A multi-objective SCOR-based decision alignment for supply chain performance management

Document Type : Article

Authors

Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., 15875-4413 Tehran, Iran

Abstract

A dynamic integrated solution for three main problems through integrating all metrics using SCOR are proposed in this research. This dynamic solution comprises strategic decisions in high-level, operational decisions in low-level and alignment of these two decision levels. In this regard, a human intelligence-based process for high level decisions and machine-intelligence based decision support systems (DSSs) for low-level decisions is then proposed using a novel approach. The operational presented model considers important supply chain features thoroughly such as different echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., NSGAII where its parameters is tuned using Taguchi method. Afterward, an intermediate machine-intelligence module is used to determine the best operational solution based on the strategic decision maker’s idea. The efficiency of the proposed framework is shown through numerical example where a sensitivity analysis is then conducted over the obtained results so as to show the impact of the strategic scenario planning on the considered supply chain’s performance.

Keywords

Main Subjects


References
1. Masoumi, M.K., Salwa Hanim, A., Ezutah, O., et
al. \An integrated framework-for designing a strategic
green supply chain with an application to the automotive
industry", International Journal of Industrial
Engineering, 22(1), pp. 46-61 (2015).
2. Bai, C. and Sarkis, J. \Supply-chain performancemeasurement
system management using neighbourhood
rough sets", International Journal of Production
Research, 50(9), pp. 2484-2500 (2012).
3. Sellitto, M.A., Pereira, G.M., Borchardt., M., et al.
\A SCOR-based model for supply chain performance
measurement: application in the footwear industry",
International Journal of Production Research, 53(16),
pp. 4917-4926 (2015).
4. Alomar, M. and Pasek, Z.J. \Linking supply chain
strategy and processes to performance improvement",
Procedia CIRP, 17, pp. 628-634 (2014).
5. Badri, H., Ghomi, S.F., and Hejazi, T. \A two-stage
stochastic programming model for value-based supply
chain network design", Scientia Iranica. Transaction
E, Industrial Engineering, 23(1), p. 348 (2016).
6. Skipworth, H., Godsell, J., Wong, C.Y., et al. \Supply
chain alignment for improved business performance:
an empirical study", Supply Chain Management: An
International Journal, 20(5), pp. 511-533 (2015).
7. Estampe, D., Lamouri, S., Paris, J.L., et al., \A
framework for analysing supply chain performance
evaluation models", International Journal of Production
Economics, 142(2), pp. 247-258 (2013).
8. Singh, R.K. and Acharya, P. \Performance evaluation
of supply chain management systems: a critical review
of literature", International Journal of Procurement
Management, 7(2), pp. 201-218 (2014).
9. Stefan Schaltegger, P.R.B., Dr, Bai, C., and Sarkis,
J. \Determining and applying sustainable supplier key
performance indicators", Supply Chain Management:
An International Journal, 19(3), pp. 275-291 (2014).
10. Ntabe, E.N., Lebel, l., Munson, A.D., et al. \A
systematic literature review of the supply chain operations
reference (SCOR) model application with special
attention to environmental issues", International Journal
of Production Economics, 169, pp. 310-332 (2015).
11. Agami, N., Saleh, M., and Rasmy, M. \A hybrid
dynamic framework for supply chain performance improvement",
Systems Journal, IEEE, 6(3), pp. 469-478
(2012).
12. Kocaoglu, B., Gulsun, B., and Tanyas, M. \A SCOR
based approach for measuring a benchmarkable supply
chain performance", Journal of Intelligent Manufacturing,
24(1), pp. 113-132 (2013).
13. Beamon, B.M. \Measuring supply chain performance",
International Journal of Operations & Production
Management, 19(3), pp. 275-292 (1999).
