A study on demand forecasting using a collective intelligence mechanism

Akihiro Nakatsuka, Hiroaki Matsukawa

Research output: Contribution to journalArticlepeer-review


Traditional demand forecasting methods are categorized as scientific methods (e.g., time series analysis or regression analysis) or methods based on experience and tacit knowledge (e.g., delphi method or market research). Recently, research that combines these forecasting methods has become a hot topic and categorized as a demand forecasting method based on the prediction market. It is known that the prediction market was able to accurately forecast the vote ratio for the US presidential election. In the field of supply chain management, the research is applied to forecast the future demand of products. In this study, we propose a demand forecasting method that uses a voting system based on a collective intelligence mechanism. We examine forecasting accuracy using real business data from a five-month period. According to statistical tests, we show that the forecasting method we propose performs more accurately than the existing method used in our company.

Original languageEnglish
Pages (from-to)143-152
Number of pages10
JournalJournal of Japan Industrial Management Association
Issue number3
Publication statusPublished - 2018 Jan 1


  • Collective intelligence mechanism
  • Demand forecasting
  • Diversity
  • Prediction market
  • Voting system

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics


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