How Artificial Intelligence Will Transform Tomorrow’s Digital Supply Chain

Alina Gross

Artificial intelligence (AI) may sound futuristic, but it’s a real-life breakthrough that exists in the present. Anyone who interacts with an online search engine, shops on Amazon, owns a self-parking car, or talks to voice-powered personal assistants like Siri or Alexa is using AI.

AI is a field of computer science in which a machine is equipped with the ability to mimic the cognitive functions of a human. An AI machine can make decisions or predictions based on its past experiences, or it can respond to entirely new scenarios. When given a goal, not only does it attempt to achieve its objective, it continuously tries to improve upon its past performance.

Revolutionizing the digital supply chain

Within five years, 50% of manufacturing supply chains will be robotically and digitally controlled and able to provide direct-to-consumer and home shipments, according to IDC Manufacturing Insights. Additionally, 47% of supply chain leaders believe AI is disruptive and important with respect to supply chain strategies, per a 2016 SCM World survey. With that in mind, 85% of organizations have already adopted or will adopt AI technology into their supply chains within one year, according to a 2016 Accenture report.

Supply chains need AI to aggregate their mass amounts of data. In the supply chain, AI can analyze large data sets and recommend customer service and operations improvements while supporting better working capital management. As corporate systems become more interconnected, providing access to a wider breadth of supply chain data, the opportunity to leverage AI increases.

Let’s look at the potential benefits of using AI to link transportation data with order data:

A logistics enterprise ensures the delivery of a product within two days. With AI, the carrier can view past performances from shipping a similar product on a specific day, using a particular route, which reveals there’s a 25% chance the order will arrive in four days, not two. This information supplies customer service and supply chain professionals with proactive alerts of potential fulfillment challenges.

To take this a step further, AI could also compare historical shipping data to the customer’s requested delivery date to provide recommendations on whether this particular carrier’s performance meets requirements, or if you need to consider a different logistics enterprise that is 15% more expensive, but 25% more likely to deliver the product on time.

Step by step to a more efficient supply chain with AI

There are many opportunities to use AI throughout the supply chain, from buying raw materials/components and converting them into finished products to selling and delivering items to customers. Supply chains can also use AI to end repetitive manual tasks and begin automating processes. This can enable companies to reallocate time and resources to their core business, and other high-value, judgment-based jobs, by using AI for low-value, high-frequency activities.

In an AI-driven selling platform, chatbots can manage many of the sales, customer service, and operations tasks traditionally handled by humans, including interacting with buyers, taking orders, and passing those orders through the supply chain. In warehouse operations, AI-capable robotics and sensors can enable organizations to enhance stacking and retrieval, order picking, stock-level management, and re-ordering processes.

Amazon is currently combining automation with human labor to increase productivity by using robots that can glide quickly across the floor to rearrange items on shelves into neatly organized rows, or alert human workers when they need to stack the shelves with new products or retrieve goods for packaging. And Logistics company DHL is using AI and automation to create self-sufficient forklifts that understand what products need to be moved, where they need to be moved, and when they need to be moved.

Supply chain companies see a path forward with AI

Leveraging AI is an important next step for supply chain companies looking to lower costs and improve productivity. It can enable your organization to spend less time on repetitive processes, such as planning, monitoring, and coordinating, and focus more on innovation and growth.

AI still needs careful monitoring, however, as well as experienced and knowledgeable logistics and operations professionals to ensure it’s being used to its maximum potential.

For more on how AI and advanced tech can help boost your business, see Next-Gen Technology Separates Digital Leaders From The Rest.

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Alina Gross

About Alina Gross

Alina Gross is currently pursuing her BA in international business at Heilbronn University. She plans on deepening her knowledge by adding an MA in international marketing. During her six-month, full-time internship at SAP, she has focused on marketing and project management topics within the field of supply chain, especially around event management and social media.

Underfit Vs. Overfit: Why Your Machine Learning Model May Be Wrong

Paul Kurchina

Just shy of 60 years old, machine learning has never looked so good. Exponential data growth, advanced algorithms, and powerful computer processing are enabling the technology to fulfill its ultimate destiny: identifying profitable opportunities and avoiding unknown risks by evaluating massive volumes of complex data and delivering accurate results in real time.

