ORIGINAL_ARTICLE
Investigating the Effects of New Corporate Liquidity and Market Operational Performance Indicators on the Markowitz Model Portfolio Returns Using Genetic Algorithm: A Case Study on Refineries and Petrochemical Companies Listed on Tehran Stock Exchange
The research on the Markowitz model and optimization of its portfolio using a variety of evaluation indicators and metaheuristic-algorithms has always been the focus of attention of accounting and finance researchers. The results of studies carried out by various types of optimization method are different in the Markowitz modified models. The purpose of this study is to measure the optimal portfolio and its corresponding return with respect to the portfolio in the traditional Markowitz model as well as comparing the position of the refining and petrochemical companies versus stock market outperformers through integrating the operational criteria and the new indicators of liquidity by using the genetic algorithm in the Markowitz model. Therefore, financial data related to the research variables of 35 cases of refinery and petrochemical companies listed on Tehran Stock Exchange (TSE) from 2012 to 2016 fiscal years were extracted from Rahavard Novin database software and simulated by the genetic algorithm. The results show that returns on the stock portfolios optimized using the genetic algorithm without considering the liquidity limitations and filters are significantly and positively different from the returns on the stock portfolios optimized with regarding the liquidity limitations and filters. Furthermore, the application of liquidity limitations and filters to the formation of the optimal stock portfolios leads to a conservative increase in the choice of stocks (portfolio formation), which results in a reduction in the risk and return of investment in such portfolios.
https://pbr.put.ac.ir/article_104107_9ec41570a0bdb74eb49cbb9c8b75d4b5.pdf
2019-03-01
1
15
10.22050/pbr.2019.104107
Liquidity Indicators
Operational efficiency
Genetic Algorithm
Markowitz Model
Optimum Portfolio
Mohammad
Tavakkoli Mohammadi
mtavakoli@put.ac.ir
1
Assistant Professor, Accounting and Finance Department, Petroleum Faculty of Tehran, Petroleum University of Technology, Tehran, Iran.
LEAD_AUTHOR
Abbas
Alimoradi
alimoradi@put.ac.ir
2
Assistant Professor, Accounting and Finance Department, Petroleum Faculty of Tehran, Petroleum University of Technology, Tehran, Iran.
AUTHOR
Mohsen
Sarvi
mstzak@gmail.com
3
M.A. Student in Finance, Petroleum Faculty of Tehran, Petroleum University of Technology, Tehran, Iran.
AUTHOR
Aitken, M., & Comerton-Forde, C. (2003). How should liquidity be measured?. Pacific-Basin Finance Journal, 11(1), pp. 45–59.
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Simon Hodrick, L., & Moulton, P. (2007). Liquidity: Considerations of a Portfolio Manager.
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Zhang, W. G., Chen, W., & Wang, Y. L. (2006). The adaptive genetic algorithms for portfolio selection problem. IJCSNS International Journal of Computer Science and Network Security, 6(1), 196–200.
22
ORIGINAL_ARTICLE
Oil and Gas Investor-State Dispute Settlement: Is Mediation-Arbitration Considered a Mechanism for Common Interests?
International oil and gas investment disputes constitute an important part of investor-state dispute settlement (ISDS) system. Investment arbitration which is regarded as a prevalent dispute settlement mechanism in this area has come under severe criticism since it creates huge costs, lengthens the process, and devastates the parties’ long-term investment relationship. In recent years, the possibility of applying alternative dispute resolution (ADR) and hybrid dispute settlement mechanisms has largely been discussed. Mediation-arbitration (Med-Arb) is one of the hybrid integrated dispute settlement mechanisms which embodies flexibility, nonjudicial, and negotiate-oriented benefits of mediation and the finality advantage of arbitration simultaneously in a single process. In this method, mediation is first attempted by the parties before arbitration could be started; if settlement is not reached during the mediation phase, the appointed neutral or mediator will then act as (an) independent arbitrator(s), will continue the case under the arbitration process, and will render a binding arbitration award. In this method, if parties reach an agreement during the first phase (mediation process), they will not incur huge costs of lengthy investment arbitration. In this method, even if the first stage (mediation process) fails, since it has further clarified and narrowed down the disputes, then the arbitration process will be less lengthy and proceed more efficiently. Moreover, both investors and host states in oil and gas investment area do have strong ambitions to maintain the investment relationships. These goals are achieved better via adopting Med-Arb proceedings. The most noted concerns in this method relates to the issue of the impartiality of the neutral (mediator in the first stage) who acts as an arbitrator at the next stage. In other words, it may be argued that the confidential information learned by the neutral from the parties in the mediation stage may seriously impact on his/her impartiality in the arbitration stage. This issue can be responded in light of respecting party autonomy principle which selects the Med-Arb clearly and correctly for dispute settlement. This approach is affirmed and proposed by the UNCITRAL model law on international commercial conciliation (2002) as well. Also, concerns regarding the enforcement of international agreements resulting from mediation have already been addressed in the United Nations Convention on International Settlement Agreements Resulting from Mediation (Singapore Convention on Mediation), which has attained international acceptance by 51 state members so far.
