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HONG KONG SAR - Media OutReach - 22 March 2021 - The Hong Kong Productivity Council (HKPC), with the funding support of the Innovation and Technology Fund of the HKSAR Government, joined hands with the Hong Kong Society of Artificial Intelligence and Robotics to organise the Hong Kong Forum of Artificial Intelligence and Robotics (HKFAIR 2021) on 20 March. Through the presentations and panel discussion with the participation from experts, local enterprises can grasp the latest applications and development trends of Artificial Intelligence (AI) and robotics technology to help establish the relevant technology development roadmap and smart industries in Hong Kong and the Greater Bay Area (GBA). The opening ceremony of HKFAIR 2021 was officiated by Mr Paul Chan, Financial Secretary, and Mr Alfred Sit, Secretary for Innovation and Technology.

HKFAIR 2021 brought together leading experts in AI and information technology from Mainland China and Hong Kong, featuring six academy fellows including Professor Tan Tieniu, Member of the Chinese Academy of Sciences and Vice Minister of the Liaison Office of the Central People's Government in the HKSAR, who along with 18 experts and representatives from world renowned companies shared on the latest development and innovative applications of AI technology covering different sectors.

In the panel discussion, all speakers highlighted that "data is the foundation for AI technology". The epidemic has expedited digital transformation of enterprises, with more data being collected to support AI technology for precise personalised services to enhance customer experience and facilitate business expansion. Also for 'Industry 4.0', innovative technology, as a critical component of the smart factory, can significantly improve production process to bring about breakthrough for smart manufacturing. As the GBA opens up its market to more industries, Hong Kong enterprises are expected to possess large amount of data and application scenarios in the future, realising and facilitating the development of AI and robotics while generating vast opportunities.

Mr Willy Lin, Chairman of HKPC, said in his welcoming address, "HKPC strives to help Hong Kong businesses to achieve digital transformation with diversified R&D services, enabling enterprises to accelerate reindustrialisation with 'Industry 4.0' and 'Enterprise 4.0' so as to overcome adversity and can plan for the future. AI and robotics technology is one of the areas in the National Key Technologies Research and Development Program. Hong Kong not only possesses internationally-recognised R&D results but also plays the unique role as the 'super connector' in the development of GBA. All these will certainly expedite technology and industry upgrading. HKPC is also active in collaborating with local and overseas R&D organisations to promote the wider relevant applications in Hong Kong and GBA for the development of smart industries to create new impetus for the economy."

At the opening ceremony, Professor Yang Qiang, President of HKSAIR, said, "There is no need to explain the importance of AI and robotics technology as we are at the centre of the technology revolution era. HKSAIR aims to promote the successful local application of AI and robotics industry and serve as the bridge and catalyst between the academia and industry, and between Mainland China and Hong Kong development. We hope to leverage the forum to foster closer cooperation among distinguished professors and outstanding entrepreneurs in Hong Kong and GBA, conjuring up a local AI and robotics development roadmap to sow the seeds of prosperous development and contribute towards the synergy of government planning, scientific research, product development and industrial manufacturing."

One of the highlights of the forum was the summary presentation of the "Hong Kong AI and Robotics Technology and Economic Development Research Report". Compiled by HKPC and HKSAIR through thorough industry interviews and surveys, the report analyses the research and application level of AI and robotics in Hong Kong to assist business planning. The full report will be released in the second quarter of this year, with five accompanying webinars to elaborate on the report content in the realms of smart city, intelligent manufacturing, smart health, smart education and FinTech for enterprises to seize on the trends and look ahead. Those interested to learn more can visit the HKFAIR 2021 website: bit.ly/HKFAIR-2021 for more details and enrol.

HKPC is committed to strengthening the technology application of AI and robotics among local enterprises. Apart from organising seminars, it is also collaborating with the internationally-renowned RWTH Aachen Campus on a number of AI and robotics-related R&D projects to promote advanced manufacturing based on the relevant technology for the continuous and long-term development of Hong Kong. In addition, HKPC is offering relevant technology courses for the industries through the "Reindustrialisation and Technology Training Programme".

