Living The Live Supply Chain: Why You Need Data

Hans Thalbauer

In this post, Part 1 of this series, we explore the essentials of deploying a live supply chain. In Part 2 we’ll look at why data scientists will be increasingly key to supply chain success.

Supply chain management is both science and art, and the supply chain operations of leading retailers, consumer products companies, and other manufacturers have been honed to the highest degree.

Unfortunately, the highest degree is no longer sufficient. That’s because established processes are labor-intensive, prone to error, and too slow in providing relevant information to the systems and people who need it. Meanwhile, market dynamics – your customers, your competitors, and the business conditions that affect you – take place in real time.

The solution is to replace your established but now inadequate operations with a live supply chain.

Running on real-time data

A live supply chain runs on real-time data, or at least “right-time” data. It connects employees, partners, customers, assets, and devices. It lets you make predictions and take actions at the speed of the marketplace.

Until very recently, we didn’t have the tools to make this possible. So we made sales forecasts based on sales history – which someone once said is like driving a car forward while looking in the rearview mirror.

But today we do have the tools, and that’s changing the competitive landscape. That is to say, your competitors are actively moving toward live supply chains. And that means you have to respond. Because your competitors aren’t just becoming more efficient. They’re actually reimagining your industry – like when Uber leveraged real-time data to upend ride services.

That real-time data, and where it comes from, will vary depending on your sector. It might come from commerce networks. It might come from social media. It might come from IoT sensors. It will cover everything from how your suppliers are sourcing raw materials at one end of your supply chain to how your products are being used by customers at the other.

The quantity of data is potentially enormous. Just think of the sensors on the average delivery vehicle. You can measure tire pressure and engine performance to predict when maintenance is needed. You can monitor driver behavior to make sure delivery is safe. You can track GPS coordinates to ensure delivery is on time. You can sense the temperature of the storage unit to make sure goods remain saleable. You can track the products themselves to be sure they haven’t been tampered with.

Changing business, changing business models

All this data needs to be fed into your business systems to drive design, planning, logistics, and other operational processes in sync with changing conditions. Some of that data is structured, but much of it is unstructured. It also comes in a vast array of types; that delivery truck probably has more than 100 sensors generating data in nearly as many formats. So you need a real-time system in which you can harmonize and analyze that data.

What does that entail? You have to store it at the lowest level of granularity. You need to parse it so that you’re managing only the data you need while ignoring the data you don’t need. And you must summarize the results at the right level for each job function or stakeholder. Without investing in sophisticated systems and advanced analytics to turn data into actionable information, your supply chain won’t come close to being live.

But the payoffs of that investment include better customer insights, more accurate supply visibility, improved demand forecasts, and real-time decisions that can lead to improved profitability.

They can also lead to competitive advantage through new business models. The example we often cite at SAP is our customer Kaeser Compressor, which transformed itself from a maker of industrial air compressors into a provider of compressed air. In the past, Kaeser sold air compressors that customers had to maintain themselves. Today, the company sells compressed air produced by air compressors that Kaeser maintains for them. Customers get the compressed air they need without the hassle of managing the equipment, while Kaeser achieves higher profit margins.

But Kaeser never could have achieved that transformation without real-time data. For its new business model to be profitable, Kaeser has to ensure that its air compressors operate with the highest uptime possible. That requires smart sensors that provide real-time visibility into operating conditions to allow for preventive maintenance.

In the same way, your supply chain need to capture, analyze, and act on real-timed data. It’s what will make your supply chain live. And what will help your new business models come to life.

Learn more about how running a live supply chain can help you thrive today and innovate for tomorrow at SAP.com.

 

 

 

 

 

 

 

This story originally appeared on EBN

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Hans Thalbauer

About Hans Thalbauer

Hans Thalbauer is globally responsible for solution management and the go-to-market functions for SAP digital supply chain solutions and the SAP Leonardo portfolio of Internet of Things solutions. In this role, he is engaged in creative dialogues with businesses and operations worldwide, addressing customer needs and introducing innovative business processes, including the vision of creating a live business environment for everyone working in operations. Hans has more than 17 years with SAP and is based out of Palo Alto, CA, USA. He has held positions in development, product and solution management, and the go-to-market organization. Hans holds a degree in Business Information Systems from the University Vienna, Austria.

How To Choose A Winning Supplier Management Solution

Tanya Bragg

Many companies today face the nearly impossible task of managing a large supplier base that has become increasingly diverse and potentially global. Traditional supply chain management and procurement practices just can’t keep up. Procurement teams are faced with too many suppliers and too few answers. The need for a centralized repository of supplier information to mitigate risk and assist in the evaluation and selection of suppliers has become paramount. This why so many companies are now looking into digital supplier management solutions.

