ORIGINAL_ARTICLE
Providing Sustainable Supply Chain Agility Model in the Brick Industry of Isfahan province
Objective: Today, manufacturing companies must pay attention to environmental and social issues throughout the supply chain in order to survive. Many efforts have been made to examine the sustainable supply chain, but the agility of sustainable supply chain has been only observed in very few studies. Therefore, the aim of this study is to present an agility model of sustainable supply chain in the brick industry of Isfahan Province. Methods: This study was a developmental applied research and was done in a qualitative way. A sample of 10 experts was determined by snowball sampling who were familiar with the research. Then, thematic analysis was used to help the semi-structured interview to extract concepts, categories and elements and the interpretive-structural modeling was used to establish the relationship between elements and formation of the model. Results: Based on the obtained resulted of thematic analysis, 11 factors were determined (namely sociability, responsiveness, compliance with laws, speed, information technology, environmental protection, competence, flexibility, executive management commitment, quality management and economics. And with the help of descriptive-structural modeling method, executive management commitment was identified as the infrastructure of the model and two factors of socialization and environmental protection were as the head of the model. Conclusion: To achieve sustainable supply chain agility in the bricks industry, Brick Industry executives should have the required commitment and readiness for the sustainable supply chain agility.
https://imj.ut.ac.ir/article_68900_663f4177bc433b6777d958e46cb50c27.pdf
2018-09-23
335
352
10.22059/imj.2018.261444.1007459
Agility supply chain
sustainability
Database theory
Interpretive structural modeling
Farhad
Farhadi
farhadi.farhad@ut.ac.ir
1
Ph.D. Candidate, Department of Industrial Management, Alborz Campus, University of Tehran, Tehran, Iran
AUTHOR
Mohammad Reza
Taghizadeh Yazdi
mrtaghizadeh@ut.ac.ir
2
Associate Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Mansour
Momeni
mmomeni@ut.ac.ir
3
Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
AUTHOR
Seyed Mojtaba
Sajadi
msajadi@ut.ac.ir
4
Assistant Prof., Department of New Business Group, Faculty of Entrepreneurship, Tehran University, Tehran, Iran
AUTHOR
آذر، عادل؛ تیزرو، علی؛ مقبل باعرض، عباس؛ انواری رستمی، علی اصغر (1389). طراحی مدل چابکی زنجیره تأمین؛ رویکرد مدل سازی تفسیری ـ ساختاری. پژوهشهای مدیریت در ایران، 14(4)، 1-25.
1
آریافر، سیدحامد؛ موسوی راد؛ آرزو؛ عظیمی فرد، شهرام (1396). اولویتبندی معیارهای زنجیره تأمین سبز پایدار در صنعت فولاد. چهارمین کنفرانس بینالمللی برنامهریزی و مدیریت محیط زیست. تهران: دانشکده محیط زیست دانشگاه تهران، 2 و 3 خرداد 1396.
2
تقیزاده یزدی، محمد رضا؛ امراللهی بیوکی، ناهید؛ محمدی بالانی، عبدالکریم (1395). سنجش روابط میان عوامل تأثیرگذار بر پیادهسازی مدیریت زنجیرۀ تأمین سبز و رتبهبندی شرکتهای حاضر در زنجیرۀ تأمین (مطالعۀ موردی: صنعت کاشی و سرامیک استان یزد)، مجله مدیریت صنعتی، 8(4)، 555-574.
3
خراسانی، محمد؛ اسمعیلزاده، عارفه؛ تجاری، مهرناز ( 1396). شناسایی و اولویتبندی معیارهای تأثیرگذار بر پایداری زنجیره تأمین ناب چابک در شرکت کاله، هشتمین کنفرانس بینالمللی حسابداری و مدیریت و پنجمین کنفرانس کارآفرینی و نوآوری های باز، تهران: شرکت همایشگران مهر اشراق.
4
رجبزاده، علی؛ کرامتپناه، محسن؛ شاهرودی، کامبیز؛ کرامتپناه، امین (1394). طراحی تطبیقی مدل نابی ـ چابکی زنجیره تأمین با رویکرد مدلسازی ساختاری ـ تفسیری و دیمتل، پژوهشهای مدیریت منابع انسانی، 5(2)، 49-71.
5
رمضانی، یعقوب؛ اسماعیلیان، غلامرضا (۱۳۹۵)، ارائه مدل چابکی زنجیره تأمین برای شرکتهای تولید کننده قطعات خودرو با رویکرد مدلسازی تفسیری ـ ساختاری. چهارمین کنفرانس ملی مدیریت، اقتصاد و حسابداری، تبریز، سازمان مدیریت صنعتی آذربایجان شرقی، دانشگاه تبریز.
6
ضیایی، محمود؛ محمودزاده، سید مجتبی؛ شاهی، طاهره (1396). اولویتبندی عوامل مؤثر بر پیادهسازی مدیریت زنجیره تأمین سبز در صنعت گردشگری. فصلنامه جغرافیا و توسعه، 15(46)، 19-34.
7
عارفی، سارا؛ عندلیب اردکانی، داوود (1395)، طراحی مدل زنجیره تأمین پایدار با رویکرد DEMATEL فازی و پویایی سیستم در صنایع فولاد استان یزد. کنفرانس جامع علوم مدیریت و حسابداری، تهران: دبیرخانه کنفرانس جامع علوم مدیریت و حسابداری.
8
قاسمی، احمدرضا؛ رعیت پیشه، محمدعلی؛ حدادی، احد؛ رعیت پیشه، سعید (1396). شناسایی و اولویتبندی شاخصهای دخیل در پایداری زنجیره تأمین مواد غذایی. فصلنامه علوم و تکنولوژی محیط زیست، 19(4)، 369-382.
9
محقر، علی؛ صادقی مقدم، محمدرضا (1390)، هماهنگی زنجیره تأمین در صنعت خودروسازی: رویکرد تئوری برخاسته از داده ها. چشماندازمدیریتصنعتی،(4)، 29-63.
10
References
11
Arefi, S., Andalib Ardakani, M. (2016). Design of Sustainable Supply Chain Model with Fuzzy DEMATEL Approach and System Dynamics in Steel Industries of Yazd Province, Comprehensive Conference on Management and Accounting Sciences. Tehran, The Secretariat of the Comprehensive Management and Accounting Sciences. (in Persian)
12
Azar, A., Tizro, A., Moghbal, A., Anvari Rostami, A. (2010). Designing of Agile Supply Chain Model; Interpretative-Structural Modeling Approach. Management Researches in Iran, 14 (4), 1-25. (in Persian)
13
Bag, S., Anand, N., & Pandey, K. K. (2017). Green Supply Chain Management Model for Sustainable Manufacturing Practices. In Green Supply Chain Management for Sustainable Business Practice, (pp. 153-189). IGI Global.
14
Balon, V., Sharma, A. K., & Barua, M. K. (2016). Assessment of barriers in green supply chain management using ISM: A case study of the automobile industry in India. Global Business Review, 17(1), 116-135.
15
Dastyar, H., Mohammadi, A., & Mohamadlou, M. A. (2018). Designing a Model for Supply Chain Agility (SCA) Indexes Using Interpretive Structural Modeling (ISM). In International Conference on Dynamics in Logistics (pp. 58-66). Springer, Cham.
16
Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability and alignment: empirical evidence from the Indian auto components industry. International Journal of Operations & Production Management, 38(1), 129-148.
17
Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S. J., Shibin, K. T., & Wamba, S. F. (2017). Sustainable supply chain management: framework and further research directions. Journal of Cleaner Production, 142, 1119-1130.
18
Fernando, Y., & Saththasivam, G. (2017). Green supply chain agility in EMS ISO 14001 manufacturing firms: empirical justification of social and environmental performance as an organisational outcome. International Journal of Procurement Management, 10(1), 51-69.
19
Ghasemi, A., Rayatpishe, M., Haddadi, A., Rayatpishe, S. (2017). Identification and Prioritization of Indicators Involved in the Stability of Food Supply Chain. Journal of Environmental Science and Technology, 19 (4), 369-382. (in Persian)
20
Ivascu, L., Mocan, M., Draghici, A., Turi, A., & Rus, S. (2015). Modeling the green supply chain in the context of sustainable development. Procedia Economics and Finance, 26, 702-708.
21
Jain, P., & Gupta, N. (2016). Role of Agility in Green Supply Chain Management. International Journal of Science Technology and Management, 5(9), 482-488.
22
Khorasani, M., Esmaeilzadeh, A., Tejari, M. (2017). Identification and Prioritization of Measuring Criteria for the Sustainable Supply Chain Leakage in Kaleh Co., 8th International Accounting and Management Conference and 5th Conference on Entrepreneurship and Innovations, Tehran , Isfahr Symonics Co. (in Persian)
23
Li, Y., & Mathiyazhagan, K. (2018). Application of DEMATEL approach to identify the influential indicators towards sustainable supply chain adoption in the auto components manufacturing sector. Journal of Cleaner Production, 172, 2931-2941.
24
Li, X., & Zhu, Q. (2017). Evaluating the green practice of food service supply chain management based on fuzzy DEMATEL-ANP model. In Seventh International Conference on Electronics and Information Engineering (pp. 103222J-103222J). International Society for Optics and Photonics
25
Mathivathanan, D., Kannan, D., & Haq, A. N. (2018). Sustainable supply chain management practices in Indian automotive industry: A multi-stakeholder view. Resources, Conservation and Recycling, 128, 284-305.
26
Mohaghar, A., Sadeghi Moghaddam, M.R. (2011). Supply Chain Coordination in the Automotive Industry: Theory Approach to Data. Industrial Management Outlook, 4, 29-63. (in Persian)
27
Mohamamdi, A., Alimohammadloo, M., & Dastyar, H. (2015). Proposing a Model of Agility in Supply Chain using Interpretive Structural Modeling. Applied mathematics in Engineering, Management and Technology, 3(2), 192-205.
28
Rajabzadeh, A., Karmat Panah, M., Shahroudi, K., KarmatPanah, A. (2015). Adaptive Design of agile-leanSupply Chain Modeling with Structural-Interpretative Modeling and DEMATEL Modeling Approach. Human Resources Management Researches, 5 (2), 49-71.
