High streets and shopping malls face a ‘domino effect’ from major store closures

Another one bites the dust: House of Fraser plans to close the majority of its stores. Image: Getty.

Traditional retail is in the centre of a storm – and British department store chain House of Fraser is the latest to succumb to the tempest. The company plans to close 31 of its 59 shops – including its flagship store in Oxford Street, London – by the beginning of 2019. The closures come as part of a company voluntary arrangement, which is an insolvency deal designed to keep the chain running while it renegotiates terms with landlords. The deal will be voted on by creditors within the month.

Meanwhile in the US, the world’s largest retail market, Sears has just announced that it will be closing more than 70 of its stores in the near future.

This trend of major retailers closing multiple outlets exists in several Western countries – and its magnitude seems to be unrelated to the fundamentals of the economy. The US, for example, has recently experienced a clear decoupling of store closures from overall economic growth. While the US economy grew a healthy 2.3 per cent in 2017, the year ended with a record number of store closings, nearly 9,000 while 50 major chains filed for bankruptcy.

Most analysts and industry experts agree that this is largely due to the growth of e-commerce – and this is not expected to diminish anytime soon. A further 12,000 stores are expected to close in the US before the end of 2018. Similar trends are being seen in markets such as the UK and Canada.

Pushing down profits

Perhaps the most obvious impact of store closures is on the revenues and profitability of established brick-and-mortar retailers, with bankruptcies in the US up by nearly a third in 2017. The cost to investors in the retail sector has been severe – stocks of firms such as Sears have lost upwards of 90 per cent of their market value in the last ten years. By contrast, Amazon’s stock price is up over 2,000 per cent in the same period – more than 49,000 per cent when considering the last 20 years. This is a trend that the market does not expect to change, as the ratio of price to earnings for Amazon stands at ten times that of the best brick-and-mortar retailers.

Although unemployment levels reached a 17-year low in 2017, the retail sector in the US shed a net 66,500 jobs. Landlords are losing longstanding tenants. The expectation is that roughly 25 per cent of shopping malls in the US are at high risk of closing one of their anchor tenants such as a Macy’s, which could set off a series of store closures and challenge the very viability of the mall. One out of every five malls is expected to close by 2022 – a prospect which has put downward pressure on retail real estate prices and on the finances of the firms that own and manage these venues.

In the UK, high streets are struggling through similar issues. And given that high streets have historically been the heart of any UK town or city, there appears to be a fundamental need for businesses and local councils to adapt to the radical changes affecting the retail sector to preserve their high streets’ vitality and financial viability.


The costs to society

While attention is focused on the direct impacts on company finances, employment and landlord rents, store closures can set off a “domino effect” on local governments and businesses, which come at a significant cost to society. For instance, closures can have a knock-on effect for nearby businesses – when large stores close, the foot traffic to neighbouring establishments is also reduced, which endangers the viability of other local businesses. For instance, Starbucks has recently announced plans to close all its 379 Teavana stores. Primarily located inside shopping malls, they have harshly suffered from declining mall traffic in recent years.

Store closures can also spell trouble for local authorities. When retailers and neighbouring businesses close, they reduce the taxable revenue base that many municipalities depend on in order to fund local services. Add to this the reduction in property taxes stemming from bankrupt landlords and the effect on municipal funding can be substantial. Unfortunately, until e-commerce tax laws are adapted, municipalities will continue to face financial challenges as more and more stores close.

It’s not just local councils, but local development which suffers when stores close. For decades, many cities in the US and the UK, for exmaple Detroit and Liverpool, have heavily invested in efforts to rejuvenate their urban cores after years of decay in the 1970s and 1980s. Bringing shops, bars and other businesses back to once derelict areas has been key to this redevelopment. But today, with businesses closing, cities could once again face the prospect of seeing their efforts unravel as their key urban areas become less attractive and populations move elsewhere.

Commercial ecosystems featuring everything from large chain stores to small independent businesses are fragile and sensitive to change. When a store closes it doesn’t just affect employees or shareholders – it can have widespread and lasting impacts on the local community, and beyond. Controlling this “domino effect” is going to be a major challenge for local governments and businesses for years to come.

Omar Toulan, Professor in Strategy and International Management, IMD Business School and Niccolò Pisani, Assistant Professor of International Management, University of Amsterdam.

This article was originally published on The Conversation. Read the original article.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.