Soma Bay Prospers with ERP in the CloudSoma Bay is a 10-millio.docx

Soma Bay Prospers with ERP in the Cloud
Soma Bay is a 10-million-square-mile resort community on the
Egyptian shore of the Red Sea. It has many attractions that
make it a first-class vacation paradise, including five hotels, a
championship golf course, water sport facilities, a world-class
spa, and luxury vacation homes. Soma Bay Development
Company is headquartered in Hurghada, Egypt and has more
than 2,000 employees.
Unfortunately, political upheavals and economic conditions
have taken a toll on occupancy rates and profitability. When
President Hosni Mubarak was overthrown during the Egyptian
revolution of 2011, there was a sharp devaluation of Egyptian
currency. In the years that followed, political conditions
stabilized and the Egyptian economy recovered, but the tourism
industry lost U.S. $1.3 billion after the downing of a
commercial airliner over the Sinai Desert in late 2015. Soma
Bay Development Company’s hotel occupancy rates plummeted
from more than 50 percent in 2015 to 25 percent in the first
quarter of 2016.
Foreign exchange fluctuations and political upheavals are forces
beyond Soma Bay’s control, but what the company’s
management can do during downturns is react intelligently by
closely monitoring operations and costs. This is possible thanks
to the company’s use of a JD Edwards Enterprise One ERP
system from Oracle with applications and data residing in
Oracle’s Cloud Infrastructure as a Service (Oracle Cloud IaaS).
In the past, Soma Bay Development Company had tried to run
much of the company using unwieldy Excel-based systems.
Managers had to manually manipulate spreadsheets to
understand the basic drivers of profitability, and it often took

too long to obtain the information needed for sound decision
making. These systems made it difficult for Soma Bay to
manage its aggressive growth plans, which included
construction of 1000 new homes over a five-year period.
Mohammed Serry, Soma Bay Company’s CFO, and his team
selected JD Edwards Enterprise One for a solution because it
could create standardized business processes across functional
areas and provide timely reports that explain the profitability of
each business unit using a standard chart of accounts. The
software can identify the profitability drivers and growth
drivers of a business. Enterprise One seamlessly combines data
from the general ledger and other financial systems with data
from operational systems.
Soma Bay’s Enterprise One cloud platform makes it easy to
create cash flow reports, project management reports, accounts
receivable aging reports, facility management reports, and key
performance indicator reports throughout Soma Bay’s
distributed organization. Company management also appreciates
Oracle Cloud IaaS disaster recovery capabilities. Several years
ago, water from an upper floor flooded Soma Bay’s Cairo data
center. The company was able to restore data and resume
operations quickly because it had backups stored in Oracle
Cloud.
JD Edwards Enterprise One contains more than 80 separate
application modules designed to support a wide range of
business processes. The software suite also features mobile
applications that support both iOS and Android and can be used
on smartphones and tablets. Soma Bay uses the JD Edwards
Enterprise One modules for Financials, Procurement, Inventory
Management, Job Cost, Real Estate Management, Homebuilder
Management, Capital Asset Maintenance, Service Management,
and Time and Labor. JD Edwards Enterprise One Homebuilder
Management helps Soma Bay coordinate activities and analyze

profitability throughout its home-building cycle down to the lot
level. JD Edwards Enterprise One Real Estate Management
streamlines financial, operational, and facilities management
processes for finished properties, coordinating tasks among
teams and providing a comprehensive management view of each
unit. The Job Cost module shows ongoing costs for the real
estate business, which helps management allocate expenses for
materials, labor, and other needs and also track expenses against
the budgets and forecasts established at the outset of each
facilities management project. Managers can identify projects
with codes and merge them with financial account numbers to
determine budget expenses versus actual expenses. They can
thereby verify if complex projects are on track and share
expense data among divisions.
The Enterprise One software creates currency-neutral financial
reports. This helps reconcile revenue from Soma Bay’s tourism
division (which caters to Germany and other parts of Europe)
with its home sales division (which is aimed primarily at
Egyptians) to neutralize the effect of different currencies on
financial results. Home building accounts for about 25 percent
of corporate revenue.
Having a dual revenue stream mitigates risks. If the tourism
business is slow, Soma Bay still has revenue from the real
estate business, and vice versa. The ERP system provides the
data required to closely track costs. For example, in 2017 Soma
Bay spent 100 million Egyptian pounds (equivalent to
approximately U.S. $5.7 million) on new construction. The
Enterprise One system provided the information about cash
management and cash flow for sustaining this level of
expansion. Soma Bay can carefully monitor cash flow and
payments to contractors.
During the 2016 downturn, Soma Bay used the Enterprise One
cost management and profitability capabilities to provide