29
(in Persian)
30
Ramezani, Y, Ismailyan, Gh. (2016). Supply chain agility model for automotive parts manufacturers with interpretive-structural modeling approach, fourth national conference on economics and management, Tabriz, East Azerbaijan Industrial Management Organization, Tabriz University. (in Persian)
31
Silvestre, B. S., Monteiro, M. S., Viana, F. L. E., & Souza-Filho, J. M. (2018). Challenges for Sustainable Supply Chain Management: When Stakeholder Collaboration Becomes Conducive to Corruption. Journal of Cleaner Production, 194, 766-776.
32
Su, C. M., Horng, D. J., Tseng, M. L., Chiu, A. S., Wu, K. J., & Chen, H. P. (2016). Improving sustainable supply chain management using a novel hierarchical grey-DEMATEL approach. Journal of Cleaner Production, 134, 469-481.
33
TaqizadehYazdi, M. R., AmrollahiBiuki, N, Mohammadi Balani, A. (2016). Measurement of the Relationship between Effective Factors on Implementation of Green Supply Chain Management and Ranking of Suppliers in the Supply Chain (Case Study: Tile and Ceramics Industryof Yazd Province).Industrial Management Magazine, 8 (4), 555-574. (in Persian)
34
Wang, Z., Mathiyazhagan, K., Xu, L., & Diabat, A. (2016). A decision making trial and evaluation laboratory approach to analyze the barriers to Green Supply Chain Management adoption in a food packaging company. Journal of Cleaner Production, 117, 19-28.
35
Wu, K. J., Tseng, M. L., Chiu, A. S., & Lim, M. K. (2017). Achieving competitive advantage through supply chain agility under uncertainty: A novel multi-criteria decision-making structure. International Journal of Production Economics, 190, 96-107.
36
Ziaie, M., Mahmoudzadeh, S., Shahi, T. (2017). Prioritizing the factors affecting the implementation of green supply chain management in the tourism industry, Geography and Development Quarterly, 15 (46), 19-34.
37
ORIGINAL_ARTICLE
Providing a New Model to Improving DEA-based Models in Multi-criteria Inventory Classification (Case Study: Pars Khazar)
Abstract
Objective: Many organizations use the ABC classification method to control their large amount of inventories. The most common way to classify inventories is the ABC method. In traditional ABC classification, items are only classified according to one criteria. But there are other criteria that need to be considered in the inventory classification. The purpose of this study is to present a new model for multi-criteria inventory classification.
Methods: Among the multi-criteria inventory classification methods, DEA-based methods do not require decision makers to determine the weight of the criteria; however, in the literature, only the radial methods of data envelopment analysis are used to classify inventory items. In this paper, the cross-efficiency of a non-radial model is proposed in order to improve the average cross-efficiency of the R model, which is a radial model.
Results: Therefore, the proposed method does not have the weakness of R model due to the use of a non-radial model and also it has benefits the cross-efficiency method.
Conclusion: The models were executed on 47 items of inventory related to a common numerical example in the research literature as well as on 80 items of inventory of the Pars Khazar Industrial Company and the results of the implementation of the models have been analyzed. The results of comparing the proposed model with some of the existing models in the literature indicate the superiority of the proposed model.
https://imj.ut.ac.ir/article_68901_c15be117f66cf136b6ff16efdba7d11a.pdf
2018-09-23
353
366
10.22059/imj.2018.259236.1007438
R model
RAM model
Cross efficiency
Data Envelopment Analysis
Multi-criteria inventory classification
Mohamadrahim
Ramazaniyan
ramazanian@guilan.ac.ir
1
Associate Prof., Department of Management, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran
LEAD_AUTHOR
keikhosro
Yakideh
yakideh@guilan.ac.ir
2
Assistant Prof., Department of Management, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran
AUTHOR
Atefeh
Alidous Saravani
alidostsaravani@gmail.com
3
MA., Department of Industrial Management, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran
AUTHOR
زارعی محمودآبادی، محمد؛ طحاری مهرجردی، محمد حسین؛ مهدویان، علیرضا (1393). ارزیابی فعالیتهای تحقیق و توسعه در ایران: رویکرد تحلیل پوششی دادهها. نشریه مدیریت صنعتی، 6(1)، 55-74.
1
علیپور جورشری، ارمغان؛ یاکیده، کیخسرو؛ محفوظی، غلامرضا (1396). بهینهسازی سبد سهام با حداقل میانگین انحرافات مطلق کاراییهای متقاطع. مدیریت صنعتی، 9(3)، 475- 496.
2
گودرزی، مهشید؛ یاکیده، کیخسرو؛ محفوظی، غلامرضا (1395). بهینهسازی سبد سهام با تلفیق کارایی متقاطع و نظریه بازیها. مدیریت صنعتی، 8(4)، 685-706.
3
مؤمنی، منصور (1393). مباحث نوین تحقیق در عملیات. تهران: گنج شایگان.
4
نمازی، م.؛ ابراهیمی، شهلا (1390). بررسی کارایی بانکهای ایران با استفاده از تکنیک DEA به روش پلهای. نشریه مدیریت صنعتی، 2(5)، 159-174.
5
References
6
Alipor Jorshari, A., Yakideh, K., Mahfoozi, GH. (2017). Portfollio optimization by minimum absolute deviation of cross efficiencies. Journalof Industrial Management, 9(3), 475-496. (in Persian)
7
Chen, J. X. (2011). Peer-estimation for multiple criteria ABC inventory classification. Computers & Operations Research, 38 (12), 1784–1791.
8
Cooper, W.W. & Park, K.S. & Pastor, J.T. (1999). RAM:A range adjusted measure of inefficiency for use with additive models, and relations to other models and measurrs in DEA. Journal of Productivity Analysis, 11(1), 5-42.
9
Flores, B.E. & Whybark, D.C. (1987). Implementing multiple criteria ABC analysis. Journal of Operation Management, 7(1-2), 79-84.
10
Goodarzi, M., Yakideh, K., Mahfoozi, Gh. (2017). Portfollio optimization by synthesis of cross efficiency and Game theory. Journalof Industrial Management, 8(4), 685-706.
11
(in Persian)
12
Guvenir, H.A. & Erel, E. (1998). Multicriteria inventory classification using a genetic algorithm.European Journal of Operational Research, 105 (1), 29-37.
13
Hadi-Vencheh, A. (2010). An improvement to multiple criteria ABC inventory classification. European Journal of Operational Research, 201, 962–965.
14
Hatefi, S.M., Torabi, S.A. & Bagheri, P. (2014). Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. International Journal of Production Research, 52(3), 776–786.
15
Hatefi, S.M. & Torabi, S.A. (2015). A common weight linear optimization approach for multicriteria ABC inventory classification. Advances in Decision Sciences, 2015.
16
Keren, B., & Hadad, Y. (2016). ABC Inventory Classification Using AHP and Ranking Methods via DEA. In Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO), 2016 Second International Symposium on (pp. 495-501). IEEE.
17
Momeni, M. (2015). New Operational Research Topics. Gange Shaygan, Tehran. (in Persian)
18
Namazi, M., Ebrahimi, S. (2011). The Investigation of the Iranian Banks' Efficiency by Using Stepwise DEA Technique,Journal of Industrial Management, 2(5), 159-332. (in Persian)
19
Ng, W.L. (2007). A simple classifier for multiple criteria ABC analysis. European Journal of Operational Research, 177 (1), 344-353.
20
Park, J., Bae, H., & Bae, J. (2014). Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification. Computers & Industrial Engineering, 76, 40-48.
21
Partovi, F. Y. & Anandarajan, M. (2002). Classifying inventory using an artificial neural network approach. Computers and Industrial Engineering, 41 (4), 389–404.
22
Ramanathan, R. (2006). ABC inventory classification with multiple criteria using weighted linear optimization. Computers & Operations Research, 33, 695–700.
23
Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory management and production planning and scheduling (Vol. 3, p. 30). New York: Wiley.
24
Zarei Mahmoudabad, M., Tahari Mehrjerdi, M.H., Mahdavian, A. (2014). Evaluation of R&D Activities in Iran: Data Envelopment Analysis Approach. Journal of Industrial Management, 6(1), 55-79. (in Persian)
25
Zheng, S., Fu, Y., Lai, K. K., & Liang, L. (2017). An improvement to multiple criteria ABC inventory classification using Shannon entropy. Journal of Systems Science and Complexity, 30(4), 857-865.
26
Zhou, P. & Fan, L. (2007). A note on multi-criteria ABC inventory classification using weighted linear optimization. European Journal of Operational Research, 182, 1488-1491.
27
ORIGINAL_ARTICLE
Proposing a Human Resource Balanced Scorecard based on Dynamic Systems Simulation s-based
Objective: The purpose of this study is to provide a dynamic system model for modeling the growth and learning perspective of a manufacturing company. The main focus of the model is on the company's human resources management process. Methods: In this study, an approach based on dynamic simulation has been proposed to evaluate the human resource condition. In order to do that, the researchers aimed to identify the most important variables in the learning and growth perspective of the balanced scorecard. Besides, two qualitative and quantitative models were designed and implemented. The qualitative model is describing causal relationships and the presence of system feedbacks between the variables and the quantitative model is based on the mathematical relations between them. Results: The proposed model was implemented in an Iranian production company working in the food and beverage industry. Different scenarios were defined based on the human resource strategic objectives in that company. The future status of human resource indicators of the company was predicted and analyzed through running scenarios in the dynamics model. Conclusion: The company managers will be able to evaluate the effective factors and their impact on the most important human resource indicators using the proposed model and to make the necessary decisions to achieve success in the future.
https://imj.ut.ac.ir/article_68906_be88c2a3adbc8097592e08d2be6edf79.pdf
2018-09-23
367
386
10.22059/imj.2018.264205.1007478
Human resource balanced scorecard
Workforce planning
Dynamic system simulation
scenario planning
Growth and learning perspective
Hossein
Safari
hsafari@ut.ac.ir
1
Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Mostafa
Zandieh
m_zandieh@sbu.ac.ir
2
Associate Prof., Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Ira
AUTHOR
Ehsan
Khanmohammadi
e.khanmohammadi@ut.ac.ir
3
Ph.D., Department of Operation Research, Faculty of Management, University of Tehran, Tehran, Iran
AUTHOR
آذر، عادل؛ معزز، هاشم (1393). اندازهگیری همسویی راهبردی سازمانی: رویکرد پویایی سیستم. فصلنامه مدیریت صنعتی، 6 (2)، 197- 218.
