高科技玻璃基板之高溫連續製程需長年不間斷運轉，任何廠務公用系統（水、電、氣）發生異常，皆可能導致熔爐毀損與長時間停機等災難性後果，因此廠務公用系統之穩定性是決定工廠營運韌性與長期獲利能力的關鍵。面對規模龐大且高度複雜的廠務公用系統資本支出，傳統僅仰賴投資報酬率與淨現值的財務評估方式已難充分因應實務需求，主因在於其未能納入資產壽命折損來設計彈性價值，以及專案初期預算高度不確定性等重要因素。為此，本研究提出一套跨領域之資本支出決策架構，將工程可靠度、財務選擇權與預算管理納入決策流程。其步驟包括：先以不可靠度成本量化事故風險，再以實質選擇權與期望總成本分析設計彈性，並結合 AACE（Association for the Advancement of Cost Engineering）成本估算分級系統及計畫評核術（Program Evaluation and Review Technique，簡稱 PERT）加權估算建立合理預算基準，最後以韌性價值與成本規避之回收期作為管理決策指標。此架構突破了既有方法未納入資產壽命折損、忽略設計彈性價值，以及未妥善處理初期預算不確定性之限制。經由製程冷卻水系統擴充與 ECDA（Emergency Clean Dry Air）系統導入等實務案例驗證，本研究所建構之模型能將防禦性投資之避險效益具體財務量化。即使傳統 ROI（Return on Investment）呈現負值，仍可客觀呈現其規避億元等級資產毀損之真實價值。本研究有效彌平了工程風險與財務語言之間的鴻溝，進而提升連續性製程企業在韌性投資與資本配置上的決策品質。此外，本論文亦建構了一套適用於廠務單位之資本支出提案標準作業程序，讓第一線工程團隊能無縫轉換財務語言來展現投資價值，有效凝聚高層共識並加速決策放行。

This study focuses on capital expenditure decision-making for utility systems in high-tech glass substrate manufacturing, where high-temperature continuous processes must operate without interruption for many years. Any abnormality in water, power, or gas supply may cause furnace damage, prolonged shutdown, and severe financial loss. Therefore, utility system stability is critical to operational resilience and long-term profitability. Traditional evaluation methods based only on ROI and NPV are insufficient because they often fail to reflect asset life degradation, the value of design flexibility, and the high uncertainty of early-stage budgeting. To address this issue, this study proposes an interdisciplinary CAPEX decision framework that integrates engineering reliability, real options, and budget management into one process. The framework applies Cost of Unreliability to quantify risk, uses real options and total cost analysis to evaluate flexibility, and combines AACE classification with PERT estimation to establish a reasonable budget baseline. It is further supported by Value of Resilience and avoided-cost payback period as management indicators. Case studies on cooling water expansion and ECDA implementation show that the model can translate defensive investment into explicit financial value and improve communication between engineering and management.

[1] Y. Lai, K. Pan, and S. Park, “Thermo-mechanical reliability of glass substrate and Through Glass Vias (TGV): A comprehensive review,” Microelectronics Reliability, vol. 161, Art. no. 115477, 2024.
[2] R. Pereira, A. A. L. Ferreira, and S. M. Toffoli, “An overview and the recent advancement of combustion applied to glass melting furnaces,” Cerâmica, vol. 71, Art. no. eNEAN2178, 2025.
[3] Glass for Europe, “Continuous energy supply is essential for the flat glass industry,” Mar. 15, 2022.
[4] AXA XL Risk Consulting, Property Risk Consulting Guidelines: Glass Manufacturing, AXA XL Risk Consulting, 2020.
[5] G. H. Volden and M. Welde, “Public project success? Measuring the nuances of success through ex post evaluation,” International Journal of Project Management, vol. 40, no. 6, pp. 703–714, 2022.
[6] M. Compare et al., “A general model for life-cycle cost analysis of condition-based maintenance enabled by PHM capabilities,” Reliability Engineering & System Safety, vol. 224, Art. no. 108499, 2022.
[7] J. Kang et al., “Availability and cost-benefit evaluation for a repairable retrial system with warm standbys and priority,” Statistical Theory and Related Fields, vol. 7, no. 2, pp. 164–175, 2023.