detailed financial data that helped managers carefully control
fixed operating expenses, helping to minimize losses. Enterprise
One provided a solid understanding of costs and profitability,
even though revenue came from different currencies and
markets. It was able to show the impact of falling occupancy
rates on the business, excluding foreign exchange effect, to help
management measure overall performance by legal entity. This
knowledge helped Soma Bay weather the downturn and
implement an aggressive turnaround plan.
Today, 95 percent of Soma Bay staff members use the
Enterprise One software in some capacity. The company has a
more stable operating model. Occupancy rates at its five hotels
are rising. Soma Bay Development Company is building 500
vacation homes in six seaside communities. According to Cherif
Samir, Financial Controller for Soma Bay, being able to track
every penny the company spends on a project has revolutionized
the business.
A Nasty Ending for Nasty Gal
In 2006, Sophia Amoruso was a 22-year-old hitchhiking,
dumpster-diving community college dropout with a lot of time
on her hands. After reading a book called Starting an eBay
Business for Dummies, she launched an eBay store called Nasty
Gal Vintage, named after a song and 1975 album by the jazz
singer Betty Davis, second wife of the legendary Miles Davis.
Nasty Gal’s styling was edgy and fresh—a little bit rock and
roll, a little bit disco, modern, but never hyper-trendy. Eight
years after its founding, Nasty Gal had sold more than $100
million in new and vintage clothing and accessories, employed
more than 350 people, had more than a million fans on
Facebook and Instagram, and was a global brand. It looked like
a genuine e-commerce success story. Or was it?

When Amoruso began her business, she did everything herself
out of her tiny San Francisco apartment—merchandising,
photographing, copywriting, and shipping. She got up at the
crack of dawn to make 6 a.m. estate sales, haggled with thrift
stores, spent hours photoshopping the images she styled and
shot photos herself using models she recruited herself, and
ensured that packaging was high quality.
She would inspect items to make sure they were in good enough
shape to sell. She zipped zippers, buttoned buttons, connected
hooks, folded each garment, and slid it into a clear plastic bag
that was sealed with a sticker. Then she boxed the item and
affixed a shipping label on it. She had to assume that her
customers were as particular and as concerned with aesthetics as
she was.
Amoruso had taken photography classes at a community
college, where she learned to understand the importance of
silhouette and composition. She bought vintage pieces with
dramatic silhouettes—a coat with a big funnel collar, a ’50s
dress with a flared skirt, or a Victorian jacket with puffy
sleeves. Exaggerating everything about the silhouette through
the angle from which it was photographed helped Amoruso
produce tiny thumbnails for eBay that attracted serious bidders.
She was able to take an object, distill what was best about it,
and then exaggerate those qualities so they were visible even in
its tiniest representation. When the thumbnail was enlarged, it
looked amazing.
Amoruso has been a heavy user of social tools to promote her
business. When she first started out, she used MySpace, where
she attracted a cult following of more than 60,000 fans. The
company gained traction on social media with Nasty Gal’s
aesthetic that could be both high and low, edgy and glossy.
Amoruso took customer feedback very seriously and believed