https://pbr.put.ac.ir/article_104109_eeb4289c42df77e868f3fc03329e4d0b.pdf
2019-03-01
17
28
10.22050/pbr.2019.104109
Med-Arb
Investor-State Disputes
Oil and gas industry
Efficient Dispute Settlement
Naqmeh
Javadpour
naghmeh.javadpour@gmail.com
1
PhD. Student in law, Faculty of Law and Political Sciences, Allameh Tabataba'i University, Tehran, Iran
LEAD_AUTHOR
Hamid reza
Oloumi Yazdi
holoumiyazdi@yahoo.com
2
Associate Professor, Faculty of Law and Political Sciences, Allameh Tabataba'i University, Tehran, Iran
AUTHOR
Seyyed Nasrollah
Ebrahimi
snebrahimi@yahoo.com
3
Assistant Professor, Faculty of Law and Political Sciences, Allameh Tabataba'i University, Tehran, Iran
AUTHOR
Chua, Eunice. (2018, August ). A Contribution to the Conversation on Mixing the Modes of Mediation and Arbitration: of definitional Consistency and Process Structure. Transnational Dispute Management, 5th issue 5, 1–14.
1
Crawford, Bishop & W. Reisman Bishop, Doak R. James R. Crawford, W. Michael Reisman (2005). Foreign Investment Disputes, Cases, Materials and Commentary. The Hague: Kluwer Law International, 1st Edition, 1–62.
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Dmitry & K. Yaraslau Kryvoi (2015). Consent Awards in International Arbitration: From Settlement to Enforcement. Brooklyn Journal of International Law, Vol.40 3rd Issue(3), 828–866.
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Douglas, Zachary. (2009). The International Law of Investment Claims. New York: Cambridge University Press,1st Edition.
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E. Mason, Paul. (2011). The Arbitrator as Mediator, and Mediator as Arbitrator. Journal of International Arbitration, Vol. 286 Issue (6), 45154–551.
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Echandi, Roberto. (2007). Investor-State Dispute Settlement and Impact on Investment Rulemaking. New York & Geneva: United Nations Conference on Trade and Development UNCTAD. Retrieved from UNCTAD Database: https://unctad.org/en/Docs/iteiia20073_en.pdf.
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International Investment Law for the 21st Century: Essays in Honor of Christoph Schreuer, 2009 Oxford: Oxford University Press, DOI: 10.1093/law/9780199571345.001.0001, 894–912.
8
Javadpour, Naghmeh, Oloumiyazdi, Hamidreza., & Ebrahimi, Seyyed Nasrollah. (2019). Med-Arb in International Commercial Contracts with focus on Iran's Legal System. Journal of Private Law Review, Allameh Tabataba'e University,29. 50–70.
9
Kayali, Didem. (2010). Enforceability of Multi-Tiered Dispute Resolution Clauses. Kluwer Law International, 27 (6), 551–577.
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Kilangi, Adelardus. (2019, July 26). Audiovisual Library of International Law. Retrieved from the United Nations database at: http://legal.un.org/avl/ha/ga_1803/ga_1803.html
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Langford, Malcolm, Behn , Daniel and Fauchald, Ole Kristian, (2018). Backlash and State Strategies in International Investment Law. In Tanja Aalberts and Thomas Gammeltoft-Hansen A. T. H. T, The Changing Practices of International Law (pp. 85–100). Cambridge: Cambridge University Press.
12
Levesque, Celine, (2013). Encouraging Greater Use of Alternative Dispute Resolution in Investor-State Dispute Settlement: Opportunities and Challenges. Proceedings of the Annual Meeting (American Society of International Law) Proceedings of the Annual Meeting (American Society of International Law)
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Pappas, Brian. A. (2015 ). Med-Arb and the Legalization of Alternative Dispute Resolution. Harvard Negotiation Law Review, 20 (157), 158–198.
17
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18
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Shahla F. Ali, Odysseas G. Repousis. (2018). Investor-state mediation and the rise of transparency in international investment law: opportunity or threat? Denver Journal of International Law and Policy, 45, 4–9. Denver Journal of International Law and Policy, Vol. 45, No. 2, 2018
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21
Stevens, Paul. (2018). The Role of Oil and Gas in the Economic Development of the Global Economy. In T. A. Roe, Extractive Industries: The Management of Resources as a Driver of Sustainable Development (pp. 1–20). Oxford: Oxford Scholarship Online.Retrievedfrom:https://www.oxfordscholarship.com/view/10.1093/oso/9780198817369.001.0001/oso-9780198817369-chapter-4.
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Walde, Thomas, (2002). Law, Contract and Reputation in International Business: What Works. Business Law International, 7–10.
26
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27
Walde, Thomas, (2006, August). Efficient Management of Transnational Disputes: Case Study of a Successful Interconnector Dispute Resolution. Oil, Gas and Energy Law Intelligence, OGEL 2, 6–20.
