ORIGINAL_ARTICLE
A Mathematical Model for the Single Machine Scheduling Considering Sequence Dependent Setup Costs and Idle Times
Planning and scheduling are among the most important parts of the management’s duties. Development of an efficient scheduling method can results in productivity improvement of an organization. Given the importance of production scheduling in an organization, this research seeks to propose a solution for one of the important problems for the production managers. This problem occurs if a considerable percentage of available production times is allocated to machine setup times. The objective of this research is to find a scheduling method to reach minimum of total production time, earliness and tardiness times. In previous researches not all effective factors on this scheduling method such as machine idle times and machine setup costs have been studied simultaneously. A mathematical model for the optimization of multi-product single-machine scheduling problem have been developed which considered sequence dependent setup costs, costs due to delay in delivery, holding costs, and costs related to machine idle time. Comparative results for the random small size test cases show that the proposed mathematical model can obtained an optimal solution in a relatively low computation time, however, for the large-scale cases this model is not efficient and an approximate method is required for these cases.
https://www.journal-aprie.com/article_42683_5a4cdf8b5393f9c051f7fa4ca9cc4a15.pdf
2015-06-01
77
85
“Production Scheduling”
“Sequence Dependent Setup Costs”
“Tardiness”
“Earliness”
Ali
Rafiei
1
Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran
AUTHOR
Seyed Mahdi
Homayouni
homayouni@iauln.ac.ir
2
Department of Industrial Engineering, Lenjan Branch, Islamic Azad University, Isfahan, Iran
LEAD_AUTHOR
Amir
Shafiei Alavijeh
3
Department of Industrial Engineering, Lenjan Branch, Islamic Azad University, Isfahan, Iran
AUTHOR
Allahverdi, A. Ng, C.T. Cheng, T.C.E. Kovalyov, M.Y. 2008. A survey of scheduling problems with setup times or costs, European Journal of Operational Research, Vol. 187, pp. 985–1032.
1
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2
Bigras, L.F., Gamache, M. and Savard, G. 2008. The time-dependent traveling salesman problem and single machine scheduling problems with sequence dependent setup times, Discrete Optimization, Vol. 5, pp. 685–699.
3
Choobineh, F.F., Mohebbi, E. and Khoo, H. 2006. A multi-objective tabu search for a singlemachine scheduling problem with sequence-dependent setup times, European Journal of Operational Research, Vol. 175, pp. 318–337.
4
Georgios, M., Kopanos, L. and Puigjaner 2009. Multi-Site Scheduling/Batching and Production Planning for Batch Process Industries, Computer Aided Chemical Engineering, Vol. 27, pp. 2109-2114.
5
Hajinejad, D., Salmasi, N. and Mokhtari, R. 2011. A fast hybrid particle swarm optimization algorithm for flow shop sequence dependent ,group scheduling problem, Scientia Iranica, Vol. 3, pp. 759-764.
6
Karimi-Nasab, M., Haddad, H., Feili, H. and Babaie, M.H. 2013, Solving a Batching-Scheduling Problem on a Multi-Operational Parallel Machine Using Two Meta Heuristic Algorithms. International Journal of Industrial Engineering & Production Management, Vol. 24, No. 2, pp. 225-236 (In Persian).
7
Khowala, K., Fowler, J., Keha, A., and Balasubramanian, H. 2014, Single machine scheduling with interfering job sets. Computers & Operations Research, Vol. 45, pp. 97-107.
8
Lee, S.M., Asllani, A.A. 2004. Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming, Omega, Vol.32, pp.145 – 153.
9
Mokhtari, H., Nakhaei-Kamalabadi, A. and Zogerdi, S. 2012. Development of upper bound and Heuristic methods for order scheduling problem aiming to minimize machine idle time, Journal of Operations and Production Management, Vol. 3, pp. 41-58.
10
Naderi, B., Fatemi Ghomi, S.M.T. and Aminnayeri, M. 2010. A high performing metaheuristic for job shop scheduling with sequence-dependent setup times, The Applied Soft Computing, Vol. 10, pp. 703-710.
