrease the time it takes to travel

from New York City to Los Angeles by up to more than 50%. Decreasing lead-times make it

more beneficial for supply chain managers to place smaller orders more frequently, improving

the reactivity of the entire chain and freeing up capital that was previously tied up in inventories.

The reduction will also lead help mitigate the influence of the bullwhip effect, a phenomenon

that has decreased supply chain efficiency, which would result in even greater cost savings.

AGVs decrease labor costs in the long run and significantly improve worker safety. Firms that

invest in AGVs will begin to see positive returns on labor costs between 45.8 and 68.8 weeks, on

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average. AGVs also eliminate human performance variations, leading to increased personnel

safety and a reduction in damaged inventory. (Citation?)Citation?

However, these benefits do come with significant costs. Ports and fulfillment centers will

need to switch to 24/7 schedules, the roles of drivers will transition to more “support” jobs and

infrastructural improvements must be made to support the autonomous trucks. Warehouses will

also need to develop infrastructure to support AGVs. Additionally, there are ethical concerns

surrounding the replacement of some workers with AI and the shift of workforce demands that

will follow. These, among other factors may slow the adoption of autonomous trucks.

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Table of Contents

Abstract 2

Executive Summary 3

Acknowledgements 6

Introduction 7

What are Autonomous Trucks, and How Do They Work? 9

A Brief History of Artificial Intelligence 9

Autonomous Trucks and How They Work 10

Applications to Supply Chain Management 12

Impact on Multi-Period Inventory Models 13

Mitigating the Bullwhip Effect 17

Industry Disruption 18

Case Study 19

Automated Guided Vehicles 22

Introduction to Automated Guided Vehicles and Warehousing 22

How Automated Guided Vehicles are Effective 25

Issues and Potential Threats 29

Conclusion 32

Citations 34

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Acknowledgements

I would like to thank Professor Zhengping Wu and Professor Gary La Point for agreeing to be my advisor and reader. Your knowledge and advice both in and out of the classroom was crucial to my success completing this project. I would also like to thank Professor Steve Sawyer, whose class inspired me to explore this topic, and provided the groundwork for what would eventually become my thesis. Finally, massive thanks go out to my friends and family for supporting me throughout this process.

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Introduction

Technology is advancing quicker than ever before. Technological developments, such as

autonomous trucks (ATs) may be disruptive to many industries and alter how they function. One

of the keys to industrial success is a responsive supply chain; products need to get from place to

place as efficiently as possible. Autonomous trucks are going to have a significant impact on

inventory management and decrease lead times and costs. However, there are several ethical and

economic issues that arise. The roles truck drivers play will significantly change. Shifting

demands in the workforce will orient truck drivers towards technological support functions and

last-mile deliveries. Infrastructure to support ATs will need to be developed, which may be

costly. What type of infrastructure will be required? Examining the impact of autonomous

trucks, measuring their costs and benefits and analyzing how they will change the way people

work is critical for successful adoption and integration.

Supply chain management (SCM) is the broad range of activities required to plan,

control and execute a product's flow, from acquiring raw materials and production through

distribution to the final customer, in the most streamlined and cost-effective way possible

(Rouse, 2018). Logistics and transportation are a critical part of supply chain management;

goods need to get from point A to point B as quickly and efficiently as possible. The supply

chain management program at Syracuse University, the first program of its kind in the United

States, was initially dedicated to solely study transportation, eventually adopting the other

aspects that characterize the field. Is this comment needed or relevant to your topic? It seems to

be out of place with everything else. Firms spend $1.0093 trillion every year on transportation

costs, including the costs of shipping goods and the costs of vehicles and maintenance. Citation?

Additionally, the value of time spent by firms on transportation totals $2.1723 trillion, combining

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for a total value of over $3 trillion a year (Winston, 2010). This presents a prime area of

opportunity for firms to cut down on time and save costs. The utilization of artificial intelligence

in autonomous vehicles, such as trucks, may be the solution managers have long sought after.

Autonomous trucks will have enormous impact on the way supply chains are managed

and will certainly alter the strategies managers will employ. First (and most obviously), the

gradual elimination of human drivers will lower labor costs significantly and bypassing the

driver’s required breaks will reduce lead times. Additional technology keeps trucks a certain

distance apart, maximizing fuel efficiency and further reducing costs. These benefits are just the

surface of many that could be realized with this introduction, however, significant changes to the

industry are on the horizon. Autonomous trucks will totally alter the function of “driver”, change

the way managers operate and strategize and would require significant upgrades to current

infrastructure to support these developments. Despite these changes, McKinsey and Co.

estimates that fully autonomous vehicles will save the trucking industry between $85 billion and

$125 billion. Another study by Clements and Kockelman estimates that if autonomous vehicles

capture a large share of the market, the overall economic impact would be about $1.2 trillion, or

$3,800 per American, per year (Clements and Kockelman, 2017). The purpose of the paper is to

holistically examine the impact autonomous trucks and automated guided vehicles will have on

Supply Chain Management and the work that managers do, as well as provide analysis on the

costs and benefits of these changes.