The Hong Kong Productivity Council (HKPC) is a multi-disciplinary organisation established by statute in 1967, to promote productivity excellence through integrated advanced technologies and innovative service offerings to support Hong Kong enterprises. HKPC is the champion and expert in facilitating Hong Kong's reindustrialisation empowered by i4.0 and e4.0 focusing on R&D, IoT, big data analytics, AI and Robotic technology development, digital manufacturing, etc., to help enterprises and industries upgrade their business performance, lower operating costs, increase productivity and enhance competitiveness.

The Council is a trusted partner with comprehensive innovative solutions for Hong Kong industries and enterprises, enabling them to achieve resources and productivity utilisation, effectiveness and cost reduction, and enhanced competitiveness in both local and international marketplace. It offers SMEs and startups immediate and timely assistance in coping with the ever-changing business environment, accompanying them on their innovation and transformation journey.

In addition, HKPC partners and collaborates with local industries and enterprises to develop applied technology solutions for value creation. It also benefits a variety of sectors through product innovation and technology transfer, with commercialisation of multiple market-driven patents and technologies, bringing enormous opportunities abound for licensing and technology transfer, both locally and internationally.

For more information, please visit HKPC's website: http://www.hkpc.org.

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Hong Kong Forum of Artificial Intelligence and Robotics Promotes Development of Smart Industries in Hong Kong and Greater Bay Area - IT News Online

With many individuals worrying about privacy as well as the possible dangers that may arise with societys reliance on computer technology,artificial intelligence (AI) is becoming particularly relevant.Artificial intelligencedescribes the ability that computers have developed to perform tasks that are commonly attributed to intelligent beings. From the Google search engine to the newest facial recognition software on cellphones, individuals interact daily with artificial intelligence. Hot topics in AI often revolve around the technologys risks and benefits, yet artificial intelligences ability to intersect a vast number of fields is rarely discussed. Why is this? The answer possibly lies in the lack of understanding of AIs true benefits as we focus too highly on its possible risks.

For example, the field of linguistics, which uses AI as part of its applications, is often overlooked in AI matters. While many associate linguists with polyglots, thestudy of linguisticsencapsulates the scientific study of speech perception, sounds, grammatical structure, and meaning. UTM Linguistic Professor Barend Beekhuizen is a linguist who advocates for the use of computational methods in the field, and uses these methods in his research.

Professor Barend Beekhuizenspecializes incomputational linguisticsresearch, a subfield of the linguistics department, where computational mechanisms are applied to linguistic data in order to extract particular information on language. It has allowed us to look at linguistics from a perspective that just wasnt possible before, says Professor Beekhuizen, explaining that the fast generation of linguistic data across languages was not accessible until the introduction of computer science methods into the field.

Growing up in the Netherlands,ProfessorBeekhuizens love for prose influenced him to study Dutch literature at Leiden University. However, he quickly realized that he was more interested in language: thats where my passion for linguistics began, Professor Beekhuizen says. While completing his masters in linguistics at the same university, he discovered that his questions about how language works were not easy to test, so he began using computational techniques to build models that made linguistic predictions. He completed his Ph.D. at Leiden University, focusing his studies on computational models of language acquisition in children. Professor Beekhuizen is also interested in studying the variation of word categorization between languages and what those variations in linguistic discourse tell us about individuals representation of the world. More of Professor Beekhuizens research interests includes computational models in the use of colour term acquisition, as well as work in lexical ambiguities.

The University of Toronto has long offered courses with computational linguistics integrated into the computer science department; however, only recently did the university hire computational linguists, such as Professor Beekhuizen. Although not a new field study, using computation methods as part of the linguistic field is a novel approach, leaving some traditional linguists hesitant in its use.Like any new method, there will be people who embrace it right away and people who are more skeptical, explains Professor Beekhuizen. Fortunately, the subfields accessibility is drawing more attention to the study and exposing more individuals to its utility. The field has embraced computational methods as part of what they want to give to future generations of students, says Professor Beekhuizen.

