Abstract
In this paper, the impacts of dual overconfidence behavior and demand updating on the decisions of port service supply chain are studied with the real case study method. A port service supply chain consists of Tianjin Port, a shipping company, and a customer is investigated. A dual overconfidence behavior-based decision model under a demand updating background is built. Then the effects of Tianjin Port’s overconfidence (TPO) behavior and the shipping company’s overconfidence (SCO) behavior on the port service level (PSL), carrying volume and utilities are analyzed. Numerical analysis with real case data is conducted. Several important conclusions are obtained in this paper. Firstly, it is identified that the SCO leads to a decrease in the shipping company’s utilities, while dual overconfidence behavior causes the increase of Tianjin Port’s utilities. Secondly, it is found that the effect of the SCO on the PSL and the effect of TPO on the PSL offset each other which will lead to a threshold in TPO. This threshold is affected by the SCO, and demand updating amplifies this effect. Thirdly, demand updating not only adds to the shipping company’s carrying volume and PSL, but also increases the utilities of both parties. Lastly, it is unexpectedly found that there are interactions between demand updating and dual overconfidence behavior.
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References
Agrawal, A., Muthulingam, S., & Rajapakshe, T. (2017). How sourcing of interdependent components affects quality in automotive supply chains. Production & Operations Management, 26(8), 1512–1533.
Alexandrou, G., Gounopoulos, D., & Thomas, H. M. (2014). Mergers and acquisitions in shipping. Transportation Research Part E,61(1), 212–234.
Asadabadi, A., & Miller-Hooks, E. (2017). Timal transportation and shoreline infrastructure investment planning under a stochastic climate future. Transportation Research Part B Methodological, 100, 156–174.
Bao, X. Y., Hu, B. Y., & Tang, X. W. (2011). Incentive contract design of reverse supply chain on basis of overconfidence. Application Research of Computers,9, 3378–3380.
Berk, E., Gürler, Ü., & Levine, R. A. (2007). Bayesian demand updating in the lost sales newsvendor problem: A two-moment approximation. European Journal of Operational Research,182(1), 256–281.
Bichou, K., & Gray, R. (2004). A logistics and supply chain management approach to port performance measurement. Maritime Policy & Management,31(1), 47–67.
Bichou, K., & Gray, R. (2005). A critical review of conventional terminology for classifying seaports. Transportation Research A,39(1), 75–92.
Bolton, G. E., & Katok, E. (2008). Learning by doing in the newsvendor problem: A laboratory investigation of the role of experience and feedback. Manufacturing & Service Operations Management,10(3), 519–538.
Bostian, A. A., Holt, C. A., & Smith, A. M. (2008). Newsvendor pull-to-center effect: Adaptive learning in a laboratory experiment. Manufacturing & Service Operations Management,10(4), 590–608.
Bradford, J. W., & Sugrue, P. K. (1990). A Bayesian approach to the two-period style-goods inventory problem with single replenishment and heterogeneous Poisson demands. Journal of Operations Research Society, 41(3), 211–218.
Carbone, V., & De Martino, M. (2003). The changing role of ports in supply-chain management: An empirical analysis. Maritime Policy and Management,30(4), 305–320.
Carlo, H. J., Vis, I. F. A., & Roodbergen, K. J. (2014). Transport operations in container terminals: Literature overview, trends, research directions and classification scheme. European Journal of Operational Research,236(1), 1–13.
Caschili, S., Medda, F., Parola, F., & Ferrari, C. (2014). An analysis of shipping agreements: The cooperative container network. Networks & Spatial Economics,14(3–4), 357–377.
CCTV. 2012. huge loss of China cosco shipping group is otherwise hidden: Blind long-term high-rent charter. http://finance.sina.com.cn/chanjing/sdbd/20120516/101812076251.shtml. Accessed on May 16th, 2012.
Chen, H., Chen, J., & Chen, Y. F. (2006). A coordination mechanism for a supply chain with demand information updating. International Journal of Production Economics,103(1), 347–361.
Chen, H., Chen, Y. F., Chiu, C. H., et al. (2010). Coordination mechanism for the supply chain with leadtime consideration and price-dependent demand. European Journal of Operational Research,203(1), 70–80.
Chen, K., Wang, X., & Huang, M. (2017). Salesforce contract design, joint pricing and production planning with asymmetric overconfidence sales agent. Journal of Industrial & Management Optimization,13(2), 51–51.
