Lifecycle Revenue Analysis and Predictive Forecasting

Abstract

The marine safety systems industry is defined by its critical aftermarket segment, with revenue generation that is more reliant on aftermarket activities like spare parts sales and services. As the trend goes with other sectors, the marine industry is increasingly reliant on data, with manufacturers seeking data-driven revenue optimization tools, analytics, and forecasts for the entire product life cycle. This masters thesis develops a data-driven framework for life cycle revenue analysis and predictive forecasting for a global safety systems provider in the marine sector. In the thesis, a mixed method approach was adopted, literature review, qualitative interviews, and quantitative data modeling with Power BI. This modeling focused on sales order data from ERP with a subset of vessels and products and tracked revenue generation across the market life cycle, enabling the forecasting of revenue generation and product demand. Also, a structured decision model for End-of-Life (EOL) was developed to capture decaying trend lines for sales and revenue volume while adhering to product category-specific thresholds. In addition, a simulation of pricing strategy was modeled to analyze the recovery of margins across the life cycle. As spotlighted in the key analysis and results, spare parts revenue tends to peak after several years of the vessel delivery and initial sales, although the patterns differ across fire and gas systems. The EOL model effectively mitigated the risks of phaseout by utilizing defined decline thresholds, thereby offering valuable contributions to aftermarket planning and inventory optimization. Within Power BI, the defined and limit-sensitive control frameworks utilizing historical quantity trends demonstrated actionable short-term forecasting capabilities, which expedited decision-making processes. This study adds to the existing body of knowledge from an academic and practical perspective: in the form of case study integration from the marine industry to life cycle revenue theories, ERP-based forecasting, and data-driven decisionmaking dashboards for the case company. Other capital goods industries with long service life cycles would benefit from the methodology and insights presented, which offer a replicable approach to planning and revenue optimization.