A leading semiconductor equipment manufacturing firm wanted to set their buyback pricing model based on actual costs incurred to ensure target margins were attained.
We created a dynamic model to factor in equipment age, upgrade status, geographical location, and refurbishment costs. The custom Python models are now IP owned by the firm to use across all mature tool set families.
Discovered the existing pricing model was missing substantial costs and therefore not hitting targeted margin structures. Deploying the new model allows the company to price more accurately and generate $10Ms in additional margin annually. Quantification of harvesting to modules or spare parts is now possible.
This model created the framework for data-driven decision making for a global sales team. The new model is more responsive and has real-time linkages to the ERP system for dynamic tracking of costs and value.
