IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v138y2019icp243-253.html
   My bibliography  Save this article

Investment cost optimization for industrial project portfolios using technology mining

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162517313896
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2018.09.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    2. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    3. Zhou, Xiao & Huang, Lu & Porter, Alan & Vicente-Gomila, Jose M., 2019. "Tracing the system transformations and innovation pathways of an emerging technology: Solid lipid nanoparticles," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 785-794.
    4. 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.
    5. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    6. Huang, Ying & Porter, Alan L. & Zhang, Yi & Lian, Xiangpeng & Guo, Ying, 2019. "An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs)," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 831-843.
    7. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
    8. Sangsung Park & Sunghae Jun, 2017. "Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    9. Daiho Uhm & Jea-Bok Ryu & Sunghae Jun, 2017. "An Interval Estimation Method of Patent Keyword Data for Sustainable Technology Forecasting," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    10. Tingcan Ma & Ruinan Li & Guiyan Ou & Mingliang Yue, 2018. "Topic based research competitiveness evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 789-803, November.
    11. Uwe Beyer & Oliver Ullrich, 2022. "Organizational Complexity as a Contributing Factor to Underperformance," Businesses, MDPI, vol. 2(1), pages 1-15, March.
    12. Coccia, Mario & Wang, Lili, 2015. "Path-breaking directions of nanotechnology-based chemotherapy and molecular cancer therapy," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 155-169.
    13. Gyula Dörgő & Viktor Sebestyén & János Abonyi, 2018. "Evaluating the Interconnectedness of the Sustainable Development Goals Based on the Causality Analysis of Sustainability Indicators," Sustainability, MDPI, vol. 10(10), pages 1-26, October.
    14. Juhwan Kim & Sunghae Jun & Dongsik Jang & Sangsung Park, 2018. "Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models," Sustainability, MDPI, vol. 10(1), pages 1-12, January.
    15. Morgan Dwyer & Bruce Cameron & Zoe Szajnfarber, 2015. "A Framework for Studying Cost Growth on Complex Acquisition Programs," Systems Engineering, John Wiley & Sons, vol. 18(6), pages 568-583, November.
    16. Félicia Saïah & Diego Vega & Harwin de Vries & Joakim Kembro, 2023. "Process modularity, supply chain responsiveness, and moderators: The Médecins Sans Frontières response to the Covid‐19 pandemic," Production and Operations Management, Production and Operations Management Society, vol. 32(5), pages 1490-1511, May.
    17. Hyejin Park & Han Woo Park, 2018. "Two-side face of knowledge building using scientometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(6), pages 2815-2836, November.
    18. Junguang Zhang & Xiwei Song & Hongyu Chen & Ruixia (Sandy) Shi, 2016. "Determination of critical chain project buffer based on information flow interactions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1146-1157, September.
    19. Pedota, Mattia & Cicala, Francesco & Basti, Alessio, 2024. "A Wild Mind with a Disciplined Eye: Unleashing Human-GenAI Creativity Through Simulated Entity Elicitation," OSF Preprints 3bn95, Center for Open Science.
    20. Ogden, Joan & Jaffe, Amy Myers & Scheitrum, Daniel & McDonald, Zane & Miller, Marshall, 2018. "Natural gas as a bridge to hydrogen transportation fuel: Insights from the literature," Energy Policy, Elsevier, vol. 115(C), pages 317-329.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:138:y:2019:i:c:p:243-253. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.