Signify – Signify Capital Market Day 2026

EMR Analysis

More information on Signify: See the full profile on EMR Executive Services

More information on Jeroen Drost (Chairman of the Supervisory Board, Signify): See the full profile on EMR Executive Services

More information on As Tempelman (Member of the Board + Chief Executive Officer, Signify): See the full profile on EMR Executive Services

More information on Željko Kosanović (Member of the Board + Chief Financial Officer + Senior Vice President, Group Controller, Signify): See the full profile on EMR Executive Services

 

More information on the Sustainability Program (Brighter Lives, Better World 2030) + The Climate Transition Plan 2040 by Signify: See the full profile on EMR Executive Services 

More information on Maurice Loosschilder (Head of Sustainability, Signify): See the full profile on EMR Executive Services

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• EBIT:
  • Earnings Before Interest and Taxes (EBIT) is a measure of a company’s operating profitability before accounting for interest expenses and income taxes. It is also known as operating profit and shows how effectively a company’s core business is generating profit from its operations.
• EBITA:
  • Earnings before interest, taxes, and amortization (EBITA) is a measure of company profitability used by investors. It is helpful for comparing one company to another in the same line of business.
  • EBITA = Net income + Interest + Taxes + Amortization
• EBITDA: 
  • Earnings before interest, taxes, depreciation, and amortization (EBITDA) is an alternate measure of profitability to net income. By including depreciation and amortization as well as taxes and debt payment costs, EBITDA attempts to represent the cash profit generated by the company’s operations.
  • EBITDA and EBITA are both measures of profitability. The difference is that EBITDA also excludes depreciation.
  • EBITDA is the more commonly used measure because it adds depreciation—the accounting practice of recording the reduced value of a company’s tangible assets over time—to the list of factors.
• EV/EBITDA (Enterprise Multiple):
  • Enterprise multiple, also known as the EV-to-EBITDA multiple, is a ratio used to determine the value of a company.
  • It is computed by dividing enterprise value by EBITDA.
  • The enterprise multiple takes into account a company’s debt and cash levels in addition to its stock price and relates that value to the firm’s cash profitability.
  • Enterprise multiples can vary depending on the industry.
  • Higher enterprise multiples are expected in high-growth industries and lower multiples in industries with slow growth.

 

 

 

• Cash Flow (CF):
  • Cash flow (CF) is the net amount of money (cash and cash equivalents) moving into and out of a business over a given period. It is a critical indicator of a company’s financial health and liquidity, revealing its ability to pay expenses, service debt, and fund growth, independent of non-cash accounting items like depreciation.
• Free Cash Flow (FCF):
  • Free cash flow (FCF) is a company’s available cash repaid to creditors and as dividends and interest to investors. Management and investors use free cash flow as a measure of a company’s financial health. FCF reconciles net income by adjusting for non-cash expenses, changes in working capital, and capital expenditures. Free cash flow can reveal problems in the financial fundamentals before they become apparent on a company’s income statement. A positive free cash flow doesn’t always indicate a strong stock trend. FCF is money that is on hand and free to use to settle liabilities or obligations.

 

 

 

• Topline and Bottom Line:
  • In finance and business, topline (or “top line”) refers to a company’s gross revenue or total sales. It represents the total amount of money a business brings in from selling its goods or services before any expenses, operating costs, interest, or taxes are deducted (i.e., revenue as reported at the top of the income statement).
  • “Topline growth” demonstrates that a company is expanding its market share, attracting new customers, or raising prices successfully (or a combination of volume growth and pricing power), but a rising topline does not automatically guarantee profit; a business can have record-breaking sales but still lose money if its expenses are too high.
  • The bottom line refers to a company’s net income, net profit, or net earnings. It represents the actual amount of money a business keeps after subtracting all expenses, interest, taxes, and depreciation from its total revenue (i.e., the final profit figure at the bottom of the income statement).
  • A growing bottom line proves that a company is managing its costs, production, and overhead effectively and converting revenue into sustainable profitability and cash generation.

 

 

 

• Build or Harvest Mandate:
  • A Build or Harvest mandate is a strategic directive given to portfolio managers or corporate executives to either aggressively grow a business unit (“Build”) or maximize its short-term cash flows while phasing out investment (“Harvest”).
  • This framework is highly utilized in private equity, corporate portfolio management, and investment banking to allocate capital based on a business unit’s market position and lifecycle.