The digital transformation of the supplier management process is long overdue. Faced with an inconsistent onboarding process, fragmented performance reporting, inaccurate risk assessment, and out-of-date information, many companies are seeking a better way to do business and manage their supply chain.

Why use a supplier management solution?

Today’s supplier management process often comes up short. It’s often a redundant, disconnected process from onboarding to engagement that creates greater risk with every decision. Fragmented supplier records and assessments make it impossible to collaborate with stakeholders and make appropriate choices regarding preferred suppliers. And when preferred suppliers aren’t used, the costs and risks tend to soar. Traditional, manual risk scoring and segmentation often misses critical signals, making timely action difficult. And because they aren’t part of a large, established network, suppliers can’t easily update their information in one place, which creates inaccurate vendor records.

A supplier management solution can solve these problems and streamline the process. All technology has a function, but the best technology has a purpose as well. The purpose of a supplier management solution, at its most basic, is about knowing your suppliers inside and out and managing them day-to-day. But it can be so much more. The right solution can help you be proactive rather than reactive. It can help you think strategically and add value to your company instead of just filling orders. The right supplier management solution can make your supply chain more reliable, more ethical, and ultimately, unstoppable.

Finding a winning solution

A successful supplier management solution often hinges as much on managing expectations and priorities as it does on the technology. Proper planning and evaluation is necessary. A poor process, plus new technology, often leads to nothing more than a poor, expensive process. This is an equation that has sunk many technology implementations. Your plan for success needs to be as good as the solution you find.

Finding a winning solution begins with making sure everyone is on the same road to same destination. Developing a plan that takes into account everyone’s expectations and priorities requires collaboration, and a clearly defined process consists of determining a starting point, defining a finish line, and finding technology that will enable you to create supplier management with a purpose through an integrated, end-to-end procurement processes.

Greater integration for better decision-making

A holistic view of supplier information, performance, and risk, along with the largest network of suppliers, can guide decisions all along the source-to-settle continuum. A winning solution works vertically, allowing you to deal with every supplier, at every tier, based on the criteria most relevant to you. Whether it’s in one category or region or many, it also works horizontally, granting you insights that allow you to make smarter choices, from your ERP backend all the way to your sourcing and procurement applications. But most of all, a winning solution connects seamlessly, eliminating holes in the process by providing all of your source-to-settle technology in one package, from one company.

Imagine using supplier management and risk insights when you need them, at key decision points in the process. Imagine being able to check supplier onboarding status, halt purchases, or switch out non-performing suppliers, renegotiate contracts based on risk and performance, and get alerted to changes in supplier risk so you can drive the right supplier relationships all with one integrated solution. A winning solution is driven by a powerful network, improving your business from end-to-end.

Keeping supplier information in sync

A unified source of truth for supplier information is the foundation of an effective supplier management solution. No matter what the task, the ability to view the most current, most accurate version of supplier information is critical. A winning supplier management solution achieves just that. With the right solution, your sourcing, procurement, and supplier management applications and backend ERP systems are connected and share the same information. So, when information is changed anywhere, it’s changed everywhere.

On the ground, this means information about supplier lifecycles, performance, and risk is the same whether someone is at a desk in Denver or in a factory in Saigon. Data needs to be entered into the system just once, and it is changed throughout. Suppliers can even update information themselves, which saves them time, saves you time, and results in more current information to make more accurate risk and performance decisions.

Intelligent flexibility

Every business, and every part of it, is unique. A winning-supplier management solution is designed to accommodate those differences from the beginning of the supplier lifecycle to the end. A winning solution recognizes the differences within your business, and then builds functionality with the purpose of making supplier management work your way. Intelligent flexibility is key.

When it comes to evaluating suppliers, one size does not fit all. Onboarding and qualifying suppliers, questionnaires, risk assessments, and approval processes should be customizable by category, location, and business unit. Such flexibility in a winning solution allows you to easily designate preferred suppliers based on specific needs and parameters. Sharing that information across all sourcing and procurement applications means more people will use preferred suppliers more often.

Knowledge is the key

Not all supplier information is created equal. A winning supplier management solution provides the most relevant data, from the best sources, delivered in the easiest way possible. It’s the best aggregator of content, rather than a creator of content. It includes a robust ecosystem of data and service providers to get and verify a wide range of information, focused application interfaces (APIs) that reach out to hundreds of thousands of sources for the most relevant data, and APIs targeted to different industries and issues like financial solvency, slave labor, and cybersecurity. A winning supplier solution uses artificial intelligence (AI) so that the information is easy to consume, predictive, actionable, and customizable to region, business unit, and category. The most relevant, most reliable, most accessible information is the most valuable.

