Research Article
BibTex RIS Cite

Integrated Use of AHP and K-means Clustering Methods for Spare Part Demand Forecasting: An Application in Healthcare Equipment

Year 2025, Volume: 8 Issue: 3, 660 - 671, 15.05.2025

Doğukan İlbey Süslü , Kumru Didem Atalay , Tusan Derya

https://doi.org/10.34248/bsengineering.1633685

Abstract

In this study, an integrated approach has been developed to improve the accuracy of spare parts demand forecasting for magnetic resonance imaging devices in the international market. The proposed method consists of three main components: multi-criteria decision-making to determine the most critical criterion, clustering to group countries based on similar demand characteristics, and regression techniques to forecast demand for each cluster. First, the Analytic Hierarchy Process is employed to calculate the relative weights of the criteria influencing spare parts demand, and the most significant criterion is selected. Then, the K-means clustering algorithm is applied to categorize countries into groups based on this criterion. Customized regression models are developed for each cluster to enhance demand forecasting accuracy. The proposed approach improves spare parts supply processes, particularly in countries without local manufacturing facilities, and enhances forecast accuracy. The findings indicate that a cluster-based demand forecasting approach can increase efficiency in inventory management and supply chain operations. In conclusion, the developed model contributes significantly to optimizing the maintenance and repair processes of high-cost magnetic resonance imaging devices in the healthcare sector and improving the management of the spare parts supply chain.

