Price Calibration in Generator Parts: Innovative Approaches Against Economic Volatilities

Abstract

The fundamental factors affecting product prices over time, such as inflation, exchange rates, market fluctuations, and tech-nological developments, are examined with a specific focus on the generator sector. The importance of comparing past costs with current values is emphasized in order to develop accurate pricing strategies and optimize profitability. The role of inflation and exchange rates in price calibration is discussed, along with the data deficiencies and modeling challenges posed by products that have not been purchased for extended periods. This study addresses the necessity of determining the current prices of products purchased in the past, a significant issue for various industries and businesses. Various statistical methods to ensure data integrity and the importance of sufficient observations for accurate statistical measurements are explored. Detailed analyses are presented on the use of text-based similarity measures in product grouping to improve the reliability of price calibrations, as well as on transition techniques for grouping by active components. Finally, the conversion of data into time series for predictive analysis, the effect of Turkish Lira volatility on price calibration, and the methods used to mitigate inconsistencies caused by exchange rate fluctuations are discussed. The research highlights the importance of meticulous data handling and statistical rigor in achieving reliable price calibrations, ultimately aiming to assist businesses in better budget planning and financial resource management.

Keywords

• Price calibration
• exchange rates
• demand forecasting algorithms
• text similarity

Jeneratör Parçalarında Fiyat Kalibrasyonu: Ekonomik Belirsizliklere Karşı Yenilikçi Yaklaşımlar

Abstract

Enflasyon, döviz kurları, piyasa dalgalanmaları ve teknolojik gelişmeler gibi ürün fiyatlarını zaman içinde etkileyen temel faktörler, jeneratör sektörü özelinde incelenmektedir. Doğru fiyatlandırma stratejileri geliştirmek ve karlılığı optimize etmek için geçmiş maliyetleri mevcut değerlerle karşılaştırmanın önemi vurgulanmaktadır. Enflasyonun ve döviz kurlarının fiyat kalib-rasyonundaki rolü tartışılırken, uzun süre satın alınmayan ürünlerin yarattığı veri yetersizlikleri ve modelleme zorlukları da tartışılmaktadır. Bu çalışma kapsamında, çeşitli endüstriler ve işletmeler için önemli bir sorun olan, geçmişte satın alınan ürünlerin mevcut fiyatlarının belirlenmesi gerekliliğini ele alınmaktadır. Veri bütünlüğünü sağlamaya yönelik çeşitli istatistiksel yöntemler ve doğru istatistiksel ölçümler için yeterli gözlemlerin önemi araştırılmaktadır. Fiyat kalibrasyonlarının güvenilirliğini artırmak için ürün gruplamasında metin tabanlı benzerlik önlemlerinin kullanımı ve aktif bileşenlere göre gruplamaya geçiş teknikleri, detaylı analizler ile sunulmaktadır. Son olarak, tahmine dayalı analiz için verilerin zaman serilerine dönüştürülmesi, Türk Lirası oynaklığının fiyat kalibrasyonu üzerindeki etkisi ve kur dalgalanmalarından kaynaklanan tutarsızlıkları azaltmak için kullanılan yöntemler tartışılmaktadır. Araştırma, güvenilir fiyat kalibrasyonlarına ulaşmada titiz veri manipülasyonunun ve istatistiksel titizliğin öneminin altını çizmekte ve sonuçta işletmelere daha iyi bütçe planlaması ve finansal kaynak yönetimi konusunda yardımcı olmayı hedeflemektedir.

Keywords

• Fiyat kalibrasyonu
• döviz kurları
• talep tahmin algoritmaları
• metin benzerliği

References

1. R. Dornbusch, “Exchange Rates And Prices,” Dec. 1985, National Bureau Of Economic Research: 1769. Doi: 10.3386/W1769.
2. N. U. Rehman And G. Nunziante, “The Effect Of The Digital Economy On Total Factor Productivity In European Regions,” Telecommunications Policy, Vol. 47, No. 10, P. 102650, Nov. 2023, Doi: 10.1016/J.Telpol.2023.102650.
3. T. Cogley And T. J. Sargent, “Drifts And Volatilities: Monetary Policies And Outcomes In The Post WWII US,” Review Of Economic Dynamics, Vol. 8, No. 2, Pp. 262–302, Apr. 2005, Doi: 10.1016/J.Red.2004.10.009.
4. “The Real Exchange Rate And Development Theory, Evidence, Issues And Challenges - Demir - 2022 - Journal Of Economic Surveys - Wiley Online Library.” Accessed: Mar. 10, 2025. [Online]. Available: Https://Onlinelibrary.Wiley.Com/Doi/Full/10.1111/Joes.12418.
5. Handbook Of International Economics. Elsevier, 2022.
6. “Monetary Policy And Redistribution In Open Economies | Journal Of Political Economy Macroeconomics: Vol 1, No 1.” Accessed: Mar. 10, 2025. Available: Https://Www.Journals.Uchicago.Edu/Doi/Full/10.1086/723410.
7. “Competition And Price Dispersion In The U.S. Airline Industry | Journal Of Political Economy: Vol 102, No 4.” Accessed: Sep. 29, 2024. [Online]. Available: Https://Www.Journals.Uchicago.Edu/Doi/Abs/10.1086/261950.
8. “Full Article: Production Planning And Scheduling In Multi-Factory Production Networks: A Systematic Literature Review.” Accessed: Mar. 10, 2025. [Online]. Available: Https://Www.Tandfonline.Com/Doi/Full/10.1080/00207543.2020.1797207?Casa_Token=Kstbjmwtqy4aaaaa%3AT0Rs7Twx25F_R-Lejtgpah6becta84a0v8laago7varqnu3uagq6kcmrq7os6pv_Fv0coix2rl96fw.

