Tech Solutions For IFRS: Posted At 35,000 Feet

Dina Medland

Technology can give a real boost to better corporate governance. We saw that with the tech challenge to #rethinksupplychains I wrote about on Forbes earlier this year (Competing To End Labor Trafficking in Global Supply Chains: With Technology). Making the process of documentation quick and easy is an important first step to transparency. It is also critical for compliance.

New International Financial Reporting Standards (IFRS) will address a suite of measures by the International Accounting Standards Board to overhaul accounting in the long wake of the financial crisis and to increase international regulatory cooperation.

But with less than 18 months to go before the new IFRS rules go into effect, a survey by Deloitte reveals that nearly two-thirds of banks are unclear on the effect the rules may have on their balance sheets – and uncertainty abounds.

A staggering 99% of respondents to Deloitte’s Sixth Global Banking IFRS survey said their local financial regulator had yet to say how they might incorporate IFRS 9 numbers into regulatory capital requirements. The survey included 91 banks, including 16 global, systemically important financial institutions (but excluding U.S. banks).

Almost half of banks think they do not have enough technical resources to deliver their IFRS 9 project and almost a quarter of these do not think that there will be sufficient skills available in the market to cover shortfalls, says Deloitte. Some 60% of banks either did not or could not quantify the transition impact of IFRS 9. Of the banks who responded, the majority estimate that total impairment provisions will increase by up to 25% across asset classes.

Seventy percent of respondents anticipate a reduction of up to £50 in core tier 1 capital ratio due to IFRS 9, according to the survey.

But amid the guesswork, time is running out. One answer comes from SAP; I was at 35,000 feet, en route to California to learn more when I wrote this blog. SAP has just announced the latest enhancements to its revenue accounting platform designed to help CFOs and chief accounting officers master the new IFRS 15 (which is known as ASC 606 in the U.S.) revenue-recognition standards.

It is not just the banking sector that’s taking note. These new accounting standards will apply to all entities – public, private, and not-for-profit – that have contracts with customers, and will supersede virtually all current revenue accounting requirements.

IFRS 15/ASC 606 eliminates the transaction- and industry-specific revenue recognition guidance under current U.S. GAAP and replaces it with a principle-based approach for determining revenue recognition. The change can affect companies’ reported revenue, how and when they report financial performance, and overall financial decision making.

It sounds like a discussion for the boardroom. But time to implement the new process is running out. To find out how much time you have, visit the IFRS 15 Doomsday clock here.

For more on making sense of the new IFRS regulations, see The Digitalist’s post on Wrestling With IFRS 15.

This article appeared originally in Board Talk. It is republished by permission.


Dina Medland

About Dina Medland

Dina Medland is an independent writer, editor, and commentator with a strong focus on issues around corporate governance, ethics, and the workings of the boardroom. She is on the team of contributors to @ForbesEurope with a page on corporate leadership, the boardroom and governance, and is an ex-Financial Times permanent staff member who has been a regular contributor in recent years.

Enforcement: The Achilles Heel Of EU Late Payments Regulation

Scott Pezza

When discussing late payments in the B2B arena, we are referring to two separate yet related issues. The first is the ordinary meaning of “late,” meaning that buyers are paying some time after the agreed upon payment date. The second is the recognition that buyers have been actively renegotiating payment terms to push the agreed upon date much further out into the future. As we covered in DPO and On-Time Payment Performance, from Metrics to Legal Mandates, existing legislation addresses both of these scenarios.

Why, then, do these problems persist?

Reading over the text of the European Commission’s Late Payments Directive (2011/7/EU), which has been adopted in regulations within individual member states, the rules are clear. For truly late payments, suppliers are entitled to interest and a fixed recovery fee. For payment terms, 60 days is the default, and while they can be extended further via negotiation, the result cannot be “grossly unfair.” Compliance is not monitored by the member states, however, and enforcement is not automatic. While suppliers have these rights, the only way to assert them is through litigation against their (usually larger) buyer.

Between a rock and a hard place

In a 2016 report on the implementation of the directive, the European Commission recognized this difficulty directly. According to its review, “[a]pproximately half of all creditors do not exercise their rights to claim late payment interest, compensation and recovery costs as provided for by the directive for fear of damaging their commercial relationships” (emphasis added). The commission recognized the same fear driving acceptance of extended payment terms and preventing supplier challenges under the “grossly unfair” standard. Both scenarios illustrate the same point: When regulatory enforcement requires a smaller business to risk future sales by confronting a larger buyer, the intended results of that regulation can go unrealized.

Leveraging corporate peer (or PR) pressure

An interesting potential solution to this enforcement problem comes from the UK, in the form of a duty to report. For larger businesses, this duty requires them to report their payment practices (including standard terms) and performance publicly. That information will be available online for review. As a result of this new reporting requirement, large buyers’ exploitation of smaller suppliers will be a matter of public record, with the data centrally available to any members of the press who might find such situations of particular interest.

It seems like a fair bet that the information will be submitted: It is a criminal offense (not just for the business entity, but for the directors as well) to fail to report. The same applies for reporting any “misleading, false, or deceptive” information. Unlike with the Late Payments Directive, enforcement here is governmental. It does not guarantee that payment terms will be honored or that such terms will not be extended greatly, but it does ensure that such behaviors will be made public.

A good first step

Any solution that pushes buyers closer toward the ideals laid out in the Late Payment Directive without forcing smaller suppliers to risk future revenues by asserting their rights is a welcome addition. Certainly, it adds costs for data collection and reporting, and it increases the amount of governmental oversight—two facts that may limit its adoption in other countries where this approach is less politically attractive. For smaller suppliers in the UK, however, the April onset of voluntary reporting and upcoming October launch of mandatory reports should provide some measure of support as they continue to navigate commercial relationships where they can be at a significant disadvantage.

To learn more about B2B payment regulations and establish your action plan to improve source-to-settle and payment performance, read “Mitigating the Risk, Cost and Cash Impacts of European Source-to-Settle Regulations.

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


Scott Pezza

About Scott Pezza

As part of SAP Ariba's Nework Value Organization Center of Excellence, Scott researches, compiles, and shares best-practice information to help customers get the most out of their investments. With a focus on the financial supply chain (invoice management, payments, discounting, and supply chain finance), his research helps inform strategic planning, performance measurement, and program execution. He has spent the past 15 years in the B2B technology space, in roles ranging from software development and support to research and consulting. Scott earned his BA in English and Philosophy from Clark University, his MBA from Boston University Graduate School of Management, and his JD from Boston University School of Law, where he served on the Executive Board of the Annual Review of Banking and Financial Law.

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.

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


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.


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.


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.