Distribution network design with big data: model and analysis

This study addresses the problem of locating distribution centers in a single-echelon, capacitated distribution network. Such network consists of several potential distribution centers and various demand points dispersed in different regional markets. The distribution operations of this network generate massive amounts of data. The problem is how to utilize big data generated to identify the right number of distribution centers to open and the right assignment of customers to opened distribution centers while minimizing the total handling and operation costs of distribution centers, transportation, and penalty. Restrictions on both network capacity and single sourcing strategy are also considered. This study formulates this problem as mixed-integer nonlinear program. The effects of different scenarios on distribution-center locations as demand, the operation costs of distribution centers and outbound transportation, and the number of customers are analyzed through simulation on randomly generated big datasets. Empirical results indicate that the model presented is appropriate and robust. The operational value of big data in the distribution network design is revealed through a case study in which several design alternatives are evaluated.

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

[2]  Shu-Hsien Liao,et al.  A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems , 2011, Ann. Oper. Res..

[3]  Kangbok Lee,et al.  Supply chain scheduling with receiving deadlines and non-linear penalty , 2015, J. Oper. Res. Soc..

[4]  Michael Amberg,et al.  Designing Global Manufacturing Networks Using Big Data , 2015 .

[5]  Gang Wang,et al.  Polynomial-time solvable cases of the capacitated multi-echelon shipping network scheduling problem with delivery deadlines , 2012 .

[6]  Lei Lei,et al.  Integrated operations scheduling with delivery deadlines , 2015, Comput. Ind. Eng..

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

[8]  Peter Trkman,et al.  Business analytics in supply chains - The contingent effect of business process maturity , 2012, Expert Syst. Appl..

[9]  Qiang Ma,et al.  Integrated location and two-echelon inventory network design under uncertainty , 2010, Ann. Oper. Res..

[10]  Kuldip Singh Sangwan,et al.  A bibliometric analysis of green manufacturing and similar frameworks , 2015 .

[11]  Lianbiao Cui,et al.  Environmental performance evaluation with big data: theories and methods , 2016, Annals of Operations Research.

[12]  Kim Hua Tan,et al.  Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph , 2015 .

[13]  Angappa Gunasekaran,et al.  The impact of big data on world-class sustainable manufacturing , 2015, The International Journal of Advanced Manufacturing Technology.

[14]  Yu-Chung Tsao,et al.  Multi-item distribution network design problems under volume discount on transportation cost , 2016 .

[15]  B. Chae,et al.  Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research , 2015 .

[16]  David L. Olson,et al.  The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective , 2014, Decis. Support Syst..

[17]  David L. Olson,et al.  Business Analytics for Supply Chain: a Dynamic-Capabilities Framework , 2013, Int. J. Inf. Technol. Decis. Mak..

[18]  Omar Ben-Ayed.  Parcel distribution network design problem , 2013, Oper. Res..

[19]  Renato de Matta,et al.  Contingency planning during the formation of a supply chain , 2017, Ann. Oper. Res..

[20]  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 .

[21]  Gilvan C. Souza,et al.  Supply Chain Analytics , 2016 .

[22]  Benita M. Beamon,et al.  Green supply chain network design with stochastic demand and carbon price , 2017, Ann. Oper. Res..

[23]  J Shu,et al.  Logistics distribution network design with two commodity categories , 2013, J. Oper. Res. Soc..

[24]  S. Fawcett,et al.  Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management , 2013 .

[25]  Umut Rifat Tuzkaya,et al.  A two-stage stochastic mixed-integer programming approach to physical distribution network design , 2015 .

[26]  A. Gunasekaran,et al.  Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .