The aviation industry is committed to net zero emissions by 2050.

To enable them to achieve this, aerospace manufacturers are adopting a range of approaches, including sustainable aviation fuels (SAFs), lighter airframes, optimising and automating flights, and whole new propulsion technologies based on hydrogen and batteries.

These systems-level engineering challenges are consuming the top minds in aeronautics. And they want to get there fast. If one company were to create a breakthrough product, there would likely be a rush from airlines to acquire it. Being two years ahead of your competitors could mean capturing billions of Euros, while others fall behind by the same amount.

Different companies are pursuing different approaches to reaching net zero, but all rely on rapid engineering innovation to redesign airframes, wings and engines.

Doing that fast and well requires new digital technologies.

The technologies of digital engineering

Such technologies are coming of age. Digital product design tools, model-based system engineering (MBSE) and Product Lifecycle Management (PLM) systems, amongst others, are advancing. But the real benefit comes from joining them up, allowing engineering design to be done smoothly and collaboratively in the cloud.

Cloud-based engineering lets engineers around the world collaborate on design. It offers digital continuity – so teams can see and work on the whole system in one continuous flow. It supports whole system-level simulations, so designers can experiment in silico and understand the impact on not just design but supply chains and in-use emissions. Global test data can be collected and shared to rapidly iterate designs.

Embracing the digital technologies that underpin rapid digital engineering – and more importantly making them work in a joined-up way – will need focus and expertise.

Companies cannot just buy PLM or design software off-the-shelf and slot it into their organisation. They need to make the right choices about the right combinations of products, from a vast and complicated landscape. Deployment into the organisation needs end-to-end changes to IT architecture, data management, and integration with cloud providers. It also needs customization and plugins across the entire system, in line with the organization’s engineering standards, lifecycle methodologies, and innovation plans, in order to ensure that continuity.

But by getting this right – with the right setup and technology choices – aerospace companies can create a cloud-based engineering system that could make net zero innovation as much as ten times faster than current industry-standard approaches.

Learn the lessons of disruption from the Electric Vehicle (EV) industry

This joined-up approach to digital innovation is not without precedent. Tesla was able to create rapid and disruptive innovations by working in a cloud-based PLM, which was directly updated with vehicle sensor and test data. This provided real-time insight, and allowed them to use AI on that data to gather insights and build simulations. It could rapidly experiment, keep operational costs low, and launch products faster than competitors. The company is sometimes said to be more of a software company than a car manufacturer.

Right now the aerospace leaders in this type of rapid digital engineering are regional innovators, such as Lilium, Ascendance, and Universal Hydrogen. These companies are focused on small aircraft, so are not yet competing directly with major aircraft manufacturers. But they are digital natives and have built their products, and companies, around digital engineering technologies. That is allowing them to innovate and iterate very rapidly, much as Tesla did.

Larger companies have other advantages, in the form of deep engineering know-how on larger planes, deep pockets to invest in R&D, and the pressing incentives to deliver big transformative innovations like hydrogen propulsion.

Some large companies aspire to design zero-emissions planes that will fly and carry passengers by the 2040s. To succeed, they will need to mimic the startups when it comes to cloud-based digital engineering. However, unlike the startups, they are not building their business from scratch, so they must overcome the technical and cultural challenges of retrofitting digital technologies into complex global engineering and IT systems that were not designed for them.

Aerospace is an exciting space right now. Lots of innovation will be needed, and there is opportunity for multiple approaches from both startups and longstanding players. And indeed we may see a more supportive environment than the one that disrupted automotive. Planes are harder for startups to sell than cars, and we can easily imagine regional innovators using digital technologies to build new propulsion systems, then partnering with – or being acquired by – larger company with the scale to deploy them into global fleets.

But the big companies must not be complacent – it is not unheard of for a digital native company with a laser focus on sustainable propulsion, to unexpectedly reinvent an industry, leaving established companies on the backfoot. Just ask automotive.

How Capgemini can help

At Capgemini we are constantly investing to ensure we are at the cutting edge of digital engineering, and can support low-carbon innovation across aerospace. We are delivering digital engineering for OEMs like Airbus, for regional disruptors such as Lilium, Ascendance, and Universal Hydrogen, and for governments.

We support our clients in engineering sustainable products, from feasibility studies, to software selection, deployment, customization, and certification. We are the only company that combines expertise in aeronautics and engineering design, with software, data management, and IT, enabling us to help clients build the digital systems that allow them to innovate for net zero aviation

Capgemini’s award-winning Digital Avatar solution enables healthcare providers to deliver critical information to their digitally excluded patients at speed.

