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.

Beyond Spare Parts: 3D Printing And Machine Learning

Stefan Krauss

The concept of 3D printing isn’t a new one. In fact, it’s been around for more than 30 years – long before it became popular in consumer settings. In industries like automotive and aerospace, we call it additive manufacturing – the process of creating something new by layering materials, like plastic, metal, or concrete, using computer-modeled designs.

This approach is extremely versatile, allowing manufacturing teams to visualize large design projects through miniature scale models, design and create small runs of custom parts and equipment for customers, and prototype new products. As 3D printing speeds increase, Gartner predicts the 3D printing industry will be a $4.6 billion market by 2019.

Until now, the primary application for 3D printing in discrete industries has been prototyping new parts and equipment. But there’s significant room for expansion, especially in the efficient fabrication of spare parts.

Most discrete manufacturers are already producing spare parts, but few have adopted tactical 3D printing as an update to their process. The lead time currently required to create many spare parts can be both long and expensive, so the only way to ensure these parts are available to the customer in a timely fashion is to create and store them in advance. This process is inefficient and cost-prohibitive for the manufacturer – resulting in higher costs and longer wait times for customers. 3D printing provides a turnkey solution to this problem, and gives manufacturers the opportunity to supply their customers with high-quality parts, on-demand, when they are needed most.

Even more exciting, with innovations in other emerging technologies concurrently maturing, 3D printing is just the start of what manufacturers can do to enhance their production process for spare parts. While 3D printing certainly expedites creation, storage and delivery, it’s still a reactionary operation at its core. Instead of relying on customers to tell them when to print these parts, discrete manufacturers must transform their operations to think proactively – leveraging machine learning (ML) to solve maintenance issues before they occur.

As 3D printing capabilities grow, maintenance teams face a variety of challenges, including the number of parts that can be printed and increasing demand from customers for faster delivery. Regardless of these challenges, their goals remain the same: to ensure that parts are available and shipped to a customer in a timely fashion. As such, it’s critical that manufacturers evolve to meet this demand by incorporating machine learning into their process.

Machine learning technology identifies, analyzes, and monitors nearly infinite amounts of data, allowing it to provide a real-time status of processes and machinery. When implemented in a discrete manufacturing setting, teams can use ML to analyze the life remaining on a specific part or piece of equipment, and flag system failures before they happen. Similarly, when synchronized with a predetermined replacement schedule, ML can help proactively identify when it’s time for a customer to replace their parts – thereby avoiding unplanned downtime for machinery that would otherwise need to be taken out of service.

Manufacturers could combine this predictive maintenance with their ability to 3D print spare parts efficiently to become full-service vendors for their customers. Those who do so will not only serve as true leaders in spare parts manufacturing, but also in customer service.

With technology disrupting nearly every type of enterprise business model, customers are demanding more, and have higher expectations than ever before. They expect materials on time and on-hand when they need them, and they expect their suppliers to adjust accordingly. Discrete manufacturers producing spare parts must meet this demand by incorporating 3D printing, in conjunction with ML, to help quickly deliver high-quality spare parts to customers ahead of demand.

Manufacturers who can take advantage of ML to predict when equipment and parts will fail, then subsequently employ 3D printing to proactively print and ship replacement parts ahead of these failures, will enjoy significantly reduced spare parts costs and delivery times, and higher customer satisfaction.

For more on implementing advanced technology to your business processes, see Managing Digital Disruption Requires The Right Strategy And Mindset.

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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.

Three Digital Innovations That Will Beat 'Rock, Paper, Scissors'

Frank Klipphahn

Rock breaks scissors. Paper covers rock. Scissors cut paper. Those are the rules of Rock-Paper-Scissors, the ancient game people play with their hands. But wouldn’t it be better if those tools could join forces for the common good?

The question also applies to three digital innovations: blockchain, digital solutions based on the Internet of Things (IoT), and machine learning (ML). These are technological tools that support each other in the process of digital transformation (DX).

The IoT connects the users and things across the world. Blockchain helps secure sharing of sensitive information via the IoT. And ML speeds cybersecurity checks to make IoT connectivity safer.

