Value Analysis & Engineering Director

Director Of Value Analysis & Engineering (VAE)

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The Director of Value Analysis & Engineering (VAE) is responsible for driving enterprise value through the strategic application of value engineering analysis methodologies and advanced analytics using generative AI techniques. This role leads a high-impact team of engineers, partnering across operations, manufacturing technology, R&D and supply chain stakeholders to identify, prioritize, and implement initiatives that optimize cost, quality, outcomes, and performance.

The role serves as both a strategic advisor and change leader, leveraging value engineering practices to maximize product, process, or project value while minimizing costs. The position drives local and enterprise-wide improvements through shop-floor technology innovations, productivity, and optimized design for Front-End Loading of capital projects.

This leader establishes the value engineering roadmap, governs portfolio performance, mentors and develops team capability, and influences senior leadership through clear, actionable recommendations. Success requires strong change leadership, experience with generative AI techniques, the ability to translate analytics into impact, and a commitment to building a culture of continuous improvement.

• Lead the enterprise value engineering strategy focused on optimizing raw materials, formulations, components, and manufacturing processes to deliver significant, sustainable cost reductions
• Own the value pipeline for material and process initiatives, prioritizing opportunities based on financial impact, feasibility, supply risk, and operational readiness
• Partner with executive leadership to align VAV&E initiatives to business strategy, margin improvement goals, and growth plans
• Drive value analysis of raw materials, including material substitution, specification optimization, grade rationalization, and alternative sourcing strategies
• Collaborate with Procurement and Suppliers to identify cost-down opportunities through should-cost modeling, AI-driven benchmarking, and innovation partnerships
• Implement AI-supported tools for predictive cost modeling, scenario analysis, supplier performance evaluation, and demand-driven material optimization
• Evaluate supplier proposals and cost-reduction programs using total cost of ownership, lifecycle cost, and risk-adjusted value frameworks
• Lead governance for material changes, ensuring performance, quality, and regulatory requirements are maintained or improved
• Lead cross-functional efforts to optimize manufacturing processes, including yield improvement, cycle time reduction, scrap and waste elimination, and energy efficiency
• Apply value engineering, Lean, Six Sigma, and AI-enabled analytics to identify root causes of cost and performance variation
• Drive standardization and simplification of processes, formulations, and bill-of-materials across sites where appropriate
• Partner with Engineering, Operations, Quality, and Manufacturing Technology, and R&D teams to enable scalable, sustainable process improvements
• Act as a strong change leader to drive adoption of material and process changes across manufacturing sites and functions
• Lead stakeholder engagement, communication, and risk mitigation for high-impact cost-reduction initiatives
• Build credibility and alignment with Operations, Quality, Supply Chain, and Plant Leadership to ensure implementation success
• Anticipate and manage resistance, ensuring changes are practical, well-understood, and operationally sustainable
• Lead, mentor, and develop a high-performing VAE team focused on materials, manufacturing, and process engineering
• Establish best practices, standards, and tools for value analysis and value engineering across the team
• Build AI literacy and analytical capability within the team to elevate outcomes and productivity
• Foster a culture of continuous improvement, data-driven thinking, and innovation
• Establish KPIs to track cost savings, cost avoidance, productivity gains, and value realization
• Ensure savings are validated with Finance and sustained through governance, monitoring, and controls
• Report clear, transparent results and progress to senior and executive leadership

• Bachelor's degree in Engineering, Computer/Data Science, MBA or AI Business Masters
• 15+ years of experience in Manufacturing, Supply Chain, Computer Science related field
• Proven experience with Advanced AI and analytics leadership, including experience sponsoring or deploying AI-enabled cost modeling, process optimization, and decision-support capabilities across…