Choi, T. M. (2016). Impacts of retailer’s risk averse behaviors on quick response fashion supply chain systems. Annals of Operations Research,9, 1–19.
Choi, T. M., & Cai, Y. J. (2018). Impacts of lead time reduction on fabric sourcing in apparel production with yield and environmental considerations. Annals of Operations Research,1, 1–22.
Croson, D., Croson, R., & Ren, Y. (2011). The overconfident newsvendor. Working paper, University of Texas at Arlington, Arlington.
De, G. A., Beresford, A. K. C., & Pettit, S. J. (2003). Liner shipping companies and terminal operators: Internationalization or globalization? Maritime Economics and Logistics,5(4), 393–412.
Eppen, G. D., & Iyer, A. V. (1997a). Backup agreements in fashion buying the value of upstream flexibility. Management Science,43(11), 1469–1484.
Eppen, G. D., & Iyer, A. V. (1997b). Improved fashion buying with Bayesian updates. Operation Research,45(6), 805–819.
Gervais, S., Odean, T. & Heaton, J. B. (2002). Overconfidence, investment policy and executive stock options. Rodney L. White Center for Financial Research Working Paper.
Grubb, M. D. (2009). Selling to overconfident consumers. Social Science Electronic Publishing,99(5), 1770–1807.
Gurnani, H., & Tang, C. S. (1999). Note: Optimal ordering decisions with uncertain cost and demand forecast updating. Management Science, 45(10), 1456–1462.
Ho, T. H., Lim, N., & Cui, T. H. (2010). Reference dependence in multilocation newsvendor models: A structural analysis. Management Science,56(11), 1891–1910.
Johnson, D. D., & Tierney, D. (2011). The Rubicon theory of war: how the path to conflict reaches the point of no return. International Security,36, 7–40.
Katok, E., & Wu, D. Y. (2009). Contracting in supply chains: A laboratory investigation. Management Science,55(12), 1953–1968.
Lee, J. S., Lee, C. Y., & Lee, K. S. (2012). Forecasting demand for a newly introduced product using reservation price data and Bayesian updating. Technological Forecasting and Social Change, 79, 1280–1291.
Li, M., Petruzzi, N., & Zhang, J. (2015). Overconfident competing newsvendors. Rochester: Social Science Electronic Publishing.
Lichtenstein, S., Fischhoff, B., & Phillips, L. D. (1982). Calibration of probabilities: The state of the art to 1980 (pp. 275–324). Dordrecht: Springer.
Liu, W. H., Liu, X. Y., & Li, X. (2015a). The two-stage batch ordering strategy of logistics service capacity with demand updating. Transportation Research Part E,83, 65–89.
Liu, W., Wang, D., Tang, O., et al. (2018). The impacts of logistics service integrator’s overconfidence behaviour on supply chain decision under demand surge. European Journal of Industrial Engineering,12(4), 558.
Liu, J. J., Wang, Z., Yao, D. Q., & Yue, X. (2016). Transaction cost analysis of supply chain logistics services: Firm-based versus port-focal. Journal of the Operational Research Society,67(2), 176–186.
Liu, W., Wang, S., Zhu, D. L., et al. (2017). Order allocation of logistics service supply chain with fairness concern and demand updating: Model analysis and empirical examination. Annals of Operations Research,2, 1–37.
Liu, W., Xie, D., Liu, Y., & Liu, X. Y. (2015b). Service capability procurement decision in logistics service supply chain: a research under demand updating and quality guarantee. International Journal of Production Research,53(2), 488–510.
Lu, X., Shang, J., Wu, S. Y., Hegde, G. G., Vargas, L., & Zhao, D. Z. (2015). Impacts of supplier hubris on inventory decisions and green manufacturing endeavors. European Journal of Operational Research,245, 121–132.
Ma, L. J., Zhao, Y. X., Xue, W. L., Cheng, T. C. E., & Yan, H. M. (2012). Loss-averse newsvendor model with two ordering opportunities and market information updating. International Journal of Production Economics, 140(2), 912–921.
Mafakheri, F., Breton, M., & Ghoniem, A. (2011). Supplier selection-order allocation: A two-stage multiple criteria dynamic programming approach. International Journal of Production Economics, 132(1), 52–57.
