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Investment cost optimization for industrial project portfolios using technology mining

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  • Azimi, Sasan
  • Rahmani, Rouhollah
  • Fateh-rad, Mahdi

Abstract

Large technology-intensive enterprises and companies face a constant challenge: How can a set of selected high-tech projects get done in a manner that would minimize the total cost across all projects? In majority of cases, projects are assumed independent, leading to a separate cost evaluation. This assumption often does not hold for real-world project portfolios, frequently sharing overlapping technologies. In this paper, we show how the order of the execution of the projects can directly affect the total cost of the portfolio, due to shared dependencies. Modeling the problems in this area can be achieved by combining two main fields: graph theory and technology mining. A novel method is introduced to create an infrastructure to perceive the dependencies of projects, estimate the cost and optimize the investment cost of the portfolio by considering the priority. This infrastructure utilizes a technology association graph model and then the graph is enhanced in several stages to compute the optimal prioritized order of execution of the projects. We excavate a sub-graph from the primary graph and simplify it to one of the typical models. We show mathematically how this prioritized list minimizes the investment cost in compare with regular method at which costs are calculated separately and ordered by lowest to highest. The proposed model can use other metrics for prioritization such as the ‘value’ that each project can deliver to the organization.

Suggested Citation

  • Azimi, Sasan & Rahmani, Rouhollah & Fateh-rad, Mahdi, 2019. "Investment cost optimization for industrial project portfolios using technology mining," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 243-253.
    • Handle: RePEc:eee:tefoso:v:138:y:2019:i:c:p:243-253
    DOI: 10.1016/j.techfore.2018.09.011
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    References listed on IDEAS

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    Cited by:

    1. Tian, Yuanyuan & Bai, Libiao & Wei, Lan & Zheng, Kanyin & Zhou, Xinyu, 2022. "Modeling for project portfolio benefit prediction via a GA-BP neural network," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    2. Wang, Yongli & Zhou, Minhan & Zhang, Fuli & Zhang, Yuli & Ma, Yuze & Dong, Huanran & Zhang, Danyang & Liu, Lin, 2021. "Chinese grid investment based on transmission and distribution tariff policy: An optimal coordination between capacity and demand," Energy, Elsevier, vol. 219(C).

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