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July  2018, 14(3): 1203-1218. doi: 10.3934/jimo.2018006

Competition of pricing and service investment between iot-based and traditional manufacturers

Zhiping Zhou 1,2, , Xinbao Liu 1,3,, , Jun Pei 1,2,3,, , Panos M. Pardalos 2, and  Hao Cheng 1,3,

1. 

School of Management, Hefei University of Technology, Hefei 230009, China
2. 

Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA
3. 

Key Laboratory of Process Optimization, and Intelligent Decision-Making of Ministry of Education, Hefei 230009, China

* Corresponding author:Xinbao Liu, Jun Pei

Received  June 2016 Revised  September 2017 Published  January 2018

This paper develops a multi-period product pricing and service investment model to discuss the optimal decisions of the participants in a supplier-dominant supply chain under uncertainty. The supply chain consists of a risk-neutral supplier and two risk-averse manufacturers, of which one manufacturer can provide real-time customer service based on the Internet of Things (IoT). In each period of the Stackelberg game, the supplier decides its wholesale price to maximize the profit while the manufacturers make pricing and service investment decisions to maximize their respective utility. Using the backward induction, we first investigate the effects of risk-averse coefficients and price sensitive coefficients on the optimal decisions of the manufacturers. We find that the decisions of one manufacturer are inversely proportional to both risk-averse coefficients and its own price sensitive coefficient, while proportional to the price sensitive coefficient of its rival. Then, we derive the first-best wholesale price of the supplier and analyze how relevant factors affect the results. A numerical example is conducted to verify our conclusions and demonstrate the advantages of the IoT technology in long-term competition. Finally, we summarize the main contributions of this paper and put forward some advices for further study.

Keywords: Stackelberg game, IoT, pricing, service investment, risk.
Mathematics Subject Classification: Primary: 91A80; Secondary: 91B24.
Citation: Zhiping Zhou, Xinbao Liu, Jun Pei, Panos M. Pardalos, Hao Cheng. Competition of pricing and service investment between iot-based and traditional manufacturers. Journal of Industrial & Management Optimization, 2018, 14 (3) : 1203-1218. doi: 10.3934/jimo.2018006
References:
[1]

M. R. Amin-Naseri and M. A. Khojasteh, Price competition between two leader-follower supply chains with risk-averse retailers under demand uncertainty, The International Journal of Advanced Manufacturing Technology, (2015), 377-393.  doi: 10.1007/s00170-014-6728-0.  Google Scholar

[2]

O. Besbes and D. Saure, Product assortment and price competition under multinomial logit demand, Production and Operations Management, 25 (2016), 114-127.   Google Scholar

[3]

W. Chen, Z. G. Zhang and Z. Hua, Analysis of price competition in two-tier service systems Journal of the Operational Research Society (2016). doi: 10.1057/jors.2015.123.  Google Scholar

[4]

Y. Dai, X. Chao, S. C. Fang and H. L. W. Nuttle, Pricing in revenue management for multiple firms competing for customers, International Journal of Production Economics, 98 (2005), 1-16.  doi: 10.1016/j.ijpe.2004.06.056.  Google Scholar

[5]

B. Dan, G. Xu and C. Liu, Pricing policies in a dual-channel supply chain with retail services, International Journal of Production Economics, 139 (2012), 312-320.   Google Scholar

[6]

H. Demirkan and D. Delen, Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud, Decision Support Systems, 55 (2013), 412-421.  doi: 10.1016/j.dss.2012.05.048.  Google Scholar

[7]

L. Duan, J. Huang and B. Shou, Duopoly competition in dynamic spectrum leasing and pricing, IEEE Transactions on Mobile Computing, 11 (2012), 1706-1719.  doi: 10.1109/TMC.2011.213.  Google Scholar

[8]

B. C. Giri and B. R. Sarker, Coordinating a two-echelon supply chain under production disruption when retailers compete with price and service level, Operational Research, 16 (2016), 71-88.  doi: 10.1007/s12351-015-0187-8.  Google Scholar

[9]