Malmendier, U., & Tate, G. (2005). CEO overconfidence and corporate investment. Journal of Finance,60, 2661–2700.
Martin, J., & Thomas, B. (2001). The container terminal community. Maritime Policy and Management,28(3), 279–292.
Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review,115, 502–517.
Nie, K., & Yu, M. (2013). Research on fresh agricultural product based on the retailer’s overconfidence under options and spot markets. International Journal of Managing Information Technology,5(4), 33–41.
Özen, U., Sošić, G., & Slikker, M. (2012). A collaborative decentralized distribution system with demand forecast updates. European Journal of Operational Research,216(3), 573–583.
Pu, X., & Zhuge, R. (2014). Bilateral efforts of supply chains considering supplier’s overconfidence and fairness. Computer Integrated Manufacturing Systems, Beijing,20(6), 1462–1470.
Qin, J. J. (2011). Contracts analysis based on information updating and loss aversion. International Conference on Intelligent Computation Technology & Automation,1, 1034–1037.
Ren, Y., & Croson, R. (2013). Overconfidence in newsvendor orders: An experimental study. Management Science,59(11), 2502–2517.
Robinson, R. (2006). Port-oriented landside logistics in Australian ports: A strategic framework. Maritime Economics and Logistics,8(1), 40–59.
Rodrigue, J. P., & Notteboom, T. (2009). The terminalization of supply chains: Reassessing the role of terminals in port/hinterland logistical relationships. Maritime Policy & Management,36(2), 165–183.
Sarvary, M., & Padmanabhan, V. (2001). The informational role of manufacturer returns policies: How they can help in learning the demand. Marketing letters, 12(4), 341–350.
Schweitzer, M. E., & Cachon, G. P. (2000). Decision bias in the newsvendor problem with a known demand distribution: Experimental evidence. Management Science,46(3), 404–420.
Sethi, S. P., Yan, H., Zhang, H., & Zhou, J. (2007). A supply chain with a service requirement for each market signal. Production & Operations Management,16(3), 322–342.
Shi, K. R., Zhou, Y., & Jiang, F. (2014). Supply chain decisions and coordination considering overconfident behavior of the retailer. Industrial Engineering Journal,3, 009.
Slack, B., & Frémont, A. (2005). Transformation of port terminal operations: From the local to the global. Transport Reviews,25(1), 117–130.
So, K. C., & Zheng, X. (2003). Impact of supplier’s lead time and forecast demand updating on retailer’s order quantity variability in a two-level supply chain. International Journal of Production Economics,86(2), 169–179.
Song, H., Yang, H., Bensoussan, A., & Zhang, D. (2014). Optimal decision making in multi-product dual sourcing procurement with demand forecast updating. Computers & Operations Research,41, 299–308.
Tsay, A. A. (1999). The quantity flexibility contract and supplier–customer incentives. Management Science, 45(10), 1339–1358.
Wang, X. W. (2014). The effects of overconfidence in supply chain systems. Applied Mechanics and Materials,543(547), 4218–4222.
Wang, Z., Yao, D., & Yue, X., et al. (2016). Impact of IT Capability on the Performance of Port Operation. Production & Operations Management, 1–14.
Wang, Z. G., Zhang, Z. C., Li, C. F., Xu, L., & Chang, Y. (2015). Optimal ordering and disposing policies in the presence of an overconfident retailer: A Stackelberg game. Mathematical Problems in Engineering,2015(2), 1–12.
Wang, F., Zhuo, X., Niu, B., et al. (2017). Who canvasses for cargos? Incentive analysis and channel structure in a shipping supply chain. Transportation Research Part B Methodological, 97, 78–101.
Wu, Y., Yu, F. & Tu, B. (2013). Decision of the supply chain with bidirectional option based on demand information update and funding constraints. In International conference on service systems & service management, IEEE, 562–567.
Zha, Y., Zhang, J., Yue, X., et al. (2015). Service supply chain coordination with platform effort-induced demand. Annals of Operations Research,235(1), 785–806.
Zhang, Z., Li, C., Chun, F., Du, P. & Xu, L. (2015). Does overconfident effect affect the performance of a duopoly market? A theoretical analysis. In 12th international conference on service systems and service management (ICSSSM).
Zhang, J., Shou, B., & Chen, J. (2013). Postponed product differentiation with demand information update. International Journal of Production Economics,141(2), 529–540.
