Companies delivering large-scale renewable energy investments operate in an environment of high volatility in component prices and strict administrative deadlines. In the wind and photovoltaic sectors, the profitability of the entire project lifecycle depends on precision at the bidding stage and the accuracy of assumptions regarding Total Cost of Ownership (TCO). Errors in estimating service costs or overlooking hidden logistics fees directly reduce return on investment and may lead to liquidity issues during the construction phase.
Relying on fragmented data and manual verification of subcontractor bids generates operational risk which, in an era of growing competition, becomes an unacceptable barrier. Inconsistencies in documentation submitted to regulatory institutions and delays in obtaining approvals often result from information chaos at the interface between technical and procurement teams. Organizations need a systemic mechanism that transforms a stream of inquiries and price lists into a unified, reliable financial model, enabling informed portfolio-level project management.
Moving away from a reactive operating model toward process automation enables not only faster responses to market inquiries, but above all the creation of an advantage based on hard historical data. Digitizing this area is a necessary step toward a modern management structure, where investment decisions are detached from intuition and instead based on verifiable performance indicators and real operating costs.
Our client is a renewable energy developer and integrator managing a portfolio of projects with a total capacity of several hundred MW. The company cooperates with dozens of turbine, inverter, and mounting-structure suppliers, delivering contracts under a “design and build” model. For years, bidding processes were based on extensive Excel spreadsheets which, as the business scaled, became increasingly opaque. Management recognized that the time-consuming nature of analyses was limiting participation in a greater number of auctions and tenders, thereby constraining the company’s growth dynamics.
The main barrier was the lack of centralized cost data and the dispersion of knowledge across project teams. Information about inquiry statuses, warranty terms, and technical specifications existed across hundreds of PDF files and email messages, making rapid offer comparison impossible. Manually re-entering data into comparison tables (TPO) consumed hundreds of engineering hours each month, increasing the risk of formal errors that could result in bid rejection by investors.
An additional issue was the low quality of as-built documentation and the absence of a feedback loop from the service phase back to the bidding team. Failing to account for actual repair and downtime costs in TCO analyses meant that selected technologies proved more expensive in the long term than rejected alternatives. The organization needed a tool that would eliminate manual data processing and ensure full control over contract profitability throughout their entire lifecycle.
We adopted a structured, phased approach, starting with a Proof of Concept focused on automating the analysis of bids from critical component suppliers. The first step involved analyzing the process logic embedded in existing tools in order to transfer it into a coherent environment without losing engineering flexibility. We focused on the data import process from various formats into a central repository based on SharePoint.
As part of the PoC architecture, we implemented an OCR mechanism supported by classification algorithms, enabling automatic extraction of pricing and technical parameters from subcontractor bids. The system was designed to generate alerts for specialists whenever inconsistencies were detected. Subsequent phases included integration with the CRM system and the development of analytical dashboards, enabling instant comparison of offers using a Total Cost of Ownership (TCO) model.
The implementation delivered measurable outcomes that directly translated into increased competitiveness. The time required to prepare comprehensive bid comparisons and TCO analyses was reduced by more than 55%, enabling the team to handle a greater number of procedures. Thanks to automated data import, critical errors in calculation sheets were eliminated, ensuring full consistency of documentation sent to external partners.
At the project level, the return on investment (ROI) for the bid automation module was estimated at 210%, making it one of the most effective elements of the transformation. The average purchasing decision time was reduced by 40% due to immediate access to financial data. From the management’s perspective, the most significant change is the ability to obtain a reliable view of project profitability, which reduced financial risk associated with unforeseen operating costs during the warranty phase.
Structuring the bidding process became the foundation for further analytical development and the implementation of advanced sales-support models. The system safeguarded organizational know-how by making processes independent of knowledge scattered across employees’ private files, which is critical for maintaining business continuity amid staff turnover.
Jakub Smeda
Karol Zielinski