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What are Autonomous Trucks and How Do They Work?

To fully comprehend the effect autonomous trucks will have on supply chains, it’s critical

to understand the history and functions of artificial intelligence, the technologies used in

autonomous vehicles and how they work. Knowing how autonomous trucks function will

prepare workers for what’s coming and eliminates some of the fear surrounding them. It is a

cornerstone in understanding how it will impact their work and is one of the first steps in

understanding how they will need to adapt.

A Brief History of Artificial Intelligence

When imaginingyou imagine artificial intelligence, scenes from the future and characters

from mid-20th century science fiction may immediately come to mind.jump to the forefront of

your mind: C-3PO and R2D2 from Star Wars, The Robot from Lost in Space that famously

warned “Danger, Will Robinson!” or more nefariously, HAL 9000 from Kubrick’s 2001, A

Space Odyssey. These fictional manifestations may seem like they can only exist in the distant

future. However, there has been research and work performed on artificial intelligence for almost

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three quarters of a century. AI can be found everywhere, from our phones, to our cars, to our

workplace.

Artificial intelligence is the concept that a computer, through a series of complex scripts

and algorithms, can “think” by taking in and processing large amounts of data. The possibility

was first explored by British mathematician Alan Turing in his 1950 paper, Computing

Machinery and Intelligence. He suggested that if humans can process information and use reason

to make decisions and solve problems, a machine should be able to do the same thing. Turing’s

vision was aimed at the future; at that point, computers were incredibly expensive and were only

able to execute commands, not store them. However, as technology developed over time, that

would change. The first artificial intelligence program, Logic Theorist made its debut in 1956.

Over the following decades, AI grew and developed, thanks to dedicated research and some

government funding. Now, with data being collected by every source imaginable, AI is

beginning to have more practical applications.in its prime. It has uses everywhere, from banking

and marketing, to entertainment, to autonomous vehicles. (Anyoha, 2019)

Autonomous Trucks and How They Work.

According to the California Department of Motor Vehicles, “Autonomous Technology”

refers to any technology that is capabilecapable of driving a vehicle without the active physical

control or monitoring by a human operator, while a vehicle in “Autonomous Mode” is in the

status of operation where a combination of hardware and software, remote and/or on-board,

performs the dynamic driving task, with or without a natural person actively supervising the

autonomous technology’s performance of driving. (California Department of Motor Vehicles,

2019) In simpler terms, autonomous vehicles combine a series of complex technologies, such as

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interfacing with sensors and artificial intelligence, to operate without the input of a human

operator.

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Automation is most effective when completing very repetitive tasks where a clear set of

rules are in place. It seems driving, especially for long distances on the interstate, fits this bill

quite well. Autonomous trucks will be rolled out in four waves. The first wave is currently in the

works. Here, two drivers can create a “connection” between their trucks, not dissimilar to how

you can connect your iPhone with a Bluetooth speaker. The drivers form a “platoon” and the

trucks will maintain speed, stay in the same lane and remain a specific distance apart on the

interstate to maximize fuel efficiency, until either driver inputs anything. When off the interstate,

drivers operate individually. The second wave will likely take place between 2022 and 2025,

according to McKinsey and Co. In this stage, the second driver is absent on the interstate

between dedicated truck-stops. The driver in the lead truck provides all the necessary inputs,

while the second, driverless truck will follow behind. When they reach a truck-stop close to their

destination, another driver will climb aboard the second truck and take it the last few miles. Like

the previous stage, the two trucks operate individually when off the interstate. In the third phase,

the trucks operate under constrained autonomy. A driver will still bring the truck to a dedicated

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truck-stop, but now the trucks can operate entirely without a driver when on the interstate. When

possible, trucks will “platoon” two or more vehicles when available. At this point, a driver will

still have to climb aboard and take the truck the last few miles to its destination. Finally, the

terminal stage of autonomous trucks will start sometime after 2027. Drivers are eliminated

entirely, both on and off the interstate and will continue to platoon to maximize efficiency when

available. (Chottani et al., 2018). With current technological advancements, it’s reasonable to

assume we may be sharing the road with totally driverless vehicles within the next 15 to 20

years.

Autonomous vehicles work by leveraging AI alongside imaging, positioning and

communication technologies that vary from one level of automation to the next. In general, AT’s

utilize cameras, GPS, lasers, sonar and radar systems to measure distance and get a sense of their

surroundings. The AI acts as the vehicle’s “brain,” responding to the inputs that the sensors

provide through advanced algorithms and predictive modeling. The AI then makes decisions

based on those inputs, following the rules of the road, staying in the same lane, avoiding

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Figure 1: A visual depiction of the four stages of autonomous trucks. Source: Chottani et al., 2018

obstacles and keeping a reasonable distance between other vehicles. In the case of semi-

autonomous vehicles, a driver may have to manually intervene if the AI faces any uncertainty,

requiring the driver to always remain alert, even if they aren’t fully in control. (Union of

