ORIGINAL_ARTICLE A Novel Algorithm for Estimating Reliability of Ready-to-use Systems in Designing Phase for Designed Lifetime Based on Markov Method and Fuzzy Approach Reliability is one of the most important factors of complex systems which play a crucial role in performance of modern systems. In this study, a novel algorithm for estimating reliability of ready-to-use systems in designing phase for designed lifetime is proposed. At first stage, the related studies are checked, and then fundamental theories of each section are presented. According to the particular structure of ready-to-use systems and Markov Chain conditions, a new model based on Markov method and Fuzzy approach is suggested. The performance of proposed model is validated by testing on a real system. Therefore, the reliability and mean time to failure of the industrial system is estimated by the algorithm. Finally, practical suggestions are recommended for optimizing the system reliability. http://www.ijsom.com/article_2761_e622b00a5f85a3ce28888e8e35b331ef.pdf 2018-11-01 289 297 10.22034/2018.4.1 Reliability Ready-to-use systems Markov chain Fuzzy theory Designed lifetime Designing phase Youness Javid youness.javid@gmail.com 1 Department of industrial engineering, Faculty of engineering, Kharazmi University, Tehran, Iran LEAD_AUTHOR Mostafa Abouei Ardakan mabouei2001@gmail.com 2 Department of industrial engineering, Faculty of engineering, Kharazmi University, Tehran, Iran AUTHOR Mohammad Yaghtin farzin.ideh12@gmail.com 3 Department of industrial engineering, Faculty of engineering, Kharazmi University, Tehran, Iran AUTHOR Mohammad N. juybari std_m_najafi@khu.ac.ir 4 Department of industrial engineering, Faculty of engineering, Kharazmi University, Tehran, Iran AUTHOR Billinton, R., and Allan, R. N. (1992). Reliability evaluation of engineering systems. New York: Plenum press. 1 Bobbio, A., Portinale, L., Minichino, M., and Ciancamerla, E. (2001). Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering & System Safety, Vol. 71(3), pp. 249–260. 2 Bucci, P., Kirschenbaum, J., Mangan, L. A., Aldemir, T., Smith, C., and Wood, T. (2008). Construction of event-tree/fault-tree models from a Markov approach to dynamic system reliability. Reliability Engineering & System Safety, Vol. 93(11), pp. 1616–1627. 3 Clemens, P.L., Fault Tree Analysis, Fourth Edition, Lecture Presentation, Sverdrup Technology, Inc (1992). 4 Dominguez-Garcia, A. D., Kassakian, J. G., Schindall, J. E., and Zinchuk, J. J. (2008). An integrated methodology for the dynamic performance and reliability evaluation of fault-tolerant systems. Reliability Engineering & System Safety, Vol. 93(11), pp. 1628–1649. 5 Jianzhong, Y., and Julian, Z. (2011). 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Fuzzy dynamic reliability evaluation of a deteriorating system      under imperfect repair. International Journal of Reliability, Quality and Safety Engineering, Vol. 11(04), pp. 387-398. 16 Van DerHorn, E., and Mahadevan, S. (2018). Bayesian model updating with summarized statistical and reliability data. Reliability Engineering & System Safety, Vol. 172, pp. 12-24. 17 Zhou, Q., Wong, Y. D., Loh, H. S., and Yuen, K. F. (2018). A fuzzy and Bayesian network CREAM model for human reliability analysis–The case of tanker shipping. Safety science, Vol.105, pp. 149-157. 18
ORIGINAL_ARTICLE Bank Mediated Financial Supply Chains: Implications for Supply Chain Strategy and Operations The purpose of this paper is to examine how bank enabled electronic financial supply chain management (FSCM) systems influence the relationship between business partners in dyadic supply chains in emerging economies such as India. Specifically, we utilize transaction cost theoretic lens to: (1) explore how banks, via FSCM, influence the financial and material flows in supply chains (2) detail the changes to exchange characteristics between supply chain partners and (3) evaluate the performance outcomes of changes to the exchanges characteristics. We utilized inductive, multiple case study as the methodological approach. We collected data via semi structured interviews from seven firms. In all, we conducted 20 in-depth interviews lasting over 20 hours. Our findings are that Supply chain members would adopt FSCM to make transactions cost efficient. Banks would motivate their clients to encourage adoption of FSCM system to expand market and reduce business uncertainty. Adoption of FSCM system would increase when the focal supply chain member gives assurance about the business prospects and creditworthiness of their trading partners. Adoption of FSCM system is more likely when they build on prior e-enabled exchange systems with their clients. Trustworthy, cooperative and disciplined behavior among the firms is crucial for a FSCM system to function well. We identified several important constructs and relationships that help to understand the exchange dynamics in financial supply chains. http://www.ijsom.com/article_2769_e63b57a27383dba518e155f2311a1bca.pdf 2018-11-01 298 318 10.22034/2018.4.2 Financial Supply Chain Management Coordination and Control Banks Buyer Behavior Supplier Behavior Transaction Cost Somendra Pant spant@clarkson.edu 1 The David D. Reh School of Business, Clarkson University, Potsdam, New York, USA LEAD_AUTHOR Santosh Mahapatra smahapat@clarkson.edu 2 The David D. Reh School of Business, Clarkson University, Potsdam, New York, USA AUTHOR Argyres, N. S., and Liebeskind, J. P. (1999). Contractual commitments, bargaining power, and governance inseparability: Incorporating history into transaction cost theory, Academy of Management Review, Vol. 24(1), pp. 49-63. 