[8] M. Mir et al., “Neural network-based interval forecasting of construction material prices,” Journal of Building Engineering, vol. 39, Art. no. 102288, 2021.
[9] KPMG, Postpandemic Supply Chain Disruption and Cost Escalation, KPMG, 2022.
[10] 林文德，《營建業缺工問題探討》，碩士論文，亞洲大學，台中，台灣，2022。
[11] I. A. Kiral, “Contextual evaluation of risk identification techniques for construction projects: Comparative insights and a decision-support model,” Buildings, vol. 15, no. 20, Art. no. 3806, 2025.
[12] K. Atli and I. Krystallis, “Design flexibility in managing infrastructure projects: Contributing factors and research avenues,” International Journal of Project Management, vol. 43, no. 1, Art. no. 102675, 2025.
[13] IPCC, Climate Change 2021: The Physical Science Basis—Summary for Policymakers, Intergovernmental Panel on Climate Change, 2021.
[14] D. Hulse et al., “How uncertain is too uncertain? Validity tests for early resilient and risk-based design processes,” Journal of Mechanical Design, vol. 143, no. 1, Art. no. 011702, 2021.
[15] D. Thomas and J. Fung, “Measuring downstream supply chain losses due to power disturbances,” Energy Economics, vol. 114, Art. no. 106314, 2022.
[16] T. Jin, “A meta-analysis of outage cost for value-based reliability planning,” Energy Strategy Reviews, vol. 62, Art. no. 101975, 2025.
[17] J. Deng and W. Jian, “Estimating construction project duration and costs upon completion using Monte Carlo simulations and improved earned value management,” Buildings, vol. 12, no. 12, Art. no. 2173, 2022.
[18] M. S. Mirković, “Triangular distribution and PERT method vs. payoff matrix for decision-making support in risk analysis of construction bidding: A case study,” Facta Universitatis, Series: Architecture and Civil Engineering, pp. 287–307, 2021.
[19] C. Caputo and M.-A. Cardin, “Analyzing real options and flexibility in engineering systems design using decision rules and deep reinforcement learning,” Journal of Mechanical Design, vol. 144, no. 2, Art. no. 021705, 2022.
[20] G. N. Nur, C. A. MacKenzie, and K. J. Min, “A real options analysis model for generation expansion planning under uncertain demand,” Decision Analytics Journal, vol. 8, Art. no. 100263, 2023.
[21] AACE International, “Cost estimate classification system—As applied in engineering, procurement, and construction for the process industries,” AACE International Recommended Practice No. 18R-97, 2020.
[22] Project Management Institute, A Guide to the Project Management Body of Knowledge (PMBOK Guide), Project Management Institute, 2025.
[23] A. Touran, “Probabilistic model for cost contingency,” Journal of Construction Engineering and Management, vol. 129, no. 3, pp. 280–284, 2003.
[24] D. G. Malcolm et al., “Application of a technique for research and development program evaluation,” Operations Research, vol. 7, no. 5, pp. 646–669, 1959.
[25] K. Komonen, “A cost model of industrial maintenance for profitability analysis and benchmarking,” International Journal of Production Economics, vol. 79, no. 1, pp. 15–31, 2002.
[26] R. Pascual, V. Meruane, and P. A. Rey, “On the effect of downtime costs and budget constraint on preventive and replacement policies,” Reliability Engineering & System Safety, vol. 93, no. 1, pp. 144–151, 2008.
[27] L. Goel and R. Billinton, “Determination of reliability worth for distribution system planning,” IEEE Transactions on Power Delivery, vol. 9, no. 3, pp. 1577–1583, 1994.
[28] H.-C. Liu, L. Liu, and N. Liu, “Risk evaluation approaches in failure mode and effects analysis: A literature review,” Expert Systems with Applications, vol. 40, no. 2, pp. 828–838, 2013.
[29] A. Pillay and J. Wang, “Modified failure mode and effects analysis using approximate reasoning,” Reliability Engineering & System Safety, vol. 79, no. 1, pp. 69–85, 2003.
