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Making real humanoid robots, for real customers, with real ROI *today* is what distinguishes DCVC-backed Agility Robotics

A detailed article that outlines what Ford is doing in the software space. Key highlights: 1️⃣ Ford’s cloud first strategy is driving a larger business transformation including the move to #sdv and revenue from software as a service. 2️⃣ Ford is unique among large automotive manufacturers in its selection of GCP, which provides Ford a strong foundation for data-driven operations. 3️⃣ The cloud has been the primary catalyst for the SDV blueprint, driving technology advances such as EVs, automated driving capabilities, and tailored services for multiple customer segments, in each unit of the three org units (Ford Pro, Model-E and Blue). 4️⃣ Ford continues to move internal workloads to Google’s cloud and to deploy Salesforce throughout the workforce. 5️⃣ Ford has developed software platforms such as the Ford Pro API, which makes data from highly connected commercial vehicles available for both internal and partner applications. This has allowed Ford to create a SaaS offering that provides customers with data and analytics to help them maximize fleet performance and efficiency. For example, data available via these APIs can inform customers about how much capacity they need for fleet usage and for planning their long-term charging strategies. 6️⃣ Technology acquisitions and partnerships are another key enabler of Ford’s high-tech transformation (e.g. 2021 acquisition of Silicon Valley startup Electriphi for its EV charging management and EV fleet monitoring software.) 7️⃣ Ford Pro’s services arm also uses digital tools to provide automated reporting and maintenance of commercial pickups and vans to help customers optimize EV mileage and annual battery use. 8️⃣ Ford has also employed UiPath and Pega robotic process automation (RPA) software to automate business processes and, in conjunction with IoT devices, many subsystems within vehicles. 9️⃣ Supply chain optimization and customer demand matching among the key machine learning algorithms Ford is developing today, as it increases the use of AI. 🔟 Ford also plans to leverage generative AI across the company, though that initiative remains in its infancy. Current use cases include using natural-language AI in call centres (sentiment analysis, information retrieval etc) and within the development ecosystem. #automotiveindustry #cloudcomputing #digitaltransformation https://lnkd.in/evbFttH5

Complete Guide of "Data Center Robotics" Download free sample PDF : (https://lnkd.in/de22xw6M) The Data Center Robotics Market, valued at USD 9 billion in 2022, is anticipated to witness a robust CAGR of over 20% from 2023 to 2032. This growth mirrors the broader industrial trend towards automation, as evidenced by the World Robotics 2023 report which recorded a 5% increase in industrial robot installations worldwide, totaling 553,052 units in factories. As businesses across sectors strive for efficiency, cost reduction, and operational enhancement, there's a growing inclination towards automation solutions, including within data centers. Integration of industrial robots has showcased promising results in tasks like maintenance, monitoring, and management, prompting increased interest in leveraging robotics for data center optimization. This escalating adoption of data center automation is poised to propel market expansion, driven by companies seeking streamlined and efficient data center operations. The data center robotics industry is witnessing a surge in the adoption of autonomous robots, heralding a transformative shift in operational paradigms. These robots have the capacity to autonomously execute diverse tasks ranging from server maintenance to equipment inspection and security monitoring, thereby diminishing the reliance on human intervention. This burgeoning trend is propelled by the pursuit of heightened operational efficiency, cost optimization, and enhanced uptime reliability. In the face of escalating complexity and expansion in data center infrastructures, autonomous robots emerge as a scalable remedy to oversee both routine and critical operations, thus positioning themselves as indispensable assets in contemporary data center management.

Capgemini is Navigating the Autonomous Telco Networks Odyssey: Challenges, Benefits, and Strategies The journey towards autonomous telco networks presents immense opportunities for operational efficiency, cost savings, and enhanced customer experiences. With comprehensive strategies, investment in talent development, and a commitment to innovation, telcos can navigate this transformative journey successfully, positioning themselves for future competitiveness and sustainability. • Autonomous Network Maturity Levels: The TM Forum's model defines six levels, ranging from manual management to fully autonomous networks. • Telco Aspirations: Majority (61%) aim for Level 3 autonomy by 2028, with only 1% targeting Level 5. Investment in autonomous networks expected to reach $87 million on average over the next five years. • Leadership and Strategy: Few organizations (15%) have dedicated leaders for autonomous networks, and only 17% have comprehensive transformation strategies. • Challenges: Cultural mindset, integration issues, data sovereignty, and lack of technological maturity pose significant barriers. • Benefits: Improved operational efficiency, OpEx savings, reduced energy consumption, and enhanced customer experience are among the tangible benefits. • Leading Telcos: Those with advanced autonomous network strategies outperform in OpEx reduction and time-to-market for new services. • Sustainability: Autonomous networks contribute to energy optimization and reduced carbon emissions, with potential for significant environmental impact. ACTION PLANS : • Low Adoption Rate: Majority of telcos lag behind in autonomous network adoption, with only a fraction having robust strategies and leadership. • Cultural Challenges: Resistance to change and traditional operational mindsets hinder progress, requiring significant efforts in workforce upskilling and mindset transformation. • Technological and Regulatory Hurdles: Integration issues, data sovereignty concerns, and regulatory challenges add complexity to the transformation journey. Aiman Ezzat | Paul Hermelin | Anirban Bose | Anil Agarwal | Jim Bailey | Andrea Falleni | Eric de Quatrebarbes | Eduardo Ferreira | Mahesh Pinge| Cristina Rodrigues | Ashwin Yardi | Niraj Parihar | Olivier Sevillia | Aarti Srivastava | Amy Dale | Guillaume Renaud | Kalpana Ramesh | Vijay Amballa| Nandkumar Bane | Parag Rede |Franco A. | Gagan Mehta | Adarsh Jayram| Andy Heppelle 🍁|Michael Hansen|Jacques Assaraf| Alfred Aue| Sandeep Arora | Yannick Martel| Isabelle Maillard| Karl Bjurstrom| Johannes Rutter | Camille Juguet| Finn Alkskog| Anne-Flore AGARD | Ehsan Dadrasnia, Dr.-Ing., MBA| Arun Santhanam| Sanjay Nand| Elhadji Mbodji,| https://lnkd.in/dv3UtXmb

