The use of Business Analytics in Supply Chain Systems: a Perspective on Prescriptive Analytics

Tipi, Nicoleta (2021). The use of Business Analytics in Supply Chain Systems: a Perspective on Prescriptive Analytics. In: 25th International Symposium on Logistics (ISL 2021) (Pawar, K. S. and Woo, S. eds.), 12-13 Jul 2021, Online.

Abstract

Purpose of this paper:
Different business analytics approaches are used by organisations to create values from data available to them. However, organisations are not operating in isolation as they form links and connections with many other organisations who all seek to create value from data and maintain their competitive position in the current market. The scope of this paper is to clarify the possible opportunities offered by business analytics in the context of complex infrastructures such as the supply chain.

Design/methodology/approach:
From reviewing current literature, it is evident that the debate between the use of technology (Frederico et al., 2019), the application of business analytics and modelling in supply chains with their reported benefits but also limitations is a continuing battle (Tipi, 2021).
The approach adopted in this research is to take a critical view on understanding the opportunities offered by business analytics and more specifically prescriptive analytics in the context of supply chain systems by reviewing current literature and reporting on key findings. Prescriptive analytics approaches (including methods such as mathematical programming, simulation, evolutionary computation, machine learning and others) have been reviewed in recent years (e.g. Lepenioti et al, 2020), however more is still expected in the context of supply chain.

Findings:
Based on findings from the literature on business analytics the discussion brings a critical view on the opportunities offered by prescriptive analytics in the context of complex supply chain systems and management.
A categorisation of various business analytics and modelling techniques that have listed benefits are emphasised in this paper but not only. The discussion goes further and brings not only the benefits, but also current limitations (Arunachalam et al., 2018) reported by practitioners when using analytics and modelling tools with specific reference to prescriptive analytics.

Value:
This work will capture practical benefits offered by business analytics to complex systems in the context of supply chain with specific reference to prescriptive analytics. Several insights are provided that contribute to the current research agenda in the field of business analytics and supply chain modelling.

Research implications:
This work brings theoretical contributions to the field of business analytics, system complexity and supply chain modelling.

References:
Arunachalam, D., Kumar, N. and Kawalek, J. P. (2018) Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E, 114, 416-436.

Lepenioti, K., Bousdekis, A., Apostolou, D. and Mentzas, G. (2020) Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57-70.

Frederico, G. F., Garza-Reyes, J. A., Anosike, A. and Kumar, V. (2019) Supply Chain 4.0: concepts, maturity and research agenda. Supply chain management. doi:10.1108/SCM-09-2018-0339

Tipi, N. (2021) Supply Chain Analytics and Modelling, Kogan Page 2021.

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