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Digital Transformation In Discrete Manufacturing

Stefan Krauss

Forklifts might be the best example of how technology is shaping the future of discrete manufacturing. Consider these two companies.

  • Company A looks to increase profits by cutting costs and asking employees to find ways to make forklifts cheaper. It seeks to increase market share by offering steeper price discounts.
  • Company B has asked customers, employees, and suppliers whether forklifts will be needed in the next decade. Should the company just be selling forklifts? Should it consider becoming a “warehouse as a service” provider instead, running warehouses for other businesses?

Which company is forward-thinking, recognizing that automation, smart products, and innovation will drive new business models? Which company is considering how to best provide the services that customers expect in addition to the products sold?

The answer is clear. For companies that recognize the pivotal role that digital transformation plays in driving innovation, the future is bright.

CIO role transforming with digital change

For CIOs, digital transformation changes their roles within organizations. The CIO of tomorrow must bring his or her skills and insights on new digital technologies to bear on the rest of the organization.

Instead of staying in traditional silos, transformative CIOs will lead engagements that bring IT departments together to collaborate deeply with other parts of the organization – sales, marketing, research and development, software development, manufacturing, and supply chain.

When successful, CIOs will help organizations rethink business models and business processes. Digital transformation will change how people are hired, trained, and deployed, leveraging the use of business networks and contingent workers to ensure companies are ready for the future.

While technology is enabling companies to reconsider their businesses, the fundamentals remain relevant: how companies can grow the business and improve efficiency. The difference is that digital changes are critical to that growth.

Automakers, high-tech companies offering services

Consider the automobile industry. Nontraditional competitors such as Google, Tesla, and Uber are causing consumers to ask, “Do I need to own a car?” Particularly in urban, congested areas, mobility is more relevant today. What’s important is optimizing the way to get from point A to point B. That demand means rapid flexibility and service. It may mean using different car models for different seasons.

Automakers are responding by thinking about services that eliminate or reduce the costs of taxes and parking. Connected vehicles are placing more emphasis on services rather than horsepower. Consumers, they realize, want services such as pre-ordered parking spots, gas station location services, or prepaid coffee in their favorite drive-through.

High-tech companies are leveraging the Internet of Things in two significant ways. Companies are producing not just hardware, but also pure or embedded software built into products. This means they can offer services in addition to or instead of just selling. For example, a printer company is now giving companies the option to purchase printing services instead of printers. The manufacturer owns and maintains the printers, using embedded software to detect needed maintenance, such as toner replacement, and replaces the printers as needed. It’s selling output, not the printers themselves.

Industrial machines, aircraft companies rethink business models

Similarly, in industrial machines and components companies, the business models are changing. One compressor manufacturer we work with is shifting its business to selling not air compressors, but air itself.

Customers purchase an amount of compressed air, with the company controlling costs via predictive maintenance software. Many call this shift Industry 4.0, allowing manufacturers to have more flexible manufacturing processes that can better react to customer demands.

The aircraft and defense industry has been at the forefront of digital transformation for years, using sensors to track and assess performance and improve safety. With such increased demand for new airplanes and passenger capacity, some OEMs are considering not selling aircrafts and engines, but rather the performance of those machines.

For the CIO of the future, a top-down approach is crucial. CIOs need to consider how to inspire and lead other C-suite executives to an innovative, connected future.

For more on how Innovator CIOs are reimagining business models to uncover new revenue, read the Digital Bridge report on Digital Business and CIO Innovation Imperative

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Stefan Krauss

About Stefan Krauss

Stefan Krauss is the general manager for Discrete Industries at SAP. Together with his team, he is responsible for the integrated management of the industries Aerospace & Defense, Automotive, High Tech and Industrial Machinery & Components – spanning development, solution management, sales and marketing, value engineering, partner management, services and support. The mission of this unit is to deliver industry cloud solutions that help SAP customers sustainably innovate and grow their business, operate safely, and develop their people.