However, during the Americas’ SAP Users’ Group (ASUG) Webcast “Guide to the Machine Learning Galaxy: How Your ERP Knowledge Enables Value-Driven Intelligent Processes,” Darwin Deano, principal and chief SAP Leonardo officer, and Denise McGuigan, senior manager and Deloitte reimagine platform leader (both from Deloitte Consulting LLP), forewarned that machine learning is only as good as the algorithm. And the algorithm is only as good as the data.

Deano advised, “Data evolves over time. Even though ERP systems provide a strong foundation for identifying opportunities and delivering on the promise of machine learning, it does not factor in information outside the core structure, nor does it move with information as it changes.”

Adding to Deano’s observation, McGuigan noted the importance of understanding data well. “Businesses must know all of the variables and data sets that drive certain decisions. Doing so will reduce the risk of bringing information into the analysis that will only cause noise or false positives within machine learning results,” she said.

Machine learning success depends on finding the right data “fit”

Although it’s tempting to jump into machine learning by automating heavily used transactions, McGuigan warned that this view misses the cognitive advantages of machine learning. “Companies have a considerable opportunity to operate with tremendous efficiency and speed,” she said. “They should also consider enabling processes and tasks that free up resources, time, and talent for entering new markets; offering breakthrough products and services; and innovating industry-disruptive business models.”

To successfully execute such an advanced form of machine learning, organizations must ensure that the right data is being applied to the machine learning model. Understanding how each data category impacts the training data helps businesses fine-tune the model to increase prediction accuracy and efficient automation. However, as McGuigan suggested, one of the most common causes of underperforming or inaccurate models can be attributed to an imbalance of data used, commonly referred as biased invariance.

One form of disparity is experienced when the model underfits the training data when assumptions are oversimplified to the point where either the wrong information or too little insight is applied. This condition leads to the inability to capture the relationship between the programmed input examples and the targeted outcomes.

On the flip side, a model can overfit training data when too much information is used and there is too much complexity. Even though it performs well with training data, the model cannot accurately evaluate data to deliver the expected outcome. The model only memorizes data, instead of learning from it to generalize how unseen examples should be treated.

It’s also important to remember that this exercise is an iterative process of trial and error. The model may be calibrated well enough at one moment to deliver expected outcomes consistently and predictively; however, as Deano suggested, “what may be overfitting today may not be the same situation six months from now as data evolves.”

For more insights into putting machine intelligence to work for your organization, watch the replay of the Americas’ SAP Users’ Group (ASUG) Webcast “Guide to the Machine Learning Galaxy: How Your ERP Knowledge Enables Value-Driven Intelligent Processes,” featuring Darwin Deano, principal and chief SAP Leonardo officer for Deloitte Consulting LLP, and Denise McGuigan, senior manager and Deloitte reimagine platform leader for Lights Out Finance at Deloitte Consulting LLP

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Paul Kurchina

About Paul Kurchina

Paul Kurchina is a community builder and evangelist with the Americas’ SAP Users Group (ASUG), responsible for developing a change management program for ASUG members.

How To Cope With Decision Fatigue

Lauren Pytel

Decision fatigue refers to the deteriorating quality of decisions as one is forced to engage in more and more decision-making. If you’ve ever planned an event, redesigned a home or purchased a new car, you probably remember the exhaustion and indifference that eventually resulted from being faced with so many choices.

Many companies put managers at risk of decision fatigue by creating performance management, compensation, staffing and development processes that require evaluating multiple people on multiple factors in a short period of time. For example, annual talent or compensation reviews where managers are required to make significant and critical decisions about multiple employees in less than four weeks, while also performing their full-time job.

Decision fatigue can lead to impulsive and irrational decision-making that can impact the accuracy of critical decisions related to employee promotion, staffing and compensation. Research shows that when our brains are tired, we tend to use mental short-cuts or heuristics to make decisions, rather than engage in effortful thought about a choice’s pros and cons. While heuristics can save decision-makers time and energy, they can also prove highly problematic for talent management decisions for several reasons.

Overly simplified decision-making

Fatigued managers may choose to pay everyone the same amount because it’s quick and easy, rather than critically evaluate whether some employees warrant greater levels of investment given their past contributions and future potential. Heuristics can result in biased decision-making. For example, people exhibit the ‘familiarity heuristic’ or ‘similarity heuristic’ when their decision-making reflects a bias for familiarity over novelty. This suggests that employees who were promoted by a manager in the past could be favored over employees who were not, or that employees who are demographically similar to a manager might be favored over employees who are not. This bias could have disastrous consequences for underrepresented demographic groups.