Concerned Scientists, 2018)

Applications to Supply Chain Management

The introduction of autonomous trucks will create many opportunities for firms to save

costs. Platoon technology keeps multiple trucks a designated distance apart, improving

aerodynamics, maximizing fuel efficiency and decreasing the cost of fuel. As automation

improves, long-haul drivers may be phased out or transition into another role, decreasing labor

costs. Drivers are also subject to federal regulation regarding how many hours they can work in a

day. Currently, drivers can work for a period of 14 consecutive hours in a day, 11 of which can

be spent driving. If more than eight hours have passed since the last time that they were off-duty,

they are required to take a half hour break. At the end of that 14-hour period, drivers are required

to spend 10 consecutive hours off-duty (United States Department of Transportation, 2015).

Even when working in teams of two, there are still 2 hours of downtime per day where neither

can drive. Autonomous trucks are not subject to the same regulations, so the time a product

spends in transit is reduced. According to MarketResearch.com, the introduction of ATs will

help the long-haul trucking industry grow 60% by 2024. However, these benefits are not only

realized in the trucking industry. Improvements in the supply chain benefit nearly every firm.

(MarketResearch.com, 2019) Reductions in lead-time lead to smaller inventories, which results

in increased cost savings. The same lead-time reductions also mitigate a phenomenon known as

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the bullwhip effect, which has long been the bane of supply chain managers. ATs will ultimately

change the strategies supply chain managers employ, but the return may be huge.

Impact on Multi-Period Inventory Models

Firms hold inventories for three reasons: to hedge against uncertain demand, to hedge

against uncertain supply and supply delays and to take advantage of discounts for large order

quantities or save on fixed ordering costs (advantages from making fewer large orders, rather

than many small orders). Companies may have a lot of capital tied up in inventory, which has its

inherent risks and ultimately impacts the bottom line. The more money that is tied up in

inventory, the less there is to spend in other, more productive areas. Reducing inventories is a

primary focus for supply chain managers.

In a continuously monitored system, an order quantity is determined and a re-order point

(ROP) is established. The ROP is the inventory level where a new order is placed. Optimal order

quantity, notated as Q*, is the point where total cost (TC) is minimized and carrying cost (Cc) is

equal to ordering cost (Co). Ordering costs, as the name implies, consist of the costs per order.

This may include shipping and handling costs. Carrying costs are the cost per unit per unit time,

multiplied by the quantity of units and the duration of time they are held. This may include costs

for storage, insurance, pilferage, utilities, intra-company transportation, and the opportunity cost

of capital. Annual Demand (D) is the amount of a product that consumers want to purchase. The

formula for minimizing total cost (MinTC) is as follows:

MinTC=CODQ¿+CC

Q¿

2

From this we can derive:

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Q¿=√ 2 CO DCc

By decreasing the cost of labor and fuel, autonomous trucks will likely decrease the cost

of ordering, Co. This, in turn, decreases the optimal order quantity, Q*. Both effectively lead to a

decrease in total cost. In other words, the introduction of autonomous trucks will make it more

economical for firms to place smaller orders, meaning less money is spent on inventory and

profits increase. For example, imagine there’s a store that sells a product called a widget. The

demand (D) for widgets is a constant 1 million per year. It costs the store $500 to have their

supplier deliver an order (Co). Widgets can’t expire, but the cost of capital tied up in inventory

amounts to 10% per year (Cc). The retailer’s optimal order quantity would be as follows:

Q¿=√ 2∗500∗1,000,000.1

=100,000

We can use Q* to find the minimal total cost per order:

MinTC=500 1,000,000100,000

+.1 100,0002

=$10,000

In other words, to reach the minimal total cost of $10,000, the store should order 100,000

widgets per order. What would happen to the optimal order quantity if the supplier switched to a

fleet of autonomous vehicles and the cost of ordering was reduced to $450?

Q¿=√ 2∗450∗1,000,000.1

≈ 94,868

Again, we use that Q* to find minimal total cost:

MinTC=450 1,000,00094,868

+.1 94,8682

=$9,486.83

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In this case, if autonomous trucks reduce ordering costs by 10% from $500 to $450, the

optimal order quantity is reduced by 5,132 widgets and the minimal total cost is reduced by

$513.17.

The re-order point is equal to the expected demand during lead time plus safety stock, the

amount of inventory held in excess of expected demand. Average demand (µ) is the average

amount of the product sold. Lead-time (LT) is the amount of time between when the order is

placed and when it is received. The standard deviation of demand (σ) represents how spread out

the demand figures are. The target service level is the desired percentage of customers that won’t

experience a stockout. The z-score (z) represents how many standard deviations a point is away

from the mean and is calculated using the target service level on a standard normal table. The

equation for finding the re-order point is as follows:

ROP=μ∗¿+ zσ √¿

All other things being equal, if the target service level is above 50%, the reduction of lead

times stemming from the use of autonomous trucks will decrease the re-order point, meaning that

firms will place orders more often. Let’s return to the widget store example. If the annual

demand is a constant 1,000,000 units, the average daily demand for widgets (µ) is equal to

2739.73 units. Assume the standard deviation is 50 and the supplier’s lead time (LT) is 9 days.

