Prioritization of Customer Order Selection Factors by Utilizing Conjoint Analysis: A Case Study for a Structural Steel Firm
In today’s business environment, companies should
make strategic decisions to gain sustainable competitive advantage.
Order selection is a crucial issue among these decisions especially for
steel production industry. When the companies allocate a high
proportion of their design and production capacities to their ongoing
projects, determining which customer order should be chosen among
the potential orders without exceeding the remaining capacity is the
major critical problem. In this study, it is aimed to identify and
prioritize the evaluation factors for the customer order selection
problem. Conjoint Analysis is used to examine the importance level
of each factor which is determined as the potential profit rate per unit
of time, the compatibility of potential order with available capacity,
the level of potential future order with higher profit, customer credit
of future business opportunity, and the negotiability level of
production schedule for the order.
[1] H. H. Guerreroand G. M. Kern, "How to more effectively accept and
refuse orders,” Production and Inventory Management Journal,vol. 29,
no. 4, 1988, pp. 59-63.
[2] D. C. Whybark and J. Wijngaard, "Editorial: manufacturing-sales
coordination,” International Journal of Production Economies, vol. 37,
no. 1, 1994, pp. 1-4.
[3] F. H. Harris and J. P. Pinder, "A revenue management approach to
demand management and order booking in assemble-to-order
manufacturing,” Journal of Operations Management, vol. 13, no. 4,
1995, pp. 299-309.
[4] V. Sridharan, "Managing capacity in tightly constrained systems,”
International Journal of Production Economics, vol. 56-57, no. 1, 1998,
pp. 601-610.
[5] P.E. Green and V. Srinivasan, "Conjoint Analysis in Consumer
Research: Issues and Outlook,” Journal of Consumer Research, vol. 5,
no. 2, 1978, pp. 103–212.
[6] J. Wang, J. Q. Yang, and H. Lee, "Multicriteria Order Acceptance
Decision Support in Over-Demand Job Shops: A Neural Network
Approach,” Mathematical Computer Modeling, vol. 19, no. 5, 1994, pp.
1-19.
[7] Y. F. Hung and T. Y. Lee, "Capacity rationing decision procedures with
order profit as a continuous random variable,” International Journal of
Production Economies, vol. 125, 2010, pp. 125-136.
[8] F. Arredondo and E. Martinez, "Learning and adaptation of a policy for
dynamic order acceptance in make-to-order manufacturing,” Computers
& Industrial Engineering, vol. 58, 2010, pp. 70-83.
[9] S. Mestry, P. Damodaran, and C-S Chen, "A branch and price solution
approach for order acceptance and capacity planning in make-to-order
operations,” European Journal of Operational Research, vol. 211, 2011,
pp. 480–495.
[10] S. Nahmias and W. S. Demmy, "Operating characteristics of an
inventory system with rationing,” Management Science, vol. 27, no. 11,
1981, pp. 1236-1245.
[11] H. C. Haynsworth and B. A. Price, "A model for use in the rationing of
inventory during lead-time,” Naval Research Logistics, vol. 36, no. 4,
1989, pp. 491-506.
[12] D. B. Rinks, "Rationing safety stock in the USAF’s multi-echelon
inventory system,” Engineering Costs and Production Economics, vol.
17, no. 1-4, 1989, pp. 99-109.
[13] A. Y. Ha, "Inventory rationing in a make-to-stock production system
with several demand classes and lost sales,” Management Science, vol.
3, no. 8, 1997, pp. 1093-1103.
[14] N. Balakrishnan, V. Sridharan, and J. W. Patterson, "Rationing capacity
between two product classes,” Decision Sciences, vol. 27, no. 2, 1996,
pp. 185-214.
[15] J. W. Patterson, N. Balakrishnan, and V. Sridharan, "An experimental
comparison of capacity rationing models,” International Journal of
Production Research, vol. 35, no. 6, 1997, pp. 1639-1649.
[16] M. Barut and V. Sridharan, "Revenue management in order-driven
production systems,” Decision Sciences, vol. 36, no. 2, 2005, pp. 287-
316.
[17] C. Oğuz, F. S. Salman, and Z. Bilgintürk Yalçın, "Order acceptance and
scheduling decisions in make-to-order systems,” International Journal
of Production Economics, vol. 125, 2010, pp. 200–211.
[18] K. P. Yoon and C-L. Hwang, Multi Attribute Decision Making: An
Introduction. Sage Univ. Papers Series, Quantitative Applications in the
Social Sciences, No 07-104, London: Sage Pub., 1995.
[19] B. K. Orme, Getting Started with Conjoint Analysis: Strategies for
Product Design and Pricing Research, Research Publishers, 2005.
[20] D. Raghavarao, J. B. Wiley, and P. Chitturi, Choice-Based Conjoint
Analysis: Models and Designs, Chapman and Hall, 2010.
[21] V.R. Rao,Applied Conjoint Analysis, Springer, 2013.
[22] URL-1 <http://www.sawtoothsoftware.com></http:>,accessed at 15.10.2013
(Sawtooth software).
[1] H. H. Guerreroand G. M. Kern, "How to more effectively accept and
refuse orders,” Production and Inventory Management Journal,vol. 29,
no. 4, 1988, pp. 59-63.