Cashing In On Space Data [VIDEO]

Robin Meyerhoff

If you want to know what’s happening on Earth, the European Space Agency (ESA) has your back. Every day dozens of ESA satellites generate around ten terabyte of data. Billed as “Europe’s gateway to space,” ESA is the largest provider of Earth observation information in the world, constantly monitoring the planet’s security and environment.

Until recently, that information was held under lock and key, unless you were a scientist with clearance to use it. However, in 2007, the European Union (which works closely with ESA and provides some 20 percent of its funding) changed its policy, allowing the agency to make its data freely available to the public.

This change has opened a new world of opportunity for ESA, the EU and businesses. Nicolaus Hanowski, who heads the ESA Earth Observation Programme, said, “When the EU decided a few years ago that all that observation data was free and open, it triggered new possibilities for ESA and the industrial world.”

Particularly with the maturation of Internet of Things, Big Data, and cloud technologies, the commercial sector now has effective ways to access this data and use it in real time.

Space data helps business and society

Here’s how it works: Satellites, drones, and other airborne “things” can transmit data, which is combined and turned into usable information by Big Data solutions like geospatial, real-time, and predictive analytics. Cloud computing makes it possible for the ESA to deliver specific sets of information to organizations that can use it to solve problems like evaluating agriculture land use, managing gas pipelines, and measuring the effect of climate change.

Hanowski explains ESA already has thematic data repositories including coastal, forestry, urban development, climate, and hydrology.  “Our mission is to make the data consumable. We want to the uptake to be as big as possible — and economically influential. We need to understand what kind of data is interesting to commercial organizations.”

Once they understand key topic areas for businesses, ESA can combine its satellite data with additional types of airborne and ground data to help companies bring new digital business models to life.

With the release of an Earth observation analysis service, organizations can now analyze historic and real-time satellite from ESA, which will help businesses better understand current conditions – and predict future situations.

Through an in-memory computing platform, decision makers can predict future scenarios, their probability, and potential actions to take. Farmers, for instance, will not only know about upcoming storms, but also how to optimize water and fertilizer use on their fields based on satellite information. Even better, the farmer can detect imminent onset of the common crop diseases – and start a preventive treatment immediately.

Munich Re, one of the world’s largest reinsurance companies, is one of the first companies using the analysis service. The increasing frequency of natural disasters like wildfires due to climate change pose a huge challenge for the insurance industry. By analyzing real-time and historic satellite data of wildfires in different regions, Munich Re can more accurately calculate insurance risks and costs. Munich Re can use wildfire data to do predictive analysis that estimates the probability of future wildfires and potential damage to people, homes, and businesses, thus minimizing costs for clients.

Dr. Carsten Linz, head of the SAP Center for Digital Leadership, said, “Like many organizations, ESA is going through a digital transformation, and this technology is helping them pave the way by closing the gap between a traditional Earth observation institution and the digital business world. ESA’s mission is to disseminate space data that is relevant to businesses – and was previously only available to scientists and data specialists. Hence, a major part of our work together is to make the information usable, accessible, and secure, which is why the in-memory computing platform and cloud technologies are so important to ESA.”

While commercial data use is a priority for ESA, Hanowski is hopeful that with analytic services, they will be able to help unite scientific and relief communities on pressing topics like smart cities, food security, and water management.

Eventually businesses will use the data to improve efficiency and offer better products, ESA will gain a revenue stream, and NGOs and the public sector can use it to improve people’s lives. In other words, everyone wins.

For more on the transformative scientific potential of data analytics, see The Promise Of The Internet Of Things.

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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.

Using Data Science For Predictive Maintenance

Sandeep Raut

A few years ago, there were two recall announcements from the National Highway Traffic Safety Administration, warning of problems that could cause fires in two auto brands. For both automakers, these defect required significant money and time to resolve.

Manufacturers in the aerospace, rail, equipment, and automotive industries face the challenge of ensuring maximum availability of critical assembly line systems and keeping those assets in good working order, while simultaneously minimizing the cost of maintenance and time-based or count-based repairs. Identifying root causes of faults and failures must also happen without labs or testing. As more vehicles, industrial equipment, and assembly robots communicate their status to a central server, detection of faults becomes easier and more practical.

