Sections

Accelerate Your Digital Transformation With Packaged Business Services

Meinolf Kaimann

From industry publications to TED Talks, it seems that people everywhere are talking about digitalization as the next big thing. Here’s a little secret, though: Digitalization isn’t coming. It’s already here.

Disruptive technological advances are shaking up businesses and markets. Although you may be expecting to do battle with your traditional competition, digitalization is making it easier for previously unseen challengers to sneak up and steal your market share. Just ask the competitors to Netflix, Airbnb, and Uber.

To avoid this fate, companies need to embrace new strategies that will help them speed their digital transformation. Meeting this goal requires partnerships with service providers that will support a laser focus on business outcomes while realizing quick wins that will help transform your enterprise. Skilled partners can help their clients save money, freeing up funds to allow companies to adopt additional innovation and unleash new business value. It’s a proven formula for maximum value, minimum risk, and rapid success.

Not all service providers are created equal

How can you choose the right service provider? Look for organizations offering services that act as accelerators, such as industrialized services, to deliver value faster. Most providers use product development as a starting point and adjust their services and processes to meet customer needs. Instead, select services that begin with best practices and proven processes that are designed for your specific industry. In other words, the center of the effort should always be your success, not the vendor’s convenience.

Predefined service packages that are based on best practices, methodologies, and tools can help you jump-start your digital transformation. When structured this way, these service packages also enable providers to systematize on service content and quality. This standardization is likely to provide consistent, positive business outcomes no matter which consultant is assigned to your initiative.

You should be able to tailor these reusable, renewable service packages to your organizational structures and preferred delivery methods. More important, each service offering should have a clear, outcome-driven scope, address the different phases of your transformation, and be delivered quickly and cost-effectively.

Industry expertise and benchmarks reveal progress

Most organizations can also benefit from a model company approach that defines the majority of processes for each industry or line of business. By combining best practices with integrated end-to-end processes, a model company approach not only acts as an accelerator, but also frees consultants to focus on any missing pieces identified during a fit-gap analysis. The expertise gathered from other customer implementation experiences helps your provider implement the latest innovations while sharply reducing implementation complexity, time, and cost.

Finally, keep in mind that it can be highly advantageous to include your software vendor in the search for services that accelerate digital transformation. Involving the vendor early in internal innovation initiatives can help you identify, prioritize, and refine ideas. With extensive knowledge of current industry best practices and familiarity with other deployments, the vendor can offer insight and recommendations that help pave the way for a successful transformation.

A software provider that supports your solution well beyond implementation is highly motivated to help your deployment succeed throughout the life of the software. This ongoing engagement provides the vendor with the opportunity – and privilege – to guide you through current and future transformations.

The importance of this customer vote of confidence cannot be overstated. Unlike a system integrator that considers your digital transformation a limited-term “project,” a software vendor views your success as a reflection on the value of the solutions and services delivered. What’s more, your software vendor can also help measure, assess, and analyze your outcomes compared with other customers, offering insight you can use to benchmark your true digital transformation progress.

Learn more about how SAP Digital Business Services can help you accelerate business opportunities through digital transformation and reduce capital expenditures, risk, and total cost of ownership.

Comments

Meinolf Kaimann

About Meinolf Kaimann

Meinolf Kaimann is vice president, global head value assurance and premium engagements product management at SAP. You can follow him on LinkedIn.

Simpler IT Begins With Hybrid Data Models

Carl Dubler

We hear a lot about simplicity in IT today. It’s a goal that is galvanizing every organization – large, small, commercial, and not-for-profit. Simpler data models, especially in core enterprise resource planning (ERP), should be the beginning of this quest.

Simpler data models in ERP not only help IT departments achieve streamlined IT landscapes; they transform the entire enterprise by fundamentally changing the way we do business. This happens by combining transactions and analytics into a unified, hybrid data model.

Breakthrough technology for database design

For decades, companies dealt with transactional and analytical information separately because database systems could not cope with both types of data. The result is the complex IT landscape we see now: numerous data silos, operational data stores, data warehouses, data cubes, and myriad tools for extracting, transforming, and loading data to feed them.

Now, using in-memory technology and columnar design, databases can handle both transactional and analytical workloads at the same time. Referred to as “Hybrid Transactional and Analytic Processing (HTAP)” by Gartner or “Translytics” by Forrester, these new databases enable a dramatically simpler IT landscape. No redundant systems are needed, and users can perform analytics directly on the transactional data at the source.