1
الهی، مرتضی؛ لطفی، محمد مهدی (1392). تحلیل تغییرات نیروی انسانی صنعت تایر خودروی سواری کشور با رویکرد پویاییهای سیستمی. فصلنامه مدیریت صنعتی، 5 (2)، 23- 48.
2
محمودی، زیرار؛ صیادی، احمد رضا؛ رجبزاده قطری، علی (1395). ارائه مدل پویای ارزیابی بهرهوری نیروی کار معادن (مطالعه موردی: مجمتع معدنی و صنعتی چادر ملو). فصلنامه مدیریت صنعتی، 8 (2)، 287-308.
3
References
4
Abdel-Hamid, T. K., & Madnick, S. E. (1991). Software project dynamics: an integrated approach (Vol. 1). Prentice Hall Englewood Cliffs, NJ.
5
Anantadjaya, S. (2007). Financial Aspects of HR Scorecard & Business Process Evaluation: An Empirical Study in Retail & Service Industries. 4th Universitas Surabaya International Annual Symopsium on Management, Available at: SSRN: https://ssrn.com/abstract =1006714.
6
Andersen, D. F., & Emmerichs, R. M. (1982). Analyzing US military retirement policies. Simulation, 39(5), 151–158.
7
Azar, A. & Moazzez, H. (2014).Measuring Organizational Strategic Alignment: A Systems Dynamics Approach. Industrial Management Journal, 6(2), 197-218. (in Persian)
8
Becker, B. E., Huselid, M. A., Huselid, M. A., & Ulrich, D. (2001). The HR scorecard: Linking people, strategy, and performance. Harvard Business Press.
9
Bohlandt, F. (2006). Is Your HR Scorecard Up To Date. Master of Business Administration, Graduate School of Business of the University of Stellenbosch.
10
Boselie, P., Paauwe, J., & Jansen, P. (2001). Human resource management and performance: lessons from the Netherlands. International Journal of Human Resource Management, 12(7), 1107–1125.
11
Boxall, P. (2003). HR strategy and competitive advantage in the service sector. Human Resource Management Journal, 13(3), 5–20.
12
Brassington, K., & Slemen, S. (1997). SUN LIFE’S human resources SCORECARD. Measuring Business Excellence, 1(2), 60–62.
13
Chadwick, C., & Dabu, A. (2009). Human resources, human resource management, and the competitive advantage of firms: Toward a more comprehensive model of causal linkages. Organization Science, 20(1), 253–272.
14
Cunningham, J. B., & Kempling, J. (2011). Promoting organizational fit in strategic HRM: Applying the HR scorecard in public service organizations. Public Personnel Management, 40(3), 193–213.
15
Delery, J. E., & Doty, D. H. (1996). Modes of theorizing in strategic human resource management: Tests of universalistic, contingency, and configurational performance predictions. Academy of Management Journal, 39(4), 802–835.
16
Doman, A., Glucksman, M. A., Tu, N.-L., & Warren, K. (2000). The talent-growth dynamic. The McKinsey Quarterly, 106.
17
Elahi, M. & Lotfi, M-M. (2013). A System Dynamics Approach for Analyzing the Human Resource Changes in Sedan Tire Industry of Iran. Industrial Management Journal, 5(2), 23-48. (in Persian)
18
Fathi M. R., Safari H., Faghih A. (2013) Integration of graph theory and matrix approach with fuzzy AHP for equipment selection. Journal of Industrial Engineering and Management, 6(2), 477-494.
19
Ferris, G. R., Perrewé, P. L., Ranft, A. L., Zinko, R., Stoner, J. S., Brouer, R. L., & Laird, M. D. (2007). Human resources reputation and effectiveness. Human Resource Management Review, 17(2), 117–130.
20
Fleetwood, S., & Hesketh, A. (2008). Theorising under-theorisation in research on the HRM-performance link. Personnel Review, 37(2), 126–144.
21
Forrester, J. W. (1961). Industrial dynamics, vol. 2. Cambridge, MA: MIT Press.
22
Gregoriades, A., & Karakostas, B. (2004). Unifying business objects and system dynamics as a paradigm for developing decision support systems. Decision Support Systems, 37(2), 307–311.
23
Gupta, Y. P., & Gupta, M. C. (1990). A process model to study the impact of role variables on turnover intentions of information systems personnel. Computers in Industry, 15(3), 211–238.
24
Hafeez, K., & Abdelmeguid, H. (2003). Dynamics of human resource and knowledge management. Journal of the Operational Research Society, 54(2), 153–164.
25
Harney, B., & Dundon, T. (2006). Capturing complexity: developing an integrated approach to analysing HRM in SMEs. Human Resource Management Journal, 16(1), 48–73.
26
Josiane, F.-S. (2009). The HR Scorecard: Linking People, Strategy and Performance. Management Research News, 32(3), 297–299.
27
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press: New York.
28
Ketchen Jr, D. J., Boyd, B. K., & Bergh, D. D. (2008). Research methodology in strategic management: Past accomplishments and future challenges. Organizational Research Methods, 11(4), 643–658.
29
Liou, D., & Lin, C. (2008). Human resources planning on terrorism and crises in the Asia Pacific region: Cross‐national challenge, reconsideration, and proposition from western experiences. Human Resource Management: Published in Cooperation with the School of Business Administration, The University of Michigan and in Alliance with the Society of Human Resources Management, 47(1), 49–72.
30
Mahmoodi, Z., Sayadi, A. & Rajabzadeh Ghatari, A. (2015). Dynamic modelling of labor productivity in mining- Case study: Chadormaluo mining and industry complex. Industrial Management Journal, 8(2), 287-308. (in Persian)
31
Nag, R., Hambrick, D. C., & Chen, M. (2007). What is strategic management, really? Inductive derivation of a consensus definition of the field. Strategic Management Journal, 28(9), 935–955.
32
Norton, D., & Kaplan, R. (1992). The Balanced Scorecard. Measures that drive performance. Harvard Business Review, enero-febrero. AAA https://steinbeis-bi.de/images/artikel/hbr_ 1992.pdf.
33
Packer, D. W. (1964). Resource acquisition in corporate growth (Vol. 2). Mit Press.
34
Powell, T. C. (2001). Competitive advantage: logical and philosophical considerations. Strategic Management Journal, 22(9), 875–888.
35
Reid, W. M., & Taylor, R. G. (1989). An application of absorbing Markov analysis to human resource issues in public administration. Review of Public Personnel Administration, 10(1), 67–74.
36
Roehling, M. V., Boswell, W. R., Caligiuri, P., Feldman, D., Graham, M. E., Guthrie, J. P., … Tansky, J. W. (2005). The future of HR management: Research needs and directions. Human Resource Management: Published in Cooperation with the School of Business Administration, The University of Michigan and in Alliance with the Society of Human Resources Management, 44(2), 207–216.
37
Ronda‐Pupo, G. A., & Guerras‐Martin, L. Á. (2012). Dynamics of the evolution of the strategy concept 1962–2008: a co‐word analysis. Strategic Management Journal, 33(2), 162–188.
38
Runcie, J. F. (1980). Dynamic-systems and the quality of work life. Personnel, 57(6), 13–24.
39
Saadat, E. (2007). Human resource management (12th ed.). SAMT. Tehran.
40
Safari H., Machado V. C., Zadeh Sarraf A., Maleki M. (2014). Multidimensional personnel selection through combination of TOPSIS and Hungary assignment algorithm. Management and Production Engineering Review, 5(1), 42-50.
41
Sterman, J. D. (2000). Business dynamics: systems thinking and modeling for a complex world (Vol. 19). Irwin/McGraw-Hill Boston.
42
Thomas, P., Wilson, J., & Leeds, O. (2013). Constructing ‘the history of strategic management’: A critical analysis of the academic discourse. Business History, 55(7), 1119–1142.
43
TURNER, G. (2000). Using human resource accounting to bring balance to the balanced scorecard. Journal of Human Resource Costing & Accounting, 5(2), 31–44.
44
Tzafrir, S. S. (2005). The relationship between trust, HRM practices and firm performance. The International Journal of Human Resource Management, 16(9), 1600–1622.
45
Vancouver, J. B. (2008). Integrating self-regulation theories of work motivation into a dynamic process theory. Human Resource Management Review, 18(1), 1–18.
46
Wacker, J. G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16(4), 361–385.
47
Wheelen, T. L., Hunger, J. D., Hoffman, A. N., & Bamford, C. E. (2017). Strategic management and business policy. pearson.
48
Wiese, D. S., & Buckley, M. R. (1998). The evolution of the performance appraisal process. Journal of Management History, 4(3), 233–249.
49
ORIGINAL_ARTICLE
The Architecture of Talent Identifying Process at National Elite Foundation: CM and SSM Hybrid Algorithm
Objective: Within the knowledge based economy, talents are known as a strategic talent in order to achieve sustainable competitive advantage as well as a criterion for growth in different organizational and social level. Thus, in recent studies, talent management architecture has emphasized on exploring and identifying talents in different fields. Accordingly, the present study aimed to provide a model of talent identifying process at the Iran National Elite Foundation (INEF).
Methods: This study is conducted based on a descriptive-exploratory study with a developmental-applied approach. The statistical population of the research includes the scientific lasting faces of the country as well as top managers of the INEF. The statistical sample was selected through a purposeful judgment of 25 people. Data collection tools were library studies and semi-structured interviews with experts. Data were analyzed based on qualitative approach and based on a hybrid algorithm of the soft system methodology and cognitive mapping.
Results: The findings proposed a model to identify talents in INEF including two methods of elite selection as active and passive manner.
