datasets manually downloaded and consolidated
up to ~60 min.to perform a routine planning update
~6 monthsof typical forward visibility
1 personholding most of the planning knowledge
An 18-month material planning and purchasing decision system for approximately $3M in on-hand raw material inventory.
Alloy planning required downloading four to five separate datasets, consolidating them manually in Excel, and refreshing a pivot-based view about every two weeks. Planning typically extended no farther than six months.
Purchasing rules, supplier timing, test-bar requirements, historical usage, safety-stock considerations, and special alloy behavior also lived largely as tribal knowledge. One person had maintained the process for roughly four years before teaching it to me as they left.
Alloy shortages occurred roughly two to three times per quarter. A shortage could stop affected production until replacement material arrived and completed a 2–8 week verification cycle.
datasets manually downloaded and consolidated
up to ~60 min.to perform a routine planning update
~6 monthsof typical forward visibility
1 personholding most of the planning knowledge
plus a maintained scheduled-receipts table
~5 min.to refresh routine planning inputs
up to ~18 monthsof selectable forward planning visibility
documentedmethodology, operating instructions, and constraints
The workbook converts operational exports into material requirements, reconciles supply, applies purchasing constraints, and presents decision horizons tailored to different users.
The planner suggests quantities and order timing while leaving room for customer expedites, overdue orders, business conditions, and current production priorities to shape the final decision.
The purchasing calendar staggers incoming material to avoid overloading test-bar verification or creating unnecessary mix-up risk on the production floor.
Purchasing needs the next quarter’s actions; operations needs six-month readiness; leadership and suppliers benefit from longer-range forecasting.
MOQ rules, supplier lead time, test-bar timing, historical consumption, special-material behavior, and planning notes are documented instead of left in one person’s memory.
Quarter-level logic could imply urgency from the start of the quarter.
The model now uses the next relevant customer due date to locate the actual material need inside the quarter.
I validated the planner by tracing discrepancies against operational expectations and refining formulas or business logic when recommendations did not reflect the real decision.
The planner entered deployment after formula validation, stakeholder review, and functional refinement with purchasing and operational leadership.
Because deployment is recent, I do not yet attribute shortage reductions or inventory savings to the system. Long-term impact will be evaluated as it accumulates operating history.
A portfolio version will preserve the workbook’s planning architecture and decision logic using fictionalized alloy, part, demand, and supplier data.