A big opportunity to reduce IT costs

All this has far-reaching implications for the IT department. Numerous systems can be consolidated into a single data platform, and cost savings from reduced data storage can be refocused to more strategic areas.

But what does it mean if you work in finance, marketing, supply chain management, or any other line of business? HTAP databases have important implications enterprise-wide.

Whereas analytics were traditionally performed on stale data, the new hybrid databases enable real-time analytics as they’re needed. And because IT staffers no longer must anticipate requirements in advance, users now have unprecedented flexibility in interrogating data to find insights to inform decision making.

Immediate results for the business

The new flexibility, immediacy, and ease with which you can analyze data is already revolutionizing business processes across every function. Finance, for example, can run a “soft close” of the books any time it wants, just to see what will happen. This means finance managers can identify issues early, before the actual close, and provide financial reports on an ad hoc basis.

Supply chain managers can run their material requirements planning (MRP) reports whenever they like, instead of only daily, and perform simulations to gauge the impact on production levels of any drop in supply of a certain component.

Leveraging superior processing capabilities

So far, so good. But simply deploying an HTAP-style database isn’t the end of the story. You also need to ensure that you’re using software that makes the most of what the database offers.

Many database systems have an in-memory option to accelerate queries. This isn’t enough. Applications – ERP in particular, since it is at the core of your business – must be completely redesigned to take advantage of the powerful analytics and superior processing capabilities that an HTAP database can offer.

The new generation of ERP software that is now emerging enables you to connect your enterprise with people, business networks, the Internet of Things, and Big Data in real time, providing live information and insights. The result: a way of working that is more agile, efficient – and simpler than ever.

To learn more about the value that simpler data models can bring to your organization, try our SAP S/4HANA Value Advisor.

Comments

Carl Dubler

About Carl Dubler

Carl Dubler is a senior director of Product Marketing for SAP S/4HANA. With an IT career stretching back to the late 1980s, he has done nearly every role in the business. In his ten years at SAP, he also managed SAP’s first commercially available cloud product and first cloud product on SAP HANA.

IT Control And The Business Value Of Data

Eamon Ida

In a recent Q&A with SAP, “The Value of Data and Analytics in Digital Transformation,” Dan Vesset at IDC makes an interesting observation. Deep into the exchange, he points out that today, “there is significantly greater acceptance by IT that it shouldn’t control all things analytics.”

The reasons why are intriguing. IT control, of course, is a long-standing issue when it comes to enterprise technology in general – and models have swung from one extreme to the other. In the interest of security, some IT groups wield considerable control over who uses what technology. In the interest of flexibility, others are quite open – allowing almost anyone to access anything with minimal controls.

Ideally, you want to find the sweet spot that balances these two extremes so that your company can minimize risk on the one hand while enabling the flexibility to innovate and serve customers more effectively on the other. This, I think, is fairly obvious. But the dichotomy between security and flexibility is not really what Dan has in mind.

Complexity and control

What he has in mind is complexity. The fact is, many IT organizations exert control over enterprise technology due to its complexity. Let’s call it “control by necessity.” A classic case is analytics, which until recently has almost always been no more than a step away from the complexities of data management. Analytical tools were complex – because using them required a fair amount of data management expertise.

In the past, analytics involved data warehouses stored on disk where experts ran batch jobs on data subsets and generated reports that were then delivered to the business. Dashboards were a nice advance, but IT had to build them, and they quickly lost relevance. IT controlled analytics because they needed to.

The new face of analytics

Today, things have shifted. Today, businesses are using new technologies in new ways to bring analytics directly to the consumer. Dan runs through a few examples:

  • Advances in user interface design: Today’s state-of-the-art UIs take a cue from the mobile world, where data can be accessed and manipulated with touch-screen simplicity. Not only are interfaces increasingly designed with the user in mind; they’re designed by the user to meet individual needs and preferences.
  • In-memory databases: Rather than storing transactional data and analytical data in separate silos, companies can now store both kinds of data in active memory, where it is easier to work with. This is helping companies sense and respond to developments faster and more effectively.
  • Cloud computing: Modern analytics, Dan says, is architected for the cloud. New data platforms that run in the cloud – or at least deliver analytics for consumption via the cloud – are helping companies meet demand for data by business users with greater flexibility at lower costs.
  • Machine learning: Companies can now use intelligent algorithms to analyze process data, identify issues, and take action to improve processes – often without human intervention. This only makes things easier for consumers of analytics, who can now spend more time on higher-value tasks.