Identifying potential issues early helps organizations deploy maintenance teams more cost-effectively and maximizes parts and equipment uptime. All the critical factors that help predict failure may be deeply buried in structured data (including equipment year, make, model, and warranty details) and unstructured data comprising millions of log entries that include sensor data, error messages, odometer readings, speeds, engine temperatures, engine torque and acceleration records, and repair and maintenance reports.

Predictive maintenance, a technique for predicting when an in-service machine will fail so that maintenance can be planned in advance, encompasses failure prediction, failure diagnosis, failure type classification, and recommendation of maintenance actions after failure. For example, TrenItalia has invested 50 million euros in an Internet of Things project to cut maintenance costs by up to 130 million euros and increase train availability and customer satisfaction.

The benefits of using data science with predictive maintenance include:

  • Minimized maintenance costs. Don’t waste money through over-cautious, time-bound maintenance. Repair equipment only when repairs are actually needed.
  • Reduced unplanned downtime. Implement predictive maintenance to predict future equipment malfunctions and failures, and minimize the risk for unplanned disasters that could put your business at risk.
  • Root-cause analysis. Find causes for equipment malfunctions and work with suppliers to switch off reasons for high failure rates. Increase return on your assets.
  • Efficient labor planning. Stop wasting time replacing and fixing equipment that doesn’t need it.
  • Avoidance of warranty cost to recover failure. Minimize recalls and assembly-line production loss.

Sudden machine failures can result in contract penalties and lost revenue, and can even ruin the reputation of a business. Data science can help avoid problems in real time and before they happen.

For more on how predictive analytics can improve business efficiency, see Using Algorithms To Add Science To Human Judgement In HR.

This article originally appeared in Simplified Analytics.

 

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How Emotionally Aware Computing Can Bring Happiness to Your Organization

Christopher Koch


Do you feel me?

Just as once-novel voice recognition technology is now a ubiquitous part of human–machine relationships, so too could mood recognition technology (aka “affective computing”) soon pervade digital interactions.

Through the application of machine learning, Big Data inputs, image recognition, sensors, and in some cases robotics, artificially intelligent systems hunt for affective clues: widened eyes, quickened speech, and crossed arms, as well as heart rate or skin changes.




Emotions are big business

The global affective computing market is estimated to grow from just over US$9.3 billion a year in 2015 to more than $42.5 billion by 2020.

Source: “Affective Computing Market 2015 – Technology, Software, Hardware, Vertical, & Regional Forecasts to 2020 for the $42 Billion Industry” (Research and Markets, 2015)

Customer experience is the sweet spot

Forrester found that emotion was the number-one factor in determining customer loyalty in 17 out of the 18 industries it surveyed – far more important than the ease or effectiveness of customers’ interactions with a company.


Source: “You Can’t Afford to Overlook Your Customers’ Emotional Experience” (Forrester, 2015)


Humana gets an emotional clue

Source: “Artificial Intelligence Helps Humana Avoid Call Center Meltdowns” (The Wall Street Journal, October 27, 2016)

Insurer Humana uses artificial intelligence software that can detect conversational cues to guide call-center workers through difficult customer calls. The system recognizes that a steady rise in the pitch of a customer’s voice or instances of agent and customer talking over one another are causes for concern.

The system has led to hard results: Humana says it has seen an 28% improvement in customer satisfaction, a 63% improvement in agent engagement, and a 6% improvement in first-contact resolution.


Spread happiness across the organization

Source: “Happiness and Productivity” (University of Warwick, February 10, 2014)

Employers could monitor employee moods to make organizational adjustments that increase productivity, effectiveness, and satisfaction. Happy employees are around 12% more productive.




Walking on emotional eggshells

Whether customers and employees will be comfortable having their emotions logged and broadcast by companies is an open question. Customers may find some uses of affective computing creepy or, worse, predatory. Be sure to get their permission.


