Ethernet networks are relied upon to transport real time data and critical business information. Such networks provide critical end-to-end services to subscribers and enterprise users. They must be reliable and resilient. As such, network designers must design their networks to be highly fault tolerant and capable of rapid recovery, to ensure near zero service downtime. The focus of this blog is to detail the various transport protocols implemented in routers/switches used by service providers to achieve a high degree of network protection, and therefore service availability.

Protection broadly involves three phases. These are i) service establishment along with the establishment of alternate/backup services ii) failure detection, and iii) a repair and recovery process. A failure event in a network can occur due to link flapping, a node failure, or a path failure. The protocols required for protection, monitoring and repair vary, depending on the transport mechanism used (i.e. Ethernet vs IP).

Protection techniques must also ensure that service restoration happens within 50 milliseconds, which has become the de-facto industry standard. To meet such stringent performance requirements, software implementations should take advantage of hardware assist techniques supported by silicon vendors.

One such hardware assist technique is ‘offloading’. Offloading involves packet generation and processing of the received packets used for monitoring at a very high frequency. Errors are reported by the hardware offloading mechanism back to the software, in the event of non-reception of packets, which then leads to switching to protected paths. To achieve protection, resources must be provisioned such that when the protected resource becomes unavailable, the backup resource takes over. The backup resource(s) can be placed in standby mode or can actively transport traffic in a load-balanced fashion. Standby mode is easier to implement but consumes more resources. The backup resources are used only when the primary goes down. Active mode is difficult to implement but conserves resources since sharing happens on the protection path.

Given this background, let us examine the plethora of protocols available for protection, starting with Layer2 Ethernet traffic, IP traffic and MPLS transport.

Ethernet links transporting Layer2 traffic can be connected in a mesh fashion to protect against failures. The usage of STP (Spanning Tree Protocol) blocks the redundant links from forwarding traffic. This protocol ensures that redundant links become available on primary link or node failures. Since STP does not meet the convergence time requirements, evolutions of STP, namely RSTP (Rapid Spanning Tree Protocol) and MSTP (Multiple Spanning Tree Protocol) – which guarantee faster convergence and load balancing – are deployed instead.

To ensure predictable topology convergence, operators now use ring topologies instead of mesh networks. The elusive 50 ms convergence with load balancing finally was achieved with the advent of ERPS (Ethernet Ring Protocol Switching). ERPS uses ECFM (Ethernet Connectivity Fault Management) as the monitoring mechanism for faster failure detection. ECFM using hardware offloading support detects failures within ten 10 msec, providing ERPS a 40 msec window for switchover. Hardware-aided MAC flushing and hardware-assisted failover support ensures that 50 msec service protection via ERPS is a reality. Operators can also aggregate links using LACP (Link Aggregation Control Protocol) for higher bandwidth and availability. The usage of micro-BFD (Bidirectional Forwarding Detection) with LACP enables faster failure detection and the convergence of aggregated links.

The routing technology used in IP networks inherently supports redundancy. The best path is used for packet forwarding and the alternate/inferior path, though established, is used for forwarding only when the best path goes down. Since convergence time is high in the plain vanilla routing methodology, IP networks use BFD as a monitoring protocol, along with fast reroute support in routing protocols. This allows them to realize link and node protection and achieve faster convergence. BFD sessions are offloaded after the initial packet exchange and, just like ECFM, can detect failures within 10msec. BFD sessions associated with routing protocols assist in faster routing protocol convergence. To achieve the 50 msec industry benchmark, backup paths are computed using the LFA (Loop free alternate) mechanism and installed in the forwarding path beforehand. Protection techniques supported in the hardware ensure backup paths are installed on failover scenarios, providing service availability even in route-scaled deployments. IP networks also support ECMP (Equal Cost Multi-Path) which provides protection and load balancing. An alternate mechanism in IP networks for node redundancy is achieved via the usage of the VRRP (Virtual Router Redundancy Protocol) protocol along with BFD for monitoring.

