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Optimizing Business Planning For The Modern Consumer [INFOGRAPHIC]

Ashley Wetherhold

In today’s world, being a consumer has unlimited advantages. At any time, I can integrated business planning through mobile technologysearch, shop and purchase products on my smartphone or tablet. If I have any questions or concerns about the product, I can easily do a quick search online and ask my friends on social media for their feedback. After all, peer reviews are the most important. It’s easy to collect all the data I need in a matter of minutes. Sounds great, right? Maybe for the consumer, but for consumer products companies, it presents big challenges.

How can consumer products companies keep up with the changing consumer?

In the world of the “modern” consumer, expectations are high and when they want something, they want it fast, which puts pressure on consumer products companies’ supply chains. According to research by IDC, many companies have less than a 48 hour lead time with less than optimal capabilities, which makes it difficult to keep up with the increasing demand. So, how can they rise to the occasion?

With integrated business planning.

Integrated business planning: Helping companies understand demand

Integrated business planning enables all the planning functions of an entire organization align to both operational and functional performance goals. It spans the entire consumer products enterprise including supply chain along with marketing, sales, manufacturing, finance and more; however, coordinating between all these business areas can be a daunting task.

The good news? Consumer products companies can get help from sales and operating planning, which is a key enabler of integrated business planning. IDC research shows that even though this type of planning has been around for 25 years, it remains a top priority, and allows consumer products companies to come to a consensus view of demand.

And that’s not it.

Below are 4 benefits consumer products companies can see as a result of successful sales and operating planning based on research by IDC:

  • Between 10% and 20% inventory reductions
  • Between 15% and 25% faster time to market for NPDI
  • Between 2% and 5% improvement in on-time, in-full deliveries
  • Between 2% and 5% revenue gains

Sales and operating planning is key

By leveraging sales and operating planning as a key component of overall integrated business planning strategy, consumer products companies will have a greater understanding of what demand looks like and how to respond to it. However, one critical question remains: to what extent are critical stakeholders from different planning areas of the organization getting actively involved in helping build this view of demand?

The sooner all hands are on board, the sooner your consumer products company can start seeing the results.

For additional information about how sales and operation planning can help your consumer products company, explore these resources

Download this infographic here!

integrated business planning infographic
future-of-business
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About Ashley Wetherhold

Ashley Wetherhold previously held the role of Digital Strategist at SAP. She managed the SAP North America social channels by building awareness, engagement and community while driving conversion.

Zhena’s Gypsy Tea Brews Sustainable Growth On Cloud ERP

David Trites

Recently I had the pleasure of hosting a podcast with Paula Muesse, COO and CFO of Zhena’s Gypsy Tea, a small, organic, fair-trade tea company based in California, and Ursula Ringham from SAP. We talked about some of the business challenges Zhena’s faces and how the company’s ERP solution helped spur growth and digital transformation.

Small but complex business

~ERP helped Zhena’s sustain growthZhena’s has grown from one person (Zhena Muzyka) selling hand-packed tea from a cart, into a thriving small business that puts quality, sustainability, and fair trade first. And although the company is small its business is complex.

For starters, tea isn’t grown in the United States, so Zhena’s has to maintain and import inventory from multiple warehouses around the world. Some of their tea blends have up to 14 ingredients, and each one has a different lead time. That makes demand-planning difficult. In addition, the FDA and US Customs require designated ingredients be traced and treated a certain way to comply with regulations.

Being organic and fair trade also makes things more complicated. Zhena’s has to pass an annual organic compliance audit for all products and processing facilities. And all products need to be traceable back to the farms where the tea was grown and picked to ensure the workers (mostly women) are paid fair wages.

Sustainable growth

Prior to implementing its new ERP system, Zhena’s was using a mix of tools like QuickBooks, Excel, and paper to manage the business. But to sustain growth and ensure future success, the company had to make some changes. Zhena’s needed an integrated software solution that could handle all facets of the business. It needed a tool that could help with cost control and profitability analysis and facilitate complex reporting and regulatory requirements.