1 Barratt, M., Choi, T. Y., and Li, M. (2011). Qualitative case studies in operations management: Trends, research outcomes, and future research implications, Journal of Operations Management, Vol. 29(4), pp. 329-342. 2 Blackman, I.D., Christopher P. Holland, C. P, and Westcott, T. (2013), Motorola's global financial supply chain strategy, Supply Chain Management: An International Journal, Vol. 18(2), pp.132-147. 3 Blanchard, D (2013). Managing the Financial Supply Chain, IndustryWeek, April 8, 2013. 4 Bowersox, D. J., Closs, D. J., and Cooper, M. B. (2002). Supply chain logistics management (Vol. 2). New York, NY: McGraw-Hill. 5 Caridi, M., Crippa, L., Perego, A., Sianesi, A., and Tumino, A. (2010). Do virtuality and complexity affect supply chain visibility?, International Journal of Production Economics, Vol. 127(2), pp. 372-383. 6 Dong, Y., Xu, K., and Dresner, M. (2007). Environmental determinants of VMI adoption: An exploratory analysis”, Transportation Research Part E: Logistics and Transportation Review, Vol. 43(4), pp. 355-369. Eisenhardt, K. M. (1989). Building theories from case study research, Academy of Management Review, Vol. 14(4), pp. 532-550. Fairchild, A. (2005). Intelligent matching: integrating efficiencies in the financial supply chain, Supply Chain Management: An International Journal, Vol. 10(4), pp. 244-248. 7 Gelsomino, L. M., Mangiaracina, R., Perego, A., and Tumino, A. (2016). Supply chain finance: a literature review”, International Journal of Physical Distribution & Logistics Management, Vol. 46(4), pp. 348-366. 8 Grosse-Ruyken, P. T., Wagner, S. M., and Jönke, R. (2011). What is the right cash conversion cycle for your supply chain?, International Journal of Services and Operations Management, Vol. 10(1), pp. 13-29. 9 Gulati, R., and Singh, H. (1998). The architecture of cooperation: Managing coordination costs and appropriation concerns in strategic alliances, Administrative Science Quarterly, Vol. 43(4), pp. 781-814. 10 Gunasekaran, A., Subramanian, N., & Papadopoulos, T. (2017). Information technology for competitive advantage within logistics and supply chains: A review. Transportation Research Part E: Logistics and Transportation Review, Vol. 99, pp. 14-33. 11 Gupta, A. K., and Govindarajan, V. (2000). Knowledge flows within multinational corporations”, Strategic Management Journal, Vol. 21(4), pp. 473-496. 12 Hennart, J. F. (1993). Explaining the swollen middle: Why most transactions are a mix of “market” and “hierarchy”. Organization Science, Vol.  4(4), pp. 529-547. 13 Hofman, D., O’Marah, K., and Elvy, C. (2011). The Gartner supply chain top 25 for 2011, Gartner, Editor, Gartner Research. Vol. 31 No. 2, pp. 305-330. 14 Hofmann, E., and Kotzab, H. (2010). A supply chain‐oriented approach of working capital management, Journal of Business Logistics, Vol. 31(2), pp. 305-330. 15 Holcomb, T. R., Holmes, R. M., and Hitt, M. A. (2006). Diversification to achieve scale and scope: The strategic implications of resource management for value creation, In Ecology and Strategy (pp. 549-587). Emerald Group Publishing Limited. 16 Hugos, M. H. (2018). Essentials of supply chain management. John Wiley & Sons. 17 Jacobides, M. G., and Winter, S. G. (2005). The co‐evolution of capabilities and transaction costs: Explaining the institutional structure of production, Strategic Management Journal, Vol. 26(5), pp. 395-413. 18 Ketokivi, M., and Choi, T. (2014). Renaissance of case research as a scientific method, Journal of Operations Management, Vol. 32(5), pp. 232-240. 19 Klapper, L. (2006). The role of factoring for financing small and medium enterprises. Journal of Banking & Finance, Vol.30 (11), pp. 3111-3130. 20 Konsynski, B. R. (1993). Strategic control in the extended enterprise”, IBM systems journal, Vol. 32(1), pp. 111-142. 21 Kumar, R. L., and Crook, C. W. (1999). A multi-disciplinary framework for the management of interorganizational systems, ACM SIGMIS Database, Vol. 30(1), pp. 22-37. 22 Lorentz, H., Solakivi, T., Töyli, J., and Ojala, L. (2016). Trade credit dynamics during the phases of the business cycle – a value chain perspective, Supply Chain Management: An International Journal, Vol. 21(3), pp.363-380. 23 Malone, T. W., Yates, J., and Benjamin, R. I. (1987). Electronic markets and electronic hierarchies, Communications of the ACM, Vol. 30(6), pp. 484-497. 24 McCutcheon, D., and Stuart, F. I. (2000). Issues in the choice of supplier alliance partners, Journal of Operations Management, Vol. 18(3), pp. 279-301. 25 More, D., and Basu, P. (2013). Challenges of supply chain finance: A detailed study and a hierarchical model based on the experiences of an Indian firm, Business Process Management Journal, Vol. 19(4), pp. 624-647. 26 Nobanee, H., Juma Abbas, F., Khan, M., and Varas, J. (2017). The Influence of Supply Chain Management and Net Trade Cycle on Financial Performance. International Journal of Supply Chain Management, Vol. 6(4), pp. 51-60. 27 Patnayakuni, R., Rai, A., and Seth, N. (2006). Relational antecedents of information flow integration for supply chain coordination, Journal of Management Information Systems, Vol. 23(1), pp. 13-49. 28 Pfohl, H. C., and Gomm, M. (2009). Supply chain finance: optimizing financial flows in supply chains, Logistics Research, Vol. 1(3-4), pp. 149-161. 29 Protopappa-Sieke, M., and Seifert, R. W. (2010). Interrelating operational and financial performance measurements in inventory control, European Journal of Operational Research, Vol. 204(3), pp. 439-448. 30 Rai, A., Patnayakuni, R., and Seth, N. (2006). Firm performance impacts of digitally enabled supply chain integration capabilities, MIS Quarterly, Vol. 30(2), pp. 225-246. 31 Robinson, P. (2007). The 2007 guide to Financial Supply Chain Management, HSBC. 32 Sari, K. (2007). “Exploring the benefits of vendor managed inventory”, International Journal of Physical Distribution & Logistics Management, Vol. 37 No. 7, pp. 529-545. 33 Seifert, D., Seifert, R. W., and Protopappa-Sieke, M. (2013). “A review of trade credit literature: Opportunities for research in operations”, European Journal of Operational Research, Vol. 231 No. 2, pp. 245-256. 34 Silvestro, R., and Lustrato, P. (2014). Integrating financial and physical supply chains: the role of banks in enabling supply chain integration, International Journal of Operations & Production Management, Vol. 34, No. 3, pp. 298-324. 35 Tanrisever, F., Morrice, D., and Morton, D. (2012). Managing capacity flexibility in make-to-order production environments. European Journal of Operational Research, Vol. 216(2), pp. 334-345. 36 Williamson, O. E. (1996). Transaction cost economics and the Carnegie connection, Journal of Economic Behavior & Organization, Vol. 31(2), pp. 149-155. 37 Wuttke, D. A., Blome, C., and Henke, M. (2013a). Focusing the financial flow of supply chains: An empirical investigation of financial supply chain management, International Journal of Production Economics, Vol. 145(2), pp. 773-789. 38 Wuttke, D. A., Blome, C., Foerstl, K., and Henke, M. (2013b). Managing the innovation adoption of supply chain finance-Empirical evidence from six European case studies, Journal of Business Logistics, Vol. 34(2), pp. 148-166. 39 Yin, R. K. (2009). Case study research: Design and methods (applied social research methods). London and Singapore: Sage. 40
ORIGINAL_ARTICLE Improving Maintenance Strategy by Physical Asset Management in Petrochemical: Applying Developments in Air Craft Maintenance Considering the use of MFOP instead of MTBF The aim of this study is to compare the aviation derived reliability metric known as the Maintenance Free Operating Period (MFOP), with the traditionally used, and commonly found, reliability metric Mean Time Between Failure (MTBF), which has over the years shown some innate disadvantages in the field of maintenance. It will be shown that this is mainly due to MTBF’s inherent acceptance of failure and the unscheduled maintenance therewith directly connected. Moreover, MFOP is successfully applied to a Petrochemicals specific Case study, as to date, no other application of the MFOP concept to the Petrochemicals sector is known. An extensive literature study is presented, which covers concepts relevant to the overall study and which helps to contextualize the problem, revealing the major shortcomings of the commonly accepted MTBF metric. A methodology to analyze systems MFOP performance, making use of failure statistics to analyze both repairable and non-repairable systems, is presented. Validation makes use of a case study, which applies the MFOP methodology to a system, specifically in the Petrochemicals sector. It was shown that MFOP could be applied to the data obtained from the Petrochemicals sector, producing estimates, which were accurate representations of reality. These findings provide an exciting basis on which to begin to facilitate a paradigm shift in the mindset of maintenance personnel, setting reliability targets and dealing with unscheduled maintenance stops. http://www.ijsom.com/article_2771_de9a1c45f161dca223508f11bf7430de.pdf 2018-11-01 319 337 10.22034/2018.4.3 Maintenance strategy Petrochemicals Physical asset management Repairable system and non-repairable system Farid Najari farid.najari@gmail.com 1 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran AUTHOR Mohammad Ali Sohanallahi sobhanallahi@yahoo.com 2 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran LEAD_AUTHOR Mohammad Mohammadi mohammad_9091@yahoo.com 3 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran AUTHOR Abate, M., Lijesen, M., Pels, E., and Roelevelt, A. (2013). 