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Cost-to-serve analysis on service operations of a heating, ventilation, and air conditioning (HVAC) company through development of a cost-to-serve framework
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School of Engineering |
Master's thesis
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en
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57
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The global Heating, Ventilation, and Air Conditioning (HVAC) industry is undergoing a strategic transformation known as "servitization," where the stance of value creation has shifted from just the transactional sale of hardware to the provision of lifecycle climate solutions. While this paradigm shift opens-up new revenue streams, it introduces complex, indirect cost structures that traditional gross-margin or contribution-margin accountings fail to capture. This thesis investigates the service operations of an Ireland-based HVAC company, focusing on the end-to-end lifecycle from equipment installation and commissioning to ongoing warranty support, and lifelong maintenance backup.
The research highlights a critical "financial blind spot": the failure to assign indirect service costs—such as unallocated travel time, on-call coordination, logistics inefficiencies, and administrative overheads—to specific customers or contracts. To address this, the study developed a structured Cost-to-Serve (CTS) framework based on Time-Driven Activity-Based Costing (TDABC) principles. This framework is constructed through the successful implementation of a rigorous four-phase methodology, spanning from the identification of cost heads to comprehensive CTS estimation, resource capacity planning, and Customer Profitability Analysis (CPA), as well as the operationalization of a real-time Microsoft Power BI dashboard.
The study also analysed sales data of three major customer sectors (commercial, industrial, and residential), and findings reveal significant disparities in sector profitability. While the residential sector drives 53.66% of sales volume, its high Cost-to-Serve (17.5%)—driven by fragmented travel and administrative burden—erodes net margins to just 6.5%, highlighting a strategic risk. Conversely, the Commercial sector emerges as a "Cash Cow" with a 23% net margin due to efficient, clustered service delivery. The research demonstrates that implementing a real-time data-driven CTS framework enables the organization to transition from reactive cost control to proactive Value-Based Pricing, optimize manpower scheduling, minimize CTS by strategically managing its customer portfolio, and secure long-term sustainable profitability.