ABB – ABB to divest Robotics division to SoftBank Group

EMR Analysis

More information on ABB: See full profile on EMR Executive Services

More information on Peter Voser (Chairman of the Board of Directors, ABB Ltd + Chairman of the Governance and Nomination Committee, ABB Ltd): See full profile on EMR Executive Services

More information on Morten Wierod (Chief Executive Officer and Member of the Group Executive Committee, ABB): See full profile on EMR Executive Services 

More information on Timo Ihamuotila (Chief Financial Officer and Member of the Executive Committee, ABB): See full profile on EMR Executive Services

 

More information on Robotics & Discrete Automation Business Area by ABB: See full profile on EMR Executive Services

More information on Sami Atiya (President, Robotics & Discrete Automation Business Area and Member of the Executive Committee, ABB till end of 2025 + Strategic Advisor, ABB from January 2026 till December 2026): See full profile on EMR Executive Services

More information on Machine Automation Division by Robotics & Discrete Automation Business Area by ABB: See full profile on EMR Executive Services

More information on Jörg Theis (President, Machine Automation Division, Robotics & Discrete Automation Business Area, ABB + Chief Executive Officer, B&R Automation): See full profile on EMR Executive Services 

More information on Robotics Division by Robotics & Discrete Automation Business Area by ABB: See full profile on EMR Executive Services

More information on Marc Segura (President, ABB Robotics Division, Robotics & Discrete Automation Business Area, ABB): See full profile on EMR Executive Services

 

More information on Process Automation Business Area by ABB: See the full profile on EMR Executive Services

More information on Peter Terwiesch (President, Process Automation Business Area and Member of the Executive Committee, ABB): See full profile on EMR Executive Services

 

 

 

More information on SoftBank Group Corp.: https://group.softbank/en + The SoftBank Group is a global technology player that aspires to drive the Information Revolution. The SoftBank Group is comprised of the holding company SoftBank Group Corp. (TOKYO: 9984) and its global portfolio of companies, which includes advanced telecommunications, Internet services, AI, smart robotics, IoT and clean energy technology providers. In September 2016, Arm Limited, the world’s leading semiconductor IP company, joined the SoftBank Group. SoftBank Group Corp. is investing in the SoftBank Vision Fund, which is deploying up to $100 billion in committed capital to support the global businesses and technologies that the SoftBank Vision Fund believes will enable the next stage of the Information Revolution.

More information on Masayoshi Son (Founder, Representative Director, Corporate Officer, Chairman & Chief Executive Officer, SoftBank Group Corp.): https://group.softbank/en/about/officer + https://group.softbank/en/about/officer/son  

More information on SoftBank Corp. by SoftBank Group Corp.: https://www.softbank.jp/en/ + Guided by the SoftBank Group’s corporate philosophy, “Information Revolution — Happiness for everyone,” SoftBank Corp. (TOKYO: 9434) operates telecommunications and IT businesses in Japan and globally. Building on its strong business foundation, SoftBank Corp. is expanding into non-telecom fields in line with its “Beyond Carrier” growth strategy while further growing its telecom business by harnessing the power of 5G/6G, IoT, Digital Twin and Non-Terrestrial Network (NTN) solutions, including High Altitude Platform Station (HAPS)-based stratospheric telecommunications. While constructing AI data centers and developing homegrown LLMs specialized for the Japanese language, SoftBank is integrating AI with radio access networks (AI-RAN), with the aim of becoming a provider of next-generation social infrastructure.

More information on Yasuyuki Imai (Director and Chairman, SoftBank Corp., SoftBank Group Corp.): https://www.softbank.jp/en/corp/aboutus/governance/corporate-governance/officer/ + https://www.linkedin.com/in/yasuyuki-imai-76709196/ 

More information on Junichi Miyakawa (President and Chief Executive Officer, SoftBank Corp., SoftBank Group Corp.): https://www.softbank.jp/en/corp/aboutus/governance/corporate-governance/officer/ 

 

 

 

More information on the SIX Swiss Exchange: https://www.six-group.com/en/home.html + SIX operates the infrastructure for the financial centers in Switzerland and Spain, thus ensuring the flow of information and money between financial market players. SIX offers exchange services, financial information and banking services with the aim of increasing efficiency, quality and innovative capacity along the entire value chain. SIX is also building a digital infrastructure for the new millennium.