IP MPLS networks support a wide variety of protection schemes, with BFD again acting as the monitoring mechanism. RSVP-TE (Resource Reservation Protocol – Traffic Engineering) inherently contains FRR (Fast Re-route) support and further supports the provision of backup tunnels to protect traffic-engineered tunnels. LDP (Label Distribution Protocol) supports FRR techniques, including LFA and RLFA (remote LFA) for protection. Segment routed LSPs (Label Switched Path) not only support LFA and RLFA, but also TI-LFA (Topology Independent LFA) for complete protection coverage.

Conclusion

Service providers design the edge and core of their networks with service protection and high availability as one of their key objectives. Redundancy at every level, including link, node and path, is the cornerstone of such networks. The transport protocols and network topology vary, depending on the services and applications supported. Capgemini’s ISS switching solution supports a rich set of protocols with full support for redundancy, allowing equipment manufacturers to build devices for high service scale and availability.

Part 1: Unlocking value from commerce with data

In my role as global Data and Analytics leader for Capgemini’s Consumer Products and Retail Sector, I often see business leaders having a blind spot when it comes to the increasing value of data in driving sales for their organization. So perhaps it is not surprising that so few of them know how to unlock this at scale within their companies.

Recently at the Consumer Goods Forum Global Summit in Kyoto, Nestlé’s global head of sales and customers, Jordi Bosch, made an inspiring statement, when he said: “Data and tech are the future of sales.”

In this three-part blog series, I will explore the implications of this statement for all consumer goods and retail companies as they strive to transform their sales and marketing organisations in this new, more data-driven era of commerce.

Data-driven transformation to channel-less commerce

Consumer behavior has undergone a permanent transformation, along with their expectations of the companies that serve them. Nestlé refers to this as “channel-less commerce” (others call it ‘Connected’ or ‘eco-system led’ Commerce) where consumer goods brands and retailers must be able to provide personally relevant and consistent experiences across an eco-system of owned and third-party channels and touchpoints.

On stage in Kyoto, my colleague Owen McCabe, who is supporting Nestlé in their transformation, presented the impact this has on companies, including different metrics (customer lifetime value) and next-levels of data capabilities (e.g. first-party data), in order to offset additional costs of customer acquisition and fulfilment.

Data flows to connect capabilities end-to-end

To thrive in this changing business model landscape, the consumer goods industry needs to master a new set of end-to-end capabilities turbo-powered by data. These can be split into front-end capabilities such as search and merchandising, personalized content generation, integrated marketing automation, comprehensive product information, dynamic pricing and promotions and channel-less carts and checkout experiences.

These front-end capabilities must then seamlessly align with back-end capabilities like inventory and order management, last-mile logistics and fulfilment, customer management and loyalty, risk management, customer service, and sales management. All functions must be rewired to work together as one demand-team, in effective collaboration with selected eco-system partners. With data as the ‘electric current’ flowing ‘end-to-end’, powering the organisation to meet consumer expectations.

Data value: Benefits for all

What I often find when working with clients, is that ultimately those who best leverage the data are those who best understand the value of the data. Specifically, by linking that data to the concrete business benefits it enables for consumers, retailers, consumer goods companies, and the wider ecosystem of value-chain partners. Consumers will see value when they get relevant inspiration, personalized experiences and can seamlessly shop across channels.

For companies, data value translates into growth, profitability, and customer satisfaction. By using data to understand consumer needs, companies can develop products and services that meet those needs, improve reach, engagement and conversion across channels (in collaboration with channel partners) and touchpoints, increase repeat purchases, and deliver on promises. Smart analytics play a crucial role in improving decision-making and optimizing the entire customer journey.

Does your organization leverage data to connect front-end and back-end operations in a seamless way, and does your organization truly understand the business benefits that such data can unlock?

