シラバス Syllabus

授業名 Business Analytics
Course Title Business Analytics
担当教員 Instructor Name 笹沼　克信（Katsunobu Sasanuma）
科目ナンバリングコード Course Numbering Code
授業形態 Class Type 講義 Regular course
授業形式 Class Format On Campus
単位 Credits 2
言語 Language EN
科目区分 Course Category 応用科目200系 / Applied
学位 Degree MBA
開講情報 Terms / Location 2026 GSM Nagoya Spring
コード Couse Code GLP125_G26N

授業の概要　Course Overview

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

This course focuses on how business leaders design, evaluate, and govern analytics and data and AI-driven initiatives across organizations and markets.
Through comparative case discussions, students will examine how analytics and data and AI systems reshape work, organizational capabilities, platform governance, and global competition.

授業の目的（意義） / Importance of this course

As organizations increasingly rely on analytics and AI to support strategic and operational decisions, future business leaders must be equipped to understand, evaluate, and responsibly apply data-driven solutions. This course develops managerial insight into how business analytics creates value and risk across industries, preparing students to lead in a data-intensive world.

学修到達目標 / Achievement Goal


By the end of this course, students will be able to critically analyze data-driven business cases, assess analytics and AI-based strategies, and formulate managerial recommendations that account for organizational, ethical, and societal implications.

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
LG2 Diversity Awareness
LG3 Ethical Decision Making
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


By the end of the course, students will be able to:

- Design analytics and AI initiatives as part of organizational and strategic systems
- Evaluate trade-offs between value creation, organizational readiness, and governance risks
- Compare analytics strategies across industries and institutional environments
- Formulate leadership-level recommendations for AI-driven transformation

SDGsとの関連性　Relevance to Sustainable Development Goals

Goal 4 質の高い教育をみんなに（Quality Education）

教育手法　Teaching Method

教育手法 Teaching Method % of Course Time
インプット型 Traditional 20 %
参加者中心型 Participant-Centered Learning ケースメソッド Case Method 80 %
フィールドメソッド Field Method 0 %
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

授業スケジュール　Course Schedule

第1日（Day1）

Analytics and Organizational Transformation

●使用するケース
- Drishti Technologies Inc.: Managing Operations through Computer Vision, AI, and Video Analytics
- Moderna: Democratizing Artificial Intelligence

第2日（Day2）

Creating Business Value with Analytics

●使用するケース
- Coursera’s Foray into GenAI
- AI, creativity, and the future of work-insights from james cameron's avatar

第3日（Day3）

Analytics in Digital Markets

●使用するケース
- Safeguarding Creativity in e-Commerce-Alibaba's Original Design Protection Program
- Alibaba’s Innovation-Driven Approach to Intellectual Property Rights Governance

第4日（Day4）

Governance and Risk in Analytics Systems

●使用するケース
- Meta's Quagmire: AI Algorithms and Social Media's Legal-Ethical Maze

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 70 %
クラス貢献度合計 Class Contribution Total 70 %
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 0 %
最終レポート Final Report 0 %
期末試験 Final Exam 30 %
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

使用ケース一覧　List of Cases

    ケースは使用しません。

配布教材と教室における電子機器の利用マナーについて Guidelines for Classroom Technology and Proper Use of Course Materials

  1. ケースメソッド教育の中核は、積極的な参加と知識の共有です。この教育を支えるため参加者は授業中の電子機器（例：スマートフォン、ノートパソコン）の使用を制限するよう求められます。許可を得た場合でも、教室内では電子機器は、ケース討議に資する目的でのみ使用してください。授業中は、たとえケース討議に関連していても、検索エンジンや生成AIの使用は避けて下さい。
  2. 配布教材（ケースを含む）は指定された授業への参加以外の目的で利用しないで下さい。著者の権利、著作権、特定情報の機密性を保護するため、許可なく教材を個人や組織（生成AI を含む）に提供することはできません。このルールは、印刷物・電子教材のいずれにも適用されます。
  1. Active participation and shared learning is at the core of the case method learning.Participants are asked to limit their use of electronic devices (e.g., laptops, smartphones) during classroom sessions in support of this model. Even with permission granted, devices should only be used in the classroom in service to the case discussion. Online searches and generative AI tools, even if related to the case discussion, are discouraged while class is in session.
  2. Students are prohibited from using the course materials (including cases) distributed by the university for any purpose other than participation in the designated class.Students must not input, process or test course materials with any artificial intelligence (AI) tools, bots, software, or platforms without the author’s permission. These actions violate the terms of use for the course materials and may also constitute copyright infringement.