[2] D. C. Whybark and J. Wijngaard, "Editorial: manufacturing-sales
coordination,” International Journal of Production Economies, vol. 37,
no. 1, 1994, pp. 1-4.
[3] F. H. Harris and J. P. Pinder, "A revenue management approach to
demand management and order booking in assemble-to-order
manufacturing,” Journal of Operations Management, vol. 13, no. 4,
1995, pp. 299-309.
[4] V. Sridharan, "Managing capacity in tightly constrained systems,”
International Journal of Production Economics, vol. 56-57, no. 1, 1998,
pp. 601-610.
[5] P.E. Green and V. Srinivasan, "Conjoint Analysis in Consumer
Research: Issues and Outlook,” Journal of Consumer Research, vol. 5,
no. 2, 1978, pp. 103–212.
[6] J. Wang, J. Q. Yang, and H. Lee, "Multicriteria Order Acceptance
Decision Support in Over-Demand Job Shops: A Neural Network
Approach,” Mathematical Computer Modeling, vol. 19, no. 5, 1994, pp.
1-19.
[7] Y. F. Hung and T. Y. Lee, "Capacity rationing decision procedures with
order profit as a continuous random variable,” International Journal of
Production Economies, vol. 125, 2010, pp. 125-136.
[8] F. Arredondo and E. Martinez, "Learning and adaptation of a policy for
dynamic order acceptance in make-to-order manufacturing,” Computers
& Industrial Engineering, vol. 58, 2010, pp. 70-83.
[9] S. Mestry, P. Damodaran, and C-S Chen, "A branch and price solution
approach for order acceptance and capacity planning in make-to-order
operations,” European Journal of Operational Research, vol. 211, 2011,
pp. 480–495.
[10] S. Nahmias and W. S. Demmy, "Operating characteristics of an
inventory system with rationing,” Management Science, vol. 27, no. 11,
1981, pp. 1236-1245.
[11] H. C. Haynsworth and B. A. Price, "A model for use in the rationing of
inventory during lead-time,” Naval Research Logistics, vol. 36, no. 4,
1989, pp. 491-506.
[12] D. B. Rinks, "Rationing safety stock in the USAF’s multi-echelon
inventory system,” Engineering Costs and Production Economics, vol.
17, no. 1-4, 1989, pp. 99-109.
[13] A. Y. Ha, "Inventory rationing in a make-to-stock production system
with several demand classes and lost sales,” Management Science, vol.
3, no. 8, 1997, pp. 1093-1103.
[14] N. Balakrishnan, V. Sridharan, and J. W. Patterson, "Rationing capacity
between two product classes,” Decision Sciences, vol. 27, no. 2, 1996,
pp. 185-214.
[15] J. W. Patterson, N. Balakrishnan, and V. Sridharan, "An experimental
comparison of capacity rationing models,” International Journal of
Production Research, vol. 35, no. 6, 1997, pp. 1639-1649.
[16] M. Barut and V. Sridharan, "Revenue management in order-driven
production systems,” Decision Sciences, vol. 36, no. 2, 2005, pp. 287-
316.
[17] C. Oğuz, F. S. Salman, and Z. Bilgintürk Yalçın, "Order acceptance and
scheduling decisions in make-to-order systems,” International Journal
of Production Economics, vol. 125, 2010, pp. 200–211.
[18] K. P. Yoon and C-L. Hwang, Multi Attribute Decision Making: An
Introduction. Sage Univ. Papers Series, Quantitative Applications in the
Social Sciences, No 07-104, London: Sage Pub., 1995.
[19] B. K. Orme, Getting Started with Conjoint Analysis: Strategies for
Product Design and Pricing Research, Research Publishers, 2005.
[20] D. Raghavarao, J. B. Wiley, and P. Chitturi, Choice-Based Conjoint
Analysis: Models and Designs, Chapman and Hall, 2010.
[21] V.R. Rao,Applied Conjoint Analysis, Springer, 2013.
[22] URL-1 <http://www.sawtoothsoftware.com></http:>,accessed at 15.10.2013
(Sawtooth software).
@article{"International Journal of Business, Human and Social Sciences:66531", author = "Burcu Akyildiz and Cigdem Kadaifci and Y. Ilker Topcu and Burc Ulengin", title = "Prioritization of Customer Order Selection Factors by Utilizing Conjoint Analysis: A Case Study for a Structural Steel Firm", abstract = "In today’s business environment, companies should
make strategic decisions to gain sustainable competitive advantage.
Order selection is a crucial issue among these decisions especially for
steel production industry. When the companies allocate a high
proportion of their design and production capacities to their ongoing
projects, determining which customer order should be chosen among
the potential orders without exceeding the remaining capacity is the
major critical problem. In this study, it is aimed to identify and
prioritize the evaluation factors for the customer order selection
problem. Conjoint Analysis is used to examine the importance level
of each factor which is determined as the potential profit rate per unit
of time, the compatibility of potential order with available capacity,
the level of potential future order with higher profit, customer credit
of future business opportunity, and the negotiability level of
production schedule for the order.
", keywords = "Conjoint analysis, order prioritization, profit management, structural steel firm.", volume = "8", number = "2", pages = "504-5", }