Stay tuned for the second and third parts of this blog series, where I will delve deeper into data sources, data collaboration, predictive and generative analytics, data foundations, composable tech architectures, data cultures, and the transformational data journey.

The future of commerce is channel-less, and it’s essential for companies to embrace it. Stay tuned…

Data and AI are redefining both the broadcasting of rugby matches and the game itself – revolutionizing the fan experience, improving athlete performance, and developing women’s sports in the process.

To take the pulse of this ongoing revolution, the Capgemini Research Institute has published the second edition of its report, A whole new ball game: Why sports tech is a game changer.

Does this mean that sport comes down to mathematics? What we can say for certain is that, for several years now, technology has been making inroads into all sports, for professionals and fans alike. The Capgemini Research Institute’s 2023 report, A whole new ball game: Why sports tech is a game changer, even underlines, with figures to back it up, that tech is transforming the experience of players and fans alike, both inside and outside stadiums. As a partner of World Rugby, the sport’s global governing body, Capgemini supports the organization’s digital transformation, enabling it to benefit from the latest technological innovations.

#1: Technology for improved performance

In rugby, as in other sports, digital technology is already improving the game and individual team performance with advances like body sensors to record players’ physiological data and game statistics transmitted in real time to coaches and television viewers. Today, thanks to new sources of data, but also artificial intelligence, data processing is becoming much more precise, refining the feedback of information in real time and even enabling predictions to be made. Clothing, smartwatches, heart rate monitors, virtual reality helmets, and connected mouthguards are tools that can be used to monitor players’ state of health, analyze playing technique, identify vulnerabilities, and even predict the outcome of a match.

Building on the Momentum Tracker solution, originally launched at the World Rugby Sevens Series in Dubai in 2020, we are now using artificial intelligence to measure the performance of men’s and women’s teams as well as their ability to improve during competition.

Momentum Tracker’s algorithm also provides each team’s coaching staff with reliable statistics on the performance of their opponents, enabling them to better guide their game strategy.

Data has also become an important management tool. It now lies at the heart of team management. In November 2022, the French Rugby Federation announced its partnership with an American data analytics company to bring together on a single platform the data collected over the last few years during international and top 14 matches. Still, this invaluable aid, which will be used by coaches to build their game strategy, does not replace the intuition and know-how of the experts who coach the French national team.

#2: Technology for better player health

Tech is also opening up new prospects for preventing injuries during games, which is of particular interest in contact sports such as rugby. Prevent Biometrics, an American company, has developed a connected mouthguard to capture the actual intensity of the impacts players undergo. All of this data, impossible to detect by human observation alone, enables players to be protected and for a library of data to be built up, with the aim of developing measures to protect and monitor players’ health. The system was tested by World Rugby at the Women’s Rugby World Cup in 2021.

#3: Technology for an enhanced fan experience

Technology is revolutionizing the fan experience both inside and outside the stadium.

The Capgemini Research Institute’s report reveals that almost 7 out of 10 sports fans prefer to watch a sports match outside the stadium if tech sufficiently enhances the viewing experience. Better camera angles, better broadcast quality, real-time statistics, and immersive experiences have profoundly transformed the fan experience outside stadiums. As a result, the number of spectators in stadiums has tended to decline over the last three years. While major international competitions are still very popular, this trend has seriously affected lower-level events. Responding to the question, “Have you attended a match in a stadium in the last 12 months?”, only 37% of fans surveyed answered in the affirmative in 2023, compared with 80% in the last quarter of 2019.

In response to this trend, and with a view to democratizing rugby, Capgemini is supporting World Rugby by developing innovative statistics with three objectives in mind: to provide live analysis of game phases and players, to help players understand the often complex rules, and to gamify the experience. Relying on data and artificial intelligence, new predictive statistics are being developed – for example, the percentage chance of scoring a try in a given type of situation, or even real-time victory predictions.

As these three examples show, technology has become omnipresent in sport. However, it still faces certain limits.

