The work

Planning, end to end.

Fourteen years in demand and supply planning across SAP IBP, o9, OMP and S&OP — the tool changes; the monthly process has to change with it. Scroll the freight: road, sea, air.

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S&OP / IBP Process Design

Standing up the monthly cadence — demand review, supply review, reconciliation, exec S&OP — so decisions get made and held.

SAP IBP & o9 Implementation

Modeling, configuration and deployment across demand, inventory and supply planning — blueprint to hypercare.

Network & Inventory Optimization

Multi-echelon inventory, safety-stock policy and network design that frees cash without starving service.

Demand Planning & Forecasting

Statistical baselines plus a real consensus process — measurable accuracy, less downstream firefighting.

Signal

A demand pattern breaks from trend before anyone notices.

Fails as a surprise: the break was visible in week one and noticed in week four.

Plan

S&OP reconciles the read against supply and cost.

Fails as an argument: the room re-litigates the data instead of choosing between options.

Execute

The plan becomes purchase orders, allocations, moves.

Fails as a handoff: the plan was right and the orders were old.

Learn

Forecast error feeds back into the next cycle.

Fails as a rerun: error never reaches the next baseline, so every quarter is the first quarter.

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Advisory on planning cycles, control towers and AI-assisted decision-making. One conversation first.

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Results, not logos. ↓

The work

From signal to shelf.

Four moves, one monthly loop. I've watched it hold for fourteen years, and I've watched it break at 11 p.m. on a cutoff night — demand signalled the break three weeks out, the plan reconciled cleanly, and Execute still shipped last month's allocation because the ERP couldn't ingest the new one. The loop rarely fails at the glamorous step. It fails at the handoff. That night is a composite — the shape of it is not.

S&OP / IBP Process Design

Standing up the monthly cadence — demand review, supply review, reconciliation, exec S&OP — so decisions get made and held.

The calendar is the product. If the monthly cycle isn’t on a fixed cadence people can plan their own work around, no amount of tooling fixes the meeting.
Every review needs one number the room agrees is real before it argues about what to do next — consensus demand, not three spreadsheets with three different totals.
Escalation criteria get written down before the first exec S&OP, not improvised in the room when the first real conflict shows up.

Common failure mode: The most common shape this takes: a beautifully documented S&OP process binder that nobody follows past month two, because the design skipped the unglamorous part — who actually owns the number when demand and supply disagree, and what happens if they miss the deadline.

SAP IBP & o9 Implementation

Modeling, configuration and deployment across demand, inventory and supply planning — blueprint to hypercare.

Configuration should mirror how the business actually plans, not how the tool’s reference architecture assumes a generic company plans — the gap between those two is where most rollouts lose a year.
Hypercare is where the real requirements surface. Budget for it as a design phase, not a warranty period.
A planning model with more granularity than the business can act on is a maintenance liability, not a capability.

Common failure mode: The common failure: a technically correct blueprint that models the org chart instead of the decision flow — so the system produces a number nobody in the actual meeting knows how to challenge or override.

Network & Inventory Optimization

Multi-echelon inventory, safety-stock policy and network design that frees cash without starving service.

Safety stock is a policy decision, not a formula output — the formula just prices the policy you already chose (service level) in units of cash.
Multi-echelon only pays for itself if the network’s node-to-node lead times are trustworthy; garbage lead-time data makes a multi-echelon model worse than a single-echelon one, not better.
The right question is never "how do we hold less inventory" — it’s "which SKUs are we overpaying to protect, and which are we underprotecting."

Common failure mode: The common failure: an optimization run that frees up cash on paper by cutting safety stock uniformly, then gets quietly reversed SKU by SKU over the next two quarters as service failures roll in — because the cut wasn’t risk-weighted.

Demand Planning & Forecasting

Statistical baselines plus a real consensus process — measurable accuracy, less downstream firefighting.

A statistical baseline’s job is to be a credible starting point for a human argument, not a final answer — forecast accuracy is a property of the process around the number, not the algorithm.
Bias is a more useful diagnostic than error. A forecast that’s consistently 8% high is a coaching conversation; a forecast that’s randomly ±15% is a data problem.
The consensus meeting should change the number sometimes — if it never does, it’s a status update wearing a planning meeting’s clothes.

Common failure mode: The common failure: a forecasting tool upgrade sold as an accuracy fix, deployed against a consensus process that was never actually adjusting the baseline — so accuracy doesn’t move, because the tool was never the bottleneck.

Fails as a surprise: the break was visible in week one and noticed in week four.

Fails as an argument: the room re-litigates the data instead of choosing between options.

Fails as a handoff: the plan was right and the orders were old.

Fails as a rerun: error never reaches the next baseline, so every quarter is the first quarter.

The work

Don't take my word for it — move the levers.

Three live tools — scenario simulator, inventory calculator, bullwhip chart. No slides, no hand-waving: every figure is computed in your browser as you move the levers.

S&OP Scenario Simulator

Live

Move the levers a planner actually controls — watch the trade-offs move in real time. Illustrative model.

Demand vs plan0%

Production capacity100%

Target service level95%

Replenishment lead time4 wk

Projected service level90.5%

Capacity utilization100%

Inventory index (plan = 100)100

Obsolescence risk0%

Balanced operating point. Service holds near target with inventory and utilization in a healthy range.

What this hides: one SKU, one line, one season. Obsolescence here is a proxy, not a write-off model. The version I get paid for has a demand plan arguing with the statistical baseline — that argument is where the trade-off actually gets settled.

Inventory Optimization

Live

EOQ, safety stock and reorder point from your own numbers — the math behind every replenishment policy.

Annual demand

Order cost ($)

Unit cost ($)

Holding %/yr

Avg daily demand

Lead time (days)

Daily demand σ

Service level %

4,767

EOQ (units)

563

Safety stock

5,183

Reorder point

Order 4,767 units about 25.2× a year. Reorder at 5,183 on-hand — carrying 563 units of safety stock to hold a 97% service level across a 14-day lead time.

What this hides: σ is daily demand variability, not forecast error — the production version uses forecast error, which is bigger and less polite. The z-value comes from a ten-row table, not the full normal curve. And 97% is only a target — check whether the safety stock is sized for the service level you actually promised.

Bullwhip Visualizer

Live

A small wiggle at the shelf becomes a storm upstream. Watch demand variability amplify tier by tier. Illustrative model.

Demand variability (CV)20%

Supply-chain echelons4

Small demand swings at the shelf amplify into large order swings upstream — by tier 4 (Regional DC), a 20% CV becomes an order index of 173. Demand visibility, shorter lead times and order smoothing dampen the whip.

What this hides: each tier multiplies the swing by one-plus-CV — a teaching model, not a simulation. Real whips are gamed rationing and batched orders. The dampeners in the note are the ones that survive contact with a real order book.

02The work

Results, not logos.

Engagements anonymised; results as ranges — situation, my role, what I did, and the honest width of the outcome.

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Plans that hold look boring from the outside — one demand number, one cadence, a range instead of a promise. That's the whole trick.