11
Rabadi, G., Georgios, M. and Anagnostopoulos, C. 2004. A branch-and-bound algorithm for the early/tardy machine scheduling problem with a common due-date sequence-dependent setup time, Computers & Operations Research, Vol. 31, pp.1727 – 1751.
12
Subramanian, A., Battarra, M. and Potts C.N. 2014. An Iterated Local Search heuristic for the single machine total weighted tardiness scheduling problem with sequence-dependent setup times, International Journal of Production Research, Vol. 52, No. 9, pp. 2729 – 2742.
13
Tan, K.C. and Narasimhan, R. 1997. Minimizing Tardiness on a Single Processor with Sequencedependent Setup Times: a Simulated Annealing Approach, Omega, Vol. 25, No. 6, pp. 619 – 634.
14
Tavakkoli Moghaddam, R., Moslehi, G., Vaseia, M. and Azaronc, A. 2005. Optimal scheduling for a single machine to minimize the sum of maximum earliness and tardiness considering idle insert, Applied Mathematics and Computation, Vol. 167, pp. 1430–1450.
15
Vanchipura, R., Sridharan, R. and Subash Babu, A. 2014. Improvement of constructive heuristics using variable neighbourhood descent for scheduling a flow shop with sequence dependent setup time, Journal of Manufacturing Systems, Vol. 33, pp. 65– 75.
16
Yin, Y., Wu, W. H., Cheng, T. C. E., & Wu, C. C. 2014. Single-machine scheduling with timedependent and position-dependent deteriorating jobs. International Journal of Computer Integrated Manufacturing, Vol. 28, No. 7, pp. 781-790.
17
Zhu, X., and Wilhelm, W.E. 2006. Scheduling and lot sizing with sequence-dependent setup: A literature review, IIE Transactions, Vol. 38, No. 1, pp. 987–1007.
18
ORIGINAL_ARTICLE
Studying the Role of Cost of Quality Information in Improvement
The objective of this study is to investigate the relationship between the ability to apply quality cost information and planning and implementing quality improvement activities. The factors leading to increase in application capacity of quality cost information in organizations are also explored in this study. The research model explains how the application capacity of quality cost information affects the planning and implementation of quality improvement activities. The research model and assumptions are examined through “structural equation model” testing approach. The findings of this research show that there is a positive and direct relationship between the application capacity of quality cost information and planning and implementation of quality improvements activities. The data used in the present study were gathered from 102 Iranian companies manufacturing automotive spare parts. Due to the volume of samples, there are limitations in generalizing the study results. This study, through identifying the factors influencing the increase in ability of an organization to apply quality cost information, offers a framework for investment and improvement of these factors with the aim of using quality cost information.
https://www.journal-aprie.com/article_42684_69bd5d0a36c736c044194bd525ee9681.pdf
2015-06-01
86
96
Quality cost information
quality improvement
Structural Equation Model
Somayeh
Maleki
smaleki82@yahoo.com
1
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Farzad
Movahedi Sobhani
farzad1348@yahoo.com
2
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Ali, H. and Arif, W. and Pirzada, D.S. and Khan, A.A. and Hussain, J. (2012). “Classical model based analysis of cost of poor qualityin a manufacturing organization”, African Journal of Business Management. Vol. 6, No. 2, pp. 670-680.
1
Al-Tmeemy, S.M.H. and Hamzah Abdul- Rahman, H. and Harun, Z. (2012). “Contractors' perception of the use of costs of quality system in Malaysian building construction projects”, International Journal of Project Management, Vol. 30, No. 7, pp. 827–838.
2
Anderson, J.C., Gerbing, D.W., (1988), “Structural equationmodeling in practice: A review and recommended two-step approach”. Psychological Bulletin, Vol. 103, No. 3, pp. 411–423.