In stadiums, although technology has enabled several improvements to the fan experience (video replay, online drink ordering, information on previous matches thanks to apps, etc.), its use is still limited. On the one hand, this is due to the lack of connectivity in today’s stadiums, and, on the other, because the fans who attend matches also, above all, come to enjoy an atmosphere, an experience, and emotions that the screen can’t match yet, or may even hinder. So, while technology continually improves the spectator experience as well as play, there are still issues and use cases to be addressed.

The 2023 Rugby World Cup in France, which kicks off in September, will bring together fans from around the world, both at venues and on screens, to enjoy the game’s highest level of play, enabled by the most advanced technology. The tournament aims to offer spectators­ – veterans and novices alike – an unprecedented level of immersion during matches. I look forward to experiencing it.

As we approach the era of quantum advantage, where quantum computers outperform classical computers for specific tasks, exploring how we can harness the potential of quantum computing and algorithms for real-world applications becomes crucial.

Among the industries eagerly anticipating the impact of quantum computing, material science and drug discovery hold immense promise. However, transitioning from quantum computational advantage to practical implementation won’t be a walk in the park and will require addressing complementary challenges. Without dedicated research into practical applications, we risk having powerful quantum computers sitting idle while companies are still unprepared to adopt them.

This article will delve into the essential requirements for quantum computers and algorithms to become useful in these fields. It will highlight the significance of focused application research in driving advancements in quantum technology.

Unleashing quantum potential In material science and drug discovery

Material science, at the forefront of innovation, presents abundant opportunities for leveraging quantum computing. By delving into the atomic and molecular levels, material scientists strive to enhance properties and functionalities. Advanced alloys, nanomaterials or topological materials are just a few examples of materials defined by quantum behavior and therefore offer exciting avenues for exploration.

Another promising industry for a quantum advantage is the pharmaceutical industry. The industry faces significant challenges in the drug discovery process, with escalating costs and time requirements. Accelerating the virtual screening of potential drugs in an expanding chemical space is imperative.

Both industries are heavy users of molecular and material simulation. Approximate solutions like density functional theory (DFT) are invaluable for exploring and designing drugs and materials and can model many desired properties. However, other simulating other chemical features can be more challenging. In material science, for example—which approximates solutions of charge transfer processes, photochemical reactions and catalytic reactions—often fails to capture the underlying quantum physics determinative for the material’s behavior. In many cases, these characteristics are defined by electron dynamics and, in some cases, strong many-electron correlations, which is notoriously difficult for classical solvers.

This is where a potential quantum advantage comes in. As we scale up the power of quantum computers, simulating electron dynamics is one area in which scientists hope for a quantum computational advantage.

Still, material scientists and pharmaceuticals aspire to understand higher-level questions such as material behavior and performance or potency and selectivity of candidate drugs. Simulating electron dynamics for small systems alone is insufficient to answer these questions. On the other hand, the genuine quantum effects that determine the behavior of some materials or molecules make quantum computers an attractive tool. Therefore, we’ll likely need a combination of quantum and classical computers and solvers to answer these questions.

The urgent need for application research

To make quantum computers useful, despite all their limitations, we must emphasize the criticality of extensive application research. Applied research goes beyond developing isolated quantum algorithms. It focuses on identifying applications that can genuinely benefit from a quantum approach and integrating quantum technologies into existing computational workflows. Note that this will not be easy. It must deal with complex matters in an interdisciplinary environment of quantum information scientists, domain knowledge experts and business owners. It must deal with the limits of today’s quantum computers while building tomorrow’s applications, all with uncertain specifications and timelines.

However, if done well, it will usher companies into the era of quantum computing. It will allow companies to benefit from quantum computers as soon as they’re available. In the first place, by developing the right capabilities and knowledge. Given the steep and interdisciplinary learning curve, companies should expect several years before becoming quantum-ready.

