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Digital twins – where are we now and what are the benefits?

Mariia Nalapko
5 Jul 2022

Digital twins have many benefits – they drive a cycle of continuous improvement to deliver specific and incremental gains in efficiency, productivity, and quality. As part of a fully integrated, connected model, digital twins can help organizations transition to the Frictionless Enterprise.

For many organizations, digital twins started life near the manufacturing production line. The idea was that new physical processes could have virtual, digital test-runs, enabling businesses to find errors and room for improvement before real-world rollout.
 
Digital twins aren’t just a good idea, they’re a great idea – and that’s why organizations have been quick to see the benefits they can bring to other areas of business. It’s not just about the factory floor. If you can try out new processes, say in HR, in customer operations, in the supply chain, and in finance, secure in the knowledge that live systems are being insulated from disruption, you’re not only reducing risk – you can also be more creative.
 
You can try new things, and you can keep trying them. Because as someone once said, while nothing is ever perfect, things can always be better.

Key drivers

So then – what’s the current status? A recent report by the Capgemini Research Institute (CRI) aimed to find out. It’s called “Digital Twins: Adding Intelligence to the Real World ,” and it examines where organizations are now, and what results they are seeing.

In a survey of 1,000 organizations worldwide, the CRI found that over 80% of them have an on-going digital twin program, and the rest are planning to start one. As many as 60% said they consider digital twins a strategic element of their overall digital transformation.

What’s prompting this enthusiasm? The key drivers relate as much to horizontal processes such as customer operations and finance as they do to vertical sectors such as manufacturing and life sciences. Around two-thirds of respondents said introducing new business models (67%) and customer-centricity (65%) were top-line drivers, and even more of them (73%) mentioned reducing time to market.

Bottom-line drivers are also pretty much universal, and are probably as predictable as those top-line factors. A sizeable 79% mentioned cost savings, while almost as many (71%) mentioned improvements in operational efficiency.

Value…

To what extent are these ambitions being achieved? The reported benefits of digital twins are impressive. On average, survey respondents said they were seeing a 17% increase in customer engagement and satisfaction, and the same increase in operational efficiency; a 16% increase in sales; a 15% decrease in turnaround time; and a 14% decrease in costs.

The report breaks down the benefits by digital twins by types in the physical world – in other words, in the areas of product development, of asset management, and of logistics performance.

But once again, there’s nothing exclusive about these performance gains. A service-based business, or a horizontal administrative function such as accounts payable or an employee helpdesk, can also employ digital twins and achieve similar outcomes in terms of, say, supplier and staff satisfaction, or of overall efficiency.

… and sustainability

Similarly, the sustainability benefits of digital twins aren’t restricted to physical environments. Yes, digital simulations in manufacturing, aviation, automotive industries and building management can help to identify and reduce product and energy waste – but gains are possible in other areas, and in other ways.

For instance, take accounts payable (AP) processes. AP is a key element of sustainability best-in-class practice. It means resource efficiency and the best use of scare materials to minimize impact on environment as well as reduce risk of finance supply chain disruption caused by climate change.

When digital twins are applied to AP processes, it helps to streamline operations, resulting in the elimination of paper invoices and a reduced need to send documents physically via post or courier, which creates a reduction in CO2 emissions. The same approach can be applied to other sectors of F&A. Everybody wins.

In the cash conversion cycle, AP is the mechanism for sustainable business practice and financing of debt and ratios of solvency, etc. It provides adaptability to circumstances for example diversification in markets and ensuring financial certainty, as well as diversification to give competitive advantage and strong stakeholder support – cash flow is critical to sustainability of any business and attention to detail in AP yields positive sustainability benefits.

Bringing it all together

Digital twins bring all kinds of specific benefits and incremental gains in efficiency, productivity, and quality. But one of the most important deliverables is holistic by nature.

For instance, digital twins can identify the bottlenecks from the purchase request/purchase order side and the various types of spend (e.g., maverick spend), which has a negative impact on processing AP invoices. As the core of all processes, digital twins enable us to discover the inefficiencies in master data that help to further streamline the payment processing.

A digital twin can replicate and demonstrate this cause-and-effect while everything is still in the pilot phase. But – and this is an important but – it can only do this if everything is connected. The specific supply chain function can’t be twinned and trialed in isolation. To address the potential wider problem, it needs to be part of a fully integrated model.

It needs, in short, to be part of what we at Capgemini call the Frictionless Enterprise  – and in the second and final article in this short series, I’ll be looking at how it can be achieved, and at how the transformation it delivers can be accelerated.

To learn more about the benefits of digital twins and how they can help your organization to drive improved business operations and transition to the Frictionless Enterprise, contact: mariia.nalapko@capgemini.com

Author

Mariia Nalapko

Digital Twin Global Process Owner, Capgemini’s Business Services
Mariia Nalapko focuses on digital transformation and enablement, developing Digital Twin & Transformation and Innovation Office (TIO) concepts, ways of working, and delivery.

    Why the public sector must prioritize sustainable IT

    Maëlle Bouvier
    05 July 2022

    How will IT transformation enable public sector organizations to become low carbon and net zero leaders in pursuit of their sustainability goals?

    Mitigating carbon-hungry tech

    With evidence of the climate emergency now irrefutable, public-sector organizations are upping their game in pursuit of net-zero carbon emissions. To this end, we hear about sustainable transport strategies, zero-emission fleet vehicles, smart cities, and investment in green public spaces. What’s not so frequently discussed is the impact of IT on the environment and what needs to be done to mitigate it.