The store’s target service level is 98%, which results in a z-score (z) of about 2.05, according to

a standard normal table. In this case, the re-order point would be:

ROP=2739.73∗9+2.05∗50∗√9 ≈ 24,965

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Here, the store should place an order when they have 24,965 widgets left in stock. What

would happen if the supplier began using autonomous trucks, resulting in lead time being

reduced to 8 days?

ROP=2739.73∗8+2.05∗50∗√8 ≈ 22,208

By reducing lead time by 1 day, the reorder point was reduced by 2,757 units. Moral of

the story: the introduction of ATs could make it more economical to hold fewer items in

inventory and place orders more often. This means that supply chains are more responsive to

changes in demand and firms have less capital in inventory, allowing them to spend elsewhere

and improving profits.

Little’s Law provides more evidence that Autonomous Trucks will reduce inventory

levels. Little’s Law is as follows:

(Little, 1961) Assuming flow rate remains the same, the lead time reduction associated with

autonomous vehicles will reduce a firm’s average inventory.

Mitigating the Bullwhip Effect

The bullwhip effect,

first described by Jay Forrester

in 1961, is a phenomenon in

supply chain management

where changes in demand order

variabilities are amplified by

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Figure 2: A graphical depiction of the bullwhip effect. Source: Lee, 1997

demand fluctuations as you move up the supply chain. Put simply, a small variation in demand

on the retail end of the chain leads to a fluctuation in order size by the retailer that results in

larger order fluctuations at each ascending level of the supply chain (Lee, 1997).

The bullwhip effect represents a problem for supply chain managers, as the distorted

information ultimately leads to massive inefficiencies and wasted money. Minimizing the threat

of the phenomenon is a primary goal for many managers.

According to Lee, long lead times give firms an incentive to hold more safety stock. This

ultimately results in greater fluctuations in order quantities over time when that stock is depleted

than the demand data would indicate, exacerbating the bullwhip effect. However, the lead-time

reduction associated with autonomous trucks would mitigate the effect, creating more accurate

and responsive supply chains and saving firms money at every level of the supply chain.

Industry Disruption

Even with these benefits, autonomous trucks are going to be disruptive and require a

significant amount of planning and infrastructure development to ensure that the transition is

smooth. Warehouses and distribution centers will have to operate 24 hours a day, 7 days a week

to accommodate ATs; the trucks aren’t subject to human schedules anymore and may arrive at

any time. Getting them loaded and departed as quickly as possible is crucial. Warehouses may

also have to build loading docks and entrances that are compatible with ATs and would probably

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want to locate in areas with prime access to the interstate. Ports will face similar challenges as

they need to keep up with ATs. Like the warehouses, they will also have to operate 24/7 and

develop infrastructure to support the vehicles. Minimizing bottlenecks is also critical, so vehicles

won’t get backed up. (Chottani et al., 2018) These developments require workers and firms to

adapt. Constant operation may require a change in shift structure or hire more workers to meet

new demands. In some career paths, such as the drivers themselves, their daily duties may

change entirely.

Truck drivers may see their roles change significantly, as they transition from long-haul

driving to shorter deliveries and technological support. According to a Michigan State

University study, once ATs are adopted on a wide scale, drivers must understand how to monitor

the vehicles hardware, software and safety systems. Drivers must be able to diagnose issues and

plan an immediate course of action when things go awry. Additionally, combined with the need

to resolve the current shortage of long-haul drivers, current drivers may transition into localized

logistics and delivery jobs. According to the American Trucking Association, 60,800 trucking

jobs went unfilled in 2018, up almost 20% from 2017. If current trends hold, that number is

predicted to grow to over 160,000 by 2028. (Costello et al, 2019) Whether there is a driver in the

seat or not, a person is needed to attach the truck to the trailer, connect hoses from the truck to

the trailer, perform routine maintenance checks or fill the truck with fuel. In the early stages of

automation, a driver will have to remain in the truck and will have to drive it the last few miles to

its destination. However, once more automation is introduced and trucks can operate

independently on the interstate, the duties of a long-haul trucker will be radically different.

Instead of driving, the “operators” are may have to keep an eye on the software, hardware and

safety systems to make sure it’s running smoothly, as well as diagnose any immediate issues.

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They may also have to input data for trip scheduling and logistics and understand logistics

software to make sure the planned route is effective (Yankelevich et al., 2018). As the need for

driving skills decreases, the need for technological training and knowledge significantly

increases. New in-service training and certification programs relevant to emerging technologies

need to be developed to ensure the workforce is prepared for potential disruption.

Case Study:

While we know that the introduction of ATs may reduce lead-times, it is difficult to

quantify the magnitude of these reductions. With no fully autonomous trucks on the road at this

point, a simulated case is necessary to capture the difference in transport time between a human

driver subject to federal regulations and an autonomous vehicle. The following case is very

simplified and makes several assumptions. However, with all else being equal, it creates a model

that demonstrates the scale of reductions solely due to the elimination of regulation.