SIX connects financial market participants in Switzerland, Spain and throughout the world. The company is owned by around 120 national and international financial institutions. They are the main users of our services and our most important customers. Our close relationship with them guarantees stability of the financial infrastructure and processes, proximity to clients’ evolving business needs and competitive prices.

More information on Bjørn Sibbern (Chief Executive Officer, SIX Swiss Exchange): https://www.six-group.com/en/company/governance/executive-board.html + https://www.linkedin.com/in/bj%C3%B8rnsibbern/ 

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• Cobots (Collaborative Robots):
  • A collaborative robot, also known as a cobot, is a robot designed to assist human worker by performing tasks in close proximity and collaboration with them.  In contrast, autonomous robots are hard-coded to repeatedly perform one task, work independently and remain stationary.
  • Intended to work hand-in-hand with employees. These machines focus more on repetitive tasks, such as inspection and picking, to help workers focus more on tasks that require problem-solving skills.
  • A robot is an autonomous machine that performs a task without human control. A cobot is an artificially intelligent robot that performs tasks in collaboration with human workers.
  • According to ISO 10218 part 1 and part 2, there are four main types of collaborative robots: safety monitored stop, speed and separation, power and force limiting, and hand guiding.
• Automated Guided Vehicles (AGV): 
  • An AGV system, or automated guided vehicle system, otherwise known as an automatic guided vehicle, autonomous guided vehicle or even automatic guided cart, is a system which follows a predestined path around a facility.
  • Three types of AGVs are towing, fork trucks, and heavy load carriers. Each is designed to perform repetitive actions such as delivering raw materials, keep loads stable, and complete simple tasks.
  • The main difference between an AGV and an AMR is that AMRs use free navigation by means of lasers, while AGVs are located with fixed elements: magnetic tapes, magnets, beacons, etc. So, to be effective, they must have a predictable route.
• Autonomous Mobile Robot (AMR): 
  • Any robot that can understand and move through its environment without being overseen directly by an operator or on a fixed predetermined path.
  • AMRs have an array of sophisticated sensors that enable them to understand and interpret their environment, which helps them to perform their task in the most efficient manner and path possible, navigating around fixed obstructions (building, racks, work stations, etc.) and variable obstructions (such as people, lift trucks, and debris).
  • Though similar in many ways to automated guided vehicles (AGVs), AMRs differ in a number of important ways. The greatest of these differences is flexibility: AGVs must follow much more rigid, preset routes than AMRs. Autonomous mobile robots find the most efficient route to achieve each task, and are designed to work collaboratively with operators such as picking and sortation operations, whereas AGVs typically do not.
• Autonomous Case-handling Robots (ACR): 
  • Autonomous Case-handling Robot (ACR) systems are highly efficient “Goods to Person” solutions designed for totes & cartons transportation and process optimization, providing efficient, intelligent, flexible, and cost-effective warehouse automation solutions through robotics technology.

 

 

• AI – Artificial Intelligence:
  • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
  • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but several, including Python, R and Java, are popular.
  • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
  • 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. 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. 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. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
  • Machine Learning (ML):
    • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • ML algorithms use historical data as input to predict new output values.
    • 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 machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
    • 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.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • 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):
    • 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) :
      • 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.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
    • 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.
  • Agentic AI:
    • Agentic AI is an artificial intelligence 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 multiagent 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:
    • Edge artificial intelligence 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. 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.

 

 

 

• Spin-Off:
  • To “spin-off” a company means a parent company creates a new, independent company by separating one of its business units, often distributing the new company’s shares to the parent company’s existing shareholders. This process allows the spun-off division to focus on its own strategy and allows parent company management to concentrate on their core business, potentially increasing the overall value for both entities. 

 

 

• 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.