The interaction between artificial intelligence and linguistics is more prominent and practical than many believe due to linguistics being central to everyday tools such as speech recognition and search engines. Additionally, the field is increasingly important in medical and healthcare-related language processes, where computational linguistic mechanisms can be used to extract information from patient files by using keywords. Moreover, computational linguistics techniques have been adapted for use by marketing companies to receive feedback from consumers and better understand consumer needs.

More students are also showing interest in the field.Ive noticed students on [both] undergraduate and graduate levels are interested to learn about computational methods to supplement their tool kit in other linguistic fields, says Professor Beekhuizen. Hes had the opportunity to work with many of these students on research projects using artificial intelligence. Recently, a group of five undergraduate students completed a computational linguistics research project through U of Ts Jackman Scholars-in-Residence (SiR) program under the supervision of Professor Beekhuizen. The study investigated the context that certain translated words are used.

The English wordstrue, real, actual, and right were analyzed in the subtitles of online TedTalks. The subtitles, translated into a dozen of languages, were analyzed through computational methods to understand the context of their use, as well as make deductions on how each word is translated in other languages. Were currently working on a write-up of this project with the students to send to an academic journal, says Professor Beekhuizen. The initial research period was successfully conducted virtually last year.

To learn more about computational linguistics, students should consider takingProfessorBeekhuizenscourse, LIN340:Language and Computers. LIN340 introduces the field by examining issues concerning computational linguistics and societies, such as equity concerns in speech recognition software. The course also provides insight on how artificial intelligence aids in answering specific linguistic questions, such as detecting sentence structures and grammatical relationships.

Computational linguistics offers an innovative perspective to AI that will hopefully help us understand its advantages. The linguistics subfield also raises awareness and develops tools for people requiring accessibility services, such as those needing aid in navigating disability or language barriers.

More:
The use of artificial intelligence in linguistics - The Medium

This article was originally published here

NPJ Digit Med. 2021 Mar 19;4(1):54. doi: 10.1038/s41746-021-00423-6.

ABSTRACT

Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors.

PMID:33742085 | DOI:10.1038/s41746-021-00423-6

Read more:
The potential of artificial intelligence to improve patient safety: a scoping review - DocWire News

Mathematics Literacy is a key skill for Board Directors and CEO's to ensure Artificial Intelligence ... [+] foundations of excellence and ensure Duty of Care.

This blog is a continuation of theBuilding AI Leadership Brain Trust Blog Serieswhich targets board directors and CEOs toaccelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results.

In this blog series, I have identified forty skill domains in an AI Leadership Brain Trust Framework to guide board directors and CEOs to ensure they can develop and accelerate their investments in successful AI initiatives. You can see the full roster of the forty leadership Brain Trust skills in myfirst blog.

In the last two blogs, the focus has been on Mathematics Literacy, which is one of the ten technical skills to develop in building a strong foundation of AI Literacy in board directors and in CEOs to lead and govern AI effectively and efficiently.

The premise I have is that AI will underpin all business processes and practices and will be more than the new electricity in our organizations, rather it will be like the new oxygen, in every business process, every software system, every infrastructure, and eventually hard wired into every human. It is simply a matter of time - so boards that want to think beyond the next five years or the next 25 years must accelerate AI literacy and ensure their duty of care on corporate oversight starts to ask far more precise questions of their CEOs on how AI is positioned in their company operations, products and services to simply stay - relevant.

Technical Skills:

1.Research Methods Literacy

2.Agile Methods Literacy

3.User Centered Design Literacy

4.Data Analytics Literacy

5.Digital Literacy

6.Mathematics Literacy

7.Statistics Literacy

8.Sciences (Computing Science, Complexity Science, Physics) Literacy

9.Artificial Intelligence (AI) and Machine Learning (ML) Literacy

10.Sustainability Literacy

This is the last blog in the three part Mathematics Literacy blog series which blog one: defined mathematics literacy, and explored linear algebra concepts, one of the most important skills in advancing AI methods, blog two explained graph theory, a subset of Algebra, basic statistical and probability concepts which underlie diverse AI methods, in particular predictive analytics. This last blog in the series will discuss basic calculus concepts.