[30] J. Hassan and F. Khan, “Risk-based asset integrity indicators,” Journal of Loss Prevention in the Process Industries, vol. 25, no. 3, pp. 544–554, 2012.
[31] F. Khan, S. Rathnayaka, and S. Ahmed, “Methods and models in process safety and risk management: Past, present and future,” Process Safety and Environmental Protection, vol. 98, pp. 116–147, 2015.
[32] S. C. Myers, “Determinants of Corporate Borrowing,” Journal of Financial Economics, vol. 5, no. 2, pp. 147–175, 1977.
[33] A. K. Dixit and R. S. Pindyck, Investment Under Uncertainty. Princeton University Press, 1994.
[34] L. Trigeorgis, Real Options: Managerial Flexibility and Strategy in Resource Allocation. MIT Press, 1996.
[35] R. de Neufville, S. Scholtes, and T. Wang, “Real Options by Spreadsheet: Parking Garage Case Example,” Journal of Infrastructure Systems (ASCE), vol. 12, no. 2, pp. 107–111, 2006.
[36] T. Wang, “Real Options ‘in’ Projects,” MIT Engineering Systems Division, Real Options Conference paper, 2005.
[37] T. Wang and R. de Neufville, “Identification of Real Options ‘in’ Projects,” INCOSE International Symposium, vol. 16, no. 1, pp. 1124–1133, 2006.
[38] A. Gamba and N. Fusari, “Valuing Modularity as a Real Option,” Management Science, vol. 55, no. 11, pp. 1877–1896, 2009.
[39] C. Y. Baldwin, “Design rules: past and future,” Industrial and Corporate Change, vol. 32, no. 1, pp. 11, 2023.
[40] H.-J. Lin and W.-K. Chang, “Design of a sheet forming apparatus for overflow fusion process by numerical simulation,” Journal of Non-Crystalline Solids, vol. 353, no. 30–31, pp. 2817–2825, Oct. 2007.
[41] D. P. H. Hasselman, “Unified theory of thermal shock fracture initiation and crack propagation in brittle ceramics,” Journal of the American Ceramic Society, vol. 52, no. 11, pp. 600–604, 1969.
[42] J. Hell et al., “Blistering phenomenon of molten glass in contact with zirconia-based refractories,” Journal of the European Ceramic Society, vol. 41, no. 10, pp. 5359–5366, 2021.
[43] M. Velez, J. Smith, and R. E. Moore, “Refractory degradation in glass tank melters: A survey of testing methods,” Cerâmica, vol. 43, no. 283–284, 1997.
[44] 蕭安政，「TFT LCD玻璃基板熱端製程的現況與挑戰」，化工，第51卷，第5期，頁58–67，2004.
[45] L. Galuppi, “Transformable curved thin glass greenhouse,” International Journal of Structural Glass and Advanced Materials Research, vol. 2, pp. 198–217, 2018.
[46] D. Li, W. Li, D. Li, Y. Shi, D. Fang,”Theoretical Research on Thermal Shock Resistance of Ultra-High Temperature Ceramics Focusing on the Adjustment of Stress Reduction Factor",2013.
[47] I. Dziaduch, “Unreliability costs in life cycle cost analysis (LCCA): Comparison of calculation methods,” Journal of Polish Safety and Reliability Association, vol. 1, no. 1, pp. 75–82, 2010.
[48] Business Insider, “Amazon’s one hour of downtime on Prime Day may have cost it up to $100 million in lost sales,” Business Insider, Jul. 17, 2018.
[49] B. Beyer, C. Jones, J. Petoff, and N. R. Murphy, Site Reliability Engineering: How Google Runs Production Systems. Sebastopol, CA, USA: O’Reilly Media, 2016.
[50] D. G. Malcolm et al., “Application of a technique for research and development program evaluation,” Operations Research, vol. 7, no. 5, pp. 646–669, 1959.
[51] S. C. Myers, “Determinants of corporate borrowing,” Journal of Financial Economics, vol. 5, no. 2, pp. 147–175, 1977.
