A wealth of data is continually being produced by supply chain networks. This data contains trends and patterns that can be found using analytics to provide useful information. The supply chain network's overall effectiveness and efficiency can then be stated to benefit greatly from analytics. The value and function of analytics in the supply chain industry are examined in this study through a critical analysis of the prior literature, a look at some major obstacles to supply chain analytics implementation, and actual case studies from the sector. Predictive forecasting and logistics were the two main applications of analytics in the supply chain that were comprehensively covered in the research.
The usefulness of implementing supply chain analytics in the supply chain sector as well as the difficulties encountered are examined in the next part, which is a critical evaluation of the existent literature from various sources. The analysis part follows, which exhibits the ability to use R software to implement and evaluate SC analytics.
Yunusa, A. & Abdulraqib, O (2023). Supply Chain Analytics. Afribary. Retrieved from https://track.afribary.com/works/supply-chain-analytics
Yunusa, Abdulhameed, and Olayanju Abdulraqib "Supply Chain Analytics" Afribary. Afribary, 24 Apr. 2023, https://track.afribary.com/works/supply-chain-analytics. Accessed 25 Dec. 2024.
Yunusa, Abdulhameed, and Olayanju Abdulraqib . "Supply Chain Analytics". Afribary, Afribary, 24 Apr. 2023. Web. 25 Dec. 2024. < https://track.afribary.com/works/supply-chain-analytics >.
Yunusa, Abdulhameed and Abdulraqib, Olayanju . "Supply Chain Analytics" Afribary (2023). Accessed December 25, 2024. https://track.afribary.com/works/supply-chain-analytics