 

 

 

• LED:
  • LED stands for light emitting diode. LED lighting products produce light significantly more efficiently (up to ~80–90% depending on comparison conditions) than incandescent light bulbs. How do they work? An electrical current passes through a microchip, which illuminates the tiny light sources we call LEDs and the result is visible light.
  • A light-emitting diode is a semiconductor light source that emits light when current flows through it. Electrons in the semiconductor recombine with electron holes, releasing energy in the form of photons.
• LED vs. Halogen:
  • Halogen bulbs, while lasting longer than incandescent bulbs, only last up to ~2,000–4,000 hours (depending on type). In contrast, LED bulbs can last up to 25,000 hours, and LED tubes are rated for up to 50,000 hours. LED bulbs can use as much as ~70–80% less energy than halogen bulbs.
  • There’s obviously a clear winner when it comes to LED vs halogen lighting. LED lights are more energy-efficient, have a longer lifespan, and offer more choices in color temperature. They do cost a little more, but their extremely long lifespan often offsets the higher upfront cost.
• microLED:
  • Compared to widespread LCD technology, microLED displays offer better contrast, response times, and energy efficiency. They are also capable of high speed modulation, and have been proposed for chip-to-chip interconnect applications.
  • MicroLED prototype displays have been shown to offer up to ~5–10 times higher peak brightness (depending on comparison and conditions) than the best OLED panel while being potentially more power efficient (technology still evolving), making them an exciting new technology in the world of displays.
• OLED:
  • An Organic Light-Emitting Diode is a solid-state device consisting of a thin, carbon-based semiconductor layer that emits light when electricity is applied by adjacent electrodes. In order for light to escape from the device, at least one of the electrodes must be transparent.
  • OLED devices (television screens, computer monitors, and portable systems such as smartphones …) use light-emitting diode principles with organic materials (not conventional inorganic LEDs) as a light emitting layer. Organic LEDs can produce high quality displays with high contrasts, high viewing angles and true blacks. They are among the best display technologies currently available.

 

 

 

• Supply Chain: 
  • A supply chain is the end-to-end network of individuals, organizations, resources, activities, data, and technologies involved in the creation and delivery of a product or service—from raw materials to the final customer.
  • A supply chain includes not only physical flows (goods), but also information flows and financial flows across all participants.
  • At the most fundamental level, Supply Chain Management (SCM) is the integrated planning, coordination, and optimization of the flow of:
    • goods
    • information
    • and finances
    • from raw material sourcing to final delivery.
  • At its core, SCM is not just “management of flows” but the optimization of those flows across cost, service level, speed, and risk.
  • Supply Chain vs Logistics:
    • Supply Chain: entire ecosystem (end-to-end)
    • Logistics: subset focused on movement and storage of goods

 

 

 

• AI – Artificial Intelligence:
  • Artificial Intelligence (AI) is the broad field of computer science focused on building systems that perform tasks requiring human-like intelligence, such as learning, reasoning, perception, and decision-making.
  • AI systems typically:
    • ingest large datasets
    • identify patterns
    • make predictions or decisions
  • AI is an umbrella term that includes machine learning, deep learning, and other approaches (rule-based systems, optimization, etc.), not just Machine Learning (ML).
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. Most modern AI systems. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Research stage. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. Does not yet exist. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state.
  • Machine Learning (ML):
    • Subset of AI that enables systems to learn from data without explicit programming.
    • ML uses historical data to detect patterns and make predictions.
    • ML is the dominant paradigm in modern AI, replacing most rule-based systems.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches:
      • supervised learning,
      • unsupervised learning,
      • semi-supervised learning and
      • reinforcement learning.
  • Deep Learning (DL):
    • Subset of ML using multi-layered neural networks to learn complex representations.
    • DL is not always “more sophisticated” in all contexts—it is more powerful for unstructured data (images, text, audio), but classical ML can outperform it in structured/tabular data.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition. Face recognition is a good example.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • AI systems that generate new content (text, images, code, audio, etc.) based on learned patterns.
    • GenAI is typically powered by large deep learning models (e.g., transformers), not a separate paradigm.
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Broad AI field for interpreting visual data.
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • lndustrial application of Computer Vision. MV is a subset of CV, not a parallel category.
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates multiple data types (text, image, audio, video).
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
    • The defining feature of an agent is not just decision-making, but the ability to take actions toward a goal in an environment.
  • Agentic AI:
    • Agentic AI is a system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multi-agent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • AI executed locally on devices (IoT, sensors, cameras) instead of centralized cloud.
    • Edge AI refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. It is an infrastructure trend (AI data centers / GPU clusters), not a distinct AI category. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs.
    • While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.
  • Physical AI & Embodied AI: 
    • Physical AI refers to a branch of AI that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Embodied AI, as a subset, focuses on the sensory, decision-making, and interaction capabilities that enable these systems to function effectively in dynamic and unpredictable environments via sensors and actuators.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning is a machine-learning technique where data stays where it is, and only the learned model updates are shared. “Training AI without sharing your data”.
    • Reinforcement Learning is a type of AI where an agent learns by interacting with an environment and receiving rewards or penalties. “Learning by trial and error”
    • Federated Learning (FL) and Reinforcement Learning (RL) can be combined into a field called Federated Reinforcement Learning (FRL), where multiple agents learn collaboratively without sharing their raw data. In this approach, each agent trains its own RL policy locally and shares model updates, like parameters or gradients, with a central server. The server aggregates these updates to create a more robust, global model. FRL is used in applications like optimizing resource management in communication networks and enhancing the performance of autonomous systems by learning from diverse, distributed experiences while protecting privacy (still niche and mostly experimental.)
  • AI Factories:
    • AI Factories are specialized, high-performance computing centers designed to train, tune, and deploy artificial intelligence models at scale.
    • Companies and organizations involved in AI factory infrastructure and development include Nvidia, AWS, Microsoft, OpenAI, CoreWeave, Lambda, Nebius, Supermicro, and HPE. The European Union is also establishing AI Factories through its EuroHPC Joint Undertaking to foster regional innovation.
    • “AI factory” is a conceptual term (not standardized), referring to industrial-scale AI production systems.