28
Walde, Thomas,. (2004). Pro-Active Mediation of International Business and Investment Disputes Involving Long-Term Contracts: From Zero-Sum Litigation to Efficient Dispute Management. Transnational Dispute-Management (TDM ),2, 5–15.
29
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32
ORIGINAL_ARTICLE
An Integrated Knowledge Management Framework for Sustainable Supply Chain Using a System Dynamics Model for POGC
Supply chains have experienced rapid growth in recent years. Focusing purely on economic performance so as to optimize costs or return on capital can no longer guarantee development or sustainability in the chain. Hence, the concepts of green supply chain management and sustainable supply chain management emerged to emphasize the importance of social and environmental concerns along with economic factors in supply chain programming. Using the system dynamics method and considering knowledge management, this study investigates the variables related to this topic and the variables of sustainable supply chain management, and it determines the relationships between these variables and their impact on the research purpose. To achieve this, first, previous studies are reviewed, and the relevant variables are extracted and finalized according to the experts. Next, a system dynamics model is designed, and various scenarios are defined by changing the effective values of the system. Eventually, several policies are presented to achieve the optimal situation. The optimal values of the ten main influential variables are extracted according to the expert opinion, and the effects revealed by the model are determined by these changes.
https://pbr.put.ac.ir/article_104182_d00604bfab1b4977ee8b25d8c6493476.pdf
2019-03-01
29
50
10.22050/pbr.2019.104182
Sustainable Supply Chain
Knowledge Management
System Dynamics National Iranian Oil Company (NIOC)
Pars Oil and Gas Company (POGC)
Sayyed Abolfazl
Hosseini Moghaddam
sahosseinim@ut.ac.ir
1
Ph.D. Student in Management, Department of Industrial Management, University of Tehran, PARDIS International Campus, Tehran, Iran
LEAD_AUTHOR
Mohammad Reza
Mehregan
mehregan@ut.ac.ir
2
Professor, Department of Industrial Management, University of Tehran, PARDIS International Campus, Tehran, Iran
AUTHOR
Mehdi
Shamizanjani
shamizanjani@ut.ac.ir
3
Associate Professor, Department of Industrial Management, University of Tehran, PARDIS International Campus, Tehran, Iran
AUTHOR
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1
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4
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17
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38
Tseng, M. L., Chiang, J. H., & Lan, L. W. (2009). Selection of optimal supplier in supply chain management strategy with analytic network process and choquet integral. Computers and Industrial Engineering, 57(1), 330–340. https://doi.org/10.1016/j.cie.2008.12.001
39
Vafaeinezhad, M., Kia, R., & Shahnazari-Shahrezaei, P. (2016). Robust optimization of a mathematical model to design a dynamic cell formation problem considering labor utilization. Journal of Industrial Engineering International, 12(1), 45–60. https://doi.org/10.1007/s40092-015-0127-5
40
Wong, L. T., Mui, K. W., Lau, C. P., & Zhou, Y. (2014). Pump efficiency of water supply systems in buildings of Hong Kong. Energy Procedia, 61, 335–338. https://doi.org/10.1016/j.egypro.2014.11.1119
41
Xiu, G., Liu, D., Li, G., Hu, N., & Hou, J. (2019). System Dynamics Modeling: A Prototype Technical-Economic Analyzation Tool for Supporting Sustainable Development in Operational Metal Mines. IEEE Access, 7, 121805–121815. https://doi.org/10.1109/access.2019.2937939
42
ORIGINAL_ARTICLE
Selecting the Appropriate Physical Asset Life Cycle Model with a Multi-Criteria Decision-Making Approach (Case Study: Petroleum Pipelines)
Companies need to exactly manage their assets to balance performance, risk, and cost. The ability of equipment to provide a certain level of performance is influenced by its design, utilization, deterioration, and life. On the other hand, in order to obtain the desired level of performance and reduce risk, proper planning of maintenance activities during the period must be done. To manage this issue, organizations must develop a suitable method for their assets from the acquisition stage to the disposal to obtain the required processes and, ultimately, to earn the desired profit. In this study, petroleum pipelines have been considered as a case study, and life cycle cost (LCC), risk, and key performance indicators (KPI) have been identified as the criteria for decision making. KPI is itself composed of three sub criteria, including reliability, availability, and maintainability. They are weighted by using the opinions of eight expert and DANP method. The final weights of LCC, risk, and KPI (reliability, availability, and maintainability) are 0.269, 0.301, and 0.429 respectively. Considering different strategies in each phase of the asset life cycle, different scenarios are described for the asset life cycle as follows: 1) RCM-replacement, 2) RCM-overhaul, 3) CBM-replacement, 4) CBM-overhaul, 5) TPM-replacement, and 6) TPM-overhaul. Finally, based on the gained experts’ viewpoint from questionnaire and MOORA technique to rank the scenarios, the desired scenario, namely Buy-TPM-Replacement, is selected. Due to the use of experts’ opinions, these results will vary with the change of people, and due to the lack of relevant data, it is not possible to avoid this issue.