Corporate officers of companies investing in DX may view blockchain, IoT solutions, and ML as competitors for IT budget. Yes, they are distinct digital tools. But together they form a powerful team supporting the aerospace industry and its many clients.

Defense and aerospace contractor Lockheed Martin is at the forefront of companies combining the capabilities of these tools.

Launching into the connected digi-verse

The Internet of Things (IoT) is a vast network of digital objects with embedded intelligence connected to the Internet via sensors. Connected factories, smart equipment, and users connected via mobile devices have been successfully leveraging IoT for years to optimize A&D operations and products.

Other IoT devices range from global positioning systems (GPS) in vehicles to digital twin computer models of aircraft. A digital twin is a software model of a physical thing/asset that relies on sensor data to understand the full state of the thing, enables digital simulations and learning to improve operations, and adds value by aiding predictive maintenance. Engineers can use these IoT-enabled 3D models to visualize where repairs are necessary based on data the sensors produce.

Lockheed Martin has relied on 3D modeling for many years. Until recently, the company referred to its immersion in DX as creating a digital tapestry of seamless connections between the “conceptualization, design, verification, manufacturing and sustainment” of its ideas.

The company made a major announcement at the 2017 ML Summit in early June. A report in the Manufacturing Leadership Council blog indicated that Lockheed Martin intends to expand into the use of digital twins to improve simulations of all its products and processes. The company also announced that the tapestry analogy no longer fits its ecosphere of innovations, which it now calls the Lockheed Martin Digi-Verse.

You might say that Lockheed Martin actually shot off into the digi-verse two months earlier with the launch of its iSpace (intelligent space) software to protect space assets. The company announced that the software “tasks, processes, and correlates data from a worldwide network of government, commercial, and scientific community sensors and command centers.” It added that the system automatically sends users real-time information gathered from “optical, radar, infrared, and radio sensors” and recommends “the best course of action.”

Seeking blockchain protection of data

CNET reported in early May that the company is adopting blockchain as a strategy for speeding the discovery and solution of cybersecurity problems. CNET added that the company is using blockchain to protect its data and “secure software development and supply chain risk management.”

Blockchain is a digital tool for creating decentralized ledgers that are secure. Many industries are exploring potential uses of this tool, which developed as a way to exchange the universal digital currency bitcoin.

It works this way: Participants in a blockchain network add individual transactions (blocks) to a shared ledger. In addition to people, the participants may include machines (ML again!) that can make quick decisions for an organization based on rapid data analysis.

Blockchain technology allows participants to read but not change blocks created by others. This characteristic protects information in a shared ledger.

Blockchain networking saves time and money for individual participants and organizations. It also makes the ledger process transparent for all involved. In aAerospace, it helps to build trust and integrity, and allows cyber-secure provenance and traceability of parts and other assets across shared business processes.

Bitcoin news service News BTC reports that machine learning can team with blockchain technology to ensure the accuracy of data and limit false positive results when testing for cyber threats.

Turning Big Data into action with machine learning

Machine learning is a form of artificial intelligence. ML applications turn computers into machines that can learn from other objects such as Digital Twins without being explicitly programmed.

These programs access the flood of information created by IoT objects, which is called big data. They sort, analyze, and learn from it automatically. Big Data is so massive that it takes rapid computing power to process it in real time. People cannot do it.

Lockheed Martin says that ML “turns data into action.” In a post about warfare innovation at its blog, the company notes, “Today, the military gathers data through sensors on a range of platforms, including aircraft, weapon systems, ground vehicles and even troops in the field.”

Lockheed Martin defense products use ML to aid military intelligence gathering and identification of threats. The company reports that the goal is to increase automation in decision-making.

Speed in decision-making decreases damage to targets, whether they are troops and cities in warzones or databases concerning company inventions and operations. Lockheed Martin uses the IoT, blockchain, and ML to protect people as well as products. Together, these digital tools are a powerful force.

It quickly becomes clear that topics like the Internet of Things, machine learning, blockchain, analytics, artificial intelligence, and Big Data often need to be viewed in combination: This is the key to creating a framework for harnessing the latest digital breakthroughs.

