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Why 3D Printed Food Just Transformed Your Supply Chain

Hans Thalbauer

Numerous sectors are experimenting with 3D printing, which has the potential to disrupt many markets. One that’s already making progress is the food industry.

The U.S. Army hopes to use 3D printers to customize food for each soldier. NASA is exploring 3D printing of food in space. The technology could eventually even end hunger around the world.

What does that have to do with your supply chain? Quite a bit — because 3D printing does more than just revolutionize the production process. It also requires a complete realignment of the supply chain.

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And the way 3D printing transforms the supply chain holds lessons for how organizations must reinvent themselves in the new era of the extended supply chain.

Supply chain spaghetti junction

The extended supply chain replaces the old linear chain with not just a network, but a network of networks. The need for this network of networks is being driven by four key factors: individualized products, the sharing economy, resource scarcity, and customer-centricity.

To understand these forces, imagine you operate a large restaurant chain, and you’re struggling to differentiate yourself against tough competition. You’ve decided you can stand out by delivering customized entrees. In fact, you’re going to leverage 3D printing to offer personalized pasta.

With 3D printing technology, you can make one-off pasta dishes on the fly. You can give customers a choice of ingredients (gluten-free!), flavors (salted caramel!), and shapes (Leaning Towers of Pisa!). You can offer the personalized pasta in your restaurants, in supermarkets, and on your ecommerce website.

You may think this initiative simply requires you to transform production. But that’s just the beginning. You also need to re-architect research and development, demand signals, asset management, logistics, partner management, and more.

First, you need to develop the matrix of ingredients, flavors, and shapes you’ll offer. As part of that effort, you’ll have to consider health and safety regulations.

Then, you need to shift some of your manufacturing directly into your kitchens. That will also affect packaging requirements. Logistics will change as well, because instead of full truckloads, you’ll be delivering more frequently, with more variety, and in smaller quantities.

Next, you need to perfect demand signals to anticipate which pasta variations in which quantities will come through which channels. You need to manage supply signals source more kinds of raw materials in closer to real time.

Last, the source of your signals will change. Some will continue to come from point of sale. But others, such as supplies replenishment and asset maintenance, can come direct from your 3D printers.

Four key ingredients of the extended supply chain

As with our pasta scenario, the drivers of the extended supply chain require transformation across business models and business processes. First, growing demand for individualized products calls for the same shifts in R&D, asset management, logistics, and more that 3D printed pasta requires.

Second, as with the personalized entrees, the sharing economy integrates a network of partners, from suppliers to equipment makers to outsourced manufacturing, all electronically and transparently interconnected, in real time and all the time.

Third, resource scarcity involves pressures not just on raw materials but also on full-time and contingent labor, with the necessary skills and flexibility to support new business models and processes.

And finally, for personalized pasta sellers and for your own business, it all comes down to customer-centricity. To compete in today’s business environment and to meet current and future customer expectations, all your operations must increasingly revolve around rapidly comprehending and responding to customer demand.

Want to learn more? Digitalize your operations to uncover new business models and a new world of work.

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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.

Transform Or Die: What Will You Do In The Digital Economy?

Scott Feldman and Puneet Suppal

By now, most executives are keenly aware that the digital economy can be either an opportunity or a threat. The question is not whether they should engage their business in it. Rather, it’s how to unleash the power of digital technology while maintaining a healthy business, leveraging existing IT investments, and innovating without disrupting themselves.

Yet most of those executives are shying away Businesspeople in a Meeting --- Image by © Monalyn Gracia/Corbisfrom such a challenge. According to a recent study by MIT Sloan and Capgemini, only 15% of CEOs are executing a digital strategy, even though 90% agree that the digital economy will impact their industry. As these businesses ignore this reality, early adopters of digital transformation are achieving 9% higher revenue creation, 26% greater impact on profitability, and 12% more market valuation.

Why aren’t more leaders willing to transform their business and seize the opportunity of our hyperconnected world? The answer is as simple as human nature. Innately, humans are uncomfortable with the notion of change. We even find comfort in stability and predictability. Unfortunately, the digital economy is none of these – it’s fast and always evolving.

Digital transformation is no longer an option – it’s the imperative

At this moment, we are witnessing an explosion of connections, data, and innovations. And even though this hyperconnectivity has changed the game, customers are radically changing the rules – demanding simple, seamless, and personalized experiences at every touch point.