D. Guinard, V. Trifa, S. Karnouskos, P. Spiess and D. Savio, Interacting with the SOA-based internet of things: discovery, query, selection, and on-demand provisioning of web services, IEEE Transactions on Services Computing, 3 (2010), 223-235.  doi: 10.1109/TSC.2010.3.  Google Scholar

[10]

D. Honhon, V. Gaur and S. Seshadri, Assortment planning and inventory decisions under stockout-based substitution, Operations research, 58 (2010), 1364-1379.  doi: 10.1287/opre.1090.0805.  Google Scholar

[11]

Z. Hua and S. Li, Impacts of demand uncertainty on retailer's dominance and manufacturer-retailer supply chain cooperation, Omega-International Journal of Management Science, 36 (2008), 697-714.  doi: 10.1016/j.omega.2006.02.005.  Google Scholar

[12]

W. Huang and J. M. Swaminathan, Introduction of a second channel: Implications for pricing and profits, European Journal of Operational Research, 194 (2009), 258-279.  doi: 10.1016/j.ejor.2007.11.041.  Google Scholar

[13]

G. Jiang, B. Hu and Y. Wang, Agent-based simulation of competitive and collaborative mechanisms for mobile service chains, Information Sciences, 180 (2010), 225-240.  doi: 10.1016/j.ins.2009.09.014.  Google Scholar

[14]

A. Khorana and H. Servaes, What drives market share in the mutual fund industry?, Review of Finance, 16 (2012), 81-113.  doi: 10.1093/rof/rfr027.  Google Scholar

[15]

D. Kiritsis, Closed-loop PLM for intelligent products in the era of the Internet of things, Computer-Aided Design, 43 (2011), 479-501.  doi: 10.1016/j.cad.2010.03.002.  Google Scholar

[16]

G. Kong, S. Rajagopalan and H. Zhang, Revenue sharing and information leakage in a supply chain, Management Science, 59 (2013), 556-572.   Google Scholar

[17]

H. Markowitz, Mean-variance approximations to expected utility, European Journal of Operational Research, 234 (2014), 346-355.  doi: 10.1016/j.ejor.2012.08.023.  Google Scholar

[18]

D. Martín, F. García, B. Musleh, D. Olmeda, G. Peláez, P. Marín, A. Ponz, C. Rodríguez, A. Al-Kaff, A. de la Escalera and J. M. Armingol, IVVI 2.0: An intelligent vehicle based on computational perception, Expert Systems with Applications, 41 (2014), 7927-7944.   Google Scholar

[19]

V. Milanés, D. F. Llorca, J. Villagrá, J. Pérez, C. Fernández, I. Parra, C. González and M. A. Sotelo, Intelligent automatic overtaking system using vision for vehicle detection, Expert Systems with Applications, 39 (2012), 3362-3373.   Google Scholar

[20]

P. M. Pardalos and V. K. Tsitsiringos (Eds), Financial Engineering, E-commerce and Supply Chain Kluwer Academic Publishers, 2002. doi: 10.1007/978-1-4757-5226-7.  Google Scholar

[21]

M. E. Porter and J. E. Heppelmann, How smart, connected products are transforming competition, Harvard Business Review, 92 (2014), 64-88.   Google Scholar

[22]

J. Shi and T. Xiao, Service investment and consumer returns policy in a vendor-managed inventory supply chain, Journal of Industrial and Management Optimization, 11 (2015), 439-459.   Google Scholar

[23]

A. Sinha, P. Malo, A. Frantsev and K. Deb, Finding optimal strategies in a multi-period multi-leader-follower Stackelberg game using an evolutionary algorithm, Computers & Operations Research, 41 (2014), 374-385.  doi: 10.1016/j.cor.2013.07.010.  Google Scholar

[24]

C. W. Tan, I. Benbasat and R. T. Cenfetelli, IT-mediated customer service content and delivery in electronic governments: An empirical investigation of the antecedents of service quality, MIS quarterly, 37 (2013), 77-109.   Google Scholar

[25]