Keywords

Spare part demand forecasting Analytic hierarchy process K-means clustering

References

  • Ashour M, Mahdiyar A. 2024. A comprehensive state-of-the-art survey on the recent modified and hybrid analytic hierarchy process approaches. Appl Soft Comput, 150: 111014.
  • Bacchetti A, Saccani N. 2012. Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega, 40(6): 722-737.
  • Bait S, Lauria SM, Schiraldi MM. 2022. A risk-based hybrid multi-criteria approach to support managers in the industrial location selection in developing countries: A case study of textile sector in Africa. J Clean Prod, 335: 130325.
  • Barak S, Mokfi T. 2019. Evaluation and selection of clustering methods using a hybrid group MCDM. Expert Syst Appl, 138: 112817.
  • Bhalla S, Alfnes E, Hvolby HH, Sgarbossa F. 2021. Advances in spare parts classification and forecasting for inventory control: A literature review. IFAC-Papers OnLine, 54(1): 982-987.
  • Bottani E, Rizzi A. 2008. An adapted multi-criteria approach to suppliers and products selection - An application oriented to lead-time reduction. Int J Prod Econ, 111(2): 763-781.
  • Chaudhuri A, Gerlich HA, Jayaram J, Ghadge A, Shack J, Brix BH, Hoffbeck LH, Ulriksen N. 2021. Selecting spare parts suitable for additive manufacturing: a design science approach. Product Plan Control, 32(8): 670-687.
  • Chopra S, Meindl P. 2020. Supply chain management: strategy, planning, and operation. Pearson, Boston USA, pp: 254.
  • Coşkun SS, Kumru M, Kan NM. 2022. An integrated framework for sustainable supplier development through supplier evaluation based on sustainability indicators. J Clean Prod, 335: 130287.
  • Crispim JA, Pinho de Sousa J. 2009. Partner selection in virtual enterprises: a multi-criteria decision support approach. Int J Prod Res, 47(17): 4791-4812.
  • Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, Akinyelu AA. 2022. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng Appl Artif Intell, 110: 104743.
  • Ho W. 2008. Integrated analytic hierarchy process and its applications A literature review. Eur J Oper Res, 186(1): 211-228.
  • Ishizaka A, Labib A. 2011. Review of the main developments in the analytic hierarchy process. Expert Syst Appl, 38(11): 14336-14345.
  • Jain AK. 2010. Data clustering: 50 years beyond K-means. Pattern Recognit Lett, 31(8): 651-666.
  • Kang X, Pang H. 2022. City selection for fresh produce e-commerce’s market entry strategy: Based on the perspective of urban logistics competitiveness. Transp Res Interdiscip Perspect, 13: 100537.
  • Kennedy WJ, Patterson JW, Fredendall LD. 2002. An overview of recent literature on spare parts inventories. Int J Prod Econ, 76(2): 201-215.
  • Keskin GA, İlhan S, Özkan C. 2010. The Fuzzy ART algorithm: A categorization method for supplier evaluation and selection. Expert Syst Appl, 37(2): 1235-1240.
  • Li H, Wu X, Liang Y, Zhang C. 2021. A multistakeholder approach to the airport gate assignment problem: Application of fuzzy theory for optimal performance indicator selection. Comput Intell Neurosci, 2021(1): 2675052.
  • Liberatore MJ, Nydick RL. 2008. The analytic hierarchy process in medical and health care decision making: A literature review. Eur J Oper Res, 189(1): 194-207.
  • Liu Y, Eckert CM, Earl C. 2020. A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Syst Appl, 161: 113738.
  • Lund B, Ma J. 2021. A review of cluster analysis techniques and their uses in library and information science research: k-means and k-medoids clustering. Performance Measurement and Metrics, 22(3): 161-173.
  • Maghsoodi AI, Kavian A, Khalilzadeh M, Brauers WK. 2018. CLUS-MCDA: A novel framework based on cluster analysis and multiple criteria decision theory in a supplier selection problem. Comput Ind Eng, 118: 409-422.
  • Maghsoodi AI, Riahi D, Herrera-Viedma E, Zavadskas EK. 2020. An integrated parallel big data decision support tool using the W-CLUS-MCDA: A multi-scenario personnel assessment. Knowl Based Syst, 195: 105749.
  • Pinçe Ç, Turrini L, Meissner J. 2021. Intermittent demand forecasting for spare parts: A Critical review. Omega, 105: 102513.
  • Porter ME, Teisberg EO. 2006. Redefining Health Care: Creating Value-Based Competition on Results. Harvard Business School Press, Boston, USA, pp: 56.
  • Saaty T. 1972. An eigenvalue allocation model for prioritization and planning. In Working paper, Energy Management and Policy Center: University of Pennsylvania, Pennsylvania, USA, pp:321.
  • Saaty TL. 1977. A scaling method for priorities in hierarchical structures. J Math Psychol, 15(3): 234-281.
  • Saaty TL. 1980. The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill., New York, USA, pp:126.
  • Sharifi E, Chaudhuri A, Vejrum Waehrens B, Guldborg Staal L, Lindemann CF, Davoudabadi Farahani S. 2022. Part selection for Freeform Injection Moulding: comparison of alternate approaches using a novel comprehensive methodology. Int J Prod Res, 60(23): 6996-7012.
  • Sironen S, Kangas J, Leskinen P. 2020. Restructuring a correlated multilevel decision hierarchy in multicriteria decision analysis. Journal of Multi‐Criteria Decision Analysis, 27(5-6): 266-285.
  • Subramanian N, Ramanathan R. 2012. A review of applications of Analytic Hierarchy Process in operations management. Int J Prod Econ, 138(2): 215-241.
  • Tatlıdil H. 1996. Uygulamalı çok değişkenli istatistiksel analiz. Cem Ofset, Eylül, Ankara, Türkiye, ss: 200.
  • Topan E, Eruguz AS, Ma W, Van der Heijden MC, Dekker R. 2020. A review of operational spare parts service logistics in service control towers. Eur J Oper Res, 282(2): 401-414.
  • Topol E. 2019. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, New York, USA, pp: 54.
  • Van der Auweraer S, Boute RN, Syntetos AA. 2019. Forecasting spare part demand with installed base information: A review. Int J Forecast, 35(1): 181-196.
  • Wu Y, Zhang T, Zhong K, Wang L, Xu C, Xu R. 2021. Optimal planning of energy storage technologies considering thirteen demand scenarios from the perspective of electricity Grid: A Three-Stage framework. Energy Convers Manag, 229: 113789.
  • Zyoud SH, Fuchs-Hanusch D. 2017. A bibliometric-based survey on AHP and TOPSIS techniques. Expert Syst Appl, 78: 158-181.

Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama

Year 2025, Volume: 8 Issue: 3, 660 - 671, 15.05.2025

Doğukan İlbey Süslü , Kumru Didem Atalay , Tusan Derya

https://doi.org/10.34248/bsengineering.1633685

Abstract

Bu çalışmada, uluslararası pazarda manyetik rezonans cihazlarının yedek parça taleplerini daha doğru tahmin edebilmek için bütünleşik bir yöntem geliştirilmiştir. Önerilen yöntem üç temel bileşenden oluşmaktadır: En önemli kriterin belirlenmesi için çok kriterli karar verme, benzer talep özelliklerine göre ülkelerin gruplandırılması için kümeleme ve her bir küme için talep tahmini yapmak amacıyla regresyon teknikleri kullanılmıştır. İlk olarak, Analitik Hiyerarşi Süreci ile yedek parça taleplerini etkileyen kriterlerin göreli ağırlıkları hesaplanmış ve en önemli kriter seçilmiştir. Daha sonra, K-ortalama kümeleme algoritması kullanılarak ülkeler bu kritere göre benzer taleplere sahip gruplara ayrılmıştır. Her bir küme için özelleştirilmiş regresyon modelleri oluşturularak talep tahminleri gerçekleştirilmiştir. Önerilen yaklaşım, özellikle yerel üretim tesisi bulunmayan ülkelerde yedek parça tedarik süreçlerinin iyileştirilmesini ve talep tahmin doğruluğunun artırılmasını sağlamaktadır. Çalışmanın bulguları, küme bazlı talep tahmini yaklaşımının stok yönetimi ve tedarik zinciri süreçlerinde verimliliği artırabileceğini göstermektedir. Sonuç olarak, geliştirilen model, sağlık sektöründe yüksek maliyetli manyetik rezonans cihazlarının bakım ve onarım süreçlerini iyileştirmekte ve yedek parça tedarik zincirinin daha etkin yönetilmesini sağlamaktadır.