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ORIGINAL_ARTICLE An Applicable Heuristic for Scheduling Multi-mode Resource Constraint Projects Using PERT Technique in the Presence of Uncertain Duration of Activities In project planning process, resource over-allocation is a major shortcoming. The resource over-allocation causes the schedules not to be applicable in practice. Besides, in real projects, it is hard to predict the duration of activities since they may be changed due to lack of resources, delays in delivering resources, improper workers etc. that cause activities not to complete as predicted. Hence, it is important to develop a method that can schedule activities by considering different execution conditions. In this research, we focused on another aspect of solving resource over-allocation problem by considering uncertain activity duration. For this purpose, a mixed integer programming model is developed where the objective function is maximizing net present value of the project while duration of activities are not deterministic. Then a number of examples are solved using a heuristic algorithm. The results showed that the proposed algorithm can effectively solve the case studies with no over-allocated resources. Afterward, the algorithm is solved using the data of constructing a hospital. The results showed that the algorithm can successfully use for real projects. http://www.ijsom.com/article_2770_984789689bea57af13c5c796a78374f2.pdf 2018-11-01 338 360 10.22034/2018.4.4 Multi-mode resource constraint Heuristic Activity duration uncertainty Resource over-allocation Aidin Delgoshaei delgoshaei.aidin@gmail.com 1 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran and Faculty of Engineering, university of Putra Malaysia, Serdang, Kuala Lumpur, Malaysia LEAD_AUTHOR Ahad Ali aali@ltu.edu 2 Department of Mechanical Engineering, Lawrence Technological University, Southfield, Michigan, USA AUTHOR Masih Parvin masih_2002uk@yahoo.com 3 Faculty of Engineering, university of Putra Malaysia, Serdang, Kuala Lumpur, Malaysia AUTHOR Maryam Ghoreishi m_ghoreishi14@yahoo.com 4 Department of Economics and Business, BSS, Aarhus University, Aarhus, Denmark AUTHOR Abbasi, B.; Shadrokh, S.; Arkat, J. (2006), Bi-objective resource-constrained project scheduling with robustness and makespan criteria, Applied Mathematics and Computation. Vol. 180(1), pp. 146-152. 1 Ballestín, F.; Valls, V.; Quintanilla, S. (2008), Pre-emption in resource-constrained project scheduling, European Journal of Operational Research. Vol. 189(3), pp. 1136-1152. 2 Buddhakulsomsiri, J.; Kim, D. S. (2006), Properties of multi-mode resource-constrained project scheduling problems with resource vacations and activity splitting, European Journal of Operational Research. Vol. 175(1), pp. 279-295. 3 Castejón-Limas, M.; Ordieres-Meré, J.; González-Marcos, A.; González-Castro, V. (2011), Effort estimates through project complexity, Annals of Operations research. Vol. 186(1), pp. 395-406. 4 Chen, J.; Askin, R. G. (2009), Project selection, scheduling and resource allocation with time dependent returns, European Journal of Operational Research. Vol. 193(1), pp. 23-34. 5 Chtourou, H.; Haouari, M. (2008), A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling, Computers & Industrial Engineering. Vol. 55(1), pp. 183-194. 6 Damay, J.; Quilliot, A.; Sanlaville, E. (2007), Linear programming based algorithms for preemptive and non-preemptive RCPSP, European Journal of Operational Research. Vol. 182(3), pp. 1012-1022. 7 Delgoshaei, A.; Ali, A. (2019), Review evolution of cellular manufacturing system’s approaches: Human resource planning method, Journal of Project Management. Vol. 4(1), pp. 31-42. 8 Delgoshaei, A.; Ali, A.; Ariffin, M. K. A.; Gomes, C. (2016a), A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty, Computers & Industrial Engineering. Vol. 100, pp. 110-132. 9 Delgoshaei, A.; Ariffin, M.; Baharudin, B.; Leman, Z. (2015), Minimizing makespan of a resource-constrained scheduling problem: A hybrid greedy and genetic algorithms, International Journal of Industrial Engineering Computations. Vol. 6(4), pp. 503-520. 10 Delgoshaei, A.; Ariffin, M.; Baharudin, B.; Leman, Z. (2016b), A new method for decreasing cell-load variation in dynamic cellular manufacturing systems, International Journal of Industrial Engineering Computations. Vol. 7(1), pp. 83-110. 11 Delgoshaei, A.; Ariffin, M. K.; Baharudin, B. H. T. B.; Leman, Z. (2014), A Backward Approach for Maximizing Net Present Value of Multi-mode Pre-emptive Resource-Constrained Project Scheduling Problem with Discounted Cash Flows Using Simulated Annealing Algorithm, International Journal of Industrial Engineering and Management. Vol. 5(3), pp. 151-158. 12 Delgoshaei, A.; Ariffin, M. K. A.; Ali, A. (2017), A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS, International Journal of Production Research. Vol. 55(4), pp. 997-1039. 13 Delgoshaei, A.; Ariffin, M. K. M.; Baharudin, B. H. T. (2016c), Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: A dynamic forward approach, Journal of Industrial Engineering and Management. Vol. 9(3), pp. 732-785. 14 Delgoshaei, A.; Gomes, C. (2016), A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost, Applied Soft Computing. Vol. 49, pp. 27-55. 