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Keep It Simple And Innovate: Digital Transformation Challenges Discussed

Chris Finnamore

At SAP Hybris LIVE: Digital Summit, a panel of digital transformation veterans shared their experiences of implementing commerce solutions. Coming from industries as diverse as diesel engines, online groceries, and nutritional supplements, the five companies drew on their experience to provide some valuable insights.

The first hurdle was winning over the cynics. For Philip Murphy, head of digital center of excellence at Glanbia, a particular challenge was getting the rest of the company to buy into what could be major changes to the business. “It takes a bit to get people on board due to the high cost of investment, the big timelines, and [the fact that] a lot of senior stakeholders… don’t understand the complexity of what you’re doing. You’ve got to show them a vision of what the future could look like for the organization.” You also need to show what the commercial benefits of transformation will be – Glanbia’s CEO is a former finance director and wants to see a return on investment, after all.

Helle Pedersen Georgakis, senior project manager at MAN Diesel & Turbo, made the point that it’s important to not to sell the product, but the benefits, “What’s in it for me?” Her team also made great efforts to involve end users in the deployment of the new solution. “They were in it from the beginning,” said Georgakis, “and could see and help IT with what they should focus on. It was not an IT tool that was rolled out – it was a business tool.”

Panelists also shared their stories about what went well, and what didn’t, when they finally went live with their new commerce systems. For Erik Lindqvist, solution architect and project manager for e-business at Alfa Laval, the go-live date went without a hitch – it was the 18 months leading up to it that were hard. In fact, the journey leading up to deployment led to the final product being overly complex.

According to Lindqvist, “I think we may have listened too hard to all the nitty-gritty business demands that came to us from different directions. It led to that we customized the solution quite heavily and we still suffer from that… we should have started more simply, with fewer features.”

Of course, as Frank Niemann, vice president, software, at Pierre Audoin Consultants, said, “The notion ‘go live’ is a term from the past.” Digital transformation is about continuous innovation but, as Ulf Bonfert showed, it’s not easy to innovate while staying on top of your business.

The panelists were willing to share their experiences with this balancing act. Eberhardt Weber, founder and CEO of SAAS AG and Lieferladen.de, has a particular advantage. He runs a company that sells groceries online, as well as the software other groceries need to become an online business.

This puts his company in a position where it can try out new solutions on its own supermarket, then feed those that work back into the software side of the business. Weber is aware that this gives his company an unusual advantage: “I know it’s not so easy if you run a big enterprise, you cannot just try stuff out, but if you have the opportunity I would recommend to everybody… just do it.”

And the parting advice from the panelists to those companies about to undertake a digital transformation? “Start small, think big.” “No matter how well prepared you are, it’s never enough.” “Try things out! Make mistakes.” “It’s not going to be easy. Be resilient when things go wrong.” And finally, “There are more opportunities than threats.”

Leverage your Data – The Hidden Treasure Inside Your Business.

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How Digital Disruptors Are Changing The Automotive Industry

Branwell Moffat

It could be argued that the automotive is currently facing the biggest period of change since Henry Ford created the production line. All major automotive companies, as well as many technology companies, are piling investments into autonomous driving.

While fully driverless cars are still a little way off (2020 seems to be the year when we will start to see them become mainstream), some major digital disruptors are changing the way we buy new cars right now.

For many years, dealerships have mainly been franchises affiliated with one or two brands, located out of town, often on an industrial estate filled with many other car dealerships. As a prospective car buyer, we would visit these dealerships, maybe more than once, book a test drive with a salesperson, spend a lot of time discussing options, haggle, organize finances, negotiate on a trade-in value, and finally, make a purchase.

This is a time-consuming and often daunting process, especially since we are generally restricted to a specific geographical location. Like many of us, you may have stood in a dealership on a Saturday morning waiting for a dealer to become free and give you some attention. You are likely to have your eye on only one or two brands and will visit those specific dealerships rather than browsing through all.

Two particular companies are changing this model drastically and have the potential to completely change the way we buy new cars.

Carwow

Carwow is based on a simple but very effective idea: As a prospective new car buyer, you visit the Carwow website and input details of your desired car. The website then emails your requirements to dealers that have registered with them. The dealers respond with an offer of a price for that car, while the site displays only the first five offers so that shoppers aren’t overwhelmed with options.

