Manufurrt: A Feasibility Investment and Operational Desktop Calculator

Handi Wilujeng Nugroho, Kurniawan Hamidi

Abstract


The prospect of success of a manufacturing project is determined by the results of feasibility tests such as investment and operations. In its implementation, the feasibility test is often constrained by several things such as data entry errors, calculation errors, and a long calculation time. The ideal solution that can be used to solve these three obstacles is to design an offline and stand-alone desktop-based feasibility calculation application. This research uses the SDLC Waterfall method and User Acceptance Test (UAT) based on questionnaires and scenario tests. In the design and coding completion phase, the SDLC waterfall approach is combined with generative AI to speed up the application design time. As a result, a calculation application named Manufurrt has been created with an interactive executable file format and is able to provide the same calculation results between the expected spreadsheet and the real calculation results from the application.


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References


M. Peña Abreu, C. R. Rodríguez Rodríguez, P. Y. Piñero Pérez, and Y. García García, “New Methods for Feasibility Analysis of Investment Projects in Uncertain Environments,” in Artificial Intelligence in Project Management and Making Decisions, P. Y. Piñero Pérez, R. E. Bello Pérez, and J. Kacprzyk, Eds., Cham: Springer International Publishing, 2022, pp. 143–154. doi: 10.1007/978-3-030-97269-1_8.

R. Abdulwahhab, S. Naimi, and R. Abdullah, “Managing Cost and Schedule Evaluation of a Construction Project via BIM Technology and Experts’ Points of View,” Math. Model. Eng. Probl., vol. 9, no. 6, pp. 1515–1522, 2022, doi: 10.18280/MMEP.090611.

M. Dombrowski, Z. Dombrowski, J. Woloszyn, A. Sachenko, O. Sachenko, and I. Melnychuk, “Adaptive Management of Digitalization Projects for Efficiency Increasing,” in Proceedings of the 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2021, 2021, pp. 1195–1199. doi: 10.1109/IDAACS53288.2021.9660388.

C. Markou, G. K. Koulinas, and A. P. Vavatsikos, “Project resources scheduling and leveling using Multi-Attribute Decision Models: Models implementation and case study,” Expert Syst. Appl., vol. 77, pp. 160–169, 2017, doi: 10.1016/j.eswa.2017.01.035.

I.-M. Jiang, Y.-H. Liu, and S. Pakavaleetorn, “Optimal Sequential Investment Decision-Making with Jump Risk,” Asia-Pacific J. Oper. Res., vol. 39, no. 04, p. 2140035, Sep. 2021, doi: 10.1142/S0217595921400352.

J. R. Bertini Junior, M. A. Funcia, A. A. S. Santos, and D. J. Schiozer, “A comparison of machine learning algorithms as surrogate model for net present value prediction from wells arrangement data,” in 2019 International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1–8. doi: 10.1109/IJCNN.2019.8851708.

T. N. Uryadova and M. G. Leshcheva, “Analytical Tools to Justify Investment Decisions,” Res. Econ. Financ. Probl., no. 03, pp. 1–10, 2022, doi: 10.31279/2782-6414-2022-3-7-1-10.

F. Alkaraan and D. Northcott, “Strategic capital investment decision-making : A role for emergent analysis tools ? A study of practice in large UK manufacturing companies,” vol. 38, pp. 149–173, 2006, doi: 10.1016/j.bar.2005.10.003.

D. Tikhomirov and V. Plotnikov, “The minimisation of risks in project finance : approaches to financial modelling and structuring,” vol. 05069, 2018.

A. F. Shorikov, E. V Butsenko, and V. A. Tyulyukin, “Intelligent software system for optimizing adaptive control of investment projecting processes,” AIP Conf. Proc., vol. 2302, no. 1, p. 60014, Dec. 2020, doi: 10.1063/5.0033499.

R. Tormosov, I. Chupryna, G. Ryzhakova, V. Pokolenko, D. Prykhodko, and A. Faizullin, “Establishment of the rational economic and analytical basis for projects in different sectors for their integration into the targeted diversified program for sustainable energy development,” in 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), 2021, pp. 1–9. doi: 10.1109/SIST50301.2021.9465993.