The SAP Business ByDesign solution was the perfect choice. The cloud-based ERP solution reduced both business and IT costs, simplified processes from demand planning to accounting, and enabled mobile access and real-time reporting.

Check out the podcast to hear more about how Zhena’s successfully transformed its business by moving to SAP Business ByDesign.

 This article originally appeared on SAP Business Trends.

Building a successful company is hard work. SAP’s affordable solutions for small and midsize companies are designed to make it easier. Simple to install and use, SAP SME Solutions help you automate and integrate your business processes to give real-time, actionable insights. So you can make decisions on the spot. Find out how Run Simple can work for you. Visit sap.com/sme.

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About David Trites

David Trites is a Director of SAP Global Marketing. He is responsible for producing interesting and compelling customer stories that will humanize the SAP brand, support sales and marketing teams across SAP, and increase the awareness of SAP in key markets.

Haier Asia Builds A Digital Platform To Speed Innovation And Win Consumers’ Loyalty [VIDEO]

Dinesh Sharma

08 Apr 2013 --- Intersection, Germany. --- Image by © Markus Hanke/www.MarkusHanke.de/CorbisFew words scare the corporate world like the term “disruption.” No matter the language, disruption conjures the fear of dilution, alteration, and disturbance. And as the world becomes increasingly hyperconnected, disruption seems to be an ever-present threat.

Nevertheless, the C-suite is remaining vigilant by embracing the digital economy as the new reality. According to a recent study conducted by the Economist Intelligence Unit, 80% of executives view hyperconnectivity positively – indicating that it presents more opportunities than threats. All the while, they are carefully watching the competitive landscape and anticipating the arrival of overnight digital sensations and the inventiveness of long-time adversaries.

However, this is only one side of the transformational change hyperconnectivity is bringing. Disruption is not just happening on the corporate side of the consumer market – consumers are steadily disrupting everything a business touches.

The secret? Go beyond the competition to find disruptive opportunity

Not that long ago, most businesses followed a one-time transaction model. They would manufacture the product and ship it to the retailer, and consumers would purchase it. However, hyperconnectivity has changed the rules – making this experience a distant memory.

Consumers are more connected to information and no longer interested in listening corporate rhetoric. By drastically changing everything in our lives, the Internet is giving more power to the consumer, putting them in a position to guide the conversation and dictate product and service offerings. From this perspective, it is easy to see that hyperconnectivity and its impact on social behavior are the true disruptors.

Haier Asia, a top-ranking multinational consumer electronics and home appliances company, is one of those few companies that quickly recognized how hyperconnectivity is powering consumer-based disruption. “When you look around, no consumer life business is making money. Why is that? Margins are so slim,” cites Yoshiaki Ito, president and CEO of Haier Asia. “Consumers are far, far faster than manufacturers because they are getting new information on a daily basis. In the meantime, traditional companies produce their products – taking 24 months. So the gap is just widening every second.”

Instead of surrendering to these challenges, Haier decided to disrupt itself and the market it serves. With a two-prong approach to digital transformation, the company created a service-based model to seize the potential of new consumer behaviors and accelerate its product development cycles.

“My strategy for Haier Asia is to double up the digital platform. This is a great opportunity to bring us to the next level by becoming a services provider and gaining a steady stream of new revenue,” says Ito.

How did Haier take advantage of hyperconnectivity to gain the attention of stakeholders and consumers? Watch the video below to find out.

This article originally appeared on SAP Business Trends.

Want more insight on managing digital disruption? See Three Keys To Winning In A World Of Disruption.

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About Dinesh Sharma

Dinesh Sharma is the Vice President, Marketing, Internet of Things, at SAP. He is a GM-level technology executive with leadership, technical innovation, effective strategic planning, customer and partner engagement, turnaround management and focused operational execution experience at both large enterprise and startup companies.

