Hazen et al. (2014)Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). 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. International Journal of Production Economics, 154, 72-80. http://dx.doi.org/10.1016/j.ijpe.2014.04.018. http://dx.doi.org/10.1016/j.ijpe.2014.04...
/International Journal of Production Economics |
Studying the importance of data quality in supply chain management decisions |
Statistical process control / Field study |
Remanufacturing company for jet engines and related components for military aircraft |
527 |
-Developing new methods for controlling data |
Chen et al. (2015)Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4-39. http://dx.doi.org/10.1080/07421222.2015.1138364. http://dx.doi.org/10.1080/07421222.2015....
/Journal of Management Information Systems |
Studying the role of big data analytics in value creation and competitive advantage |
Technological, organizational, and environmental (TOE) framework |
Collected data from supply chain executives through a questionnaire |
192 |
-Examining the influence of firm-level employment of big data analytics on organizational performance |
-Examining the intervening variables between organizational IT practices and performance outcomes |
Tan et al. (2015)Tan, K. H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223-233. http://dx.doi.org/10.1016/j.ijpe.2014.12.034. http://dx.doi.org/10.1016/j.ijpe.2014.12...
/International Journal of Production Economics |
Providing firms an analytic infrastructure to combine their competence sets |
Deduction graph technique |
SPEC company, a leading eyeglasses manufacturer in China |
252 |
-Testing the contributed approach on other supply chains to determine its general applicability |
-Simplifying the contributed mathematical approach |
Giannakis & Louis (2016)Giannakis, M., & Louis, M. (2016). A multi-agent based system with big data processing for enhanced supply chain agility. Journal of Enterprise Information Management, 29(5), 706-727. http://dx.doi.org/10.1108/JEIM-06-2015-0050. http://dx.doi.org/10.1108/JEIM-06-2015-0...
/Journal of Enterprise Information Management |
Developing a big data analytics system that exerts autonomous corrective control actions in a supply chain |
Analytical study / Supply chain agility theories |
NA |
77 |
-Studying the application of an agent-based technology in supply chain sustainability |
-Studying the influence of the attributes of supply chain managers on the implementation of agent-based technology in decision making |
Prasad et al. (2018)Prasad, S., Zakaria, R., & Altay, N. (2018). Big data in humanitarian supply chain networks: a resource dependence perspective. Annals of Operations Research, 270(1-2), 383-413. http://dx.doi.org/10.1007/s10479-016-2280-7. http://dx.doi.org/10.1007/s10479-016-228...
/Annals of Operations Research |
Developing a model to connect big data analytics to superior humanitarian outcomes |
Resource dependence theory |
Three focal non-governmental organizations’ supply network in India |
42 |
-Doing research to clearly identify stages regarding big data attributes |
-Examining the scenarios of non-linear patterns emanating from distributed supply chain networks |
Richey Junior et al. (2016)Richey Junior, R. G., Morgan, T. R., Lindsey-Hall, K., & Adams, F. G. (2016). A global exploration of big data in the supply chain. International Journal of Physical Distribution & Logistics Management, 46(8), 710-739. http://dx.doi.org/10.1108/IJPDLM-05-2016-0134. http://dx.doi.org/10.1108/IJPDLM-05-2016...
/International Journal of Physical Distribution & Logistics Management |
Developing a framework in which supply chain managers can use big data |
Native category approach |
Interviewing 27 supply chain experts in 6 countries |
68 |
-Developing unbiased managerial guidance for using big data analytics in supply chain management |
Gunasekaran et al. (2017)Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317. http://dx.doi.org/10.1016/j.jbusres.2016.08.004. http://dx.doi.org/10.1016/j.jbusres.2016...
/Journal of Business Research |
Studying the impact of big data and predictive analytics on supply chain performance |
Statistical analysis / Field study |
E-mail survey of a sample of companies in India |
279 |
-Investigating top managers’ commitment towards developing big data predictive analytics capabilities |
Kache & Seuring (2017)Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10-36. http://dx.doi.org/10.1108/IJOPM-02-2015-0078. http://dx.doi.org/10.1108/IJOPM-02-2015-...
/International Journal of Operations & Production Management |
Investigating the impacts of big data analytics on information usage in a supply chain |
Delphi survey / Statistical analysis |
Collect data from 15 experts by questionnaire |
195 |
-Studying the constituents of a big data ecosystem as keys for optimal supply chain productivity |
Roßmann et al. (2018)Roßmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2018). The future and social impact of big data analytics in supply chain management: results from a Delphi study. Technological Forecasting and Social Change, 130, 135-149. http://dx.doi.org/10.1016/j.techfore.2017.10.005. http://dx.doi.org/10.1016/j.techfore.201...