customers were at the center of everything Nasty Gal did. When
she sold on eBay, she learned to respond to every customer
comment to help her understand precisely who was buying her
goods and what they wanted. Amoruso said that the content
Nasty Gal customers created has always been a huge part of the
Nasty Gal brand. It was very important to see how customers
wore Nasty Gal’s pieces and the types of photographs they took.
They were inspiring.
Social media is built on sharing, and Nasty Gal gave its
followers compelling images, words, and content to share and
talk about each day. They could be a crazy vintage piece, a
quote, or a behind-the-scenes photo. At most companies the
person manning the Twitter and Facebook accounts is far
removed from senior management. Amoruso did not always
author every Nasty Gal tweet, but she still read every comment.
If the customers were unhappy about something, she wanted to
hear about it right away. At other businesses, it might take
months for customer feedback to filter up to the CEO. When
Nasty Gal first joined Snapchat, Amoruso tested the water with
a few Snaps, and Nasty Gal followers responded in force.
In June 2008, Amoruso moved Nasty Gal Vintage off eBay and
onto its own destination website, www.nastygal.com. In 2012,
Nasty Gal began selling clothes under its own brand label and
also invested $18 million in a 527,000-square-foot national
distribution center in Shepherdsville, Kentucky, to handle its
own shipping and logistics. Venture capitalists Index Ventures
provided at least $40 million in funding. Nasty Gal opened a
brick-and-mortar store in Los Angeles in 2014 and another in
Santa Monica in 2015.
With growing direct-to-consumer demand and higher inventory
replenishment requirements driven by new store openings,
Nasty Gal invested in a new warehouse management system.
The warehouse management system investment was designed to

increase warehouse productivity and shorten order cycle times
so that Nasty Gal’s supply chain could better service its
mushrooming sales. (Order cycle time refers to the time period
between placing of one order and the next order.) The company
selected HighJump’s Warehouse Management System (WMS)
with the goal of increasing visibility and overall productivity
while keeping fill rates above 99 percent. (The fill rate is the
percentage of orders satisfied from stock at hand.)
Key considerations were scalability and capabilities for
handling retail replenishment in addition to direct-to-consumer
orders. HighJump’s implementation team customized the WMS
software to optimize the business processes that worked best for
an e-commerce retailer that ships most of its items straight to
the customer, with a small subset going to retail stores. The
WMS software was also configured to support processes that
would scale with future growth. Picking efficiency and fill rates
shot up, with fill rates above 99 percent, even though order
volume climbed.
Nasty Gal experienced tremendous growth in its early years,
being named INC Magazine’s fastest-growing retailer in 2012
and earning a number one ranking in Internet Retailer’s Top 500
Guide in 2016. By 2011, annual sales hit $24 million and then
nearly $100 million in 2012. However, sales started dropping to
$85 million in 2014 and then $77 million in 2015. Nasty Gal’s
rapid expansion had been fueled by heavy spending in
advertising and marketing. This is a strategy used by many
start-ups, but it only pays off in the long run if one-time buyers
become loyal shoppers. Otherwise, too much money is spent on
online marketing like banner ads and paying for influencers. If a
company pays $70 on marketing to acquire a customer and that
customer only buys once from it, the company won’t make
money. A company that spends $200 million to make $100
million in revenue is not a sustainable business. Nasty Gal had a
“leaky bucket” situation: Once it burned through its fundraising

capital and cut down on marketing, sales continued to drop.
Nasty Gal couldn’t hold onto customers. Some were dissatisfied
with product quality, but many were more attracted to fast-
fashion retailers such as Zara and H&M, which both deliver a
wider array of trendy clothes through online and bricks-and-
mortar stores at lower prices and are constantly changing their
merchandise. The actual market for the Nasty Gal brand was
quickly saturated. There was a limit to the number of women
Nasty Gal appealed to: Nasty Gal had a California cool, young
girl look, and it was unclear how attractive it was in other parts
of the United States and around the world.
Nasty Gal also wasted money on things that didn’t warrant large
expenditures. The company quintupled the size of its
headquarters by moving into a 50,300-square-foot location in
downtown Los Angeles in 2013—far more space than the
company needed, according to industry experts. The company
had also opened a 500,000-square-foot fulfillment center in
Kentucky to handle its own distribution and logistics as well as
two bricks-and-mortar stores in Los Angeles and Santa Monica.
Even in the hyper-trendy fashion business, companies have to
closely monitor production, distribution, and expenses for
operations to move products at a scale big enough to make a
profit. Nasty Gal’s mostly young staff focused too much on the
creative side of the business.
While it was growing, Nasty Gal built its management team,
hiring sizzling junior talent from retail outlets such as Urban
Outfitters. But their traditional retail backgrounds clashed with
the start-up mentality. As Nasty Gal expanded, Amoruso’s own
fame also grew, and she was sidetracked by other projects. She
wrote two books. The first, titled #Girlboss, described the
founding of Nasty Gal and Amoruso’s business philosophy and
was adapted by Netflix into a show with Amoruso as executive
producer. (The series was cancelled in June 2017 after just one