    Yet, enterprise IT has a substantial carbon footprint. A recent Capgemini Research Institute report, Sustainable IT — Why it’s time for a green revolution for your organization’s IT, points out that data centers across both the public and private sectors represent nearly 1% of the world’s energy demand, with the majority still largely powered by fossil fuels. The report cites a forecast stating that the growth rate of enterprise IT’s contribution to global CO2 emissions could grow from 8.4% in 2020 to 20.5% in 2025. That’s not all. There’s a distinct lack of recycling, with 89% of organizations recycling less than 10% of their IT hardware. Further, as more and more organizations seek to use artificial intelligence (AI) to inform efficiency gains and service excellence, it’s important to recognize that AI itself has a carbon footprint. One estimate suggests that training an AI language processing system produced 1,400 pounds of emissions, which is equivalent to the amount produced by one person taking a round-trip flight between New York and San Francisco.

    Advancing the sustainability agenda

    For the public sector with its vast IT estate and growing technology spend, there is thus potential for IT leaders to play a significant role in advancing the sustainability agenda through the implementation of sustainable IT strategies. This is where IT adopts an environmental, sustainability-focused approach to the design, usage, and disposal of IT, software applications, and accompanying business processes.

    However, while the global findings show that the wider technology industry is responding to the climate urgency, respondents in public sector organizations exhibit relatively low levels of awareness of the environmental impact of their IT (36%) compared with other industries, such as banking (52%) and consumer products (51%).

    More broadly, outside IT functions, the awareness and implementation of sustainable IT initiatives is very low. For example, awareness/implementation amongst HR respondents in the public sector is just 5%, although this is a little higher than the all-sector average for HR of 1%. Likewise, public sector finance functions have a low awareness/implementation score at 5%, just below the all-sector average of 7%.

    Tapping into the capabilities of tech firms

    Only 10% of public sector organizations (against an 18% global average) have a sustainable IT strategy with well-defined goals and target timelines. This compares to the broader sustainability strategy for the entire enterprise, which 52% of public sector organizations say they have. This low figure for sustainable IT in the public sector comes despite the existence of national regulations and frameworks for eco-responsible digital strategies. So, what’s stopping the adoption of these frameworks? Above all, there remains a major challenge in measuring the carbon impact of public IT and this hinders decision-makers in committing to reduction trajectories/targets.

    Clearly, help is needed to accelerate the adoption of sustainable IT in this sector. And public sector leaders recognize this, with 62% saying technology firms can help them measure the environmental impact of IT. A further 53% believe that the tech firms should incorporate a sustainable IT dimension in their products and services, and 47% would be willing to pay a premium of up to 5% for them.

    The barriers to sustainable IT

    The biggest roadblock for sustainable IT implementation in the public sector is a lack of domain expertise, with 60% of organizations citing this as a challenge versus the all-sector average of 53%. Cost is another factor, with 55% of public sector organizations citing the high cost of setting up sustainable IT infrastructure. Again, this is above the all-industry average, which stands at 48%. When decarbonization is not an administration’s priority, it becomes difficult to justify both the recruitment effort and cost.

    Of course, sustainability maturity varies from country to country, and this will have an impact on how these barriers are handled. For example, Germany has set in law its objective of carbon neutrality for public administrations (Federal Climate Act), which means public sector organizations are legally obliged to find ways to overcome whatever barriers to climate neutrality they face. In contrast the ‘burning platform’ for the public sector in other countries is lacking because there is no legally binding target for them to achieve.

    As a provider of essential citizen and business services and national infrastructures, it is no surprise that the public sector also has a heightened concern (55% v 45%) about the impact on business continuity of shifting from legacy systems to more sustainable alternatives.

    Nonetheless, removing these barriers will bring substantial benefit aside from the huge value of reducing the public sector IT carbon footprint. For example, organizations that have scaled up their sustainable IT use cases have achieved on average a 12% cost reduction. Those deemed to have highly mature sustainable IT practices score  higher in terms of improved customer/client satisfaction at 56% versus 43% for less mature organizations. Just one measure alone, that of implementing auto switch-off of hardware/features, can deliver up to 14% in operational cost savings from energy reduction, while also reducing carbon emissions.

    Recommendations for sustainable IT implementation

    As leaders in shaping policy to mitigate climate change and working towards net zero, government and public sector bodies must accelerate their own implementation of sustainable IT strategies and approaches.

    The following recommendations form a roadmap for achieving this.

    • Assess: Set the foundations for sustainable IT initiatives with a qualitative and quantitative diagnostic assessment and a sustainable IT strategy that aligns with the broader organizational sustainability strategy. This should include the definition of key performance indicators, targets, frameworks, and the setting of a carbon cost against IT operations so that different functions really understand the impact of their IT footprint.
    • Target and trajectory: Equip the organization with the ability to determine the impact of the measures envisaged when setting a target and trajectory, whether these measures concern the evolution of existing IT or the development of new digital offers.
    • Governance plan: Set up robust governance approaches to be implemented at both national and local level. The national plan will be key to establishing common rules, such as a sustainable procurement guide, for implementation across the public sector.  Empower a dedicated sustainable IT team with support from the top leadership team to ensure all stakeholders are committed to implementing sustainable IT initiatives. The governance plan should align services and citizen-engagement strategies with sustainable IT.
    • Implement: Operationalize sustainable IT initiatives, with sustainability a key pillar of software architecture, a sustainability culture across teams, and by identifying the environmental impact of AI during design and training. Environmental impact should also be an IT vendor selection criterion, while selecting and scaling the right sustainable IT use cases is vital, such as moving to enterprise cloud applications.

    Responding to growing digitalization

    Public sector organizations are on a steep and rapid digital trajectory. As our Public Sector IT Trends 2021-22 research showed, the pandemic has demonstrated a requirement for contactless digital services, while in Europe the EU’s new goals stipulate that all public administration services should be available online by 2030. This will inevitably have an impact on C02 emissions and the volume of e-waste generated.