9. “Full Article: Researchers’ Perspectives On Industry 4.0: Multi-Disciplinary Analysis And Opportunities For Operations Management.” Accessed: Mar. 10, 2025. [Online]. Available: Https://Www.Tandfonline.Com/Doi/Full/10.1080/00207543.2020.1798035?Casa_Token=Xiiomzv1qhwaaaaa%3apumswm2c-Hyvhjak9g6f0godmxxx6r6xku_Dhvotx32isrgrrqw0eumyrdn05smdm0vx_XEYCK_Awa.
10. B. K. Meher, I. T. Hawaldar, C. M. Spulbar, And F. R. Birau, “Forecasting Stock Market Prices Using Mixed ARIMA Model: A Case Study Of Indian Pharmaceutical Companies,” Jan. 22, 2021, Social Science Research Network, Rochester, NY: 3771334. Doi: 10.2139/Ssrn.3771334.
11. A. Krause, A. Martin, And M. Fix, “Uncertainty In Automated Valuation Models: Error-Based Versus Model-Based Approaches,” Journal Of Property Research, Vol. 37, No. 4, Pp. 308–339, Oct. 2020, Doi: 10.1080/09599916.2020.1807587.
12. S. Doğan And Y. Büyükkör, “Makine Öğrenmesi Ile Finansal Zaman Serisi Tahminleme,” AHBVÜ İİBF Dergisi, Vol. 24, No. 3, Art. No. 3, Dec. 2022, Doi: 10.26745/Ahbvuibfd.1191080.
13. Z. Rowland, G. Lazaroiu, And I. Podhorská, “Use Of Neural Networks To Accommodate Seasonal Fluctuations When Equalizing Time Series For The CZK/RMB Exchange Rate,” Risks, Vol. 9, No. 1, Art. No. 1, Jan. 2021, Doi: 10.3390/Risks9010001.
14. M. Osman, “LSTM Ile Hisse Senedi Fiyat Tahmini”, Accessed: Mar. 10, 2025. [Online]. Available: Https://Www.Researchgate.Net/Profile/Muharrem-Topakkaya/Publication/378003903_LSTM_Ile_Hisse_Senedi_Fiyat_Tahmini/Links/65c25440790074549769a564/LSTM-Ile-Hisse-Senedi-Fiyat-Tahmini.Pdf.
15. O. Féron And E. Daboussi, “Calibration Of Electricity Price Models,” In Commodities, Energy And Environmental Finance, R. Aïd, M. Ludkovski, And R. Sircar, Eds., New York, NY: Springer, 2015, Pp. 183–210. Doi: 10.1007/978-1-4939-2733-3_7.
16. J. L. Castle, J. A. Doornik, And D. F. Hendry, “Forecasting Principles From Experience With Forecasting Competitions,” Forecasting, Vol. 3, No. 1, Pp. 138–165, Feb. 2021, Doi: 10.3390/Forecast3010010.
17. “Optimal Dynamic Pricing Of Inventories With Stochastic Demand Over Finite Horizons | Management Science.” Accessed: Sep. 29, 2024. [Online]. Available: Https://Pubsonline.Informs.Org/Doi/Abs/10.1287/Mnsc.40.8.999.
18. D. Bertsimas And N. Kallus, “The Power And Limits Of Predictive Approaches To Observational Data-Driven Optimization: The Case Of Pricing,” INFORMS Journal On Optimization, Vol. 5, No. 1, Pp. 110–129, Jan. 2023, Doi: 10.1287/Ijoo.2022.0077.
19. S. Kim And D.-J. Seo, “Improving Operational Ensemble Streamflow Forecasting With Conditional Bias-Penalized Post-Processing Of Precipitation Forecast And Assimilation Of Streamflow Data,” Aug. 01, 2024, Rochester, NY: 4912528. Accessed: Sep. 29, 2024. [Online]. Available: Https://Papers.Ssrn.Com/Abstract=4912528.
20. “An Introduction To Statistical Learning: With Applications In Python | Springerlink.” Accessed: Oct. 03, 2024. [Online]. Available: Https://Link.Springer.Com/Book/10.1007/978-3-031-38747-0.
21. B. T. Hazen, I. Russo, I. Confente, And D. Pellathy, “Supply Chain Management For Circular Economy: Conceptual Framework And Research Agenda,” The International Journal Of Logistics Management, Vol. 32, No. 2, Pp. 510–537, Dec. 2020, Doi: 10.1108/IJLM-12-2019-0332.