In the context of AI, the two most important concepts from calculus are gradient and gradient descent. On the other hand, f(x) points in the direction of steepest descent from x. To become skilled in AI, linear algebra is key to understanding most AI or machine learning methods (See prior blogs on linear algebra).

The two most important terms relevant to AI in calculus are understanding what a gradient is mathematically and appreciating that the machine learning algorithm, gradient descent, is one of the most well used machine learning methods.

1.) Gradient - gradientis a fancy word for derivative, or the rate of change of a function. It's a vector (a direction to move) that points in the direction of greatest increase of a function (intuition on why). Gradientgroups are all partial derivatives, the gradient is just the vector containing all the partial derivatives. In summary, a gradient is a vector-valued function that represents the slope of the tangent of the graph of the function, pointing the direction of the greatest rate of increase of the function. It is a derivative that indicates the incline or the slope of the cost function. In essence, it generalizes derivatives to scalar functions of several variables. Well, just like the first derivative of a function with one variable equals to zero in stationary points, the same goes for gradient for the functions with multiple variables (More definition insights on Gradient and a simple video defining gradient with mathematical context and as it is so important, here is another short video).

2.) Gradient Descent - is an optimization AI algorithm that's used when training a machine learningmodel. It's based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum. Notes: In mathematics, a real-valued function defined on an n-dimensional interval is called a convex if the line segment between any two points on the graph of the function lies above the graph between the two points. Equivalently, a function is convex if its epigraph is a convex set (Source: WikiEncyclopedia). This algorithm is one of the most used algorithms for solving machine learning optimization problems and is often used in deep learning methods, regression methods and if you want to understand one type of AI algorithm, this is the one term to understand. Two good videos that further explain gradient descent is here and here.

Summary

This three part Mathematics skill literacy blog series was written not to be a full representation of all the relevant concepts to appreciate more the language of AI, but more to illustrate that everyone can improve and learn mathematics at any time in ones life. Understanding graph theory, vectors, probabilities, are all germaine to the field of artificial intelligence.

As discussed in the second blog on mathematics literacy, artificial intelligences primary goal is to create an accurate AI model that gains confidence for human understanding and decision making. The AI models are prepared with the strategies and methods from various branches of mathematics. Mathematics is a discipline that explains data models and guides leaders to validate their business knowledge. Mathematics is a core skill to deepen the rigour of AI on understanding key concepts: like probability, correlation, causation to predict future outcomes, often much better and more accurately than humans. Behind all of the innovations and advances, mathematics is at the core.

Key Leadership Questions : Building Mathematical Literacy is Key to Artificial Intelligence ... [+] leadership Competency.

What questions can board directors and CEOs ask to ensure their companies have a mathematics literacy capability focus? (this list adds another five additional mathematic literacy questions concluding this three part blog series to accelerate board director and CEOs investing time to learn more rapidly to advance their AI literacy to help their companies modernize).

1.) Does your company have a mathematics literacy strategy integrated with your digital literacy and artificial intelligence programs?

2.) Is your company testing for digital and mathematical literacy for all employees to evaluate your overall risk in advancing into deeper analytics capabilities? (See my last weeks blog on digital literacy )

3.) Is your company testing for statistical literacy in any of your operational roles?

4.) Does your company know what your competitors are doing in advancing their mathematical and digital literacy strategies and how does your company compare?

5.) Is AI skill competency development integrated into your digital literacy strategy, where mathematics literacy is a foundational skill in digital enablements?

6.) Do you have the ability to scan skills across your talent base to know the depth of math skills, statistical skills, AI skills and mobilize the right talent to solve the right use case in real time?

7.) How skilled are your board directors in mathematics and AI to guide your companies foreward into AI enablements?

8.) Is your company discussing the importance of Mathematics Literacy in your corporate boardrooms?

9.) Are you testing your senior executives on their basic mathematics proficiency levels

10.) Are you investing in your overall workforce training on relevant mathematical concepts to ensure all talent can communicate with your AI and Data science professionals?

11.) Has your company identified coaching communities to develop stronger skills in mathematics and statistics?

12.) Has your company developed a knowledge or learning competency center so your employees can easily can find relevant content on mathematics and artificial intelligence concepts that can help them build relevant skills?