    Public sector leaders can mitigate the environmental impact of this digitalization by implementing sustainable IT. At the same time, a sustainable IT strategy must consider both the positive and negative environmental impact of the changes proposed. For example, a digital citizen service will have its own IT carbon footprint, yet it could also make it possible to significantly reduce user travel and the ensuing carbon impact.

    Moving forward, from the adoption of diagnostic tools and the definition of a strategy to the development of a roadmap for sustainable IT and internal behavioral change, the stage is set for a more resilient, sustainable public sector IT landscape.

    Find out more

    Read the full Capgemini Research Institute report, Sustainable IT — Why it’s time for a green revolution for your organization’s IT. For more insights on the public sector, follow us on LinkedIn.

    How to “Go FAIR”: The key business decisions to be made

    Natalie Stanford
    4 Jul 2022

    There are many ways to “go FAIR”, choosing the right approach for your organization is critical for success.

    The FAIR Guiding Principles detail characteristics of data that support better data management. They stand for Findable, Accessible, Interoperable and Reusable.

    When implementing FAIR in your organization, there is no “right way” to do it. You approach will depend on factors including: current data management maturity; what data you manage; company culture; your budget, time, and skills, among others. Here are things you should consider during your journey of “going FAIR”.

    Enabling Findability

    Findability is about ensuring that data can be searched, identified, and retrieved by a range of users. Enabling Findability involves getting the right balance of technology, processes, expertise, and data management support. You need to consider the following: 

    • Metadata development and capture: you need to ensure that all data is labelled with enough key attributes that your users can easily search for it. For instance a user might want all data collected within the last 12 months that relates to Breast Cancer. Therefore, you would need to introduce key attributes such as Date of Collection, and Disease Type.
    • Storage / Centralisation of data: for data to be physically findable it needs to be ‘centralised’, so your users know where to look. You could achieve this through physical centralisation e.g. a data lake, or a hub and spoke style centralisation where data is kept in domain specific storage, and a catalogue us used to direct users to its location. What makes sense for your business will depend on your tech and process history, and future expectations.
    • Searching for data: you need to enable effective querying of metadata. Solutions such as cataloguing software supports key-word searches and filtering of data, to support identification of data and location.

    Enabling Accessibility

    Accessibility is about making the right data available to the right people, at the right time, with the right supporting information. This is not a free-for-all, and data access should be restricted as much as needed (e.g. privacy, sensitivity etc.) and as open as allowed (e.g. licensing, regulations etc.). You will need to consider the following:

    • Data Vs Metadata: FAIR makes a key distinction between metadata and data. You will need to decide what information about the data to expose to ensure staff can identify suitable access permission channels as needed.
    • Legal and contractual requirements: what legal or contractual restrictions does your data have?  You need to ensure that users are only able to view and access data that they have adequate permissions for, which also means you need to consider how to match users to permissions.
    • Technical considerations: What is your current technology stack? FAIR emphasizes making data accessible through a standardized communication protocol, for instance through a secure internal web app.

    Enabling Interoperability

    Interoperability is the ability of people, computer systems and/or software to exchange and use information. You then need to decide what levels of interoperability are both feasible and beneficial to your use cases over your expected timescales to ensure you get the most value from what you implement.

    There are varying degrees of Interoperability, each incrementally improving the holistic interoperation of information: 

    • Foundational Interoperability: this involves business information moving between different systems in a way that the purpose and meaning of the data is preserved e.g., a report in Word, or a PDF. In this instance it is the user that interprets the understanding; the systems only support the transfer of the file structure. 
    • Structural Interoperability: this involves business information being exchanged between systems, with the systems able to absorb the data automatically using pre-defined context mapping of data columns. In this instance the systems don’t ‘understand’ the data, simply implement programmed mapping.
    • Semantic interoperability: this involves systems exchanging information seamlessly through full understanding of the origin, context, and meaning of the data. It relies on structured Formats (e.g. JSON, SML, RDF, API, etc.), and Controlled Vocabularies, Ontologies and Synonym libraries to ensure that each system can seamlessly interpret the true meaning of each piece of information.

    Enabling Reusability

    Formulating the right data management approach to Findability, Accessibility, and Interoperability, are the foundations of Reusable data. In addition, to ensure Reusability you need to ensure you have the right:

    • Data Linking / Provenance: for data to be truly reusable a user needs to understand the original context of the data, including processing methods or transformations that were performed to achieve the current data. This is particularly important in scientific data. The origins are the key to valid reuse.
    • Technology: data that relies on specific and / or legacy software to interpret and understand needs supporting technology to ensure suitable re-use. This includes e.g. containerization, virtual machines, or legacy computer set-ups to support older data.
    • Culture: for effective re-use, culture is king. Through establishing good sharing practices, suitable organizational support and structures, and creating incentives for FAIR data sharing, a company can rapidly improve the reusability of its data.

    Conclusion

    Strategically implementing the FAIR Guiding Principles enables you to establish a data-drive organization, impacting many aspects of your business value, including: enabling analytics capabilities; improving the speed and reliability of auditing; and ensuring long term value of laboratory data. If you are unsure where to start, Capgemini’s many years of experience with FAIR and data management will help you identify the best strategies for you, and enable you to truly embrace becoming a data-driven R&D organization

    Meet our expert

    Natalie Stanford

    Life Sciences and Data Management Consultant, Capgemini Engineering
    Natalie is a Chemist, Biologist and a FAIR data expert. She has been supporting clients to achieve their R&D data management goals since 2013, delivering support, strategy, and roadmaps to clients across academia, Fast Moving Consumer Goods, and Big Pharma.