https://pbr.put.ac.ir/article_110990_e3c6d72df9d6c4eb21041c80c76cc120.pdf
2019-03-01
51
62
10.22050/pbr.2019.110990
Physical Asset Management (PAM)
Life Cycle Cost (LCC)
risk
Ram
Mohammad Reza
Shokouhi
shokouhi72@gmail.com
1
Assistant Professor, Energy Economics and Management Department, Petroleum Faculty of Tehran , Petroleum University of Technology, Tehran, Iran
LEAD_AUTHOR
Mohammad Reza
Moniri
m_moniri@sbu.ac.ir
2
PhD Candidate in Operation and Production Management, Faculty of Management & Accounting, Shahid Beheshti University, Tehran, Iran
AUTHOR
Behnaz
Shahheidar
behnazshahheidar@yahoo.com
3
M.A. Student in Project Management, Energy Economics and Management Department, Petroleum Faculty of Tehran , Petroleum University of Technology, Tehran, Iran
AUTHOR
Aditya, P. (2012). Asset performance assessment in asset management. The state of the art in Europe from a life cycle perspective, by Paulien Herder, Ype Winia Telli van der Lei, pp. 101–113.
1
Bashiri, M., Badri, H., Hejazi, T. (2011). Selecting optimum maintenance strategy by fuzzy interactive linear assignment method. Applied Mathematical Modeling, 35, 152–164.
2
Brauers, W. K., Zavadskas, E. (2012). Robustness of MULTIMOORA: A method for multi-objective optimization. Informatica, 23(1), 1–25.
3
Campbell, J.D, Jardin, A. K. S, McGlynn, J. (2011). Asset management excellence: optimizing equipment life-cycle decisions, Second Edition, Taylor & Francis, New York.
4
Campos, J., Sharm, P., Gabiria, U. G., Jantunen, E., Baglee, D. (2017). A big data analytical architecture for the Asset Management. Procedia CIRP 64, 369 – 374.
5
Chiu, W. Y., Tzeng, G. H., & Li, H. L. (2013). A new hybrid MCDM model combining DANP with VIKOR to improve e-store business. Knowledge-Based Systems, 37, 48–61.
6
Hajshirmahammadi, A. (2004). Industrial Maintenance Planning & Control. Ghazal. Isfahan.
7
ISO 14224. (2016). Petroleum, petrochemical and natural gas industries: collection and exchange of reliability and maintenance data for equipment.
8
ISO 55000, (2014). Asset management- overview, principles and terminology. International Standard, Switzerland.
9
Jafari-Moghadam, S., Zali, M., Sanaeepour, H. (2017). Tourism entrepreneurship policy: a hybrid MCDM model combining DEMATEL and ANP (DANP). Decision Science Letters, 6(3), 233–250.
10
Jooste, W. (2004). A performance management model for physical asset management. South African Journal of Industrial Engineering, 15(2), 45–66.
11
Karande, P., Chakraborty, S. (2012). Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Materials & Design, 37, 317–324.
12
Maletič, D., Maletič, M., Al-Najjar, B., Gotzamani, K., Gianni, M., Kalinowski, T. B., Gomiscek, B. (2017). Contingency factors influencing implementation of physical asset management practices. Organizacija, 50(1), March.
13
Mkhatshwa, C. (2011). The decision to buy or lease equipment from an engineering economics perspective. BSc Thesis, University of Pretoria, MA, October.
14
Premanathan, T., Rajini, D., Weerainghe, A. (2018). Risks associated with physical asset management: a literature review. The 7th World Construction Symposium, July, Colombo.
15
Sayyah, A. (2017). Developing a mathematical model for determination of asset management strategy based on maintenance and procurement factors. Journal of Modeling in Engineering, 15(51), 1–13.
16
Smit S. J., Vlok, P.J. (2014). The possible influence of risk management, forecasting, and personnel training in physical asset management. South African Journal of Industrial Engineering, 25(2), 96–104.
17
Sokri, A. (2014). Life cycle costing of military equipment. International conference of control, dynamic systems, and robotics, Ottawa, Ontario, Canada, May, No.45, 1–9.
18
Streimikiene, D., Balezentis, T. (2013). Multi-objective ranking of climate change mitigation policies and measures in Lithuania. Renewable and Sustainable Energy Reviews, 18, 144–153.
19
Waghmode, L. Y., Sahasrabudhe, A. D. (2012). Modelling maintenance and repair costs using stochastic point processes for life cycle costing of repairable systems. International Journal of Computer Integrated Manufacturing, 25(4–5), 353–367.
20
Woodward, D. G. (1997). Life cycle costing- theory, information acquisition and application. International journal of project management, 15(6), 335–344.
