Emerging Future Research areas in Statistics and Operational Research for Logistics

Introduction 

Logistics are an integral part of the functioning of today’s world. In the urban areas, they deal with the goods picking and delivery. Some issues have been identified concerning logistics, such as traffic congestion, the necessity to deliver goods within the time window, and the optimisation of routes have gained importance in the field of logistics (Gayialis, 2022). In addition, logistics are crucial for determining customer satisfaction, and the goods and services delivered on time are associated with satisfaction. In this era of rapid technological advancements and evolving business landscapes, Statistics and Operational Research (OR) fields have become vital tools for optimising logistics processes. Several potential research areas exist for Statistics and Operational Research (OR) in Logistics that aim to improve goods and service delivery 

Research areas in Statistics and Operational Research for logistics 

Inventory Management: One area of research is optimising inventory management by using statistical models to forecast demand and determine the optimal inventory level to meet customer demand while minimising inventory holding costs. Research can also include investigating optimal inventory policies and considering factors like lead time variability, demand uncertainty, and service-level requirements. Research can focus on inventory control methods, safety stock optimisation, dynamic pricing, and the impact of new technologies (Winkelhaus, 2019).  

Vehicle Routing: Another area of research is developing vehicle routing and scheduling algorithms to minimise transportation costs and delivery times while considering factors such as vehicle capacity, driver availability, and delivery deadlines (Konstantakopoulos, 2020).  

Quality Control: The quality of the products and services directly correlates with customer satisfaction. Statistical quality control methods can be used to monitor and improve the quality of products and services in the logistics industry. Research areas can include statistical process control, design of experiments, and other quality improvement methods (Mukhammedrizaevna, 2020).  

Supply Chain Analytics: Supply chain analytics involves using statistical and OR techniques to analyse supply chain data and identify areas for improvement, such as reducing lead times, increasing efficiency, and minimising costs. Research on supply chain management would help address the problems occurring in logistics, significantly improving good and service delivery and customer satisfaction (Winkelhaus, 2019). 

Risk Management: Finally, statistical and OR techniques can be used to assess and manage risks in the logistics industry, such as identifying and mitigating risks associated with supply chain disruptions, transportation accidents, or natural disasters (Zimon, 2020). 

Conclusion 

There is immense scope for research in statistics and operational research in logistics. The potential research areas include quality control, risk management, supply chain analytics, vehicle routing and inventory management. These areas could serve as dissertation topics for business management, which can help to address the research gaps existing in Operational Research and Logistics.   

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References 

Gayialis, S.P., Kechagias, E.P. & Konstantakopoulos, G.D. A city logistics system for freight transportation: integrating information technology and operational research. Oper Res Int J 22, 5953–5982 (2022). https://doi.org/10.1007/s12351-022-00695-0 

Winkelhaus, S., & Grosse, E. H. (2019). Logistics 4.0: a systematic review towards a new logistics system. International Journal of Production Research, 1–26. doi:10.1080/00207543.2019.1612964 

Konstantakopoulos, G.D., Gayialis, S.P. & Kechagias, E.P. Vehicle routing problem and related algorithms for logistics distribution: a literature review and classification. Oper Res Int J 22, 2033–2062 (2022). https://doi.org/10.1007/s12351-020-00600-7 

Mukhammedrizaevna, T. M., & Bakhriddinovna, A. N. (2020). Requirements for quality, logistics and safety when growing agricultural products. Achievements of science and education, (10 (64)), 13-15. 

Zimon, D., & Madzík, P. (2020). Standardized management systems and risk management in the supply chain. International Journal of Quality & Reliability Management, 37(2), 305-327.