It would be difficult to completely eliminate decision fatigue as a risk, but there are things your organization can do to mitigate its negative effects:

Conduct calibration talent reviews

Calibration sessions can reduce individual manager decision fatigue, but only if they are conducted the right way. One of the most compelling reasons to hold calibration sessions is that the conversations they promote provide managers with a deeper understanding of the unique skills and capabilities their employees have to offer. But this only works if you provide adequate time to discuss employees. Including too many employees or failing to dedicate enough time to talk about the employees in a session defeats their purpose. In fact, when designed ineffectively, calibration reviews can actually make decision fatigue more likely as people may rush through evaluating employees so they can end the meeting on time.

Two additional calibration session best practices can also help mitigate the effects of decision fatigue. First, include a diverse group of raters with a variety of viewpoints and perspectives. Research suggests that group members can act as a ‘check & balance’ system against individuals’ biases, but only if all group members do not exhibit the same bias. Intentionally including raters who are not like other members of the rater group can help ensure that biases are detected and resolved before they affect decision-making. Second, randomize the order of evaluation.

Having to evaluate a large number of employees in a small period of time may, in some cases, be unavoidable. In such cases, it is useful to randomize the order in which employees are evaluated. Research has shown that the order in which individuals are assessed can influence how they are assessed. Rather than using some arbitrary criteria like an employee’s last name or their manager to order assessment, create a standardized process for defining assessment order that can be used across the organization.

Encourage managers to engage in continuous feedback

Managers can simplify the number of critical decisions during annual reviews by engaging in continuous dialogue with employees about their performance, progress and expectations throughout the year. Addressing these topics in small chunks can help reduce the burden of decision-making in terms of both the number of decisions that must be made as well as their level of criticality.

Managers are faced with an enormous number of decisions each day. While some of these may be insignificant, others, such as deciding who is up for promotion or a bonus, have critical long-term consequences. It is amazing how often managers suffering from decision fatigue make these critical decisions in a way that emphasizes efficiency over quality. Ensuring that your organization’s talent review process and philosophy protects managers against experiencing decision fatigue is important, not only for your managers’ senses of well-being but for the fairness and accuracy of your talent management decisions.

For more insight on decision-making in HR, see Succession Management: Why Do We Still Fail To Get It Right?

This article originally appeared on Forbes SAPVoice.

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Lauren Pytel

About Lauren Pytel

Lauren Pytel is a human capital management researcher at SAP SuccessFactors (SAP).

Why Strategic Plans Need Multiple Futures

By Dan Wellers, Kai Goerlich, and Stephanie Overby , Kai Goerlich and Stephanie Overby

When members of Lowe’s Innovation Labs first began talking with the home improvement retailer’s senior executives about how disruptive technologies would affect the future, the presentations were well received but nothing stuck.

“We’d give a really great presentation and everyone would say, ‘Great job,’ but nothing would really happen,” says Amanda Manna, head of narratives and partnerships for the lab.

The team realized that it needed to ditch the PowerPoints and try something radical. The team’s leader, Kyle Nel, is a behavioral scientist by training. He knows people are wired to receive new information best through stories. Sharing far-future concepts through narrative, he surmised, could unlock hidden potential to drive meaningful change.

So Nel hired science fiction writers to pen the future in comic book format, with characters and a narrative arc revealed pane by pane.

The first storyline, written several years before Oculus Rift became a household name, told the tale of a couple envisioning their kitchen renovation using virtual reality headsets. The comic might have been fun and fanciful, but its intent was deadly serious. It was a vision of a future in which Lowe’s might solve one of its long-standing struggles: the approximately US$70 billion left on the table when people are unable to start a home improvement project because they can’t envision what it will look like.

When the lab presented leaders with the first comic, “it was like a light bulb went on,” says Manna. “Not only did they immediately understand the value of the concept, they were convinced that if we didn’t build it, someone else would.”

Today, Lowe’s customers in select stores can use the HoloRoom How To virtual reality tool to learn basic DIY skills in an interactive and immersive environment.

Other comics followed and were greeted with similar enthusiasm—and investment, where possible. One tells the story of robots that help customers navigate stores. That comic spawned the LoweBot, which roamed the aisles of several Lowe’s stores during a pilot program in California and is being evaluated to determine next steps.

And the comic about tools that can be 3D-printed in space? Last year, Lowe’s partnered with Made in Space, which specializes in making 3D printers that can operate in zero gravity, to install the first commercial 3D printer in the International Space Station, where it was used to make tools and parts for astronauts.