Zhu, X. (2017). Outsourcing management under various demand Information Sharing scenarios. Annals of Operations Research,257(1–2), 449–467.
Acknowledgements
This research is supported by the National Natural Science Foundation of China (Grant No. 71672121; 71372156), sponsored by Independent Innovation Foundation of Tianjin University. The suggestions of the reviewers are also gratefully acknowledged.
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Appendices
Appendix A : Proof of Lemma 1
Similarly, \( \frac{{\partial Q_{k2}^{*} }}{\partial k} > 0 \), \( \frac{{\partial \beta_{l2}^{*} }}{\partial k} > 0 \), and \( \frac{{\partial \beta_{l2}^{*} }}{\partial l} < 0 \) can be proved
Appendix B: Proof of proposition 1
As \( F^{ - } \left( x \right) \) is a monotonically increasing function, therefore: when \( k > 1 \), \( F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right) > F^{ - 1} (\frac{p + h - \gamma w}{p + h + g}) \), and \( Q_{k1}^{*} - Q_{1}^{*} > 0 \) when \( \frac{\gamma w + g}{p + h + g} < k < 1 \), \( F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right) < F^{ - 1} (\frac{p + h - \gamma w}{p + h + g}) \), and \( Q_{k1}^{*} - Q_{1}^{*} > 0 \)
,We must compare the value of \( \frac{{k(p + h + g) - \left( {\gamma w + g} \right)}}{k(p + h + g)} + \frac{{I_{k} \left( {\gamma w + g} \right)}}{k(p + h + g)} \) and
Further, we have:
Appendix C: Proof of proposition 2
We must compare the value of \( F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right) \) and \( lF^{ - 1} (\frac{p + h - \gamma w}{p + h + g}) \)
As \( F^{ - 1} \left( x \right) \) is a monotonically increasing function, therefore:
We only must compare the value of \( \frac{(p + h + g)k - \gamma w - g}{k(p + h + g)} \) and \( \frac{p + h - \gamma w}{p + h + g} \)
-
(1)
When \( k > 1 \) and \( l_{\hbox{min} } < l < 1 \)
We have \( F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right) > lF^{ - 1} (\frac{p + h - \gamma w}{p + h + g}) \), so \( \beta_{l1}^{*} > \beta_{1}^{*} \)
-
(2)
When \( \frac{\gamma w + g}{p + h + g} < k < 1 \) and \( 1 < l < l_{\hbox{max} } \)
We have \( F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right) < lF^{ - 1} (\frac{p + h - \gamma w}{p + h + g}) \), so \( \beta_{l1}^{*} < \beta_{1}^{*} \)
-
(3)
When \( \frac{\gamma w + g}{p + h + g} < k < 1 \) and \( l_{\hbox{min} } < l < 1 \), we have:
and we cannot confirm the positive and negative of \( \beta_{l1}^{*} - \beta_{1}^{*} \)
Let \( \beta_{l1}^{*} - \beta_{1}^{*} = 0 \), we have
In the case of \( \frac{\gamma w + g}{p + h + g} < k < 1 \)
we have \( 0 < l < \frac{{F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right)}}{{F^{ - 1} (\frac{p + h - \gamma w}{p + h + g})}} < 1 \), and \( \beta_{l1}^{*} > \beta_{1}^{*} \)
In the case of \( \frac{\gamma w + g}{p + h + g} < k < 1 \)
we have \( \frac{{F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right)}}{{F^{ - 1} (\frac{p + h - \gamma w}{p + h + g})}} < l < 1 \), and \( \beta_{l1}^{*} < \beta_{1}^{*} \)
(4) When \( k > 1 \) and \( 1 < l < l_{\hbox{max} } \)
Further, we cannot confirm the positive and negative of \( \beta_{l1}^{*} - \beta_{1}^{*} \)
Let \( \beta_{l1}^{*} - \beta_{1}^{*} = 0 \); we have \( l = \frac{{F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right)}}{{F^{ - 1} (\frac{p + h - \gamma w}{p + h + g})}} \)
In the case of \( k > 1 \)
We have \( 1 < l < \frac{{F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right)}}{{F^{ - 1} (\frac{p + h - \gamma w}{p + h + g})}} \), and \( \beta_{l1}^{*} > \beta_{1}^{*} \)
In the case of \( k > 1 \)
We have \( 1 < \frac{{F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right)}}{{F^{ - 1} (\frac{p + h - \gamma w}{p + h + g})}} < l \), and \( \beta_{l1}^{*} < \beta_{1}^{*} \)
In summary
When \( l < \frac{{F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right)}}{{F^{ - 1} (\frac{p + h - \gamma w}{p + h + g})}},\beta_{l1}^{*} > \beta_{1}^{*} \);
While \( l > \frac{{F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right)}}{{F^{ - 1} (\frac{p + h - \gamma w}{p + h + g})}},\beta_{l1}^{*} < \beta_{1}^{*} \)
Since \( \frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}{ = 1} - \frac{{\left( {\gamma w + g} \right)}}{k(p + h + g)} \) increases monotonically with \( k \), \( F^{ - 1} \left( x \right) \) is an increase function as well.