Predicting the future, at least when it comes to your supply chain, is actually possible. A solution with proactive risk due diligence and automated supplier monitoring can make potential issues known before they impact your business. A winning supplier solution provides a 360-degree view of your suppliers, including risk insights that allow you to see the entire picture for all active supplier engagements. Insights tailored to your priorities, and delivered at the right times, lead to more proactive decisions.

In the end, a winning supplier solution can help bring your supply chain into the 21st century. With the right solution, you have the power to make an impact, to think more strategically, operate more proactively, and work more ethically so you can change your company… and the world.

For more insight, download How to Choose a Winning Supplier Management Solution.

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Tanya Bragg

About Tanya Bragg

As a product marketing manager on the SAP Ariba Integrated Marketing team, Tanya Bragg creates content designed to help customers gain greater value from the supplier management solutions they use. Her areas of expertise include supply chain, sustainability, sourcing, and procurement.

Why Four Weeks Of Design Thinking Offers Better Digital Transformation

Derek Klobucher

Digital transformation can do a lot to help the mining industry, such as improving onsite operations and decision making. Design thinking can optimize those improvements – if properly utilized.

Metals and mining corporation Vale has a compelling story of a successful implementation that took only four weeks. The result was a completely new process, according to Vale’s IT innovation manager at SAP Leonardo Live in Chicago.“The mining sector has embraced the introduction of new technologies, which have resulted in significant productivity benefits,” professional services firm EY stated in a recent mining update. “But there is the gap between the potential from digital transformation and the poor track record for successful implementations.”

Management assets across a global supply chain

The mining industry is on the upswing, enjoying growth in the U.S., Europe, and China, according to EY. Vale has been growing too, investing more than $120 billion during the last decade and expanding to 27 countries, according to Vale’s Helio Mosquim.

This growth brought a lot of challenges, such as managing new assets across the globe, according to Mosquim; intelligent maintenance would help the company increase uptime, boost productivity, and cut costs. Supply chain was also a big concern for the Rio de Janeiro-based multinational, which produces iron ore, copper, and more.

“Imagine producing ore in the middle of the Amazon, and transporting it all the way to the ports, and going through the distribution centers in Malaysia and Oman – and on to China,” Mosquim said. “Optimizing production, optimizing logistics and shipping … it’s a big challenge for us to optimize the whole chain.”

Saving time by automating critical tasks

Workforce effectiveness was another challenge for Vale, which has about 110,000 employees, according to Mosquim. Inventory is about $1.6 billion of Vale’s $11 billion annual spend, so the company automated purchasing processes to make them more intelligent.

For example, maintenance workers used to ask someone else to create a purchase requisition before acquiring a new part for damaged equipment, according to Mosquim. This completely manual process often overlooked parts that were already available (perhaps the previous shift ordered the same thing), and finding the missing information required searching through multiple screens.

These steps often resulted in a lot of redundant work and wasted time.

“Between 25% to 40% of rejections of all the purchase requisitions [occurred] because [the part] was either available on contract or in inventory,” Mosquim said. “And the equipment was out there waiting for the part.”

A “totally different approach” in just four weeks

Vale only expected to enhance its legacy platform, as opposed to taking full advantage of innovation services. But Vale’s vendor helped the company connect APIs directly to its system and use everything in the cloud – and implement it quickly.

“We set up a plan to innovate in four weeks,” Mosquim said. “And that was an amazing experience.”

Vale put its procurement team through a design thinking session to sort major pain points. They had a draft prototype by week one; by the following week, they had feedback – and were making adjustments.

“The result was very effective, very impressive,” Mosquim said. “When we saw the totally different approach, we were very confident that it was going to be able to deliver.”

“We had an opportunity to have a totally new process … we had other managers come in saying, ‘We would like to invest in this innovation,’” Mosquim said. “In the end, we got a little bit from each solution, and we put it in the cloud.”

Design thinking helped Vale digitally transform its supply chain, asset management, workforce effectiveness, and more. Digital technologies – including Internet of Things and machine learning – could also help mining companies improve safety, optimize site-wide operating systems, and more, according to a smart mining conference last week.

“You can only truly achieve a sustainable productivity improvement by adopting an integrated end-to-end business approach from market to mine,” as the EY mining update stated.

Design thinking could be your key to a successful implementation.

Learn more about SAP’s approach to design thinking.

This story originally appeared on SAP Business Trends. Follow Derek on Twitter@DKlobucher

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Derek Klobucher

About Derek Klobucher

Derek Klobucher is a Brand Journalist, Content Marketer and Master Digital Storyteller at SAP. His responsibilities include conceiving, developing and conducting global, company-wide employee brand journalism training; managing content, promotion and strategy for social networks and online media; and mentoring SAP employees, contractors and interns to optimize blogging and social media efforts.

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.


Comments

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