The comics are the result of sending writers out on an open-ended assignment, armed with trends, market research, and other input, to envision what home improvement planning might look like in the future or what the experience of shopping will be in 10 years. The writers come back with several potential story ideas in a given area and work collaboratively with lab team members to refine it over time.

The process of working with writers and business partners to develop the comics helps the future strategy team at Lowe’s, working under chief development officer Richard D. Maltsbarger, to inhabit that future. They can imagine how it might play out, what obstacles might surface, and what steps the company would need to take to bring that future to life.

Once the final vision hits the page, the lab team can clearly envision how to work backward to enable the innovation. Importantly, the narrative is shared not only within the company but also out in the world. It serves as a kind of “bat signal” to potential technology partners with capabilities that might be required to make it happen, says Manna. “It’s all part of our strategy for staking a claim in the future.”

Planning must become completely oriented toward—and sourced from—the future.

Companies like Lowe’s are realizing that standard ways of planning for the future won’t get them where they need to go. The problem with traditional strategic planning is that the approach, which dates back to the 1950s and has remained largely unchanged since then, is based on the company’s existing mission, resources, core competencies, and competitors.

Yet the future rarely looks like the past. What’s more, digital technology is now driving change at exponential rates. Companies must be able to analyze and assess the potential impacts of the many variables at play, determine the possible futures they want to pursue, and develop the agility to pivot as conditions change along the way.

This is why planning must become completely oriented toward—and sourced from—the future, rather than from the past or the present. “Every winning strategy is based on a compelling insight, but most strategic planning originates in today’s marketplace, which means the resulting plans are constrained to incremental innovation,” says Bob Johansen, distinguished fellow at the Institute for the Future. “Most corporate strategists and CEOs are just inching their way to the future.” (Read more from Bob Johansen in the Thinkers story, “Fear Factor.”)

Inching forward won’t cut it anymore. Half of the S&P 500 organizations will be replaced over the next decade, according to research company Innosight. The reason? They can’t see the portfolio of possible futures, they can’t act on them, or both. Indeed, when SAP conducts future planning workshops with clients, we find that they usually struggle to look beyond current models and assumptions and lack clear ideas about how to work toward radically different futures.

Companies that want to increase their chances of long-term survival are incorporating three steps: envisioning, planning for, and executing on possible futures. And doing so all while the actual future is unfolding in expected and unexpected ways.

Those that pull it off are rewarded. A 2017 benchmarking report from the Strategic Foresight Research Network (SFRN) revealed that vigilant companies (those with the most mature processes for identifying, interpreting, and responding to factors that induce change) achieved 200% greater market capitalization growth and 33% higher profitability than the average, while the least mature companies experienced negative market-cap growth and had 44% lower profitability.

Looking Outside the Margins

“Most organizations lack sufficient capacity to detect, interpret, and act on the critically important but weak and ambiguous signals of fresh threats or new opportunities that emerge on the periphery of their usual business environment,” write George S. Day and Paul J. H. Schoemaker in their book Peripheral Vision.

But that’s exactly where effective future planning begins: examining what is happening outside the margins of day-to-day business as usual in order to peer into the future.

Business leaders who take this approach understand that despite the uncertainties of the future there are drivers of change that can be identified and studied and actions that can be taken to better prepare for—and influence—how events unfold.

That starts with developing foresight, typically a decade out. Ten years, most future planners agree, is the sweet spot. “It is far enough out that it gives you a bit more latitude to come up with a broader way to the future, allowing for disruption and innovation,” says Brian David Johnson, former chief futurist for Intel and current futurist in residence at Arizona State University’s Center for Science and the Imagination. “But you can still see the light from it.”

The process involves gathering information about the factors and forces—technological, business, sociological, and industry or ecosystem trends—that are effecting change to envision a range of potential impacts.

Seeing New Worlds

Intel, for example, looks beyond its own industry boundaries to envision possible future developments in adjacent businesses in the larger ecosystem it operates in. In 2008, the Intel Labs team, led by anthropologist Genevieve Bell, determined that the introduction of flexible glass displays would open up a whole new category of foldable consumer electronic devices.