Billions of people are using social and digital communities to provide services, share insights, and engage in commerce. All the while, new channels for engaging with customers are created, and new ways for making better use of resources are emerging. It is these communities that allow companies to not only give customers what they want, but also align efforts across the business network to maximize value potential.

To seize the opportunities ahead, businesses must go beyond sensors, Big Data, analytics, and social media. More important, they need to reinvent themselves in a manner that is compatible with an increasingly digital world and its inhabitants (a.k.a. your consumers).

Here are a few companies that understand the importance of digital transformation – and are reaping the rewards:

  1. Under Armour:  No longer is this widely popular athletic brand just selling shoes and apparel. They are connecting 38 million people on a digital platform. By focusing on this services side of the business, Under Armour is poised to become a lifestyle advisor and health consultant, using his product side as the enabler.
  1. Port of Hamburg: Europe’s second-largest port is keeping carrier trucks and ships productive around the clock. By fusing facility, weather, and traffic conditions with vehicle availability and shipment schedules, the Port increased container handling capacity by 178% without expanding its physical space.
  1. Haier Asia: This top-ranking multinational consumer electronics and home appliances company decided to disrupt itself before someone else did. The company used a two-prong approach to digital transformation to create a service-based model to seize the potential of changing consumer behaviors and accelerate product development. 
  1. Uber: This startup darling is more than just a taxi service. It is transforming how urban logistics operates through a technology trifecta: Big Data, cloud, and mobile.
  1. American Society of Clinical Oncologists (ASCO): Even nonprofits can benefit from digital transformation. ASCO is transforming care for cancer patients worldwide by consolidating patient information with its CancerLinQ. By unlocking knowledge and value from the 97% of cancer patients who are not involved in clinical trials, healthcare providers can drive better, more data-driven decision making and outcomes.

It’s time to take action 

During the SAP Executive Technology Summit at SAP TechEd on October 19–20, an elite group of CIOs, CTOs, and corporate executives will gather to discuss the challenges of digital transformation and how they can solve them. With the freedom of open, candid, and interactive discussions led by SAP Board Members and senior technology leadership, delegates will exchange ideas on how to get on the right path while leveraging their existing technology infrastructure.

Stay tuned for exclusive insights from this invitation-only event in our next blog!
Scott Feldman is Global Head of the SAP HANA Customer Community at SAP. Connect with him on Twitter @sfeldman0.

Puneet Suppal drives Solution Strategy and Adoption (Customer Innovation & IoT) at SAP Labs. Connect with him on Twitter @puneetsuppal.

 

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Scott Feldman and Puneet Suppal

About Scott Feldman and Puneet Suppal

Scott Feldman is the Head of SAP HANA International Customer Community. Puneet Suppal is the Customer Co-Innovation & Solution Adoption Executive at SAP.

What Is Digital Transformation?

Andreas Schmitz

Achieving quantum leaps through disruption and using data in new contexts, in ways designed for more than just Generation Y — indeed, the digital transformation affects us all. It’s time for a detailed look at its key aspects.

Data finding its way into new settings

Archiving all of a company’s internal information until the end of time is generally a good idea, as it gives the boss the security that nothing will be lost. Meanwhile, enabling him or her to create bar graphs and pie charts based on sales trends – preferably in real time, of course – is even better.

But the best scenario of all is when the boss can incorporate data from external sources. All of a sudden, information on factors as seemingly mundane as the weather start helping to improve interpretations of fluctuations in sales and to make precise modifications to the company’s offerings. When the gusts of autumn begin to blow, for example, energy providers scale back solar production and crank up their windmills. Here, external data provides a foundation for processes and decisions that were previously unattainable.

Quantum leaps possible through disruption

While these advancements involve changes in existing workflows, there are also much more radical approaches that eschew conventional structures entirely.

“The aggressive use of data is transforming business models, facilitating new products and services, creating new processes, generating greater utility, and ushering in a new culture of management,” states Professor Walter Brenner of the University of St. Gallen in Switzerland, regarding the effects of digitalization.

Harnessing these benefits requires the application of innovative information and communication technology, especially the kind termed “disruptive.” A complete departure from existing structures may not necessarily be the actual goal, but it can occur as a consequence of this process.