14. Gunasekaran, A., Patel, C., and Tirtiroglu, E. \Performance
measures and metrics in a supply chain
environment", International Journal of Operations &
Production Management, 21(1/2), pp. 71-87 (2001).
15. Cai, J., Liu, X., Xiao, Z., et al. \Improving supply
chain performance management: A systematic approach
to analyzing iterative KPI accomplishment",
Decision Support Systems, 46(2), pp. 512-521 (2009).
16. Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., et al.
\Linking supply chain processes' performance to a
company's nancial strategic objectives", European
Journal of Operational Research, 223(1), pp. 276-289
(2012)
17. Rooeinfar, R., Azimi, P., and Pourvaziri, H. \Multiechelon
supply chain network modelling and optimization
via simulation and metaheuristic algorithms",
Scientia Iranica, 23(1), pp. 330-347 (2016).
18. Agami, N., Saleh, M., and Rasmy, M. \An innovative
fuzzy logic based approach for supply chain performance
management", Systems Journal, IEEE, 8(2),
pp. 336-342 (2014).
19. Blanco, V. \A mathematical programming approach
to the computation of the omega invariant of a numerical
semigroup", European Journal of Operational
Research, 215(3), pp. 539-550 (2011).
20. Liu, S. and Papageorgiou, L.G. \Multiobjective optimisation
of production, distribution and capacity planning
of global supply chains in the process industry",
Omega, 41(2), pp. 369-382 (2013).
21. Hamta, N., Fatemi Ghomi, S.M.T., Jolai, F., et al.
\A hybrid PSO algorithm for a multi-objective assembly
line balancing problem with
exible operation
2822 M. Rezaei et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 2807{2823
times, sequence-dependent setup times and learning effect",
International Journal of Production Economics,
141(1), pp. 99-111 (2013).
22. Kolahan, F. and Kayvanfar, V. \A heuristic algorithm
approach for scheduling of multi-criteria unrelated
parallel machines", World, Academy of Science, Engineering
and Technology, 59, p. 102 (2009).
23. Celik, N., Nageshwaraniyer, S.S., and Son, Y.-J. \Impact
of information sharing in hierarchical decisionmaking
framework in manufacturing supply chains",
Journal of Intelligent Manufacturing, 23(4), pp. 1083-
1101 (2012).
24. Aalaei, A. and Davoudpour, H. \Revised multi-choice
goal programming for incorporated dynamic virtual
cellular manufacturing into supply chain management:
a case study", Engineering Applications of Arti cial
Intelligence, 47, pp. 3-15 (2016).
25. Tamiz, M., Jones, D., and Romero, C. \Goal programming
for decision-making: An overview of the current
state-of-the-art", European Journal of Operational Research,
111(3), pp. 569-581 (1998).
26. Slomp, J., Chowdary, B.V., and Suresh, N.C. \Design
of virtual manufacturing cells: a mathematical
programming approach", Robotics and Computer-
Integrated Manufacturing, 21(3), pp. 273-288 (2005).
27. Mahdavi, I., Aalaei, A., Paydar, M.M., et al. \Multiobjective
cell formation and production planning in
dynamic virtual cellular manufacturing systems", International
Journal of Production Research, 49(21),
pp. 6517-6537 (2011).
28. Wong, J.-T. \DSS for 3PL provider selection in global
supply chain: combining the multi-objective optimization
model with experts' opinions", Journal of
Intelligent Manufacturing, 23(3), pp. 599-614 (2012).
29. Xu, J., Jiang, B., Tang, L., et al., A Multi-Objective
Coordinated Operation Model for Supply Chain with
Uncertain Demand Based on Fuzzy Interval (2013).
30. Cai, Z., Wang, Y., Xiao, R., et al. \A multi-agentdriven
closed-loop quality chain model and coordinated
optimization", Communications in Information
Science and Management Engineering, 3(11), p. 524
(2013).
31. Zhang, W. and Reimann, M. \Towards a multiobjective
performance assessment and optimization
model of a two-echelon supply chain using SCOR
metrics", Central European Journal of Operations Research,
22(4), pp. 591-622 (2014).
32. Ramaa, A., Rangaswamy, T., and Subramanya, K. \A
review of literature on performance measurement of