Many healthcare providers currently face challenges related to how health-related information and advice is being distributed to their patients. In particular, the health of digitally excluded, often older, patients who are unaccustomed to using digital platforms, mobile applications, and chatbots is being impacted, and healthcare providers are looking to transform the way they serve these customers.

At Capgemini, we saw this as an opportunity to build a solution that gives patients quick and easy access to all their medical records, ensuring they get the critical information they need, when they need it – without having to explain their medical history every time they connect with their healthcare provider.

Enhancing patient satisfaction and digital inclusion

Our Digital Avatar solution responds to situations and requests just like a human agent to deliver a more digitally inclusive service and enhanced customer satisfaction through an easy-to-use and intuitive interface.

By simplifying how patients and customers in the healthcare sector receive critical information, our solution eliminates the need for servicing by expensive human resources and reducing the time patients need to access information. It can also be easily integrated with an organization’s business operations and data, without causing any disruptions to business operations.

The solution combines robotic process automation (RPA), conversational AI, multi-lingual natural language voice processing, digital twin technology, and enterprise platforms with next-generation human avatar digitalization technology to translate incoming calls in multiple source languages into the patient’s desired language. This eliminates language barriers, enabling older, digitally excluded patients to engage with their healthcare provider quickly, easily, and confidently.

Providing information quickly and reducing costs

Capgemini’s Digital Avatar solution delivers a wide range of business and customer experience outcomes to healthcare providers, including enhanced patient satisfaction and digital inclusion, 90% reduced language dependency, and reduced operational costs.

All of this is why Capgemini’s Digital Avatar solution was recently announced as a winner in Business Intelligence Group’s 2023 BIG Innovation Awards and BIG Artificial Intelligence Excellence Awards. And although the solution is still highly experimental, further research suggests significant benefits in hyper-personalized services and next-generation analytics across all business process families.

To learn how Capgemini’s Intelligent Process Automation infuses robotic process automation, AI, and smart analytics into your ways of working to deliver an unprecedented level of self-service and automation to your organization contact: pawel.bochenek@capgemini.com

We will one day be able to predict sustainability impact of design changes at the push of a button. We need to start preparing our data and IT now.

In previous articles, we discussed the importance of measuring the environmental impact of complex products – such as planes, trains and cars. The next big thing in sustainable engineering, we believe, is to move beyond manual data collection, and automate it via a model-of-models. This would allow design engineers to experiment in silico, making ‘virtual’ changes to design, materials, or supply chains, and immediately understanding how combinations of decisions change the overall lifecycle environmental impact.

The idea is to create live models of every aspect of your product’s lifetime environmental impact – from raw materials and manufacturing footprint, to in-use emissions, to end-of-life disposal or better yet re-use in a 2^nd life. Then, to build an overarching model that combines all of these models into one. This would dramatically improve their ability to make sustainable design choices.

Moving from life cycle analysis to life cycle modelling

This challenge is often underestimated. Many think data management software and access to SAP is all that will be needed. But in reality, significant is work needed on data and IT architecture, as well as supplier and customer engagements, before these sub-models – let alone the overarching model – can be reliably built, connected, and trusted.

Life Cycle Assessment (LCA) tools for reporting and even planning are advancing (we have a methodology for calculating the carbon impact of projects, for example). However, Capgemini Research Institute (CRI) research found that 45% of organizations are not using their emissions data for decision-making in any way, beyond mandatory reporting. No one we are aware of is successfully using autonomous tools that take such data and use it to support decisions by modelling their impact on complex systems.

However, progress is being made and best practice is starting to emerge from early experimentation.

Gather the right data in the first place

The first challenge is gathering all the data you need for the sub-models.

Your own emissions (Scope 1 & 2) can be captured by deploying electricity and gas meters, weighing fuel, tracking vehicles, and energy invoices. That, combined with data on fuel values on the local energy mix, can be used to build models that calculate emissions. This is not technically challenging, but deployment can be a sizeable project in a large organisation.

Emissions beyond your organisation – supply chains, product emissions, end-of-life (Scope 3) are trickier.

For in-use emissions, products such as cars and planes now have sensors which collect detailed usage data. That data can feed physics-based models to derive energy use and emission, which can be updated in near real-time. Products without such sensors, however, will need to rely on models which approximate their impact.

Suppliers, manufacturers, and disposal are harder to collect data on. Whilst some suppliers do their own LCAs, many do not collect even basic energy data, and there is little international standardisation.

