About

Senior planning architecture, grounded in execution.

A first-person view of the experience, judgment, and operating style behind the advisory work.

My Background

I bring 26+ years of total supply chain, manufacturing, SAP planning, and IT services experience, including 20+ years in IT services and SAP consulting/transformation. My foundation is not only system design. It is also manufacturing and shop-floor grounding, where schedules, materials, capacity, constraints, and people determine whether a plan is realistic. That experience still shapes how I evaluate planning architecture, transformation readiness, and the decisions planners need to make every week. I look past configuration lists and ask how the planning cycle will run, who owns exceptions, and how decisions move from signal to action.

How I Got Here

Over time, my work expanded into global SAP implementations, planning modernization, and cross-functional program leadership. I have worked across SAP IBP, PP/DS, APO DP/SNP, ECC, and S/4HANA planning contexts in complex enterprise settings. I am comfortable moving between executive conversations, architecture choices, process design, master data dependencies, integration questions, and the adoption work needed for planners and business leaders to trust the solution. I have learned that transformation succeeds when the roadmap is honest about sequence, ownership, readiness, and the amount of change the business can absorb.

Senior Judgment

The value of senior advisory work is not only knowing the systems. It is knowing what to sequence first, which risks deserve executive attention, and where a practical decision is more useful than a perfect design. I bring that judgment to planning programs where architecture, data, governance, testing, and adoption must move together.

Why AI-Augmented Planning Now

Planning teams are being asked to respond faster while managing more volatility. I see AI-enabled forecasting as a disciplined extension of planning judgment, not a replacement for it. Used well, AI can improve signal detection, exception prioritization, scenario comparison, and forecast confidence. Used poorly, it adds another layer of noise. My direction is practical AI-augmented planning: statistical baselines, scenario logic, business context, SAP planning design, and decision governance working together in a way leaders can understand and planners can use. I am cautious about broad claims and focused on the planning decisions that actually improve readiness.

Clinical Trial and Regulated Supply Chain Focus

Clinical trial supply chains require a planning mindset that respects uncertainty, regulation, patient continuity, and supply assurance. Enrollment variability, site activation, country requirements, shelf-life, labeling, depot strategy, and supply constraints can all affect the forecast. My clinical trial supply chain work centers on scenario-based planning intelligence, clear risk views, and planning models that help regulated teams make better decisions without overcomplicating the operating model. In these environments, the quality of the planning narrative matters because leaders need to understand risk before they choose a response. I am especially interested in approaches that make uncertainty visible early, before supply risk becomes operational pressure.

Credentials and Availability

My professional development includes SAP ICSP completion, SAP IBP certification in progress, an MBA, MITx MicroMasters in Supply Chain Management, CSCP, data science coursework, and continued learning in AI-enabled planning. I am U.S.-based and available for remote, hybrid, advisory, and senior SAP architecture conversations. I am most useful where the work requires both architectural clarity and the judgment to guide senior stakeholders through complex tradeoffs with calm, credible planning leadership.

How I Work

A practical advisory rhythm

The engagement model is designed to move from understanding to executive-ready recommendations without unnecessary ceremony.

Discover

Understand the planning landscape, pain points, and business priorities.

Diagnose

Assess process, architecture, forecasting logic, and decision gaps.

Design

Define a practical target-state model and transformation path.

Pilot

Build a small proof of concept or planning framework using safe simulated data.

Recommend

Deliver a clear, executive-ready roadmap with next steps.