نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت صنعتی ،دانشگاه آزاد واحد تهران مرکزی،تهران،ایران

2 دانشیارگروه ریاضی کاربردی، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران، ایران

3 دانشیارگروه ریاضی کاربردی، دانشگاه آزاد اسلامی، واحد تهران جنوب، تهران، ایران

4 دانشیار، گروه مدیریت صنعتی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

کارخانجات سیمان در گوشه و کنار ایران پراکنده شده است و بنابراین، محصول سیمان در شرایط آب و هوایی متفاوت تولید می‌گردد. فناوری‌های تولید سیمان با توجه به دسترسی به نوع مصالح، سوخت و از جمله منبع مهمی چون آب متفاوت است. بر این اساس، ورودی‌ها و خروجی‌های متفاوتی در هر یک از فناوری‌ها قابل ملاحظه است که باعث ایجاد ناهمگنی مراحل تولید بین کارخانجات می‌شود و از آنجا که همگی کار تولید سیمان را انجام می‌دهند، لازم است با یکدیگر مقایسه شوند. در این پژوهش، فرآیندهای عملیاتی پنج شیوه خشک، نیمه‌خشک، مرطوب، مرطوب دوغابی و نیمه مرطوب در چهار شرکت دارای مجموعا نه کارخانه مورد مطالعه قرار گرفته است و کارایی آنها با توجه به اطلاعات موجود از سال 1396 تا سال 1399 بر اساس تحلیل پوششی شبکه‌ای داده‌ها در شرایط ناهمگن و به صورت سه‌مرحله‌ای، پس از مدلسازی محاسبه گردید. از مهمترین ویژگی‌های این پژوهش می‌توان به تمرکز بر زنجیره تامین از مواد خام متنوع تا محصول نهایی اشاره کرد. گرچه بر اساس فناوری‌های یاد شده، مواد خام اولیه در فناوری‌های مختلف تولید سیمان متفاوت است، لیکن محصول نهایی تقریبا یکسان است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Efficiency evaluation of cement production companies using nonhomogeneous network DEA

نویسندگان [English]

  • Homa Abedi Dehkordi 1
  • Ghasem Tohidi 2
  • Shabnam Razavyan 3
  • Mohammad Ali Keramati 4

1 PhD student in industrial management, Central Tehran Azad University, Tehran, Iran

2 Department of Mathematics, Islamic Azad University Central Tehran Branch, Tehran, Iran

3 Department of Mathematics, Islamic Azad University South Tehran Branch, Tehran, Iran

4 Associate Professor, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

چکیده [English]

