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AI as your strategic ally: customer-centric portfolio decisions, powered by Artificial Intelligence

Munich, December 2023

Featured Insights

Portfolio decisions have become increasingly complex as the pace of transformation in the automotive industry accelerates. Munich, December 2023

P

ortfolio decisions have become increasingly complex as the pace of transformation in the automotive industry accelerates. Driven by intensifying competition to meet customer demands ever more accurately, we expect this trend to continue for the foreseeable future.

A well-crafted portfolio strategy has never been more essential. Selecting the right products, services, and features that meet customer desires and needs is the key challenge in portfolio decision making, alongside managing risk, allocating resources, and maintaining agility. Could AI be the new standard for how portfolios can be managed more effectively? Think of AI as your most versatile team member, equipped for a variety of tasks. Much like a Swiss Army knife in the field of portfolio strategy. The only question is: how do you best use it?

Our approach offers a practical roadmap for using AI effectively. Not all pieces of the puzzle allowing usage of AI’s full potential are yet in place. However, the picture of how these pieces will fall into place is becoming increasingly clear. By integrating AI into portfolio strategy, a variety of benefits, such as time efficiency, deeper analytical insights, and easier scenario analyses, can be expected. The urgency to integrate AI into strategic planning cannot be emphasized enough. By doing so, not only present work and decision making can be improved, but also the groundwork for significantly greater benefits in the future is laid. Now is a crucial time to consider AI integration; those who act promptly are likely to be better positioned in an increasingly competitive market.

This article is the first in a series of articles exploring the potential of AI for portfolio strategy. As this technology is evolving fast, we look at AI with a dual lens: assessing what is already possible, and subsequently, identifying the next steps for leading automotive players towards (even more) customer-centric portfolios. In this context, this first article will focus primarily on the opportunities for automotive OEMs.

Artificial Intelligence (AI) is a broad field of computer science dedicated to developing systems capable of performing tasks that normally require human intelligence; for example, solving problems, recognizing patterns, and understanding natural language.

Large Language Models (LLMs), such as the latest GPT models, are advanced AI models trained on vast datasets and designed to understand, process, and generate human-like text based on the context provided. LLMs have revolutionized the way we interact with AI systems and bear great potential for applications like customer segmentation and – when done right – can even support strategy work.

 

Unleashing AI’s potential for customer-centric portfolio decision making:

The transformative power of AI for customer and portfolio strategy lays in the ability to inform human decision making. If complemented by a robust framework and accurate data, AI has the potential to enhance customer understanding and increase the speed of analysis in portfolio decision-making. This can lead to greater cost-efficiency compared to traditional data science methods such as advanced analytics or big data. By harnessing vast amounts of structured and unstructured data from different sources, such as customer demographics, purchasing behaviour, vehicle usage, and online interactions, AI can unlock nuanced insights about customer preferences and behaviour enabling a dynamic, real-world understanding of customers. From the discerned patterns, AI can then offer a glimpse into the future, fundamentally informing human decision-making. This granular understanding empowers automotive companies to refine their portfolio strategy with timely insights and tailor their products and services in a way that resonates with customer preferences fostering competitiveness as well as strategic, sustainable growth.

We believe that harnessing the potential of AI for portfolio strategy offers distinct key benefits for OEMs:

Authors
Malte Broxtermann

Partner

Timo Krall

Project Manager

Philipp Brandner

Consultant

Claudius Feldmann

Thesis Student

Benefit 1
Benefit 2
Benefit 3
Benefit 1

1. Dynamic customer understanding & sensing tomorrow’s landscape:

The utilization of AI as a tool for dynamic understanding of customers and market prediction can be a strategic advantage for OEMs. AI can provide timely updates on customer behaviors and needs as required, coupled with detailed segment and persona analysis. This allows automotive companies to gain comprehensive insights into their target audience's preferences and anticipate shifting demands. Through pattern identification AI can anticipate emerging market changes before they fully emerge. By this proactive detection of trends, risks, and opportunities as they emerge, OEMs are better equipped to make more effective decisions in an industry that is more dynamic than ever before.

Benefit 2
2. Aligning strategy with real-world insights:

In today’s rapidly evolving market, aligning strategy with real-world insights is crucial, and AI can play a pivotal role in this process. By comparing strategic concepts with a wealth of internal and external data, AI performs well in scenario recognition. It detects inconsistencies effectively and allows the refinement of strategies to ensure they align more closely with actual market conditions. With actionable insights, these strategies are not only robust on paper but also practical in real-world conditions.
Benefit 3

3. Empowering decision-making through human-AI synergy:
Consider AI as the perfect partner for complex analysis. When humans and AI collaborate, decision-making is supercharged. While humans bring empathy, ethical considerations, and intricate practical understanding to the table, AI ensures precision, speed, and vast data handling. Together, this partnership takes decision-making to the next level, combining the depth of human intuition with the efficiency and analytical capabilities of AI.

After exploring the key benefits and understanding the significant impact of AI, we recognize that the automotive industry is on the threshold of a new era. This leads us to these key questions:

“How do we transition from recognizing these advantages to implementing them in tangible, actionable strategies for portfolio management? How can OEMs leverage AI to not only understand but also anticipate the dynamic demands of the market?”

Operationalizing AI insights: A strategic approach to portfolio management

A clear roadmap is essential to harness the AI benefits and close the gap between AI’s potential and its practical application in portfolio management. This is where we, at Berylls, step in. As agents of operationalization, our goal is to transform abstract concepts into tangible, actionable strategies.