Bring analytics to the masses – but start small

Surely other technologies and trends have played a role in simplifying analytics for the business consumer. And we can expect more technologies to emerge over time. But whatever specific technologies a company chooses to adopt, Dan warns against big-bang digital transformation projects that are implemented enterprise-wide for ill-defined reasons.

Most companies are better off with targeted projects that fulfill defined needs or answer specific questions. Fortunately, today’s leading-edge cloud analytics platforms are designed for rapid expansion. The best approach is to get comfortable first, expand as needed, and then evaluate if cloud analytics is something that could help enterprise-wide.

For more on the issues covered in Dan’s interview, get the full text here. It’s worth a read.

Comments

Eamon Ida

About Eamon Ida

Eamon is a passionate, creative, and data-driven analytics product marketer at SAP. He has a special interest in technology and its role at the forefront of how we live and work together.

Primed: Prompting Customers to Buy

Volker Hildebrand, Sam Yen, and Fawn Fitter

When it comes to buying things—even big-ticket items—the way we make decisions makes no sense. One person makes an impulsive offer on a house because of the way the light comes in through the kitchen windows. Another gleefully drives a high-end sports car off the lot even though it will probably never approach the limits it was designed to push.

We can (and usually do) rationalize these decisions after the fact by talking about needing more closet space or wanting to out-accelerate an 18-wheeler as we merge onto the highway, but years of study have arrived at a clear conclusion:

When it comes to the customer experience, human beings are fundamentally irrational.

In the brick-and-mortar past, companies could leverage that irrationality in time-tested ways. They relied heavily on physical context, such as an inviting retail space, to make products and services as psychologically appealing as possible. They used well-trained salespeople and employees to maximize positive interactions and rescue negative ones. They carefully sequenced customer experiences, such as having a captain’s dinner on the final night of a cruise, to play on our hard-wired craving to end experiences on a high note.

Today, though, customer interactions are increasingly moving online. Fortune reports that on 2016’s Black Friday, the day after Thanksgiving that is so crucial to holiday retail results, 108.5 million Americans shopped online, while only 99.1 million visited brick-and-mortar stores. The 9.4% gap between the two was a dramatic change from just one year prior, when on- and offline Black Friday shopping were more or less equal.

When people browse in a store for a few minutes, an astute salesperson can read the telltale signs that they’re losing interest and heading for the exit. The salesperson can then intervene, answering questions and closing the sale.

Replicating that in a digital environment isn’t as easy, however. Despite all the investments companies have made to counteract e-shopping cart abandonment, they lack the data that would let them anticipate when a shopper is on the verge of opting out of a transaction, and the actions they take to lure someone back afterwards can easily come across as less helpful than intrusive.

In a digital environment, companies need to figure out how to use Big Data analysis and digital design to compensate for the absence of persuasive human communication and physical sights, sounds, and sensations. What’s more, a 2014 Gartner survey found that 89% of marketers expected customer experience to be their primary differentiator by 2016, and we’re already well into 2017.

As transactions continue to shift toward the digital and omnichannel, companies need to figure out new ways to gently push customers along the customer journey—and to do so without frustrating, offending, or otherwise alienating them.

The quest to understand online customers better in order to influence them more effectively is built on a decades-old foundation: behavioral psychology, the study of the connections between what people believe and what they actually do. All of marketing and advertising is based on changing people’s thoughts in order to influence their actions. However, it wasn’t until 2001 that a now-famous article in the Harvard Business Review formally introduced the idea of applying behavioral psychology to customer service in particular.

The article’s authors, Richard B. Chase and Sriram Dasu, respectively a professor and assistant professor at the University of Southern California’s Marshall School of Business, describe how companies could apply fundamental tenets of behavioral psychology research to “optimize those extraordinarily important moments when the company touches its customers—for better and for worse.” Their five main points were simple but have proven effective across multiple industries:

  1. Finish strong. People evaluate experiences after the fact based on their high points and their endings, so the way a transaction ends is more important than how it begins.
  2. Front-load the negatives. To ensure a strong positive finish, get bad experiences out of the way early.
  3. Spread out the positives. Break up the pleasurable experiences into segments so they seem to last longer.
  4. Provide choices. People don’t like to be shoved toward an outcome; they prefer to feel in control. Giving them options within the boundaries of your ability to deliver builds their commitment.
  5. Be consistent. People like routine and predictability.