15 Delgoshaei, A.; Rabczuk, T.; Ali, A.; Ariffin, M. K. A. (2016d), An applicable method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources, Annals of Operations Research. Vol.259(1-2), pp. 1-33. 16 Hartmann, S.; Briskorn, D. 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(2008), Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm, Expert Systems with Applications. Vol. 34(4), pp. 2480-2490. 25 Mika, M.; Waligóra, G.; Węglarz, J. (2005), Simulated annealing and tabu search for multi-mode resource-constrained project scheduling with positive discounted cash flows and different payment models, European Journal of Operational Research. Vol. 164(3), pp. 639-668. 26 Naber, A.; Kolisch, R. (2014), MIP models for resource-constrained project scheduling with flexible resource profiles, European Journal of Operational Research. Vol. 239(2), pp. 335-348. 27 Papke-Shields, K. E.; Boyer-Wright, K. M. (2017), Strategic planning characteristics applied to project management, International Journal of Project Management. Vol. 35(2), pp. 169-179. 28 Pérez, E.; Posada, M.; Lorenzana, A. (2016), Taking advantage of solving the resource constrained multi-project scheduling problems using multi-modal genetic algorithms, Soft Computing. Vol. 20(5), pp. 1879-1896. 29 Rabbani, M.; Ravanbakhsh, M.; Farrokhi-Asl, H.; Taheri, M. (2017), Using metaheuristic algorithms for solving a hub location problem: application in passive optical network planning, International Journal of Supply and Operations Management. Vol. 4(1), pp. 15-32. 30 Seifi, M.; Tavakkoli-Moghaddam, R. (2008), A new bi-objective model for a multi-mode resource-constrained project scheduling problem with discounted cash flows and four payment models, Int. J. of Engineering, Transaction A: Basic. Vol. 21(4), pp. 347-360. 31 Sharon, A.; Dori, D. (2015), A Project–product model–based approach to planning work breakdown structures of complex system projects, IEEE Systems Journal. Vol. 9(2), pp. 366-376. 32 Vaez, P. 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ORIGINAL_ARTICLE Purchasing Planning and Order Allocation in the Pharmaceutical Sustainable Supply Chain with Using Theoretical-Graph (GT-MP- DM) (Case Study: Supplying the clotting factor for patients with hemophilia) In view of the growing environmental consciousness among product users, the issue of product sustainability is one of the challenging tasks being faced by product designers, manufacturers,environmentalists and, decision makers.This article presents a framework for supplier selection from sustainability perspective.In this study, along with the sustainability criteria, the other criteria and sub-criteria involved in the selection of drug suppliers for patients with hemophilia have been investigated. Regarding to the significance of the clotting agents in health and life of the hemophilia patients, it is especially important to provide and supply this drug from the safe companies and the amount of ordering it is significant. The studied criteria and sub-criteria in this article have been investigated and concluded through using library studies, filed assessments of the experts’ comments; and the desired supplier has been determined, and the others have been ranked through the Graph Theoretical Matrix Permanent Dicision Making(GT-MP-DM) Approach. Finally, with presenting the bi-objective mathematical model, the amount of order to the suppliers has been determined and then the model has been solved by means of fuzzy MAX-MIN method and GAMS software; then the model has been validated by sensitivity analysis. http://www.ijsom.com/article_2772_21af07b0aedbe3f4b91fa8d1c717b390.pdf 2018-11-01 361 378 10.22034/2018.4.5 Pharmaceutical supplier selection Sustainable supply chain Hemophilia patients Fuzzy graph theoretical approach Multi criteria decision making Mahdi Moradi mahdimoradi1362@yahoo.com 1 College of Industrial Engineering, Campus of Technical Colleges, University of Tehran LEAD_AUTHOR Fariborz Jolai f.jolai@ut.ac.ir 2 College of Industrial Engineering, Campus of Technical Colleges, University of Tehran AUTHOR Amit Kumar Sinha and Ankush Anand, (2018), Development of sustainable supplier selection index for new product development using multi criteria decision making, Journal of cleaner Production, Vol. 197 (1), pp. 1587-1596. 1 Amid, A,, Ghodsypour ,S.H,O Brien,c,(2011), Aweighted max-min model for fuzzy multi-objective supplier selection in asuooly chain . Internatinal journal of production Economics, Vol. 131 (1), pp. 139-145. 2 Ageron B., Gunasekaran A. and Spalanzani A. (2012), Sustainable Supply Management: An Empirical Study. International Journal of Production Economics, Vol.140 (1), pp. 168-182. 3 Büyükozkan, G., and Çifçi, G. (2012), A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers, Expert Systems with Applications, Vol. 39, pp. 3000–3011. 4 Baykasoglu A,(2012), A review and analysis of " Graph theoretical-matrix permanent"approach to decision making with example application . Artificial intelligence review, Vol. 42 (4), pp. 576-605. 5 Dickson, G. W. (1966), An analysis of vendor selection systems and decisions, Journal of Purchasing, Vol. 2(1), pp. 28-41. 