Our approach follows five clear steps for portfolio strategy development:

A) It begins with summarizing existing relevant aspects of corporate, functional, and business unit strategies into key statements, forming a comprehensive understanding of the strategic landscape. Where feasible, key statements are also quantified to enable more meaningful data-based analysis.

B) This is followed by a detailed analysis of the portfolio environment, which is crucial for understanding the stakeholder landscape and market trends. This analysis especially highlights the significant role of understanding customers and competitors to ensure a thorough evaluation of latest leading portfolio strategies and best practices. There is no one-size-fits-all solution, and we pay close attention to what makes a portfolio work (or fail) depending on each specific company and its target customers.

C) We then conduct a comprehensive review of the current internal portfolio in question, analyzing its composition, performance, and enablers.

D) These initial steps lay the foundation for the subsequent phases, where we identify and evaluate potential improvements to the portfolio, focusing on portfolio gaps, key levers, and scenarios for enhancement.

E) Finally, the analysis concludes with defining or updating the portfolio strategy, the setting of specific objectives, and the development of a detailed implementation roadmap.

This approach is optimized for AI integration with the first three steps (A-C) providing the relevant input for AI-supported decision making and the last two steps (D-E) involving AI to analyze all available information effectively.

Figure 1: Berylls framework for portfolio strategy

Source: Berylls Strategy Advisors

The integration of AI into portfolio strategy development represents a significant improvement. Already in the initial stages where qualitative and quantitative data is made accessible for AI, it can play a key role in improving the speed, cost efficiency and quality of strategic decision making. In later steps, AI supports in identifying data patterns, pinpointing missing data points, suggesting portfolio improvements, and preparing meetings by decision makers effectively. Moreover, this integration of AI enables a more dynamic, agile, and efficient iteration of the entire process that closely aligns with evolving market needs.

Figure 2: Berylls framework for AI-enabled portfolio strategy

Source: Berylls Strategy Advisors

An example ChatGPT case illustrating the framework can be found by clicking on this box

Output generated by current LLMs should never be used without diligent review and confidential data must not be submitted to ChatGPT. The example ChatGPT output above was created based on dummy data for a hypothetical company. This output was left completely unchanged by Berylls to help provide a realistic example of the type of quality that is feasible with the latest generation of LLMs. Replicating similar results for real applications requires access to the latest generation of LLMs that guarantees full data security. A real-world project covers drastically more inputs, analytical steps, and output than is included in the simplified example case.
Click here

An example ChatGPT case illustrating the framework can be found by clicking on this box

Output generated by current LLMs should never be used without diligent review and confidential data must not be submitted to ChatGPT. The example ChatGPT output above was created based on dummy data for a hypothetical company. This output was left completely unchanged by Berylls to help provide a realistic example of the type of quality that is feasible with the latest generation of LLMs. Replicating similar results for real applications requires access to the latest generation of LLMs that guarantees full data security. A real-world project covers drastically more inputs, analytical steps, and output than is included in the simplified example case.
Click here

Using AI to take smarter portfolio decisions by putting the customer front-and-center:

AI can demonstrate significant advantages over traditional analyzing methods. AI’s speed and adaptability enable quicker decisions, eliminate human biases, reduce manual research, and efficiently process large datasets for precise market alignment and feedback analysis. Correctly providing relevant input and gradually refining the prompts provided to AI does require expertise and AI currently cannot (and in our assessment: should not) replace human decision making in matters of strategic importance. Latest AI models, nevertheless, can be applied to accelerate analysis and help human experts take more informed and well-prepared decisions. Remarkably, an analysis as outlined in the example case linked above can be replicated in minutes, assuming an AI model comparable to the latest version used by ChatGPT has access to all essential data in a way that ensures data security. While the case linked above is hypothetical, AI indeed can create significant value once it has access to an organization’s real-world data and strategies. Berylls invites you to start working towards unlocking the full potential of AI to achieve a deep, data-driven understanding of customer behaviors, preferences, and expectations.

Engage several powerful AI flywheels and lay the foundation for success by embracing the following enablers:
1. Redefine data for AI

Quality data is the backbone of meaningful AI insights. Ensure your data is well-structured, high-quality, meaningful, and thus prepared to enable accurate results.

2. Forge AI-ready strategies

Condense your core business strategies into written key statements to make them AI-readable. This allows AI to analyze data and generate insights with your strategy in mind.

3. Human-AI decision synergy

Use AI to support your decision-making process, ensuring faster responses to dynamic market demands. Combining the strengths of human intuition with AI’s analytical capabilities creates a powerful duo, maximizing both precision and efficiency.

4. Tailor your AI approach

Different needs require different solutions. Whether it's leveraging AI for specific tasks or a more comprehensive AI-driven strategy, aligning the technology with individual organizational goals and capabilities is crucial.

Engage several powerful AI flywheels and lay the foundation for success by embracing the following enablers:

1. Redefine Data for AI

Quality data is the backbone of meaningful AI insights. Ensure your data is well-structured, high-quality, meaningful, and thus prepared to enable accurate results.

2. Forge AI-ready strategies

Condense your core business strategies into written key statements to make them AI-readable. This allows AI to analyze data and generate insights with your strategy in mind.

3. Human-AI decision synergy

Use AI to support your decision-making process, ensuring faster responses to dynamic market demands. Combining the strengths of human intuition with AI’s analytical capabilities creates a powerful duo, maximizing both precision and efficiency.

4. Tailor your AI approach

Different needs require different solutions. Whether it's leveraging AI for specific tasks or a more comprehensive AI-driven strategy, aligning the technology with individual organizational goals and capabilities is crucial.

