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3D Printing Gives U.S. Manufacturer A Leg Up On Competitors

Robin Meyerhoff

You might not have heard of Jabil, but you have probably have a product made by the company that you use daily in your home or office. As one of the largest and most technologically advanced manufacturers, Jabil creates products for more than 250 well-known global brands.

Founded in Michigan 50 years ago, Jabil started out making electronic parts for the automotive industry. Since then, the company has grown from a small family business on the outskirts of Detroit to one of the largest global manufacturers, working with industries ranging from household appliances to healthcare–and almost everything in between.

Over the past several years, manufacturers have been digitizing operations, embracing Industry 4.0 using new technologies like Internet of Things (IoT) to improve performance and efficiency. But as consultancy KPMG points out, “The vast majority of leading manufacturers admit they are not yet prepared to fully integrate the lessons of Industry 4.0 into the way they view and manage their products.”

Jabil is bucking that trend. Recognizing that manufacturers need to be more nimble and personal in how they relate to customers, Jabil has been adopting new technologies like 3D printing to help clients meet those challenges head on.

“The digital transformation in manufacturing is going to be enormously impactful for companies like Jabil,” said John Dulchinos, vice president of digital manufacturing for Jabil. “We see it as a tremendous opportunity for us to respond to customers more quickly, to build products in regions that are closer to where the end customers are and to open up entirely new business models.”

Touring Jabil’s Blue Sky Innovation Center and manufacturing operations in San Jose, evidence of how these digital technologies can impact products was everywhere. The lobby displayed smart watch bands and electric toothbrushes in personalized colors, and tailored packaging for household products. There was a room of robots, including one that was creating Disney MagicBands. Most impressive were the 3D printers, which ranged from small (comparable to a microwave) to HP printers the size of a small room.

John said, “3D printing is one of those really amazing technologies. As we look at where 3D printing will take us in the future, we think it’s going to impact the entire product lifecycle from very early innovation and ideation, through manufacturing and product introduction. Ultimately we can provide support for spare parts and other needs later in the product lifecycle. We think 3D printing will impact all of that.”

He continued, “What really excites us about 3D printing is it gives us the ability to free up designers to create the most optimized, intricate designs. We now have a way to bring manufacturing options closer to where customers are, and deliver goods that are more targeted and responsive to their needs.” 

John explains that Jabil has been on top of the transition to digital and computer-driven manufacturing since the 1980s. It shows in Jabil’s strong numbers: For the past 30 years they’ve continued to grow, and their stock price recently hit a 52-week high.

As Jabil embarks on its next growth phase, John believes “a digital backbone is the most important asset for manufacturers like us to achieve those goals.”

Jabil relies on an enterprise resource planning (ERP) system to support critical supply chain and manufacturing operations. Supply chain experts at Jabil use an in-memory computing platform for real-time analytics and reporting.

Manufacturers around the world like Jabil are facing similar issues: They want to optimize their supply chain, bring production closer to customers and offer new personalized products and services. The company is using a distributed manufacturing solution and a digital innovation system that brings together the Internet of Things, machine learning, blockchain and advanced analytics to its cloud platform. Through this technology, Jabil is helping its customers deliver innovative, locally manufactured and customized goods. The entire process – from inception to delivery – is transformed into a 100% digitally native process.

In addition, the company is integrating 3D printing into procurement, sales, inventory, and logistics systems with greater ease. By combining this rising technology with an Internet of Things-based distributed manufacturing solution, Jabil is better positioned to evaluate which parts should be digitized and can more easily collaborate to approve parts for 3D printing.

For more on digital transformation strategies, see Five Pillars Of Digital Transformation: Invest In Digital Technology Capabilities.

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Robin Meyerhoff

About Robin Meyerhoff

Robin Meyerhoff is the Senior Director, Content Team, Global Corporate Affairs, at SAP, responsible for telling key corporate stories via multiple formats: cartoons, video, infographics, opinion pieces. Lead integrated internal-external approach to rolling out content, including comprehensive editorial calendar, regional coordination and alignment with key business objective.

