Investment Decision-Making and Coordination of Supply Chain: A New Research in the Big Data Era

In a Big Data environment, in order to study the decision-making problem of Big Data information investment and the effects of using Big Data information to improve industry cost on supply chain coordination, firstly the importance of Data Company in supply chain was analyzed, and the original supply chain model was built. Meanwhile, some changes of consumer behavior were analyzed in a Big Data environment. Based on these, the market demand function and the benefit model of stakeholder were built and analyzed. Findings: The first finding is whether an enterprise was suitable for gaining Big Data to improve its costs, which was determined by the cost improvement coefficient; namely, it was related to the ability of excavating and using the value of Big Data. Whether the supply chain was the decentralized decision-making and the centralized decision-making, the thresholds of acquisition costs on Big Data information were equal. Moreover, the maximum value that they could undertake was same. Meanwhile the fact that the quantity discount contract could achieve a win-win outcome for supply chain members was proved. The discount coefficient was related to consumers’ behavior preference in a Big Data environment.

[1]  Z. K. Weng,et al.  Channel coordination and quantity discounts , 1995 .

[2]  G. Gallego,et al.  Supply Chain Coordination in a Market with Customer Service Competition , 2004 .

[3]  Erik Hofmann,et al.  Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect , 2017, Int. J. Prod. Res..

[4]  Wei Ming Wang,et al.  Coordinating ordering and pricing decisions in a two-stage distribution system , 2009 .

[5]  Benjamin T. Hazen,et al.  Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications , 2014 .

[6]  Sachchidanand Singh,et al.  Big Data analytics , 2012 .

[7]  Shibaji Panda Coordinating Two-echelon Supply Chains under Stock and Price dependent Demand rate , 2013, Asia Pac. J. Oper. Res..

[8]  Kim Hua,et al.  Harvesting Big Data to Enhance Supply Chain Innovation Capabilities : An Analytic Infrastructure Based on Deduction Graph , 2016 .

[9]  Pengju Li,et al.  The Mechanism of “Big Data” Impact on Consumer Behavior , 2014 .

[10]  Gérard P. Cachon Supply Chain Coordination with Contracts , 2003, Supply Chain Management.

[11]  Sarada Prasad Sarmah,et al.  Supply chain coordination under retail competition using stock dependent price-setting newsvendor framework , 2011, Oper. Res..

[12]  Ziming Liu Perceptions of credibility of scholarly information on the web , 2004, Inf. Process. Manag..

[13]  Cheryl Ann Alexander,et al.  Big Data Driven Supply Chain Management and Business Administration , 2015 .

[14]  Shuai Yang,et al.  Supply chain coordination with stock-dependent demand rate and credit incentives , 2014 .

[15]  M. Taisch,et al.  The value of Big Data in servitization , 2015 .

[16]  Nada R. Sanders Big Data Driven Supply Chain Management: A Framework for Implementing Analytics and Turning Information Into Intelligence , 2014 .

[17]  David Van Horn,et al.  Design Analytics: Capturing, Understanding, and Meeting Customer Needs Using Big Data , 2012 .

[18]  Arnold Picot,et al.  Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda , 2015, J. Strateg. Inf. Syst..

[19]  Gérard P. Cachon,et al.  Supply Chain Coordination with Revenue-Sharing Contracts: Strengths and Limitations , 2005, Manag. Sci..

[20]  Melnned M. Kantardzic Big Data Analytics , 2013, Lecture Notes in Computer Science.

[21]  Ren Ruo-en Study on Consumption Structure of Urban Household Based on Heterogeneous Preferences , 2007 .

[22]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[23]  Tiaojun Xiao,et al.  Coordination of a supply chain with consumer return under demand uncertainty , 2010 .

[24]  Maria L. Gini,et al.  Agent-assisted supply chain management: Analysis and lessons learned , 2014, Decis. Support Syst..

[25]  Bob Frisch Quién realmente toma las decisiones importantes en su empresa , 2012 .

[26]  Zhang Quan-ling Liu Zhi-hui Research overview of big data technology , 2014 .

[27]  John Gantz,et al.  The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East , 2012 .

[28]  Charles W. Chase,et al.  Demand-Driven Forecasting: A Structured Approach to Forecasting , 2009 .

[29]  P. Weill,et al.  Thriving in an increasingly digital ecosystem , 2015 .