[52] L. Trigeorgis, Real Options: Managerial Flexibility and Strategy in Resource Allocation. Cambridge, MA, USA: MIT Press, 1996.
[53] A. K. Dixit and R. S. Pindyck, Investment Under Uncertainty. Princeton, NJ, USA: Princeton University Press, 1994.
[54] T. E. Copeland and V. Antikarov, Real Options: A Practitioner’s Guide. New York, NY, USA: Texere, 2001.
[55] D. M. Lander and G. E. Pinches, “Challenges to the practical implementation of modeling and valuing real options,” The Quarterly Review of Economics and Finance, vol. 38, no. 3, pp. 537–567, 1998.
[56] R. A. Brealey, S. C. Myers, and F. Allen, Principles of Corporate Finance, 14th ed. New York, NY, USA: McGraw Hill, 2023.
[57] J. R. Graham and C. R. Harvey, “The theory and practice of corporate finance: Evidence from the field,” Journal of Financial Economics, vol. 60, no. 2–3, pp. 187–243, 2001.
[58] A. Botchkarev, P. Andru, and R. Chiong, “A return on investment as a metric for evaluating information systems: Taxonomy and application,” Interdisciplinary Journal of Information, Knowledge, and Management, vol. 6, no. 6, pp. 245–269, 2011.
[59] Tipalti., “ROI calculation guide: Understanding return on investment with examples,” Tipalti, Dec. 26, 2025.
[60] Vested Finance, “ROI explained: Meaning, formula, examples & key limitations,” Vested Finance, Jan. 12, 2026.
[61] 財團法人中技社, “淨零轉型下產業園區電力可靠與韌性研究, “專題報告 2024-15, 2024.
[62] W. Weibull, “A statistical distribution function of wide applicability,” Journal of Applied Mechanics, vol. 18, no. 3, pp. 293–297, 1951.
[63] W. Q. Meeker, L. A. Escobar, and F. G. Pascual, Statistical Methods for Reliability Data, 2nd ed. Hoboken, NJ, USA: John Wiley & Sons, 2021.
[64] D. N. P. Murthy, M. Xie, and R. Jiang, Weibull Models. Hoboken, NJ, USA: John Wiley & Sons, 2004.
[65] M. Bradley and R. Dawson, “The cost of unreliability: a case study,” Journal of Quality in Maintenance Engineering, vol. 4, no. 3, pp. 212–218, 1998.
[66] J. R. Salsabila, I. A. Marie, and H. H. Purba, “Maintenance cost analysis using cost of unreliability,” Jurnal Ilmiah Teknik Industri, vol. 19, no. 2, pp. 223–234, 2020.
[67] A. Parida and U. Kumar, “Maintenance performance measurement (MPM): issues and challenges,” Journal of Quality in Maintenance Engineering, vol. 12, no. 3, pp. 239–251, 2006.
[68] M. Rausand, “Reliability centered maintenance,” Reliability Engineering & System Safety, vol. 60, no. 2, pp. 121–132, 1998.
[69] J. M. Juran and F. M. Gryna, Juran’s Quality Control Handbook, 4th ed. New York, NY, USA: McGraw-Hill, 1988.
[70] American Society for Quality, “Pareto chart,” ASQ, 2026.
[71] D. Curto, F. Acebes, J. M. González-Varona, and D. Poza, “Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves,” International Journal of Production Economics, vol. 253, Art. no. 108626, 2022
[72] M. A. Ashtari, R. Ansari, E. Hassannayebi, and J. Jeong, “Cost overrun risk assessment and prediction in construction projects: A Bayesian network classifier approach,” Buildings, vol. 12, no. 10, Art. no. 1660, 2022
[73] J. Du, S. Liu, L. Tao, and W. Dong, “Three-way failure mode and effect analysis approach for reliability management in multigranular unbalanced linguistic contexts,” Computers & Industrial Engineering, vol. 175, Art. no. 108909, 2023
[74] A. Benhanifia, Z. Ben Cheikh, P. M. Oliveira, A. Valente, and J. Lima, “Systematic review of predictive maintenance practices in the manufacturing sector,” Intelligent Systems with Applications, vol. 26, Art. no. 200501, 2025