Unlock Your Digital Super Powers: How Digitization Helps Companies Be Live Businesses

Erik Marcade and Fawn Fitter

The Port of Hamburg handles 9 million cargo containers a year, making it one of the world’s busiest container ports. According to the Hamburg Port Authority (HPA), that volume doubled in the last decade, and it’s expected to at least double again in the next decade—but there’s no room to build new roads in the center of Hamburg, one of Germany’s historic cities. The port needed a way to move more freight more efficiently with the physical infrastructure it already has.

sap_Q216_digital_double_feature1_images1The answer, according to an article on ZDNet, was to digitize the processes of managing traffic into, within, and back out of the port. By deploying a combination of sensors, telematics systems, smart algorithms, and cloud data processing, the Port of Hamburg now collects and analyzes a vast amount of data about ship arrivals and delays, parking availability, ground traffic, active roadwork, and more. It generates a continuously updated model of current port conditions, then pushes the results through mobile apps to truck drivers, letting them know exactly when ships are ready to drop off or receive containers and optimizing their routes. According to the HPA, they are now on track to handle 25 million cargo containers a year by 2025 without further congestion or construction, helping shipping companies bring more goods and raw materials in less time to businesses and consumers all across Europe.

In the past, the port could only have solved its problem with backhoes and building permits—which, given the physical constraints, means the problem would have been unsolvable. Today, though, software and sensors are allowing it to improve processes and operations to a previously impossible extent. Big Data analysis, data mining, machine learning, artificial intelligence (AI), and other technologies have finally become sophisticated enough to identify patterns not just in terabytes but in petabytes of data, make decisions accordingly, and learn from the results, all in seconds. These technologies make it possible to digitize all kinds of business processes, helping organizations become more responsive to changing market conditions and more able to customize interactions to individual customer needs. Digitization also streamlines and automates these processes, freeing employees to focus on tasks that require a human touch, like developing innovative strategies or navigating office politics.

In short, digitizing business processes is key to ensuring that the business can deliver relevant, personalized responses to the market in real time. And that, in turn, is the foundation of the Live Business—a business able to coordinate multiple functions in order to respond to and even anticipate customer demand at any moment.

Some industries and organizations are on the verge of discovering how business process digitization can help them go live. Others have already started putting it into action: fine-tuning operations to an unprecedented level across departments and at every point in the supply chain, cutting costs while turbocharging productivity, and spotting trends and making decisions at speeds that can only be called superhuman.

Balancing Insight and Action

sap_Q216_digital_double_feature1_images2Two kinds of algorithms drive process digitization, says Chandran Saravana, senior director of advanced analytics at SAP. Edge algorithms operate at the point where customers or other end users interact directly with a sensor, application, or Internet-enabled device. These algorithms, such as speech or image recognition, focus on simplicity and accuracy. They make decisions based primarily on their ability to interpret input with precision and then deliver a result in real time.

Edge algorithms work in tandem with, and sometimes mature into, server-level algorithms, which report on both the results of data analysis and the analytical process itself. For example, the complex systems that generate credit scores assess how creditworthy an individual is, but they also explain to both the lender and the credit applicant why a score is low or high, what factors went into calculating it, and what an applicant can do to raise the score in the future. These server-based algorithms gather data from edge algorithms, learn from their own results, and become more accurate through continuous feedback. The business can then track the results over time to understand how well the digitized process is performing and how to improve it.

sap_Q216_digital_double_feature1_images5From Data Scarcity to a Glut

To operate in real time, businesses need an accurate data model that compares what’s already known about a situation to what’s happened in similar situations in the past to reach a lightning-fast conclusion about what’s most likely to happen next. The greatest barrier to this level of responsiveness used to be a lack of data, but the exponential growth of data volumes in the last decade has flipped this problem on its head. Today, the big challenge for companies is having too much data and not enough time or power to process it, says Saravana.

Even the smartest human is incapable of gathering all the data about a given situation, never mind considering all the possible outcomes. Nor can a human mind reach conclusions at the speed necessary to drive Live Business. On the other hand, carefully crafted algorithms can process terabytes or even petabytes of data, analyze patterns and detect outliers, arrive at a decision in seconds or less—and even learn from their mistakes (see How to Train Your Algorithm).

How to Train Your Algorithm 

The data that feeds process digitization can’t just simmer.
It needs constant stirring.

Successfully digitizing a business process requires you to build a model of the business process based on existing data. For example, a bank creates a customer record that includes not just the customer’s name, address, and date of birth but also the amount and date of the first deposit, the type of account, and so forth. Over time, as the customer develops a history with the bank and the bank introduces new products and services, customer records expand to include more data. Predictive analytics can then extrapolate from these records to reach conclusions about new customers, such as calculating the likelihood that someone who just opened a money market account with a large balance will apply for a mortgage in the next year.

Germany --- Germany, Lower Bavaria, Man training English Springer Spaniel in grass field --- Image by © Roman M‰rzinger/Westend61/CorbisTo keep data models accurate, you have to have enough data to ensure that your models are complete—that is, that they account for every possible predictable outcome. The model also has to push outlying data and exceptions, which create unpredictable outcomes, to human beings who can address their special circumstances. For example, an algorithm may be able to determine that a delivery will fail to show up as scheduled and can point to the most likely reasons why, but it can only do that based on the data it can access. It may take a human to start the process of locating the misdirected shipment, expediting a replacement, and establishing what went wrong by using business knowledge not yet included in the data model.

Indeed, data models need to be monitored for relevance. Whenever the results of a predictive model start to drift significantly from expectations, it’s time to examine the model to determine whether you need to dump old data that no longer reflects your customer base, add a new product or subtract a defunct one, or include a new variable, such as marital status or length of customer relationship that further refines your results.

It’s also important to remember that data doesn’t need to be perfect—and, in fact, probably shouldn’t be, no matter what you might have heard about the difficulty of starting predictive analytics with lower-quality data. To train an optical character recognition system to recognize and read handwriting in real time, for example, your samples of block printing and cursive writing data stores also have to include a few sloppy scrawls so the system can learn to decode them.

On the other hand, in a fast-changing marketplace, all the products and services in your database need consistent and unchanging references, even though outside the database, names, SKUs, and other identifiers for a single item may vary from one month or one order to the next. Without consistency, your business process model won’t be accurate, nor will the results.

Finally, when you’re using algorithms to generate recommendations to drive your business process, the process needs to include opportunities to test new messages and products against existing successful ones as well as against random offerings, Saravana says. Otherwise, instead of responding to your customers’ needs, your automated system will actually control their choices by presenting them with only a limited group of options drawn from those that have already received the most
positive results.

Any process is only as good as it’s been designed to be. Digitizing business processes doesn’t eliminate the possibility of mistakes and problems; but it does ensure that the mistakes and problems that arise are easy to spot and fix.

From Waste to Gold

Organizations moving to digitize and streamline core processes are even discovering new business opportunities and building new digitized models around them. That’s what happened at Hopper, an airfare prediction app firm in Cambridge, Massachusetts, which discovered in 2013 that it could mine its archives of billions of itineraries to spot historical trends in airfare pricing—data that was previously considered “waste product,” according to Hopper’s chief data scientist, Patrick Surry.

Hopper developed AI algorithms to correlate those past trends with current fares and to predict whether and when the price of any given flight was likely to rise or fall. The results were so accurate that Hopper jettisoned its previous business model. “We check up to 3 billion itineraries live, in real time, each day, then compare them to the last three to four years of historical airfare data,” Surry says. “When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now,’ or ‘no, we think that fare is too expensive, we predict it will drop, and we’ll alert you when it does.’ And we can give them that answer in less than one second.”

When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now’.

— Patrick Surry, chief data scientist, Hopper

While trying to predict airfare trends is nothing new, Hopper has told TechCrunch that it can not only save users up to 40% on airfares but it can also find them the lowest possible price 95% of the time. Surry says that’s all due to Hopper’s algorithms and data models.