Having had to contend with “only” one new technology at a time in the past, be it PCs, SAP software, SQL databases, or the Internet itself, companies are now facing an array of concurrent topics, such as the Internet of Things, social media, third-generation e-business, and tablets and smartphones. Professor Brenner thus believes that every good — and perhaps disruptive — idea can result in a “quantum leap in terms of data.”

Products and services shaped by customers

It has already been nearly seven years since the release of an app that enables customers to order and pay for taxis. Initially introduced in Berlin, Germany, mytaxi makes it possible to avoid waiting on hold for the next phone representative and pay by credit card while giving drivers greater independence from taxi dispatch centers. In addition, analyses of user data can lead to the creation of new services, such as for people who consistently order taxis at around the same time of day.

“Successful models focus on providing utility to the customer,” Professor Brenner explains. “In the beginning, at least, everything else is secondary.”

In this regard, the private taxi agency Uber is a fair bit more radical. It bypasses the entire taxi industry and hires private individuals interested in making themselves and their vehicles available for rides on the Uber platform. Similarly, Airbnb runs a platform travelers can use to book private accommodations instead of hotel rooms.

Long-established companies are also undergoing profound changes. The German publishing house Axel Springer SE, for instance, has acquired a number of startups, launched an online dating platform, and released an app with which users can collect points at retail. Chairman and CEO Matthias Döpfner also has an interest in getting the company’s newspapers and other periodicals back into the black based on payment models, of course, but these endeavors are somewhat at odds with the traditional notion of publishing houses being involved solely in publishing.

The impact of digitalization transcends Generation Y

Digitalization is effecting changes in nearly every industry. Retailers will likely have no choice but to integrate their sales channels into an omnichannel approach. Seeking to make their data services as attractive as possible, BMW, Mercedes, and Audi have joined forces to purchase the digital map service HERE. Mechanical engineering companies are outfitting their equipment with sensors to reduce downtime and achieve further product improvements.

“The specific potential and risks at hand determine how and by what means each individual company approaches the subject of digitalization,” Professor Brenner reveals. The resulting services will ultimately benefit every customer – not just those belonging to Generation Y, who present a certain basic affinity for digital methods.

“Think of cars that notify the service center when their brakes or drive belts need to be replaced, offer parking assistance, or even handle parking for you,” Brenner offers. “This can be a big help to elderly people in particular.”

Chief digital officers: team members, not miracle workers

Making the transition to the digital future is something that involves not only a CEO or a head of marketing or IT, but the entire company. Though these individuals do play an important role as proponents of digital models, it also takes more than just a chief digital officer alone.

For Professor Brenner, appointing a single person to the board of a DAX company to oversee digitalization is basically absurd. “Unless you’re talking about Da Vinci or Leibnitz born again, nobody could handle such a task,” he states.

In Brenner’s view, this is a topic for each and every department, and responsibilities should be assigned much like on a soccer field: “You’ve got a coach and the players – and the fans, as well, who are more or less what it’s all about.”

Here, the CIO neither competes with the CDO nor assumes an elevated position in the process of digital transformation. Implementing new databases like SAP HANA or Hadoop, leveraging sensor data in both technical and commercially viable ways, these are the tasks CIOs will face going forward.

“There are some fantastic jobs out there,” Brenner affirms.

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

Image via Shutterstock

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Andreas Schmitz

About Andreas Schmitz

Andreas Schmitz is a Freelance Journalist for SAP, covering a wide range of topics from big data to Internet of Things, HR, business innovation and mobile.

Running Future Cities on Blockchain

Dan Wellers , Raimund Gross and Ulrich Scholl

Building on the Blockchain Framework

Some experts say these seemingly far-future speculations about the possibilities of combining technologies using blockchain are actually both inevitable and imminent:


Democratizing design and manufacturing by enabling individuals and small businesses to buy, sell, share, and digitally remix products affordably while protecting intellectual property rights.
Decentralizing warehousing and logistics by combining autonomous vehicles, 3D printers, and smart contracts to optimize delivery of products and materials, and even to create them on site as needed.
Distributing commerce by mixing virtual reality, 3D scanning and printing, self-driving vehicles, and artificial intelligence into immersive, personalized, on-demand shopping experiences that still protect buyers’ personal and proprietary data.