Keywords

Yedek parça talep tahmini Analitik hiyerarşi süreci K-ortalama kümeleme

References

  • Ashour M, Mahdiyar A. 2024. A comprehensive state-of-the-art survey on the recent modified and hybrid analytic hierarchy process approaches. Appl Soft Comput, 150: 111014.
  • Bacchetti A, Saccani N. 2012. Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega, 40(6): 722-737.
  • Bait S, Lauria SM, Schiraldi MM. 2022. A risk-based hybrid multi-criteria approach to support managers in the industrial location selection in developing countries: A case study of textile sector in Africa. J Clean Prod, 335: 130325.
  • Barak S, Mokfi T. 2019. Evaluation and selection of clustering methods using a hybrid group MCDM. Expert Syst Appl, 138: 112817.
  • Bhalla S, Alfnes E, Hvolby HH, Sgarbossa F. 2021. Advances in spare parts classification and forecasting for inventory control: A literature review. IFAC-Papers OnLine, 54(1): 982-987.
  • Bottani E, Rizzi A. 2008. An adapted multi-criteria approach to suppliers and products selection - An application oriented to lead-time reduction. Int J Prod Econ, 111(2): 763-781.
  • Chaudhuri A, Gerlich HA, Jayaram J, Ghadge A, Shack J, Brix BH, Hoffbeck LH, Ulriksen N. 2021. Selecting spare parts suitable for additive manufacturing: a design science approach. Product Plan Control, 32(8): 670-687.
  • Chopra S, Meindl P. 2020. Supply chain management: strategy, planning, and operation. Pearson, Boston USA, pp: 254.
  • Coşkun SS, Kumru M, Kan NM. 2022. An integrated framework for sustainable supplier development through supplier evaluation based on sustainability indicators. J Clean Prod, 335: 130287.
  • Crispim JA, Pinho de Sousa J. 2009. Partner selection in virtual enterprises: a multi-criteria decision support approach. Int J Prod Res, 47(17): 4791-4812.
  • Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, Akinyelu AA. 2022. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng Appl Artif Intell, 110: 104743.
  • Ho W. 2008. Integrated analytic hierarchy process and its applications A literature review. Eur J Oper Res, 186(1): 211-228.
  • Ishizaka A, Labib A. 2011. Review of the main developments in the analytic hierarchy process. Expert Syst Appl, 38(11): 14336-14345.
  • Jain AK. 2010. Data clustering: 50 years beyond K-means. Pattern Recognit Lett, 31(8): 651-666.
  • Kang X, Pang H. 2022. City selection for fresh produce e-commerce’s market entry strategy: Based on the perspective of urban logistics competitiveness. Transp Res Interdiscip Perspect, 13: 100537.
  • Kennedy WJ, Patterson JW, Fredendall LD. 2002. An overview of recent literature on spare parts inventories. Int J Prod Econ, 76(2): 201-215.
  • Keskin GA, İlhan S, Özkan C. 2010. The Fuzzy ART algorithm: A categorization method for supplier evaluation and selection. Expert Syst Appl, 37(2): 1235-1240.
  • Li H, Wu X, Liang Y, Zhang C. 2021. A multistakeholder approach to the airport gate assignment problem: Application of fuzzy theory for optimal performance indicator selection. Comput Intell Neurosci, 2021(1): 2675052.
  • Liberatore MJ, Nydick RL. 2008. The analytic hierarchy process in medical and health care decision making: A literature review. Eur J Oper Res, 189(1): 194-207.
  • Liu Y, Eckert CM, Earl C. 2020. A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Syst Appl, 161: 113738.
  • Lund B, Ma J. 2021. A review of cluster analysis techniques and their uses in library and information science research: k-means and k-medoids clustering. Performance Measurement and Metrics, 22(3): 161-173.
  • Maghsoodi AI, Kavian A, Khalilzadeh M, Brauers WK. 2018. CLUS-MCDA: A novel framework based on cluster analysis and multiple criteria decision theory in a supplier selection problem. Comput Ind Eng, 118: 409-422.
  • Maghsoodi AI, Riahi D, Herrera-Viedma E, Zavadskas EK. 2020. An integrated parallel big data decision support tool using the W-CLUS-MCDA: A multi-scenario personnel assessment. Knowl Based Syst, 195: 105749.
  • Pinçe Ç, Turrini L, Meissner J. 2021. Intermittent demand forecasting for spare parts: A Critical review. Omega, 105: 102513.
  • Porter ME, Teisberg EO. 2006. Redefining Health Care: Creating Value-Based Competition on Results. Harvard Business School Press, Boston, USA, pp: 56.
  • Saaty T. 1972. An eigenvalue allocation model for prioritization and planning. In Working paper, Energy Management and Policy Center: University of Pennsylvania, Pennsylvania, USA, pp:321.
  • Saaty TL. 1977. A scaling method for priorities in hierarchical structures. J Math Psychol, 15(3): 234-281.
  • Saaty TL. 1980. The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill., New York, USA, pp:126.
  • Sharifi E, Chaudhuri A, Vejrum Waehrens B, Guldborg Staal L, Lindemann CF, Davoudabadi Farahani S. 2022. Part selection for Freeform Injection Moulding: comparison of alternate approaches using a novel comprehensive methodology. Int J Prod Res, 60(23): 6996-7012.
  • Sironen S, Kangas J, Leskinen P. 2020. Restructuring a correlated multilevel decision hierarchy in multicriteria decision analysis. Journal of Multi‐Criteria Decision Analysis, 27(5-6): 266-285.
  • Subramanian N, Ramanathan R. 2012. A review of applications of Analytic Hierarchy Process in operations management. Int J Prod Econ, 138(2): 215-241.
  • Tatlıdil H. 1996. Uygulamalı çok değişkenli istatistiksel analiz. Cem Ofset, Eylül, Ankara, Türkiye, ss: 200.
  • Topan E, Eruguz AS, Ma W, Van der Heijden MC, Dekker R. 2020. A review of operational spare parts service logistics in service control towers. Eur J Oper Res, 282(2): 401-414.
  • Topol E. 2019. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, New York, USA, pp: 54.
  • Van der Auweraer S, Boute RN, Syntetos AA. 2019. Forecasting spare part demand with installed base information: A review. Int J Forecast, 35(1): 181-196.
  • Wu Y, Zhang T, Zhong K, Wang L, Xu C, Xu R. 2021. Optimal planning of energy storage technologies considering thirteen demand scenarios from the perspective of electricity Grid: A Three-Stage framework. Energy Convers Manag, 229: 113789.
  • Zyoud SH, Fuchs-Hanusch D. 2017. A bibliometric-based survey on AHP and TOPSIS techniques. Expert Syst Appl, 78: 158-181.
There are 37 citations in total.