However, with the high pace of current developments, that time might be now. Benefits go beyond capability and knowledge development, too. Essential technologies must be developed to integrate quantum computing into workflows. Additionally, application research serves as a compass for the quantum industry. By understanding the computational requirements companies face, they can influence the direction of research and guarantee systems support industry requirements.

Conclusion

As we inch closer to quantum advantage, the pressing question arises: How do we make quantum computing truly useful? While numerous companies are exploring quantum computing, the percentage of them publishing their findings remains minimal. This highlights the urgent need for dedicated teams comprised of chemists, material scientists and computational experts to bridge the gap between theoretical advancements and practical applications. If we want to prevent the transformative power of quantum computing from going unused while companies are still getting ready, then initiating application research today is key.

This article first appeared on Forbes.com

A Q&A with Vincent de Montalivet, Principal, Data for Net Zero offer leader, and Christopher Scheefer, Vice President, North American Lead for Intelligent Industry at Capgemini

The world’s manufacturers are more connected than ever, and they simply cannot afford to be at the mercy of unreliable, insecure supply chains. Yet supply chains are among the business operations most vulnerable to outside forces and the cost can be significant, ranging from lost sales and production time to lower brand image and increased difficulty in raising capital.

Insights derived from top-quality data can help manufacturers avoid disruptions in their supply chains – or at least mitigate the effects of climate change – by making these vital operations more sustainable.

Capgemini’s Vincent de Montalivet and Christopher Scheefer talk about how they help manufacturers leverage data to transform their operations. Here, they explain how the sustainable supply chain creates financial and operational advantages even as it reduces environmental impact and mitigates risk.

What is a sustainable supply chain? Is it one that’s environmentally responsible?

Vincent de Montalivet: Environmental excellence is certainly an important part of it. Consumers, investors, and regulators are increasingly concerned about carbon emissions and other environmental issues, and companies must respect and respond to those concerns. Green supply chains are particularly important as companies make net-zero commitments and need to track their emissions across their ecosystem and the life cycle of their products. But sustainability is more than just being green. What we’re really talking about is the transformation of the supply chain beyond its traditional role in which partners work with an enterprise to design and manufacture a product and then bring it to market. The sustainable supply chain encompasses that, but then expands to cover the entire lifecycle of the product in a way that reduces that product’s impact on the environment and minimizes risks to the business. It accounts for how a manufacturer’s decisions and activities impact climate change, but also how climate change impacts the organization – in terms of both financial and organizational risks.

Can you provide an example?

Christopher Scheefer: Sure. A manufacturer buys from a partner in its supply chain that experiences a chemical spill and has to suspend business while it addresses this. From an organizational perspective, the manufacturer needs to find a new source for that chemical so it can continue to operate. But that spill could also negatively impact the manufacturer’s brand because the manufacturer is associated with that supplier. It could also have financial implications – both in terms of sales but also in the form of making it harder to attract capital from investors who are increasingly concerned about environmental, social, and governance (ESG) issues. So it’s absolutely paramount that organizations include sustainable risk analysis in their network.

That’s a greatly expanded role for the traditional supply chain, isn’t it?

Scheefer: Absolutely. A supply chain has traditionally been treated as a means to manage disruptions – for example, those caused by the COVID-19 pandemic or by international conflicts – and, in legacy supply chains, the chief procurement officer would rank members of their network primarily on time, quality, and cost.

de Montalivet: We call those the currencies of a supply chain: they’re the main criteria that companies consider as they make decisions. Now, manufacturers need to add carbon as a currency. We’re witnessing increasing risks to supply chains arising from climate catastrophes and, as we look ahead, extreme weather events are only expected to grow more frequent and more severe. So there’s a real need for companies to make their supply chains more sustainable because that also means making them more resilient.

Scheefer: In the modern business environment,manufacturers must also deal with the rise of the circular economy. Chief procurement officers now must also manage reverse logistics – for example, they must figure out how to bring a product back into the company for reuse, refurbishment, or remanufacture after the customer is finished with it. And once the product has reached its end of life, companies must be able to determine its ultimate waste footprint in order to report on its environmental performance to shareholders, potential investors, and regulators.

