Optimizing Port Data & Analytics: A Maturity Model for Competitive Edge

Problem

Background

Many companies treat data as a strategic asset but struggle to extract value. Proper management of this resource can provide a competitive advantage, so firms need an integrated Data & Analytics (D&A) system with the right people, technology, and processes.

Your consulting team developed a D&A assessment model to help executives evaluate three key elements—people, technology, and processes—and determine system maturity. The model guides companies in optimizing their analytics capabilities, building client confidence, and gaining a competitive edge.

Requirements

ICM (Intercontinental Cargo Management) operates a large seaport and cannot share detailed internal data. Using the provided general description of its business and data types, develop a model to assess ICM’s D&A system maturity, including:

Metrics and KPIs to measure current maturity of people, technology, and processes.

Demonstration of how the model suggests system changes to maximize data asset potential.

Recommended protocols for measuring D&A system effectiveness.

Analysis of applying the model to larger or smaller ports and other industries, such as trucking.

Finally, write a one‑page letter to ICM’s port users summarizing the recommended measurement approach and expressing confidence in the D&A system.

Methodology

We built a DAS (Data & Analytics System) assessment model focusing on three components—people, technology, and processes—and defined twelve quantitative KPIs.

Two scoring methods were applied:

Fuzzy comprehensive evaluation, using expert‑scored matrices across five performance levels.

EWM‑AHP (entropy weight method combined with analytic hierarchy process) for more objective weighting of KPIs.

The two scores are aggregated to obtain a final D&A system score, which is mapped to a maturity level using a five‑level scale.

Optimization Model

A vector optimization model places the three component scores in a three‑dimensional space, with the target maturity represented as a parametric surface. Selecting an optimal vector moves the system toward the surface, visualizing the improvement path.

Scale‑Demand Method

Inspired by economic scale‑economies, we introduced a Scale‑Demand Method (SDM) to adjust component weights based on firm size, recognizing that larger ports may assign different importance to people, technology, and processes.

Extended Applications

The model was tested on various port sizes and industries. Monte‑Carlo simulations generated synthetic KPI data, which fed into a principal component analysis (PCA) to produce quantitative maturity scores. Results showed less than 5 % error compared with the original DAMM model, confirming broad applicability.

Network analysis using PageRank identified critical indicators across people, technology, and process layers, guiding targeted improvement actions.

Effectiveness Protocol

We propose a measurement protocol based on queuing theory for data transmission, network average degree and diameter for data cascade, and five operational rules covering processing speed, capacity, and cascade levels.

Client Communication

A concise one‑page letter is drafted for ICM’s port users, summarizing the assessment approach, highlighting the maturity score, and expressing confidence in the system’s ability to support reliable, data‑driven decision making.

References

https://www.contest.comap.com/undergraduate/contests/mcm/contests/2022/results/