Conclusion: The results showed that the active process of elite selection includes two nominating and searchable methods and the passive process of elite selection includrs two selective and self- assertive methods.
https://imj.ut.ac.ir/article_68907_d9b2004444347bb2433548967ac2a0ce.pdf
2018-09-23
387
406
10.22059/imj.2018.262586.1007469
Talent management architecture
Talent identifying
Soft System Methodology
Cognitive mapping
INEF
Behnam
Golshahi
bgolshahi@semnan.ac.ir
1
Ph.D. Candidate, Department of Human Resources Management, Faculty of Economic, Management and Political Sciences, Semnan University, Semnan, Iran
AUTHOR
Abbas Ali
Rastegar
a_rastgar@semnan.ac.ir
2
Associate Prof., Department of Management, Faculty of Economic, Management and Political Sciences, Semnan University, Semnan, Iran
LEAD_AUTHOR
Davood
Feiz
feiz1353@semnan.ac.ir
3
Associate Prof., Department of Management, Faculty of Economic, Management and Political Sciences, Semnan University, Semnan, Iran
AUTHOR
Azim
Zarei
a_zarei@semnan.ac.ir
4
Associate Prof., Department of Management, Faculty of Economic, Management and Political Sciences, Semnan University, Semnan, Iran
AUTHOR
آذر، عادل؛ خسروانی، فرزانه؛ جلالی، رضا (1395). تحقیق در عملیات نرم: رویکردهای ساختاردهی مسئله. چاپ دوم، تهران: انتشارات سازمان مدیریت صنعتی.
1
آذر، عادل؛ واعظی، رضا؛ محمدپور سرایی، وحید (1396). طراحی مدل خطیمشیگذاری تجاریسازی فناوری نانو با رویکرد متدولوژی سیستمهای نرم. فصلنامه مدیریت سازمانهای دولتی، 5 (2)، 89- 106.
2
تولایی، روحاله؛ بامداد صوفی، جهانیار؛ رشیدی، محمد مهدی؛ رضائیان، علی؛ صالحی صدقیانی، جمشید (1393). طراحی الگوی توسعه شبکههای دانش در هابهای پژوهش و فناوری صنعت نفت با بهکارگیری رویکرد تفکر نرم و مدل نگاشت شناختی. فصلنامه مدیریت منابع انسانی در صنعت نفت، 6 (20)، 181- 200.
3
چکلند، پیتر؛ پولتر، جان (1393). یادگیری برای عمل. ترجمه محمدرضا مهرگان، محمود دهقان، محمدرضا اخوان و کامیار رئیسیفر. تهران: انتشارات مهربان نشر.
4
سپهریراد، رامین؛ رجبزاده قطری، علی؛ آذر، عادل؛ زارعی، بهروز (1394). استفاده از روششناسی سیستمهای نرم برای ساختاردهی به مسئله مراقبت در برابر مواجهات شغلی سرطانزا: مورد مطالعه صنایع نفت. فصلنامه پژوهشهای مدیریت در ایران، 19 (3)، 161- 184.
5
قلیپور، آرین؛ افتخار، نیره (1396). مدیریت استعدادها (نخبهیابی، نخبهداری، نخبهپروری). تهران: انتشارات مهربان نشر.
6
References
7
Alnıaçık, E., Alnıaçık, U., Erat, S. & Akcin, K. (2014). Attracting Talented Employees to the Company: Do We Need Different Employer Branding Strategies in Different Cultures? Procedia - Social and Behavioral Sciences, 150, 336-344.
8
Ambler, T. & Barrow, S. (1996). The employer brand. Journal of Brand Management, 4, 185-206.
9
Azar, A., Khosravani, F. & Jalali, R. (2016). Soft Operational Research, Second Ed, Tehran: Industrial Management Institute. (in Persian)
10
Azar, A., Vaezi, R. & Mohammadpour Saraiy, V. (2017). Designing a Model of Policy Making of Commercialization of Nanotechnology Using Soft Systems Methodology. Quarterly Journal of Public Organzations Management, 5(2), 89-106. (in Persian)
11
Bersin, J., Geller, J., Wakefield, N. & Walsh, B. (2016). Global Human Capital Trends: The New Organization, Different by Design. New Jersey: Deloitte University Press.
12
Bhattacharya, C.B., Sen, S. & Korschun, D. (2008). Using Corporate Social Responsibility to Win the War for Talent. MIT Sloan Management Review, 49 (2).
13
Checkland, P. & Davies L. (1986). The use of the term ‘Weltanschauung’ in soft systems methodology. Journal of Applied Systems Analysis, 13, 109-115.
14
Checkland, P. & Polter, J. (2014). Learning for action. Translated by Mehregan, M.R. Dehghan, M. Akhavan, M.R. & Raissifar, K. Tehran: Mehraban Nashr. (in Persian)
15
Checkland, P. & Scholes, J. (1990). Soft systems methodology in action, Chichester: Wiley.
16
Checkland, P. & Winter, M. (2006). Process and content: Two ways of using SSM. Journal of the Operational Research Society, 57(12), 1435-1441.
17
Checkland, P., & Poulter, J. (2006). Learning for Action: A Short Definitive Account of Soft Systems Methodology and its Use, for Practitioners, Teachers and Students. Chichester: John Wiley and Sons Ltd.
18
Collings, D. G. & Mellahi, K. (2009). Strategic Talent Management: A review and research agenda. Human Resource Management Review, 19(4), 304-313.
19
Falk, B., Lidor, R., Lander, Y. & Lang, B. (2014). Talent identification and early development of elite water-polo players: a 2-year follow-up study. Journal of Sports Sciences, 22, 347–355.
20
Gholipor, A. & Eftekhar, N. (2017). Talents Management (elite-selecting, elite-keeping and elite-training). Tehran: Mehraban Nashr Publisher. (in Persian)
21
Kostman, J. T., & Schiemann, W. A. (2005). People equity: The hidden driver of quality. Quality Progress, 38(5), 37–42.
22
Lewis, R. E. & Heckman, R. J. (2006). Talent management: A critical review. Human Resource Management Review, 16(2), 139-154.
23
Liakopoulos, A., Barry, L. & Schwartz, J. (2013). The Open Talent Economy: People and Work in a Borderless Workplace. Available in: https://www2.deloitte.com/content/dam/Deloitte /global/Documents/HumanCapital/dttl-hc-english-opentalenteconomy.pdf.
24
Lidor, R. & Lavyan, N.Z. (2002). A retrospective picture of early sport experiences among elite and near-elite Israeli athletes: developmental and psychological perspectives.
25
Mingers, J. (2011). Soft OR comes of age-but not everywhere! Omega, 39 (6), 729-741.
26
Morris, S., Snell, S., & Björkman, I. (2016). An architectural framework for global talent management. Journal of International Business Studies, 47(6), 723-747.
27
Schiemann, W. A. (2006). People equity: A new paradigm for measuring and managing human capital. Human Resource Planning, 29 (1), 34–44.
28
Schiemann, W. A. (2013). From talent management to talent optimization. Journal of World Business, 49(2), 281-288.
29
Sepehrirad, A., Rajabzadeh, A., Azar, A., & Zarei, B. (2015). A Soft System Methodology Approach for Structuring Surveillance against Occupational Carcinogenic Exposures Problem (Case Study: Petroleum Industries). Management research in Iran, 19(3), 167-190. (in Persian)
30
Silzer, R. & Church, A. H. (2009a). Identifying and assessing high-potential talent: Current organizational practices. In R. Silzer & B. E. Dowell (Eds.), Strategy-driven talent-management: A leadership imperative (pp. 213–279). San Francisco, CA: Jossey-Bass.
31
Sparrow, P. R. & Makram, H. (2015). What is the value of talent management? Building value-driven processes within a talent management architecture. Human Resource Management Review, 25(3), 249-263.
32
Talent Architecture.(2013). On the World Wide Web:Advantage Performance Group. www.advantageperformance.com.
33
Tansley, C. (2011). What do we mean by the term “talent” in talent management?. Industrial and Commercial Training, 43(5), 266-274.
34
Tavalaie, R., Soufi, B. G., Rashidi, M., Rezaiean, A. & Salehi Sedghiyani, J. (2015). Designing a Model for the Development of Knowledge Networks in the Research and Technology Industry Hubs Using the Soft Thinking Approach and Cognitive Modeling Model. Human resource management in oil industry, 6(20), 181-200. (in Persian)
35
Wacker, J. G. (2004). A theory of formal conceptual definitions: Developing theory building measurement instruments. Journal of Operations Management, 22, 629–650.
36
Wilson, B. (1993). Systems: concepts, methodologies and applications, Wiley, UK.
37
Wilson, B. (2001). Soft Systems Methodology, Conceptual Model Building and its Contribution. John Willy and Sons LTD, UK.
38
ORIGINAL_ARTICLE
Developing an Integrated Simulation Model of Bayesian-networks to Estimate the Completion Cost of a Project under Risk: Case Study on Phase 13 of South Pars Gas Field Development Projects
Objective: The aim of this paper is to propose a new approach to assess the aggregated impact of risks on the completion cost of a construction project. Such an aggregated impact includes the main impacts of risks as well as the impacts of interactions caused by dependencies among them.
Methods: In this study, Monte Carlo simulation and Bayesian Networks methods are combined to present a framework to assess the aggregated impact of risks on a construction project’s completion cost.
Results: Project risk assessment, regardless of the interactions between them, leads to prioritization of risks and does not provide any indicator to assess the aggregated effect of risks on the entire project. Achieving a nearly accurate estimate of the project completion time or cost requires consideration of the probabilities and effects of the risks, as well as the interdependencies among them simultaneously.
Conclusion: The integrated model presented in this paper, in addition to providing a framework to evaluate the direct impact of risks on activities or work packages of a construction project, is able to assess the sensitivity of the project completion cost to the occurrence of the risks by considering the probabilities, effects and interdependencies.According to the results of the sensitivity analysis, the probabilities of “shortage of resources”, “inefficiency in project financing” and “poor design” are the main causes of delay in a gas refinery construction project.
https://imj.ut.ac.ir/article_68908_a556234888504c0aa565c528233c96da.pdf
2018-09-23
407
428
10.22059/imj.2018.228669.1007202
risk assessment
Aggregated risk
Bayesian networks
simulation
Construction project
Ali
Namazian
a.namazian@ut.ac.ir
1
PhD. Candidate, Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Siamak
Haji Yakhchali
yakhchali@ut.ac.ir
2
Assistant Prof., Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
آتشسوز، علی؛ فیضی، کامران؛ کزازی، ابوالفضل؛ الفت، لعیا (1394). مدلی برای روابط ریسکهای زنجیرة تأمین صنعت پتروشیمی در ایران. نشریه مدیریت صنعتی، 7(3)، 405- 424.