22. R. J. Hyndman And G. Athanasopoulos, Forecasting: Principles And Practice. Otexts, 2018.
23. T. Leimbach, “The SAP Story: Evolution Of SAP Within The German Software Industry,” IEEE Annals Of The History Of Computing, Vol. 30, No. 4, Pp. 60–76, Oct. 2008, Doi: 10.1109/MAHC.2008.75.
24. S. Chakraborti And M. Graham, Nonparametric Statistical Process Control. John Wiley & Sons, 2019.
25. E. Ilzetzki, C. M. Reinhart, And K. S. Rogoff, “Rethinking Exchange Rate Regimes☆,” In Handbook Of International Economics, Vol. 6, G. Gopinath, E. Helpman, And K. Rogoff, Eds., In Handbook Of International Economics: International Macroeconomics, Volume 6, Vol. 6. , Elsevier, 2022, Pp. 91–145. Doi: 10.1016/Bs.Hesint.2022.02.010.
26. D. Hoaglin, “Volume 16: How To Detect And Handle Outliers,” 2013. Accessed: Sep. 29, 2024. [Online]. Available: Https://Www.Semanticscholar.Org/Paper/Volume-16%3A-How-To-Detect-And-Handle-Outliers-Hoaglin/D524a172b49e25f888376d662ee364aa77d99e8a.
27. W. J. Dixon, “Processing Data For Outliers,” Biometrics, Vol. 9, No. 1, Pp. 74–89, 1953.
28. B. Rosner, “Percentage Points For A Generalized ESD Many-Outlier Procedure,” Technometrics, Vol. 25, No. 2, Pp. 165–172, May 1983, Doi: 10.1080/00401706.1983.10487848.
29. A. R. Lahitani, A. E. Permanasari, And N. A. Setiawan, “Cosine Similarity To Determine Similarity Measure: Study Case In Online Essay Assessment,” In 2016 4th International Conference On Cyber And IT Service Management, Apr. 2016, Pp. 1–6. Doi: 10.1109/CITSM.2016.7577578.
30. “Probabilistic Time Series Forecasting With Deep Non‐Linear State Space Models - Du - 2023 - CAAI Transactions On Intelligence Technology - Wiley Online Library.” Accessed: Mar. 10, 2025. [Online]. Available: Https://Ietresearch.Onlinelibrary.Wiley.Com/Doi/Full/10.1049/Cit2.12085.
31. F. S. Mishkin And S. G. Eakins, Financial Markets And Institutions 8e. Pearson Education Limited, 2016. Accessed: Oct. 03, 2024. [Online]. Available: Http://Localhost:8080/Xmlui/Handle/123456789/176.
32. Z. Ouattara, “THE IMPACT OF EXCHANGE RATE VOLATILITY ON INTERNATIONAL TRADE IN DEVELOPING COUNTRIES: EVIDENCE FROM TURKIYE,” PAP, Vol. 17, No. 1, Art. No. 1, Jul. 2023, Doi: 10.17261/Pressacademia.2023.1767.
33. A. Cieslak And H. Pang, “Common Shocks In Stocks And Bonds,” Journal Of Financial Economics, Vol. 142, No. 2, Pp. 880–904, Nov. 2021, Doi: 10.1016/J.Jfineco.2021.06.008.
34. Á. Fernández, J. Bella, And J. R. Dorronsoro, “Supervised Outlier Detection For Classification And Regression,” Neurocomputing, Vol. 486, Pp. 77–92, May 2022, Doi: 10.1016/J.Neucom.2022.02.047.
35. G. Li, A. Zhang, Q. Zhang, D. Wu, And C. Zhan, “Pearson Correlation Coefficient-Based Performance Enhancement Of Broad Learning System For Stock Price Prediction,” IEEE Transactions On Circuits And Systems II: Express Briefs, Vol. 69, No. 5, Pp. 2413–2417, May 2022, Doi: 10.1109/TCSII.2022.3160266.
36. N. Roustaei, “Application And Interpretation Of Linear-Regression Analysis,” Med Hypothesis Discov Innov Ophthalmol, Vol. 13, No. 3, Pp. 151–159, Oct. 2024, Doi: 10.51329/Mehdiophthal1506.