We expect a broad spectrum of relevant use cases for applying AI to portfolio strategy. The role of AI can range from fundamental applications in stakeholder analysis to more complex scenarios in business case preparation and continuous improvement. Each use case demonstrates a specific aspect of AI’s contribution to portfolio strategy and has unique requirements for successful implementation. Different use cases can be rolled out sequentially, gradually creating more value with AI over time.

Four types of portfolio strategy use cases for AI

Low Complexity
Low Output

High Complexity
High Output

Use case I: stakeholder analysis

LLMs enable detailed profiling and analysis of customers, competitors, and other key stakeholders. Profiles can also cover the portfolios and best practices by key competitors. LLMs can provide frequent, cost-effective updates based on AI-assisted research, competitor monitoring, and customer data analysis. This speed and breadth of analysis helps improving the understanding of market expectations, behaviors, and dynamics.

Main requirements:

- Introduce LLM integration: Provide access to a Large Language Model (LLM) like ChatGPT for analyzing anonymized stakeholder data.

- Establish customer data platform: Develop a platform to manage and analyze customer data across relevant data sources, ensuring it is structured and AI-ready.

- Ensure data updates: Implement a system for continuous updating of stakeholder data to keep the analysis relevant.

Use case II: Identification of decision option

AI aids in broadening decision options for portfolio adjustments. It helps in generating new ideas for portfolio changes by using pattern recognition in the available data to identify potential opportunities and optimize strategic choices. Specifically, AI can help identify untapped potential by highlighting discrepancies of the current portfolio with available customer insights, market trends and best practices by comparable competitors.

Main requirements:

- Ensure available strategies are AI-readable: Turn the relevant aspects of current strategies into AI-readable (quantified) key statements.

- Integrate results of stakeholder analysis: Grant AI access to all relevant analysis of the impact and interests of various key stakeholders, especially by customers and competitors. This should include a thorough review of competitor portfolio best practices and strategies with clear assessments of the company-specifics that make each competitor succeed (or fail).

- Describe portfolio for AI Analysis: Submit a comprehensive documentation of the current product and service portfolio, the portfolio performance, and any relevant information about company-specific portfolio enablers. The information about enablers should include the main resources allocated across the portfolio as well as key competitive strengths (or weaknesses). Examples for relevant competitive attributes would be information about the sales and manufacturing model used, describing (e.g.) the resulting supported production speed, variety, flexibility, and cost-efficiency

Use case III: Assessment of decision options

Latest generation LLMs can assist in qualitative comparison of decision alternatives, for example by suggesting (dis)advantages for each available decision. For quantitative decisions, they can also support in preparing business cases. At the current technological level of LLMs, business cases are mainly manually crafted, with LLMs offering suggestions that require in-depth expert review for accuracy. Over time and as LLMs evolve, their suggestions become more reliable, allowing them to draft business cases based on company data. This evolution will gradually shift the human role towards systematically refining these AI-generated suggestions, leading to more complex and accurate business cases that account for scenarios involving competitor responses, thus enhancing strategic decision-making

Main requirements:

- Integrate results of stakeholder and decision option analysis: Inform the AI about the final set of decision alternatives and the related scenario assumptions. This will be the starting point for the business case. In addition, provide input to the AI with any relevant findings from the stakeholder analysis such as identified customer preferences. This information, for example, can help the AI to suggest meaningful parameters and assumptions for the business case.

- Provide access to operational and financial data: Both internal and market-related operational and financial data must be accessible for a comprehensive AI analysis. Where available, validated forecast data will improve the accuracy of the analysis.

- Involve industry experts: From start to finish, AI prompts should be engineered by industry experts to ensure relevant and accurate AI outputs.

Use case IV: Continuous improvement

AI supports the ongoing refinement of a portfolio strategy. It quickly identifies opportunities for action based on current data and ensures that the portfolio remains aligned with market trends and customer preferences, leading to continuous improvement and strategic agility.

Main requirements:

- Establish regular data updates: Ensure AI analyses are based on most current information.

- Implement feedback mechanisms: Integrate customer feedback and performance data into ongoing AI analysis.

- Develop a dynamic strategy adaption process: Allow regular strategy refinements based on AI insights.

Leveraging AI for your company… but different

The potential of AI to drastically change the way strategy work is done grows rapidly. Berylls stands ready to help you transform successfully and in a way that is uniquely centered around your customers, company, situation, and aspirations. Stepping into the AI realm can be complex; however, having a dedicated partner ensures that you harness its full potential efficiently and strategically. Berylls provides a tailored way to make AI work best for your needs, from initial assessment to final implementation. Backed by a team of experts and collaboration partners with custom software solutions at the forefront of AI advancements. Embrace the AI-driven future of automotive customer strategy and gain the AI advantage with us. The automotive industry faces a time of accelerating change, and those who can sense and adapt to these changes first as AI matures will have the upper hand.

Malte Broxtermann

Malte Broxtermann (1986) joined the Berylls team in 2014. After extensive experience as emergency medical technician, he has been working in consulting since 2012. He helps customer to leverage digital strategies & products across the entire automotive value chain. He is an expert in deploying machine learning-powered applications. As Partner at Berylls’ own unit for digital solutions, Berylls Digital Ventures, he focuses on scaling start-ups as part of our venturing practice.
Studied economics and international business at Maastricht University (Netherlands) and Queen’s University (Canada).

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Automotive Software’s Next Challenge Is Procurement – And It Needs to Be Addressed Today

Munich, November 2023

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Automotive Software’s Next Challenge Is Procurement – And It Needs to Be Addressed Today Munich, November 2023

T

he structured and linear procurement model of the past must give way to a new collaborative approach. 