The Hopper app launched on iOS in January 2015 and on Android eight months later. The company also switched in September 2015 from directing customers to external travel agencies to taking bookings directly through the app for a small fee. The Hopper app has already been downloaded to more than 2 million phones worldwide.

Surry predicts that we’ll soon see sophisticated chatbots that can start with vague requests from customers like “I want to go somewhere warm in February for less than $500,” proceed to ask questions that help users narrow their options, and finally book a trip that meets all their desired parameters. Eventually, he says, these chatbots will be able to handle millions of interactions simultaneously, allowing a wide variety of companies to reassign human call center agents to the handling of high-value transactions and exceptions to the rules built into the digitized booking process.

Port of Hamburg Lets the Machines Untangle Complexity

In early 2015, AI experts told Wired magazine that at least another 10 years would pass before a computer could best the top human players at Go, an ancient game that’s exponentially harder than chess. Yet before the end of that same year, Wired also reported that machine learning techniques drove Google’s AlphaGo AI to win four games out of five against one of the world’s top Go players. This feat proves just how good algorithms have become at managing extremely complex situations with multiple interdependent choices, Saravana points out.

The Port of Hamburg, which has digitized traffic management for an estimated 40,000 trucks a day, is a good example. In the past, truck drivers had to show up at the port to check traffic and parking message boards. If they arrived before their ships docked, they had to drive around or park in the neighboring residential area, contributing to congestion and air pollution while they waited to load or unload. Today, the HPA’s smartPORT mobile app tracks individual trucks using telematics. It customizes the information that drivers receive based on location and optimizes truck routes and parking in real time so drivers can make more stops a day with less wasted time and fuel.

The platform that drives the smartPORT app also uses sensor data in other ways: it tracks wind speed and direction and transmits the data to ship pilots so they can navigate in and out of the port more safely. It monitors emissions and their impact on air quality in various locations in order to adjust operations in real time for better control over environmental impact. It automatically activates streetlights for vehicle and pedestrian traffic, then switches them off again to save energy when the road is empty. This ability to coordinate and optimize multiple business functions on the fly makes the Port of Hamburg a textbook example of a Live Business.

Digitization Is Not Bounded by Industry

Other retail and B2B businesses of all types will inevitably join the Port of Hamburg in further digitizing processes, both in predictable ways and in those we can only begin to imagine.

sap_Q216_digital_double_feature1_images4Customer service, for example, is likely to be in the vanguard. Automated systems already feed information about customers to online and phone-based service representatives in real time, generate cross-selling and upselling opportunities based on past transactions, and answer customers’ frequently asked questions. Saravana foresees these systems becoming even more sophisticated, powered by AI algorithms that are virtually indistinguishable from human customer service agents in their ability to handle complex live interactions in real time.

In manufacturing and IT, Sven Bauszus, global vice president and general manager for predictive analytics at SAP, forecasts that sensors and predictive analysis will further automate the process of scheduling and performing maintenance, such as monitoring equipment for signs of failure in real time, predicting when parts or entire machines will need replacement, and even ordering replacements preemptively. Similarly, combining AI, sensors, data mining, and other technologies will enable factories to optimize workforce assignments in real time based on past trends, current orders, and changing market conditions.

Public health will be able to go live with technology that spots outbreaks of infectious disease, determines where medical professionals and support personnel are needed most and how many to send, and helps ensure that they arrive quickly with the right medication and equipment to treat patients and eradicate the root cause. It will also make it easier to track communicable illnesses, find people who are symptomatic, and recommend approaches to controlling the spread of the illness, Bauszus says.

He also predicts that the insurance industry, which has already begun to digitize its claims-handling processes, will refine its ability to sort through more claims in less time with greater accuracy and higher customer satisfaction. Algorithms will be better and faster at flagging claims that have a high probability of being fraudulent and then pushing them to claims inspectors for investigation. Simultaneously, the same technology will be able to identify and resolve valid claims in real time, possibly even cutting a check or depositing money directly into the insured person’s bank account within minutes.