/Technological Forecasting and Social Change |
Studying expert assessments of big data analytics applications in supply chain management |
Delphi survey / Fuzzy c-means clustering |
Interview with 73 experts |
38 |
-Interviewing other fields’ experts |
-Studying the impact of potential technological applications on social dynamics in supply chain management |
Choi (2018)Choi, T.-M. (2018). Incorporating social media observations and bounded rationality into fashion quick response supply chains in the big data era. Transportation Research Part E, Logistics and Transportation Review, 114, 386-397. http://dx.doi.org/10.1016/j.tre.2016.11.006. http://dx.doi.org/10.1016/j.tre.2016.11....
/Transportation Research Part E |
Studying the impact of social media comments on quick response supply chains in fashion |
Analytical mathematical modeling / Newsvendor model |
NA |
30 |
-Incorporate the correlation of consumer voices and a product’s demand |
-Studying the impact of a government’s role in local sourcing and emissions taxes on a supplier-market relationship |
Coble et al. (2018)Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T. (2018). Big data in agriculture: a challenge for the future. Applied Economic Perspectives and Policy, 40(1), 79-96. http://dx.doi.org/10.1093/aepp/ppx056. http://dx.doi.org/10.1093/aepp/ppx056...
/Applied Economic Perspectives and Policy |
Studying the challenges and opportunities of using big data analytics in an agricultural value chain |
Analytical study |
NA |
65 |
-Studying data ownership rules in an agriculture supply chain |
-Developing access to technology infrastructure for rural areas |
Dubey et al., 2019)/Management Decision |
Studying how to use big data analytics to improve the agility of a supply chain |
Statistical analysis / Hypotheses tests |
Collected data from 173 experts by questionnaire |
46 |
-Using other theoretical perspectives to study the effect of big data analytics on the agility of a supply chain |
-Using case-based methods instead of survey-based research |
Dubey et al. (2018b)Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B. T., & Douglas, M. A. (2018b). Big data and predictive analytics in humanitarian supply chains. International Journal of Logistics Management, 29(2), 485-512. http://dx.doi.org/10.1108/IJLM-02-2017-0039. http://dx.doi.org/10.1108/IJLM-02-2017-0...
/The International Journal of Logistics Management |
Studying big data predictive analytics’ impact on coordination and visibility in humanitarian supply chains |
Least squares regression / Hypothesis tests |
Survey responses from 205 International Non-Government Organizations |
26 |
-Considering country culture and/or supply base complexity in a predictive model |
-Applying agent-based simulation methods |
Irani et al. (2018)Irani, Z., Sharif, A. M., Lee, H., Aktas, E., Topaloğlu, Z., van’t Wout, T., & Huda, S. (2018). Managing food security through food waste and loss: Small data to big data. Computers & Operations Research, 98, 367-383. http://dx.doi.org/10.1016/j.cor.2017.10.007. http://dx.doi.org/10.1016/j.cor.2017.10....
/Computers & Operations Research |
Studying organizational factors that impact the amount of waste in a food supply chain |
Fuzzy cognitive map / Simulation |
Data from surveying 34 stakeholders in food industry in Qatar |
21 |
-Use Delphi method to involve a wider set of participants |
-Develop the same approach in countries besides Qatar |
Jeble et al. (2018)Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. International Journal of Logistics Management, 29(2), 513-538. http://dx.doi.org/10.1108/IJLM-05-2017-0134. http://dx.doi.org/10.1108/IJLM-05-2017-0...
/The International Journal of Logistics Management |
Studying the impact of big data and predictive analytics on sustainable business development |
Resource-based view logic / Contingency theory |
Survey data from 205 individuals in auto components industry |
40 |
-Studying the actual impact of big data and predictive analytics on a business firm rather than just the perception of the impact |
-Explore data that can be more generalized |
Lai et al. (2018)Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management. International Journal of Logistics Management, 29(2), 676-703. http://dx.doi.org/10.1108/IJLM-06-2017-0153. http://dx.doi.org/10.1108/IJLM-06-2017-0...
/The International Journal of Logistics Management |
Studying the factors that determine the adoption of big data analytics in supply chains |
Technology-organization-environment (TOE) framework |
Survey data from 210 Chinese IT managers and business analysts |
28 |
-Increase the environmental safety of big data |
-Studying the other factors that may affect the adoption of big data analytics, such as supply chain scale and delivery complexity |
Lau et al. (2018)Lau, R. Y. K., Zhang, W., & Xu, W. (2018). Parallel aspect‐oriented sentiment analysis for sales forecasting with big data. Production and Operations Management, 27(10), 1775-1794. http://dx.doi.org/10.1111/poms.12737. http://dx.doi.org/10.1111/poms.12737...