Details

Primary Language Turkish
Subjects Multiple Criteria Decision Making, Industrial Engineering
Journal Section Research Articles
Authors

Doğukan İlbey Süslü 0009-0004-8947-0953

Kumru Didem Atalay 0000-0002-9021-3565

Tusan Derya 0000-0002-2851-4463

Publication Date May 15, 2025
Submission Date February 5, 2025
Acceptance Date March 15, 2025
Published in Issue Year 2025 Volume: 8 Issue: 3

Cite

APA Süslü, D. İ., Atalay, K. D., & Derya, T. (2025). Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama. Black Sea Journal of Engineering and Science, 8(3), 660-671. https://doi.org/10.34248/bsengineering.1633685
AMA Süslü Dİ, Atalay KD, Derya T. Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama. BSJ Eng. Sci. May 2025;8(3):660-671. doi:10.34248/bsengineering.1633685
Chicago Süslü, Doğukan İlbey, Kumru Didem Atalay, and Tusan Derya. “Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama”. Black Sea Journal of Engineering and Science 8, no. 3 (May 2025): 660-71. https://doi.org/10.34248/bsengineering.1633685.
EndNote Süslü Dİ, Atalay KD, Derya T (May 1, 2025) Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama. Black Sea Journal of Engineering and Science 8 3 660–671.
IEEE D. İ. Süslü, K. D. Atalay, and T. Derya, “Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama”, BSJ Eng. Sci., vol. 8, no. 3, pp. 660–671, 2025, doi: 10.34248/bsengineering.1633685.
ISNAD Süslü, Doğukan İlbey et al. “Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama”. Black Sea Journal of Engineering and Science 8/3 (May 2025), 660-671. https://doi.org/10.34248/bsengineering.1633685.
JAMA Süslü Dİ, Atalay KD, Derya T. Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama. BSJ Eng. Sci. 2025;8:660–671.
MLA Süslü, Doğukan İlbey et al. “Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama”. Black Sea Journal of Engineering and Science, vol. 8, no. 3, 2025, pp. 660-71, doi:10.34248/bsengineering.1633685.
Vancouver Süslü Dİ, Atalay KD, Derya T. Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama. BSJ Eng. Sci. 2025;8(3):660-71.
 Download Cover Image

                                                24890