Some may respond to encouragement, especially if you are one of their big customers. Workshops and guidance on what you need may help, as may paying to install sensors at their site, share product and material related information, or access to shared reporting software that feeds your own supplier models. Sticks may support carrots, such as audits and threats to switch to suppliers with better environmental data. Making PCF (Product carbon footprint) data reporting a condition for any new customer will help in future.

If all else fails, there are industry benchmarks to fall back on to calculate emissions of materials and parts, based on the materials and local energy mix where they were mined and processed.

A particular challenge is integrating new concepts. Creating values for different steel types is not too tricky, since there is lots of historical data. Understanding the impact of an untested biomaterial is harder. Evaluating the impact of a whole new technology, such as hydrogen, is a real challenge. There’s some chicken and egg; we need data to make the projection, but we want to project before we make the investment. The best middle ground is to make sensible projections based on scientific and engineering expertise, then gather data as the product evolves, which feeds directly back into the model to improve its predictive power.

Cleaning and clarifying your data

All the data coming into your models needs to be well-defined, consistent, and in machine-readable formats.

This starts with setting consistent policies for data collection and formats across your own organisation, and where possible communicating these to your value chain. An industry-led project in automotive, Catena-X, provides a good model for how data may be shared across the supply chain in future, and designing data capture and modelling that will integrate with this ecosystem is advisable.

That may set a path for the future. But a lot of legacy data – internal and external – has evolved in silos over the years, from engineering data, to excel spreadsheets, to PDFs of technical drawings. That will necessitate an exercise to find, clean, convert and tag data.

AI tools can – crawl IT systems, screen for the right data, and pull it from PDFs, Excel and so on, checking it, filling in gaps with industry standard figures, and pumping it out in a format that is Creating the right IT infrastructure for a model-or-modelsreadable by your model.

Creating the right IT infrastructure for a model-or-models

All of this data is spread out in different parts of the organisation, suppliers, and customers. But we want a single source of truth so that all data will be ‘Findable, Accessible, Interoperable, and Reusable’ or FAIR.

That means setting up a sustainability data hub – a master database in the cloud – where all relevant data is fed and validated.

It will also need software customisation to ensure all sources of data – whether sensor management platforms or CRM databases – are collecting data in the right format and updating the master database in real-time.

For sharing data across supply chains, or between manufacturers and customers, privacy and cross-border data-sharing rules also need to be considered. Blockchain-based databases offer good solutions to tracking parts and products securely as they move along the value chain. We already see companies like BMW using blockchain to track supply chains, and Siemens has a blockchain-based tool that lets suppliers share verified emissions data with customers, whilst keeping any underlying sensitive data confidential.

Conclusion: Aim high, create value along the way

All of this will be very bespoke to each organisation. It will mean working with data and software experts, as well as domain experts familiar with the materials and processes that the data represents.

This will not happen overnight, but the journey will also provide value. The best strategy is to have an eye on long-term value, whilst delivering more immediate returns. Improving data, building sub-models, and connecting up data streams will help life cycle assessments and small-scale modelling projects, as you gradually build to a systems-level model-of-models.

Ultimately – as companies test ideas in silico and see how they ripple through the supply chain and product life – they will become better able to make smarter, and often disruptive, decisions in sustainable design. No one is there yet, but this is the direction of travel.

HR leadership needs to invest in building a culture of unbiased talent assessments that boost collaboration and professional development to drive collective success of the entire organization.

The word “talent” is defined by Merriam-Webster as “a special often athletic, creative, or artistic ability.” In reality, however, my experience is far from this dictionary definition.

While many people live up to their potential by working on their strength areas and exploring ways to build self-actualization, there are just as many people who continue to work in accidental, forced careers that may not necessarily align with their actual talent.

Talent assessments, power, and politics

Science has long suggested that investing in the right people will maximize the return of an organizations. You only have to see how organizations leverage Pareto’s principle or power law distribution to create a highly motivated, self-driven talent pool through assessing, building, and developing individual capabilities that give people the winning edge in business.

Some organizations start this journey at the recruitment stage through aptitude tests and psychometric assessments to understand the variety of potential being added to the organization’s talent pool. While others carry out annual assessments to identify top talent and build a pool of high-potential (HIPO) employees that receive special attention.

Both of these approaches can create division among individuals that results in power games and politics within the organization. Indeed, the very culture of collaboration is broken or crushed when a performance or potential-based hierarchy is created, as such identification efforts are perceived to be loaded with cognitive biases.

Of course, bias exists in the idea of low performance, because as human beings we have an innate desire to be seen as winners or at least be associated with the winning team.

High-potential talent – ego vs. partnership

The key question to be answered in any talent assessment then becomes “assessment for what?”