6 Esfandiari, N., Seifbarghy, M. (2013), Modeling a stochastic multi-objective quota allocation problem with price-dependent ordering, Applied Mathematical Modeling, Vol. 37, pp. 5790-5800. 7 Gopalakrishnan K., Yusuf A., Abubakar T. and Ambursa H. (2012). Sustainable supply chain management: A case study of British Aerospace (BAe) Systems. International Journal of Production Economics, Vol.140 (1), pp.193–203. 8 Govindana k., Khodaveric R. and Jafarian A. (2013). A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, Vol. 47 (5), pp.345–354. 9 Hemmelmayr, V., et al. (2010), Vendor managed inventory for environments with stochastic product usage, European Journal of Operational Research, Vol. 202 (3), pp. 686-695. 10 Kahraman, C.(2008), Fuzzy Multi-Criteria Decision Making and fuzzy sets, Springer Optimization and Its Applications Vol. 16, pp. 1-18. 11 Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., Diabat, A. (2013), Integrated fuzzy multi criteria decision making method and multi objective programming approach for supplier selection and order allocation in a green supply chain, Journal of Cleaner Production. Vol. 47, pp. 355-367. 12 Kuo, R. J., Wang, Y. C., and Tien, F. C. (2010), Integration of artificial neural network and MADA methods for green supplier selection, Journal of Cleaner Production, Vol. 18(12), pp. 1161–1170. 13 Kendall, K.E. (1980), multiple objective planning for regional blood centers, Long Range Planning, Vol. 13(4), pp. 98-104. 14 Mehralian Gh., Rajabzadeh A., Morakabati M. and Vatanpour H. (2012). Developing a Suitable Model for Supplier Selection Based on Supply Chain Risks: An Empirical Study from Iranian Pharmaceutical Companies Services. Iranian Journal of Pharmaceutical Research, Vol.11 (1), pp.209-219. 15 Mehralian Gh., Nazari J. A., Rasekh, H. R. and Hosseini S. (2016), TOPSIS approach to prioritize critical success factors of TQM: evidence from the pharmaceutical industry.The TQM Journal, Vol. 28 (2), pp.235-249. 16 N. Shah, (2004), Pharmaceutical supply chains: key issues and strategies for optimisation, Computers & Chemical Engineeringvol. Vol. 28, pp. 929-941. 17 Nazari-Shirkouhi, S., Shakouri, H., Javadi, B., Keramati, A. (2013), Supplier selection and order allocation problem using a two-phase fuzzy multi-objective linear programming, Applied Mathematical Modelling, Vol. 37(22), pp. 9308–9323. 18 Nagurney, A.,   Masoumi, A., Yu, M. (2012),  Supply chain network operations management of a blood banking system with cost and risk minimization, Computational Management Science, Vol. 9(2), pp. 205-251. 19 Prasad, K., Subbaiah, K. and Prasad, M., (2017). Supplier evaluation and selection through DEA AHP-GRA integrated approach-A case study. Uncert. Supply Chain Manag, Vol. 5(4), pp. 369-382. 20 Razmi, J., and Maghool, E. (2009). Multi-item supplier selection and lot sizing planning under multiple price discounts using augmented ε-constraint and Tchebycheff method, International Journal of Advanced Manufacturing Technology, Vol. 49, pp. 379–392. 21 Sahin, G., H.sural, and S. Meral. (2007). Locational analysis for regionalization of Turkish Red Crescent blood services, Computers & Operations Research, Vol. 34(3),pp. 692-704.   22 Vishwakarma V., Prakash C. and Barua M. K. (2016). A fuzzy-based multi criteria decision making approach for supply chain risk assessment in Indian pharmaceutical industry. International Journal of Logistics Systems and Management, Vol.25 (2), pp.245-265. 23
ORIGINAL_ARTICLE Performance Measurement and Productivity Management in Production Units with Network Structure by Identification the Most Productive Scale Size Pattern Managers tend to improve the use of resources (inputs) of organizations to obtain the most productivity. Additionally, many industrial units have multi-stage structure in which the output of one stage is the input to the next stage. This paper, for the first time, presents data envelopment analysis (DEA) approaches to obtain the most productivity in two-stage decision making units (DMUs). Radial and non-radial models, by considering internal activities in system, are proposed to evaluate network DMUs and radial model is developed to identify most productive scale size (MPSS) pattern. Proposed models are applied to optimize the performance of bank branches as units with two-stage structure. Results show efficiency scores and also improvements are needed in costs and paid interests (inputs) to get values of incomes and loans (outputs) which results in the most productivity. This study provides managers with information to propose better strategies to improve not only the overall performance but also the efficiency of each stage. http://www.ijsom.com/article_2773_86db9022960057887a5aaa49e05dc2b8.pdf 2018-11-01 379 395 10.22034/2018.4.6 Data envelopment analysis (DEA) Network DEA Most productive scale size (MPSS) Scale efficient target Fereshteh Koushki fkoushki@gmail.com 1 Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran LEAD_AUTHOR Amir Teimoori, A. (2013). A DEA two-stage decision processes with shared resources. Central European Journal of Operations Research, Vol. 21, pp. 141-151. 1 Banker, R. D.  (1984). Estimating most productive scale size using data envelopment analysis. European Journal of Operational Research, Vol. 17, pp. 35–44. 2 Banker, R. D. and Thrall, R. M. (1992). Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research, Vol. 62, pp. 74–84. 3 Banker, R.D., Cooper, W.W., Seiford, L.M. and Zhu, J. (2004) Returns to Scale in DEA.  Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science, Vol. 71, pp. 41-73. 4 Barros, C.P. and Wanke, P. (2015). An analysis of African airlines efficiency with two-stage TOPSIS and neural networks. Journal of Air Transport Management, Vol. 44, pp. 90-102. 5 Chao, C-M, Yu, M-M, Lee, U-T and  Hsiao, B. (2016). Measurement of Banking Performance in a Dynamic Multi-activity Network Structure: Evidence from Banks in Taiwan. Journal of Emerging Markets Finance and Trade, Vol. 53(4), pp. 786-805. 6 Chao, C-M, Yu, M-M and Wu, H-N. (2015). An application of the Dynamic Network DEA Model: The case of banks in Taiwan. Journal of Emerging Markets Finance and Trade, Vol. 51(1), pp. 133-151. 7 Charnes, A., Cooper, W.W. and Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, Vol. 2, pp. 429–444. 8 Chen, M-J, Chiu, Y-H, Jan, Ch., Chen, Y-C and Liu, H-H. (2015). Efficiency and Risk in Commercial Banks – Hybrid DEA Estimation. Journal of Emerging Markets Finance and Trade, Vol. 44(3), pp. xx-xx. 9 Chen, Y., Cook, W. D., Li, N. and Zhu, J.  (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research, Vol. 196, pp. 1170-1176. 10 Chen, Y. and Zhu, J.  (2004). Measuring information technology’s indirect impact on firm performance. Information Technology and Management, Vol. 5(12), pp. 9-22. 11 Färe, R. and Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, Vol. 34, pp. 35-49. 12 Färe, R. and Primont, D.  (1984). Efficiency measures for multi plant firms. Operations Research Letters, Vol. 3, pp. 257-260. 13 Fukuyama, H. and Matousek, R. (2016). Modeling Bank Performance: A Network DEA Approach. European Journal of Operational Research, Vol. 259 (2), pp. 721-732. 14 Fukuyama, H. and Mirdehghan, S. M.  (2012). Identifying the efficiency status in network DEA. European Journal of Operational Research, Vol. 220(1), pp. 85-92. 15 Galagedera, D. U. A., Roshdi, I., Fukuyama, H. and Zhu, J. (2018). A new network DEA model    for mutual fund performance appraisal: An application to U.S. equity mutual funds. Omega, Vol. 77, pp. 168-179 16 Hu, J-L and Yu, H-E. (2015). Risk, Capital, and Operating Efficiency: Evidence from Taiwan’s Life Insurance Market. Journal of Emerging Markets Finance and Trade, Vol. 51(1), pp. 121-132. 17 Kao, C. and Hwang, S. N. (2011). Decomposition of technical and scale efficiencies in two-stage production systems. European Journal of Operational Research, Vol. 211, pp. 515–519. 18 Kao, C. and Hwang, S-N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European journal of operational research, Vol. 185(1), pp. 418–429. 19 Koushki, F. (2017). Modeling Dynamic Production Systems with Network Structure. Iranian Journal of Mathematical Sciences and Informatics, Vol. 12(1), pp. 13-26. 20 Li, H., Chen, Ch., Cook, W.D., Zhang, J. and Zhu, J. (2018). Two-stage network DEA: Who is the leader? Omega, Vol. 24(C), pp. 15-19. 21 Lin, R., Chen, Z., Hu, Q. and Li, Z. (2017). Dynamic network DEA approach with diversification to multi-period performance evaluation of funds. OR Spectrum, Vol. 39(3), pp. 821-860. 22 Liu, S-T.  (2014). Fuzzy efficiency ranking in fuzzy two-stage data envelopment analysis. Optimization Letters, Vol. 8(2), pp. 633-652. 23 Liu, WB, Zhoua, ZB, Maa, CQ, Liu DB and Shen, WF. (2015). Two-stage DEA models with undesirable input-intermediate-outputs. Omega, Vol. 56, pp. 74–87. 24 Lu, W-M, Kweh, Q. L. and Huang, C-L. (2014). Intellectual capital and national innovation systems performance.  Knowledge-Based Systems, Vol. 71, pp. 201–210. 25 Paradi, J.C., Rouatt, S. and Zhu, H.  (2011.) Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, Vol. 39(1), pp. 99-109. 26 Sahoo, B. K., Zhu, J., Tone, K. and Klemen, B. M. (2014). Decomposing technical efficiency and scale elasticity in two-stage network DEA. European Journal of Operational Research, Vol. 233(3), pp. 584–594. 27 Seiford, LM and Zhu, J.  (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, Vol. 45(9), pp. 1270-88. 28 Shokri Kahi, V., Yousefi, S., Shabanpour, H. and Farzipoor Saen, R. (2017). How to evaluate sustainability of supply chains? A dynamic network DEA approach. Industrial Management & Data Systems, Vol. 117(9), pp. 1866-1889. 29 Tone, K. and Tsutsui, M. (2014). Dynamic DEA with network structure: a slacks-based measure approach. Omega, Vol. 42(1), pp. 124-131. 30 Wang, C.H., Gopal, R. and Zionts, S. (1997). Use of data envelopment analysis in assessing information technology impact on firm performance. Annals of Operations Research, Vol. 73, pp. 191-213. 31 Wang, K., Huang, W., Wu, J. and Liu, Y. N.  (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, Vol.  3, pp. 445-20. 32 Wankea, P, Maredzab, A. and Guptac, R. (2017). Merger and acquisitions in South African banking: A network DEA model. Research in International Business and Finance, Vol. 41, pp. 362–376. 33 Yang, F., Wu, D., Liang, L., Bi, G. and Wu, D.D.  (2011). Supply chain DEA: production possibility set and performance evaluation model. Annals of Operations Research, Vol. 185, pp. 195-211. 34 Zhou, Z., Lin, L., Xiao, H., Ma, Ch. and Wu, Sh. (2017). Stochastic network DEA models for two-stage systems under the centralized control organization mechanism. Computers & Industrial Engineering, Vol. 110, pp. 404-412. 35