3
Bajpei, A.K. and Willey, P.C.T , (1994). “A dynamic model of quality costs and benefits system for design quality”, International system Dynamics Conference.
4
Bamford, D.R. and Land, N. (2006). “The application and use of the PAF quality costing model within a footwear company”, International Journal of Quality & Reliability Management, Vol. 23, No. 3, pp. 265–278.
5
Campenella, J. (1999). “Principles of Quality Costs: Principles Implementation, and Use”, ASQC Quality Press, New York.
6
Chase, N. (1998). “Accounting for quality: counting costs, reaping rewards”, Quality, Vol. 37, No. 10, pp. 38-42.
7
Cheah, S.J., Md. Shahbudin, A.S and Md. Taib, F. (2010). “Tracking hidden quality costs in a manufacturing company: an action research”, International Journal of Quality & Reliability Management, Vol. 28, No. 4, pp. 405-425.
8
Chopra, A. and Garg, D. (2011). “Behavior patterns of quality cost categories”, The TQM Journal, Vol. 23, No. 5, pp. 510-515.
9
Czuchry, A.J., Yasin, M.M. and Little, G.S. (1999). “A practical, systematic approach to understanding cost of quality: a field study”, Industrial Management & Data Systems, Vol. 99 No. 8, pp. 362–366.
10
Eldridge, S. and Balubaid, M. and Barber, K.D. (2006). “Using a knowledge management approach to support quality costing”, International Journal of Quality & Reliability Management, Vol. 23, No. 1, pp. 81–101.
11
Gupta, M. and Campbell, V. (1995). “The cost of quality”, Production & Inventory Management Journal, Vol. 36, No. 3, pp. 43-49.
12
Hair, JR, and Black, W.C. and Babin, B.J. and Anderson, R.E. (2010). “Multivariate data Analysis”, Pearson Prentice Hall , Seven Editon .
13
Harrington, H.J. (1999). “Performance improvement: a total poor-quality cost system”, The TQM Magazine, Vol. 11, No. 4, pp. 221–230.
14
Juran, J.M. (1998). “Juran‘s Quality Handbook”, McGrawhill, Fifth edition.
15
Kiani, B., Shirouyehzad, H., KhoshsalighehBafti, F. and Fouladgar, H. R. (2009). “System dynamics approach to analyzing the cost factors effects on cost of quality “, International Journal of Quality & Reliability Management, Vol. 26, No. 7, pp. 685-698.
16
Loduca, D.P. (2011) “Exploratory Study of Barriers to Use of Feigenbaum’s Quality Cost Strategy within Design Engineering Firms”, Dissertation, Missouri University of Science and Technology. Low, S.P. and Yeo, H.K.C., (1998). “A construction quality costs quantifying system for the building industry”, International Journal of Quality & Reliability Management, Vol. 15, No. 3, pp. 329–349.
17
Mohandas ,V.P. and Sankaranarayanan S.R. (2008). “Cost of Quality Analysis: Driving Bottom-line Performance”, International Journal of Strategic Cost Management, Vol. 3, No. 2, pp. 1-8.
18
Montgomery, D. (1996), Introduction to Statistical Quality Control, Wiley, New York, NY
19
Morse, W.J., Roth, H.P. and Poston, K.M. (1987). Measuring, Planning and Controlling Quality Costs, NAA Publication,
20
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21
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22
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23
Schiffauerova, A. and Thomson, V. (2006), “A review of research on cost of quality models and best practices”, International Journal of Quality & Reliability Management, Vol. 23, No. 6, pp. 647.
24
Sharma, R.K., Kumar, D. and Kumar, P. (2007). “A framework to implement QCS through process cost modeling”, The TQM Magazine, Vol. 19, No. 1, pp. 18–36.
25
Sharma, S. (1996). “Applied Multivariate techniques” ,Wiley, New York, NY. Sower, V.E. and Quarles, R. (2007). “Cost of quality usage and its relationship to quality system maturity”, International Journal of Quality & Reliability Management, Vol. 24, No. 2, pp. 121- 140.