There is a long-

distance trucking route

between New York

City, NY and Los

Angeles, CA. The

distance between the

two points is 2,790

miles. The route

involves taking I-80 W

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Figure 3: Route Between New York City and Los Angeles Source: Google Maps, 2019

to I-76 W to I-70 W to I-15 S. Along those roads, the average speed for a truck is 57.7 MPH,

54.5 MPH, 56.8 MPH and 56.7MPH, respectively. (United States Department of Energy, 2011)

This means that the average speed along all four roads is 56.425 MPH. The route is driven by a

solo driver in a semi and the same truck fitted with autonomous driving technology. The two

leave from the same point at the same time. The size of the load, traffic and road conditions are

the same for both vehicles and no fuel stops are made. How much faster will the autonomous

truck complete the trip?

If driven straight through with no rest breaks or fuel stops, the trip would take 49.45

hours, or 2.06 days at that speed. However, truck drivers are subject to the following regulations

regarding hours worked: (United States Department of Transportation, 2015)

Regulations Regarding Hours Worked:How much can a driver work per day?

The driver can work 14 consecutive hours in a day.

How long can they drive for in a day?

Within 14-hour working period, the driver can drive for 11 hours.

How long do drivers need to rest for?

After 14 hour working period, the driver must rest for 10 consecutive hours.

Do they have to take breaks?

If 8 consecutive hours have passed since the last off-duty period, the driver must take a 30-minute break.

How long can they The driver is not allowed to be on duty more than 60 hours in 7

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work per week? consecutive days, or 70 hours in 8 consecutive days.When do their weekly hours reset?

60/70-hour limits "restart" after 34 consecutive hours off-duty.

Figure 4: Summary of Regulations Regarding Hours Worked by Truck Drivers Source: United States Department of Transportation

These regulations significantly improve driver health and safety. Tired, overworked

drivers have a decreased ability to focus on the road, leading to more accidents, injuries and

deaths. Regulations are necessary to protect the truck drivers and those who share the road with

them. However, on long-distance trips, the regulations increase the amount of time it takes to

reach the destination. The calculations to determine the difference in driving times due to

regulations are summarized in the table below:

Total Route Distance

2,790 miles

Average Driving Speed

57.7mph+54.5 mph+56.8mph+56.7 mph4 = 56.425 miles per hour.

How many hours of nonstop driving will the trip take?

2,790 miles56.425miles per hour

=49.446hours or 2.06 days at minimum to

complete the trip. This is how long it would take the autonomous truck without any breaks.

How long would it take a human?

49.45 hours11hoursof driving per day

=4.495days or 107.89 total hours to

complete the trip.% Change 4.495 days−2.06 days

4.495 days=54.17 %

Figure 5: Summary of Calculations

The difference in transportation times directly resulting in regulation to protect human

drivers is significant. All other things, changing from a solo human driver to an autonomous

truck on a trip from NYC to LA will reduce travel time by 54.17%. Time reductions, especially

of this size, will result in more responsive, accurate supply chains, more efficient companies and

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higher profits. Ideally, cost savings will also be passed down to the consumer, resulting in

cheaper goods for everyone.

Automated Guided Vehicles

Introduction to Automated Guided Vehicles and Warehousing

Trucks are not the only autonomous vehicles that are making a large impact on supply

chain strategies. Warehouses and distribution centers are another key link in the supply chain.

Warehouses are the center of a firm’s inventory and distribution operations. Collectively,

American firms spent $1.64 trillion on warehousing and transportation costs in 2018. (Smith,

2019) According to the United States Department of Labor, over the past decade, the number of

people employed in warehouses has near doubled from an average of 641,700 in 2009 to

1,188,500 between January and August 2019. (United States Bureau of Labor Statistics, 2019)

With so much inventory moving in and out of warehouses, and so much money spent keeping

them running, making warehouse operations more efficient and cost-effective are important

opportunities to improve a firm’s profitability.

Warehouses and their operations are evolving rapidly. For one, they’re getting bigger.

The rise of e-commerce and demand for global distribution has led firms to build massive

warehouses and distribution centers to effectively satisfy demand. As of October 2019, Trammel

Crow Co. is building a $280 million, 3.8 million square foot distribution center in Liverpool,

New York for a company that has yet to be revealed. Only one warehouse in the world surpasses

that size, a 4.3 million square foot warehouse owned by Boeing in Everett, Washington.

(Moriarty, 2019) With warehouses of that size handling such intense inventory loads, it is simply

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too much space and work to cover with human labor alone. One way that companies are looking

to improve their warehousing operations is through the addition of autonomous warehouse

vehicles, also known as Automated Guided Vehicles (AGVs). While autonomous trucks are

currently emerging, Automated Guided Vehicles have been around for decades, and have made

significant impacts.