season.) Employees complained about Amoruso’s management
style and lack of focus.
Amoruso resigned as chief executive in 2015 but remained on
Nasty Gal’s board of directors until the company filed for
Chapter 11 bankruptcy on November 9, 2016. Between 2015 and
2016, Nasty Gal had raised an additional $24 million in equity
and debt financing from venture-focused Stamos Capital
Partners LP and Hercules Technology Growth Capital Inc. Even
though the funding helped Nasty Gal stay afloat, the company
still had trouble paying for new inventory, rent, and other
operating expenses.
Within weeks of filing for Chapter 11 protection, Nasty Gal
sold its brand name and other intellectual property on February
28, 2017, for $20 million to a rival online fashion site, the
United Kingdom’s Boohoo.com. Boohoo is operating Nasty Gal
as a standalone website, but Nasty Gal’s stores are closing.
Boohoo believes Nasty Gal’s arresting style and loyal customer
base will complement Boohoo and expand global opportunities
for growth. Many customers have complained about the quality
of fabric and customer service.
Amoruso subsequently turned to developing Girlboss—a media
company that hosts a website, a podcast, and two annual
conferences, called the Girlboss Rally. She also launched the
Girlboss Foundation, which has given out $130,000 to women-
owned small businesses.
Can Cars Drive Themselves—And Should They?
Will cars really be able to drive themselves without human
operators? Should they? And are they good business
investments? Everyone is searching for answers.

Autonomous vehicle technology has reached a point where no
automaker can ignore it. Every major auto maker is racing to
develop and perfect autonomous vehicles, believing that the
market for them could one day reach trillions of dollars.
Companies such as Ford, General Motors, Nissan, Mercedes,
Tesla, and others have invested billions in autonomous
technology research and development. Ford invested $1 billion
in AI firm Argo AI, and GM bought a self-driving car startup
called Cruise. Ford has set a goal of producing a self-driving
car with no pedals by 2021. Ride-hailing companies like Uber
and Lyft believe driverless cars that eliminate labor costs are
key to their long-term profitability. Cars that drive themselves
have been on the road in select locations in California, Arizona,
Michigan, Paris, London, Singapore, and Beijing. Waymo, the
company that emerged from Google’s self-driving car project,
predicts that by 2020 its fleet of self-driving Jaguars will make
as many as one million trips per day.
A car that is supposed to take over driving from a human
requires a very powerful computer system that must process and
analyze large amounts of data generated by myriad sensors,
cameras, and other devices to control and adjust steering,
accelerating, and braking in response to real-time conditions.
Key technologies include:
SENSORS: Self-driving cars are loaded with sensors of many
different types. Sensors on car wheels measure car velocity as it
drives and moves through traffic. Ultrasonic sensors measure
and track positions of line curbs, sidewalks, and objects very
close to the car.
CAMERAS: Cameras are needed for spotting things like lane
lines on the highway, speed signs, and traffic lights.
Windshield-mounted cameras create a 3-D image of the road
ahead. Cameras behind the rear-view mirror focus on lane
markings. Infrared cameras pick up infrared beams emitted from