      Procurement – the challenges and its changing role

      Greg Bateup
      Greg Bateup
      July 04, 2022

      Procurement is becoming an integrated, cognitive function that supports your end-to-end value chain and business ecosystem of customers and suppliers.

      The key grip in a film crew. The back four of a soccer team. The quartermaster in an army unit.

      What do they all have in common? It’s this: while they may all lack the glamor of the stars of their respective shows, they are all of them vital to collective success.

      Take the last one, for instance. Quartermasters don’t train the troops, or lead them into combat – but without them, there would be no food, fuel, or water. Or clothing. Or field services.

      Procurement in business is just the same. It’s a beyond the scenes function, but it’s vital – and it faces challenges.

      Cinderella, disconnected

      Those challenges fall into two broad categories.

      The first is this back-room, Cinderella status. It’s not glamorous, and so the value it brings to the business is not understood by the rest of the organization. It’s seen as functional or maybe tactical, rather than strategic. In many cases, it has to compete for talent with other parts of the enterprise, which leads to retention issues for key members of staff, who may feel their career prospects would be better elsewhere.

      The second can be summarized as disconnect. Procurement’s role makes it prey to multiple, non-integrated processes and systems, making it hard to share data or to achieve visibility across the organization. It’s not the lack of integrated systems, either: frequently, systems are also outdated and leave gaps in the process, leading to excessive manual processing and a poor experience for end-users.

      The disconnect extends to supplier interaction. Procurement often has only limited visibility of the picture beyond its major suppliers. There are challenges in interactions between procurement and other parts of the business, such as operations, business, IT, and finance. As a result, business stakeholders don’t keep the procurement team in the loop when they begin to work with suppliers. This means that, in turn, there is less collaboration with, and management of, the supplier portfolio than there might be, and weaker risk processes, too.

      A new and better future

      Many organizations are waking up to these challenges. They see that a new and better future for procurement is possible.

      This is a future in which the procurement function is digitally woven not just into the operation, but the strategy, of the business. A centralized organization model can, for example, facilitate innovation and competitiveness, rather than merely act in a fulfillment capacity. This same model can incorporate the increased use of shared services and business process outsourcing (BPO) for service delivery, maintaining a sustainable set of processes and metrics for managing the external team. A Center of Excellence can carry out strategic activity, and digitization can make the most of the efficiency and value of sourcing and procurement tools and enablers.

      A new role for procurement will also see supplier relationships improve. When data is better managed, suppliers can be better profiled – but also, innovation can be more easily initiated, and better monitored. Better supplier management also makes it likely that procurement can play a greater role in meeting the organization’s corporate social responsibility (CSR) commitments.

      Also, right now, the skills of the procurement team are frequently misaligned to the changing needs of the business, especially in the areas of digitization and analytics – but the growing awareness of procurement’s potential is increasing the pressure to implement bespoke training and digital skills programs. This is another way in which the procurement role is evolving.

      Procurement is changing in order to address broader issues, too. The global pandemic has, of course, made an impact on businesses’ ability to meet their own supply requirements, and has created a greater need for flexibility.

      In addition, and finally, the drive for sustainability, which is now pretty much universal, means that organizations are collaborating ever more closely with their suppliers to reduce the ecological footprint of product sourcing.

      Integrated procurement – frictionless and intelligent

      In short, we are witnessing a significant change in the role of procurement within the enterprise. In response to new as well as long-standing challenges, there is pressure for it to become an integrated, cognitive function supporting the end-to-end value chain, fully embedded into business processes, increasing the extent to which it supports customers, suppliers, and the business as a whole.

      In the next article in this short series, we’ll take a look at how digitization can facilitate this change. A frictionless, cognitive approach to procurement, incorporating technologies such as cloud, artificial intelligence (AI), robotic process automation (RPA), and blockchain, will tackle the disconnect issues, and increase the efficiency and value that procurement can bring to the business.

      As we’ll see, when all this happens, and procurement goes frictionless, then, perhaps, it will get the recognition it deserves.

      To find out how Capgemini’s Cognitive Procurement Services offer can transform your organization to drive effective, sustainable, and frictionless procurement, contact: greg.bateup@capgemini.com

      Author

      Greg Bateup

      Greg Bateup

      Head of Cognitive Procurement Services, Capgemini’s Business Services
      Greg Bateup has worked with clients to deliver business transformation and BPO services for almost 30 years. For the last few years, Greg has focused on the digital transformation of the source-to-pay function, and how organizations can not only drive efficiencies in the procurement function, but also drive compliance and savings.

        Procurement – going frictionless

        Greg Bateup
        Greg Bateup
        July 04, 2022

        When everything is cognitive and seamless, procurement ceases to be an operational function, and evolves into something much more strategic, providing a dependable, tailored way to manage supplier performance.

        In the first article in this short series, we considered the challenges facing the procurement function, and also the changing nature of its role. This time, we’ll be looking at integrating it into a cognitive, enterprise-wide, digital model.

        The Frictionless Enterprise

        Let’s start with that very point. If procurement is to address its challenges and meet changing responsibilities, it does indeed need to be properly plugged into the organization as a whole.

        But that’s not enough. It’s not just about procurement tapping into other business functions – it’s about all of those functions tapping into one another.

        This is a concept that we at Capgemini call the Frictionless Enterprise[HB1] . It’s an approach that seamlessly connects processes and people, intelligently, and as and when needed. Everything works together.

        It’s an approach that dynamically adapts to the circumstances of individual organizations, and that addresses each and every point of potential operational friction – regardless of whether that friction is between individual departments, between functions, or apps, or data sources, or devices, or involving something else altogether.

        Cohesion and improvement across the function

        When the entire enterprise is cognitive and cohesive in this way, procurement becomes a constituent part of it, like any other. It may perhaps have been previously regarded by some as a backroom function, lacking glamor – but no longer. More importantly, when everything else, from finance through marketing to logistics, is working seamlessly, procurement can start to deliver far greater value than was ever possible before.