To take advantage of that advance, Intel would need to be able to make silicon small enough to fit into some imagined device of the future. By the time glass manufacturer Corning unveiled its ultra-slim, flexible glass surface for mobile devices, laptops, televisions, and other displays of the future in 2012, Intel had already created design prototypes and kicked its development into higher gear. “Because we had done the future casting, we were already imagining how people might use flexible glass to create consumer devices,” says Johnson.

Because future planning relies so heavily on the quality of the input it receives, bringing in experts can elevate the practice. They can come from inside an organization, but the most influential insight may come from the outside and span a wide range of disciplines, says Steve Brown, a futurist, consultant, and CEO of BaldFuturist.com who worked for Intel Labs from 2007 to 2016.

Companies may look to sociologists or behaviorists who have insight into the needs and wants of people and how that influences their actions. Some organizations bring in an applied futurist, skilled at scanning many different forces and factors likely to coalesce in important ways (see Do You Need a Futurist?).

Do You Need a Futurist?

Most organizations need an outsider to help envision their future. Futurists are good at looking beyond the big picture to the biggest picture.

Business leaders who want to be better prepared for an uncertain and disruptive future will build future planning as a strategic capability into their organizations and create an organizational culture that embraces the approach. But working with credible futurists, at least in the beginning, can jump-start the process.

“The present can be so noisy and business leaders are so close to it that it’s helpful to provide a fresh outside-in point of view,” says veteran futurist Bob Johansen.

To put it simply, futurists like Johansen are good at connecting dots—lots of them. They look beyond the boundaries of a single company or even an industry, incorporating into their work social science, technical research, cultural movements, economic data, trends, and the input of other experts.

They can also factor in the cultural history of the specific company with whom they’re working, says Brian David Johnson, futurist in residence at Arizona State University’s Center for Science and the Imagination. “These large corporations have processes and procedures in place—typically for good reasons,” Johnson explains. “But all of those reasons have everything to do with the past and nothing to do with the future. Looking at that is important so you can understand the inertia that you need to overcome.”

One thing the best futurists will say they can’t do: predict the future. That’s not the point. “The future punishes certainty,” Johansen says, “but it rewards clarity.” The methods futurists employ are designed to trigger discussions and considerations of possibilities corporate leaders might not otherwise consider.

You don’t even necessarily have to buy into all the foresight that results, says Johansen. Many leaders don’t. “Every forecast is debatable,” Johansen says. “Foresight is a way to provoke insight, even if you don’t believe it. The value is in letting yourself be provoked.”

External expert input serves several purposes. It brings everyone up to a common level of knowledge. It can stimulate and shift the thinking of participants by introducing them to new information or ideas. And it can challenge the status quo by illustrating how people and organizations in different sectors are harnessing emerging trends.

The goal is not to come up with one definitive future but multiple possibilities—positive and negative—along with a list of the likely obstacles or accelerants that could surface on the road ahead. The result: increased clarity—rather than certainty—in the face of the unknown that enables business decision makers to execute and refine business plans and strategy over time.

Plotting the Steps Along the Way

Coming up with potential trends is an important first step in futuring, but even more critical is figuring out what steps need to be taken along the way: eight years from now, four years from now, two years from now, and now. Considerations include technologies to develop, infrastructure to deploy, talent to hire, partnerships to forge, and acquisitions to make. Without this vital step, says Brown, everybody goes back to their day jobs and the new thinking generated by future planning is wasted. To work, the future steps must be tangible, concrete, and actionable.

Organizations must build a roadmap for the desired future state that anticipates both developments and detours, complete with signals that will let them know if they’re headed in the right direction. Brown works with corporate leaders to set indicator flags to look out for on the way to the anticipated future. “If we see these flagged events occurring in the ecosystem, they help to confirm the strength of our hypothesis that a particular imagined future is likely to occur,” he explains.

For example, one of Brown’s clients envisioned two potential futures: one in which gestural interfaces took hold and another in which voice control dominated. The team set a flag to look out for early examples of the interfaces that emerged in areas such as home appliances and automobiles. “Once you saw not just Amazon Echo but also Google Home and other copycat speakers, it would increase your confidence that you were moving more towards a voice-first era rather than a gesture-first era,” Brown says. “It doesn’t mean that gesture won’t happen, but it’s less likely to be the predominant modality for communication.”

How to Keep Experiments from Being Stifled

Once organizations have a vision for the future, making it a reality requires testing ideas in the marketplace and then scaling them across the enterprise. “There’s a huge change piece involved,”
says Frank Diana, futurist and global consultant with Tata Consultancy Services, “and that’s the place where most
businesses will fall down.”

