Optimizing container terminal and port operations at Maersk

[Peter van der Putten]

Dear all,

See below for an interesting project on an interesting real world problem at Maersk, evaluating and optimizing container operations across a large proportion of ports worldwide. This could be relevant for all programs taught in Leiden, as long as there is an interesting in economical modeling, machine learning and/or optimization. 

 

Reach out to me for further details.

 

Peter van der Putten

 

Sub: Request for Resource to Develop Port Performance Model

Introduction The Global Data and Analytics team at Maersk is currently working on a transformative data product called DOPM, which stands for Daily Operations Performance Management. DOPM is a product that revolutionizes our container terminal operations at APM Terminals globally. It offers real-time insights into vital aspects such as vessel operations, yard management, gate operations, safety, and equipment operations. By providing granular data-driven information, DOPM empowers us to make informed decisions and enhance our container terminal operations effectively. DOPM goes beyond traditional monitoring by offering over 50 Key Performance Indicators (KPIs) specifically tailored to our terminal operations. This comprehensive dashboard allows us to track performance in real-time, optimizing our operational efficiency. Key metrics include Moves, CMPH (Crane Moves Per Hour), PMPH (Port Moves Per Hour), Port hour save (PHS), Crane Intensity, Yard Utilization, Truck turnaround time and many more. We have set ambitious goals to elevate DOPM to new heights by introducing performance ranking and optimization for our terminals. Our aim is to enhance our operations by evaluating and ranking terminals based on their productivity scores and gaining valuable insights into the factors influencing these rankings. Furthermore, we strive to establish performance benchmarks by determining the optimal number of terminals in different KPI categories. To achieve these objectives, we propose the implementation of a robust data model capable of accurately assessing terminal productivity, identifying key performance drivers, and providing valuable input for our decision-making process regarding terminal optimization. In order to accomplish this, we recognize the need for a specialist with expertise in this field to join our team and contribute to the success of this initiative. Problem Statement:

At present, our DOPM initiatives gather operational data from all 31 of our terminals and calculate the most KPIs. However, it doesn't give us a detailed understanding of why certain things happen or why performance varies between terminals. As a result, we're missing out on the opportunity to identify and address specific issues or improve our operations effectively. To fix this, we need a better data model that can help us figure out the reasons behind terminal performance and make smarter decisions based on that information.

Need for a Performance Model:

To tackle these challenges, we need to create a robust port performance model that integrates a wide range of methodologies. This model will explore heuristic methods as well as mathematical approaches such as parametric or econometric techniques like cost or production frontier performance functions, stochastic frontier analysis (SFA), data envelopment analysis (DEA), confirmatory factor analysis (CFA), structural equation modelling (SEM), gap analysis etc.

Role of the Resource:

We are seeking a resource with a strong background in quantitative analysis methodologies to successfully implement a data model for terminal performance ranking and optimization. This expert will play a crucial role in designing and implementing algorithms, analysing large datasets, and deriving valuable insights to enhance our decision-making process and drive improvements in terminal operations. Additionally, proficiency in statistical analysis, data modelling, and familiarity with relevant software tools will be an added advantage.

Expected Outcomes:

Once the port performance model is developed and implemented, we anticipate several positive outcomes:

Terminal Ranking: The model will provide a systematic and unbiased assessment of terminal productivity, allowing us to rank the terminals based on their performance. This information will be invaluable in identifying the highest-performing terminals and potential areas for improvement.

Performance Drivers: By analysing the performance data and incorporating various methodologies, the model will help identify the underlying factors that contribute to the productivity differences among terminals. This insight will enable us to address bottlenecks and implement targeted improvements where necessary.

Optimal Resource Allocation: With a clearer understanding of terminal performance and the factors influencing it, we can make informed decisions about resource allocation. This will ensure that our resources are distributed optimally among the terminals, maximizing overall efficiency and profitability.

Conclusion:

implementing a comprehensive port performance indicator model will provide us with valuable insights into our terminal operations. By ranking the terminals based on productivity and identifying the reasons behind performance variations, we can make data-driven decisions to improve efficiency and optimize resource allocation. The resource we seek should have a solid background in quantitative analysis methodologies and a deep understanding of port performance factors.

I kindly request your approval and support for the acquisition of the resource to initiate this project.

Thank you for your attention to this matter.

Sincerely,

Forruk Ahmed