F. Tao, L. Zhang, Y. Liu, Y. Cheng, L. Wang and X. Xu, Manufacturing service management in cloud manufacturing: Overview and future research directions Journal of Manufacturing Science and Engineering 137(2015), 040912, 11pp. doi: 10.1115/1.4030510.  Google Scholar

[26]

H. Wang and J. Ma, Complexity analysis of a Cournot-Bertrand duopoly game with different expectations, Nonlinear Dynamics, 78 (2014), 2759-2768.  doi: 10.1007/s11071-014-1623-7.  Google Scholar

[27]

D. D. Wu, Bargaining in supply chain with price and promotional effort dependent demand, Mathematical and Computer Modelling, 58 (2013), 1659-1669.  doi: 10.1016/j.mcm.2010.12.035.  Google Scholar

[28]

T. Xiao and D. Yang, Price and service competition of supply chains with risk-averse retailers under demand uncertainty, International Journal of Production Economics, 114 (2008), 187-200.  doi: 10.1016/j.ijpe.2008.01.006.  Google Scholar

[29]

Y. Zhang, G. Zhang, J. Wang, S. Sun and T. Yang, Real-time information capturing and integration framework of the internet of manufacturing things, International Journal of Computer Integrated Manufacturing, 28 (2015), 811-822.   Google Scholar

[30]

J. Zhao, W. Liu and J. Wei, Competition under manufacturer service and price in fuzzy environments, Knowledge-Based Systems, 50 (2013), 121-133.  doi: 10.1016/j.knosys.2013.06.003.  Google Scholar

show all references

References:
[1]

M. R. Amin-Naseri and M. A. Khojasteh, Price competition between two leader-follower supply chains with risk-averse retailers under demand uncertainty, The International Journal of Advanced Manufacturing Technology, (2015), 377-393.  doi: 10.1007/s00170-014-6728-0.  Google Scholar

[2]

O. Besbes and D. Saure, Product assortment and price competition under multinomial logit demand, Production and Operations Management, 25 (2016), 114-127.   Google Scholar

[3]

W. Chen, Z. G. Zhang and Z. Hua, Analysis of price competition in two-tier service systems Journal of the Operational Research Society (2016). doi: 10.1057/jors.2015.123.  Google Scholar

[4]

Y. Dai, X. Chao, S. C. Fang and H. L. W. Nuttle, Pricing in revenue management for multiple firms competing for customers, International Journal of Production Economics, 98 (2005), 1-16.  doi: 10.1016/j.ijpe.2004.06.056.  Google Scholar

[5]

B. Dan, G. Xu and C. Liu, Pricing policies in a dual-channel supply chain with retail services, International Journal of Production Economics, 139 (2012), 312-320.   Google Scholar

[6]

H. Demirkan and D. Delen, Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud, Decision Support Systems, 55 (2013), 412-421.  doi: 10.1016/j.dss.2012.05.048.  Google Scholar

[7]

L. Duan, J. Huang and B. Shou, Duopoly competition in dynamic spectrum leasing and pricing, IEEE Transactions on Mobile Computing, 11 (2012), 1706-1719.  doi: 10.1109/TMC.2011.213.  Google Scholar

[8]

B. C. Giri and B. R. Sarker, Coordinating a two-echelon supply chain under production disruption when retailers compete with price and service level, Operational Research, 16 (2016), 71-88.  doi: 10.1007/s12351-015-0187-8.  Google Scholar

[9]

D. Guinard, V. Trifa, S. Karnouskos, P. Spiess and D. Savio, Interacting with the SOA-based internet of things: discovery, query, selection, and on-demand provisioning of web services, IEEE Transactions on Services Computing, 3 (2010), 223-235.  doi: 10.1109/TSC.2010.3.  Google Scholar

[10]

D. Honhon, V. Gaur and S. Seshadri, Assortment planning and inventory decisions under stockout-based substitution, Operations research, 58 (2010), 1364-1379.  doi: 10.1287/opre.1090.0805.  Google Scholar

[11]

Z. Hua and S. Li, Impacts of demand uncertainty on retailer's dominance and manufacturer-retailer supply chain cooperation, Omega-International Journal of Management Science, 36 (2008), 697-714.  doi: 10.1016/j.omega.2006.02.005.  Google Scholar