headlamps to extend vision for night driving.
LIDARS: Lidars are light detection and ranging devices which
sit on top of most self-driving cars. A lidar fires out millions of
laser beams every second, measuring how long they take to
bounce back. The lidar takes in a 360-degree view of a car’s
surroundings, identifying nearby objects with an accuracy up to
2 centimeters. Lidars are very expensive and not yet robust
enough for a life of potholes, extreme temperatures, rain, or
snow.
GPS: A global positioning system (GPS) pinpoints the car’s
macro location, and is accurate to within 1.9 meters. Combined
with reading from tachometers, gyroscopes, and altimeters, it
provides initial positioning.
RADAR: Radar bounces radio waves off of objects to help see
a car’s surroundings, including blind spots, and is especially
helpful for spotting big metallic objects, such as other vehicles.
COMPUTER: All the data generated by these technologies
needs to be combined, analyzed, and turned into a robot-
friendly picture of the world, with instructions on how to move
through it, requiring almost supercomputer-like processing
power. Its software features obstacle avoidance algorithms,
predictive modeling, and “smart” object discrimination (for
example, knowing the difference between a bicycle and a
motorcycle) to help the vehicle follow traffic rules and navigate
obstacles.
MACHINE LEARNING, DEEP LEARNING, AND COMPUTER
VISION TECHNOLOGY: The car’s computer system has to be
“trained” using machine intelligence and deep learning to do
things like detect lane lines and identify cyclists, by showing it
millions of examples of the subject at hand. Because the world
is too complex to write a rule for every possible scenario, cars

must be able to “learn” from experience and figure out how to
navigate on their own.
MAPS: Before an autonomous car takes to the streets, its
developers use cameras and lidars to map its territory in
extreme detail. That information helps the car verify its sensor
readings, and it is key for any vehicle to know its own location.
Self-driving car companies are notorious for overhyping their
progress. Should we believe them? At this point, the outlook for
them is clouded.
In March 2018, a self-driving Uber Volvo XC90 operating in
autonomous mode struck and killed a woman in Tempe,
Arizona. Since the crash, Arizona has suspended autonomous
vehicle testing in the state, and Uber is not renewing its permit
to test self-driving cars in California. The company has also
stopped testing autonomous cars in Pittsburgh and Toronto and
it’s unclear when it will be revived. Even before the accident,
Uber’s self-driving cars were having trouble driving through
construction zones and next to tall vehicles like big truck rigs.
Uber’s drivers had to intervene far more frequently than drivers
in other autonomous car projects.
The Uber accident raised questions about whether autonomous
vehicles were even ready to be tested on public roads and how
regulators should deal with this. Autonomous vehicle
technology’s defenders pointed out that nearly 40,000 people
die on U.S. roads every year, and human error causes more than
90 percent of crashes. But no matter how quickly self-driving
proliferates, it will be a very long time before the robots can put
a serious dent in those numbers and convince everyday folks
that they’re better off letting the cars do the driving.
While proponents of self-driving cars like Tesla’s Elon Musk
envision a self-driving world where almost all traffic accidents

would be eliminated, and the elderly and disabled could travel
freely, most Americans think otherwise. A Pew Research Center
survey found that most people did not want to ride in self-
driving cars and were unsure if they would make roads more
dangerous or safer. Eighty-seven percent wanted a person
always behind the wheel, ready to take over if something went
wrong.
There’s still plenty that needs to be improved before self-
driving vehicles could safely take to the road. Autonomous
vehicles are not yet able to operate safely in all weather
conditions. Heavy rain or snow can confuse current car radar
and lidar systems—autonomous vehicles can’t operate on their
own in such weather conditions. These vehicles also have
trouble when tree branches hang too low or bridges and roads
have faint lane markings. On some roads, self-driving vehicles
will have to make guidance decisions without the benefit of
white lines or clear demarcations at the edge of the road,
including Botts’ Dots (small plastic markers that define lanes).
Botts’ Dots are not believed to be effective lane-marking for
autonomous vehicles.
Computer vision systems are able to reliably recognize objects.
What remains challenging is “scene understanding”—for
example, the ability to determine whether a bag on the road is
empty or is hiding bricks or heavy objects inside. Although
autonomous vehicle vision systems are now capable of picking
out traffic lights reliably, they are not always able to make
correct decisions if traffic lights are not working. This requires
experience, intuition, and knowing how to cooperate among
multiple vehicles. Autonomous vehicles must also be able to
recognize a person moving alongside a road, determine whether
that person is riding a bicycle, and how that person is likely to
respond and behave. All of that is still difficult for an
autonomous vehicle to do right now. Chaotic environments such
as congested streets teeming with cars, pedestrians, and cyclists