Automakers need to adapt their procurement systematics and acquire new skills in order to source software successfully.

Earlier this year, Mercedes-Benz announced it would no longer be offering a proprietary automotive navigation and map system in favor of collaborating with Google. This signaled a significant change in the company’s software and procurement strategy. The German OEM was effectively announcing that developing, running, and maintaining core elements of its vehicle software stack required new supplier relationships in order to stay competitive with other car manufacturers – including collaboration with companies from non-automotive sectors.

Today, an estimated 73% of value creation in automotive R&D is hardware-related, but before long, the majority of value will be delivered by the software running on it. We can already identify some successful frontrunners today like Tesla and new entrants from China.

In recent years, OEMs have realized that they do not possess the in-house capabilities and resources to develop a full software stack including E/E architectures, underlying operating systems, and consumer-facing applications and services. BMW’s Senior Vice President of Electronics and Software Christoph Grote recently said: “It is completely unrealistic to build almost all software components yourself as a car manufacturer. Those who do so isolate themselves.”

However, the balance of power is also shifting. Large tech companies like Apple, Google, Tencent, and Huawei become essential contributors to software-defined vehicles. Driven by high customer expectations for the standardized integration of their platforms and services across car brands, the bargaining power of OEMs is steadily decreasing.

Consequently, OEMs needed to learn that while in the past, they were in the position to dictate a customer/supplier collaboration with Tier-1 and Tier-2 suppliers, this role may be subject to change in the era of the software-defined vehicle.

Pricing stands as the most potent lever for increasing profitability within the automotive sector. Consequently, it should take center stage for all Original Equipment Manufacturers (OEMs). However, it’s essential to recognize that Revenue Management extends beyond Pricing. It involves a strategic business practice adopted by companies to maximize their revenue and profitability through the optimization of product or service pricing. This discipline entails the application of diverse pricing strategies, data analysis, and forecasting techniques to make informed decisions about price setting, resource allocation, and capacity management. The primary components are depicted in the chart below. Pricing emerges as the most significant lever for enhancing profitability, making it of utmost importance for top management.

A new collaborative and flexible software procurement framework is needed

This significant role and power shift implies that OEMs will have to source key elements of their vehicle software externally while at the same time ensuring state-of-the-art integration into their vehicles in order to maintain a competitive edge. The traditional “design-bid-build” model, which is characterized by highly specified requirements, strict cost discipline, and high-volume orders, is unlikely to work in this case.

A new collaborative and flexible software procurement framework for design, development, and delivery is needed to enable co-development and partnerships. You cannot plan software development efforts years in advance. As a result, the framework must reflect the needs of the fast-moving and fast-changing nature of software projects where scope, timeline, and technology change more frequently than in classical procurement processes. This will require both internal empowerment and new evaluation criteria to assess software partners.

Figure: Software Procurement is different

Source: Berylls Strategy Advisors

In terms of internal empowerment at OEMs, we have identified four success factors:

  • Eye-level collaboration: The classic relationship between an OEM and its suppliers is a hierarchical one in which the OEM sets targets and directions and makes the important decisions. If a project goes wrong, many OEMs have specialized task force teams that will “invade” the supplier and take over operations. Due to the existing lack of software competencies at OEMs and the power of large tech providers, this approach will not work for many software projects. Software collaboration demands shared beliefs and a strategy underlying a joint approach toward the development of software products. This is why our first success factor is “eye-level collaboration.” This means that shared responsibility allows each party to contribute their unique expertise and capabilities while sharing risks related to technology, market, and regulatory compliance. Procurement must take this position on the OEM side and constantly steer this relationship with supplier management. This establishes the foundation for more flexible requirements and target setting as well as new contract and business models.

 

  • Flexible requirements and target setting: The playing field of software-defined products needs a completely different approach to target setting and documentation. Moving away from rigid specification sheets and towards flexible requirements will allow for changes and adaptations in the scope, timeline, and deliverables of the software. This approach will benefit from agile development methodologies, which emphasize iterative and incremental delivery of software and may include performance metrics that are tied to the achievement of project milestones and goals (e.g., achievement of a defined number of story points), rather than strict adherence to predefined functional requirements. Challenges arising out of this “softer” target agreement must also be addressed through new contract and business models.

 

  • New contract and business models: Traditionally, the classic contract model between an OEM and a supplier has focused on two parameters – unit price and development cost. Software was usually included in the (one-off) unit price of the hardware component. Today, however, new business models are needed for continuously developed and delivered software components. OEMs may need to evolve beyond a software license model based on production volume, irrespective of software usage intensity, and consider time-/usage-based payment models that better take into account ongoing supplier development and maintenance costs. Indeed, we predict that software-as-a-service business models will become the new normal. A further step is the participation of software suppliers in vehicle or function sales (e.g., autonomous driving functionality). This business model is applicable, especially in terms of long-term strategic partnerships and cooperation.

 

  • Software assessment competence: Procurement needs to increase its ability to understand and evaluate software and deepen its connection to hardware. This includes knowledge of the overall software architecture with all its dependencies, common programming languages, software development methods, and systematic procedures for the management of critical projects. Therefore, procurement needs to establish a close relationship to development departments in order to build up this understanding in addition to its commercial competence. This competence is crucial in order for procurement to remain a neutral partner between the supplier and the OEM development departments. It is the foundation for negotiation in the initiation phase and the basis for a successful partnership.