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The first AGV, known as the Guide-O-Matic was introduced by Arthur Barrett and

Barrett Electronics in 1954. The machine was a simple tow truck that followed a wire imbedded

in the floor of a warehouse. Since then, AGVs have developed significantly more, especially

their navigation systems. While early AGVs relied on hardware, such as wires or nails to find

their paths, modern ones rely on systems like those seen in autonomous trucks. The

effectiveness, flexibility, and efficiency of Automatic Guided Vehicles has led to a growth in

popularity, and their wide adoption across many industries. (Olmi, 2011)

Figure 6: Article on Barrett's Guide-O-Matic in the June 1958 edition of Popular Electronics Magazine. Source: Popular Electronics Magazine, 1958.

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The most modern AGVs take four main forms.

The first type, known as trains, moves goodsmove goods

between workers picking them and other areas. This can

eliminate significant time workers spend moving from

place to place in the warehouse. The second type is self-

driving forklifts. Autonomous forklifts use similar

technology to autonomous trucks, such as laser navigation

and camera setups to safely move around the warehouse

floor, away from human workers. The third type monitors

inventory. These machines go up and down the aisles of

the warehouse scanning RFID tags on the materials in

stock, keeping track of what is being used and what is in

stock. This not only eliminates some of the need for

physical inventory counts but can also help eliminate inefficient use of space and labor. The final

type, drones, are being will eventually be used to move materials through the air around

distribution centers. Like the inventory monitoring machines, they may also be equipped with

RFID scanners to keep track of inventory. (Guillot, 2018)

How Automated Guided Vehicles are Effective

The immediate benefits of having autonomous warehouse vehicles are clear. First, while

expensive initially, AGVs are far less expensive over their lifetime than the cost of the labor they

supplement. According to the United States Department of Labor, the average wage of a

nonsupervisory warehouse employee was $18.50 between January and July 2019. The average

cost of an AGV is in the $100,000 to $150,000 price range, depending on the specifics of the

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Figure 7: An Autonomous Forklift Produced by Toyota. Source: toyotamaterialhandling.com.au

vehicle. (Weber, 2015) This means that it would take between 5,405.4 and 8,108.1 hours of labor

to recoup the cost of one AGV. On average, nonsupervisory warehouse employees worked 39.3

hours a week. (United States Bureau of Labor Statistics, 2019) Assuming an AGV is using a

battery exchange strategy where the battery is replaced when it reaches a certain level,

eliminating charging wait times, can operate 24 hours per day, seven days per week and is a

substitute for three workers per day (one per eight-hour shift), it would take between about 45.8

and 68.8 weeks to pay off one AGV. After that breakeven point, firms will see a positive return

on their investment in an AGV. In the short run, human workers are a less expensive option.

Maintenance and repair costs are also significant and need to be accounted for when managers

decide if an AGV is the right fit for their situation. However, in the long run, AGVs are generally

more economical compared to the labor they replace.

Labor reduction is not the only area where AGVs can provide significant benefits to

warehouse operations. AGVs can potentially improve safety, reduce workers’ movement, and

reduce workman’s compensation and insurance costs. Between 2011 and 2017, 614 people were

killed in workplace forklift accidents, and more than 7,000 people are injured in forklift

accidents per year. (United States Bureau of Labor Statistics, 2019) That means that someone is

killed by a human-driven forklift in a workplace accident approximately once every 4 days.

These incidents are tragic, unnecessary, and costly. Each year, approximately $135 million of

immediate costs are incurred each year due to forklift accidents. (Seegrid.com, 2019) AGVs can

improve worker safety and save employers money. In order to protect human workers, AGVs

are equipped with laser scanners designed to detect obstacles in the vehicle’s path with 100%

accuracy, and a set of predetermined paths the AGV can travel is determined. (Cardarelli et al.,

2017). AGVs are always paying attention, are always aware of their surroundings, and never

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stray from their programmed path. Every task is performed the same way every time. If human

workers are properly trained to safely interact with AGVs, and stay out of their path, the amount

of injuries involving vehicles may decrease significantly. There has not been a substantial

amount of research quantifying the extent of these safety improvements, but cultural attitudes

concerning AGVs and their production suggest that it is considerable. According to Fabien

Bardinet, CEO of French AGV producer Baylo, human errors are frequent and normalized, but if

an AGV malfunctions and makes an error, it is a much bigger deal. Scuff marks and dents from

humans running warehouse vehicles into walls and shelves are a normal occurrence, but AGVs

are held to a much higher standard. (Barthelemy, 2019) This higher standard ensures that AGV

producers are creating consistent, safe vehicles, otherwise, the fearful attitudes surrounding

AGVs would discourage managers from buying them entirely.