26
Sower, V.E. and Quarles, R. (2003). “Cost of Quality: Why More Organizations Do Not Use It Effectively”, American Society for Quality, pp. 1-14
27
Superville, C.R. and Gupta, S, (2001). “Issues in modeling, monitoring and managing quality costs”, The TQM Magazine, Vol. 13, No. 6, pp. 419–424.
28
Weheba, G.S. and Elshennawy, A.K. (2004). “A revised model for the cost of quality”, International Journal of Quality & Reliability Management, Vol. 21, No. 3, pp. 291–308.
29
Zimwara, D. and Mugwagwa, L. and Maringa, D. and Mnkandla, A. and Mugwagwa, L. and Ngwarati, T.T. (2013). “Cost of Quality as a Driver for Continuous Improvement - Case Study – Company X”. International Journal of Innovative Technology and Exploring Engineering, Vol. 2, No. 2, pp. 132-139.
30
ORIGINAL_ARTICLE
Identifying Effective Criteria in Agile Project Management and Ranking Projects Regarding the Employer and the Contractor’s Perception using TOPSIS Method; The Case of Foolad Technique Co.
To achieve the business goals, projects must be accurately done by project-based organizations. So, the aware of project performance is vital for this type of organizations. Given the projects play an important role in the development of any society, so the use of the best ways to implement, is very important. One of the most important methods employed is agile project management .method that the purpose of the application of this approach is delivering value to the employer. The employer in this project plays an important role. The contractor will also have an important role in the implementation of projects, knowledge of the perceptions of the employer and contractor project management agility standards play an important role in improving implementation of the projects. The purpose of this paper is that examine the criteria for agile project management, then compare it with the traditional method. Therefore, evaluates the projects in Foolad Technique according to perception of contractors and employer of the criteria for agile project management agility using MCDM. From 6 project which studying in Foolad Technique, project of Foundation equipment Abarkooh rolling Hall, received the highest level of priority based on perception of contractors and employers. The managers can increase and estimate the agility level of their organization through knowing and using these criteria.
https://www.journal-aprie.com/article_42685_e9820e522df5e22459200c4891c3f56f.pdf
2015-06-01
97
110
project management
Agility
Agile Project Management
Perception
Foolad Technique
Employer
Contractor
TOPSIS
Hadi
Shirouyehzad
hadi.shirouyehzad@gmail.com
1
Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Iran
AUTHOR
Arash
Shahin
shahinmailbox@yahoo.com
2
Department of Management,University of Isfahan, Isfahan, Iran
AUTHOR
Mina
Dayani
minadayani@gmail.com
3
Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
LEAD_AUTHOR
Boehm, B. and Turner, R. (2003). ” Using risk to balance agile and plan-driven methods”. IEEE Computer, Vol. 36, No. 6, pp. 57–66.
1
Camci, A. and Katncur, T. (2006). “Technology Complexity in Projects: Does Classical Project Management Works?. PICMET.
2
Cao, Q. and Hoffman, J.J. (2011). ” A case study approach for developing a project performance evaluation system”. International Journal of Project Management. Vol.29, No.1, pp.155–164.
3
Cui, Y. and Olsson, N.O.E. (2009). “Project flexibility in practice: And empirical study of reduction lists in large governmental projects”. International Journal of Project Management, Vol. 27, No. 5, pp. 447-455.
4
Chen, Q., Reichard,G. and Beliveau, G. (2007). ” Interface management_a facilitator of lean construction and agile project management”.Proceedings of the 15th International Conference on Lean Construction Summit,16-22 July, Michigan,USA, pp. 57-66.
5
Chin,G. (2004). ”Agile Project Management: How to Succeed in the Face of Changing Project Requirements”. 1th edition, New York: Amacon.
6
Chow, T., and Cao, D. (2008). ” A survey study of critical success factors in agile software projects”. The Journal of Systems and Software.Vol. 81, No.1, pp. 961–971.