Learn how to bring new technologies and services together to power digital transformation by downloading The IoT Imperative for Discrete Manufacturers: Automotive, Aerospace and Defense, High Tech, and Industrial Machinery.

Explore how to bring Industry 4.0 insights into your business today by reading Industry 4.0: What’s Next?

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Frank Klipphahn

About Frank Klipphahn

Frank Klipphahn is the Senior Solution Manager for the Aerospace and Defense Industry Unit at SAP.

Human Skills for the Digital Future

Dan Wellers and Kai Goerlich

Technology Evolves.
So Must We.


Technology replacing human effort is as old as the first stone axe, and so is the disruption it creates.
Thanks to deep learning and other advances in AI, machine learning is catching up to the human mind faster than expected.
How do we maintain our value in a world in which AI can perform many high-value tasks?


Uniquely Human Abilities

AI is excellent at automating routine knowledge work and generating new insights from existing data — but humans know what they don’t know.

We’re driven to explore, try new and risky things, and make a difference.
 
 
 
We deduce the existence of information we don’t yet know about.
 
 
 
We imagine radical new business models, products, and opportunities.
 
 
 
We have creativity, imagination, humor, ethics, persistence, and critical thinking.


There’s Nothing Soft About “Soft Skills”

To stay ahead of AI in an increasingly automated world, we need to start cultivating our most human abilities on a societal level. There’s nothing soft about these skills, and we can’t afford to leave them to chance.

We must revamp how and what we teach to nurture the critical skills of passion, curiosity, imagination, creativity, critical thinking, and persistence. In the era of AI, no one will be able to thrive without these abilities, and most people will need help acquiring and improving them.

Anything artificial intelligence does has to fit into a human-centered value system that takes our unique abilities into account. While we help AI get more powerful, we need to get better at being human.


Download the executive brief Human Skills for the Digital Future.


Read the full article The Human Factor in an AI Future.


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Dan Wellers

About Dan Wellers

Dan Wellers is founder and leader of Digital Futures at SAP, a strategic insights and thought leadership discipline that explores how digital technologies drive exponential change in business and society.

Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation.

Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

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The Human Factor In An AI Future

Dan Wellers and Kai Goerlich

As artificial intelligence becomes more sophisticated and its ability to perform human tasks accelerates exponentially, we’re finally seeing some attempts to wrestle with what that means, not just for business, but for humanity as a whole.

From the first stone ax to the printing press to the latest ERP solution, technology that reduces or even eliminates physical and mental effort is as old as the human race itself. However, that doesn’t make each step forward any less uncomfortable for the people whose work is directly affected – and the rise of AI is qualitatively different from past developments.

Until now, we developed technology to handle specific routine tasks. A human needed to break down complex processes into their component tasks, determine how to automate each of those tasks, and finally create and refine the automation process. AI is different. Because AI can evaluate, select, act, and learn from its actions, it can be independent and self-sustaining.

Some people, like investor/inventor Elon Musk and Alibaba founder and chairman Jack Ma, are focusing intently on how AI will impact the labor market. It’s going to do far more than eliminate repetitive manual jobs like warehouse picking. Any job that involves routine problem-solving within existing structures, processes, and knowledge is ripe for handing over to a machine. Indeed, jobs like customer service, travel planning, medical diagnostics, stock trading, real estate, and even clothing design are already increasingly automated.

As for more complex problem-solving, we used to think it would take computers decades or even centuries to catch up to the nimble human mind, but we underestimated the exponential explosion of deep learning. IBM’s Watson trounced past Jeopardy champions in 2011 – and just last year, Google’s DeepMind AI beat the reigning European champion at Go, a game once thought too complex for even the most sophisticated computer.

Where does AI leave human?

This raises an urgent question for the future: How do human beings maintain our economic value in a world in which AI will keep getting better than us at more and more things?

The concept of the technological singularity – the point at which machines attain superhuman intelligence and permanently outpace the human mind – is based on the idea that human thinking can’t evolve fast enough to keep up with technology. However, the limits of human performance have yet to be found. It’s possible that people are only at risk of lagging behind machines because nothing has forced us to test ourselves at scale.