are especially difficult for self-driving cars to navigate.
Driving a car to merge into rapidly flowing lanes of traffic is an
intricate task that often requires eye contact with oncoming
drivers. How can autonomous vehicles communicate with
humans and other machines to let them know what they want to
do? Researchers are investigating whether electronic signs and
car-to-car communication systems would solve this problem.
There’s also what’s called the “trolley problem”: In a situation
where a crash is unavoidable, how does a robot car decide
whom or what to hit? Should it hit the car coming up on its left
or a tree on the side of the road?
A less advanced version of autonomous vehicle technology is
already on the market. Cadillac Super Cruise, Nissan ProPilot
Assist, and Tesla Autopilot are capable of keeping a car in its
lane and a safe distance from other cars, allowing the “driver”
behind the wheel to take hands off the wheel, provided that
person keeps paying attention and is ready to take control if
needed. These less-advanced systems can’t see things like
stopped fire trucks or traffic lights. But humans haven’t made
good driving backups because their attention tends to wander.
At least two Tesla drivers in the U.S. have died using the
system. (One hit a truck in 2016, another hit a highway barrier
in 2018.) There is what is called a “handoff problem.” A semi-
autonomous car needs to be able to determine what its human
“driver” is doing and how to get that person to take the wheel
when needed.
And let’s not forget security. A self-driving car is essentially a
collection of networked computers and sensors linked wirelessly
to the outside world, and it is no more secure than other
networked systems. Keeping systems safe from intruders who
want to crash or weaponize cars may prove to be the greatest
challenge confronting autonomous vehicles in the future.

Self-driving cars require new ecosystems to support them, much
as today’s cars are dependent on garages, gasoline stations, and
highway systems. New roads, highways, and automotive supply
chains will have to be rebuilt for self-driving cars. The big auto
makers that build millions of cars a year rely on complex,
precise interaction among hundreds of companies, including
automotive component suppliers and the services to keep cars
running. They need dealers to sell the cars, gas pumps or
charging stations to fuel them, body shops to fix them, and
parking lots to store them. Manufacturers of autonomous
vehicles need to rethink interactions and processes built up over
a century. The highway infrastructure will need to change over
time to support autonomous vehicles. Waymo has partnered
with Avis to take care of its fleet of driverless minivans in
Arizona, and it’s working with a startup called Trov to insure
their passengers. GM is retooling one of its plants to produce
Chevrolet Bolts without steering wheels or pedals.
A computer-driven car that can handle any situation as well as a
human under all conditions is decades away at best. Many
analysts expect the first deployment of self-driving technology
will be robot taxi services operating in limited conditions and
areas, so their operators can avoid particularly tricky
intersections and make sure everything is mapped in fine detail.
The Boston Consulting Group predicts that 25 percent of all
miles driven in the U.S. by 2030 may be by shared self-driving
vehicles. To take a ride, you’d probably have to use
predetermined pickup and drop-off points, so your car can
always pull over safely and legally. The makers of self-driving
cars will be figuring out how much to charge so they can recoup
their research and development costs, but not so much as to
dissuade potential riders. They’ll struggle with regulators and
insurance companies over what to do in the inevitable event of a
crash.
Some pundits predict that in the next few decades, driverless

technology will add $7 trillion to the global economy and save
hundreds of thousands of lives. At the same time, it could
devastate the auto industry along with gas stations, taxi drivers,
and truckers. People might stop buying cars because services
like Uber using self-driving cars would be cheaper. This could
cause mass unemployment of taxi drivers and large reductions
in auto sales. It would also cut down the need for many parking
garages and parking spaces, freeing up valuable real estate for
other purposes. More people might decide to live further from
their workplaces because autonomous vehicles linked to traffic
systems would make traffic flow more smoothly and free riders
to work, nap, or watch video while commuting. Some people
will prosper. Most will probably benefit, but many will be left
behind. Driverless technology is estimated to change one in
every nine U.S. jobs, although it will also create new jobs.
Another consideration is that the tremendous investment in
autonomous vehicles, estimated to be around $32 billion
annually, might be better spent on improving public
transportation systems like trains and subways. Does America
need more cars in sprawling urban areas where highways are
already jammed?
The accidents self-driving cars have experienced so far point to
the need to create a dependable standard for measuring
reliability and safety. In 2018, twenty-nine states have enacted
legislation regulating autonomous vehicles, with a few states
requiring a …

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