The City of the Future

Imagine that every agency, building, office, residence, and piece of infrastructure has an entry on a blockchain used as a city’s digital ledger. This “digital twin” could transform the delivery of city services.

For example:

  • Property owners could easily monetize assets by renting rooms, selling solar power back to the grid, and more.
  • Utilities could use customer data and AIs to make energy-saving recommendations, and smart contracts to automatically adjust power usage for greater efficiency.
  • Embedded sensors could sense problems (like a water main break) and alert an AI to send a technician with the right parts, tools, and training.
  • Autonomous vehicles could route themselves to open parking spaces or charging stations, and pay for services safely and automatically.
  • Cities could improve traffic monitoring and routing, saving commuters’ time and fuel while increasing productivity.

Every interaction would be transparent and verifiable, providing more data to analyze for future improvements.


Welcome to the Next Industrial Revolution

When exponential technologies intersect and combine, transformation happens on a massive scale. It’s time to start thinking through outcomes in a disciplined, proactive way to prepare for a future we’re only just beginning to imagine.

Download the executive brief Running Future Cities on Blockchain.


Read the full article Pulling Cities Into The Future With Blockchain

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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.

Raimund Gross

About Raimund Gross

Raimund Gross is a solution architect and futurist at SAP Innovation Center Network, where he evaluates emerging technologies and trends to address the challenges of businesses arising from digitization. He is currently evaluating the impact of blockchain for SAP and our enterprise customers.

Ulrich Scholl

About Ulrich Scholl

Ulrich Scholl is Vice President of Industry Cloud and Custom Development at SAP. In this role, Ulrich discovers and implements best practices to help further the understanding and adoption of the SAP portfolio of industry cloud innovations.

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Are AI And Machine Learning Killing Analytics As We Know It?

Joerg Koesters

According to IDC, artificial intelligence (AI) is expected to become pervasive across customer journeys, supply networks, merchandizing, and marketing and commerce because it provides better insights to optimize retail execution. For example, in the next two years:

  • 40% of digital transformation initiatives will be supported by cognitive computing and AI capabilities to provide critical, on-time insights for new operating and monetization models.
  • 30% of major retailers will adopt a retail omnichannel commerce platform that integrates a data analytics layer that centrally orchestrates omnichannel capabilities.

One thing is clear: new analytic technologies are expected to radically change analytics – and retail – as we know them.

AI and machine learning defined in the context of retail

AI is defined broadly as the ability of computers to mimic human thinking and logic. Machine learning is a subset of AI that focuses on how computers can learn from data without being programmed through the use of algorithms that adapt to change; in other words, they can “learn” continuously in response to new data. We’re seeing these breakthroughs now because of massive improvements in hardware (for example, GPUs and multicore processing) that can handle Big Data volumes and run deep learning algorithms needed to analyze and learn from the data.

Ivano Ortis, vice president at IDC, recently shared with me how he believes, “Artificial intelligence will take analytics to the next level and will be the foundation for retail innovation, as reported by one out of every two retailers globally. AI enables scale, automation, and unprecedented precision and will drive customer experience innovation when applied to both hyper micro customer segmentation and contextual interaction.”

Given the capabilities of AI and machine learning, it’s easy to see how they can be powerful tools for retailers. Now computers can read and listen to data, understand and learn from it, and instantly and accurately recommend the next best action without having to be explicitly programmed. This is a boon for retailers seeking to accurately predict demand, anticipate customer behavior, and optimize and personalize customer experiences.

For example, it can be used to automate:

  • Personalized product recommendations based on data about each customer’s unique interests and buying propensity
  • The selection of additional upsell and cross-sell options that drive greater customer value
  • Chat bots that can drive intelligent and meaningful engagement with customers
  • Recommendations on additional services and offerings based on past and current buying data and customer data
  • Planogram analyses, which support in-store merchandizing by telling people what’s missing, comparing sales to shelf space, and accelerating shelf replenishment by automating reorders
  • Pricing engines used to make tailored, situational pricing decisions

Particularly in the United States, retailers are already able to collect large volumes of transaction-based and behavioral data from their customers. And as data volumes grow and processing power improves, machine learning becomes increasingly applicable in a wider range of retail areas to further optimize business processes and drive more impactful personalized and contextual consumer experiences and products.