Most top talent interventions focus on individual career success and paths rather than assessing or guaranteeing an individual’s contribution to the success of the organization. Nor do these interventions provide any assurance that once an individual moves into a leadership position, he or she will adopt a winning approach or “algorithm” that turns their team or the organization into high performing entity.

In fact, more often than not, HIPO employees are too focused on their own individual success to contribute as much as they should to organizational success. Such high performers either become potential targets to switch jobs or end up demotivating their peers/teams with their sense of individual success. High performers tend to become luxury commodities that might not always deliver the right economic outcomes for their organizations.

Another observation is that most top performers evaluate their own performance more critically and harshly, whereas those who perform poorly or are put on performance improvement plans think they are making a fantastic contribution. Thus, self-awareness of expectations, it seems, is a critical component of talent identification.

Building a culture of collaborative performance

To overcome these challenges, organizations need to invest in unbiased talent assessments that provide accurate information on what triggers or motivates an individual to drive success.

Foe their part, HR leaders need to build a culture of working on talent interventions that help individuals create a high-performance benchmark to drive organizational competitiveness.

A culture that heavily supports and boosts collaboration, partnership, professional development, coaching, and people empowerment for the collective good and success of the organization. A culture akin to the creation of intellectual property that increases in value and quantity each year.

Read Capgemini’s full report “The people experience advantage” to learn about 10 key actions for companies to take to improve the experience of their people.

A resignation after the interview and before the start of a new employment relationship is unfortunately not an exception.

As mentioned in our previous blog article, the optimal employee-centric Onboarding journey consists of three phases: pre-boarding, acclimation, belonging, and performance excellence. In this article, we take a closer look at the pre-boarding phase. This phase is particularly crucial since many companies experience potential employees terminating their employment contracts even before they officially start. This first phase of the employee-centric Onboarding journey begins after a successful recruitment process and the signing of the employment contract by future employees. The goal of this phase is the administrative empowerment of future employees, giving them a first impression of the corporate culture and offering the first social touchpoints. To achieve this pre-boarding goal, Onboarding activities take place within our four defined Onboarding experience layers. They are considered in more detail below:

Admin and technological layer

This layer is the main focus in pre-boarding. To ensure an optimal employee experience in Onboarding, the idea of a single source of truth is essential. In line with this, the Onboarding journey starts with timely access to the central Onboarding portal.

In the portal, future employees select the complete, desired technical equipment and fill out the relevant documents directly in the portal. For example, a pre-Onboarding task can be to upload a profile picture, which must be done by future employees. To support future employees in this regard, reminders are sent from the Onboarding portal. Independent status tracking is also offered.

Additionally, a checklist with the next upcoming Onboarding steps provides future employees with a constant overview of all the activities required until joining the company. Systems like ServiceNow, for example, provide a comprehensive Onboarding checklist for different personas (e.g., for the employee, hiring manager, HR, and IT) that guides them through the completion of Onboarding (see illustration 1).

Professional and content-related layer

Before starting a new job, only relevant updates on team projects and the overall team strategy are shared with future employees – via personal touchpoints or more formally through the Onboarding portal. In this way, employees are given the opportunity to start the job with all relevant, content-related information. To get even more support from HR Cloud technologies in pre-Onboarding and to create added value in the Onboarding experience, training courses can also be provided in advance. For example, this is offered by the HR Cloud provider, Workday. However, organizations should avoid overloading employees with job-specific induction activities before the official start.

Organizational and cultural layer

If we consider the organizational and/ cultural aspects of the Onboarding journey, it is necessary to provide future employees with relevant information in the onboarding portal before their start date. This includes information about the first day at the new employer: where and when is the new employee expected? How will the first day(s) be structured? Similarly, FAQs are a useful addition. However, this also means information about the company itself, such as company values and the organizational structure, should be provided to shape the pre-boarding on an emotional and informative level. The scope of information should be based on the principle of “as little as possible, as much as necessary.”

Communication with personal contacts, such as a buddy or the employee’s own manager, should also be easily accessible to future employees via the Onboarding portal. Of course, first contact should be made by the buddy and the manager.

Social and network layer

As briefly touched on, relevant contacts are directly involved in the pre-boarding of new employees. Buddies and managers act as the first personal contact with new employees immediately after the contract is signed. This is important to ensure regular and reliable communication until the official start. Invitations to team events and for a coffee in the office are also optimal ways to get to know new colleagues before the official start and to round off the employee’s experience in pre-boarding.

Our next Blog #3 takes a closer look at the second phase of the employee-centric Onboarding journey – the acclimation phase.