The 3D-Printed Future Needs A New Supply Chain

Denis Kefallinos

The future landed in my living room around May 12, when we bought our son a 3D printer for his birthday. I had been thinking about buying a 3D printer for some time, but hadn’t gotten around to researching brands, models, and capabilities. As a hands-on engineer, chainsaw rebuilder, home renovator, and general gearhead, the idea of concocting a design, sketching up a drawing, and building it comes to me naturally. But 3D printing is different.

While visiting a childhood friend on Vancouver Island, I noticed a peculiar device in his office. “Is that a 3D printer?” I asked. “Yes, and it’s the best one out there for the money,” he answered. Translation: my trusted friend had already done the research legwork. Excellent! The next week I ordered the same model from Amazon for $300. The printer comes as a kit, and one afternoon my son and I spent six hours putting it together on the dining room table. Sure, we could have bought a complete version, but I felt it would be good for my son to understand the device as he follows in the footsteps of his parents in studying mechanical engineering.

Once we had the printer built, we couldn’t wait to test it. After turning it on and warming it up, we connected the printer to the PC via USB. From CAD software, we exported a drawing to a slicer program. The slicer takes the design and slices it into hundreds of horizontal layers, creating the flow path for the print head. The slicer program then uploads the .gcode file to the printer, and it’s ready to start printing. Layer by layer, the melted plastic filament is laid out and the design materializes.

As our very first design began taking shape on the printer’s platform, I sat there and pondered the implications. Being an engineer-turned-enterprise software professional, I was always conscious of the intersection of the physical, human, and digital planes. No doubt about it, 3D printing will alter our working world. Not only work, but supply chains as well. It wasn’t long ago that someone said, “companies don’t compete, supply chains do.” With 3D printing, it’s time to change that mantra again.

Supply chains compete, and so do ideas

Enterprises can always come up with a great design, but that great design is compromised in the marketplace if the supply chain isn’t there to support it. Suppliers, production, transport, warehousing, distribution, and retail channels are part of the competitive arsenal, and they all need to be in sync.

With this printer sitting in my living room (and presumably millions of living rooms), the supply chain changes. In my unique case, I only need one – correction – two, supply chains. One supply chain for plastic filament, and another for ideas (or designs).

Ideas are not subject to the same constraints as physical supply chains. Ideas move instantly, and there are factors that influence the harvesting, production, refinement, distribution, and performance of ideas.

For an enterprise, an idea may take shape in a design-thinking session or in a customer focus group. It may result from a warranty claim or service ticket, from a manufacturing defect, or from a need to reduce costs. There are limitless sources for ideas.

Now more than ever, enterprises need to put significant energy into driving good, practical ideas from their “idea supply chain.” Enterprises will need to gather data from millions of people (or their IoT-enabled printers) to gauge feedback on designs and respond rapidly. Social media helps people quickly praise or criticize products to a wide audience, but currently that product is at the end of a very long supply and design chain. If the future enterprise is one that sells customers a 3D printer file with a single-use digital right, the ideas and designs will be critiqued. Competitors will launch competing ideas to try to gain market share; no need to wait for the competing product to hit the shelf.

Without the need for a manufacturing and distribution network, next-generation idea enterprises need to ramp idea-management capabilities to compete. In the 3D-printed future, the company that provides its customers with the timeliest idea will be the one that wins.

What’s different in the 3D printed realm

Earlier I said I was no stranger to designing and building, and that this 3D printing realm was different. Here’s my opinion on what’s on the horizon:

  1. The machine builds it. No getting your hands dirty! Upload the file, push the button, and go. As a hands-on builder, it’s cool to let the machine do the work.
  1. Instant gratification with value chain disruption. I dream it, or I need it, and I make it. For example, I need a new light switch cover in the hallway. I’m going to ask my son to measure it up and print a replacement. Now an OEM with a supply chain that sources plastic and has molds and injection-molding machines, employees, warehouses, trucks, and retailer relationships is going to feel that decision. I wouldn’t mind paying that OEM for a single-use digital file holding a tested and proven design.
  1. Instant digital feedback and affinity. The opportunity for designers to learn whether their design worked is amazing. For example, after I print the light switch cover, the 3D printer can send a signal back to the OEM that the print job is complete. Allowing an hour delay after the conclusion of the print, a text message from the OEM would prompt me for my feedback on the design: Am I satisfied? Enter a score – from 1 (bad) to 5 (good) – and hit reply. Feedback will be tracked in real time and displayed on the OEM website for other potential customers to see. I may allow myself to be contacted via an anonymous email alias to provide a reference to a prospective customer. For my trouble, I will get 10% off my next design purchase. As a repeat customer, I might be invited to a virtual design review or to be a beta tester.
  1. New business processes with mandatory digital transformation. In the traditional supply chain, processes such as procurement, manufacturing, quality, testing, packaging, warehousing, and distribution will be impacted by 3D printing. Other business processes will be enhanced or newly created – particularly around design acceleration, customer quality and feedback, customer design collaboration, royalty management, digital rights, and more. These will be digital-only processes.
  1. Enterprise valuations continue to evolve. More emphasis will be put on a company’s investment in R&D and enabling technology as a leading indicator of market opportunity. Traditional logistical value-add streams of buy-make-move-store will have less impact. This already happened over the last few decades with the outsourcing of manufacturing, and 3D printing is poised to bring the next evolution in the enterprise valuation model.

While I am seeing the future from my living room, I’m certain enterprises are seeing it from their boardrooms as well. Physical supply chains will continue to exist, but digital idea-based supply chains are about to rise. I am curious to see how enterprises, particularly OEMs, respond to the 3D printing phenomenon.

For more insight, download the free eBook 6 Surprising Ways 3D Printing Will Disrupt Manufacturing.

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Denis Kefallinos

About Denis Kefallinos

Denis Kefallinos is Head of Presales at SAP Canada.

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.

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.

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Artificial Intelligence: The Future Of Oil And Gas

Anoop Srivastava

Oil prices have fallen dramatically over last few years, forcing some major oil companies to take drastic actions such as layoffs, cutting investments and budgets, and more. Shell, for example, shelved its plan to invest in Qatar, Aramco put on hold its deep-water exploration in the Red Sea, Schlumberger fired a few thousand employees, and the list goes on…

In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Currently, oil and gas companies find it difficult to maintain the same level of investment in exploration and production as when crude prices were at their peak. Operations in the oil and gas industry today means balancing a dizzying array of trade-offs in the drive for competitive advantage while maximizing return on investment.

The result is a dire need to optimize performance and optimize the cost of production per barrel. Companies have many optimization opportunities once they start using the massive data being generated by oil fields. Oil and gas companies can turn this crisis into an opportunity by leveraging technological innovations like artificial intelligence to build a foundation for long-term success. If volatility in oil prices is the new norm, the push for “value over volume” is the key to success going forward.

Using AI tools, upstream oil and gas companies can shift their approach from production at all costs to producing in context. They will need to do profit and loss management at the well level to optimize the production cost per barrel. To do this, they must integrate all aspects of production management, collect the data for analysis and forecasting, and leverage artificial intelligence to optimize operations.

When remote sensors are connected to wireless networks, data can be collected and centrally analyzed from any location. According to the consulting firm McKinsey, the oil and gas supply chain stands to gain $50 billion in savings and increased profit by adopting AI. As an example, using AI algorithms to more accurately sift through signals and noise in seismic data can decrease dry wellhead development by 10 percent.

How oil and gas can leverage artificial intelligence

1. Planning and forecasting

On a macro scale, deep machine learning can help increase awareness of macroeconomic trends to drive investment decisions in exploration and production. Economic conditions and even weather patterns can be considered to determine where investments should take place as well as intensity of production.

2. Eliminate costly risks in drilling

Drilling is an expensive and risky investment, and applying AI in the operational planning and execution stages can significantly improve well planning, real-time drilling optimization, frictional drag estimation, and well cleaning predictions. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making.