Financial services firms will be able to apply machine learning, data mining, and AI to accelerate the process of rating borrowers’ credit and detecting fraud. Instead of filling out a detailed application, consumers might be able to get on-the-spot approval for a credit card or loan after inputting only enough information to be identified. Similarly, banks will be able to alert customers to suspicious transactions by text message or phone call—not within a day or an hour, as is common now, but in a minute or less.

Pitfalls and Possibilities

As intelligent as business processes can be programmed to be, there will always be a point beyond which they have to be supervised. Indeed, Saravana forecasts increasing regulation around when business processes can and can’t be digitized. Especially in areas involving data security, physical security, and health and safety, it’s one thing to allow machines to parse data and arrive at decisions to drive a critical business process, but it’s another thing entirely to allow them to act on those decisions without human oversight.

Automated, impersonal decision making is fine for supply chain automation, demand forecasting, inventory management, and other processes that need faster-than-human response times. In human-facing interactions, though, Saravana insists that it’s still best to digitize the part of the process that generates decisions, but leave it to a human to finalize the decision and decide how to put it into action.

“Any time the interaction is machine-to-machine, you don’t need a human to slow the process down,” he says. “But when the interaction involves a person, it’s much more tricky, because people have preferences, tastes, the ability to try something different, the ability to get fatigued—people are only statistically predictable.”

For example, technology has made it entirely possible to build a corporate security system that can gather information from cameras, sensors, voice recognition technology, and other IP-enabled devices. The system can then feed that information in a steady stream to an algorithm designed to identify potentially suspicious activity and act in real time to prevent or stop it while alerting the authorities. But what happens when an executive stays in the office unusually late to work on a presentation and the security system misidentifies her as an unauthorized intruder? What if the algorithm decides to lock the emergency exits, shut down the executive’s network access, or disable her with a Taser instead of simply sending an alert to the head of security asking what to do while waiting for the police to come?

sap_Q216_digital_double_feature1_images6The Risk Is Doing Nothing

The greater, if less dramatic, risk associated with digitizing business processes is simply failing to pursue it. It’s true that taking advantage of new digital technologies can be costly in the short term. There’s no question that companies have to invest in hardware, software, and qualified staff in order to prepare enormous data volumes for storage and analysis. They also have to implement new data sources such as sensors or Internet-connected devices, develop data models, and create and test algorithms to drive business processes that are currently analog. But as with any new technology, Saravana advises, it’s better to start small with a key use case, rack up a quick win with high ROI, and expand gradually than to drag your heels out of a failure to grasp the long-term potential.

The economy is digitizing rapidly, but not evenly. According to the McKinsey Global Institute’s December 2015 Digital America report, “The race to keep up with technology and put it to the most effective business use is producing digital ‘haves’ and ‘have-mores’—and the large, persistent gap between them is becoming a decisive factor in competition across the economy.” Companies that want to be among the have-mores need to commit to Live Business today. Failing to explore it now will put them on the wrong side of the gap and, in the long run, rack up a high price tag in unrealized efficiencies and missed opportunities. D!

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Erik Marcade

About Erik Marcade

Erik Marcade is vice president of Advanced Analytics Products at SAP.

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Digital Transformation Needs More Than Technology

Andreas Hauser

Digital transformation is a hyped-up topic these days. But it is much more than a buzzword. Technology trends like hyper-connectivity, Big Data, cloud, Internet of Things, and security provide new opportunities for companies to re-imagine their business and how they engage with their customers and users.

But what happens if you develop an amazing technical solution that people cannot use?

Let me tell you a story.

On a business trip recently, I had an experience that some of you might have also encountered from time to time. I wanted to enter the parking garage of a hotel and had to get a parking ticket to get in — sounds simple. The machine looked pretty modern. It had an integrated monitor and several buttons on the side. First I touched the screen, but nothing happened — it was not a touchscreen. Then I pressed some buttons on the side, and again, nothing happened. The rounded button at the bottom finally got me a ticket. Great technical solution … but not usable.

