Enterprises Using Supply Chain Processes Of The Past Will Struggle In The Future

Lindsey LaManna

With changing customer expectations and extraordinary demands on our physical future of resourcesresources in today’s global economy, supply chains are becoming more complex. Businesses are being forced to rethink resource optimization and reinvent the supply chain.

In our last interview in the Future of Resources series, James Marland explained how the growing network of suppliers and partners and the“sharing economy” are transforming the supply chain.

This week, Keertan Rai (@Keertanrai), Solutions Marketing Manager for Ariba, shares his insight on the changing landscape of resource optimization and how businesses must adapt.

Customers are demanding faster and more personalized products and services? In this context, how can enterprises address the resource optimization challenges that emerge as a result of that?

The constant shift in customer dynamics has and will continue to play a critical role on resource accessibility and utilization. Enterprises that choose to address these changes by only leveraging processes and expertise of the past have often found themselves hard pressed in striking the right balance in managing cost, quality and agility.  In the networked economy era that we are in today, organizations are better placed that ever to address such challenges.  Be it through new supply source discovery at the click of a button, seamless collaboration across all participants and processes, and elimination of manual processes, business networks have enabled enterprises to access new resources as well as optimize the usage of the existing one’s in their quest to remain nimble to the changing market environment.

How would timely and accurate insight impact resource utilization across the entire supply chain from initial design and demand to delivery?

There goes a saying that you can’t fight what you can’t see. Right utilization of resources begins with good visibility in to the supply chain. The impact is huge and across the board too.  Proactive identification and mitigation of supplier risks, material and inventory cost containment, accurate sourcing decisions enablement, supplier performance management, working capital optimization are just a few examples of how enterprises have gained with timely and accurate insights on their supply chains.  However visibility on enterprise data in its native form would provide limited insights because of the inherent inaccuracies and inconsistencies.  Its only when enterprise wide data is well aggregated, classified, enriched and analyzed does the big picture begin to emerge. Also by taking business commerce beyond the four walls of the enterprises and by providing an ever greater transparency on transactions and the ecosystem, Business Networks have created a paradigm shift on resource visibility and utilization across the supply chain.

The idea of sustainability remains mostly an idea. What things need to happen in order for it to become more of a reality in supply chains?

Sustainability today is no longer a buzzword, it’s a business imperative. Time and again we have witnessed enterprises on the wrong side of the sustainability equation facing the squeeze from competitors, clients and governments alike.  While enterprises, to some extent, have managed to bring about the sustainability change internally, it is the task managing it across their supply chains that remains a challenge. Poor visibility on the supplier base and lack of accurate information are often the culprits to blame to here.  Using supplier data enrichment and supplier performance solutions, organizations today stand to know exactly how green, sustainable and diverse their supply chain is.  Empowered with this ability to track and validate supplier initiatives, organizations are able to place the importance of sustainability practices to their supplier base and thus set off a cascading effect across the ecosystem.

Keertan RaiKeertan Rai handles product marketing for Ariba’s Spend Visibility solution. He has over 7 years of experience across multiple B2B marketing disciplines in the IT products and services industry. Prior to Ariba, he was with Microland Limited and HCL Technologies in corporate and solutions marketing roles. Keertan holds an engineering degree in Computer Science from Visvesvaraya Technological University, India and an MBA in Marketing and Finance from Amity Business School, India.


About Lindsey LaManna

Lindsey LaManna is a Marketing Manager at SAP. Her specialties include social media marketing, marketing strategy, and marketing communications.

The Future Of Procurement In A Word: Intelligent

Marcell Vollmer

The world has gone digital. People are more connected, efficient, and productive and are living life faster than ever. But today’s fast-paced world impacts more than just our personal lives. Digitization is presenting new opportunities for businesses to predict and respond more effectively to customer and market demands. From machine learning, artificial intelligence (AI), and the Internet of Things (IoT) to blockchain, cloud, and networks, digital technologies are opening the door to new, more efficient and intelligent ways of operating.

Nowhere is this more evident than in procurement. Unlocking these digital opportunities requires new approaches in how we connect with people and access information that will fundamentally change how buying and selling are done.

Machine learning and artificial intelligence have made their way into procurement applications and are fueling smarter, faster decisions. A new breed of cognitive technologies promises to push things even further.

Digital technologies drive simplicity and automation that can help procurement create advantage for their organizations today. But the future of procurement is all about intelligence. And cognitive technologies are the key to unlocking its potential.

Cognitive technologies present new opportunities to predict and respond more effectively to customer and market demands. They also open new approaches to connecting people and information—all of which will fundamentally change how buying and selling are done.