Through the site customers can communicate with the dealers, view their ratings and reviews, and even negotiate with the dealer further to get a lower price. The offers cover all purchasing options, including cash and finance, and most of the dealers deliver your car to you for free.

The very simple Carwow concept offers a radical way to purchase a new car. Why be restricted by geographical location when most of the dealers will deliver for free? Does it really matter where your car is coming from? Why play the negotiating game with one or two dealers when you can let a large number of dealers compete with one another for your business? Ultimately, you are likely to pay less for a new car when buying through Carwow than if you buy from a dealer in person, and it can involve a lot less hassle.

This model has the potential to completely change the way a dealership operates. You may be surprised to hear that a dealer’s typical gross profit on a new car sale is around 10%. When you then factor in the high overheads of having a physical dealership, the net profit is reduced to around 1-2%. You can now imagine a scenario where a dealer may be entirely digital and not actually have a physical presence. This would allow them to significantly lower their overheads and be even more competitive on price.

As you can imagine, this has not gone down all that well with some automotive brands, as it can eat into margins. In 2016 Carwow reported BMW to the Competition Markets Authority (CMA), claiming that BMW was preventing dealers from selling through Carwow. After an investigation by the CMA, BMW made a u-turn and allowed their dealers to participate.

Today’s consumers are much more tech-savvy and are likely to have significantly researched their car purchase online before visiting any dealer. In recent years, most car manufacturers have simplified their offerings where most options are bundled into packages rather than requiring the purchaser to choose from a long list of extras. Car brands have been investing in their websites, creating slick user experiences and usable configuration tools.

All of this has meant that the consumer’s reliance on face-to-face contact with a physical dealer has become less and less, which makes a service like Carwow more viable than ever before. We are all used to self-service on the web, whether it is managing our own finances to booking a flight or a holiday. Why can’t it be the same for a car?

Of course, you will still want to test-drive the car. I doubt many of us would buy a new car without test-driving it, and this is where we are constrained by a geographical location and are likely to go to a local dealer. However, if you use Carwow, you are less likely to actually buy it from them. Maybe in the future, dealerships will primarily serve as test-drive and service centers rather than focusing on actually selling new cars. After all, current dealers make most of their money on extras, financing, and servicing.

It will be interesting to watch what impact Carwow and any other imitators will have on the automotive industry in the next few years.

Rockar

Rockar is an omnichannel car dealer based online and currently within stores in two shopping centers in the UK: Bluewater and Westfield Stratford City. Yes, you read that correctly: shopping centers. They originally partnered with Hyundai, but recently partnered with Jaguar Land Rover, with the new Rockar Jaguar store opening in October 2016. Rockar aims to completely change the way we shop for and purchase new cars. Rockar was set up by Simon Dixon, a veteran of the car industry, a few years ago. Rockar’s aim is to build a business model that is entirely focused on the customer buying experience, rather than the traditional car sales model and brings the buying experience into the digital age.

The Hyundai Rockar website is more akin to a car hire website or traditional e-commerce site than a traditional car dealer’s site. As well as finding the right car, users can book a test drive or book their service through the website. Buyers can buy outright or arrange finance all online in simple and easy steps.

Since Rockar was created as an omnichannel business right from the start, the website experience is carried through into the physical stores. In a Rockar store, you will find a small number of models and plenty of touch-screen kiosks, which effectively run a version of the website. They have an office base in the shopping centre car park, where you can pick up your car for a test drive. The store does not have the usual salespeople you would expect to see in a traditional car dealership. Instead, they have people they call Rockar Angels. The aim of these staff is not to sell or make a deal, but to advise the customers on the cars.

This model works especially well for a brand like Hyundai. It is fair to say that South Korean brands such as Hyundai or Kia have not had the best reputation for quality or luxury in the past. Over the last few years, both of these brands have significantly improved their quality, especially inside the cabin, to the extent that they are certainly rivaling Japanese brands, and even knocking on the doors of German brands.

However, the perception of lower quality persists, and many of us might not consider buying from one of these brands. Therefore, in the traditional model, we would not be likely to visit one of their dealerships. This is where Rockar changes things. By placing an inviting looking showroom in a shopping center like Bluewater or Westfield, they draw in customers who would not otherwise visit an out-of-town Hyundai dealership.

