シラバス Syllabus

授業名 Design Thinking for Big Data & AI
Course Title Design Thinking for Big Data & AI
担当教員 Instructor Name Giulio Toscani
コード Couse Code GLP227_G21N
授業形態 Class Type 講義 Regular course
授業形式 Class Format Live Virtual
単位 Credits 2
言語 Language EN
科目区分 Course Category 基礎科目100系 / Basic
学位 Degree MBA
開講情報 Terms / Location 2021 GSM Nagoya Fall

授業の概要 Course Overview

Misson Statementとの関係性 / Connection to our Mission Statement

Please note that, as this intensive 3-day course will be delivered virtually via an online platform, the start and end times have been adjusted as follows:

Day 1 (10:20 group session) 11:00 to 20:00
Day 2 (10:20 group session) 11:00 to 20:00
Day 3 (10:20 group session) 11:00 to 19:50

Aspiring and established leaders in any industry must not just analyse data and use technology but understand them, expand them, and incorporate them into strategy of their organizations in order to drive innovation and organizational performance.

授業の目的(意義) / Importance of this course

What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of Big Data & Artificial Intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the business opportunities generated from these applications, practice implementing some of these systems and understand how the world and the business is changing. Specific topics include the intelligence cycle, disruptive innovation and, of course, the basics of machine learning, chatbots and robots. The main goal of the course is to equip you with the tools to tackle new opportunities offered by Big Data/AI you might encounter in life, by applying Disruptive Innovation, a methodology based on Design Thinking.

到達目標 / Achievement Goal


Objective: How Big Data and AI will give an edge over competitors. What are your data and what technologies will better connect to your customers and to learn what they expect from you by applying Disruptive innovation methodology.


本授業の該当ラーニングゴール Learning Goals

*本学の教育ミッションを具現化する形で設定されています。

LG1 Critical Thinking
LG4 Effective Communication
LG5 Executive Leadership (EMBA)
LG6 Innovative Leadership (MBA)
LG7 Global Perspective (GLP)

受講後得られる具体的スキルや知識 Learning Outcomes


• Make strategic and confident decisions using the best data and AI technology
• Learn a valuable business methodology that helps you pinpoint weaknesses and discover new opportunities
• Earn higher profits by better understanding the future of your business, your processes and your customers

SDGsとの関連性 Relevance to Sustainable Development Goals

Goal 9 産業と技術革新の基盤をつくろう(Industry, Innovation and Infrastructure)

教育手法 Teaching Method

教育手法 Teaching Method % of Course Time
インプット型 Traditional 50 %
参加者中心型 Participant-Centered Learning ケースメソッド Case Method 30 %
フィールドメソッド Field Method 20 %
合計 Total 100 %

事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods

[Teaching Methods]

Traditional - 50%

Participant-Centered Learning 50%



Total - 100%

Case    :All the cases studied
Assignment :Individual assignment
Deadline   :In the afternoon of the last day.
Submission method:Submit in hard copy to the Professor

授業スケジュール Course Schedule

第1日(Day1)

AI and Big Data Now - Why This Time it’s Different
Artificial intelligence (AI) and Big Data are rapidly emerging as the most important and transformative technology of our time. Recent advances, particularly in machine learning - a computer’s ability to improve its performance without human instruction - have led to a rapid proliferation of new applications that are changing the game for companies in almost all industries.



●使用するケース
Case 1 Bytedance
Case 2 Mcdonald’s can a behemoth lead in the era of artificial intelligence?
Disruptive Innovation Workshop

第2日(Day2)

Consolidating Your AI/Big Data Strategy
A growing proportion of human activities such as social interactions, entertainment, shopping, and gathering information are now mediated by digital devices and services. Such digitally mediated activities can be easily recorded, offering an unprecedented opportunity to study and measure intimate psycho-demographic traits using actual — rather than self-reported — behavior. Such Big Data assessment has a number of advantages: it does not require participants’ active involvement; it can be easily and inexpensively applied to large populations; and it is relatively immune to cheating or misrepresentation. The question is: What Data and Technology can push forward the company business?


●使用するケース
Case 3 Sensetime group limited: business model
Case 4 GE Digital: Racing to Lead Industry 4.0

第3日(Day3)

The Future of business and work
The only thing predictable about the future of business and work is that there will be lots of change. One day you read that there is a looming labor shortage as the population ages, the next you read that mass unemployment is right around the corner due to the advent of robots and other artificial intelligence. In this session, we will look at trends in the market and the future of work and business. We will consider how demographic changes present business and labor market opportunities, as well as challenges.