For example, McKinsey cites a major health insurance company that experimented with this framework in 2009 as part of its health management program. A test group of patients received regular coaching phone calls from nurses to help them meet health goals.

The front-loaded negative was inherent: the patients knew they had health problems that needed ongoing intervention, such as weight control or consistent use of medication. Nurses called each patient on a frequent, regular schedule to check their progress (consistency and spread-out positives), suggested next steps to keep them on track (choices), and cheered on their improvements (a strong finish).

McKinsey reports the patients in the test group were more satisfied with the health management program by seven percentage points, more satisfied with the insurance company by eight percentage points, and more likely to say the program motivated them to change their behavior by five percentage points.

The nurses who worked with the test group also reported increased job satisfaction. And these improvements all appeared in the first two weeks of the pilot program, without significantly affecting the company’s costs or tweaking key metrics, like the number and length of the calls.

Indeed, an ongoing body of research shows that positive reinforcements and indirect suggestions influence our decisions better and more subtly than blatant demands. This concept hit popular culture in 2008 with the bestselling book Nudge.

Written by University of Chicago economics professor Richard H. Thaler and Harvard Law School professor Cass R. Sunstein, Nudge first explains this principle, then explores it as a way to help people make decisions in their best interests, such as encouraging people to eat healthier by displaying fruits and vegetables at eye level or combatting credit card debt by placing a prominent notice on every credit card statement informing cardholders how much more they’ll spend over a year if they make only the minimum payment.

Whether they’re altruistic or commercial, nudges work because our decision-making is irrational in a predictable way. The question is how to apply that awareness to the digital economy.

In its early days, digital marketing assumed that online shopping would be purely rational, a tool that customers would use to help them zero in on the best product at the best price. The assumption was logical, but customer behavior remained irrational.

Our society is overloaded with information and short on time, says Brad Berens, Senior Fellow at the Center for the Digital Future at the University of Southern California, Annenberg, so it’s no surprise that the speed of the digital economy exacerbates our desire to make a fast decision rather than a perfect one, as well as increasing our tendency to make choices based on impulse rather than logic.

Buyers want what they want, but they don’t necessarily understand or care why they want it. They just want to get it and move on, with minimal friction, to the next thing. “Most of our decisions aren’t very important, and we only have so much time to interrogate and analyze them,” Berens points out.

But limited time and mental capacity for decision-making is only half the issue. The other half is that while our brains are both logical and emotional, the emotional side—also known as the limbic system or, more casually, the primitive lizard brain—is far older and more developed. It’s strong enough to override logic and drive our decisions, leaving rational thought to, well, rationalize our choices after the fact.

This is as true in the B2B realm as it is for consumers. The business purchasing process, governed as it is by requests for proposals, structured procurement processes, and permission gating, is designed to ensure that the people with spending authority make the most sensible deals possible. However, research shows that even in this supposedly rational process, the relationship with the seller is still more influential than product quality in driving customer commitment and loyalty.

Baba Shiv, a professor of marketing at Stanford University’s Graduate School of Business, studies how the emotional brain shapes decisions and experiences. In a popular TED Talk, he says that people in the process of making decisions fall into one of two mindsets: Type 1, which is stressed and wants to feel comforted and safe, and Type 2, which is bored or eager and wants to explore and take action.

People can move between these two mindsets, he says, but in both cases, the emotional brain is in control. Influencing it means first delivering a message that soothes or motivates, depending on the mindset the person happens to be in at the moment and only then presenting the logical argument to help rationalize the action.

In the digital economy, working with those tendencies means designing digital experiences with the full awareness that people will not evaluate them objectively, says Ravi Dhar, director of the Center for Customer Insights at the Yale School of Management. Since any experience’s greatest subjective impact in retrospect depends on what happens at the beginning, the end, and the peaks in between, companies need to design digital experiences to optimize those moments—to rationally design experiences for limited rationality.