1
صیادی، احمدرضا؛ حیاتی، محمد؛ منجزی، مسعود (1390). مدیریت ریسک ساخت تونل با استفاده از تکنیکهای MADM. نشریه مدیریت صنعتی، 3 (7)، 99- 116.
2
عالم تبریز، اکبر؛ خالدیان، فرنوش؛ مهدیپور، مصطفی (1395). پیشبینی زمان پروژه از طریق طول زمان کسب شده و مدیریت ریسک. نشریه مدیریت صنعتی، 8 (2)، 217- 240.
3
ولیپور خطیر، محمد؛ قاسمنیا عربی، نرجس (1395). مدلسازی سیستم استنتاج فازی برای ارزیابی ریسکهای بالقوه در تجهیزات پزشکی. نشریه مدیریت صنعتی، 8 (4)، 533- 554.
4
References
5
Alam Tabriz, A., Khaledian, F., & Mehdipour, M. (2016). Estimation of project time through earned time and risk management. Industrial Management Journal, 8(2), 217-240.
6
(in Persian)
7
Aminbakhsh, S., Gunduz, M., & Sonmez, R. (2013). Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. Journal of Safety Research, 46, 99-105.
8
Atashsouz, A., Feyzi, K., Kazazi, A., & Olfat, L. (2015). A model for relationships between risks in the supply chain of the petrochemical industry in Iran. Industrial Management Journal, 7(3), 405-424. (in Persian)
9
Chatterjee, K., Zavadskas, E., Tamošaitienė, J., Adhikary, K., & Kar, S. (2018). A Hybrid MCDM Technique for Risk Management in Construction Projects. Symmetry, 10(2).
10
Cheng, M., & Lu, Y. (2015). Developing a risk assessment method for complex pipe jacking construction projects. Automation in Construction, 58, 48-59.
11
Creemers, S., Demeulemeester, E., & Van de Vonder, S. (2014). A new approach for quantitative risk analysis. Annals of Operations Research, 213(1), 27-65.
12
Dikmen, I., Birgonul, M. T., & Han, S. (2007). Using fuzzy risk assessment to rate cost overrun risk in international construction projects. International Journal of Project Management, 25(5), 494-505.
13
Gierczak, M. (2014). The quantitative risk assessment of MINI, MIDI and MAXI Horizontal Directional Drilling Projects applying Fuzzy Fault Tree Analysis. Tunnelling and Underground Space Technology, 43, 67-77.
14
Hu, Y., Zhang, X., Ngai, E. W. T., Cai, R., & Liu, M. (2013). Software project risk analysis using Bayesian networks with causality constraints. Decision Support Systems, 56, 439-449.
15
Hyun, K.C., Min, S., Choi, H., Park, J., & Lee, I.M. (2015). Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels. Tunnelling and Underground Space Technology, 49, 121-129.
16
Jamshidi, A., Rahimi, S. A., Ait-kadi, D., Rebaiaia, M. L., & Ruiz, A. (2015, 25-27 May 2015). Risk assessment in ERP projects using an integrated method. Paper presented at the 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT).
17
Joubert, F. J., Pretorius, L. U. (2017). Using Monte Carlo simulation to create a ranked check list of risks in a portfolio of railway construction projects. South African Journal of Industrial Engineering, 28, 133-148.
18
Khodakarami, V., & Abdi, A. (2014). Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items. International Journal of Project Management, 32(7), 1233-1245.
19
Leu, S.S., & Chang, C.M. (2013). Bayesian-network-based safety risk assessment for steel construction projects. Accident Analysis & Prevention, 54, 122-133.
20
Liang, W., Hu, J., Zhang, L., Guo, C., & Lin, W. (2012). Assessing and classifying risk of pipeline third-party interference based on fault tree and SOM. Engineering Applications of Artificial Intelligence, 25(3), 594-608.
21
Liu, J., & Wei, Q. (2018). Risk evaluation of electric vehicle charging infrastructure public-private partnership projects in China using fuzzy TOPSIS. Journal of Cleaner Production, 189, 211-222.
22
Loizou, P., & French, N. (2012). Risk and uncertainty in development: A critical evaluation of using the Monte Carlo simulation method as a decision tool in real estate development projects. Journal of Property Investment & Finance, 30(2), 198-210.
23
Luu, V. T., Kim, S.-Y., Tuan, N. V., & Ogunlana, S. O. (2009). Quantifying schedule risk in construction projects using Bayesian belief networks. International Journal of Project Management, 27(1), 39-50.
24
Rodríguez, A., Ortega, F., & Concepción, R. (2016). A method for the evaluation of risk in IT projects. Expert Systems with Applications, 45, 273-285.
25
Sayadi, A., Hayati, M., & Monjezi, M. (2011). Risk management of tunnel construction using MADM methods. Industrial Management Journal, 3(7), 99-116. (in Persian)
26
Valipour khatir, M., & Ghasemnia arabi, N. (2016). Fuzzy inference system modeling for assessing the potential risks in medical equipments. Industrial Management Journal, 8(4), 533-554. (in Persian)
27
Wang, L., Zhang, H.-y., Wang, J.-q., & Li, L. (2018). Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project. Applied Soft Computing, 64, 216-226.
28
Yang, C.C., Lin, W.T., Lin, M.Y., Huang, J.T. (2006). A study on applying FMEA to improving ERP introduction: An example of semiconductor related industries in Taiwan. International Journal of Quality & Reliability Management, 23(3), 298-322.
29
Zeng, J., An, M., & Smith, N. J. (2007). Application of a fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management, 25(6), 589-600.
30
Zeng, Y., & Skibniewski, M. J. (2013). Risk assessment for enterprise resource planning (ERP) system implementations: a fault tree analysis approach. Enterprise Information Systems, 7(3), 332-353.
31
ORIGINAL_ARTICLE
Critical Systems Heuristics (CSH) to Deal with Stakeholders' Contradictory Viewpoints of Iran Performance Based Budgeting System
Objective: Performance based budgeting is an undeniable necessity for effective management of the country vital resources nowadays, which benefits all economic and social layers of the society if properly implemented. Accordingly, this has encouraged lots of studies and researches on PPB theories, concepts and models. This study deeply reviewed Iran’s PBB system within four basic issues, including motivation, knowledge, legitimacy and power through critical approach.
Methods: Exploration in this system has been made using a critical (Emancipatory) approach called Critical Systems Heuristics. The process consists of reflecting the hidden views of the stakeholders using 12 boundary questions in two states of “being” which refers to the present situation and “ought to” which refers to an ideal situation.
Results: Findings of the implementation of methodology showed that there are many technical, human and social problems involved in evaluating and implementing a performance-based budgeting system. The main criticisms of the system are targeting, focusing on the short-term political visions, ignoring the new critical expertise, and ignoring the transparency of the system.
Conclusion: Research findings provided some fruitful comments on Iran’s PBB system and its vital required changes and modifications according to stakeholders’ boundary judgments (primary judgments about and a holistic approach to the system).
https://imj.ut.ac.ir/article_68909_c4bdda917f8440d21381e4681e2d41ad.pdf
2018-09-23
429
454
10.22059/imj.2018.254206.1007404
Performance based budgeting
Soft Operation Research
system thinking
critical systems
Emancipatory
Mahmoud
Dehghan Nayeri
mdnayeri@modares.ac.ir
1
Assistant Prof., Department of Industrial Management, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Moein
Khazaei
moein.khazaei@modares.ac.ir
2
MA. Student, Department of Industrial Management, Tarbiat Modares University, Tehran, Iran
AUTHOR
Fatemeh
Alinasab Imani
f.alinasab@modares.ac.ir
3
MA. Student, Department of Industrial Management, Tarbiat Modares University, Tehran, Iran
AUTHOR
آذر، عادل؛ زاهدی، شمسالسادات؛ امیرخانی، طیبه (1389). ﻃﺮاﺣﯽ ﻣﺪل ﭘﯿﺎدهﺳﺎزی ﺑﻮدﺟﻪرﯾﺰی ﺑـﺮ ﻣﺒﻨـﺎی ﻋﻤﻠﮑـﺮد ﺑـﺎ روﯾﮑﺮد ﭘﻮﯾﺎﯾﯽ ﺳﯿﺴﺘﻢ. فصلنامه علوم مدیریت ایران، 5(18)، 29 – 53.
1
آذر، عادل (1389). بودجهریزی بر مبنای عملکرد و حسابرسی عملکرد، از اجرای حسابرسی عملکرد. کنفرانس پاسخگویی و بهرهوری، تهران.
2
آذر، عادل؛ خدیور، آمنه (1392). روﻳﻜﺮد اﻧﺘﻘﺎدی و ﭘﺴﺎ ﻣﺪرن ﺑﻪ ﺑﻮدﺟﻪرﻳﺰی ﺑﺮ ﻣﺒﻨﺎی ﻋﻤﻠﻜﺮد. پژوهشهای مدیریت در ایران، 18 (3)، 67-94.
3
آذر، عادل؛ خدیور، آمنه؛ امین ناصری، محمدرضا؛ انواری رستمی، علی اصغر (1390). اراﺋﻪ ﻣﻌﻤﺎری ﻧﻈﺎم ﺑﻮدﺟﻪرﻳﺰی ﺑﺮ ﻣﺒﻨﺎی ﻋﻤﻠﻜﺮد ﺑﺎروﻳﻜﺮد ﺳﻴﺴﺘﻢ ﭘﺸﺘﻴﺒﺎن ﺗﺼﻤﻴﻢ ﻫﻮﺷﻤﻨﺪ. پژوهشهای مدیریت در ایران، 15 (3)، 1-22.