Cement production in Iran takes place across various geographical locations, each characterized by distinct weather conditions. The technology employed in cement production varies depending on the availability of raw materials, fuel sources, and essential resources like water. Consequently, diverse inputs and outputs assume significance in each production technology, resulting in non-homogeneity among cement factories. Despite these differences, all these facilities are engaged in cement production, warranting a comparative analysis of their efficiency. This study examines the operational processes of five different cement production technologies—dry, semi-dry, humid, semi-humid, and wet slurry—across four companies comprising a total of nine factories. The study evaluates their efficiency between 2017 and 2020 using network data envelopment analysis under non-homogeneous conditions across three modeling stages. An important aspect of this study is its focus on the entire supply chain, from raw materials to the final product. Although the raw materials employed vary among different cement production technologies, the end product remains largely consistent.
Introduction
In certain real-world scenarios, even with similar production technologies, the assumption of homogeneous decision-making units may not hold true. Practical applications often involve supply chain structures that differ significantly from others. For instance, some supply chains may, at certain stages, eject intermediate products to meet specific needs, a phenomenon not universal to all supply chains, resulting in non-homogeneous chains. The cement industry, including Iran, constitutes one of the pivotal economic sectors. Therefore, mitigating shortcomings, including resource and material waste reduction, can have a substantial impact on this industry and consequently on the broader economy. Due to varying climatic conditions, cement production employs diverse technologies, primarily categorized as dry or wet processes. This study investigates the operational processes of five different cement production methods—dry, semi-dry, humid, semi-humid, and wet slurry—across four companies with a total of nine factories. Their performance between 2017 and 2020 is evaluated using network DEA under non-homogeneous conditions, encompassing three modeling stages.
Materials and Methods
In novel approaches, DEA is utilized to assess the performance of network decision-making units. The models typically assume homogeneity among decision-making units, which may not always align with real-world conditions. Practical situations often violate assumptions of unit homogeneity and uniformity in input and output parameters. Consequently, it is imperative to present and employ methods and models capable of accommodating non-homogeneous units. This study employs a scientific library research approach and practical purposive data collection to gather relevant information. This information informs specific adjustments to operational processes. Consequently, the development of a robust system for evaluating supply chain performance becomes essential. The study utilizes common models to evaluate efficiency under non-homogeneous conditions. Classification of operational processes and related data, followed by modeling using Lingo software, is employed in this research.
Discussion and Result:
This article consists of two parts. Initially, it introduces the fundamental performance evaluation model and subsequently delves into the three-stage model of data envelopment analysis (DEA) within the supply chain context. In the second part, the production processes of Portland cement are examined, covering dry, semi-dry, humid, semi-humid, and wet slurry processes. The proposed approach assesses the performance of four cement production companies over a four-year period. Efficiency calculations for nine factories are conducted in three stages:
The first stage consists of three steps as follows:

First step: Input and output parameters used across the entire production process are categorized based on the different production methods.
Second step: Processes utilizing similar production steps, as determined in the first stage, are grouped into four categories.
Third step: Efficiency assessments for factories sharing similar production stages from the previous step are conducted, resulting in the identification of nine categories.

Second stage: The efficiency of each category, characterized by a common feature from the previous step, is calculated.
Third stage: To determine the overall efficiency of each factory, the efficiencies of individual processes are multiplied.
Conclusions
The results indicate that the fourth cement production company exhibits the highest efficiency, while the first company has the lowest efficiency. Notably, the lowest efficiency for the years 2017 to 2020 was recorded by the first company in 2020, while the fourth company achieved the highest efficiency in the same year. Among the factories, the lowest efficiency was observed in 2017 for the first company's five semi-dry factories, the fourth company's four semi-humid factories in 2018, the fourth company's nine wet slurry factories in 2018, the third company's seven semi-humid factories in 2020, and the fourth company's four semi-humid factories in 2020, which recorded the highest efficiency. Further examination and identification of suitable solutions to enhance efficiency in cases with lower efficiency levels can follow this study.

کلیدواژه‌ها [English]

  • Efficiency
  • Network data envelopment analysis
  • Nonhomogeneous DEA
  • Supply chain
  • Cement
  1. جمالی، غلامرضا و کریمی اصل، الهام. (1397). موقعیت رقابتی زنجیره تأمین لارج در صنعت سیمان و تحلیل اهمیت عملکرد الزامات راهبردی مرتبط با آن، مطالعات مدیریت صنعتی. سال شانزدهم، پاییز، شماره 50.
  2. رمضانیان، محمدرحیم، یاکیده، کیخسرو و اخوان دیلمی، لعبت. (1398). بررسی کارایی مدیریت بانک‌ها با استفاده از تکنیک DEA (مطالعه موردی شعب مختلف بانک‌های تهران). مدیریت بهره‌وری، سال سیزدهم، شماره چهل‌ونه، تابستان، ص‌ص: 144-12.