        Intelligent automation and analytics can streamline processes, and save time and money across the whole function. Smart insight can deliver consolidation and improvement in:

        • Demand management – request validation; purchase order processing; expediting, receiving, and returns; invoice exception management; and procurement support
        • Intelligent sourcing – tail spend management; tactical procurement; strategic sourcing; category management; and contract and relationship management
        • Supplier management – supplier performance; supplier enablement; contract management; compliance management; and supplier support
        • Accounts payable – invoice receipt; invoice processing; invoice issue management; payment; and payables support
        • Risks and insights – spend analytics; working capital analytics; risk management and compliance; market intelligence; and corporate social responsibility (CSR) and sustainability

        Taking stock

        When everything is smart and seamless, procurement ceases to be an operational function, and evolves into something much more strategic. It becomes predictive, responsible, and reliable, delivering actionable insight. It provides a dependable way to manage supplier performance, tailoring it to the needs of the business while ensuring that the suppliers’ own needs and expectations are also met. And it establishes a means of continuous feedback in contract management compliance and risk management.

        In fact, several of these and other benefits are quantifiable. Working within an enterprise-wide digital model, frictionless procurement can:

        • Enhance compliance and risk mitigation: cognitive, connected models can achieve over 90% procurement policy compliance, and deliver significant reductions in what might be termed “maverick spend”
        • Increase productivity by up to 50%, by enabling changes such as automation of purchase orders and dynamic channel switching
        • Deliver an unobtrusive user experience – because why should doing the day job be any less straightforward for someone than, say, shopping online in the evening?
        • Enhance transparency and insights: when friction between functions and systems is removed, visibility can be increased across the enterprise, and across the supply chain in particular. This can deliver up to 26% identified supplier consolidation savings, reduce risk, and – once again – cut back on that “maverick spend”
        • Start paying back promptly. We’ve seen reductions of up to 50% in back-office costs, and savings of 15% on spend.

        These, then, are benefits that can be achieved in principle. But there’s no substitute for practice. In the third and final article in this short series, we’ll take a look at some cognitive, integrated procurement implementations in the real world. The results are pretty impressive…

        To find out how Capgemini’s Cognitive Procurement Services offer can transform your organization to drive effective, sustainable, and frictionless procurement, contact: greg.bateup@capgemini.com

        Author

        Greg Bateup

        Greg Bateup

        Head of Cognitive Procurement Services, Capgemini’s Business Services
        Greg Bateup has worked with clients to deliver business transformation and BPO services for almost 30 years. For the last few years, Greg has focused on the digital transformation of the source-to-pay function, and how organizations can not only drive efficiencies in the procurement function, but also drive compliance and savings.

          Procurement – real-world transformational benefits

          Greg Bateup
          Greg Bateup
          July 04, 2022

          Procurement is not merely a fulfillment service, but an important contributor to the strategic and tactical success of an organization that gives them the flexibility, cost-effectiveness, and resources they need in order to go out and win.

          In the first article in this short series, we headlined the challenges facing the procurement function. We also considered the changing nature of its [HB1] role. In the second article, we looked at how procurement could be integrated into a cognitive, enterprise-wide, digital model[HB2] , as part of what we at Capgemini call the Frictionless Enterprise[HB3] .

          In this, the third and final article in the series, we’re going to assess some real-world implementations of this cognitive, integrated approach to procurement. In each case, you’ll see some impressive – and measurable – operational benefits; but you’ll also see the extent to which new, intelligent procurement models can make a significant contribution to business strategy.

          Case #1 – financial services

          This multinational financial services company sought to increase procurement efficiency in general, and in particular to improve purchasing compliance in its global insurance business.

          A digital global approach was introduced that included an outsourcing model, a user-friendly buying portal, intelligent automation, a closed loop process for compliance and change management, and a Command Center concept to provide greater visibility into process bottlenecks.

          In fact, visibility was improved not just in this respect, but across the entire procure-to-pay (P2P) function. Processes were harmonized globally across business units, and scalable, fit-for-purpose platforms maintained compliance, and locked in savings.

          As a result, the organization achieved 30% productivity gains over five years. There was a 90% increase in purchase order (PO) compliance, savings of over 10% in tail-spend management, an increase in no-touch POs to 80%, and a significant improvement in end-user satisfaction.

          Case #2 – food

          One of the world’s largest food companies was experiencing delays in processing purchase requisitions which led to internal customer dissatisfaction, delayed internal projects and a loss of revenue. PO compliance was at only 40%, limiting control over purchasing, and 30% of purchases required multiple touches which further delayed on-time payment to suppliers. In addition, poor data visibility meant it was difficult to identify savings.

          A global managed service process model was introduced, with standardized desktop procedures, including the use of a catalog that was generated using improved content via analysis of repeat spend and training. Functions including PO processing, PO cancellations and changes, and invoice exception handling were monitored against new SLA-based metrics.

          The new model reduced the number of interactions per transaction, leading to a significant improvement in on-time supplier payment. In fact, over three years, there was an increase in touchless POs of up to 63%, as well as a 90% increase in PO compliance. There was a 75% improvement in invoice block resolution, and a year-on-year rise of 10% in the productivity of full-time employees.

          Procurement – key to strategic success

          As I said in the introduction to this article, while all these stats are impressive, it’s not just about measurable benefits. In this last case, for instance, what is perhaps more important than any one operational improvement is that the procurement function is now much more closely aligned to the company’s business objectives.

          We need to see procurement for what it is – not merely as a fulfillment service, but as an important contributor to the strategic and tactical success of an organization. When it’s part of a Frictionless Enterprise , procurement can give businesses the flexibility, the cost-effectiveness, and the resources they need in order to go out and win.