We can prove that \( F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right) \) increases monotonically with \( k \).
Therefore \( A_{1} = \frac{{F^{ - 1} \left( {\frac{(p + h + g)k - \gamma w - g}{k(p + h + g)}} \right)}}{{F^{ - 1} (\frac{p + h - \gamma w}{p + h + g})}} \) increases monotonically with \( k \).
Similarly, we can prove the relationship between \( \beta_{l2}^{*} \) and \( \beta_{2}^{*} \), and \( A_{2} \) increases monotonically with \( k \), as described in Proposition 2.
Appendix D: Proof of Lemma 2
We need to compare \( A_{1} \) and 1; \( A_{1} = \frac{{F^{ - 1} \left( {\frac{{k\left( {p + h + g} \right) - \left( {g + \gamma w} \right)}}{{k\left( {p + h + g} \right)}}} \right)}}{{F^{ - 1} \left( {\frac{p + h - \gamma w}{{\left( {p + h + g} \right)}}} \right)}} \)
We first compare the value of \( \frac{{k\left( {p + h + g} \right) - \left( {g + \gamma w} \right)}}{{k\left( {p + h + g} \right)}} \) and \( \frac{p + h - \gamma w}{{\left( {p + h + g} \right)}} \).
We have:
Therefore, when \( \frac{g + \gamma w}{p + h + g} < k < 1 \), \( A < 1 \); and when \( \frac{g + \gamma w}{p + h + g} \ge 1 \), \( A \ge 1 \).
Appendix E: Proof of proposition 3
In the context of no demand updating, compared with the rational situation, in the case of dual overconfidence conditions, the utility change of the shipping company is as follows:
After updating the demand, compared with the rational situation, in the case of dual overconfidence conditions, the utility change of the shipping company is as follows:
Take the first derivative of \( \Delta \varPi_{\text{S1}} \) with respect to \( k \), and we have:
When \( k = 1 \), \( \frac{{\partial \Delta \varPi_{S1} }}{\partial k} = 0 \); when \( \frac{g + \gamma w}{p + g + h} < k < 1 \), \( \frac{{\partial \Delta \varPi_{S1} }}{\partial k} > 0 \); when \( k > 1 \), \( \frac{{\partial \Delta \varPi_{S1} }}{\partial k} < 0 \).
In addition, since \( k = 1 \), we obtain \( \Delta \varPi_{S1} = 0 \), that is, \( \varPi_{S1}^{k} = \varPi_{S1} \).
Therefore \( \Delta \varPi_{S1} \le 0 \).
The proof of \( \Delta \varPi_{S2} \) is similar to \( \Delta \varPi_{S1}. \)
Appendix F: A Proof of proposition 4
Appendix G: Proof the proposition 5
Therefore, \( A_{2} - A_{1} \) increases with k.
Further, when \( k = 1 \), \( A_{2} - A_{1} = 0 \), \( A_{2} = A_{1} \).
Hence, when \( k < 1 \), \( A_{2} - A_{1} < 0 \), \( A_{2} < A_{1} \).
Otherwise, when \( k > 1 \), \( A_{2} - A_{1} > 0 \), \( A_{2} > A_{1} \).
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Liu, W., Shen, X. & Wang, D. The impacts of dual overconfidence behavior and demand updating on the decisions of port service supply chain: a real case study from China. Ann Oper Res 291, 565–604 (2020). https://doi.org/10.1007/s10479-018-3095-5
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DOI: https://doi.org/10.1007/s10479-018-3095-5