Those are significant changes and the supply chain of today must look very different from those of the past. What does today’s supply chain look like?

Scheefer: From a technology perspective, a modern, sustainable supply chain is intelligent and connected. The intelligence provides the manufacturer with the ability to track the priority, the commitment, and the execution of sustainability for all its suppliers, globally, at any given time. In the past that would have been impossible – but now we can apply advanced, data-driven solutions employing artificial intelligence and other leading-edge technologies to the problem. It’s still a challenge – especially for enterprises that rely upon a globe-spanning network of suppliers and sub-suppliers, distributors, and other partners. But data mastery makes it viable.

de Montalivet: Connectivity is also important. It enables the manufacturer to manage the business in a way not previously possible. For example, the supply chain can now be connected to research and development, to ensure new product designs are more sustainable. It can be connected more deeply into manufacturing, where it will have an effect on new methods, new systems, and new machinery. And connectivity enables more sustainable sourcing through marketplaces that provide a true view of the supply chain network and a true view of the organization’s exposure. AI and analytics play huge roles in this because they’re critical technologies for assessing risk on the scale we’re describing. As an aside, it’s why AI is playing an increasingly vital role in the insurance industry as that sector comes to grips with the risks associated with climate change.

Can you provide some examples of how AI enables or enhances the sustainable supply chain?

de Montalivet: The Capgemini Research Institute has identified more than 70 AI-enabled use cases related to climate action. It focused on some of the top use cases for its report, Climate AI: How artificial intelligence can power your climate action strategy. For the manufacturing sector, these include tracking greenhouse gas emissions and tracing GHG leaks at industrial sites, and improving the energy efficiency of manufacturing facilities and industrial processes. Capgemini researchers also cited a number of use cases that have direct implications for the sector’s supply chains – including designing new products that reduce waste and emissions during prototyping, production, and use, improving demand planning, reducing the waste of raw materials, and route optimization and fleet management.

Scheefer: AI-derived insights are giving decision makers the information they need to transition manufacturing away from “make to stock,” in which a company creates products and warehouses them on the assumption that a customer, eventually, will want them. It facilitates “make to order,” in which the product isn’t produced until there’s a customer for it. This confers several advantages – including eliminating waste, reducing warehousing space, and making better use of a company’s workers. At the same time, better management allows the organization to reduce water use, energy consumption, and associated emissions. All of this has implications for the supply chain as well.

How are successful companies enabling their supply chains to become more sustainable?

de Montalivet: It may sound obvious, but if an organization can’t see all of the parameters across its supply chains in real time, it’s almost impossible to optimize them in a sustainable manner.So companies that already employ a logistics control tower are off to a good start. The control tower isn’t a new concept – it’s been around for years – but it’s essential to incorporate sustainability data into it.

Scheefer: Successful companies also recognize this is a journey. Once they’ve defined and implemented a sustainable supply chain, they can then start to apply data and analytics to continuously improve it. Capgemini offers solutions that help with this. Our Data for Net Zero solution enables organizations to master data from different sources and suppliers, then share it across their business functions and value chain. Meanwhile, our Sustainable AI solution ensures that employing the computational power of AI is itself accomplished in the most energy efficient, sustainable way possible.

You obviously believe a sustainable supply chain is essential. Why?

Scheefer: We do, because the supply chain is linked to so many other aspects of a business. Well-managed, sustainable supply chains have become a transformational agent and a necessary capability. It’s absolutely essential that a company has a sustainable supply chain strategy. If the chief procurement officer is not making their supply chains more sustainable, they’re losing out. Increasingly, it’s a license to operate and should be a corporate imperative.

Continue the conversation, get in touch with us:

Vincent de Montalivet, Principal, Data for Net Zero Offer Leader

Christopher Scheefer, Vice President, North American Lead for Intelligent Industry at Capgemini