Many large firms have forgotten what it’s like to experiment in several new markets on a small scale to determine what will stick and what won’t, says René Rohrbeck, professor of strategy at the Aarhus School of Business and Social Sciences. Companies must be able to fail quickly, bring the lessons learned back in, adapt, and try again.

Lowe’s increases its chances of success by creating master narratives across a number of different areas at once, such as robotics, mixed-reality tools, on-demand manufacturing, sustainability, and startup acceleration. The lab maps components of each by expected timelines: short, medium, and long term. “From there, we’ll try to build as many of them as quickly as we can,” says Manna. “And we’re always looking for that next suite of things that we should be working on.” Along the way certain innovations, like the HoloRoom How-To, become developed enough to integrate into the larger business as part of the core strategy.

One way Lowe’s accelerates the process of deciding what is ready to scale is by being open about its nascent plans with the world. “In the past, Lowe’s would never talk about projects that weren’t at scale,” says Manna. Now the company is sharing its future plans with the media and, as a result, attracting partners that can jump-start their realization.

Seeing a Lowe’s comic about employee exoskeletons, for example, led Virginia Tech engineering professor Alan Asbeck to the retailer. He helped develop a prototype for a three-month pilot with stock employees at a Christiansburg, Virginia, store.

The high-tech suit makes it easier to move heavy objects. Employees trying out the suits are also fitted with an EEG headset that the lab incorporates into all its pilots to gauge unstated, subconscious reactions. That direct feedback on the user experience helps the company refine its innovations over time.

Make the Future Part of the Culture

Regardless of whether all the elements of its master narratives come to pass, Lowe’s has already accomplished something important: It has embedded future thinking into the culture of the company.

Companies like Lowe’s constantly scan the environment for meaningful economic, technology, and cultural changes that could impact its future assessments and plans. “They can regularly draw on future planning to answer challenges,” says Rohrbeck. “This intensive, ongoing, agile strategizing is only possible because they’ve done their homework up front and they keep it updated.”

It’s impossible to predict what’s going to happen in the future, but companies can help to shape it, says Manna of Lowe’s. “It’s really about painting a picture of a preferred future state that we can try to achieve while being flexible and capable of change as we learn things along the way.” D!


About the Authors

Dan Wellers is Global Lead, Digital Futures, at SAP.

Kai Goerlich is Chief Futurist at SAP’s Innovation Center Network.

Stephanie Overby is a Boston-based business and technology journalist.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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Dan Wellers

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.

Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

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Retail Tomorrow: How Today’s Technology Is Shaping Retail’s Future

Stephen Sparrow

Do you ever think about tomorrow? Many retailers don’t. They’re too concerned with what’s happening in the moment. They’re too wrapped up in managing their daily business operations or maintaining profit margins.

Don’t get me wrong – those things are important. But tomorrow matters more than they know.

With game-changing technologies like the Internet of Things (IoT), virtual reality, and machine learning reshaping the retail landscape, tomorrow can no longer be ignored. If your company wants to stay ahead of the competition – both now and in the future – you need to begin experimenting with these innovations today.

Beer, there, and everywhere: Create an immersive customer experience

Imagine you’re a Brooklyn-based brewery. You craft the most delicious beer anyone’s ever tasted, and Brooklynites are absolutely gaga over your product. But how do you spread the word? How can you make people in Seattle or San Francisco thirst for your beverage?

Virtual reality and IoT tools can help you create a more immersive customer experience – one that gives people an in-depth view into your brewery – so folks across the country can get excited about sampling your suds.

By setting up a 360-degree video camera and implementing virtual reality capabilities, you can invite people all over the world to tour your facility. They can visit the tasting room, check out the outdoor patio, and watch the kettles work their magic in the production area.

IoT sensors, meanwhile, can provide prospective customers with insight around your brewing processes. Attached to the brew kettles, these sensors enable you to share real-time data about each batch of beer, from when the hops reach a boil to when fermentation is complete.

If viewers like what they see, they can order a case of your beer online.

Creating an immersive customer experience, where people get a glance behind the curtain to see how your company operates and how your product is made, is a surefire recipe for retail success.

A passion for fashion: Predict trends so your customers are always dressed to kill

Instagram, the popular image-sharing app, has a global community of more than 800 million users. These users share upwards of 95 million photos and videos per day.