21
ORIGINAL_ARTICLE
The Impact of Oil Price Movements on Bank Nonperforming Loans (NPLs): The Case of Iran
It is generally believed that macroeconomic and financial performance in oil exporting countries is interlinked to oil price movements. Regarding that assumption, the present study aims to examine the impact of oil price movements on bank nonperforming loans (NPLs) ,as a criterion for evaluation of bank credit risk, by applying the Generalized Method of Moments (GMM) on data from 18 Iranian banks data over period 2006–2017. The result of the estimated model indicates that there is a significant relation between fluctuations of oil price and bank nonperforming loans; accordingly, any decrease in the price of oil will result in an increase in bank nonperforming loans. Also, in order to have comprehensive assessment, economic and bank specific control variables were used in the model. Findings show that the NPLs ratio increases as economic growth decreases and exchange rate and real interest rates rise. Among bank specific factors, equity ratio as a criterion for efficiency and loan growth has a negative effect on NPLs, but by raising bank industry concentration, credit risk and financial stability can be threatened. Thus, the reliance of oil rich economies on oil incomes leads to the linkage of oil prices, and macroeconomic and financial performance. Therefore, the result of this study will be useful in adapting and diversifying macroeconomic policies in the face of drastic changes in oil prices and mitigating its adverse effects.
https://pbr.put.ac.ir/article_109394_b37729ec90ad4d9e4c028d6e7abf955b.pdf
2019-03-01
63
78
10.22050/pbr.2019.109394
Bank Nonperforming Loans (NPLs)
Oil Exporting Countries
generalized
Method of Moments (GMM)
JEL Classification: E32
E44
G21
G32
Ameneh
Nadalizadeh
shahrzad.nadali@gmail.com
1
Ph.D Student, Management and Economy Faculty, Islamic Azad University, Science and Research Branch, Tehran, Iran.
AUTHOR
Kambiz
Kiani
anadalizadeh@cbi.ir
2
Professor, Management and Economy Faculty, Islamic Azad University, Science and Research Branch, Tehran, Iran.
LEAD_AUTHOR
Shamseddin
Hoseini
economic1967@gmail.com
3
Assistant Professor, Economic Department, Allameh Tabataba'i University, Tehran, Iran
AUTHOR
Kambiz
Peykarjou
r.k.peykarjou@yahoo.com
4
Assistant Professor, Economy Faculty, Islamic Azad University, Science and Research Branch, Tehran, Iran.
AUTHOR
Ajlafi, M. (2013). Investigating the relationship between Concentration and Financial Stability of Banks in Iran. MA Thesis, Iran Banking Institute (In Persian).
1
Al-Khazali, O.M., Mirzaei, A.(2017).The Impact of oil price movements on bank nonperforming loans: global evidence from oil-exporting countries, Journal of Emerging market Review, vol. 31, pp. 193–208.
2
Alodayni, S. (2016). Oil Prices, Credit Risks in Banking Systems, and Macro-Financial Linkages across GCC Oil Exporters. International Journal of Financial Studies, Vol. 4(4), pp 1–14.
3
Atkeson, A. and Kehoe, P.J., (1999). Models of energy use: putty-putty vs. putty-clay. American Economic Review 89, 1028–1043.
4
Ayadi, O.F., (2005). Oil price fluctuations and the Nigerian economy. OPEC Review 29, 3, 199–217.
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Berger, A.N.; DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. J. Bank. Finance, 21, 849–870.
6
Bernanke, B., Gertler, M. and Watson, M., (1997). Systematic monetary policy and the effect of oil price shocks. Brooking Papers on Economic Activity 1, 91–142.
7
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Elyasiani, E., Mansur, I., Odusami, B. (2011). Oil price shocks and industry stock returns. Energy Economics 33 (5), 966–974.
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Esmaeil Nia, A., Shafiee, S. (2009). Assessment the differences between the effects of the recent rise in oil prices and the shocks of the 1970s. Journal of Economic Research and Policies, 17(50), 53–76.
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Espinoza, R.A.; Prasad, A. (2010). Nonperforming loans in the GCC Banking System and Their Macroeconomic Effects. IMF Working Papers
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Hamilton, J.D. (1996). This is what happened to the oil price–macroeconomy relationship. Journal of Monetary Economics, 38, 215–20.
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Hamilton, J.D. (2003). What is an oil shock? Journal of Econometrics, 113, 363–98.
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Hamilton, J.D. and Herrera, A.M. (2004). Oil shocks and aggregate economic behavior: the role of monetary policy, Journal of Money, Credit and Banking, 36, 265–86.
20
Harris, E.S., Kasman, B.C., Shapiro, M.D. and West, K.D. (2009). Oil and the macroeconomy: lessons for monetary policy. U.S. Monetary Policy Forum Report.
21
Hassan. S.A, Nosheen. M, (2019), Estimating the Railways Kuznets Curve for high income nations A, GMM approach for three pollution indicators, Energy Reports 5 (2019) 170–186.
22
Heidari, H., Zavarian, Z., and Noorbakhsh, A. (2011). Investigating the effect of macroeconomic indicators on banks' nonperforming loans, Journal of Economic Research, 11(1), 43–65.