[12]

W. Huang and J. M. Swaminathan, Introduction of a second channel: Implications for pricing and profits, European Journal of Operational Research, 194 (2009), 258-279.  doi: 10.1016/j.ejor.2007.11.041.  Google Scholar

[13]

G. Jiang, B. Hu and Y. Wang, Agent-based simulation of competitive and collaborative mechanisms for mobile service chains, Information Sciences, 180 (2010), 225-240.  doi: 10.1016/j.ins.2009.09.014.  Google Scholar

[14]

A. Khorana and H. Servaes, What drives market share in the mutual fund industry?, Review of Finance, 16 (2012), 81-113.  doi: 10.1093/rof/rfr027.  Google Scholar

[15]

D. Kiritsis, Closed-loop PLM for intelligent products in the era of the Internet of things, Computer-Aided Design, 43 (2011), 479-501.  doi: 10.1016/j.cad.2010.03.002.  Google Scholar

[16]

G. Kong, S. Rajagopalan and H. Zhang, Revenue sharing and information leakage in a supply chain, Management Science, 59 (2013), 556-572.   Google Scholar

[17]

H. Markowitz, Mean-variance approximations to expected utility, European Journal of Operational Research, 234 (2014), 346-355.  doi: 10.1016/j.ejor.2012.08.023.  Google Scholar

[18]

D. Martín, F. García, B. Musleh, D. Olmeda, G. Peláez, P. Marín, A. Ponz, C. Rodríguez, A. Al-Kaff, A. de la Escalera and J. M. Armingol, IVVI 2.0: An intelligent vehicle based on computational perception, Expert Systems with Applications, 41 (2014), 7927-7944.   Google Scholar

[19]

V. Milanés, D. F. Llorca, J. Villagrá, J. Pérez, C. Fernández, I. Parra, C. González and M. A. Sotelo, Intelligent automatic overtaking system using vision for vehicle detection, Expert Systems with Applications, 39 (2012), 3362-3373.   Google Scholar

[20]

P. M. Pardalos and V. K. Tsitsiringos (Eds), Financial Engineering, E-commerce and Supply Chain Kluwer Academic Publishers, 2002. doi: 10.1007/978-1-4757-5226-7.  Google Scholar

[21]

M. E. Porter and J. E. Heppelmann, How smart, connected products are transforming competition, Harvard Business Review, 92 (2014), 64-88.   Google Scholar

[22]

J. Shi and T. Xiao, Service investment and consumer returns policy in a vendor-managed inventory supply chain, Journal of Industrial and Management Optimization, 11 (2015), 439-459.   Google Scholar

[23]

A. Sinha, P. Malo, A. Frantsev and K. Deb, Finding optimal strategies in a multi-period multi-leader-follower Stackelberg game using an evolutionary algorithm, Computers & Operations Research, 41 (2014), 374-385.  doi: 10.1016/j.cor.2013.07.010.  Google Scholar

[24]

C. W. Tan, I. Benbasat and R. T. Cenfetelli, IT-mediated customer service content and delivery in electronic governments: An empirical investigation of the antecedents of service quality, MIS quarterly, 37 (2013), 77-109.   Google Scholar

[25]

F. Tao, L. Zhang, Y. Liu, Y. Cheng, L. Wang and X. Xu, Manufacturing service management in cloud manufacturing: Overview and future research directions Journal of Manufacturing Science and Engineering 137(2015), 040912, 11pp. doi: 10.1115/1.4030510.  Google Scholar

[26]

H. Wang and J. Ma, Complexity analysis of a Cournot-Bertrand duopoly game with different expectations, Nonlinear Dynamics, 78 (2014), 2759-2768.  doi: 10.1007/s11071-014-1623-7.  Google Scholar

[27]

D. D. Wu, Bargaining in supply chain with price and promotional effort dependent demand, Mathematical and Computer Modelling, 58 (2013), 1659-1669.  doi: 10.1016/j.mcm.2010.12.035.  Google Scholar

[28]