          To find out how Capgemini’s Cognitive Procurement Services offer can transform your organization to drive effective, sustainable, and frictionless procurement, contact: greg.bateup@capgemini.com

          Author

          Greg Bateup

          Greg Bateup

          Head of Cognitive Procurement Services, Capgemini’s Business Services
          Greg Bateup has worked with clients to deliver business transformation and BPO services for almost 30 years. For the last few years, Greg has focused on the digital transformation of the source-to-pay function, and how organizations can not only drive efficiencies in the procurement function, but also drive compliance and savings.

            Why securing the SAP landscape is a business essential

            Marieke Van De Putte
            1 Jul 2022

            Looking at the current cybersecurity landscape calls to mind the fable The Boy Who Cried Wolf. Many businesses are fully aware of the threat of the wolf, but with repeated calls to look at a multitude of dangers, businesses are fast becoming disorientated as to where the danger really lies.

            Where it differs from the tale is that many of the cries are not unfounded, and yet, they might not always be as fatal as they’re made out to be. With SAP security, however, the risks are real and the consequences of not acting on the dangers, seriously, can be serious.

            Used by the vast majority of multinationals around the world, SAP (systems, applications, and products) security protects business processes and data of high value, such as sales, finance, and personnel information. Traditionally, businesses would lock critical information in a data center, protected by a proverbial lock and key, with peace of mind. SAP, however, is primarily focused on authorization management and segregation of duties.

            While this approach was once enough, today it is not. The digitization and movement of assets in the cloud means that online threats are increasing in number, diversity, and impact, leaving organizations under a far greater threat of opportunistic attacks. In 2019, research found that nearly two-thirds of organizations reported an ERP (enterprise resource planning) system breach over a 24-month period from attackers after critical data. More often than not, this is a result of unsuitable SAP risk management solutions.

            Shifting sands

            In 2021, SAP and Onapsis issued an intelligence report warning organizations to take immediate action and review and monitor their SAP landscape. This was aimed at companies using old and vulnerable SAP versions, which are difficult to patch by today’s security standards.

            Just the year before, the RECON vulnerability left tens of thousands of customers’ data exposed to attackers; if exploited, an unauthenticated user would have been able to create a new SAP profile with maximum privileges to circumnavigate access and authorization controls. It was patched soon after, but it exposed the risks associated with relying on a system without proper monitoring.

            Today’s leap to the digital economy creates opportunities for companies to transform and scale, and SAP encourages customers to move to SAP S/4HANA – a cloud-based ERP system – to reap such benefits. This migration is a necessary modernization for many businesses, but it’s worth bearing in mind that it doesn’t guarantee out-of-the-box security. It still requires continuous monitoring to identify threats and vulnerabilities.

            Getting ahead

            Securing SAP platforms demands a proactive approach. With more and more critical infrastructure connected to the internet, possible entry points multiply every day. Research by Statista estimates that the number of connected devices is set to triple from 8.7 billion in 2020 to 24.4 billion in 2030, and this number will only continue to grow.

            Hackers generally want to create the greatest impact possible, which is why we’re seeing increasing attacks on critical sectors such as life sciences and energy and utilities. Take the example of medicine: if an attacker can bypass security and enter through the back door, there’s a chance they could edit essential information related to the product make-up at the production stage. This goes beyond business disruption to affect consumers, sometimes with life threatening consequences.

            Ideally, SAP applications provide businesses with a way to manage their departments effortlessly. But nothing should be taken for granted, especially as businesses move data and even applications into the cloud. Whether you have public or private clouds, you must have security measures so that you know who is taking care of what when it comes to security. To understand what they are or aren’t doing, it’s important to be proactive by examining, monitoring, and assessing.

            Assessment and management

            Locking down SAP security may seem like a complex task – and it is without the right practical processes in place.

            This is why by implementing effective vulnerability assessment and vulnerability management, you’ll be able to identify new threats and weaknesses in security configurations and prioritize vulnerability remediations for mission-critical SAP systems.

            To cover these bases, Capgemini has developed a unique holistic solution that shields your system from data breaches and potential losses. With quick and real-time insights, our Vulnerability Assessment reviews, evaluates, identifies, and reports on SAP weaknesses. But with the landscape always changing, it cannot end there, which is why we combine continuous management to monitor and identify new vulnerabilities in the environment.

            For businesses to seize the advantages of cloud-based security, it is essential they understand the SAP landscape better. To navigate complexity, improve compliance, and seize the potential of cloud-based digitization they must be able to know which cry of the wolf to prioritize. This is no small feat, but through better collaboration, it is more than possible.

            Contact Capgemini today to find out how our network of global SAP experts can help your organization embrace this new generation of cybersecurity.

            Author

            Marieke Van De Putte

            Global Domain Lead Cyber Compliance | SAP & Cyber | NL Service Line Lead Security & Compliance 
            Specialized in developing practical approaches to security, risk and compliance, and applying automation possibilities. Contributing our team’s expertise to digital transformation projects, like IT outsourcing and cloud migration.

            Mark Sampson

            Principal Enterprise Architect SAP and Cloud Centers of Excellence
            Has over twenty years’ experience in both IT consultancy and end user positions with a specialization in SAP design, architecture and delivery. Since 2012 he has been focused on delivering SAP solution onto hyper scale cloud providers (AWS/Azure).

              Project bose : a smart way to enable sustainable 5G networks

              Capgemini
              Capgemini
              30 Jun 2022
              capgemini-engineering

              Energy consumption has always been a significant consideration for service providers as it is one of the highest operating costs. But it is becoming even more important due to climate change and sustainability considerations.