Are you ready to explore the thrilling world of Industry 4.0? The Fourth Industrial Revolution, otherwise known as Industry 4.0, is characterized by the convergence of smart machines, autonomous robots, cloud and edge computing and big data. Our eBook, ‘Industry 4.0: How to Connect with Confidence,’ is a treasure trove of insights on how Digi #5G #edgecomputing solutions can help you create scalable, cost-effective systems to support your #digitaltransformation. Learn more in our eBook: https://hubs.la/Q02hSpGJ0

Nice summary from PT: Ford is undergoing a business transformation by implementing a cloud-first strategy, using GCP as a foundation for data-driven operations, developing software platforms, employing RPA software, and leveraging AI for supply chain optimization and customer demand matching.

How can edge computing be leveraged to enhance the real-time decision-making capabilities of robotic systems in critical applications? Edge computing can significantly enhance the real-time decision-making capabilities of robotic systems, particularly in critical applications. By processing data locally at the edge of the network rather than relying solely on centralized cloud servers, edge computing can reduce latency, increase reliability, and improve efficiency. Here’s how edge computing can be leveraged in various ways: 1. Reduced Latency and Faster Response Times Benefit: Immediate Processing: Edge computing allows data to be processed close to where it is generated, minimizing the time it takes for data to travel to a central server and back. This is crucial for robotic systems that require real-time decision-making, such as autonomous vehicles or industrial robots. 2. Enhanced Reliability and Robustness Benefit: Offline Capabilities: Edge computing can maintain functionality even if the connection to central servers is disrupted. This is important in environments where network connectivity might be unreliable or intermittent. Application: Industrial Robotics: In manufacturing, robots equipped with edge computing capabilities can continue to operate and perform complex tasks even if they temporarily lose connection to a central control system. 3. Improved Data Privacy and Security Benefit: Local Data Processing: By processing sensitive data locally, edge computing reduces the risk of data breaches that could occur during data transmission to centralized servers. This is crucial for applications involving sensitive or personal information. 4. Efficient Resource Utilization Benefit: Optimized Processing: Edge computing can offload certain computational tasks from central servers to local devices, reducing the need for extensive cloud resources and improving overall system efficiency. 5. Real-Time Analytics and Insights Benefit: Immediate Analysis: Edge computing facilitates real-time analytics by enabling local processing of data, which helps robotic systems make informed decisions without delay. 6. Scalability and Flexibility Benefit: Modular Deployment: Edge computing can be deployed in a modular fashion, allowing for scalable and flexible integration with various robotic systems and applications. Application: Agricultural Robotics: In precision agriculture, edge computing enables multiple robots and sensors to operate autonomously and collaboratively, scaling up or down based on the needs of the farm. 7. Reduced Bandwidth Usage and Costs Benefit: Efficient Data Handling: By processing data locally, edge computing reduces the amount of data that needs to be transmitted to central servers, leading to lower bandwidth usage and reduced costs.

Latest MCFM article, our annual tech trend radar: "What technology trends are shaping the mobility sector?" Technological innovation is transforming every aspect of the mobility sector, and more companies are beginning to investigate disruptive technologies. A McKinsey analysis identifies the trends to watch. More of our latest thinking on www.mckinsey.com/mcfm. #futuremobility #futureofmobility #automotive #genai #ai #connectivity #cloud #edge #web3 #immersiveexperience #machinelearning #trust #softwaredevelopment #quantumcomputing #appliedai

💲 The Cost and Gain of AI 🤖 Calculating the ROI of #ai and ML investments in manufacturing involves considering various factors, including: 🔸 Infrastructure Costs: This includes hardware, software, cloud services, and licensing fees required for AI deployment. 🔸  Data Processing and Management: The cost associated with processing, storing, and managing large volumes of data. 🔸 Personnel Costs: The human resources required for project management, data engineering, and data science roles. 🔸 Expected Savings: Anticipated cost savings from efficiency gains, reduced downtime, improved quality, and other operational improvements. #manufacturing #dataanalytics Hyundai Motor Company (현대자동차)