The transformation of retail has already begun

The impacts of AI and machine learning are already being felt. For example:

  • Retailers are predicting demand with machine learning in combination with IoT technologies to optimize store businesses and relieve workforces
  • Advertisements are being personalized based on in-store camera detections and taking over semi-manual clienteling tasks of store employees
  • Retailers can monitor wait times in checkout lines to understand store traffic and merchandising effectiveness at the individual store level – and then tailor assortments and store layouts to maximize basket size, satisfaction, and sell through
  • Systems can now recognize and predict customer behavior and improve employee productivity by turning scheduled tasks into on-demand activities
  • Camera systems can detect the “fresh” status of perishable products before onsite employees can
  • Brick-and-mortar stores are automating operational tasks, such as setting shelf pricing, determining product assortments and mixes, and optimizing trade promotions
  • In-store apps can tell how long a customer has been in a certain aisle and deliver targeted offers and recommendations (via his or her mobile device) based on data about data about personal consumption histories and preferences

A recent McKinsey study provided examples that quantify the potential value of these technologies in transforming how retailers operate and compete. For example:

  • U.S. retailer supply chain operations that have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Using data and analytics to improve merchandising, including pricing, assortment, and placement optimization, is leading to an additional 16% in operating margin improvement.
  • Personalizing advertising is one of the strongest use cases for machine learning today. Additional retail use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics, as well as optimizing merchandising strategies.

Exploiting the full value of data

Thin margins (especially in the grocery sector) and pressure from industry-leading early adopters such as Amazon and Walmart have created strong incentives to put customer data to work to improve everything from cross-selling additional products to reducing costs throughout the entire value chain. But McKinsey has assessed that the U.S. retail sector has realized only 30-40% of the potential margin improvements and productivity growth its analysts envisioned in 2011 – and a large share of the value of this growth has gone to consumers through lower prices. So thus far, only a fraction of the potential value from AI and machine learning has been realized.

According to Forbes, U.S. retailers have the potential to see a 60%+ increase in net margin and 0.5–1.0% annual productivity growth. But there are major barriers to realizing this value, including lack of analytical talent and siloed data within companies.

This is where machine learning and analytics kick in. AI and machine learning can help scale the repetitive analytics tasks required to drive leverage of the available data. When deployed on a companywide, real-time analytics platform, they can become the single source of truth that all enterprise functions rely on to make better decisions.

How will this change analytics?

So how will AI and machine learning change retail analytics? We expect that AI and machine learning will not kill analytics as we know it, but rather give it a new and even more impactful role in driving the future of retail. For example, we anticipate that:

  • Retailers will include machine learning algorithms as an additional factor in analyzing and  monitoring business outcomes in relation to machine learning algorithms
  • They will use AI and machine learning to sharpen analytic algorithms, detect more early warning signals, anticipate trends, and have accurate answers before competitors do
  • Analytics will happen in real time and act as the glue between all areas of the business
  • Analytics will increasingly focus on analyzing manufacturing machine behavior, not just business and consumer behavior

Ivano Ortis at IDC authored a recent report, “Why Retail Analytics are a Foundation for Retail Profits,” in which he provides further insights on this topic. He notes how retail leaders will use new kinds of analytics to drive greater profitability, further differentiate the customer experience, and compete more effectively, “In conclusion, commerce and technology will converge, enabling retailers to achieve short-term ROI objectives while discovering untapped demand. But implementing analytics will require coordination across key management roles and business processes up and down each retail organization. Early adopters are realizing demonstrably significant value from their initiatives – double-digit improvements in margins, same-store and e-commerce revenue, inventory positions and sell-through, and core marketing metrics. A huge opportunity awaits.”

So how do you see your retail business adopting advanced analytics like AI and machine learning? I encourage you to read IDC’s report in detail, as it provides valuable insights to help you invest in – and apply – new kinds of analytics that will be essential to profitable growth.

For more information, download IDC’s “Why Retail Analytics are a Foundation for Retail Profits.

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Joerg Koesters

About Joerg Koesters

Joerg Koesters is the Head of Retail Marketing and Communication at SAP. He is a Technology Marketing executive with 20 years of experience in Marketing, Sales and Consulting, Joerg has deep knowledge in retail and consumer products having worked both in the industry and in the technology sector.