This often involves making multiple small changes in the way options are presented well before the final nudge into making a purchase. A paper that Dhar co-authored for McKinsey offers the example of a media company that puts most of its content behind a paywall but offers free access to a limited number of articles a month as an incentive to drive subscriptions.

Many nonsubscribers reached their limit of free articles in the morning, but they were least likely to respond to a subscription offer generated by the paywall at that hour, because they were reading just before rushing out the door for the day. When the company delayed offers until later in the day, when readers were less distracted, successful subscription conversions increased.

Pre-selecting default options for necessary choices is another way companies can design digital experiences to follow customers’ preference for the path of least resistance. “We know from a decade of research that…defaults are a de facto nudge,” Dhar says.

For example, many online retailers set a default shipping option because customers have to choose a way to receive their packages and are more likely to passively allow the default option than actively choose another one. Similarly, he says, customers are more likely to enroll in a program when the default choice is set to accept it rather than to opt out.

Another intriguing possibility lies in the way customers react differently to on-screen information based on how that information is presented. Even minor tweaks can have a disproportionate impact on the choices people make, as explained in depth by University of California, Los Angeles, behavioral economist Shlomo Benartzi in his 2015 book, The Smarter Screen.

A few of the conclusions Benartzi reached: items at the center of a laptop screen draw more attention than those at the edges. Those on the upper left of a screen split into quadrants attract more attention than those on the lower left. And intriguingly, demographics are important variables.

Benartzi cites research showing that people over 40 prefer more visually complicated, text-heavy screens than younger people, who are drawn to saturated colors and large images. Women like screens that use a lot of different colors, including pastels, while men prefer primary colors on a grey or white background. People in Malaysia like lots of color; people in Germany don’t.

This suggests companies need to design their online experiences very differently for middle-aged women than they do for teenage boys. And, as Benartzi writes, “it’s easy to imagine a future in which each Internet user has his or her own ‘aesthetic algorithm,’ customizing the appearance of every site they see.”

Applying behavioral psychology to the digital experience in more sophisticated ways will require additional formal research into recommendation algorithms, predictions, and other applications of customer data science, says Jim Guszcza, PhD, chief U.S. data scientist for Deloitte Consulting.

In fact, given customers’ tendency to make the fastest decisions, Guszcza believes that in some cases, companies may want to consider making choice environments more difficult to navigate— a process he calls “disfluencing”—in high-stakes situations, like making an important medical decision or an irreversible big-ticket purchase. Choosing a harder-to-read font and a layout that requires more time to navigate forces customers to work harder to process the information, sending a subtle signal that it deserves their close attention.

That said, a company can’t apply behavioral psychology to deliver a digital experience if customers don’t engage with its site or mobile app in the first place. Addressing this often means making the process as convenient as possible, itself a behavioral nudge.

A digital solution that’s easy to use and search, offers a variety of choices pre-screened for relevance, and provides a friction-free transaction process is the equivalent of putting a product at eye level—and that applies far beyond retail. Consider the Global Entry program, which streamlines border crossings into the U.S. for pre-approved international travelers. Members can skip long passport control lines in favor of scanning their passports and answering a few questions at a touchscreen kiosk. To date, 1.8 million people have decided this convenience far outweighs the slow pace of approvals.

The basics of influencing irrational customers are essentially the same whether they’re taking place in a store or on a screen. A business still needs to know who its customers are, understand their needs and motivations, and give them a reason to buy.

And despite the accelerating shift to digital commerce, we still live in a physical world. “There’s no divide between old-style analog retail and new-style digital retail,” Berens says. “Increasingly, the two are overlapping. One of the things we’ve seen for years is that people go into a store with their phones, shop for a better price, and buy online. Or vice versa: they shop online and then go to a store to negotiate for a better deal.”

Still, digital increases the number of touchpoints from which the business can gather, cluster, and filter more types of data to make great suggestions that delight and surprise customers. That’s why the hottest word in marketing today is omnichannel. Bringing behavioral psychology to bear on the right person in the right place in the right way at the right time requires companies to design customer experiences that bridge multiple channels, on- and offline.

Amazon, for example, is known for its friction-free online purchasing. The company’s pilot store in Seattle has no lines or checkout counters, extending the brand experience into the physical world in a way that aligns with what customers already expect of it, Dhar says.