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Henriques, I., Sadorsky, P., (2008). Oil prices and the stock prices of alternative energy companies. Energy Economics 30, 998–1010.
24
Herrera, A.M. and Pesavento, E. (2009). Oil price shocks, systematic monetary policy, and the “Great Moderation” .Macroeconomic Dynamics, 13, 107–37.
25
Husain, A.M., Tazhibyeva, K. and Ter-Martirosyan, A., (2008). Fiscal policy and economic cycles in oil-exporting countries. IMF, Working Paper.
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27
Kaminsky, G.L.; Reinhart, C.M. (1999). The twin crises: The causes of banking and balance-of-payments problems. Am. Econ. Rev., 89, 473–500.
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Khandelwal, P.M Miyajima, K., Santos, A. (2016). The Impact of oil prices on the banking system in GCC. IMF Working Papers. 2016.
29
Khermraj, T., Pasha, S. (2009). The determinants of nonperforming loans: An econometric case study of Guyana. Munich Personal Repec Archive. 1–26. Available at: https://mpra.ub.unimuechen.de
30
Kilian, L. (2008). The economic effects of energy price shocks, Journal of Economic Literature, 46, 871–909.
31
Kiviet, J., Pleus, M., Poldermans, R., 2017. Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models. Econometrics 5 (1), 14.
32
Klein, N. (2013). Nonperforming loans in CESEE: Determinants and impact on macroeconomic performance. IMF Working Papers.
33
Kourani, H. (2013). Effect of Oil shock on banks’ profitability in oil producer countries. Master’s dissertation. Islamic Azad University Tehran Branch
34
Louzis, D.P.; Vouldis, A.T.; Metaxas, V.L. (2012). Macroeconomic and bank-specific determinants of nonperforming loans in Greece: a comparative study of mortgage, business and consumer loan portfolios. J. Bank. Finance, 36, 1012–1027.
35
Matutes, C., and Vives, X. (2000), Imperfect competition, risk taking and regulation in banking, European Economic Review. 44, 1–34.
36
Mehrabi, L. (2013). Assessment of bank nonperforming in the Iranian Banking System and Comparing with Other Countries: A review of the Experiences of Other Islamic Countries. (Research Report). Tehran; Monetary and Banking Research Institute.
37
Mehrabi, l. (2014). Assessing the Status of Nonperforming loans ratio in the Iranian Banking System and comparing it with Other Countries: A Review of the Experiences of Other Islamic Countries. (Research Report). Tehran; Monetary and Banking Research Institute
38
Mishkin, F. (2012). The economics of money, banking and financial markets. (10th). United States of America: Pearson.
39
Mohammadi, T., Eskandari, F., Karimi, D (2016). The Effect of macroeconomic variables and special banking characterics on nonperforming loans in Iranian banking system. Journal of Economic Research, 16(62), 81–101.
40
Moshiri, S., Nadali, M. (2013). Identifying effective factors on Banking Crisis in Iranian Economy. Journal of Economic Research (Islamic-Iranian Approach), 13(48), 1–27.
41
Nazariyan, R., Golzarian pour, S. and MoradPour, M. (2017). The Relationship between Financial Stability and Concentration in Iran’s Banking System. Quarterly Journal of Economics and Modeling Shahid Beheshtei University. 7(28), pp. 108–137.
42
Nili, F., Mahmudzadeh, A. (2014). Bank Nonperforming Loans or Toxic Bank Asset (Research Report). Tehran; Monetary and Banking Research Institute.
43
Nkusu, M. (2011). Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies. IMF Working Papers.
44
Norouzi, P. (2013). The impact of macroeconomic variables on credit risk of banks in Iran. Journal of Monetary Research-Bank, 7(20), 237–257.
45
Pashaei Fam, R., Pazoki, M., and Amirkhani, P. (2013). Explanation and analysis of the effect of OPEC oil price fluctuations on inflation of selected OECD countries. Journal of Economic Development Research, 3(9), 89–116.
46
Sadorsky, P. (2008). Assessing the impact of oil prices on firms of different size: Its tough being in the middle. Energy Policy 36(38),54–61.
47
Sadorsky, P., (2004). Stock markets and energy prices. Elsevier, New York. Encyclopedia of Energy 5, 707–717.
48
Salimi, F.M Akhondzadeh, T., and Samei, Gh. (2013). Generalized Method of Moments Model for Panel Data and Sagan Test. First International Conference on Political and Economic Epic.
49
Samadi, S., Yahyabadi, A., and Nasabadi, H. (2009). Relationship between oil shocks and economic growth in OPEC member countries: is this a symmetrical relationship? Journal of Energy Economics Studies, 17(52), 5–26.
50
Shahchera, M., Keshishian, L. (2014). The simultaneous effect of banking concentration and monetary policy on credit channels in Iranian banking system. Journal of Monetary and Banking Research. 7(19), 27–50.