In addition to internal empowerment, OEMs should consider four evaluation criteria when assessing potential software partners:

  • Software delivery model: Compelling software products are the result of efficient development, rigorous testing, feedback loops, and appropriate adjustments throughout the product lifecycle. Suppliers need to offer a software delivery model that implements new requirements fast in agile teams using shorter release cycles and DevOps methods. Short feedback loops within the development value chain, from the OEM to the supplier, are a prerequisite for shortening time-to-market while maintaining quality. This enables continuous development, refinement, and delivery of software components “as-a-service” over the entire lifecycle. It stands in contrast to the traditional, SOP-focused delivery models used for hardware projects.

 

  • Software flexibility and efficiency: A modern vehicle with its increasingly centralized computer architecture processes hundreds of millions of lines of software code – it is now estimated that a typical electric vehicle uses four times as much code as a commercial jetliner. Even though computing power is continuously increasing, the complexity of porting software to multiple platforms makes the flexibility and efficiency of acquired software a strategic issue. Buyers and suppliers must move from developing static, monolithic software tailored to specific hardware to prioritizing a flexible and modular cross-domain software portfolio.

 

  • Automotive software steering competence: Nowadays, organization in agile teams to develop and maintain software is pretty much standard. But the implementation of these practices varies vastly across organizations and hierarchies. Currently, there is no global blueprint on how car software is developed and steered. The supplier’s management team in particular needs to be able to assess and handle the steering of software projects and the interfaces to the OEMs as part of automotive systems engineering. This includes efficient decision-making, prioritization, harmonization with hardware engineering, and communication in a much more volatile setup than with traditional hardware projects. And, if a project turns critical, the ability to manage overarching task forces up to and including the highest levels of corporate leadership is crucial.

Berylls Research

How to not mess up software projects
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  • Processes, methods, and tools: As future automotive software will consist of both prepackaged on-board components and ad-hoc additive components, this combination will require holistic underlying processes, methods, and tools. When choosing a supplier, the consistent use of PMT across the value chain needs to be considered to ensure collaboration efficiency. One special area of focus is the requirement management process that needs to be standardized via tools and aligned processes.

The big shift

The move to software-defined vehicles is transforming the automotive procurement landscape and requires procurement professionals to adapt their practices in order to effectively source and manage software suppliers.

This means that the process and the principles of procurement must reflect the business logic of software, which is closer to signing a service contract than buying a product. To establish an innovative and future-proof software procurement organization, OEMs will need a deeper understanding of software development, supplier management, industry standards and regulations, intellectual property, and talent management. The key is self-empowerment: OEMs need to develop the necessary skillset while at the same time creating flexible and resilient organizations to manage software supplier relationships that have broken free of the “captive supplier” model and more closely resemble collaborations and joint ventures. Markdowns will not be the dominant measurement for success of procurement.

This shift will not be easy, but the software-defined automotive future demands it.

Author
Sebastian Bräuer

Associate Partner

Sebastian Bräuer
Sebastian Bräuer (1982) joined the Berylls Group in 2022 as Associate Partner. He started his career in consulting where he led and supported assignments with focus on operational excellence programs primarily in automotive but also in consumer goods and chemicals. Later Sebastian worked for an German OEM in leadership roles regarding organizational development, digital product offering and digital car strategy. At Berylls Sebastian focuses on topics regarding Digital Car.
Sebastian graduated in economics and engineering at the Technical University of Dresden.
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Navigating the automotive revolution: Insights into the future of automotive operating systems

Munich, November 2023

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Navigating the Automotive revolution: Insights into the fututre of automotive operating systems
Munich, November 2023

I

n the landscape of the automotive industry, an enormous transformation is underway. The very essence of vehicles is being redefined, not under the hood, but in lines of code and algorithms.

Automotive Operating Systems (AOS) have emerged as the catalysts for this revolution, promising a future where software dictates the driving experience. In our in-depth survey conducted among industry experts spanning OEMs, Tier-1 suppliers, Big Tech players, and engineering service providers, a tapestry of insights has been woven, illuminating the path forward.

Download the full study now.
Berylls Insight
Navigating the automotive revolution: Insights into the future of automotive operating systems
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Author
Dr. Jürgen Simon

Associate Partner

Sebastian Böswald

Associate Partner

Felix Günther

Consultant

Dr. Jürgen Simon

Dr. Juergen Simon (1986) is Associate Partner at Berylls Group, an international strategy consultancy specializing in the automotive industry. He is an expert in sales and corporate strategies as well as M&A and can look back on many years of consulting experience.
Dr. Juergen Simon has been advising automotive manufacturers and suppliers since 2011 and has in-depth expert knowledge in the areas of holistic strategy development, business models and commercial due diligence. He also focuses on market entry strategies and topics related to the „Software Defined Vehicle“.
Prior to joining Berylls Strategy Advisors, he worked as senior consultant at the Droege Group, a consulting and investment firm.
As a graduate economist from the University of Hohenheim, he completed his doctorate at the Institute of Management at the Karlsruhe Institute of Technology (KIT) before joining Berylls.

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Pressemitteilung: Berylls Strategy Advisors und Rechtsexperten von Synthetic Law gehen strategische Partnerschaft ein

München, November 2023

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Pressemitteilung: KOMBINIERTES KNOW-HOW FÜR DIE DIGITALE TRANSFORMATION

München, November 2023
K

OMBINIERTES KNOW-HOW FÜR DIE DIGITALE TRANSFORMATION 

  • Kombination von Expertise in der Mobilitätsbranche mit hochaktuellem juristischem Fachwissen
  • Zukunft der Mobilität wirft komplexe juristische Herausforderungen auf, Synthetic Law ist in diesem Bereich bestens aufgestellt und idealer Partner für Berylls
  • Noch bessere Unterstützung der Berylls Kunden bei Datenschutz, Compliance und Data-Governance
  • Berylls wappnet sich und damit seine Kunden für künftige rechtliche Auswirkungen, die durch anstehende Datenschutzvorschriften und verschärfte Cybersicherheitsanforderungen entstehen

Jetzt die gesamte Pressemitteilung herunterladen!