4
آذر، عادل؛ خسروانی، فرزانه؛ خداداد حسینی، سید حمید؛ رجبزاده، علی (1395). ساختاردهی مسئله تدوین استراتژی با استفاده از رویکردهای استراتژی مذاکره و بازتاب مفاهمه و تئوری درام (مورد مطالعه: فاز پالایش بعد از تولید زنجیره تأمین سبز گاز). پژوهشهای نوین در تصمیمگیری، 1(1)، 103 – 138.
5
حسینزاده، مهناز؛ مهرگان، محمدرضا؛ امیری، مجتبی (1392). طراحی چارچوبی برای استفاده از روششناسی چندگانه در تحقیق در عملیات با استفاده از رویکرد تحلیل جامع ریختشناسی. چشمانداز مدیریت صنعتی، 3(11)، 63-87.
6
حسینزاده، مهناز؛ مهرگان، محمدرضا؛ کیانی، مجتبی (1392). تحقیق در عملیات، علم یا فناوری؟ اهمیت آن چیست؟ سیاست، علم و فناوری، 5(4)، 33-46.
7
خاتمی، محمود (1381). پراگماتیسم و اومانیسم. فلسفه، 4(5)، 97-112
8
خدیور، آمنه (1394). طراحی یک سیستم خبره هیبریدی برای بودجهریزی بر مبنای عملکرد. هفتمین کنفرانس بودجهریزی بر مبنای عملکرد، تهران: سالن همایشهای رازی، ۲۹ و ۳۰ آذر ۱۳۹۴.
9
عبدصبور، فریدون؛ راوند، مصطفی (1391). الزامات و موانع بودجهریزی عملیاتی سازمانها بر اساس «مدل سه عاملی شه» (مورد مطالعه: شرکت برق منطقهای تهران). مدیریت صنعتی، 7(20)، 109-129.
10
سبزیان موسیآبادی، ع؛ بهمن، شعیب (1389). پراگماتیسم و سیاست: بررسی و نقد پراگماتیسم در قلمرو سیاست و حکومت. تحقیقات سیاسی و بینالمللی، 2 (5)، 89-123.
11
References
12
Abdesabur, F., Ravand, M. (2012). Requirements and barriers of operational budgeting of organizations based on "Three-factor Model of Shah" (Case Study: Tehran Electric Power Company). Industrial Management, 7 (20), 109-129.
13
Azar, A. (2009). Performance based budgeting and performance auditing, proceeding ofthe performance auditing. Accountability and Productivity Conference, Tehran. (in Persian)
14
Azar, A. Zahedi, Sh., Amirkhani, T. (2010). A Model for Implementing Performance-based Budget: A System Dynamics Approach. Iranian journal of management sciences. 5 (18), 29-54. (in Persian)
15
Azar, A., Khadivar, A. (2011).A linear programming model for performance based budgeting with robust approach. Journal of Public Management, 3(8), 93-120. (in Persian)
16
Azar, A., Khadivar, A. (2013). A critical post-modern approach to performance-based budgeting. Management Research in Iran, 18 (3), 67-94 (in Persian)
17
Azar, A., Khadivar, A., Amin Naseri, M.R., Anvari Rostami, A. A. (2011). The presentation of the architecture of the budgeting system based on the intelligent decision support system. Management Research in Iran, 15 (3), 1-22 (in Persian)
18
Bogsnes, B. (2016). Implementing beyond budgeting: unlocking the performance potential. John Wiley & Sons.
19
Champion, D., & Wilson, J. M. (2010). The impact of contingency factors on validation of problem structuring methods. Journal of the Operational Research Society, 61(9), 1420–1431.
20
Curristine, T. (2006). Performance information in the budget process. OECD Journal on Budgeting, 5(2), 87-131.
21
Curristine, T. (2007). Experience of OECD Countries with Performance Budgeting. In Performance Budgeting (pp. 128-143). Palgrave Macmillan, London.
22
Flood, R. L. & Jackson, M. C. (1991). Total systems intervention: A practical face to critical systems thinking. Systems Practice, 4(3),197-213.
23
Green, P. (2014). Critical systems heuristics a basis for evaluation of service in developing countries. International Conference on Commerce, Law and Social Sciences, 4(1),1-10, Bangkok, Thailand.
24
Hart, D. & Caceres, A. P. (2013). Using Critical Systems Heuristics to Guide Second‐Order Critique of Systemic Practice: Exploring the Environmental Impact of Mining Operations in Southern Peru. System Research and Behavioral science, 31(2), 197-214.
25
Hossein Zadeh, M; Mehregan, MR; Amiri, M. (2013). Designing a framework for using multiple methodologies in operational research using a comprehensive morphological analysis approach. Industrial Management Perspective, 3 (11), 63-87. (in Persian)
26
Hossein Zadeh, M; Mehregan, MR; Kiani, M. (2013). Investigate Operations, Science or Technology? What is its significance? Politics, Science and Technology, 5 (4), 33-46.
27
(in Persian)
28
Khadivar, Amena (2015). Designing a Hybrid Expert System for Performance Based Budgeting. Seventh International Conference on Performance based Budgeting, 29 and 30 December 1394. San Razi Conference, Tehran.
29
Khatami, M (2002). Pragmatism and Humanism. Philosophy, 4 (5), 97-112.
30
Khosravani, F. Azar, A., Khodadad Hosseini, S.H. (2016). Strategy Making Problem Structuring with JOURNEY Making and Drama Theory (Case Study: Green Gas Supply Chain). Modern Research in Decision Making, 1(1), 103-138. (in Persian)
31
Melkers, J. E., & Willoughby, K. G. (2001). Budgeters’ Views of State Performance-Budgeting Systems: Distinctions across Branches. Public Administration Review, 61(1), 54–64.
32
McNab, R. M., & Melese, F. (2003). Implementing the GPRA: Examining the prospects for performance budgeting in the federal government. Public budgeting & finance, 23(2), 73-95.
33
Robinson, M., & Brumby, J. (2005). Does Performance Budgeting Work: An Analytical. Review of the Empirical Literature. IMF Working Paper No. 210, International. Monetary Fund.
34
Rosenhead, J., & Mingers, J. (2001). Rational analysis for a problematic world revisited (No. 2nd). John Wiley and Sons.
35
Rosenhead, J. (2006). Past, present and future of problem structuring methods. Journal of the Operational Research Society, 57(7), 759–765.
36
Sabzian Mosa Abadi, A.S., Bahman, Sh. (2010). Pragmatism and Politics: Reviewing and criticizing pragmatism in the realm of politics and government. Political and International Research, 2 (5), 89-123. (in Persian)
37
Schick, A. (2003). Does Budgeting have a future? OECD Journal on Budgeting, 2(2), 7-48.
38
Ulrich, W. (1983). Critical heuristics of social planning: A new approach to practical philosophy. J. Wiley & Sons.
39
Ulrich, W. (2003). Beyond methodology choice: critical systems thinking as critically systemic discourse. Journal of the Operational Research Society, 54(4), 325-342.
40
Ulrich, W. (2013). Critical systems thinking. Encyclopedia of Operations Research and Management Science, 314-326.
41
Ulrich, W. (2005). A Brief Introduction to Critical Systems Heuristics (CSH). ECOSENSUS project website. The Open University, Milton Keynes, UK, 14 October 2005.
42
Venter, C., & Goede, R. (2016). A critical systems approach to business intelligence system development. In Proceedings of the 59th Annual Meeting of the ISSS-2015 Berlin, Germany (Vol. 1, No. 1).
43
White, L. (2006). Evaluating problem-structuring methods: developing an approach to show the value and effectiveness of PSMs. Journal of the Operational Research Society, 57(7), 842–855.
44
Zafar, N. (2008). Performance budgeting in the United Kingdom. OECD Journal on Budgeting, 8)1(, 75-90.
45
ORIGINAL_ARTICLE
Identifying Significant Health Measurement of Equipment Affecting the Quality of a Continuous Product (Case Study: Unit 2, Parand Gas Turbine Power Plant)
Objective: Majorproducers consider quality as a major criterion in decision making.Quality characteristics are affected by maintenance and repair decisions. In this study, a model is developed to determine significant measurements of production equipment affecting the quality of a continuous product to identify which measurements are more critical in terms of quality.
Methods: Diversity of parameters affecting the quality and the delay until effects on quality come into view, are the main aspects of the issue. Genetic algorithm with a fitness function including prediction accuracy, convergence rate, and number of measurements is developed to obtain optimum set of measurements. Artificial neural networks are also used to evaluate the reliability and validity of the solutions.
Results: The proposed model was applied and evaluated by a case study in unit 2, Parand Gas Turbine Power Plant. The results demonstrated the optimum set of measurements which are significantly related to quality characteristic. In addition, the available data demonstrating that the terminal equipment in production process has more significant effects on quality.
Conclusion: The proposed model enjoys the capability of identifying the most important health measurements affecting the output quality of a continuous product in some limited steps of optimization algorithm by processing the history data from Condition Monitoring Process. With these significant measurements available, the decision makings in maintenance and repair can happen on the grounds of quality.
https://imj.ut.ac.ir/article_68910_8dad7718b3ab7d44fa3eaa49a1433208.pdf
2018-09-23
455
480
10.22059/imj.2018.127321.1006879
genetic algorithm
Condition monitoring
Health measurement
Identification
Quality characteristic
Amir
Heydari
amir.heydari@aut.ac.ir
1
Msc., Department of Industrial Engineering and Management Systems, Amir Kabir University of Technology, Tehran, Iran
AUTHOR
Seyed Hamidreza
Shahabi Haghighi
shahabi@aut.ac.ir
2
Assistant Prof., Department of Industrial Engineering and Management Systems, Amir Kabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Abbas
Ahmadi
abbas.ahmadi@aut.ac.ir
3
assistant Prof./Department of Industrial Engineering and Management Systems, Amirkabir University of Technology
AUTHOR
آقایی، رضا؛ آقایی، اصغر؛ حسینی ناجیزاده، رامین محمد (1394). شناسایی و رتبهبندی شاخصهای کلیدی مؤثر بر نگهداری و تعمیرات چابک با استفاده از رویکرد دلفی فازی و دیمتل فازی (مطالعه موردی: صنعتی خودروسازی ایران). مجله مدیریت صنعتی، 7(4)، 641-672.