Once this customer is drawn in, they realize that these cars are actually really quite nice. The quality and finish is nothing like they imagined. The staff are very inclusive and welcoming, and the buying experience is pleasant. This is known as a conquest sale, and it is making other car brands very nervous. They are losing sales to brands like Hyundai, and I suspect we will soon see more automotive brands in shopping centers very soon.

The future

Over the next few years, I expect to see other major disruptors enter the UK automotive market. Once fully autonomous cars become mainstream, we are likely to see a major shift in many consumer’s perception of cars. Imagine being able to hail an autonomous car whenever you want to take you wherever you want to go. Why would you own a car yourself? Would you really care about the brand of car?

Maybe we will see Google, Samsung, or Apple begin to dominate the market. During your journey, your car would be gathering data on traffic conditions, air quality, and weather. This data could be be valuable and be sold to subsidize the cost of your journey. You could even be sold premium services on your journey such as movies, news, food, or drinks, which will further subsidize the journey to the extent that it is actually free. The decade ahead is going to be a very interesting and exciting one for the automotive industry.

For more on technology and the automotive industry, see How Social Media Has Changed The Automobile Industry.

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In The Future, All Products Will Automatically Improve As People Use Them

Timo Elliott

This is the Tesla autopilot in action – and it’s a great analogy for the future of digital business.

As every Tesla car equipped with autopilot drives down the street, it’s using sensors and the Internet of Things to gather vast amounts of Big Data. And then it uses analytics, machine learning, and artificial intelligence to turn that data into a superior driving experience.

That’s what digital business is all about – using the latest technologies to create better products and services. But Tesla is taking things to a whole new level. The data from every car is sent to headquarters and shared with every other car on the road. So your car knows what to look out for even if you’ve never been on that street before.

This means that Tesla has essentially turned itself into a massively parallel learning machine. The Tesla customer experience now improves automatically the more you drive and the more other people use the product.

In addition, the company is gathering detailed information that can be used for many of other business opportunities in the future. And that’s perhaps why Tesla is now the most valuable U.S. car company, eclipsing General Motors, even though GM makes over 100 times as many cars.

These types of self-improving products are now starting to take over the world. For example, AlphaGo’s algorithms shocked the experts last year by beating one of the world’s strongest Go champions. And it’s been steadily improving ever since – to the point where one Chinese Go master says it now “plays like a God.”

So imagine — what if your products and services automatically improved as more people used them?

New customer chatbots are a simple example of self-improving interfaces. Modeled on consumer services such as Siri and Alexa, these chatbots are poised to make all interactions with computers easier, from purchasing items online to working with internal business applications. You can simply ask things like “what colors are available?” or “what are the details of this order?” and the chatbot will respond. And because these services leverage machine learning, the quality of the responses will automatically improve over time and as more people use them.

And that’s just the start. Machine learning can now be embedded into every customer experience and operational process. For example, chemicals giant BASF was able to use machine learning on repetitive decisions in the finance function, improving invoice matching from 70% to 94% – and that score should rise as the algorithms master the remaining variations.

But there’s one key prerequisite for this vision: good data

Artificial intelligence works best when you have large amounts of high-quality training data, applied to a specific, clearly defined business problem. So the first step to introducing self-improving products and services to your customers is a single, consistent, governed view of all necessary information, no matter where it’s stored, inside or outside the organization.

The self-fulfilling cycle of data, AI, and better product experiences

More data means better artificial intelligence algorithms, which means better customer experiences, which means better customers, which means more data… The winners in digital business will be the ones who first unleash this virtuous circle of self-improving AI and “self-driving business.”

Autopilot Full Self-Driving Hardware (Neighborhood Short) from Tesla, Inc on Vimeo.

Develop a machine learning strategy that will change the basis of competition in your industry. Learn Why Machine Learning and Why Now?

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About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in articles such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

Running Future Cities on Blockchain

Dan Wellers , Raimund Gross and Ulrich Scholl

Building on the Blockchain Framework

Some experts say these seemingly far-future speculations about the possibilities of combining technologies using blockchain are actually both inevitable and imminent:


Democratizing design and manufacturing by enabling individuals and small businesses to buy, sell, share, and digitally remix products affordably while protecting intellectual property rights.
Decentralizing warehousing and logistics by combining autonomous vehicles, 3D printers, and smart contracts to optimize delivery of products and materials, and even to create them on site as needed.
Distributing commerce by mixing virtual reality, 3D scanning and printing, self-driving vehicles, and artificial intelligence into immersive, personalized, on-demand shopping experiences that still protect buyers’ personal and proprietary data.