教科書　Textbook

  • 配布資料

参考文献・資料　Additional Readings and Resource

There is no set textbook for this course, but students can refer to an open-source textbook by the following weblink:

- Social Science Research: Principles, Methods, and Practices
https://open.umn.edu/opentextbooks/textbooks/social-science-research-principles-methods-and-practices

- Introductory Statistics
https://open.umn.edu/opentextbooks/textbooks/introductory-statistics

- Introduction to Data Science Using Python
https://open.umn.edu/opentextbooks/textbooks/introduction-to-data-science-using-python

- An Introduction to Data Science and AI for the nontechnical person
https://hbsp.harvard.edu/search?q=An+Introduction+to+Data+Science+and+AI+for+the+nontechnical+person&enableQuerySyntax=true&aq=%40source%3D%28product_metadata%2C+he_bundles%29&activeTab=products&searchLocation=header&action=

Additional readings and videos will be distributed and shown in class.

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　

東京大学教養学部基礎科学科卒業後、東京大学大学院理学系研究科相関理化学修了（物性物理学専攻）。東芝研究開発センター（研究員）、アルメック（コンサルタント）を経てハーバード大学とマサチューセッツ工科大学において修士課程（公共政策及びオペレーションズリサーチ専攻）を修了した後、カーネギーメロン大学においてPh.D.取得（オペレーションズマネージメント専攻）。その後ニューヨーク州立大学ストーニーブルック校アシスタントプロフェッサーを経て、現職。現在、名古屋商科大学経営学部教授、及び東北大学大学院経済学研究科客員教授。専門は確率モデル、待ち行列理論、在庫管理、交通システム、データアナリティクス等。これまでオペレーションズリサーチ、オペレーションズマネージメント、ビジネスアナリティクス等の各授業を担当し、研究成果は国際学会及び国際ジャーナルに発表されている。

Katsunobu Sasanuma is a professor at NUCB, Nagoya University of Commerce and Business. He also holds a visiting professor position at the Graduate School of Economics and Management at Tohoku University. Prior to joining the faculty at NUCB, Dr. Sasanuma was an assistant professor at College of Business at Stony Brook University, State University of New York.

His research areas include stochastic modeling, queueing theory, inventory management, transportation systems, and data analytics. He possesses an interdisciplinary background with a decade of accumulated consulting, business, and engineering experience including an R&D work at Toshiba focusing on LEDs and laser diodes. He has taught classes in Operations Research/Operations Management/Business Analytics. His work has been presented at various conferences and has appeared in professional journals.

BA and MS, University of Tokyo (1990 and 1992, resp.)
MPA, Harvard University (2005)
MS in Operations Research and Technlogy&Policy, MIT (2009)
PhD in Public Policy and Management (Operations Management), Carnegie Mellon University (2015)

Refereed Articles

  • (2025) Validity of the Case Method in Undergraduate Legal Education: An Empirical Study at a Japanese University. North East Journal of Legal Studies
  • (2025) Can Harvard’s teaching style thrive in Japan’s classrooms?. The Academic
  • (2024) Acquisition of Knowledge and Meta-Skills through the Case Method in Politics and Law Classrooms: New Empirical Insight from Japan. Journal of Political Science Education
  • (2024) Evaluating the Effectiveness of Recommendation Engines on Customer Experience Across Product Categories. International Journal of Technology and Human Interaction (IJTHI) 1548-3908
  • (2022) A marginal analysis framework to incorporate the externality effect of ordering perishables. Operations Research Perspectives (Elsevier) 9(100230): 2214-7160

Refereed Proceedings

  • (2024). Analytical approach to solving continuous-time hidden Markov models. The 2024 Fall National Conference of Operations Research Society of Japan .Operations Research Society of Japan. 1. 2. Nanzan University






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