Strategic Information and Reverse Logistics Economics Laboratory (SIRE Lab)
Research Field
Dr. Netnapha is an assistant professor in the International Master Program in Smart Manufacturing and Applied Information Science at the College of Management, National Chin-Yi University of Technology. She specializes in Supply Chain Management, Platform Economy, Operation-Finance Interface, and Economics of Information Systems. Dr. Netnapha holds a Ph.D. in Industrial and Information Management from National Cheng Kung University, Taiwan, and has a diverse professional background in academia and industry.
SIRE Lab – where strategy meets sustainability and technology meets economics.
Our lab focuses on interdisciplinary research at the intersection of Economics of Information Systems, Supply Chain Management, Sustainable Production, Reverse Logistics, Strategic Competition, and Decision Science.
We aim to address complex challenges in digital and physical supply networks by integrating analytical models, game theory, and data-driven decision-making tools. Our goal is to provide actionable insights for firms, platforms, and policymakers navigating the evolving digital economy.
Key Research Themes:
-Economic analysis of digital platforms and information flow in supply chains
-Sustainable production and reverse logistics models for circular economy systems
-Strategic competition and cooperation among firms in technology-driven markets
-Optimization and simulation techniques in decision science for real-world applications
At SIRE Lab, we collaborate across disciplines and borders to develop innovative, impactful, and sustainable solutions.
- Economics of information systems
- Supply chain management
- Sustainable production and reverse logistics
- Strategic competition
- Decision science
Publications
- Wu, C. H., Chamnisampan, N.*, & Sin, L. (2025). Freemium vs. Deterrence: Optimizing revenue in the face of piracy competition. Journal of Business Research
- Chamnisampan, N. (2025). Security Investment and Pricing Decisions in Competitive Software Markets: Bug Bounty and In-House Strategies. Systems.
- Huang, ML*, Chamnisampan, N., & Ke, YR (2025). A deep learning model integrating EEMD and GRU for air quality index forecasting. Atmosphere.
- Chamnisampan, N., & Wu, C. H.* (2024). Frenemies: Exploring interfirm credit between an incumbent and a capital‐constrained startup. International Transactions in Operational Research.
- Wu, C. H., Chamnisampan, N.*, & Liao, Y. T. (2024). Perpetual or Subscription: Incumbent sales strategy with strategic consumers and social learning. Procedia Computer Science.
- Wu, C. H.*, & Chamnisampan, N. (2021). Platform entry and homing as competitive strategies under cross-sided network effects. Decision Support Systems.
Achievements
- 2025, NSTC 114-2637-E-167-016, Combining motion economics with AI-powered human activity recognition technology to improve lean performance for SMEs, Moderator
- 2025, NSTC 114-2927-I-150-001, Smart Manufacturing and Sustainable Development in Taiwan and Asia—AI-Driven Low-Carbon Smart Manufacturing and Industrial Transformation, Co-Moderator
- 2025, MOE-114-TPREE-0043-003Y1, Research on Data-Driven Learning and Innovative Teaching Strategies in Smart Manufacturing and Information Applications, Principal Investigator
- 2024, NSTC 113-2222-E-167-005, Balancing the trade-off between privacy and application compatibility in the competition among platform providers, Moderator
- 2022, Grand Review for Outstanding Research
- 2022, International Symposium on Semiconductor Manufacturing Intelligence, Best Paper award.
- 2024, Ph.D. in Industrial and Information Management, National Cheng Kung University, Taiwan
- 2018, M.Sc. in Industrial Engineering and Management, National Formosa University, Taiwan
- 2016, M.E. in Business Engineering, The University of Thai Chamber of Commerce, Thailand
- 2013, B.Sc. in Maritime Industry Management, Burapha University, Thailand
Job Description
This position offers hands-on experience in applying optimization techniques to address carbon reduction challenges in supply chains and platform ecosystems. You will learn how to formulate models that balance cost efficiency with environmental impact, conduct scenario analysis, and translate results into practical recommendations. Our lab uses Mathematica (Wolfram) as the primary computational tool. High-performing interns may have the opportunity to co-author conference or journal papers.
Preferred Intern Educational Level
- Graduate students or Senior undergraduate students in IE or ME.
- Students who are intereted in pursuing a Master's or Ph.D. degree after completing the internships.
Skill sets or Qualities
- Knowledge of operations research methods (linear programming, multi-objective optimization, network models, etc.)
- Experience with Mathematica/Wolfram or willingness to learn
- Interest in sustainability, carbon footprint, or green supply chain topics
- Able to read and summarize English academic papers
- Strong analytical and problem-solving skills
- Detail-oriented when handling data and building models
- Self-motivated with good time management