M. Bavay, M. Reisecker, T. Egger, and D. Korhammer, “Inishell 2 . 0 : semantically driven automatic GUI generation for scientific models,” pp. 365–378, 2022.

A. F. Vianiryzki and G. S. Niwanputri, “A User-Centered Approach to Design a Financial Asset Investment Mobile Application for Building Investing Eagerness,” in 2021 8th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA), 2021, pp. 1–6. doi: 10.1109/ICAICTA53211.2021.9640286.

M. Kolhekar, S. Upadhyay, H. Yeole, N. Patil, and V. Totawar, “Development of User-friendly Stock Prediction Assistance using Ensemble Learning,” in 2024 Asia Pacific Conference on Innovation in Technology (APCIT), 2024, pp. 1–6. doi: 10.1109/APCIT62007.2024.10673583.

W. Sardjono and A. Bhagas, “ACCELERATE DECISION-MAKING PROCESS THROUGH THE IMPLEMENTATION OF FINA: FINANCIAL FEASIBILITY STUDY IN HAND,” J. Theor. Appl. Inf. Technol., vol. 101, no. 23, pp. 7512–7519, 2023.

S. Boyd, “REFeasibility: Designing a mobile application for initiating feasibility analysis,” Pacific Rim Prop. Res. J., vol. 21, no. 2, pp. 179–196, 2015, doi: 10.1080/14445921.2015.1058035.

F. K. Foo and D. S. C. Ong, “Advance Injection Strategy Optimization: Maximize Benefit-Cost Ratio by Integration of Economic Spreadsheet in Excel to Assisted History Matching Using Python Scripting,” in Abu Dhabi International Petroleum Exhibition and Conference, vol. Abu Dhabi. 2021, p. D032S237R002. doi: 10.2118/207955-MS.

M. Moshiri, M. Raza, M. Sahlab, A. A. Malik, A. Bilberg, and G. Tosello, “Value Chain Comparison of Additively and Conventionally Manufactured Multi-Cavity Tool Steel Inserts: An Injection Molding Industrial Case Study for High-Volume Production,” Appl. Sci., vol. 12, no. 20, 2022, doi: 10.3390/app122010410.

S. Pullteap, P. Samartkit, K. Kheovichai, and H. C. Seat, “A software development for investment analysis of LED lighting production project using fuzzy logic technique,” vol. 14, pp. 83–100, 2020.

A. S. Albana and Y. Andrian Saputra, “Financial Risk Assessment For Power Plant Investment Under Uncertainty Using Monte Carlo Simulation,” in 2019 International Conference on Technologies and Policies in Electric Power & Energy, 2019, pp. 1–6. doi: 10.1109/IEEECONF48524.2019.9102631.

S. Mathews, “Valuing Risky Projects with Real Options,” Res. Manag., vol. 52, no. 5, pp. 32–41, 2009, doi: 10.1080/08956308.2009.11657587.

D. LI, P. Chang, X. YANG, Z. SUN, F. JIN, and Y. YIN, “Two Dimensional Monte-Carlo Simulation Method of Risk Assessment for Strategic Asset Investment Decision Making,” in SPE Annual Technical Conference and Exhibition, vol. SPE Annual. 2015, p. D021S021R004. doi: 10.2118/174885-MS.

B. Losada, J.-M. López-Gil, and M. Urretavizcaya, “Improving Agile Software Development Methods by means of User Objectives: An End User Guided Acceptance Test-Driven Development Proposal,” in Proceedings of the XX International Conference on Human Computer Interaction, in Interacción ’19. New York, NY, USA: Association for Computing Machinery, 2019. doi: 10.1145/3335595.3335650.

K. C. Bourne, Application Administrators Handbook: Installing, Updating and Troubleshooting Software. USA: Elsevier Inc., 2013.