supply chain network", in Emerging Trends in Engineering
and Technology (ICETET), 2nd International
Conference on, IEEE (2009).
33. Damodaran, A., Applied Corporate Finance: A User's
Manual, John Wiley & Sons (2008).
34. Teran, H., Hernandez, J., Vizan, A., et al. \Performance
measurement integrated information framework
in e-Manufacturing", Enterprise Information Systems,
8(6), pp. 607-629 (2014).
35. Hearnshaw, E.J. and Wilson, M.M. \A complex network
approach to supply chain network theory", International
Journal of Operations & Production Management,
33(4), pp. 442-469 (2013).
36. Trkman, P., Budler, M., and Groznik, A. \A business
model approach to supply chain management", Supply
Chain Management: An International Journal, 20(6),
pp. 587-602 (2015).
37. Arzu Akyuz, G. and Erman Erkan, T. \Supply chain
performance measurement: a literature review", International
Journal of Production Research, 48(17), pp.
5137-5155 (2010).
38. Wang, W.Y., Chan, H.K., and Pauleen, D.J. \Aligning
business process reengineering in implementing global
supply chain systems by the SCOR model", International
Journal of Production Research, 48(19), pp.
5647-5669 (2010).
39. Li, L., Su, Q., and Chen, X. \Ensuring supply
chain quality performance through applying the SCOR
model", International Journal of Production Research,
49(1), pp. 33-57 (2011).
40. Francis, J., Supply Chain Management & Business
Financial Performance (2009).
41. Sabri, E.H. and Beamon, B.M. \A multi-objective
approach to simultaneous strategic and operational
planning in supply chain design", Omega, 28(5), pp.
581-598 (2000).
42. Deb, K., Pratap, A., Agarwal, S., et al. \A fast and
elitist multi objective genetic algorithm: NSGA-II"
IEEE transactions on evolutionary computation, 6(2),
pp. 182-197 (2002).
43. Montgomery, D.C., Design and Analysis of Experiments,
5th Ed. New York: Wiley (2000).
44. Cochran, W.G. and Cox, G.M., Experimental Designs,
2nd Ed. New York: Wiley (1992).
45. Taguchi, G., Introduction to Quality Engineering,
White Plains. Asian Productivity, pp. 21-22 (1986).
46. Tsai, J.-T., Ho, W., Liu, T., et al. \Improved immune
algorithm for global numerical optimization and jobshop
scheduling problems", Applied Mathematics and
Computation, 194(2), pp. 406-424 (2007).
47. Kayvanfar, V. and Zandieh, M. \The economic lot
scheduling problem with deteriorating items and shortage:
an imperialist competitive algorithm", The International
Journal of Advanced Manufacturing Technology,
62(5-8), pp. 759-773 (2012).
48. Al-Aomar, R. \Incorporating robustness into genetic
algorithm search of stochastic simulation outputs",
Simulation Modelling Practice and Theory, 14(3), pp.
201-223 (2006).
M. Rezaei et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 2807{2823 2823
49. Scott, J. \Fault tolerant design using single and multicriteria
genetic algorithms", Master's Thesis, Department
of Aeronautics and Astronautics, Massachusetts
Institute of Technology (1995).
50. Zitzler, E. and Thiele, L. \Multiobjective optimization
using evolutionary algorithms - a comparative case
study", in International Conference on Parallel Problem
Solving from Nature, Springer (1998).
51. Kayvanfar, V., Zandieh, M., and Mahdavi, I. \Economic
lot scheduling problem with allowable shortage:
a multi-objective approach", in Industrial Engineering
and Engineering Management (IE &EM), 2011 IEEE
18Th International Conference on, IEEE (2011).
52. Lapide, L. \What about measuring supply chain
performance", Achieving Supply Chain Excellence
Through Technology, 2, pp. 287-297 (2000).
53. Peng Wong, W. and Yew Wong, K. \A review on
benchmarking of supply chain performance measures",
Benchmarking: An International Journal, 15(1), pp.
25-51 (2008).

Volume 25, Issue 5 - Serial Number 5
Transactions on Industrial Engineering (E)
September and October 2018
Pages 2807-2823
  • Receive Date: 10 August 2016
  • Revise Date: 08 April 2017
  • Accept Date: 17 July 2017