Berylls Pressemitteilung
Pressemitteilung: Berylls Strategy Advisors und Rechtsexperten von Synthetic Law gehen strategische Partnerschaft ein
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Christian Kaiser

Christian Kaiser (1978) is partner and Head of IT at Berylls Group with focus on software and connectivity. He began his career with Daimler AG in 1997. He brings 26 years of industry and consulting experience in the automotive industry serving in various executive roles at OEMs and software companies.

At Berylls, his area of expertise is software development & architecture, digital business models, digital operating models and ADAS/AD.

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Accelerating Automotive R&D: Strategies and approaches to maximize efficiency and speed

Munich, November 2023

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Accelerating Automotive R&D: Strategies and approaches to maximize efficiency and speed

Munich, November 2022
A

utomotive OEMs face challenges in sustaining profitability due to escalating product complexity, which in turn results in heightened research and development expenditures.

Five improvement strategies can help not only to reduce R&D expenditures but also increase innovation cycles.

Download the full insight now.

Berylls Insight
Accelerating Automotive R&D: Strategies and approaches to maximize efficiency and speed
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Author
Sebastian Böswald

Associate Partner

Sebastian Böswald

Sebastian Böswald (1991) joined Berylls in April 2021. He is an Associate Partner and an expert in both transformation and operations. Over the last decade, he has focused his work on strategy and organizational design, as well as on two megatrends shaping the automotive industry: software-defined vehicles and CASE (connected, autonomous, shared, and electrified mobility). In these fields, he has advised our global OEM clients as well as Tier-1 suppliers and tech companies.

Prior to joining Berylls, he worked for PwC Strategy& and started his career at BMW as a project manager for product strategy and digital charging services.

He received a Bachelor of Science in Automotive Computer Science at the Technical University of Ingolstadt as well as a Master of Science in Management from the Technical University of Munich.

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Five battles you must win as an OEM to master Pricing & Revenue Management

Munich, October 2023

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Five battles you must win as an OEM to master Pricing & Revenue Management Munich, October 2023

T

he transformation of the automotive industry is in full swing, encompassing both upstream and downstream aspects on national and international scales.

To paint a clear picture of this evolution, various aspects of the automotive sector are being reshaped. New strengths are being cultivated, weaknesses are being addressed.

A critical aspect deserving focused attention is „Pricing and Revenue Management.“

Fig 1: Pricing as the Ultimate Profit Lever

Source: Berylls Strategy Advisors

Pricing stands as the most potent lever for increasing profitability within the automotive sector. Consequently, it should take center stage for all Original Equipment Manufacturers (OEMs). However, it’s essential to recognize that Revenue Management extends beyond Pricing. It involves a strategic business practice adopted by companies to maximize their revenue and profitability through the optimization of product or service pricing. This discipline entails the application of diverse pricing strategies, data analysis, and forecasting techniques to make informed decisions about price setting, resource allocation, and capacity management. The primary components are depicted in the chart below. Pricing emerges as the most significant lever for enhancing profitability, making it of utmost importance for top management.

Fig 2: Revenue Management Encompasses Multifaceted Strategies

Source: Berylls Strategy Advisors

With a number of years of experience across various industries, it is evident that the automotive sector can glean valuable insights and techniques in Pricing and Revenue Management from sectors such as Fast-Moving Consumer Goods (FMCG), software, and industrial fields. Currently, many OEMs are still focusing a lot on factory utilization. This can result in cars being sold with higher discounts, because production and demand are not matching good enough. Many changes occurring in the automotive industry mirror transformations witnessed by other industries in recent years:

  • From Product to Product-Service-Software Combinations: The significance of services and software is growing, necessitating different pricing structures, and enabling novel approaches such as „as-a-service.“

  • From Single- to Omnichannel: Vehicles, services, and software will be sold through an expanding array of channels, including direct and indirect, electronic, and physical, and owned or third-party channels. Decisions regarding what to sell through which channel to which customer segment at what price are becoming more intricate and, thus, more strategically crucial.

  • From Product to Customer Focus: OEMs are increasingly striving to better comprehend their customers (their journey, value drivers, willingness to pay, etc.). This understanding provides a solid foundation for more differentiated pricing, better exploitation of specific customer segments‘ willingness to pay, and catering to individual demands for products and services.

  • From Gut Feeling to Data-Based: Modern Revenue Management can be approached much more professionally using data and AI-based methods.

     

Considering these ongoing transformations, now is the ideal time to formulate and implement a 3-5-year development plan to fortify the Pricing & Revenue Management capabilities.

Best practices from Other Industries

Before delving into the specific must-win-battles (MWBs) for Pricing & Revenue Management in the automotive sector, let’s explore some best practices from other industries:

  • Marriott International: Renowned for its revenue management strategies in the hospitality industry, Marriott employs sophisticated pricing algorithms and data analytics to dynamically adjust room rates based on demand, seasonality, and other factors. This optimization maximizes revenue and occupancy rates across its global portfolio of hotels.

  • Amazon: As one of the world’s largest e-commerce companies, Amazon excels in revenue management through its advanced pricing strategies. They leverage extensive customer data to dynamically adjust prices on their marketplace, optimizing in real-time while considering factors such as competitor pricing, demand, and customer behavior.