1
اسماعیلیان، غلامرضا؛ لورکزاده، فروزان؛ زارعیان، رحمان (1394). اریابی و مقایسه اثربخشی پیادهسازی نت اصلاحی و نت پیشگیرانه با رویکرد پویاییشناسی سیستمها (مطالعه موردی: شرکت سیمکان). مجله مدیریت صنعتی، 7(2)، 189-214.
2
حسن قاسمی، جلیل؛ کاظمی، عالیه؛ حسینزاده، مهناز (1395). گسترش عملکرد کیفیت (QFD) با استفاده از مدل برنامهریزی خطی فازی. مجله مدیریت صنعتی، 8(2)، 241-262.
3
صفری، حسین؛ صادقیمقدم، محمدرضا؛ عبادی ضیایی، علی (1395). مدلسازی علی روابط میان معیارهای مدل تعالی سازمانی EFQM در بانک توسعه تعاون. مجله مدیریت صنعتی، 8(3)، 423-446.
4
نشاط، نجمه؛ محلوجی، هاشم (1388). کنترل پیشبینانه کیفیت با استفاده از شبکههای عصبی مصنوعی (ANNs) و روش ترکیبی تحلیل رگرسیون و ANNs. مجله مدیریت صنعتی، 1(3)، 153-170.
5
References
6
Aghaee, R., Aghaee, A., & Najizadeh, R. M. H. (2016). Key effective factors on Agile Maintenance in vehicle industry using fuzzy Delphi method and Fuzzy DEMATEL. Journal of Industrial Management, 7(4), 641–672. (in Persian)
7
Ahmad, R., & Kamaruddin, S. (2012). An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering, 63 (1), 135–149.
8
Alander, J.T. (1992). On optimal population size of genetic algorithms. In CompEuro’92.’Computer Systems and Software Engineering’, Proceedings. (pp. 65–70). IEEE.
9
Arunraj, N.S. & Maiti, J. (2007). Risk-based maintenance-Techniques and applications. Journal of Hazardous Materials, 142(3), 653–661.
10
Aurich, J. C., Siener, M., & Wagenknecht, C. (2006). Quality Oriented Productive Maintenance within the life cycle of a manufacturing system. In 13th CIRP international conference on life cycle engineering (pp. 669–673). Citeseer.
11
Baidya, R., Dey, P. K., Ghosh, S. K., & Petridis, K. (2018). Strategic maintenance technique selection using combined quality function deployment, the analytic hierarchy process and the benefit of doubt approach. The International Journal of Advanced Manufacturing Technology, 94(1–4), 31–44.
12
Ben-Daya, M. (1999). Integrated production maintenance and quality model for imperfect processes. IIE Transactions, 31(6), 491–501.
13
Ben-Daya, M., & Duffuaa, S. O. (1995). Maintenance and quality: the missing link. Journal of Quality in Maintenance Engineering, 1(1), 20–26.
14
Ben-Daya, M., & Rahim, M. A. (2000). Effect of maintenance on the economic design of x-control chart. European Journal of Operational Research, 120(1), 131–143.
15
Bouslah, B., Gharbi, A., & Pellerin, R. (2016). Integrated production, sampling quality control and maintenance of deteriorating production systems with AOQL constraint. Omega, 61, 110–126.
16
Budai, G., Dekker, R., & Nicolai, R. P. (2008). Maintenance and production: a review of planning models. In Complex system maintenance handbook (pp. 321–344). Springer.
17
Cassady, C. R., Bowden, R. O., Liew, L., & Pohl, E. A. (2000). Combining preventive maintenance and statistical process control: a preliminary investigation. Iie Transactions, 32(6), 471–478.
18
Coley, D. A. (1999). An introduction to genetic algorithms for scientists and engineers. World Scientific Publishing Company.
19
Esmaeilian, G., Zadeh, F. L., & Zareayan, R. (2015). Evaluating and comparing the implementation effectiveness of corrective maintenance and preventive maintenance with a systems dynamic approach (case study: Symcan company). Journal of Industrial Management, 7(2), 189–214.(in Persian)
20
Hasan Ghasemy, J., Kazemi, A., & Hoseinzadeh, M. (2016). Quality Function Deployment by Using Fuzzy Linear Programming Model. Journal of Industrial Management, 8(2), 241–262. (in Persian)
21
Hadidi, L. A., Al-Turki, U. M., & Rahim, A. (2011). Integrated models in production planning and scheduling, maintenance and quality: a review. International Journal of Industrial and Systems Engineering, 10(1), 21–50.
22
Hines, W. W., & Montgomery, D. C. (n.d.). Probability and Statistics in Engineering and Management Science, 1980. John Wiley & Sons.
23
Karray, F. O., & De Silva, C. W. (2004). Soft computing and intelligent systems design: theory, tools, and applications. Pearson Education.
24
Liu, B. (2006). Vibration data monitoring and design of multivariate ewma chart for cbm. Ph. D. Dissertation, University of Totonto.
25
Liu, L., Yu, M., Ma, Y., & Tu, Y. (2013). Economic and economic-statistical designs of an X control chart for two-unit series systems with condition-based maintenance. European Journal of Operational Research, 226(3), 491–499.
26
Mann, L., Saxena, A., & Knapp, G. M. (1995). Statistical-based or condition-based preventive maintenance? Journal of Quality in Maintenance Engineering, 1(1), 46–59.
27
Mehdi, R., Nidhal, R., & Anis, C. (2010). Integrated maintenance and control policy based on quality control. Computers & Industrial Engineering, 58(3), 443–451.
28
Montegomery, D. C. (2009). Introduction to statistical quality control. John Wiley & Sons (New York).
29
Neshat, N., & Mahlooji, H. (2009). Predictive Process Control Using Artificial Neural Networks (ANNs) and A Combined Method of Regression Analysis and ANNs. Journal of Industrial Management, 1(3), 153–170.(in Persian)
30
Panagiotidou, S., & Nenes, G. (2009). An economically designed, integrated quality and maintenance model using an adaptive Shewhart chart. Reliability Engineering & System Safety, 94(3), 732–741.
31
Panagiotidou, S., & Tagaras, G. (2007). Optimal preventive maintenance for equipment with two quality states and general failure time distributions. European Journal of Operational Research, 180(1), 329–353.
32
Panagiotidou, S., & Tagaras, G. (2008). Evaluation of maintenance policies for equipment subject to quality shifts and failures. International Journal of Production Research, 46(20), 5761–5779.
33
Pandey, D., Kulkarni, M. S., & Vrat, P. (2010). Joint consideration of production scheduling, maintenance and quality policies: a review and conceptual framework. International Journal of Advanced Operations Management, 2(1–2), 1–24.
34
Peck, C. C., Dhawan, A. P., & Meyer, C. M. (1993). Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring. In Neural Networks, 1993., IEEE International Conference on (pp. 1115–1122). IEEE.
35
Radhoui, M., Rezg, N., & Chelbi, A. (2009). Integrated model of preventive maintenance, quality control and buffer sizing for unreliable and imperfect production systems. International Journal of Production Research, 47(2), 389–402.
36
Rahim, M. A. (1993). Economic design of x control charts assuming Weibull in-control times. Journal of Quality Technology, 25(4), 296–305.
37
Rahim, M. A. (1994). Joint determination of production quantity, inspection schedule, and control chart design. IIE Transactions, 26(6), 2–11.
38
Safari, H., Moghaddam, M.R.S., & Ziaei, A.E. (2016). Causal modeling of relationships between criteria for EFQM excellence model in TOSE’E TA’AVON bank. Journal of Industrial Management, 8(3), 423–446.(in Persian)
39
Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003). A review of process fault detection and diagnosis: Part III: Process history based methods. Computers & Chemical Engineering, 27(3), 327–346.
40
Wang, W. (2012). A simulation-based multivariate Bayesian control chart for real time condition-based maintenance of complex systems. European Journal of Operational Research, 218(3), 726–734.
41
Wu, J. (2006). CBM optimization models with multivariate observations (Vol. 68).
42
Wu, J., & Makis, V. (2008). Economic and economic-statistical design of a chi-square chart for CBM. European Journal of Operational Research, 188(2), 516–529.
43
Yeung, T. G., Cassady, C. R., & Schneider, K. (2007). Simultaneous optimization of [Xbar] control chart and age-based preventive maintenance policies under an economic objective. IIE Transactions, 40(2), 147–159.
44
Yin, Z., & Makis, V. (2010). Economic and economic-statistical design of a multivariate Bayesian control chart for condition-based maintenance. IMA Journal of Management Mathematics, 22(1), 47–63.
45
Zhang, G., Deng, Y., Zhu, H., & Yin, H. (2015). Delayed maintenance policy optimisation based on control chart. International Journal of Production Research, 53(2), 341–353.
46
ORIGINAL_ARTICLE
Investigating the Role of the Components of the Knowledge-Based Economy in Iran Present Situation and the Vision Plan Countries Using Multiple- Group Discriminant Analysis and K-Mean Differentiation Analysis
Objective: One of the long-term goals and strategies of the country for development in the 20-year vision plan is the development of the knowledge-based economy, so that with pursuing this strategy, Iran could become a knowledge-based economy by 1404. The purpose of this research is to explain the economic status of Iran among regional competitors based on the components of knowledge-based economy.
Methods: This study was based on World Bank documentation using clustering methods based on the K-Mean algorithm and Multiple- Group Discriminant Analysis with the aim of calculating the knowledge-based economy index and determining the components and criteria of each country studied between1995and 1995. It should be noted that the under-study countries are clustered into three groups based on 14 variables in the form of four components of knowledge-based economy.
Results: The results of the k-mean method showed that the variables of cell phone users, the quality of regulations, the number of Internet users per thousand ones, the number of telephone lines per thousand ones, and the number of Internet users per thousand ones played the most important role in separating the clusters. For the Multiple- Group Discriminant Analysis method, in the first differentiation function, the variables of the quality of regulation, the number of Internet users per thousand ones, cell phone users, and in the second differentiation function, the variables of tariff and non-tariff barriers, rule of law, the number of telephone lines per thousand ones have the most importance in creating a distinction between different groups of countries.
