シラバス Syllabus

授業名 Analytic Frameworks for Management
Course Title Analytic Frameworks for Management
担当教員 Instructor Name 笹沼 克信(Katsunobu Sasanuma)
コード Couse Code GLP259_G22N
授業形態 Class Type 講義 Regular course
授業形式 Class Format Hybrid
単位 Credits 2
言語 Language EN
科目区分 Course Category 応用科目200系 / Applied
学位 Degree MSc in Management
開講情報 Terms / Location 2022 GSM Nagoya Fall

授業の概要 Course Overview

This course teaches various analytic techniques, which will be a key tool to deal with uncertainty and big data. The course also aims to educate innovative leaders who can contribute to the creation of new business.
The course emphasizes the understanding of the concepts of important analytical techniques, not the derivations of formulas. Through active learning teaching in this course, students will develop an ability to understand key concepts and working knowledge of various analytic techniques. Students also cooperate with their classmates and discuss their ideas in groups.
Students taking this course will acquire an ability to analyze performance of systems quantitatively, gain insights into performance characteristics, recognize various trade-offs in operational decisions, and apply concepts and analytical methods to firms/organizations to improve their operational performances.

本授業の該当ラーニングゴール Learning Goals


LG1 Critical Thinking
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG7 Global Perspective (GLP)

受講後得られる具体的スキルや知識 Learning Outcomes

Upon successful completion of this course, students will understand the key concepts of various analytic techniques under uncertainty on the topics such as
1. Optimization
2. Forecasting
3. Decision Theory
4. Statistical Control
5. Inventory Management
6. Queueing
7. Simulation

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 40 %
参加者中心型 Participant-Centered Learning ケースメソッド Case Method 60 %
フィールドメソッド Field Method 0 %
合計 Total 100 %

学習方法、レポート、課題に対するフィードバック方法 Course Approach, Report, Feedback methods

The goal of the course is to develop your ability to solve hard problems that may not have solutions. Do not memorize formulas. I do not want you to be a second-rate computer, which runs slow and can find answers only when formulas are available. When solving real-world problems, understanding the fundamental concept is much more important than memorizing formulas.

Cases and/or reading materials are provided before each class; students are expected to read them, submit assignments (if any), and prepare well for the discussions before coming to class. Students are expected to spend at least around three hours to prepare for each class.

Feedback on students’ performances (e.g. in-class discussions, reports, etc.) will be provided during class, office hours, and/or through Google Classroom.

Note: The case list is tentative and subject to change.

授業スケジュール Course Schedule


Introduction, Math Review, Optimization

Day 1-1: Why IT Fumbles Analytics (Harvard Business Review: https://hbr.org/2013/01/why-it-fumbles-analytics) (Article)

Day 1-2: DHL Supply Chain (W12888-PDF-ENG)


Forecasting, Decision Theory

Day 2-1: Wilkins, A Zurn Company: Demand Forecasting (906D06-PDF-ENG)

Day 2-2: Behavioral Time Discounting (UV7432-PDF-ENG) (Technical Note)


Statistical Control, Inventory Management

Day 3-1: Six Sigma Quality at Flyrock Tires (KEL028-PDF-ENG)

Day 3-2: Managing Inventories: The Newsvendor Model (UV6026-PDF-ENG) (Technical Note)


Queueing, Simulation

Day 4-1: Tirumala Tirupati: Wait a Moment (UV6959-PDF-ENG)

Day 4-2: Supply Chain Management Simulation: Beer Game (Simulation)




成績評価方法 Evaluation Criteria

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 60 %
クラス貢献度合計 Class Contribution Total 60 %
予習レポート Preparation Report 10 %
小テスト Quizzes / Tests 30 %
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 0 %
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

Preparation report (10% weight for the final grade): Submission of the preparation report to Google Classroom is required except for the first day of the course.

In-class discussion (60% weight for the final grade): Active participation in the class discussion is required.

Mini in-class test (30% weight for the final grade): Students should take a mini in-class test to confirm an understanding of the concepts they learn in each class. These mini tests are non-cumulative.

使用ケース一覧 List of Cases


教科書 Textbook

  • 配布資料

参考文献・資料 Additional Readings and Resource

Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics
Cliff Ragsdale
Cengage Learning
ISBN13: 978-0357132098

Additional reading materials will be provided or assigned through Google Classroom.

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

First year to teach this course. I will make adjustments to the syllabus based on the progress of the course and the feedback from students during the term. Students are also encouraged to utilize the resources at the NUCB library.

担当教員のプロフィール About the Instructor 


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

  • (2022) A marginal analysis framework to incorporate the externality effect of ordering perishables. Operations Research Perspectives (Elsevier) 9(100230): 2214-7160
  • (2022) Controlling arrival and service rates to reduce sensitivity of queueing systems with customer abandonment. Results in Control and Optimization (Elsevier) 6(100089): 2666-7207
  • (2022) An opaque selling scheme to reduce shortage and wastage in perishable inventory systems. Operations Research Perspectives (Elsevier) 9(100220): 2214-7160
  • (2021) Evaluating the Dynamic Impact of Theater Performances and Sports Events on Parking Demand in Downtown Pittsburgh. Smart Cities 4(4): 2624-6511
  • (2021) Asymptotic Analysis for Systems with Deferred Abandonment. Mathematics 9(18): 2227-7390