Other than a handful of notable individual thinkers, scientists, and artists, most of humanity has met survival-level needs through mostly repetitive tasks. Most people don’t have the time or energy for higher-level activities. But as the human race faces the unique challenge of imminent obsolescence, we need to think of those activities not as luxuries, but as necessities. As technology replaces our traditional economic value, the economic system may stop attaching value to us entirely unless we determine the unique value humanity offers – and what we can and must do to cultivate the uniquely human skills that deliver that value.

Honing the human advantage

As a species, humans are driven to push past boundaries, to try new things, to build something worthwhile, and to make a difference. We have strong instincts to explore and enjoy novelty and risk – but according to psychologist Mihaly Csikszentmihalyi, these instincts crumble if we don’t cultivate them.

AI is brilliant at automating routine knowledge work and generating new insights from existing data. What it can’t do is deduce the existence, or even the possibility, of information it isn’t already aware of. It can’t imagine radical new products and business models. Or ask previously unconceptualized questions. Or envision unimagined opportunities and achievements. AI doesn’t even have common sense! As theoretical physicist Michio Kaku says, a robot doesn’t know that water is wet or that strings can pull but not push. Nor can robots engage in what Kaku calls “intellectual capitalism” – activities that involve creativity, imagination, leadership, analysis, humor, and original thought.

At the moment, though, we don’t generally value these so-called “soft skills” enough to prioritize them. We expect people to develop their competency in emotional intelligence, cross-cultural awareness, curiosity, critical thinking, and persistence organically, as if these skills simply emerge on their own given enough time. But there’s nothing soft about these skills, and we can’t afford to leave them to chance.

Lessons in being human

To stay ahead of AI in an increasingly automated world, we need to start cultivating our most human abilities on a societal level – and to do so not just as soon as possible, but as early as possible.

Singularity University chairman Peter Diamandis, for example, advocates revamping the elementary school curriculum to nurture the critical skills of passion, curiosity, imagination, critical thinking, and persistence. He envisions a curriculum that, among other things, teaches kids to communicate, ask questions, solve problems with creativity, empathy, and ethics, and accept failure as an opportunity to try again. These concepts aren’t necessarily new – Waldorf and Montessori schools have been encouraging similar approaches for decades – but increasing automation and digitization make them newly relevant and urgent.

The Mastery Transcript Consortium is approaching the same problem from the opposite side, by starting with outcomes. This organization is pushing to redesign the secondary school transcript to better reflect whether and how high school students are acquiring the necessary combination of creative, critical, and analytical abilities. By measuring student achievement in a more nuanced way than through letter grades and test scores, the consortium’s approach would inherently require schools to reverse-engineer their curricula to emphasize those abilities.

Most critically, this isn’t simply a concern of high-tuition private schools and “good school districts” intended to create tomorrow’s executives and high-level knowledge workers. One critical aspect of the challenge we face is the assumption that the vast majority of people are inevitably destined for lives that don’t require creativity or critical thinking – that either they will somehow be able to thrive anyway or their inability to thrive isn’t a cause for concern. In the era of AI, no one will be able to thrive without these abilities, which means that everyone will need help acquiring them. For humanitarian, political, and economic reasons, we cannot just write off a large percentage of the population as disposable.

In the end, anything an AI does has to fit into a human-centered value system that takes our unique human abilities into account. Why would we want to give up our humanity in favor of letting machines determine whether or not an action or idea is valuable? Instead, while we let artificial intelligence get better at being what it is, we need to get better at being human. That’s how we’ll keep coming up with groundbreaking new ideas like jazz music, graphic novels, self-driving cars, blockchain, machine learning – and AI itself.

Read the executive brief Human Skills for the Digital Future.

Build an intelligent enterprise with AI and machine learning to unite human expertise and computer insights. Run live with SAP Leonardo.


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Dan Wellers

About Dan Wellers

Dan Wellers is founder and leader of Digital Futures at SAP, a strategic insights and thought leadership discipline that explores how digital technologies drive exponential change in business and society.

Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation.

Share your thoughts with Kai on Twitter @KaiGoe.heif Futu