The City of the Future

Imagine that every agency, building, office, residence, and piece of infrastructure has an entry on a blockchain used as a city’s digital ledger. This “digital twin” could transform the delivery of city services.

For example:

  • Property owners could easily monetize assets by renting rooms, selling solar power back to the grid, and more.
  • Utilities could use customer data and AIs to make energy-saving recommendations, and smart contracts to automatically adjust power usage for greater efficiency.
  • Embedded sensors could sense problems (like a water main break) and alert an AI to send a technician with the right parts, tools, and training.
  • Autonomous vehicles could route themselves to open parking spaces or charging stations, and pay for services safely and automatically.
  • Cities could improve traffic monitoring and routing, saving commuters’ time and fuel while increasing productivity.

Every interaction would be transparent and verifiable, providing more data to analyze for future improvements.


Welcome to the Next Industrial Revolution

When exponential technologies intersect and combine, transformation happens on a massive scale. It’s time to start thinking through outcomes in a disciplined, proactive way to prepare for a future we’re only just beginning to imagine.

Download the executive brief Running Future Cities on Blockchain.


Read the full article Pulling Cities Into The Future With Blockchain

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About Dan Wellers

Dan Wellers is founder and leader of Digital Futures at SAP, a strategic insights and thought leadership discipline that explores how digital technologies drive exponential change in business and society.

Raimund Gross

About Raimund Gross

Raimund Gross is a solution architect and futurist at SAP Innovation Center Network, where he evaluates emerging technologies and trends to address the challenges of businesses arising from digitization. He is currently evaluating the impact of blockchain for SAP and our enterprise customers.

Ulrich Scholl

About Ulrich Scholl

Ulrich Scholl is Vice President of Industry Cloud and Custom Development at SAP. In this role, Ulrich discovers and implements best practices to help further the understanding and adoption of the SAP portfolio of industry cloud innovations.

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Are AI And Machine Learning Killing Analytics As We Know It?

Joerg Koesters

According to IDC, artificial intelligence (AI) is expected to become pervasive across customer journeys, supply networks, merchandizing, and marketing and commerce because it provides better insights to optimize retail execution. For example, in the next two years:

  • 40% of digital transformation initiatives will be supported by cognitive computing and AI capabilities to provide critical, on-time insights for new operating and monetization models.
  • 30% of major retailers will adopt a retail omnichannel commerce platform that integrates a data analytics layer that centrally orchestrates omnichannel capabilities.

One thing is clear: new analytic technologies are expected to radically change analytics – and retail – as we know them.

AI and machine learning defined in the context of retail

AI is defined broadly as the ability of computers to mimic human thinking and logic. Machine learning is a subset of AI that focuses on how computers can learn from data without being programmed through the use of algorithms that adapt to change; in other words, they can “learn” continuously in response to new data. We’re seeing these breakthroughs now because of massive improvements in hardware (for example, GPUs and multicore processing) that can handle Big Data volumes and run deep learning algorithms needed to analyze and learn from the data.

Ivano Ortis, vice president at IDC, recently shared with me how he believes, “Artificial intelligence will take analytics to the next level and will be the foundation for retail innovation, as reported by one out of every two retailers globally. AI enables scale, automation, and unprecedented precision and will drive customer experience innovation when applied to both hyper micro customer segmentation and contextual interaction.”

Given the capabilities of AI and machine learning, it’s easy to see how they can be powerful tools for retailers. Now computers can read and listen to data, understand and learn from it, and instantly and accurately recommend the next best action without having to be explicitly programmed. This is a boon for retailers seeking to accurately predict demand, anticipate customer behavior, and optimize and personalize customer experiences.

For example, it can be used to automate:

  • Personalized product recommendations based on data about each customer’s unique interests and buying propensity
  • The selection of additional upsell and cross-sell options that drive greater customer value
  • Chat bots that can drive intelligent and meaningful engagement with customers
  • Recommendations on additional services and offerings based on past and current buying data and customer data
  • Planogram analyses, which support in-store merchandizing by telling people what’s missing, comparing sales to shelf space, and accelerating shelf replenishment by automating reorders
  • Pricing engines used to make tailored, situational pricing decisions

Particularly in the United States, retailers are already able to collect large volumes of transaction-based and behavioral data from their customers. And as data volumes grow and processing power improves, machine learning becomes increasingly applicable in a wider range of retail areas to further optimize business processes and drive more impactful personalized and contextual consumer experiences and products.