              As per the GSMA, energy costs account for 20-25% of a network’s total cost of ownership for 4G operators. Operators globally spend approximately USD 17 billion on energy every year. With the pending deployment of more NR base stations in 5G, such as small base stations with massive MIMO in high band and 5G networks, energy consumption is expected to increase by as much as 140% in some 5G deployment scenarios. It therefore calls for immediate action.

              As operators aiming to decrease power consumption in 5G networks through different energy-saving techniques, the complexity of non-standardized energy-saving mechanisms are becoming a blocker to achieving the desired energy reduction targets.
              However, AI/ML intelligence, combined with technology enablers like NWDAF/RIC in 5G, has unlocked various innovation paths for the telco industry to design and execute such energy efficiency solutions in an economical and standard way to overcome the challenges encountered in current methods.

              Project Bose represents a great example of such innovative use cases. Its objective is to create a sustainable 5G network – and beyond – using a data-driven approach. To achieve this, it has introduced five energy-saving levers – directional UE paging, MICO mode, energy aware NF placement, smart UPF selection, and intelligent CPU tuning – that leverage analytics information from an underlying Capgemini NWDAF framework, and work in tandem to optimize energy consumption in the network and associated IoT devices.

              This result in significant CO2 emission reductions and cost savings, with no negative impact on end users’ QoE. Project Bose also provides a platform to build new energy-saving levers in the future.
              Thanks to a collaboration with Intel for its observability framework, Project Bose is able to capture a vast variety of network and infra metrics at different levels in the 5G ecosystem and provides a holistic energy-saving solution for operators.

              In tests conducted with this solution in the lab, we have observed an average energy saving of 18%, resulting in around a 14% reduction in CO2 emissions. It is a very impressive first step. But it is just the beginning. Project Bose still has many exciting innovations to come.

              Download our white paper to learn more about Project Bose.

              Providing accurate T&E information through an AI-based chatbot

              Bartosz Grochowski
              28 Jun 2022

              Capgemini’s award-winning chatbot leverages natural language processing and machine learning to deliver more accurate travel and expenses information. This is helping our people travel more easily and safely in the post-pandemic world.

              Organizations today need to provide up-to-date travel information to consulting professionals travelling on business at speed – including accurate information on local hotel rates, food allowances, and travel entitlements.

              This process is typically handled manually, with requests often sent to a third-party system that can lengthen response times significantly. In addition, requests are often sent to teams outside of office hours, which means questions frequently go unanswered until the next business day – much to the frustration of traveling professionals.

              Enable instant and accurate travel information

              What travel and expenses (T&E) teams needed is a tool that delivers simple, quick, and user-friendly support to its business people, without any specific training, implementation costs, or additional software installation.

              On top of this, the tool would ideally leverage an extensive knowledge repository, simple plug-and-play architecture, an intuitive user interface, and intelligent automation to enable finance teams to respond accurately to a wide range of T&E, HR, IT, finance, and policy-related queries.

              NLP and machine learning drives speed and accuracy

              Enter, Capgemini’s Microsoft-based chatbot. With natural language processing (NLP) embedded at its core, the chatbot recalls all previous user conversations, while leveraging machine learning to predict future queries and inputs. This ensures that the accuracy and amount of travel information it provides continuously improves over time.

              We’re extremely proud that our Microsoft-based chatbot recently won the “Chatbot Innovation” category at the 2022 AI Breakthrough Awards – for the fourth year running. Not only that, the tool is also being currently being implemented across many of our internal engagements, which is helping our people to travel safely in a post-pandemic world.

              To learn how Capgemini can help you provide up-to-date, accurate travel information to your people through leveraging Intelligent Process Automation, contact: bartosz.grochowski@capgemini.com

              The growth power of artificial intelligence

              Dr. Lobna Karoui
              28 Jun 2022

              The rise of AI

              Since the first century BC, humans have been concerned with creating machines capable of imitating human reasoning. Artificial intelligence (AI) has been defined by Arthur Samuel as the field of study that gives computers the ability to learn without being explicitly programmed. More globally we can define AI as a process of mimicking human intelligence based on the creation and application of algorithms executed in a dynamic computing environment. Its final goal is to enable computers to think and act like human beings. In 1956, John McCarthy and his collaborators organized a conference called the Dartmouth Summer Research Project on Artificial Intelligence, which gave birth to machine learning, deep learning, predictive analytics, and more recently, prescriptive analytics. In 2007,  McCarthy published an important paper titled “What is Artificial Intelligence,” where he clearly answered multiple questions about AI and its precise branches (pattern recognition, ontology, inference, search, etc.). Also, in the last decade, data science has emerged as a new area of study.

              The current rise of AI was made possible by four enabling conditions:

              • With the advent of the internet and the development of connected objects, tremendous quantities of data are now available. In 2020, 1.7 MB of data was created each second by every person. In the past two years alone, an astonishing 90% of the world’s data has been created. Every day, 95 million photos and videos are shared on Instagram, 306.4 billion emails are sent, and 5 million Tweets are made1. IDC predicted that the global datasphere will reach 175 zb by 2025.
              • New technologies such as cloud computing have emerged, and we are witnessing an exponential increase in storage capacity and computing power.
              • Society assist to the growing progress in available algorithms developed by researchers. Libraries like TensorFlow (Google) or scikit-learn (Inria: National Institute for Research in Digital Science and Technology), which contain major AI algorithms, are available with no fees. Many communities, like Stack Overflow, are helping AI developers solve problems.
              • The support from industries is growing. Many business sectors have come to understand the importance of AI and are investing massively in this exponential technology.

              The importance of data in AI

              Data is the new oil, and some big companies like the GAFA (Google, Amazon, Facebook, and Apple) are monetizing it. Today, one of the main challenges faced by business leaders is how to improve productivity and increase profits by using their data assets efficiently.