T. Xiao and D. Yang, Price and service competition of supply chains with risk-averse retailers under demand uncertainty, International Journal of Production Economics, 114 (2008), 187-200.  doi: 10.1016/j.ijpe.2008.01.006.  Google Scholar

[29]

Y. Zhang, G. Zhang, J. Wang, S. Sun and T. Yang, Real-time information capturing and integration framework of the internet of manufacturing things, International Journal of Computer Integrated Manufacturing, 28 (2015), 811-822.   Google Scholar

[30]

J. Zhao, W. Liu and J. Wei, Competition under manufacturer service and price in fuzzy environments, Knowledge-Based Systems, 50 (2013), 121-133.  doi: 10.1016/j.knosys.2013.06.003.  Google Scholar

Figure 1.  The structure of market competition between the IoT-based manufacturer and the traditional manufacturer
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Figure 2.  The optimal retail price $p_{i, 1}^*$ versus the price sensitive coefficient $\alpha$ and $\beta$
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Figure 3.  The optimal retail price $p_{i, 1}^*$ versus the price sensitive coefficient $\alpha$ and $\beta$
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Figure 4.  First derivative of retail price $\frac{\partial p_{i, 1}^*}{\partial w_1}$ versus the service level $d_0$
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Figure 5.  The optimal retail price $p_{i, 1}^*$ versus the risk-averse coefficient $\lambda_i$
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Figure 6.  The optimal wholesale price $w_n^*$ versus the service level $d_n$
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Figure 7.  The optimal wholesale price $w_n^*$ versus the price sensitive coefficients $\alpha$ and $\beta$
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Figure 8.  The optimal wholesale price $w_n^*$ versus the risk-averse coefficients $\lambda_i$
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Table 1.  NOTATIONS
Symbol Meaning
$\widetilde{a}_{i, n}$ manufacturer i's random market base in $nth$ period with mean $q_{i, n-1}$ and variance $\sigma^2$, where $q_{i, n-1}$ denotes the expected market demand in the previous period and $q_{2, 0}>q_{1, 0}$;
$s$ marginal production cost of the supplier;
$w_n$ unit wholesale price of the supplier in period $n$;
$p_{i, n}$ unit retail price of manufacturer $i$ in period $n$;
$\alpha, \beta$ price sensitive coefficients of demands of IoT-based and traditional products respectively;
$\lambda_i$ risk-averse coefficient of manufacturer $i$, $\lambda_i\geq 0$;
$I_n$ service investment of manufacturer 1 in the $nth$ period;
$C$ investment efficiency coefficient of service expenditure;
$\eta_n$ service improvement of manufacturer 1 in the $nth$ period, $\eta_n>1$;
$d_n$ service level of IoT-based product in period $n$, $d_n=d_{n-1} \eta_n$;
$K$ influence coefficient of service level on the demand of IoT-based product, $K>0$;
$R_i$reservation utility of manufacturer $i$, $R_i>0$.
Table Options

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Symbol Meaning
$\widetilde{a}_{i, n}$ manufacturer i's random market base in $nth$ period with mean $q_{i, n-1}$ and variance $\sigma^2$, where $q_{i, n-1}$ denotes the expected market demand in the previous period and $q_{2, 0}>q_{1, 0}$;
$s$ marginal production cost of the supplier;
$w_n$ unit wholesale price of the supplier in period $n$;
$p_{i, n}$ unit retail price of manufacturer $i$ in period $n$;
$\alpha, \beta$ price sensitive coefficients of demands of IoT-based and traditional products respectively;
$\lambda_i$ risk-averse coefficient of manufacturer $i$, $\lambda_i\geq 0$;
$I_n$ service investment of manufacturer 1 in the $nth$ period;
$C$ investment efficiency coefficient of service expenditure;
$\eta_n$ service improvement of manufacturer 1 in the $nth$ period, $\eta_n>1$;
$d_n$ service level of IoT-based product in period $n$, $d_n=d_{n-1} \eta_n$;
$K$ influence coefficient of service level on the demand of IoT-based product, $K>0$;
$R_i$reservation utility of manufacturer $i$, $R_i>0$.
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