  • Walt Disney Parks and Resorts: Disney meticulously practices revenue management within its theme parks. They employ various pricing strategies, including seasonal pricing, tiered ticketing, and add-on experiences, to optimize revenue and manage crowd levels effectively.

  • Ferrero: With its diverse brands and sales channels, Ferrero has implemented professional Revenue Management on a global scale. Based on a clear governance, well-defined playbooks and extensive data utilization, Ferrero has achieved significant profitability gains.

Initial Conclusions

Considering these examples, let’s draw some initial conclusions:

1. Pricing & Revenue Management serves as the most substantial profit lever.

2. When benchmarked against other industries, there is ample room for improvement in automotive OEMs.

3. The ongoing developments within the automotive industry underscore the rising importance of Pricing & Revenue Management.

Consequently, we strongly advocate a concentrated focus on Pricing & Revenue Management, with particular emphasis on the following 5 must-win-battles (MWBs):

MWB 1 – Establish a Clear Pricing & Revenue Management Strategy

A robust strategy is fundamental to success, and this holds true for Pricing & Revenue Management. Given the complexity of the automotive environment, the strategy must provide detailed answers to critical strategic questions. These include prioritizing revenue over profitability (or vice versa?), setting objectives for each Strategic Business Unit (SBU), defining target customers, understanding their value drivers and willingness to pay, positioning prices, determining channel strategies for different customer segments, and specifying pricing methods.

Developing a Pricing & Revenue Management strategy for an automotive OEM is a comprehensive exercise that necessitates in-depth insights into and transparency about markets, customers, channels, and offerings, both internally and externally. Of course, it must be mentioned that OEMs are not starting from scratch but have already developed elements or even entire Pricing & Revenue Management strategies.

MWB 2 – Implement a Target Operating Model for Pricing & Revenue Management

Determining the organizational structure of Pricing & Revenue Management is crucial. It’s essential to decide whether the team should be centralized, decentralized, or a hybrid model. This decision should align with the overall strategy and objectives of Pricing & Revenue Management. As always, there´s not THE one solution that is always right:

  • Apple and Skype are having centralized pricing teams because they have strong global brands and high price transparency.*

  • Porsche and Michelin use a split responsibility to foster a high degree of standardization on the one hand side. At the same time, local input e.g., to differentiate local willingness to pay must be considered.*

  • At 3M and Celesio, the Pricing teams are largely decentral with responsibility for a BU or region. This makes sense because markets are heterogeneous with different regulatory requirements for example.*

*Please note that this may not correspond to the current status of the companies.

In addition to structure, harmonizing global Pricing & Revenue Management processes is crucial for automation through digitization. Technology and human resources are integral components of the Target Operating Model. As the era of direct sales is coming closer, this of particular importance, as from than on, the final pricing responsibility stays with the OEM.

MWB 3 – Understand Customer Willingness to Pay and Set Prices Accordingly

A central tenet of Pricing & Revenue Management is understanding customers and aligning strategies accordingly. Effective Pricing & Revenue Management involves precise tools and concepts, with price differentiation being a significant component. Many companies successfully charge different prices to different customer segments for the same product or service, based on willingness to pay. Other industries are already successfully doing this as we speak: Just consider the practice that hotels and airlines are applying very successfully since many years. Amazon is changing the price for certain products up to 40 times a day, based on demand, competitor prices etc. Big insurance companies offer their insurance products at differentiated prices to different customers. Automotive has made recent experience with changing willingness to pay during the time of product shortages. Customers that are used to negotiating prices with their dealer where suddenly ready to even pay way above list price as for example visible in the United States. As product shortages are more and more dissolving now, the old pattern of price negotiation is coming back. A lot more examples of price differentiation exist.

To achieve this successfully, OEMs must understand the willingness to pay of their target customer segments, employing various reliable techniques for this purpose. This not only boosts profitability but also enhances revenue. And always remember that a 1% price increase already leads to a 7% increase in profitability.

MWB 4 – Establish Transparency in Pricing & Revenue Management Performance

Effective Performance Management hinges on knowing the right performance metrics, measuring, and reporting them accurately, and having sound performance management processes in place. Performance metrics vary based on business models, and achieving granularity across different business, customer, and product segments is often necessary. When list prices exist and discounts and rebates can be granted, you might want to use “price enforcement” as one top KPI. When you are for example in an online business, where prices are set by an AI-algorithm, a suitable KPI can be “realized price improvement”, measuring the price increase over a certain period. To add even more quality, “realized price improvement” can be measured against a market price index or internal objectives. The price waterfall can be a good input to discuss suitable KPIs. For OEMs on their way to the direct sales mode, managing price enforcement will be one of the key capabilities to be built to ensure profitability targets are met.

Fig 3: Price waterfall as food-for-thoughts about pricing KPIs.

Source: Berylls Strategy Advisors

Performance metrics should align with the pricing strategy and objectives, and data should be made available in suitable reports, despite the challenges of legacy IT systems and data quality.

MWB 5 – Leverage Data and AI to Optimize Pricing & Revenue Management

Data and AI offer tremendous potential for enhancing Pricing & Revenue Management. Using AI, OEMs can optimize various facets of Revenue Management simultaneously, which is challenging with conventional methods. Additionally, understanding customer behavior and preferences from data can guide pricing strategies and offerings. However, successfully scaling AI projects in Pricing & Revenue Management requires investment in relevant data, systems, and expertise, as data quality and system readiness are often limiting factors.