7
Eden,C., Ackermann,F. and Williams, T. (2005). “The amoebic growth of project costs”. Project Management Journal, Vol. 36. No. 2, pp. 15-27.
8
Fernandez, D.and Fernandez, J. (2009). ”Agile project management agilism versus traditional approaches”.The Journal of Computer Information Systems,Vol. 49, No.2, p10.
9
Hallgren, M. and Olhager, J. (2009). ”Lean and agile manufacturing: External and internal drivers and performance outcomes”. International Journal of Operations and Production Management, Vol. 29, No. 10, pp. 976–999.
10
Hass, K. (2007). ” The Blending of Traditional and Agile Project Management”. PM World Today. Vol. 9, No. 5, pp. 18.
11
Highsmith, J. (2004a). Agile Project Management: Creating Innovative Products.4th edition,Boston: Addison-Wesley.
12
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13
Hoda, R., Noble, J. and Marshall.S. (2011). ” The impact of inadequate customer collaboration on self-organizing Agile teams. Information and Software Technology, Vol. 53, No. 1, pp. 521–534.
14
Horn, C. and Rudolf, M. (2011). ”Service quality in the private banking business”.Financial Markets and Portfolio Management, Vol. 25, No. 2, pp. 173–195.
15
Leybourne,S.(2009).” Improvisation and agile project management: a comparative consideration”. International Journal of Managing Projects in Business. Vol. 2, No. 4, pp. 519-535.
16
Mafakheri, F., Nasiri,F. and Mousavi, M. (2008). ”Project agility assessment: an integrated decision analysis approach”. Production Planning and Control, Vol.19, No.6, pp. 567- 576.
17
Munns,A.k., and Bjeirmi, B.F. (1996). ” The role of project management in achieving project success”, International Journal of Project Management, Vol. 14, No. 2, pp. 81-87.
18
Narasimhan, R., Swink, M. and Kim, S. W. (2006). ”Disentangling leanness and agility:An empirical investigation”, Journal of Operations Management, Vol. 24, No. 5, pp. 440– 457.
19
Qumer, A. and Henderson-Sellers, B. )2008(. “An evaluation of the degree of agility in six agile methods and its applicability for method engineering”. Information and Software Technology. Vol.50, No. 4, pp.280–295.
20
Schatz, B. and Abdelshafi, I. (2005). “Primavera gets agile: A successful transition to agile development”. IEEE Software . Vol. 22, No. 3, pp. 36–42.
21
Siakas,K. and Siakas,E. (2007). ”The agile professional culture: a source of agile quality”.Software Procees Improvement and Practice, Vol. 12, No. 1, pp. 597–610.
22
Stankovica,D., Nikolicb.V., Djordjevicc,M. and Caod,B. (2013). ” A survey study of critical success factors in agile software projects in former Yugoslavia IT companies”. The Journal of Systems and Software, Vol. 86, No.1, pp. 1663– 1678.
23
Stare,A. (2014). ”Agile Project Management in Product Development Projects”. Proceedings of The 27th IPMA World Congress on IPMA, 19 March, Dubrovnik, Croatia, pp. 295– 304.
24
Szoke,A.(2011).” Conceptual scheduling model and optimized release scheduling for agile environments”. Information and Software Technology.Vol.53,No. 1, pp. 574–591.
25
Wang, T.C. and Chang, T.H. (2007). “Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment”. Expert Systems with Applications, Vol. 33, No. 4, pp. 870-880.
26
Wysocki,R.K.(2009).”Effective Project Management”, 5th ed.,Indiana:Wiley publishing;InG. Zandi, F. and Tavana, M. (2011). “A fuzzy group quality function deployment model for eCRM framework assessment in agile manufacturing”. Computers and Industrial Engineering. Vol. 61, No. 1, pp. 1–19.
27
Zavadskas, E.K., Vilutiene, T., Turskis, Z. and aparauskas, J. S. (2014). ”Multi-criteria analysis of Projects' performance in construction”, Archives of Civil and Mechanic al Engineering, Vol.14, No. 1, pp.114–121.