 

  1. Bart, M., Tohidi, G., &Sanei, M. (2018). DEA for nonhomogeneous mixed networks. Journal of Asia Pacific Management Review, 24(2): 161–166. https://doi.org/10.1016/j.apmrv.2018.02.003
  2. Charnes, A., Cooper, W., & Rhodes, E.) 1978(. Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
  3. Chen, Y., WU, L. & LU, Bo. (2018). Data envelopment analysis procedure with two non-homogeneous DMU groups, Journal of Systems Engineering and Electronics, 29(4): 780–788. DOI:21629/JSEE.2018.04.12
  4. Cook, W., Harrison, J., Imanirad, R., Rouse, P., & Zhu, J. (2013). Data envelopment analysis with nonhomogeneous DMUs, Journal of Operations Research, 61(3), 666-676.DOI: https://doi.org/10.1016/j.omega.2013.09.004
  5. Cook, WD., Tone, K., & Zhu. J. (2014). Data envelopment analysis Prior to choosing a model. Journal of Omega, 44, 1-4. DOI: https://doi.org/10.1016/j.omega.2013.09.004
  6. Du, J., Chen, Y. & Huo, J. (2015). DEA for non-homogenous parallel networks, Journal of Omega, 56(1), 122– 132. DOI:10.1016/j.omega.2014.10.001
  7. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3): 253 – 290. https://doi.org/10.2307/2343100
  8. Huang, W., Chiu, H., Tu, H., Luo, Y., & Wang, Z. (2016). Using the non-homogeneous frontier two-stage DEA model to assess the efficiencies of expense utilization and operation of the Taiwanese hotel industry, International Transactions in Operational Research, 23(6): 1067–1087. https://doi.org/10.1080/01605682.2019.1578625
  9. Jablonsky, J. (2019). Data Envelopment Analysis Models in Non-Homogeneous Environment. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(6): 1535–1540. 11118/ actaun201967061535
  10. Jamali,GH.,Karimi Asl, (2019). The competitive position of the large supply chain in the cement industry and the analysis of the importance of the performance of the strategic requirements related to it.Journal of industrial management , 16(50):53-77[In Persian] https://doi.org/10.22054/jims.2018.9106
  11. Kao, C., Hwang, SN. (2010). Efficiency measurement for network systems IT impact on firm performance, Journal of Decision Support Systems, 48: 437-446. https://doi.org/10.1016/j.dss.2009.06.002
  12. Li,W., Liang, L., Cook, W. D., & Zhu, J. (2016). DEA models for non-homogeneous DMUs with different input configurations, European Journal of Operational Research, 254(3), 946- 956. https://doi.org/10.1016/j.ejor.2016.04.063
  13. Li,Y., Lei, X., & Morton, A. (2018). Performance evaluation of nonhomogeneous hospitals: The case of Hong Kong hospitals, Health Care Management Science, 22(2): 215–228. 1007/s10729-018-9433-y
  14. Ramezaniyan,M., Pakideh, K., &Akhavan Deylami, L.(2020). Investigating the efficiency of bank management using DEA technique (case study of different branches of Tehran banks. Journal of Productivity management, 13(49):123-144 [In Persian]30495/qjopm.2019.666889
  15. Singh, S. and Ranjan, P. (2018). Efficiency analysis of non-homogeneous parallel sub-unit systems for the performance measurement of higher education, Annals of Operations Research, 269(1–2): 641–666. https://doi.org/10.1007/s10479-017-2586-0
  16. Song, M.L., Peng, L., Wang, J.L., & Zhao. J.J. (2018). Environmental efficiency and economic growth of China: a Ray slack-based model analysis. European Journal of Operational Research269(1):51–63. DOI: 10.1016/j.ejor.2017.03.073
  17. Sun, J., Wang, C., JI, X., & WU, J. (2017). Performance evaluation of heterogeneous bank supply chain systems from the perspective of measurement and decomposition, Journal of Computers & Industrial Engineering, 113: 891–903. DOI:1016/j.cie.2017.05.028
  18. Ye, C., Liangpeng, W., Bo, L. (2018). Data envelopment analysis procedure with two non-homogeneous DMU groups, Journal of Systems Engineering and Electronics, 29(4), 780 – 788. DOI:21629/JSEE.2018.04.12
  19. Zhu, W., Yu, Y. & Sun, P. (2017). Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability, European Journal of Operational Research. 269(1), 99-110.https://doi.org/10.1016/j.ejor.2017.08.007