Endurance testing experiences like this one are actually easily preventable when taking into consideration human needs (desirability). This makes very clear that we need to connect three elements—viability, feasibility, and desirability—to be successful and remain competitive in the digital era.

Wikipedia defines digital transformation as “application of digital technology in all aspects of human society.” This is why companies with the most successful digital transformations have focused on people and applied a design-led approach.

One company that has excelled at creating a pleasant experience is Uber. Their app not only tells you how long it will take the car to arrive, but you can also watch the arrival on your mobile device. I like the user interface. But here’s what I personally like most about the Uber experience: You get out of the car, keep your mobile phone in your pocket, do nothing, pay automatically without thinking about how much you need to tip the driver, and get the receipt via e-mail.

That is the difference between simply focusing on the user interface and providing a great customer and user experience. To design and develop such a solution, you need to know what people really desire. Technology certainly plays a very important role to make this experience a reality, and you must be clear about the business model.

Slide1.JPG


Design-led digital transformation means leveraging breakthrough technology trends, re-imaging business processes and business models, and re-imaging the customer and user experience to achieve design-led innovations.

In today’s digital economy, companies understand that the experience their customers and users have must be the core focus of its brand and survival. Customers and users drive the current and future state of any business. Products and services, whether they are delivered to internal or external customers, must create a value for them and the company. Therefore, customers and users need to be an integral part—not an afterthought—of the entire product development process.

Design thinking to focus on human needs

To better understand what that experience can be, companies are using design thinking – a human-centered approach to innovation – and are putting the customer and user into the center of all activities. Design thinking focuses on human needs, problem finding, working in inter-disciplinary teams across the innovation lifecycle, and a fail-fast, fail-early approach.

My observation from about 500 customer projects is that more and more IT organizations are starting to apply design thinking within their organization. They are hiring designers to better understand the needs of their customers and users and are translating these needs into an experience design. In the past, they simply collected requirements from the business and implemented functions, features, and business processes. This was sufficient in last-decade enterprises, but consumerization of IT requires re-thinking of this approach.

Create business value with human-centered design

The goal is to create business value by engaging with customers and users throughout the end-to-end process—from discovery to design to delivery—and apply design thinking combined with agile methodologies. It is not about simply creating a cool design; rather it is all about creating business value and outcomes.

To do this, business and IT need to work hand in hand to take the company toward that single consumer’s experience.Slide2.JPG

Let’s look at an example.

As part of its business strategy, Mercedes-AMG, the sports car brand of Mercedes-Benz, aimed to increase its production drastically while keeping the excellent quality standards that have always characterized its products. In a co-innovation project, we have engaged on an intensive research plan and applied the principles of design thinking and agile software development to bring the Mercedes-AMG vision to life: a customizable collaborative planning solution that supports cross-functional competence teams and increases efficiency during the three-year production process. The solution, based on SAP HANA, provides access to relevant data in a holistic way and enables a seamless team collaboration in the remodeled process. One of the key success factors was engaging with users throughout the entire process by observing how they work and iterating on solutions with them.

Digital transformation is a journey, not just a one-time project. Ultimately, enterprises want to prepare their organization for sustainable design-led digital transformation.

So how can you embrace the human aspect of design in your digital transformation? This is our credo: Apply design thinking to engage with your customers and most importantly, with users, right from the beginning, in an iterative, user-centric design process.

If you are interested in more customer stories, check out the UX Design Services website. You can also find more information in this presentation, or check out this video recording.

This article originally appeared on SAP Business Trends.

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Andreas Hauser

About Andreas Hauser

Andreas is global head of the design and co-innovation center at SAP. His team drives customer & strategic design projects through Co-Innovation and Design Thinking. Before he was Vice President of User Experience at SAP SE for OnDemand Solutions.