One situation where AGVs may be most effective is when inventory is especially fragile

and expensive or has low margins. At one large pharmaceutical production facility in New York

State, warehouse workers frequently joke about the “Million Dollar Club.” Workers become

members after they have damaged $1 million worth of inventory. It can be surprisingly easy to

get to that point. In some instances, one pallet of materials can be worth tens to hundreds of

thousands of dollars, so even a few mistakes add up against the bottom line. While not at all a

source of pride, the club is a constant reminder of how costly human error can be. According to

inventory management consultant Jon Schreibfeder of Effective Inventory Management, Inc. the

equation to calculate additional sales needed to replace damaged goods is as follows:

Additional Sales Needed=Value of Damaged MaterialAverageGross Margin %

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(Schreibfeder, 2018) Therefore, if a company were to have a 20% average gross margin, and lost

a pallet of materials worth $20,000, then they would require an additional $100,000 worth of

sales to make up for that loss. If those margins were 10%, that number would double to

$200,000. No matter what the average gross margins or the value of the damaged material are,

one thing is clear: it takes more sales to make up for the loss of a damaged good than the value of

the good itself. Eliminating human error is critical to decreasing costs of damaged goods. This is

where AGVs can be beneficial. Automated systems are especially good at doing the same task

repeatedly and will have little to no variation in how the task is performed. Humans may

accidentally stray from the designated path, or not see obstacles. It’s these minor deviations that

can be a major source of mistakes and costs. An AGV will follow the same path every time and

its sensors will stop the vehicle if there is an obstacle. With these sources of potential human

error eliminated, the amount of inventory items damaged should decrease, saving firms money.

Like autonomous trucks, warehouse infrastructure must to adapt to meet the needs of

AGVs. While the Guide-o-Matic and wires imbedded in floors may be a thing of the past,

warehouses that utilize AGVs must make some accommodations for them. Simpler AGVs may

require wires or magnetic tape to navigate the facility. Once these are laid down, they are

difficult to move, meaning that if the warehouse were to be reorganized, it will take a significant

amount of work to change the designated AGV path. More advanced AGVs with vision-guided

navigation have the benefit of being more easily reprogrammed but require a lot of light to be

able to “see.” AGVs with laser and lidar navigation should be programmed to avoid being in

similar paths at the same time, because the systems may interfere with each other’s sensors,

blinding the vehicle. (Gooley, 2016) It may be a challenge to implement AGVs in a warehouse if

the existing equipment and processes aren’t complimentary. When considering an AGV,

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managers should account for the layout of the warehouse, and the potential for layout changes in

the future.

Issues and Potential Threats

While the benefits of autonomous trucks and AGVs are bound to be significant, there are

several issues and potential threats that could inhibit their adoption. These problems include both

ethical and economic concerns. Understanding the issues and how we can approach them is a

significant part of moving forward with introducing autonomous vehicles.

In 2016, there were approximately 3,292,400 people employed as heavy and tractor

trailer truck drivers, delivery drivers or driver/sales worker. (United States Department of Labor

Statistics, 2019) According to a study performed by Frey and Osborne, 79% of tasks performed

by heavy truck drivers and 69% of tasks performed by light truck and delivery drivers can be

automated. (Frey and Osborne, 2017) Additionally, almost 1.2 million people are employed in

warehouses. (United States Bureau of Labor Statistics, 2019) With so many people employed by

these industries, there is serious cause for concern among many people. What will happen when

all those jobs disappear? How will we retrain 4.5 million people? What are drivers and

warehouse workers going to do for work after? While these fears are not totally unfounded, a

2018 report published by Michigan State University and Texas A&M found that job elimination

will be more modest than many people think. Researchers estimate that long-haul trucking jobs

will only decrease from 2.03 to 1.57 million and lightweight and delivery trucking jobs will fall

from 1.5 to 1.12 million by 2028. (Yankelevich et al, 2018) They suggest that instead of

eliminating jobs, autonomous trucks are going to change the demands of the workplace. Most

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trucking jobs probably aren’t going anywhere; it’s the functions of the job that are going to

change. Drivers of the future must be more technologically inclined and be familiar with how the

truck’s systems function. As hardware and software take over the primary driving functions,

driving skills will take a backseat to technological skills. John Hitch argues in Industry Week that

AGVs have similar effects on warehouse employment. According to Hitch, AGVs are doing the

work that humans don’t want to do. Despite very low unemployment in the United States, firms

are struggling to fill warehousing positions as humans become more selective with the jobs they

perform. Some customers of Seegrid, an AGV manufacturer, state that they have 300% turnover

in material handling drivers. (Hitch, 2019) With this trend in mind, it is less likely that AGVs

will eliminate significant amounts of warehouse jobs, as they are largely replacing jobs that

aren’t being filled. Training and certification methods need to be developed to prepare upcoming

drivers for the future. People can be resistant to change sometimes, and drivers and managers

alike might be hesitant to go all-in on autonomous trucks and AGVs because of their perceived

disruptions.

Ethical concerns have also been at the forefront of the conversation surrounding

autonomous vehicles. Questions about what standards the AI will be programmed to and how it

will make decisions when faced with dilemma situations have been asked by researchers,

academics, governments and keyboard philosophers alike. If an autonomous truck was in a

situation where it had to decide between protecting its driver and driving into a crowd of people

or driving off a cliff and sparing the crowd, what should it chose? What if it came between

hitting a young person vs. an old person? A human vs. an animal? Many people say that AI

should be programmed to match human values. The issue is, which values do we use?