51
ORIGINAL_ARTICLE
Measuring Supply Network Resilience Using a Mixed Approach (Case Study: Oil and Gas Companies)
Today, random and intelligent risks have made supply management disruptive much more than before. Over the past decade, many supply network (SN) disruptions in oil and gas industry have been due to the deliberate risks posed by international sanctions. Undoubtedly, resilience in general and resilience of SN in particular has been a systematic method for firms and organizations to deal with disruptions. This study aimed to measure, assess, and compare the resilience of SNs in oil and gas companies based on a mixed approach of systematic literature review (SLR) and complex adaptive systems (CAS). The statistical population of the study consisted of 11 subsidiaries of the National Iranian Oil Company. A robust systematic review of the literature was conducted to collect all the crucial components of supply network resilience (SNR) from 608 articles that ultimately resulted in 40 key factors based on the context intervention mechanism outcome logic (CIMO-logic). Quantitative analysis was carried out in the upstream sector of three subsidiaries of Iranian Central Oil Fields Company (ICOFC) including South Zagros, East and West Oil and Gas Production Companies. The results demonstrated a relationship between components and their measurement in upstream companies. A further finding is that South Zagros Oil and Gas Production Company was more resilient than the other two companies.
https://pbr.put.ac.ir/article_107913_ca61df12ea4f2d3d76ad2d5f23deae74.pdf
2019-03-01
79
97
10.22050/pbr.2019.107913
Resilience
Supply Network
Systematic literature review
Complex adaptive systems
Hadi
Salami
hadisalami@ut.ac.ir
1
Phd Student, Production and Operations Management, Industrial Management Department, Yazd University, Yazd, Iran
AUTHOR
Seyed Haidar
Mirfakhradini
mirfakhr.dr@gmail.com
2
Associate Professor, Industrial Management Department, Yazd University, Yazd, Iran
LEAD_AUTHOR
Davood
Andalib Ardakani
andalib@yazd.ac.ir
3
Assistant Professor, Industrial Management Department, Yazd University, Yazd, Iran
AUTHOR
Seyed Mahmoud
Zanjirchi
zanjirchi@yazd.ac.ir
4
Associate Professor, Industrial Management Department, Yazd University, Yazd, Iran
AUTHOR
Adger, W.N., Hughes, T.P., Folke, C., Carpenter, S.R., and Rockstro M, J. (2005), “Social-ecological resilience to coastal disasters”, Science, Vol. 309 No. 5737, pp. 1036–1039.
1
Bahadur, A., Ibrahim, M., and Tanner, T. (2010), “The resilience renaissance? Unpacking of resilience for tackling climate change and disasters”, Strengthening Climate Resilience Discussion Paper 1, Institute of Development Studies, Brighton, 45pp.
2
Birkmann, J. (2006), “Indicators and criteria for measuring vulnerability: theoretical bases and criteria”, in Birkmann, J. (Ed.), Measuring Vulnerability to Natural Hazards, Towards Disaster Resilient Societies, UNU Press, Tokyo, 550 pp.
3
Braziotis, C., Bourlakis, M., Rogers, H., and Tannock, F. (2013), “Supply chains and supply networks: distinctions and overlaps”, Supply Chain Management: An International Journal, Vol. 18 No. 6, pp. 644–65.
4
Brusset, X., and Teller, C. (2017). Supply chain capabilities, risks, and resilience. International Journal of Production Economics 184, 59–68.
5
Buckle, P., Marsh, G., and Smale, S. (2001), “Assessment of personal and community resilience and vulnerability”, EMA Project Report No. 15/2000 49, EMA.
6
Canbolat, Y. B., Chelst, K., and Garg, N. (2007), “Combining decision tree and MAUT for selecting a country for a global manufacturing facility”, Omega, Vol. 35, pp. 312–325.
7
Cannon, T. (2007), “Reducing people’s vulnerability to natural hazards: communities and resilience”, WIDER Conference on Fragile States–Fragile Groups: Tackling Economic and Social Vulnerability, WIDER, Helsinki, 15–16 June.
8
Carvalho, H., Duarte, S., and V. C., Machado. (2011). Lean, agile, resilient and green: divergencies and synergies, International Journal of Lean Six Sigma, Vol. 2, No. 2, 151–179.
9
Choi, T.Y., and Krause, D.R. (2006), “The supply base and its complexity: implications for transaction costs, risks, responsiveness, and innovation”, Journal of Operations Management, Vol. 24 No. 5, pp. 637–652.
10
Chowdhury, M. H. (2014), “Supply chain sustainability and resilience: the case of apparel industry in Bangladesh”, Doctoral Dissertation, Curtin University.
11
Cutter, S.L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., and Webb, J. (2008), Community and regional resilience: perspectives from hazards, disasters, and emergency management, CARRI Research Report No. 1, Community and Regional Resilience Initiative Oak Ridge National Lab, Oak Ridge, TN.
12
Day Jamison M. (2014) “Fostering emergent resilience: the complex adaptive supply network of disaster relief”; International Journal of Production Research, 52 (7): 1970–1988.
