Make vs. Buy Financial Analysis

A large European financial institution, as documented by McKinsey in 2025, deployed a combination of large language models and advanced analytics to analyze indirect spending across thousands of suppliers. The organization built a detailed cost taxonomy with approximately 400 subcategories and used AI to surface cost inefficiencies through automated and semi-automated anomaly detection. The analysis revealed specific opportunities to reduce costs across energy usage, travel and transport, and facility management, together reducing costs by approximately 10% of a multibillion-euro spend base. The implementation demonstrated how AI-driven cost analysis can identify savings that traditional make-vs.-buy frameworks overlook.

In the enterprise planning domain, the global consumer goods company Unilever International Group deployed an AI-driven planning platform to process 300 million data rows for forecasting and scenario modeling, according to Anaplan. The implementation integrated statistical models at scale to support make-vs.-buy decisions across supply chain and operational planning functions. Separately, a global industrial conglomerate used AI financial modeling to achieve a 10% improvement in prediction accuracy for financial reporting, as reported by Coherent Solutions in 2024, enabling more precise total cost of ownership comparisons across manufacturing and outsourcing alternatives.

In the fulfillment domain, the apparel company Levi Strauss and Co. transitioned to a hybrid fulfillment model incorporating third-party logistics providers, as reported by Supply Chain Dive, predicting improved inventory management and greater profitability. Conversely, the specialty tea retailer DavidsTea moved fulfillment in-house in 2023, ultimately achieving higher gross profit after an initial revenue dip. These contrasting outcomes underscore the importance of dynamic, AI-informed analysis rather than static cost comparisons when evaluating fulfillment make-vs.-buy decisions.