According to an MIT study titled The Moral Machine Experiment, preferences in ideal AI

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behavior is largely dependent on region, with respondents in three distinct groups. Overall,

respondents generally prioritized saving a group of people vs a single person, humans over

animals and young people over old people. However, there was variations amongst people with

similar backgrounds. The southern group, which includes France, Hungary and Latin America

were much more likely to save young people. In the eastern group, containing most of Asia and

the Middle East, where people traditionally have high value and respect for elders, respondents

showed less preference for saving younger people. People in the western group, which includes

most of Europe and the US, showed a strong preference for saving humans over animals, while

the southern group was less likely to do so. (Awad et al, 2018)

With different preferences around the globe, who decides what the ethics of AI should be

based on? According to the German Ethics Commission on Autonomous Vehicles, dilemmas

like those discussed in The Moral Machine Experiment should never happen. They state that

decisions should never be made based on age, gender or appearance, although programming

designed to reduce the number of injuries are justified. They also state that liability for damages

caused by autonomous vehicles should be subject to the same principles as other product

liabilities. This would ensure manufacturers continue to improve their systems over time, so

damages become exceedingly rare. (Federal Ministry of Transport and Digital Infrastructure,

2018) However, will fear of liability disincentivize manufacturers from investing in developing

autonomous vehicles? Unlikely. Even with the threat of liability looming, companies have

continued to work on developing autonomous technology. If anything, it’s encouraging them to

create the safest product possible. Situations that create ethical dilemmas, are far less likely with

autonomous vehicles. Computers can’t get tired, distracted, or drunk. Accidents are going to

become less common and these discussions less relevant.

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If there is one thing that will hold back the adoption of ATs and AGVs, it is fear and

uncertainty. People are fearful of the economic implications. They worry about what will happen

to transportation and warehousing jobs. They’re afraid that the composition of their careers is

going to change. People are afraid that life and death decisions will be made by a computer.

They may not trust the people behind the curtain that program the computer. A survey conducted

by AAA found that 71% of Americans are afraid of riding in an autonomous vehicle. That

number is 8% higher than it was in 2017. These feelings are justified, as accidents involving

autonomous vehicles, including a deadly accident in March 2018, receive widespread media

coverage. (Novak, 2018) However, the leading cause of these accidents? People. A survey of car

accidents in California found that out of the 62 incidents involving moving autonomous vehicles,

all but one was the fault of human drivers. (Reisinger, 2018) It’s these valid concerns that may

ultimately slow down the introduction, but technology will continue to improve and eventually

these worries will be quelled.

Conclusion

The introduction of autonomous trucks will create several benefits and new opportunities

for drivers. In a route between New York and Los Angeles, an autonomous truck can complete

the 2,790-mile journey up to 54.17% faster than its human-driven counterpart. The reduction in

lead time that will come with their introduction makes it more economical for managers to place

smaller orders more frequently, decreasing the amount of capital spent on inventories and

positively impacting the bottom line. More efficient, reactive supply chains will be created,

which may ultimately pass lower prices to consumers. However, the introduction of ATs does

have its challenges. Autonomous trucks will be disruptive; they will change the way many

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people work, and some may be resistant to that change. There are also several ethical and

economic concerns that arise.

AGVs are making significant advances in warehousing operations. The most obvious

benefit is their labor supplementation. When accounting for the labor they supplement, AGVs

pay themselves off between 45.8 and 68.8 weeks. Additionally, advanced laser-guided

navigation systems allow AGVs to “see” everything around them and follow the same path

repeatedly. This eliminates variation and human error, increasing safety, and reducing worker

injuries and damaged goods. Workplace injuries are a significant cost for firms, with forklift

accidents alone causing $135 million in immediate costs annually. Damaged goods are also a

significant threat for firms, as it typically takes significantly more sales than the nominal value of

the damaged good to recoup costs. However, AGVs require proper infrastructure to be

implemented successfully. More basic AGVs require wires or magnetic tape to be installed,

which make it difficult to reprogram them if the warehouse is rearranged. Advanced AGVs with

vision, laser, and lidar guided navigation systems are easier to reprogram, but each come with

their own drawbacks. Vision-guided systems require large amounts of light to guide themselves,

while other systems may interfere with each other if they get too close together. Warehouses

must be designed with AGVs as part of their operating strategy. Trying to shoehorn AGVs into

preexisting warehouse facilities will often result in a poor fit, resulting in negative performance.

The fact is, autonomous vehicles haven’t been fully rolled out yet. It is impossible to

know their true impact until they are available on a broad scale and more data on the subject

exist. When that point is reached, further research with the available information will need to be

done. Two major questions cannot be answered until this research is completed: will the true

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total cost of ATs outweigh the savings on inventory and labor they create? Will the reduction in

transportation jobs reduce demand enough to counter any benefit from cost savings?

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