Conclusion: During 1995-1995, Iran has not seen significant progress in terms of knowledge-based economy index, and in the second group of countries (the average level), it has been considered as a composite index of the knowledge-based economy.
https://imj.ut.ac.ir/article_68911_ff9ff9e00885e462a58143b6c10c3109.pdf
2018-09-23
481
501
10.22059/imj.2018.266596.1007490
Knowledge-based Economy
Multiple- group discriminate analysis
K-Means method
jafar
roodari
roodari@gmail.com
1
Ph.D Candidate in Economy, Faculty of Management and Economic, Islamic Azad University, Kerman Branch, Iran.
AUTHOR
mohsen
zayandehroody
m_roody2000@yahoo.com
2
Assistant prof. In Economy, Faculty of Management and Economic, Islamic Azad University, Kerman Branch, Iran
LEAD_AUTHOR
hossein
mahrabi
hmehrabi@uk.ac.ir
3
Prof. in Agricultural Economic, Faculty of Shahid Bahonar University, Kerman, Iran.
AUTHOR
دیزجی، منیره؛ دانشور، سهند؛ بابایی اناری، علیرضا (1391). تعیین جایگاه ایران در زمینه اقتصاد دانشبنیان در میان کشورهای منتخب. مدیریت بهرهوری(فراسوی مدیریت)، 6(22)، 121-144.
1
شقاقی شهری، وحید (1396). ارزیابی وضعیت اقتصادی ایران در سند چشمانداز. فصلنامه اقتصاد و الگوسازی، 8 (30)، 1-29.
2
عزیزی، فیروزه؛ مرادی، فهیمه (1397). محاسبه شاخصهای اصلی و فرعی اقتصاددانشبنیان برای ایران. فصلنامه پژوهشها و سیاستهای اقتصادی، 26 (85)، 243- 270.
3
عظیمی، ناصر علی؛ برخورداری دورباش، سجاد (1379). شناسایی بنیانهای اقتصاد دانشبنیان، تهران: نشر مرکز تحقیقات سیاست علمی کشور.
4
عمادزاده، مصطفی؛ شهنازی، روحالله (1386). بررسی مبانی و شاخصهای اقتصاد داناییمحور و جایگاه آن در کشورهای منتخب در مقایسه با ایران. پژوهشنامه اقتصادی، 7(4)، 143- 175.
5
عمادزاده، مصطفی؛ شهنازی، روح الله؛ دهقان شبانی، زهرا (1385). بررسی میزان تحقق اقتصاد دانشمحور در ایران. فصلنامه پژوهشهای اقتصادی، 6(2)، 103- 132.
6
فرهادی کیا، علیرضا؛ ازوجی، علاءالدین (1395). مقایسه تطبیقی عملکرد اقتصاد ایران در مقایسه با کشورهای حوزه سند چشمانداز در دوره (1394- 1384) و الزامهای بهبود جایگاه آن در سالهای باقیمانده حوزه اقتصادی. سازمان برنامه و بودجه کشور، گزارش 55-3.
7
محمدی، بهمن؛ کامکار روحانی، ابوالقاسم (1396). بهکارگیری روشهای خوشهبندی k میانگین، میانگین فازی و گوستافسون کسل در تلفیق نتایج وارونسازی دادههای توموگرافی لرزههای انکساری و مقاومت ویژه الکتریکی برای ارزیابی آبرفت و سنگ بستر. علوم زمین خوارزمی، (3)2، 183-198.
8
منوریان، عباس؛ عسکری، ناصر؛ آشنا، مصطفی (1386). ابعاد ساختاری و محتوایی سازمانهای دانشمحور. اولین کنفرانس ملی مدیریت دانش، تهران.
9
نوری، جواد؛ بنیادی نائینی، علی؛ اسماعیلزاده، محمد (1395). تعیین جایگاه ایران در منطقه از منظر اقتصاد دانشبنیان بر پایه الگوریتم خوشهبندی. فصلنامه سیاستهای راهبردی و کلان. 4، 133- 155.
10
References
11
Afzal, M. N. I. (2014). Knowledge-based Economy (KBE): An Investigation of Frameworks and Measurement Techniques in the South East Asian Region. A Ph.D. dissertation, University of Southern Queensland.
12
APEC (November, 2000). Towards Knowledge Based Economies in APEC. Report by APEC Economic Committee.
13
APEC Economic Committee (2001). Towards Knowledge Based Economies in APEC. APEC Secretariat.
14
Azimi, N., & Barkhordari, S. (2008). Knowledge-Based Economy in Southeast Asian Countries. Rahyaft, 43, 32-42. (in Persian)
15
Azizi F, Moradi F. (2018). Calculating the Index and Sub-Indices of Knowledge-Based Economy for Iran, 26 (85), 243-270.
16
Despotovic, D., Cvetanovic, D., Vladimir, N. (2015). Perspectives for the Development of Knowledge Economy, Innovativeness, and Competitiveness of Cefta Countries. Economics and Organization, 12(3), 209-223.
17
Dizaji, M., Daneshvar, S., Babaei Anari, A. (2013). Determining Iran's Position among selected countries from the Perspective of Knowledge Based Economy based. Productivity Management, 6(22), 121-144.
18
Farhadi kia, A., Azvaji, A. (2015). Comparative Comparison of Iran's Economic Performance Compared to the Contries in Vision Regions in the Period (2005-2014) and the Requirements for Improving Its Position in the Residual Economic Areas. Planning and Budget Organization. Report 3-55.
19
Fucec, A. A., Corina, M. (2014). Knowledge economies in European Union: Romania’s position. Emerging Markets Queries in Finance and Business, 15, 481–489.
20
Höppner, F., Klawonn, F., Kruse R., Runkler, T. (1999). Fuzzy cluster analysis: Methods for classification, data analysis and image recognition. Journal of the Operational Research Society, 51 (1999) 769-770.
21
Imadzadeh, M., Shahnazi, R. A. (2007). The study of the bases and indicators of knowledge-based economy and its position in selected countries compared to Iran. Research Economic, 7(4), 175-143. (in Persian)
22
Imadzadeh, M., Shahnazi, R. A., & Dehghan Shabani, Z. (2006). The Study of the Realization of the Knowledge-Driven Economy in Iran. Economic Quarterly, 6(2), 103-132. (in Persian)
23
Kaufmann, L., Rousseeuw, P. J. (1990). Finding groups in data: an introduction to cluster analysis. Wiley Series in Probability and Statistics, John Wiley & Sons, Inc.
24
Lucas, R. (1988). On the Mechanics of Economic Development. Journal of Monetary Economics, 22, 3-42.
25
Maddala, G.S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge University Press.
26
Mehrara, M., Rezaei, A.A. (2015), “Knowledge Economy Index (KEI) in Iran and Comparison with other Countries of Region: The Vision 1404 Document. International Journal of applied Economic Studies, 3(2), 1-7.
27
Mohammadi, B., Kamkar Rouhani, A. (2017). The application of clustering k-mean, fuzzy, and gustaffson methods in combining the results of inversion of refractive-earthquake tomography and electrical resistivity tomography for the assessment of alluvium and bedrock. Kharazmi Earth Sciences, 2(3), 183-198. (in Persian)
28
Monavarian, A., Askari, N and Ashena., M. (2007). The structural and content dimensions of knowledge-based organizations. First National Knowledge Management Conference. Tehtan. (in Persian)
29
Noori, J., Bonyad Naeini, A & Esmailzadeh, M. (2015). Determining Iran's Position in the Region from the Perspective of Knowledge Based Economy based on Clustring Algoritm. Quarterly Journal of the Macro and Strategic Policies, 4, 133-155. (in Persian)
30
Paasche, H., Eberle, D. (2011). Automated compilation of pseudo-lithology maps from geophysical data sets: a comparison of Gustafson-Kessel and fuzzy C-means cluster algorithms. Exploration Geophysics, 42(4), 275-285.
31
Paz-Marin, M., Gutierrez-Pena, P. A., & Martinez, C. (2015). Classification of countries’ progress toward a knowledge economy based on machine learning classification techniques. Expert Systems with Applications, 42(1), 562-572.
32
Romer, P. M. (1990). Human Capital and Growth: Theory and Evidence. Carnegie Rocheser Conference Series on Public Policy, 32, 251-286.
33
Shaghaghi Shahri, V. (2015). Evaluating the Economic Condition of Iran in Comparison with “Twenty-Year Vision Document. Quarterly Journal of Economics and Modelling Shahid Beheshti University, 30(8), 1-29. (in Persian)
34
Smith, K. What is the Knowledge Economy? Knowledge Intensity and Distributed Knowledge Bases, The United Nations University, Institute for New Technologies,UNU/INTECH Discussion Papers. ISSN 1564-8370, (2002).
35
Smith, R., Sharif, N. (2007). Understanding and Acquiring Technology Assets for Global Competition. Technovation, 27(11), 643-649.
36
Sun, J., Li, Y. (2016). Joint inversion of multiple geophysical data using guided fuzzy C-Means clustering. Geophysics, 81 (3), 37-57.
37
Vinnychuk, O., Skrashchuk, L. & Vinnychuk, I. (2014). Research of Economic Growth in the context of Knowledge Economy. Intellectual Economics, 1 (19), 116-127.
38
Wilson, D.I. (2002). Derivation of the chalk superficial deposits of the North Downs, England: an application of discriminant analysis. Geomorphology, 42(3-4), 343-364.
39
World Bank (1998/99). World Development Report- Knowledge for Development. New York: Oxford University Press.
40
World Bank (2013). World Development Indicators 2003. World Bank Institute, Knowledge for Development Program. www.worldbank.org/wbi/knowledgefordevelopment
41
World Bank and World Bank Institute. (2002). Knowledge for Development; a Forum for Middle East and North Africa. Marseilles: France, 9- 12.
42
World Bank. (2008). Measuring Knowledge in the World's Economies. Knowledge for Development, World bank Institute. The World Bank's Knowledge Assessment Methodology. Available at: www.worldbank.org/kam.
43
World Bank. (2012). Knowledge Assessment Metodology (KAM). World Bank Institute. available at: www.worldbank.org/kam
44