●使用するケース
Case 5 Nedbank group: leadership and adaptive space for digital innovation
Case 6 The future of the fashion industry in a post-covid-19 world

•Individual Final exam

第4日(Day4)



第5日(Day5)



第6日(Day6)



第7日(Day7)



成績評価方法 Evaluation Criteria

*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
講師用内規準拠 Method of Assessment Weights
コールドコール Cold Call 0 %
授業内での挙手発言 Class Contribution 40 %
クラス貢献度合計 Class Contribution Total 40 %
予習レポート Preparation Report 0 %
小テスト Quizzes / Tests 0 %
シミュレーション成績 Simulation 0 %
ケース試験 Case Exam 20 %
最終レポート Final Report 0 %
期末試験 Final Exam 40 %
参加者による相互評価 Peer Assessment 0 %
合計 Total 100 %

評価の留意事項 Notes on Evaluation Criteria

Note on Grading
Participants will be graded according to their voluntary participation during case discussion and professors session. Also cold call will be, eventually, used for participants who do not participate, to test their preparation. Final exam will represent 40% of the weight.

使用ケース一覧 List of Cases

    ケースは使用しません。

教科書 Textbook

  • L. Williams「Disrupt: Think the unthinkable to spark transformation in your business」FT Press(2015)

参考文献・資料 Additional Readings and Resource

Recommended readings for the future
Davenport, T. H., & Kirby, J. (2016). Only humans need apply: Winners and losers in the age of smart machines. New York, NY: Harper Business.
Malone, T. W. (2018). Superminds: The surprising power of people and computers thinking together. Little, Brown.
Daugherty, P. R., & Wilson, H. J. (2018). Human+machine: reimagining work in the age of AI. Harvard Business Press.
Harari, Y. N. (2019). Lessons for the 21st Century. Spiegel & Grau.
Crawford K. Atlas of AI: The Real Worlds of Artificial Intelligence (2021). Yale University Press.

授業調査に対するコメント Comment on Course Evaluation

-

担当教員のプロフィール About the Instructor 


An expert on the subjects of Leadership and Disruptive Innovation by Big Data and Artificial Intelligence, Prof. Toscani caters to the board of companies, academic and not-for-profit institutions, like CCL (Belgium), SDA Bocconi (Italy), Telefonica (Spain, Argentina, Brazil, Guatemala), Deppon Logistics (China), Navozyme (Singapore), Nike and Megafon (Russia), Parexcelence (India).
Prof. Toscani started his career as a patent attorney for chemical plants in Lugano, Switzerland. He also has an MBA, and a PhD in Management from the Royal Institute of Technology, Stockholm, and is involved in research projects on Machine Learning and CEO mindset with IESE Business School. He has also published in leading publications like Marketing Intelligence and Planning, Journal of Non-Profit and Public Sector Marketing etc.
In addition to holding the extensive management and teaching experience, Prof. Toscani also presided over the Global Management Programme at Universitas Telefonica, co-managed and taught with IESE Business School. He is currently the Academic Director and professor for the Programme in Artificial Intelligence for Business Strategy in ESADE Business School, Barcelona and Madrid, Spain.
His vast experience on Big Data/AI springs from his work on location-system behavioural analysis and time perception project at Telefonica. He has also participated in the programme on AI for business at the Massachusetts Institute of Technology, and is currently setting a start-up delivering Artificial Intelligence services.
He plays the side flute in a classical music orchestra, is a yoga teacher, a vipassana meditator and an ultra-trail runner with personal record of 120 km. Based out of Barcelona, he has worked, visited or lived in over 100 countries, most recently having cycled solo halfway across the Black Sea and Caucasus, published in his youtube channel.

(実務経験 Work experience)


Professor and Advisor
• ESADE Business and Law School, Barcelona, Spain. Adjunct Professor
Law School degree: Digital Technologies Impact
MIBA (Master in Business Analytics): Human + Machine strategy
Executive education: Remote teams; Digital Entrepreneurship; Leadership in AI.
• Pacifico Business School, Lima, Perú. Adjunct Professor
• University of Bath, Bath, UK. Visiting Professor. MBA Programme: Contemporary issues at the time of Big Data/ Artificial Intelligence
• NUCB Nagoya University of Commerce and Business, Nagoya, Japan. Visiting Professor. MBA programme: Disruption by Big Data|Artificial Intelligence
• Ranepa Business School, Moscow, Russia. Visiting Professor. Global MBA: Digital Entrepreneurship
• Politecnico, Milan, Italy. Visiting Professor. Master in Strategic Design: Design Thinking in AI
• Navozyme, Singapore. Advisory Board.
• Programme Director Universitas Telefónica. Barcelona, Spain.
Direction and Teaching of the Programmes for Telefonica Global Executives

Refereed Articles

  • (2023) The effects of the COVID-19 pandemic for AI practitioners: the decrease in tacit knowledge sharing. Journal of Knowledge Management
  • (2023) Cómo el trabajo en remoto está reduciendo el conocimiento implícito. Harvard Deusto Business Review
  • (2022) Los mundos virtuales, un nuevo reto para la propiedad industrial de las marcas. Harvard Deusto Business Review
  • (2022) The role of reciprocity and reputation in service relationships with Arts organisations. Journal of Services Marketing
  • (2018) Arts Sponsorship Versus Sports Sponsorship: Which Is Better for Marketing Strategy?. Journal of Nonprofit & Public Sector Marketing






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