M. A. Dhina, G. Hadisoebroto, I. E. Setiawati, G. Undang, M. Masluh, and S. R. Mubaroq, “Digital performance assessment: Measure a pharmacy physics laboratory’s skill,” J. Phys. Conf. Ser., vol. 1869, no. 1, pp. 0–6, 2021, doi: 10.1088/1742-6596/1869/1/012099.

L. Nägele et al., “A backward-oriented approach for offline programming of complex manufacturing tasks,” in 2015 6th International Conference on Automation, Robotics and Applications (ICARA), 2015, pp. 124–130. doi: 10.1109/ICARA.2015.7081135.

A. Siddieq and I. Nurhaida, “Mobile application of BTS tower search build upon location based service (LBS),” Libr. Hi Tech News, vol. 36, no. 3, pp. 1–6, Jan. 2019, doi: 10.1108/LHTN-08-2018-0049.

N. A. Khan, “Research on Various Software Development Lifecycle Models,” in Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3, K. Arai, S. Kapoor, and R. Bhatia, Eds., Cham: Springer International Publishing, 2021, pp. 357–364.

T. Brinck, D. Gergle, and S. D. Wood, “CHAPTER 2 - User Needs Analysis,” in User Experience Re-Mastered, C. Wilson, Ed., Boston: Morgan Kaufmann, 2010, pp. 23–72. doi: https://doi.org/10.1016/B978-0-12-375114-0.00005-0.

Q. Liu et al., “Python-Based Data Analysis Tool for a 6-DoF Industrial Robot Python-Based Data Analysis Tool for a 6-DoF Industrial Robot,” 2019, doi: 10.1088/1757-899X/568/1/012098.

M. Suhaib, “Conflicts identification among stakeholders in goal oriented requirements engineering process,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 12, pp. 4926–4930, 2019, doi: 10.35940/ijitee.L3557.1081219.

K. Hamidi, L. Hanarisanty, R. I. Kurniati, and H. Wilujeng, “Rancang Bangun Aplikasi Dekstop untuk Kebutuhan Uji Fitoremediasi,” vol. 24, pp. 208–216, 2024.

J. H. Liu, H. X. Xia, and H. B. Zhang, “A Research into the UML Legend in the Waterfall Model Development,” in Computer and Information Technology, in Applied Mechanics and Materials, vol. 519. Trans Tech Publications Ltd, 2014, pp. 322–328. doi: 10.4028/www.scientific.net/AMM.519-520.322.

Z. Y. Oguz and C. Turhan, “Ontology-Based Semantic Analysis of Software Requirements: A Systematic Mapping,” in 3rd International Informatics and Software Engineering Conference, IISEC 2022, 2022. doi: 10.1109/IISEC56263.2022.9998243.

A. ALazzawi, Q. M. Yas, and B. Rahmatullah, “A Comprehensive Review of Software Development Life Cycle methodologies: Pros, Cons, and Future Directions,” Iraqi J. Comput. Sci. Math., vol. 4, no. 4, pp. 173–190, 2023, doi: 10.52866/ijcsm.2023.04.04.014.

M. Fischer and C. Lanquillon, “Evaluation of Generative AI-Assisted Software Design and Engineering: A User-Centered Approach,” in Artificial Intelligence in HCI, H. Degen and S. Ntoa, Eds., Cham: Springer Nature Switzerland, 2024, pp. 31–47.

M. Alenezi and M. Akour, “AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions,” Appl. Sci., vol. 15, no. 3, pp. 1–26, 2025, doi: 10.3390/app15031344.

S. S. Kumar and J. Shetty, “Malicious PE File Detection Using Machine Learning: An Analysis of Header Features,” in 2024 International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), 2024, pp. 66–71. doi: 10.1109/COSMIC63293.2024.10871898.

J. R. Dora and L. Hluchy, “Exploitation of Thick Client Application Vulnerabilities and a Synopsis of Mitigation: *How to Conduct Attacks to Abuse Weaknesses Present in a Windows Executable File.,” in 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2024, pp. 431–436. doi: 10.1109/SACI60582.2024.10619849.




DOI: http://dx.doi.org/10.24014/sitekin.v22i2.36764

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