28
ORIGINAL_ARTICLE
New model for ranking based on Sum Weights Disparity Index in data envelopment analysis in fuzzy condition
In this research, a new model for ranking is presented based on sum weights disparity index in data envelopment analysis in fuzzy condition. Using disparity index, the input and output of data envelopment analysis is considered according to similarity in one category and, units with the efficiency one can be ranked with this method. The new approach of this research is the evaluation of this model in uncertainty conditions and in fuzzy state. In fuzzy conditions, a new model can be provided and used when there are no definitive data and the application of this model can get closer to the actual situation. In this study, to prove the adequacy of the model, the numerical example is assessed and the results of the proposed model is compared with the results of the fuzzy BCC model; the obtained results are indicative of the superiority of the proposed model.
https://www.journal-aprie.com/article_42686_286a3f75916bb0da739e70af9976a347.pdf
2015-06-01
111
119
Data Envelopment Analysis
Sum Weights Disparity Index
Fuzzy Conditions
Navid
Torabi
torabi_na@yahoo.com
1
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Seyyed Esmaeil
Najafi
najafi1515@yahoo.com
2
Professor of Industrial Engineering, Azad University, Research & Science Branch, IRAN
AUTHOR
Azizi, H., Wang, Y.M., (2013). “Improved DEA models for measuring interval efficiencies of decision-making units”, Measurement, Vol. 46, No. 3, pp. 1325-1332.
1
Bal, H., orkcu, H.H., & Celebioglu, S.(2008). “A new method based on the dispersion of weights in data envelopment analysis”. Journal of Computers Industrial Engineering, Vol. 54, No. 3, pp. 502–512.
2
Banker, R.D., Charnes, A., and Cooper, W.W. (1984). “Some models for estimating technical and scale efficiency in data envelopment analysis”. Management Science, Vol. 30, No. 9, pp. 1078–1092.
3
Charnes, A., Cooper, W.W., and Rhodes,E.(1978). “Measuring the efficiency of decision making units”. European Journal of Operational Research, Vol. 2, No. 6, pp. 429–444.
4
Chen, Y., Djamasbi, S., Juan, D., and Lim, S., (2013). “Integer-valued DEA super-efficiency based on directional distance function with an application of evaluating mood and its impact on performance”, International Journal of Production Economics, Vol. 146, No. 2, pp. 550-556.
5
Guo, P., and Tanaka, H. (2001). “Fuzzy DEA: A perceptual evaluation method”. Fuzzy Sets and Systems, Vol. 119, No. 1, pp. 149–160.
6
Kao, C., and Liu, T.S., (2014). “Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks”. Omega, Vol. 47, No. 1, pp. 90-98.
7
Lau, K.H. (2013). “Measuring distribution efficiency of retail network through data envelopment analysis”. International journal of production economics, Vol. 146, No. 3, pp. 598-611.
8
Lee, B.L., and Worthington, C.A., (2014). “Technical efficiency of mainstream airlines and low-cost carriers: New evidence using bootstrap data envelopment analysis truncated regression”. Journal of Air Transport Management, Vol. 38, No. 1, pp. 15-20.
9
Parra, M.A., Terol, A.B., Gladish, B.P., and Rodriguez Uria, M.V. (2005). “Solving a multi objective possibilistic problem through compromise programming”, European Journal of Operational Research , Vol. 164, No. 3, pp. 748–759.
10
Sengupta, J. K. (1992). “A fuzzy systems approach in data envelopment analysis”. Computers and Mathematics with Applications. Vol. 24, No. 8-9, pp. 259-266.
11
Jahanshahloo, G. R., Shahmirzadi, P., (2013). “New methods for ranking decision making units based on the dispersion of weights and Norm 1 in Data Envelopment Analysis”. Computers & Industrial Engineering, Vol. 65, No. 2, pp. 187-193.