When drilling, machine-learning software takes into consideration a plethora of factors, such as seismic vibrations, thermal gradients, and strata permeability, along with more traditional data such as pressure differentials. AI can help optimize drilling operations by driving decisions such as direction and speed in real time, and it can predict failure of equipment such as semi-submersible pumps (ESPs) to reduce unplanned downtime and equipment costs.

3. Well reservoir facility management

Wells, reservoirs, and facility management includes integration of multiple disciplines: reservoir engineering, geology, production technology, petro physics, operations, and seismic interpretation. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance.

AI techniques can also be applied in other activities such as reservoir characterization, modeling and     field surveillance. Fuzzy logic, artificial neural networks and expert systems are used extensively across the industry to accurately characterize reservoirs in order to attain optimum production level.

Today, AI systems form the backbone of digital oil field (DOF) concepts and implementations. However, there is still great potential for new ways to optimize field development and production costs, prolong field life, and increase the recovery factor.

4. Predictive maintenance

Today, artificial intelligence is taking the industry by storm. AI-powered software and sensor hardware enables us to use very large amounts of data to gain real-time responses on the best future course of action. With predictive analytics and cognitive security, for example, oil and gas companies can operate equipment safely and securely while receiving recommendations on how to avoid future equipment failure or mediate potential security breaches.

5. Oil and gas well surveying and inspections

Drones have been part of the oil and gas industry since 2013, when ConocoPhillips used the Boeing ScanEagle drone in trials in the Chukchi Sea.  In June 2014, the Federal Aviation Administration (FAA) issued the first commercial permit for drone use over United States soil to BP, allowing the company to survey pipelines, roads, and equipment in Prudhoe Bay, Alaska. In January, Sky-Futures completed the first drone inspection in the Gulf of Mexico.

While drones are primarily used in the midstream sector, they can be applied to almost every aspect of the industry, including land surveying and mapping, well and pipeline inspections, and security. Technology is being developed to enable drones to detect early methane leaks. In addition, one day, drones could be used to find oil and gas reservoirs underlying remote uninhabited regions, from the comfort of a warm office.

6. Remote logistics

As logistics to offshore locations is always a challenge, AI-enhanced drones can be used to deliver materials to remote offshore locations.

Current adoption of AI

Chevron is currently using AI to identify new well locations and simulation candidates in California. By using AI software to analyze the company’s large collection of historical well performance data, the company is drilling in better locations and has seen production rise 30% over conventional methods. Chevron is also using predictive models to analyze the performance of thousands of pieces of rotating equipment to detect failures before they occur. By addressing problems before they become critical, Chevron has avoided unplanned shutdowns and lowered repair expenses. Increased production and lower costs have translated to more profit per well.

Future journey

Today’s oil and gas industry has been transformed by two industry downturns in one decade. Although adoption of new hard technology such as directional drilling and hydraulic fracturing (fracking) has helped, the oil and gas industry needs to continue to innovate in today’s low-price market to survive. AI has the potential to differentiate companies that thrive and those that are left behind.

The promise of AI is already being realized in the oil and gas industry. Early adopters are taking advantage of their position  to get a head start on the competition and protect their assets. The industry has always leveraged technology to adapt to change, and early adopters have always benefited the most. As competition in the oil and gas industry continues to heat up, companies cannot afford to be left behind. For those that understand and seize the opportunities inherent in adopting cognitive technologies, the future looks bright.

For more insight on advanced technology in the energy sector, see How Digital Transformation Is Refueling The Energy Industry.

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Anoop Srivastava

About Anoop Srivastava

Anoop Srivastava is Senior Director of the Energy and Natural Resources Industries at SAP Value Engineering in Middle East and North Africa. He advises clients on their digital transformation strategies and helps them align their business strategy with IT strategy leveraging digital technology innovations such as the Internet of Things, Big Data, Advanced Analytics, Cloud etc. He has 21+ years of work experience spanning across Oil& Gas Industry, Business Consulting, Industry Value Advisory and Digital Transformation.