Lifecycle Revenue Analysis and Predictive Forecasting

Sammanfattning

The marine safety systems industry is defined by its critical aftermarket segment,with revenue generation that is more reliant on aftermarket activities like spareparts sales and services. As the trend goes with other sectors, the marine industryis increasingly reliant on data, with manufacturers seeking data-driven revenueoptimization tools, analytics, and forecasts for the entire product life cycle. Thismasters thesis develops a data-driven framework for life cycle revenue analysis andpredictive forecasting for a global safety systems provider in the marine sector. Inthe thesis, a mixed method approach was adopted, literature review, qualitativeinterviews, and quantitative data modeling with Power BI. This modeling focusedon sales order data from ERP with a subset of vessels and products and trackedrevenue generation across the market life cycle, enabling the forecasting of revenuegeneration and product demand. Also, a structured decision model for End-of-Life(EOL) was developed to capture decaying trend lines for sales and revenue volumewhile adhering to product category-specific thresholds. In addition, a simulationof pricing strategy was modeled to analyze the recovery of margins across the lifecycle.As spotlighted in the key analysis and results, spare parts revenue tends to peakafter several years of the vessel delivery and initial sales, although the patterns differacross fire and gas systems. The EOL model effectively mitigated the risks of phaseoutby utilizing defined decline thresholds, thereby offering valuable contributions toaftermarket planning and inventory optimization. Within Power BI, the defined andlimit-sensitive control frameworks utilizing historical quantity trends demonstratedactionable short-term forecasting capabilities, which expedited decision-making processes.This study adds to the existing body of knowledge from an academic andpractical perspective: in the form of case study integration from the marine industryto life cycle revenue theories, ERP-based forecasting, and data-driven decisionmakingdashboards for the case company. Other capital goods industries with longservice life cycles would benefit from the methodology and insights presented, whichoffer a replicable approach to planning and revenue optimization.