12
Jimenez, M. (1996). “Ranking fuzzy numbers through the comparison of its expected intervals”, International Journal of Uncertainty, Fuzziness and Knowledge Based Systems, Vol. 4 , No. 4, pp. 379–388.
13
Jimenez, M., Arenas, A., & Bilbao, A., Rodriguez, M.V. (2007). “Linear programming with fuzzy parameters: an interactive method resolution”, European Journal of Operational Research, Vol. 177, No. 3, pp. 1599–1609.
14
Wang, Y. M., Jiang, P., (2012). “Alternative mixed integer linear programming models for identifying the most efficient decision making unit in data envelopment analysis”, Computers and Industrial Engineering, Vol. 62, No. 2, pp. 546–553.
15
Zadeh, L.A. (1978). “Fuzzy sets as a basis for a theory of possibility”. Fuzzy sets and systems, Vol. 1, No. 1, pp. 3-28.
16
ORIGINAL_ARTICLE
Using DEMATEL – ANP hybrid algorithm approach to select the most effective dimensions of CRM on innovation capabilities
Customer relationship management (CRM) and innovation are widely considered to be valuable capabilities associated with competitive advantage. CRM is a comprehensive guideline and process of management and data sharing with key customers to prompt supreme values of partnership and customers. Innovation is an important factor in setting successful mature firms apart from their competitors. Innovation capability assessments are methods to evaluate the innovation capability of enterprises, in particular, for the identification of their strengths, the improvement of their potentials, and for a good basis for a sustainable improvement of the innovation capability. The propose of this study is choosing the most effective dimensions of CRM by using the hybrid approach of Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). Thus, based on experiences of the industry firm, we draw the causal relations among innovation capabilities using DEMATEL mathematical model and determine their effects on each other. Then based on these causal relations, we choose the most effective dimensions of CRM by using the ANP model. At the result, “long-term cooperation” has the best score and can be said that it is the most effective dimensions and And then respectively followed by Customer relationship management technology-oriented, Customer participation, information sharing and consultation on problems.
https://www.journal-aprie.com/article_42688_1470ffa8a28acd4821ee9e3aaccb6a3b.pdf
2015-06-01
120
138
CRM
Innovation capabilities
DEMATEL
ANP
Elahe
Shariatmadari Serkani
e.shariatmadari@srbiau.ac.ir
1
Phd student,department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Battor, M. and Battor, M. (2010). “The impact of customer relationship management capability on innovation and performance advantages: testing a mediated model”, Journal of Marketing Management. Vol. 26, No. 9–10, pp. 842–857.
1
Becker, J.U., Greve, G. and Albers, S. (2009). “The impact of technological and organizational implementation of CRM on customer acquisition, maintenance, and retention”. Int. J. Res. Market, Vol. 26, No. 3, pp. 207–215.
2
Bose, R. (2002). “Customer relationship management: key components for IT success”. Industrial Management and Data Systems, Vol. 102, No. 2, pp. 89-97.
3
Chang, S. and Lee, M.S. (2008). “The linkage between knowledge accumulation capability and organizational innovation”, Journal of Knowledge Management, Vol. 12, No. 1, pp. 3-20.
4
Chen, I.J. and Popovich, K. (2003). “Understanding customer relationship management (CRM): People processes and technology”. Business Process Management Journal, Vol. 9, No. 5, pp. 672–688.
5
Cohen,W.M. and Levinthal, D.A. (1990). “Absorptive capacity: a new perspective on learning and innovation”, Administrative Science Quarterly, Vol. 35, No. 1, pp. 128-152.
6
Daft, R.L. (1982). Bureaucratic versus non-bureaucratic structure and the process of innovation and change, in Bacharach, S.B. (Ed.), Research in the Sociology of Organizations, Vol. 1, JAI Press, Greenwich, CT, 129-66.
7
Damanpour, F. (1991). “Organizational innovation: a meta-analysis of effects of determinants and moderators”, Academy of Management Journal, Vol. 34, No. 3, pp. 555-590.
8
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