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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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    1. Ikujiro Nonaka & Ryoko Toyama, 2007. "Strategic management as distributed practical wisdom (phronesis)," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 16(3), pages 371-394, June.
    2. Zhang, Yi & Robinson, Douglas K.R. & Porter, Alan L. & Zhu, Donghua & Zhang, Guangquan & Lu, Jie, 2016. "Technology roadmapping for competitive technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 175-186.
    3. Huang, Lu & Zhang, Yi & Guo, Ying & Zhu, Donghua & Porter, Alan L., 2014. "Four dimensional Science and Technology planning: A new approach based on bibliometrics and technology roadmapping," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 39-48.
    4. Eppinger, Steven D. & Browning, Tyson R., 2012. "Design Structure Matrix Methods and Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262017520, December.
    5. Sangsung Park & Seung-Joo Lee & Sunghae Jun, 2015. "A Network Analysis Model for Selecting Sustainable Technology," Sustainability, MDPI, vol. 7(10), pages 1-16, September.
    6. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
    7. Leung, Lawrence C. & Cao, Dong, 2001. "On the efficacy of modeling multi-attribute decision problems using AHP and Sinarchy," European Journal of Operational Research, Elsevier, vol. 132(1), pages 39-49, July.
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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. Shugang Li & Ziyi Li & Yixin Tang & Wenjing Zhao & Xiaoqi Kang & Lingling Zheng & Zhaoxu Yu, 2024. "Pioneering Technology Mining Research for New Technology Strategic Planning," Sustainability, MDPI, vol. 16(15), pages 1-26, August.
    3. 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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