13
Dormady, N., Roa-Henriquez, A., and Rose, A. (2019). Economic resilience of the firm: a production theory approach, International Journal of Production Economics, doi: 10.1016/j.ijpe.2018.07.017.
14
Faisal MN, Banwet DK., and Shankar R. (2007); “Quantification of risk mitigation environment of supply chains using graph theory and matrix methods”, European J. Industrial Engineering, Vol. 1, No. 1, pp. 29–39.
15
Gallopin, G.C. (2006), “Linkages between vulnerability, resilience, and adaptive capacity”, Global Environmental Change, Vol. 16 No. 3, pp. 293–303.
16
Håkansson, H., and Snehota, I. (1989), “No business is an island: the network concept of business strategy”, Scandinavian Journal of Management, Vol. 5, No.3, pp. 187–200.
17
Håkansson, H., and Snehota, I. (2000), “The IMP perspective: assets and liabilities of business relationships”, in Sheth, J.N. and Parvatiyar, A. (Eds), Handbook of Relationship Marketing, Sage, Thousand Oaks, CA, pp. 69–93.
18
Harrison, T. P., Houm, P. J., Thomas, D.J., and Craighead, C.W. (2013), “Supply chain disruptions are inevitable–get ready: resiliency enhancement analysis via deletion and insertion”, Transportation Journal, Vol. 52 No. 2, pp. 264–276.
19
Hohenstein, N., Feisel, E., Hartmann, E., and Giunipero, L. (2015), “Research on the phenomenon of supply chain resilience”, International Journal of Physical Distribution and Logistics Management, Vol. 45 No. 1–2, pp. 90–117.
20
Hosseini, S., and Barker, K. (2016). A Bayesian network model for resilience-based supplier selection, International Journal of Production Economics, 180: 68–87.
21
Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, M. D., Barker, K., and Al-Khaled, A. (2019). Resilient supplier selection and optimal order allocation under disruption risks, International Journal of Production Economics, doi: 10.1016/j.ijpe.2019.03.018.
22
Klibi, W., and Martel, A. (2012), “Modeling approaches for the design of resilient supply networks under disruptions”, International Journal of Production Economics, Vol. 135 No. 2, pp, 882–898.
23
Klibi, W., Martel, A., and Guitouni, A. (2010), “The design of robust value-creating supply chain networks: a critical review”, European Journal of Operational Research, Vol. 203 No. 2, pp. 283–293.
24
Kochan, S. G. (2015). The impact of cloud-based supply chain management on supply chain resilience, Doctor of Philosophy, University of North Texas.
25
Kraaijenbrink J., Spender J.-C., and Groen A. J. (2010) "The resource-based view: a review and assessment of its critiques"; Journal of Management 36 (1): 349–372.
26
Kungwalsong, K. (2013), “Managing disruptions risks in global supply chains”, Doctoral Dissertation, The Pennsylvania State University.
27
Kwesi-Bour, (2015), “Applying system dynamics modelling to building resilient logistics: a case of the Humber ports complex”, Doctoral Dissertation, University of Hull.
28
Lambert, D. M., and Cooper, M. C. (2000), “Issues in supply chain management”, Industrial Marketing Management, Vol. 29, No.1, pp. 65–83.
29
Ledwoch, A., Yasarcan, H., and Brintrup. (2018). the moderating impact of supply network topology on the effectiveness of risk management, International Journal of Production Economics, 197: 13–26.
30
Levalle, R. R., and Nof, S. Y. (2015). A resilience by teaming framework for collaborative supply networks, Computers and Industrial Engineering, Vol 90, 67: 85.
31
Li, Y. (2017). Disruption information, network topology and supply chain resilience, Doctor of Philosophy, Business Information Technology Virginia Polytechnic Institute and State University.
32
Lusch, R.F., Vargo, S.L., and Tanniru, M. (2010), “Service, value networks and learning”, Journal of the Academy of Marketing Science, Vol. 38 No. 1, pp. 19–31.
33
Manikandan, U.D. (2008), “Modeling and analysis of a four state multi-period supply chain”, Master Thesis, Department of Industrial and Manufacturing Engineering, Pennsylvania State University.
34
Mari, S. I., Lee, Y. H., Memon, M. S., Park, Y. S., and Kim, M. (2015). Adaptive of complex network topologies for designing resilient supply chain networks, International Journal of Industrial Engineering, 22 (1), 102: 116.
35
Min, H., and Zhou, G. (2002), “Supply chain modeling: past, present and future”, Computers and Industrial Engineering, Vol. 43 Nos 1–2, pp. 231–49.
36
Mizgier, K. J., Wagner, S., and Juttner, M. P. (2015), “Disentangling diversification in supply chain networks”, International Journal of Production Economics, Vol. 162, pp. 115–124.
37
Pereira, C., Christopher, M., and Da Silva, L. (2014), “Achieving supply chain resilience: the role of procurement, Supply Chain Management: An International Journal, Vol. 19 No. 5–6, pp. 626–642.
38
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