13.) Is mathematics literacy a core leadership skill and is part of your talent life-cycle (On-boarding and development practices)?

14.) Do your employees have the ability to search for internal experts on different mathematical or artificial concepts to access expertise, in house easily or externally with online coaching?

15.) Do you celebrate and recognize talent for building formalized skills in mathematics literacy, artificial intelligence and general digital literacy?

In closing this three part series, if you cannot communicate, lead and inspect in using the language of AI, how can you trust and be confident the AI experts directions and recommendations are accurate to risk your future investments. Board directors have a duty of care responsibility, so starting to appreciate the 40 skills domains required to advance a fully literature company with deeper AI skills and enabling competencies is key to modernize businesses. Core to AI is understanding mathematics, and with the declining skills in North America on mathematics competency, board directors and CEOs and educators must drive greater leadership and accountability or the longer term implications to our economic health will decline.

In closing, Albert Einstein said: "Do not worry too much about your difficulties inmathematics, I can assure you that mine are still greater. .. Puremathematicsis, in its way, the poetry of logical ideas. .. Not everything that counts can be counted.

Historical Perspective: Archimedesis known as the Father Of Mathematics. He lived between 287 BC 212 BC. Syracuse, the Greek island of Sicily was his birthplace. Give me but a firm spot on which to stand, and I shall move the earth , is one of his most famous quotes - and in the context of AI - it is giving humans the power to not just move earth, but all worlds, and things - which is perhaps why the late and visionary Dr. Steven Hawking warned leaders of the inherent longer term risks of AI.

To see the full AI Brain Trust Framework introduced in thefirst blog, reference here.

Note:

If you have any ideas, please do advise as I welcome your thoughts and perspectives.

Original post:
What Are Your Board Directors And CEO's Mathematics Literacy and Skills In Building AI Brain Trust? - Forbes

DUBLIN, March 19, 2021 /PRNewswire/ -- The "Artificial Intelligence in Supply Chain Management Market by Technology, Processes, Solutions, Management Function (Automation, Planning and Logistics, Inventory, Risk), Deployment Model, Business Type and Industry Verticals 2021 - 2026" report has been added to ResearchAndMarkets.com's offering.

This report provides detailed analysis and forecasts for AI in SCM by solution (Platforms, Software, and AI as a Service), solution components (Hardware, Software, Services), management function (Automation, Planning and Logistics, Inventory Management, Fleet Management, Freight Brokerage, Risk Management, and Dispute Resolution), AI technologies (Cognitive Computing, Computer Vision, Context-aware Computing, Natural Language Processing, and Machine Learning), and industry verticals (Aerospace, Automotive, Consumer Goods, Healthcare, Manufacturing, and others).

This is the broadest and detailed report of its type, providing analysis across a wide range of go-to-operational process considerations, such as the need for identity management and real-time location tracking, and market deployment considerations, such as AI type, technologies, platforms, connectivity, IoT integration, and deployment model including AI-as-a-Service (AIaaS). Each aspect evaluated includes forecasts from 2021 to 2026 such as AIaaS by revenue in China. It provides an analysis of AI in SCM globally, regionally, and by country including the top ten countries per region by market share.

The report provides an analysis of leading companies and solutions that are leveraging AI in their supply chains and those they manage on behalf of others, with an evaluation of key strengths and weaknesses of these solutions. It assesses AI in SCM by industry vertical and application such as material movement tracking and drug supply management in manufacturing and healthcare respectively. The report also provides a view into the future of AI in SCM including analysis of performance improvements such as optimization of revenues, supply chain satisfaction, and cost reduction.

Select Report Findings:

Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. Supply Chain Management (SCM) solutions are typically manifest in software architecture and systems that facilitate the flow of information among different functions within and between enterprise organizations.

Leading SCM solutions catalyze information sharing across organizational units and geographical locations, enabling decision-makers to have an enterprise-wide view of the information needed in a timely, reliable and consistent fashion. Various forms of Artificial Intelligence (AI) are being integrated into SCM solutions to improve everything from process automation to overall decision-making. This includes greater data visibility (static and real-time data) as well as related management information system effectiveness.