/Production and Operations Management |
Using consumer social media comments for sales forecasting |
Parallel sentiment analysis / Machine learning |
Consumer comments datasets in English and Chinese |
31 |
-Combining parallel topic models with lifelong learning strategies |
-Examining parallel ensemble models for better sales forecasting |
Gupta et al. (2019b)Gupta, S., Chen, H., Hazen, B. T., Kaur, S., & Santibañez Gonzalez, E. D. R. (2019b). Circular economy and big data analytics: a stakeholder perspective. Technological Forecasting and Social Change, 144, 466-474. http://dx.doi.org/10.1016/j.techfore.2018.06.030. http://dx.doi.org/10.1016/j.techfore.201...
/Technological Forecasting and Social Change |
Using big data analytics to support data-driven decision making in circular economical supply chains |
Stakeholder perspective on circular economy |
Interview data from 10 expert employees |
19 |
-Using larger empirical data for this study |
-Studying inter-organizational relationships, intra-organizational dynamics, and informational privacy issues in supply chains |
Lamba & Singh (2019)Lamba, K., & Singh, S. P. (2019). Dynamic supplier selection and lot-sizing problem considering carbon emissions in a big data environment. Technological Forecasting and Social Change, 144, 573-584. http://dx.doi.org/10.1016/j.techfore.2018.03.020. http://dx.doi.org/10.1016/j.techfore.201...
/Technological Forecasting and Social Change |
Using big data analytics to study a supplier’s selection and lot-sizing problem under carbon cap-and-trade regulations |
Mixed integer non-linear program |
Experimental problem sets |
15 |
-Developing heuristics that can obtain the solution via a faster method |
-Studying the same model’s behavior under various carbon emission regulations |
Lamba et al. (2019)Lamba, K., Singh, S. P., & Mishra, N. (2019). Integrated decisions for supplier selection and lot-sizing considering different carbon emission regulations in big data environment. Computers & Industrial Engineering, 128, 1052-1062. http://dx.doi.org/10.1016/j.cie.2018.04.028. http://dx.doi.org/10.1016/j.cie.2018.04....
/Computers & Industrial Engineering |
Studying a supplier selection and lot-sizing problem in dynamic supply chains |
Mixed integer non-linear program |
A randomly generated dataset |
23 |
-Studying the stochastic demand with the same problem settings |
-Focusing on the veracity and value characteristics of big data |
Shen et al. (2019)Shen, B., Choi, T.-M., & Chan, H.-L. (2019). Selling green first or not? A Bayesian analysis with service levels and environmental impact considerations in the big data era. Technological Forecasting and Social Change, 144, 412-420. http://dx.doi.org/10.1016/j.techfore.2017.09.003. http://dx.doi.org/10.1016/j.techfore.201...
/Technological Forecasting and Social Change |
Using big data analytics to find if a retailer must sell green or non-green products first, according to shelf space limitations |
Bayesian analysis |
NA |
19 |
-Studying incentive contracts in order to achieve a coordinated supply chain |
-Studying the role of government interventions on selling green products |
-Studying this case with enough shelf space for both green and non-green products |
Singh & El-Kassar (2019)Singh, S. K., & El-Kassar, A.-N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213, 1264-1273. http://dx.doi.org/10.1016/j.jclepro.2018.12.199. http://dx.doi.org/10.1016/j.jclepro.2018...
/Journal of Cleaner Production |
Studying the impact of the integration of big data with green supply chain management and human resource management on a firms’ sustainability |
Statistical analysis / Hypotheses testing |
Survey data from 215 employees in Saudi Arabia, the United Arab Emirates, Egypt, and Lebanon |
40 |
-Using the same research framework of this study with multisource and/or multi-time datasets |
-Using mixed methods instead of quantitative data within the same research framework |
Yu et al. (2019)Yu, L., Zhao, Y., Tang, L., & Yang, Z. (2019). Online big data-driven oil consumption forecasting with Google trends. International Journal of Forecasting, 35(1), 213-223. http://dx.doi.org/10.1016/j.ijforecast.2017.11.005. http://dx.doi.org/10.1016/j.ijforecast.2...
/International Journal of Forecasting |
Using Google trends to forecast the oil consumption in an oil supply chain |
Cointegration tests / Granger causality analysis |
Data from Google trends |
38 |
-Considering the dynamic between Google trends and oil consumption over time |
-Introducing other types of big data, such as social networks, to the proposed model |