              Then comes the question of what data policy to implement. An efficient data strategy must ensure that good quality data sets are collected and can be used, shared, and moved easily from one system to another. The objective being to make information usable at the right time, in the right place, and by the right person to bring added value to the organization.

              AI business applications

              Many AI applications have already been deployed in diverse sectors of activities, with great impact on our daily lives as users, consumers, customers, and more. In the following paragraphs, we propose categories of AI usage and propose with concrete examples based on our experience in the AI development area.

              Customer first

              Customer segmentation

              It is a targeted advertising approach. Customer data is used to suggest homogeneous groups for marketing. This classification approach is based on common characteristics, such as demographics (age, geography, urbanization, income, family, job type, etc.) or behaviors (basket size, share of wallet, long-term loyalty). Customer segmentation is popular because it helps you market and sell more effectively. This is because you can develop a better understanding of your customers’ needs and desires. Clustering algorithms are key techniques in building a personalized customer experience. In a Capgemini Research Institute report about customer experience, we found that “66% of consumers want to be made aware when they interact with an AI system.” Being able to implement AI in such processes while  saving the human intelligence part of it is essential.

              Weekly churn prediction

              Churn is when a customer stops doing business or ends a relationship with a company. It’s a common problem across a variety of industries. It’s one of the most well-known AI applications in the customer relationship management (CRM) and marketing areas. A company that predicts churn can take proactive action to retain valuable customers and get ahead of the competition. Consumer characteristics and history are used to give a churn score to marketing leaders every week using the cloud.

              Real-time chatbot

              We’ve all had to deal with a voice server at least once. Behind this technology, you may have a real-time chatbot. It’s a conversational robot that communicates with users in natural language. It is a permanent point of contact for customers, users, or employees. It acts as a virtual assistant and sends them the right information in real time. For the most performant virtual assistant, the benefits are reducing human interaction costs and increasing user satisfaction with immediate and 24/7 responses. Natural language understanding (NLU) algorithms and cloud infrastructure are used here. Not all instant messaging and virtual assistants are based on AI techniques utilizing NLP and NLU. Some of them are mainly rules based.

              Intelligent industry

              Prescriptive maintenance

              With the emergence of the industrial internet of things (IIoT), the field of maintenance is connecting tools, software, and sensors to collect, store, and analyze multiple data sources in one place. Those tools are already unlocking predictive maintenance, where sensors and software predict future failures. However, many maintenance leaders are looking towards a near future based on prescriptive maintenance, where AI machines not only predict failures but also identify solutions. Prescriptive maintenance uses AI with IIoT to make specific recommendations for equipment maintenance. It combines technologies that analyze histories, make assumptions, and test and retest data freely. Complex AI algorithms enable software to automatically identify and learn from trends, recognize data patterns, and apply the best maintenance plan. This AI application, which uses reinforcement learning, helps to reduce maintenance costs.

              Real-time anomaly detection

              There are three commonly accepted types of anomalies in statistics and data science:

              • Global outliers represent rare events that have likely never happened before.
              • Contextual outliers represent events that fall within a normal range in a global sense but are abnormal in the context of seasonal patterns.
              • Collective outliers represent events that on their own do not fall outside of the standard expected behavior, but when combined represent an anomaly.

              Anomalies within a company’s data set can represent opportunities and threats to the business. Real-time detection of anomalies empowers enterprises to make the right decisions to seize revenue opportunities and avoid potential losses. Data from production is used to detect anomalies in a plant in real time thanks to unsupervised learning and a SCADA (Supervisory Control And Data Acquisition) system.

              Forecast methods

              Business forecasting is the process of using time series data to estimate and predict future developments in areas such as sales, revenue, and demand for resources and inventory. Business forecasting can be divided into two main categories:

              • Demand forecasting: Anticipate demand for inventory, products, service calls, and much more.
              • Growth forecasting: Anticipate revenue growth, expenses, cash flow, and other KPIs.

              Time-series algorithms are designed for these categories. These methods are widely used to estimate the evolution of the Covid-19 pandemic.

              Many other AI applications developing computer vision and deep learning algorithms are discovering drugs, identifying cancer cells, and used for sorting devices in factories.

              Enterprise management

              Monthly KPI dashboard

              Financial data is used to display important KPIs for top managers every month in a slideshow. An automation system is set to guarantee the quality of the data and the results. The technologies used are ETL (extract, transform, load), Analytics, and Dataviz. In the context of enterprise management, connecting siloed data across the sales, finance, supply chain, and services domains and embedding AI is keyfor better and smarter decisions. Such achievements help large organizations reduce costs, optimize operating performance, and harness the power of data.

              Career management

              Digitalization entered HR departments several years ago, and AI has logically become part of this evolution. For many entities, it’s today a part of all career management processes.

              • In recruitment, AI significantly reduces delays thanks to intelligent CV sorting. Some HR departments go so far as to carry out a first interview with chatbots.
              • For training, AI allows employees to benefit from an ideal training plan for their skills development.
              • When it comes to internal mobility, today there are solutions for finding the best profile corresponding to a position that needs to be filled within a company.

              Among plenty of successful applications in the HR field, AI is bringing productivity gains, procedure reliability, HR processes improvement, and responsiveness in career management.

              Conclusion

              Artificial intelligence has so far delivered many benefits and is a huge economic growth accelerator. It embraces many sectors of activities and is already impacting our daily lives. It also raises questions about embedding humans in the process, sharing the benefits, being fair, employment, data confidentiality, privacy, violation of ethical values, ​​and trust in results. These concerns need to be addressed through global regulations, certifications of AI models, and more. In a coming blog article, we will address these necessary fundaments around trusted AI.