Sourcing using a digital procurement assistant, combined with machine learning, can transform sourcing events by helping with tasks such as defining the correct “request for proposal” type; identifying appropriate suppliers to participate based on commodity category, region, or industry; and delivering intelligence on market signals and pricing pressures to optimize results.

Contracting can also become smarter and more comprehensive with applications that automatically identify relevant terms and conditions matched to legal library and taxonomy, uncover similar contract terms for a specific commodity by industry or region based on benchmarking data, and suggest optimal prices to target based on expected volume and contractual discounts.

Intelligent platforms exist that use data insights to empower procurement professionals to make smarter, faster decisions across their supply chains. The application can impact the entire procurement process, from improving spend visibility to assisting buyers and enriching content management.

The technology brings intelligence from procurement data together with predictive insights from unstructured information to enable an even more intelligent source-to-settle process for managing all categories of spend.

And procurement is ready to embrace them. We recently conducted a global survey of procurement, finance, and supply chain function executives in partnership with the University of Applied Science Würzburg/Schweinfurt. And the top priorities for the year ahead among those polled were clearBig Data and predictive analytics (72%) followed by AI (including machine learning) and cognitive computing (22%).

Like the rest of the world, procurement has gone digital. It’s now ready to be smart. By embracing intelligent applications and the technologies underlying them, procurement can reimagine their supply chains and take insight-driven actions that go beyond savings and efficiencies to create broad business value. Forward-thinking, innovative procurement and supply chain organizations that think fast can not only drive bottom-line savings, but contribute to the top line of their organizations by fueling innovation that leads to growth. Those who stall will find themselves being lapped by the competition.

This article originally appeared in Dell EMC Tech Page One and is republished by permission.

Follow SAP Finance online: @SAPFinance (Twitter)  | LinkedIn | FacebookYouTube


Marcell Vollmer

About Marcell Vollmer

Marcell Vollmer is the Chief Digital Officer for SAP Ariba (SAP). He is responsible for helping customers digitalize their supply chain. Prior to this role, Marcell was the Chief Operating Officer for SAP Ariba, enabling the company to setup a startup within the larger SAP business. He was also the Chief Procurement Officer at SAP SE, where he transformed the global procurement organization towards a strategic, end-to-end driven organization, which runs SAP Ariba and SAP Fieldglass solutions, as well as Concur technologies in the cloud. Marcell has more than 20 years of experience in working in international companies, starting with DHL where he delivered multiple supply chain optimization projects.

Digital Breakthrough: How 3D Printing Will Blow Your Mind

Maria Estrada

3D printing is shaking up how people engage in daily tasks at work, at school, and even at home. Recognizing the powerful potential of this innovative technology, many businesses are also beginning to leverage it for commercial use.

The fashion industry, for example, is using 3D printing to launch clothing collections based on the latest trends. The automotive industry is also tapping the technology to solve technical challenges. And healthcare organizations are using it to make lifesaving breakthroughs.

As 3D printing technology matures, it will be used in countless ways yet to be imagined. Here are some examples of how 3D printing is affecting manufacturing today.

1. Product design and development

Because 3D printing can minimize design constraints, developers can create products that are built to last without needing to first work through the problems of a physical prototype. Companies will be able to produce products quickly and easily.

Steve Swaddle, technology manager at Black & Decker, says, “It is the ability to design something in the morning and have a physical representation of the concept in your hand in the afternoon that is a priceless step forward in product design.”

2. Customization for a segment of one

Businesses can use 3D printing to easily customize products, such as a new concept of business cards, according to individual customer preferences. With product design limited only by the human imagination, many industries will be poised to undergo dramatic transformation.

However, such a shift also brings cost implications. Products will likely cost more, as 3D printers are expensive—many are priced between $10,000 and $20,0000, with some specialized models up to $50,000 or more. Over time, these prices will naturally decrease, but for now, consumers will pay a price for customization.

Another concern is copyright issues. Users of 3D printers can easily copy ideas based on copyright-protected product designs, and manufacturers and designers may call the ethical use of 3D printing into question as property right and infringements occur.

Major digital breakthroughs and innovations rarely go unnoticed, and 3D printing is just one example. However, we must use them to solve problems rather than create new ones.

For more on the potential of 3D printing, see Why 3D Printed Food Just Transformed Your Supply Chain.


Maria Estrada

About Maria Estrada

Maria Estrada is the business associate of PrintMeister, an online company that offers high-quality printing services like brochures and print business card. She is a tech savvy and loves to share her knowledge through blog writing. Maria lives in Australia with her family and two lovely cats.

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


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.


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.


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.