Distribution network model using big data in an international environment.

This paper proposes dynamic mixed integer facility location model to design an international manufacturing network (IMN). The proposed model considers a broad facility network linking production and distribution facilities located internationally. The proposed model discussed in the paper assumes significance over the traditional manufacturing model as it provides a country specific analysis making it more convenient for the decision maker to devise country specific strategies within an international ecosystem. Therefore, the model considers import export cost, loan subsidies along with depreciation expense and other operating costs applicable to specific country. The objective of the model is to identify optimal facility locations and the production distribution in the entire network to meet the demand of global markets. The proposed model is illustrated and computationally tested using two cases. Model parameters are mapped using 3Vs of Big Data viz. Volume, Velocity and Variety.

[1]  Jie Wu,et al.  Efficiency evaluation based on data envelopment analysis in the big data context , 2017, Comput. Oper. Res..

[2]  Ioannis Minis,et al.  A strategic model for exact supply chain network design and its application to a global manufacturer , 2018, Int. J. Prod. Res..

[3]  Kim Hua Tan,et al.  The impact of Brexit on designing a material-based global supply chain network for Asian manufacturers , 2019 .

[4]  Y. Yue,et al.  The potential global distribution and dynamics of wheat under multiple climate change scenarios. , 2019, The Science of the total environment.

[5]  Colin O. Benjamin,et al.  A knowledge-based decision support system for locating a manufacturing facility , 1995 .

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

[7]  Angappa Gunasekaran,et al.  Exploring the relationship between leadership, operational practices, institutional pressures and environmental performance: A framework for green supply chain , 2015 .

[8]  Saman Hassanzadeh Amin,et al.  A facility location model for global closed-loop supply chain network design , 2017 .

[9]  Angappa Gunasekaran,et al.  A global optimization for sustainable multi-domain global manufacturing , 2018, Comput. Oper. Res..

[10]  Jan Olhager,et al.  Differentiating manufacturing focus , 2006 .

[11]  M. Cemal Dincer,et al.  A multifactor model for international plant location and financing under uncertainty , 1986, Comput. Oper. Res..

[12]  Benjamin T. Hazen,et al.  Big data and predictive analytics for supply chain and organizational performance , 2017 .

[13]  A. Gunasekaran,et al.  Role of decoupling point in examining manufacturing flexibility: an empirical study for different business strategies , 2019 .

[14]  Ning Zhang,et al.  An optimization model for green supply chain management by using a big data analytic approach , 2017 .

[15]  Nishikant Mishra,et al.  Integrated decisions for supplier selection and lot-sizing considering different carbon emission regulations in Big Data environment , 2019, Comput. Ind. Eng..

[16]  Ray Y. Zhong,et al.  Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives , 2016, Comput. Ind. Eng..

[17]  Liang Liang,et al.  Service outsourcing and disaster response methods in a relief supply chain , 2016, Ann. Oper. Res..

[18]  Qin Zhang,et al.  Global reverse supply chain design for solid waste recycling under uncertainties and carbon emission constraint. , 2017, Waste management.

[19]  Harpreet Kaur,et al.  Heuristic modeling for sustainable procurement and logistics in a supply chain using big data , 2017, Comput. Oper. Res..

[20]  Donald B. Rosenfield,et al.  Global and variable cost manufacturing systems , 1996 .

[21]  A. Gunasekaran,et al.  Can big data and predictive analytics improve social and environmental sustainability? , 2017, Technological Forecasting and Social Change.

[22]  Morgan Swink,et al.  An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective , 2018 .

[23]  Bongsik Shin,et al.  Data quality management, data usage experience and acquisition intention of big data analytics , 2014, Int. J. Inf. Manag..

[24]  X. Chuai,et al.  High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China. , 2019, The Science of the total environment.

[25]  Angappa Gunasekaran,et al.  Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture , 2019, British Journal of Management.

[26]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[27]  Angappa Gunasekaran,et al.  Flexible Sustainable Supply Chain Network Design: Current Trends, Opportunities and Future , 2016 .

[28]  Charles L. Munson,et al.  A mathematical programming model for global plant location problems: Analysis and insights , 2004 .

[29]  A. Gunasekaran,et al.  The role of Big Data in explaining disaster resilience in supply chains for sustainability , 2017 .

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

[31]  Francisco Saldanha-da-Gama,et al.  Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning , 2006, Comput. Oper. Res..

[32]  Malin Song,et al.  How would big data support societal development and environmental sustainability? Insights and practices , 2017 .

[33]  Shraddha Mishra,et al.  An environmentally sustainable manufacturing network model under an international ecosystem , 2019, Clean Technologies and Environmental Policy.

[34]  Panagiotis Kouvelis,et al.  Global facility network design in the presence of competition , 2013, Eur. J. Oper. Res..

[35]  Stefanie Hellweg,et al.  A new method for analyzing sustainability performance of global supply chains and its application to material resources. , 2019, The Science of the total environment.

[36]  Benjamin T. Hazen,et al.  Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda , 2016, Comput. Ind. Eng..

[37]  Yang Cheng,et al.  International manufacturing network: past, present, and future , 2015 .

[38]  Krishna G. Palepu,et al.  Winning in Emerging Markets: A Road Map for Strategy and Execution , 2010 .

[39]  Surya Prakash Singh,et al.  Big data in operations and supply chain management: current trends and future perspectives , 2017 .

[40]  B. Yu,et al.  The sightseeing bus schedule optimization under Park and Ride System in tourist attractions , 2019, Ann. Oper. Res..

[41]  Jan Olhager,et al.  Manufacturing networks and supply chains : An operations strategy perspective , 2003 .

[42]  Santhosh Srinivasan,et al.  Multi-stage manufacturing/re-manufacturing facility location and allocation model under uncertain demand and return , 2018 .

[43]  Kamesh Munagala,et al.  Local Search Heuristics for k-Median and Facility Location Problems , 2004, SIAM J. Comput..

[44]  Bert Meijboom,et al.  International manufacturing and location decisions: balancing configuration and co‐ordination aspects , 1997 .

[45]  Hau L. Lee,et al.  Strategic Analysis of Integrated Production-Distribution Systems: Models and Methods , 1988, Oper. Res..

[46]  Malin Song,et al.  Customer profitability forecasting using Big Data analytics: A case study of the insurance industry , 2016, Comput. Ind. Eng..

[47]  John Johansen,et al.  Manufacturing network evolution: a manufacturing plant perspective , 2011 .

[48]  John Johansen,et al.  Evaluating indicators for international manufacturing network under circular economy , 2019, Management Decision.

[49]  Surya Prakash Singh,et al.  Big Data analytics in supply chain management: some conceptual frameworks , 2016 .