In addition to fully automated decision-making, AI systems are also leveraging various forms of cognitive computing to optimize the combined efforts of artificial and human intelligence. For example, AI in SCM is enabling improved supply chain automation through the use of virtual assistants, which are used both internally (within a given enterprise) as well as between supply chain members (e.g. customer-supplier chains). It is anticipated that virtual assistants in SCM will leverage an industry-specific knowledge database as well as company, department, and production-specific learning.

AI-enabled improvements in supply chain member satisfaction causes a positive feedback loop, leading to better overall SCM performance. One of the primary goals is to leverage AI to make supply chain improvements from production to consumption within product-related industries as well as create opportunities for supporting "servitization" of products in a cloud-based "as a service" model. AI will identify opportunities for supply chain members to have greater ownership of "outcomes as a service" and control of overall product/service experience and profitability.

With Internet of Things (IoT) technologies and solutions taking an ever-increasing role in SCM, the inclusion of AI algorithms and software-driven processes with IoT represents a very important opportunity to leverage the Artificial Intelligence of Things (AIoT) in supply chains. More specifically, AIoT solutions leverage the connectivity and communications power of IoT, along with the machine learning and decision-making capabilities of AI, as a means of optimizing SCM by way of data-driven managed services.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction2.1 Supply Chain Management2.1.1 Challenges2.1.2 Opportunities2.2 AI in SCM2.2.1 Key AI Technologies for SCM2.2.2 AI and Technology Integration

3.0 AI in SCM Challenges and Opportunities3.1 Market Dynamics3.1.1 Companies with Complex Supply Chains3.1.2 Logistics Management Companies3.1.3 SCM Software Solution Companies3.2 Technology and Solution Opportunities3.2.1 Leverage Artificial Intelligence (AI)3.2.1.1 Integrate AI with Existing Processes3.2.1.2 Integrate AI with Existing Systems3.2.2 Integrate AI with Internet of Things (IoT)3.2.2.1 Leverage AIoT Platforms, Software, and Services3.2.2.2 Leverage Data as a Service Providers3.3 Implementation Challenges3.3.1 Management Friction3.3.2 Legacy Processes and Procedures3.3.3 Outsource AI SCM Solution vs. Legacy Integration

4.0 Supply Chain Ecosystem Company Analysis4.1 Vendor Market Share4.2 Top Vendor Recent Developments4.3 3M4.4 Adidas4.5 Amazon4.6 Arvato SCM Solutions4.7 BASF4.8 Basware4.9 BMW4.10 C. H.Robinson4.11 Cainiao Network (Alibaba)4.12 Cisco Systems4.13 ClearMetal4.14 Coca-Cola Co.4.15 Colgate-Palmolive4.16 Coupa Software4.17 Descartes Systems Group4.18 Diageo4.19 E2open4.20 Epicor Software Corporation4.21 FedEx4.22 Fraight AI4.23 H&M4.24 HighJump4.25 Home Depot4.26 HP Inc.4.27 IBM4.28 Inditex4.29 Infor Global Solutions4.30 Intel4.31 JDA4.32 Johnson & Johnson4.33 Kimberly-Clark4.34 L'Oreal4.35 LLamasoft Inc.4.36 Logility4.37 Manhattan Associates4.38 Micron Technology4.39 Microsoft4.40 Nestle4.41 Nike4.42 Novo Nordisk4.43 NVidia4.44 Oracle4.45 PepsiCo4.46 Presenso4.47 Relex Solution4.48 Sage4.49 Samsung Electronics4.50 SAP4.51 Schneider Electric4.52 SCM Solutions Corp.4.53 Splice Machine4.54 Starbucks4.55 Teknowlogi4.56 Unilever4.57 Walmart4.58 Xilinx

5.0 AI in SCM Market Case Studies5.1 IBM Case Study with the Master Lock Company5.2 BASF: Supporting smarter supply chain operations with cognitive cloud technology5.3 Amazon Customer Retention Case Study5.4 BMW Employs AI for Logistics Processes5.5 Intelligent Revenue and Supply Chain Management5.6 AI-Powered Customer Experience5.7 Rolls Royce uses AI to safely transport its Cargo5.8 Robots deliver medicine, groceries and packages with AI5.9 Lineage Logistics Company Case Study

6.0 AI in SCM Market Analysis and Forecasts 2021 - 20266.1 AI in SCM Market 2021 - 20266.2 AI in SCM by Solution 2021 - 20266.2.1 Platforms6.2.2 Software6.2.3 AI as a Service6.3 AI in SCM by Solution Components 2021 - 20266.3.1 Hardware6.3.1.1 Non-IoT Device6.3.1.2 IoT Embedded Device6.3.1.2.1 Security Devices6.3.1.2.2 Surveillance Robots and Drone6.3.1.2.3 Networking Devices6.3.1.2.4 Smart Appliances6.3.1.2.5 Healthcare Device6.3.1.2.6 Smart Grid Devices6.3.1.2.7 In-Vehicle Devices6.3.1.2.8 Energy Management Device6.3.1.3 Components6.3.1.3.1 Wearable and Embedded Components6.3.1.3.1.1 Real-Time Location System (RTLS)6.3.1.3.1.2 Barcode6.3.1.3.1.3 Barcode Scanner6.3.1.3.1.4 Barcode Stickers6.3.1.3.1.5 RFID6.3.1.3.1.6 RFID Tags6.3.1.3.1.7 Sensor6.3.1.3.2 Processors6.3.2 Software6.3.3 Services6.3.3.1 Professional Services6.4 AI in SCM by Management Function 2021 - 20266.4.1 Automation6.4.2 Planning and Logistics6.4.3 Inventory Management6.4.4 Fleet Management6.4.5 Virtual Assistance6.4.6 Freight Brokerage6.4.7 Risk Management and Dispute Resolution6.5 AI in SCM by Technology 2021 - 20266.5.1 Cognitive Computing6.5.2 Computer Vision6.5.3 Context-aware Computing6.5.4 Natural Language Processing6.5.5 Predictive Analytics6.5.6 Machine Learning6.5.6.1 Reinforcement Learning6.5.6.2 Supervised Learning6.5.6.3 Unsupervised Learning6.5.6.4 Deep Learning6.6 AI in SCM by Industry Vertical 2021 - 20266.6.1 Aerospace and Government6.6.2 Automotive and Transportation6.6.3 Retail and Consumer Electronics6.6.4 Consumer Goods6.6.5 Healthcare6.6.6 Manufacturing6.6.7 Building and Construction6.6.8 Others6.7 AI in SCM by Deployment 2021 - 20266.7.1 Cloud Deployment6.8 AI in SCM by AI System 2021 - 20266.9 AI in SCM by AI Type 2021 - 20266.10 AI in SCM by Connectivity6.10.1 Non-Telecom Connectivity6.10.2 Telecom Connectivity6.10.3 Connectivity Standard6.10.4 Enterprise6.11 AI in SCM Market by IoT Edge Network 2021 - 20266.12 AI in SCM Analytics Market 2021 - 20266.13 AI in SCM Market by Intent Based Networking 2021 - 20266.14 AI in SCM Market by Virtualization 2021 - 20266.15 AI in SCM Market by 5G Network 2021 - 20266.16 AI in SCM Market by Blockchain Network 2021 - 20266.17 AI in SCM by Region 2021 - 20266.17.1 North America6.17.2 Asia Pacific6.17.3 Europe6.17.4 Middle East and Africa6.17.5 Latin America6.18 AI in SCM by Country6.18.1 Top Ten Country Market Share6.18.2 USA6.18.3 China6.18.4 Canada6.18.5 Mexico6.18.6 Japan6.18.7 UK6.18.8 Germany6.18.9 South Korea6.18.10 France6.18.11 Russia

7.0 Summary and Recommendations

For more information about this report visit https://www.researchandmarkets.com/r/bxs2vn

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

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Worldwide Artificial Intelligence in Supply Chain Management Industry to 2026 - Featuring 3M, Adidas and Amazon - PRNewswire