Other limiting factors

The availability of the data required to infer a person’s emotional state is still limited. Further, it can be difficult to capture all the physical cues that may be relevant to an interaction, such as facial expression, tone of voice, or posture.



Get a head start


Discover the data

Companies should determine what inferences about mental states they want the system to make and how accurately those inferences can be made using the inputs available.


Work with IT

Involve IT and engineering groups to figure out the challenges of integrating with existing systems for collecting, assimilating, and analyzing large volumes of emotional data.


Consider the complexity

Some emotions may be more difficult to discern or respond to. Context is also key. An emotionally aware machine would need to respond differently to frustration in a user in an educational setting than to frustration in a user in a vehicle.

 


 

download arrowTo learn more about how affective computing can help your organization, read the feature story Empathy: The Killer App for Artificial Intelligence.


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Christopher Koch

About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

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In An Agile Environment, Revenue Models Are Flexible Too

Todd Wasserman

In 2012, Dollar Shave Club burst on the scene with a cheeky viral video that won praise for its creativity and marketing acumen. Less heralded at the time was the startup’s pricing model, which swapped traditional retail for subscriptions.

For as low as $1 a month (for five two-bladed cartridges), consumers got a package in the mail that saved them a trip to the pharmacy or grocery store. Dollar Shave Club received the ultimate vindication for the idea in 2016 when Unilever purchased the company for $1 billion.

As that example shows, new technology creates the possibility for new pricing models that can disrupt existing industries. The same phenomenon has occurred in software, in which the cloud and Web-based interfaces have ushered in Software as a Service (SaaS), which charges users on a monthly basis, like a utility, instead of the typical purchase-and-later-upgrade model.

Pricing, in other words, is a variable that can be used to disrupt industries. Other options include usage-based pricing and freemium.

Products as services, services as products

There are basically two ways that businesses can use pricing to disrupt the status quo: Turn products into services and turn services into products. Dollar Shave Club and SaaS are two examples of turning products into services.

Others include Amazon’s Dash, a bare-bones Internet of Things device that lets consumers reorder items ranging from Campbell’s Soup to Play-Doh. Another example is Rent the Runway, which rents high-end fashion items for a weekend rather than selling the items. Trunk Club offers a twist on this by sending items picked out by a stylist to users every month. Users pay for what they want and send back the rest.

The other option is productizing a service. Restaurant franchising is based on this model. While the restaurant offers food service to consumers, for entrepreneurs the franchise offers guidance and brand equity that can be condensed into a product format. For instance, a global HR firm called Littler has productized its offerings with Littler CaseSmart-Charges, which is designed for in-house attorneys and features software, project management tools, and access to flextime attorneys.

As that example shows, technology offers opportunities to try new revenue models. Another example is APIs, which have become a large source of revenue for companies. The monetization of APIs is often viewed as a side business that encompasses a wholly different pricing model that’s often engineered to create huge user bases with volume discounts.

Not a new idea

Though technology has opened up new vistas for businesses seeking alternate pricing models, Rajkumar Venkatesan, a marketing professor at University of Virginia’s Darden School of Business, points out that this isn’t necessarily a new idea. For instance, King Gillette made his fortune in the early part of the 20th Century by realizing that a cheap shaving device would pave the way for a recurring revenue stream via replacement razor blades.

“The new variation was the Keurig,” said Venkatesan, referring to the coffee machine that relies on replaceable cartridges. “It has started becoming more prevalent in the last 10 years, but the fundamental model has been there.” For businesses, this can be an attractive model not only for the recurring revenue but also for the ability to cross-sell new goods to existing customers, Venkatesan said.

Another benefit to a subscription model is that it can also supply first-party data that companies can use to better understand and market to their customers. Some believe that Dollar Shave Club’s close relationship with its young male user base was one reason for Unilever’s purchase, for instance. In such a cut-throat market, such relationships can fetch a high price.

To learn more about how you can monetize disruption, watch this video overview of the new SAP Hybris Revenue Cloud.

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