The transformation of retail has already begun

The impacts of AI and machine learning are already being felt. For example:

  • Retailers are predicting demand with machine learning in combination with IoT technologies to optimize store businesses and relieve workforces
  • Advertisements are being personalized based on in-store camera detections and taking over semi-manual clienteling tasks of store employees
  • Retailers can monitor wait times in checkout lines to understand store traffic and merchandising effectiveness at the individual store level – and then tailor assortments and store layouts to maximize basket size, satisfaction, and sell through
  • Systems can now recognize and predict customer behavior and improve employee productivity by turning scheduled tasks into on-demand activities
  • Camera systems can detect the “fresh” status of perishable products before onsite employees can
  • Brick-and-mortar stores are automating operational tasks, such as setting shelf pricing, determining product assortments and mixes, and optimizing trade promotions
  • In-store apps can tell how long a customer has been in a certain aisle and deliver targeted offers and recommendations (via his or her mobile device) based on data about data about personal consumption histories and preferences

A recent McKinsey study provided examples that quantify the potential value of these technologies in transforming how retailers operate and compete. For example:

  • U.S. retailer supply chain operations that have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Using data and analytics to improve merchandising, including pricing, assortment, and placement optimization, is leading to an additional 16% in operating margin improvement.
  • Personalizing advertising is one of the strongest use cases for machine learning today. Additional retail use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics, as well as optimizing merchandising strategies.

Exploiting the full value of data

Thin margins (especially in the grocery sector) and pressure from industry-leading early adopters such as Amazon and Walmart have created strong incentives to put customer data to work to improve everything from cross-selling additional products to reducing costs throughout the entire value chain. But McKinsey has assessed that the U.S. retail sector has realized only 30-40% of the potential margin improvements and productivity growth its analysts envisioned in 2011 – and a large share of the value of this growth has gone to consumers through lower prices. So thus far, only a fraction of the potential value from AI and machine learning has been realized.

According to Forbes, U.S. retailers have the potential to see a 60%+ increase in net margin and 0.5–1.0% annual productivity growth. But there are major barriers to realizing this value, including lack of analytical talent and siloed data within companies.

This is where machine learning and analytics kick in. AI and machine learning can help scale the repetitive analytics tasks required to drive leverage of the available data. When deployed on a companywide, real-time analytics platform, they can become the single source of truth that all enterprise functions rely on to make better decisions.

How will this change analytics?

So how will AI and machine learning change retail analytics? We expect that AI and machine learning will not kill analytics as we know it, but rather give it a new and even more impactful role in driving the future of retail. For example, we anticipate that:

  • Retailers will include machine learning algorithms as an additional factor in analyzing and  monitoring business outcomes in relation to machine learning algorithms
  • They will use AI and machine learning to sharpen analytic algorithms, detect more early warning signals, anticipate trends, and have accurate answers before competitors do
  • Analytics will happen in real time and act as the glue between all areas of the business
  • Analytics will increasingly focus on analyzing manufacturing machine behavior, not just business and consumer behavior

Ivano Ortis at IDC authored a recent report, “Why Retail Analytics are a Foundation for Retail Profits,” in which he provides further insights on this topic. He notes how retail leaders will use new kinds of analytics to drive greater profitability, further differentiate the customer experience, and compete more effectively, “In conclusion, commerce and technology will converge, enabling retailers to achieve short-term ROI objectives while discovering untapped demand. But implementing analytics will require coordination across key management roles and business processes up and down each retail organization. Early adopters are realizing demonstrably significant value from their initiatives – double-digit improvements in margins, same-store and e-commerce revenue, inventory positions and sell-through, and core marketing metrics. A huge opportunity awaits.”

So how do you see your retail business adopting advanced analytics like AI and machine learning? I encourage you to read IDC’s report in detail, as it provides valuable insights to help you invest in – and apply – new kinds of analytics that will be essential to profitable growth.

For more information, download IDC’s “Why Retail Analytics are a Foundation for Retail Profits.

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About Joerg Koesters

Joerg Koesters is the Head of Retail Marketing and Communication at SAP. He is a Technology Marketing executive with 20 years of experience in Marketing, Sales and Consulting, Joerg has deep knowledge in retail and consumer products having worked both in the industry and in the technology sector.