Omnichannel helps counter some people’s tendency to believe their purchasing decision isn’t truly well informed unless they can see, touch, hear, and in some cases taste and smell a product. Until we have ubiquitous access to virtual reality systems with full haptic feedback, the best way to address these concerns is by providing personalized, timely, relevant information and feedback in the moment through whatever channel is appropriate. That could be an automated call center that answers frequently asked questions, a video that shows a product from every angle, or a demonstration wizard built into the product. Any of these channels could also suggest the customer visit the nearest store to receive help from a human.

The omnichannel approach gives businesses plenty of opportunities to apply subtle nudges across physical and digital channels. For example, a supermarket chain could use store-club card data to push personalized offers to customers’ smartphones while they shop. “If the data tells them that your goal is to feed a family while balancing nutrition and cost, they could send you an e-coupon offering a discount on a brand of breakfast cereal that tastes like what you usually buy but contains half the sugar,” Guszcza says.

Similarly, a car insurance company could provide periodic feedback to policyholders through an app or even the digital screens in their cars, he suggests. “Getting a warning that you’re more aggressive than 90% of comparable drivers and three tips to avoid risk and lower your rates would not only incentivize the driver to be more careful for financial reasons but reduce claims and make the road safer for everyone.”

Digital channels can also show shoppers what similar people or organizations are buying, let them solicit feedback from colleagues or friends, and read reviews from other people who have made the same purchases. This leverages one of the most familiar forms of behavioral psychology—reinforcement from peers—and reassures buyers with Shiv’s Type 1 mindset that they’re making a choice that meets their needs or encourages those with the Type 2 mindset to move forward with the purchase. The rational mind only has to ask at the end of the process “Am I getting the best deal?” And as Guszcza points out, “If you can create solutions that use behavioral design and digital technology to turn my personal data into insight to reach my goals, you’ve increased the value of your engagement with me so much that I might even be willing to pay you more.”

Many transactions take place through corporate procurement systems that allow a company to leverage not just its own purchasing patterns but all the data in a marketplace specifically designed to facilitate enterprise purchasing. Machine learning can leverage this vast database of information to provide the necessary nudge to optimize purchasing patterns, when to buy, how best to negotiate, and more. To some extent, this is an attempt to eliminate psychology and make choices more rational.

B2B spending is tied into financial systems and processes, logistics systems, transportation systems, and other operational requirements in a way no consumer spending can be. A B2B decision is less about making a purchase that satisfies a desire than it is about making a purchase that keeps the company functioning.

That said, the decision still isn’t entirely rational, Berens says. When organizations have to choose among vendors offering relatively similar products and services, they generally opt for the vendor whose salespeople they like the best.

This means B2B companies have to make sure they meet or exceed parity with competitors on product quality, pricing, and time to delivery to satisfy all the rational requirements of the decision process. Only then can they bring behavioral psychology to bear by delivering consistently superior customer service, starting as soon as the customer hits their app or website and spreading out positive interactions all the way through post-purchase support. Finishing strong with a satisfied customer reinforces the relationship with a business customer just as much as it does with a consumer.

The best nudges make the customer relationship easy and enjoyable by providing experiences that are effortless and fun to choose, on- or offline, Dhar says. What sets the digital nudge apart in accommodating irrational customers is its ability to turn data about them and their journey into more effective, personalized persuasion even in the absence of the human touch.

Yet the subtle art of influencing customers isn’t just about making a sale, and it certainly shouldn’t be about persuading people to act against their own best interests, as Nudge co-author Thaler reminds audiences by exhorting them to “nudge for good.”

Guszcza, who talks about influencing people to make the choices they would make if only they had unlimited rationality, says companies that leverage behavioral psychology in their digital experiences should do so with an eye to creating positive impact for the customer, the company, and, where appropriate, the society.

In keeping with that ethos, any customer experience designed along behavioral lines has to include the option of letting the customer make a different choice, such as presenting a confirmation screen at the end of the purchase process with the cold, hard numbers and letting them opt out of the transaction altogether.

“A nudge is directing people in a certain direction,” Dhar says. “But for an ethical vendor, the only right direction to nudge is the right direction as judged by the customers themselves.” D!

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


About the Authors:

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

Sam Yen is Chief Design Officer and Managing Director at SAP.

Fawn Fitter is a freelance writer specializing in business and technology.

Comments

Tags:

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.

Comments

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.