 

 

 

• Share Buyback:
  • A share buyback, also known as a share repurchase, occurs when a company purchases its own outstanding shares from the market, reducing the total number of shares in circulation.
  • This can:
    • increase earnings per share (EPS) by reducing share count
    • return capital to shareholders (alternative to dividends)
    • signal management’s confidence in the company’s value
    • adjust ownership structure or defend against takeovers

 

 

 

• Generally Accepted Accounting Principles (GAAP): 
  • GAAP is the set of accounting rules and standards established by the Financial Accounting Standards Board (FASB) that U.S. companies are required (for public companies) or expected (for others) to follow when preparing financial statements.
  • The goal of GAAP is to ensure that financial statements are:
    • complete
    • consistent
    • comparable
    • reliable and transparent
  • GAAP may be contrasted with non-GAAP (pro forma) reporting, which adjusts standard results to exclude certain items for analytical purposes.
  • GAAP is used primarily in the U.S., while most other countries follow International Financial Reporting Standards (IFRS).
  • GAAP is also used by state and local governments, although these typically follow standards issued by the Governmental Accounting Standards Board (GASB).

 

• International Financial Reporting Standards (IFRS):
  • IFRS is a globally recognized set of accounting standards used in most jurisdictions worldwide, including the European Union.
  • It is issued by the International Accounting Standards Board (IASB) and is designed to make financial statements:
    • consistent
    • transparent
    • comparable across countries
  • IFRS replaced the earlier International Accounting Standards (IAS) framework in 2001.
  • IFRS fosters greater corporate transparency by emphasizing principles-based accounting, allowing companies to apply judgment based on economic substance.
  • Chinese companies primarily follow Chinese Accounting Standards (ASBE), which are largely converged with IFRS but not identical.

 

• => GAAP vs. IFRS:
  • GAAP: U.S.-focused, more rules-based, highly detailed
  • IFRS: Global, more principles-based, flexible application

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Financial Notes: 

• Major financial KPI’s since 2017 are available on EMR Executive Services under “Financial Results” and comparison with peers under “Market Positioning”
• Companies’ full profile on EMR Executive Services are based on their official press releases, quarterly financial reports, annual reports and other official documents.
• All members of the Executive Committee and of the Board have their full profile on EMR Executive Services
• The Signify Q1 2026 Results Presentation can be found here: https://www.signify.com/static/quarterlyresults/2026/q1_2026/signify-first-quarter-results-2026-presentation.pdf
• The Signify Annual Report 2025 can be found here: https://www.signify.com/static/2025/signify-annual-report-2025.pdf
• The Signify Q4 2025 Results Report can be found here: https://www.signify.com/static/quarterlyresults/2025/q4_2025/signify-fourth-quarter-and-full-year-results-2025-report.pdf
• The Signify Q4 2025 Results Presentation can be found here: https://www.signify.com/static/quarterlyresults/2025/q4_2025/signify-fourth-quarter-and-full-year-results-2025-presentation.pdf
• The Signify Annual Report 2024 can be found here: https://www.signify.com/static/2024/signify-annual-report-2024.pdf
• The Signify Q4 and Full Year 2024 Results Report can be found here: https://www.signify.com/static/quarterlyresults/2024/q4_2024/signify-fourth-quarter-and-full-year-results-2024-report.pdf
• The Signify Q4 and Full Year 2024 Results Presentation can be found here: https://www.signify.com/static/quarterlyresults/2024/q4_2024/signify-fourth-quarter-and-full-year-results-2024-presentation.pdf
• The Signify comparable financials for 2023 and Q1 2024 following implementation of new organizational structure can be found here: https://www.assets.signify.com/is/content/Signify/Assets/signify/global/news/2024/20240614-signify-publishes-comparable-financials-for-2023-and-q1-2024.pdf
• The Signify Annual Report 2023 can be found here: https://www.signify.com/static/2022/signify-annual-report-2022.pdf
• The Signify Q4 and full-year results 2023 Report can be found here: https://www.signify.com/static/quarterlyresults/2023/q4_2023/signify-fourth-quarter-and-full-year-results-2023-report.pdf
• The Signify Q4 and full-year results 2023 Presentation can be found here: https://www.signify.com/static/quarterlyresults/2023/q4_2023/signify-fourth-quarter-and-full-year-results-2023-presentation.pdf
• The Signify Annual Report 2022 can be found here: https://www.signify.com/static/2022/signify-annual-report-2022.pdf
• The Signify Q4 2022 Presentation can be found here:  https://www.signify.com/static/quarterlyresults/2022/q4_2022/signify-fourth-quarter-and-full-year-results-2022-presentation.pdf
• The Signify Business Highlights 2022 Video can be found here:  https://www.signify.com/global/our-company/news/press-releases/2023/20230127-signify-fourth-quarter-and-full-year-results-2022