Conclusion

In conclusion, while automotive OEMs may not be considered Pricing & Revenue Management benchmarks compared to other industries, the changing landscape demands attention. We recommend that OEMs assess their current maturity level in Pricing & Revenue Management and chart a strategic roadmap, centered around these 5 must-win-battles outlined in this paper. With the Berylls Pricing Pathfinder we combine our Pricing & Revenue Management expertise with a powerful tool to reveal what Pricing & Revenue Management excellence looks like, and how to get there.

Author
Thorsten Lips

Partner

Thorsten Lips

Thorsten Lips (1972) is a partner at Berylls Strategy Advisors. He began his career as a management consultant at PricewaterhouseCoopers Düsseldorf in 1998. After spending six years at Malik Management Centre in St. Gallen, Switzerland, he took the cross-industry, global responsibility for Pricing, Sales, Service and Marketing as a partner at Horváth. At Berylls, his area of expertise is Pricing & Revenue Management. This encompasses classical topics like new- and used-car pricing, aftersales pricing and the like. In addition, he is an expert in innovative Pricing and Revenue Management approaches for digital products and services as well as in the field of data-driven Pricing.

Industrial engineering and management studies at the Technical University of Ilmenau and the Technical University of Darmstadt.

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Semi-annual index rebalancing: WidsomTree Berylls LeanVal global automotive innovators index

Munich, October 2023

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ew things shape modern life as much as individual mobility. Be it as an expression of freedom and individuality, or as an economic driver. 

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Authors
Dr. Jan Burgard

Berylls Group CEO

Malte Broxtermann

Associate Partner

Björn Simon

Senior Consultant

Jakob Rüchardt

Consultant

Dr. Jan Burgard

Dr. Jan Burgard (1973) is CEO of Berylls Group, an international group of companies providing professional services to the automotive industry.

His responsibilities include accelerating the transformation of luxury and premium OEMs, with a particular focus on digitalization, big data, connectivity and artificial intelligence. Dr. Jan Burgard is also responsible for the implementation of digital products at Berylls and is a proven expert for the Chinese market.

Dr. Jan Burgard started his career at the investment bank MAN GROUP in New York. He developed a passion for the automotive industry during stopovers at an American consultancy and as manager at a German premium manufacturer. In October 2011, he became a founding partner of Berylls Strategy Advisors. The top management consultancy was the origin of today’s Group and continues to be the professional nucleus of the Group.

After studying business administration and economics, he earned his doctorate with a thesis on virtual product development in the automotive industry.

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Malte Broxtermann

Malte Broxtermann (1986) joined the Berylls team in 2014. After extensive experience as emergency medical technician, he has been working in consulting since 2012. He helps customer to leverage digital strategies & products across the entire automotive value chain. He is an expert in deploying machine learning-powered applications. As Partner at Berylls’ own unit for digital solutions, Berylls Digital Ventures, he focuses on scaling start-ups as part of our venturing practice.
Studied economics and international business at Maastricht University (Netherlands) and Queen’s University (Canada).

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Ein Elektrofahrzeug ist wesentlich einfacher zu bauen als ein Verbrennungsfahrzeug

München, September 2023

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Auch dadurch dass die Produktion einfacher ist, als bei einem Verbrenner, versuchen unzählige Marken weltweit sich einen Namen zu machen. Ein Problem – auch für Anleger, die mit einem „zweiten Tesla“ den Lucky Punch suchen. Könnte ein ETF auf den WisdomTree Global Automotive Innovators Index helfen?

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Autor
Dr. Jan Burgard

CEO Berylls Group

Dr. Jan Burgard

Dr. Jan Burgard (1973) ist CEO der Berylls Group, einer internationalen und auf die Automobilitätsindustrie spezialisierten Unternehmensgruppe.
Sein Aufgabengebiet umfasst die Transformation von Luxus- und Premiumherstellern, mit besonderen Schwerpunkten auf Digitalisierung, Big Data, Start-ups, Connectivity und künstliche Intelligenz. Dr. Jan Burgard verantwortet bei Berylls außerdem die Umsetzung digitaler Produkte und ist ausgewiesener Spezialist für den Markt China.
Dr. Jan Burgard begann seine Karriere bei der Investmentbank MAN GROUP in New York. Die Leidenschaft für die Automobilitätsindustrie entwickelte er während Zwischenstopps bei einer amerikanischen Beratung und als Manager eines deutschen Premiumherstellers.
Im Oktober 2011 komplettierte er die Gründungspartner von Berylls Strategy Advisors. Die Top-Management-Beratung ist die Basis der heutigen Group und weiterhin der fachliche Nukleus aller Einheiten.
An das Studium der Betriebs- und Volkswirtschaftslehre, schloss sich die Promotion über virtuelle Produktentwicklung in der Automobilindustrie an.

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Model Based Systems Engineering in Truck R&D

Munich, September 2023

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he trucking industry is currently grappling with significant pressure induced by an ongoing transformation predicated on three fundamental changes to its long-standing operations.

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Model based systems engineering in truck R&D
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Authors
Steffen Stumpp

Associate Partner

Frederik Ruhm

Associate Partner

Steffan Lemke

Consultant

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Steffen Stumpp

Steffen Stumpp (1970) joined the Berylls Group in October 2020 as Head of Business Unit Commercial Vehicles. At this point, he already looked back on extensive professional and leadership experience in the commercial vehicle industry. Stumpp started his career in an OEM and went through different roles in research, marketing, product planning and after-sales service. When he switched to the automotive supplier industry, he took over the responsibility for worldwide sales and marketing of a medium-sized tier 1 supplier. After another step as head of sales he decided to join Berylls, where he is now responsible for the commercial vehicle business.

Stumpp is a graduate engineer and has studied industrial engineering at the KIT in Karlsruhe and the Technical University of Berlin with focus on logistics.

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