Project 2

Project Activities DataFrame and Indicators, Case Study: Shipyard/Marina - CSV.

In shipbuilding activities, efficient project management is crucial to ensure not only profitability, but also long-term sustainability. With the aim of optimising the management of a shipyard's project portfolio, the Company's Project Management Office (PMO) division has carried out a comprehensive analysis of data from almost 250 projects developed over a ten-year period. This report presents the key findings of this analysis, providing a detailed view of project performance in terms of costs, schedules, complexity, risks, and other earned value indicators. The analyzed dataset includes complete information on each of the projects, covering critical aspects such as planned and actual costs, duration of activities, construction complexity factors, earned value performance indices (CPI and SPI), and identified and materialized risks. This information has been collected and systematized to provide a solid foundation that allows the shipyard's shareholders and managers to make informed and strategic decisions. One of the main objectives of this analysis is to assess the accuracy of cost and time estimates, as well as to identify areas where variability is greatest. The projects have been classified into different types: design, consultancy, construction, maintenance and reconstruction. This classification has allowed for a comparative analysis that highlights the differences in performance and management of each type of project. For example, construction projects have been observed to exhibit greater variability in actual versus planned costs, suggesting the need to improve cost estimation and control practices in these projects. In addition, the analysis has delved into the relationship between the complexity of projects and their performance. Using construction complexity factors, we have assessed how complexity affects the duration and costs, as well as the likelihood and impact of the identified risks. The results indicate that projects with higher levels of complexity tend to have more significant deviations in the schedule, underscoring the importance of detailed planning and proactive risk management. The report also addresses risk management and its impact on project performance. A significant correlation has been found between the probability and impact of the identified risks and the performance indices (CPI and SPI). Projects with a higher probability of risk tend to perform worse in terms of costs and schedules, highlighting the need to implement effective risk mitigation strategies. In addition to these analyses, the report presents a comparison of the performance of projects over time. An improvement trend has been observed in projects initiated in recent years, which could be related to the adoption of new technologies and project management practices. This improvement is reflected in key indicators such as the CPI and SPI, which show greater efficiency and effectiveness in the use of resources. This project then provides a comprehensive view of the shipyard's project performance, identifying critical areas that require attention and proposing data-driven recommendations to improve project portfolio management. The preliminary findings presented here will serve as a guide for strategic decision-making, helping the shipyard to optimize its operations and secure its competitive position in the market.

1. Cost Variance Boxplot by Project Type:

Cost Variance Boxplot by Project Type: A boxplot that shows the variance of actual vs. planned costs for each type of project, helping to identify which types of projects have the greatest variability in costs.

Visualization 2: Scatter Plot Matrix

Visualization 3: Scatter Plot Matrix for Mantenimiento

Visualization 4: Sunburst Diagram

Visualization 5: Scatter Plot Matrix for Mantenimiento and SWBS = 100

Tipo de Proyecto: Mantenimiento

SWBS: 100

Visualization 6

A dot density map is a map type that uses a dot or another symbol to show the presence of a feature or phenomenon. In a dot density map, areas with many dots indicate high concentrations of values for the chosen field and fewer dots indicate lower concentrations. Each dot on a dot-density map can either represent one single recording of a phenomenon (one-to-one) or represent a given quantity of it (one-to-many).

Radar Chart for Consultoria by Purpose and SWBS

COMMERCIAL
NAVAL
PLEASURE
WORKBOATS

Visualization 8

A bar chart is a chart with rectangular bars with lengths proportional to the values that they represent. One axis of the chart shows the specific categories being compared, and the other axis represents a discrete value. Bar charts provide a visual presentation of categorical data. Categorical data is a grouping of data into discrete groups, such as months of the year, age group, shoe sizes, and animals. These categories are usually qualitative. Bars on the chart may be arranged in any order.

Visualization 9

A bar chart is a chart with rectangular bars with lengths proportional to the values that they represent. One axis of the chart shows the specific categories being compared, and the other axis represents a discrete value. Bar charts provide a visual presentation of categorical data. Categorical data is a grouping of data into discrete groups, such as months of the year, age group, shoe sizes, and animals. These categories are usually qualitative. Bars on the chart may be arranged in any order.

Project DataFrame Indexes Tool

SWBS - () Ship Cost/Work Breakdown Structure, a comprehensive framework which can be used throughout the entire life- cycle of the ship, from early design and cost studies through production and subsequent disposal, and into which can be organized and correlated elements for cost, weight, ship production and maintenance.

Project Type - () A flexible way to categorize everything that ROMV(CMS) CORPORATE is actively working on based on its business units SEATOR Innova Boats.

Purpose - () Vessels classified according to their mission or purpose into four main categories.

Year - () Project management indexes (such as cost performance index, schedule performance index, etc.) gathered, calculated, etc. pertinent to the year.

Data Segmentation:

Radar Chart - Mean Overall Complexity Parameters

Radar Chart - Project_ID (Complexity Parameters)

Bubble Chart - Project_ID Cost & Duration variations

Filtered Dataset

Exploration Bubble Chart - Project_ID Cost & Duration variations per year

Global Complexity (GC) | Global KPI (GK)

Dataset Description

Field Name Data Type Valid Data Count Null Data Count

References