If a woman from the United States is traveling to Tokyo for an upcoming vacation and wants to make sure she looks fashionable while visiting Japan’s capital city, where can she turn?

Instagram, of course.

With a simple keyword search for “fashion” and “Tokyo,” this woman could be knee-deep in results highlighting the top trends from this chic metropolitan hotspot. Now, with a better idea of what the locals are wearing, she can pick up a few new outfits before her trip, and she won’t feel so out of place in her American attire when she visits.

Retailers, particularly fashion brands, can benefit from how consumers are using apps like Instagram. By analyzing what people are wearing in photos taken in fashion meccas like London, Paris, Tokyo, Milan, or New York, your business can have its finger firmly on the pulse.

Pairing your analysis with machine learning capabilities can enable your retailer to detect and predict the hottest fashion trends. This will help your designers tailor the clothing they create to what’s happening – or what will be happening – in the market.

If more people are wearing floral-print miniskirts, you can design matching leggings. If more people are dressing in denim, you can ramp up production on jean jackets.

Staying up to date on the latest fashion trends can keep your retailer at the top of its game. Predicting the next big thing in fashion using machine learning? That will have your business declaring “game over” to all your competitors.

Not your grandma’s kitchen: Increase customer convenience through greater connectivity

Connected products are invading our homes. We have smart TVs in our living rooms. We have showerheads equipped with Bluetooth speakers in our bathrooms. We have lights that brighten or dim based on our sleeping schedules in our bedrooms.

In the kitchen, though, things are getting really intelligent. From precision cookers that alert you when dinner’s ready to coffee makers you can operate with your smartphone, kitchen appliances are creating a whole new level of convenience for customers.

With a smart refrigerator, customers can create shopping lists using a touch screen on the door. IoT capabilities enable people to add or remove items from their lists using a mobile device. Customers can even submit their grocery orders to a nearby store through their smart fridge, a convenient click-and-collect shopping scenario.

Augmented reality, meanwhile, allows people to peek inside their refrigerators without even opening them. If a woman at work wants to see if she has enough milk for a bowl of cereal tomorrow, she can check using a tablet or smartphone.

Retailers and consumer products companies can leverage this technology to deliver a more engaging product experience. The packaging of a stick of butter, for instance, might have a code on it. When a man peers into his refrigerator using his smartphone, he could click on the code and find out the product’s expiration date. Or perhaps he can learn a few new recipes he could bake using the butter.

By creating a hassle-free shopping experience and enhancing how your buyers engage with your products, you can increase sales and earn your customers’ loyalty.

Home sweet home: Modernize retail like real-estate agents have revolutionized homebuying

Think of how the realty business has changed over the past 25 years. In the early ‘90s, prospective homebuyers had to schedule an appointment with a Realtor or attend an open house to see a home they liked.

In the mid-2000s, house hunting went online, with sites like Trulia and Zillow springing up. Today, homebuyers can snap a photo of an on-the-market house they like using a mobile app and see pictures of the home’s interior, learn the price, find out the square footage, and discover how many bathrooms it has.

Retailers should strive to modernize their industry like the realty business has revolutionized homebuying. Barcode scanning and sensor tracking are just a couple technologies that could help.

If a customer is walking through the aisles of your store, you could offer them the opportunity to scan a tag on a shirt with their mobile device and instantly give them access to outfit ideas or show them accessories that match the top.

Sensors, meanwhile, could track where a shopper is in a store, allowing your retailer to send timely and relevant offers based on their location.

Adding value to your customer experience is the name of the game in retail. And there’s no better way to create a more valuable in-store customer experience than with the latest technology.

Innovation experimentation: Forge your path to a brighter future with revolutionary tech tools

Innovations like IoT, virtual reality, and machine learning are shaping what retail’s future will look like.

Your company’s success – both today and tomorrow – will depend on your willingness to embrace these technologies and experiment with new ways to engage and satisfy your customers.

Join us at the National Retail Forum’s 2018 conference and EXPO at the Jacob K. Javits Convention Center in New York City on January 14–16 to learn how the SAP Leonardo digital innovation system can help your organization bring these exciting technologies to life.

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Stephen Sparrow

About Stephen Sparrow

Stephen Sparrow is the Director of Retail Marketing at SAP. He defines, champions and executes marketing strategies to increase penetration and capture of revenue opportunities across SAP's retail enterprise accounts. He also develops industry advancing and perception enhancing programs to drive brand preference for SAP in the retail community.