ORIGINAL_ARTICLE Exploring the Effects of Enterprise Resource Planning Systems on Direct Procurement: An Upstream Asset-intensive Industry Perspective The past two decades have experienced an unprecedented rise in enterprise resource planning (ERP) systems implementation among asset-intensive organizations. Typical asset-intensive industries such as oil & gas, energy, and mining, rely heavily on the performance of their asset investments to stay competitive. Recently, several ERP vendors have developed solutions with diverse functionalities to address different business processes within such organizations. However, challenges unique to asset-intensive industries such as multiplex global supply chains, geographically dispersed sites, and sporadic climatic conditions add to existing impediments. This paper explores the effects of ERP systems on direct procurement with a focus on upstream asset-intensive industries. The study examines existing functionalities within ERP to determine benefits and constraints and builds on a framework with which to address potential gaps and opportunities. A quantitative research method was used to address five constructs related to ERP systems functionality to support inventory levels, delivery lead-times, procure-to-pay process, engineering change management, and ERP usability. The findings reveal statistically significant relationships between ERP systems effectiveness and all mentioned constructs, except the procure-to-pay process and ERP usability. The study informs on future improvements and feasible developments in procurement management and extends the scope of ERP systems knowledge in asset intensive industries. http://www.ijsom.com/article_2768_9469b6e849dd9c444764d27a05fc4903.pdf 2018-11-01 396 402 10.22034/2018.4.7 ERP Systems Asset-intensive industries Direct procurement Lewis Njualem lewis.njualem@csusb.edu 1 School of International Business and Management, Seneca College of Applied Arts and Technology, Ontario, Canada LEAD_AUTHOR Milton Smith milton.smith@ttu.edu 2 Department of Industrial, Manufacturing and Systems Engineering Edward E. Whitacre Jr., College of Engineering, Texas Tech University Lubbock, Texas, USA AUTHOR Absoft. (2014). Efficient operations for inventory. Oil & gas SAP specialist, Accessible at: http://www.absoft.co.uk/ uploaded-files/products/efficient-operations-for-inventory.pdf 1 Cooke J.A. (2014). Protean supply chains: Ten dynamics of supply chain and demand alignment. Hoboken, John Wiley & Sons. 2 Dhillon B.S. (2002). Engineering maintenance: A modern approach. Boca Raton, Florida: CRC Press. 3 Dickersbach J.T., Keller G. and Weihrauch K. (2007). Production planning and control with SAP. Boston, MA: Galileo Press. 4 Genpact. (2014). Improving bill of materials management for an oil and gas company decreases time to market and saves cost. Generating Engineering Impact. Accessible at: https://www.genpact.com/insight/case-study/improving-bill-ofmaterials-management-for-an-oil-and-gas-company-decreases-time-to-markets-and-saves-cost 5 Halim H. (2014). Managing information system in AII: Perspective in choosing the right solution. Accessible at: https://www.scribd.com/document/321140759/Management-Information-System-in-Assetintensive-Industries-Perspective-in-Choosing-The-Right-Solution 6 Kumar A., Tadayoni R. and Sorensen L.T. (2015). Metric Based Efficiency Analysis of Educational ERP System Usability – Using Fuzzy Model. Third International Conference on Image Information Processing, Waknaghat, India, pp. 382-386. 7 Jabareen Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, Vol. 8(4). pp. 49-62. 8 Leedy P.D. and Ormrod J.E. (2013). Practical research: Planning & design. Columbus, Ohio: Pearson, Prentice Hall. 9 Magal S.R. and Word J. (2012). Integrated business processes with ERP systems. Hoboken, NJ: John Wiley & Sons. 10 Mishra A. and Mishra D. (2011). ERP project implementation: Evidence from the oil and gas sector. Acta Polytechnic Hungarica, Vol. 8(4). pp. 55-74. 11 Pierce J., Brande A., Richard D., Doerler J., Etheredge K., Zuberev O., Lvitsanis C., Re G. and Easton E. (2012). Materials management: A mine for upstream oil and gas. Korea: AT Kearney Korea. 12 Shipcom. (2012). Shipcom helps ENSCO international to significantly improve supply chain and asset management. Shipcom Wireless. Accessible at: http://www.shipcomwireless.com/wp-content/uploads/Energy-Case-StudyENSCO.pdf 13 Wallace D. (2008). The important role of the equipment bill of material: The 2nd in a series on integrated inventory management. Charleston, SC: Life Cycle Engineering. 14 Wen-sheng W. and Zhi-chao G. (2013). ERP systems construction for mining industry based on business intelligence. Information Technology Journal, Vol. 12(23), pp. 7511-7514. 15