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Learning outcomes

The learning outcomes of this paper are as follows: to understand the context of seaport logistics and supply chain design structure, to apply Monte Carlo simulation in the interface of the supply chain and to analyze the Monte Carlo simulation algorithm and statistical techniques for identifying the key seaport logistics factors.

Case overview/synopsis

It was 9:00 p.m. on November 10, 2020, and Nishadh Amonkar, the CEO of OCTO supply chain management (SCM) was glued to the television watching the final cricket match of the Indian Premier League, 2020. Amonkar’s mobile phone rang and it was a call from Vinod Nair, a member Logistics Panel of Ranji Industries Federation. Nair informed Amonkar that it was related to the rejection of several export consignments of agricultural products from Ranji (in the western part of India). The rejection was due to the deterioration in the quality of the exported agricultural products during transit from Ranji to various locations in Europe.

Complexity academic level

This course is suitable at the MBA level for the following courses: Operations research (Focus/Session: Applications on Monte Carlo Simulation). SCM (Focus/Session: Global SCM, Logistics Planning, Distribution Network). Logistics management (Focus/Session: Transportation Planning). Business statistics (Focus/Session: Application of Hypothesis Testing).

Supplementary materials

Teaching Notes are available for educators only.

Subject code

CSS 9: Operations and logistics.

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