Work · Lavi Sahu

The work

Four disciplines, one row each. Open a row for the principles I hold to and the failure mode I see most often.

principles

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: one consensus demand figure, on one sheet, with one total.
Escalation criteria get written down before the first exec S&OP, well ahead of the first real conflict in the room.

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.

principles

Configuration should mirror how the business actually plans. The tool’s reference architecture assumes a generic company, and the gap between that company and the real one is where most rollouts lose a year.
Hypercare is where the real requirements surface. Budget for it as a design phase.
A planning model with more granularity than the business can act on is a maintenance liability.

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.

principles

Safety stock is a policy decision. The formula only prices the policy you already chose (the 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.
The useful question is "which SKUs are we overpaying to protect, and which are we underprotecting?" It is sharper than "how do we hold less inventory."

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.

principles

A statistical baseline’s job is to be a credible starting point for a human argument. Forecast accuracy is a property of the process around the number.
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.

demand planning · s&op · inventory policy · forecast diagnostics · llm workflows · design systems · demand planning · s&op · inventory policy · forecast diagnostics · llm workflows · design systems · demand planning · s&op · inventory policy · forecast diagnostics · llm workflows · design systems · demand planning · s&op · inventory policy · forecast diagnostics · llm workflows · design systems ·

try it

Two games and three live tools. Open one to run it right here; nothing loads until you do.

01

Stockout

Kombucha has days on the shelf, not months. Load crates onto the truck and ship them before they turn. A 40 second shift.

02

The Beer Game

The classic supply-chain game, solo: run one desk of four for 30 weeks and watch a small change in demand turn into a swing upstream.

play

03

S&OP Scenario Simulator

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

04

Inventory Optimization

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

05

Bullwhip Visualizer

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

selected

Anonymised and NDA-safe, numbers as ranges. Open a row for the full situation → task → action → result.

Situation

The monthly S&OP cycle ran long and finished after the decisions it was meant to inform. Demand, supply and finance each planned off their own numbers, so the executive review re-argued the data instead of committing to a plan.

Task

Compress the cycle, put one reconciled set of numbers in front of the executive review, and make that review decide.

What I did

Redesigned the four-step monthly cadence (demand review, supply review, reconciliation, executive S&OP), with a named owner, entry criteria and a decision log for each step.
Configured the demand and supply review in the planning platform so every step worked from the same reconciled dataset.
Built the reconciliation view that surfaced the gaps and the trade-off options the executive review had to choose between.
Ran the first cycles hands-on and coached the client planners through hypercare.

Result

Planning cycle time down roughly 30–45%; the executive review now closes each month with signed decisions and a decision log instead of a re-run of the analysis.

S&OP
SAP IBP
consensus forecasting

Figures are directional ranges from an anonymised engagement. Client name and absolute values withheld under NDA.

Situation

Finished-goods inventory had grown faster than demand as the network added markets. Safety stock was set by rules of thumb rather than measured demand and lead-time variability, so cash was tied up without a service-level story to justify it.

Task

Take working capital out of finished goods without moving the service level the business had committed to.

What I did

Rebuilt the safety-stock model on measured demand variability and actual supply lead-time variability by segment.
Re-optimised stock targets against the promised service level rather than a flat days-of-cover rule.
Segmented SKUs so the high-runner and long-tail portfolios got different policies.
Set up the monthly review that keeps the targets current as variability shifts.

Result

Finished-goods inventory reduced by about 15–20% while the target service level held.

Multi-echelon inventory
safety-stock modelling
SAP IBP

Directional range from an anonymised engagement. Client, SKU counts and absolute inventory values withheld under NDA.

Situation

Several regions each ran their own planning process, calendar and definition of demand. There was no single number the business could point to, and cross-region supply moves were negotiated by email.

Task

Bring the regions onto one integrated planning process and a single source of demand truth without stalling the regional teams mid-cycle.

What I did

Defined one target planning process and data model, then ran a region-by-region rollout so no region went dark during cutover.
Stood up a single demand-truth layer that every region planned from.
Aligned the planning calendars so cross-region supply decisions happened inside the cycle.
Trained regional key users and ran a shared hypercare desk across time zones.

Result

Regions unified onto one integrated planning process: 5 planning regions on a single monthly cadence and one demand number, replacing separate region-by-region processes.

Integrated business planning
o9
S&OP governance

Anonymised engagement. Client, region names and volumes withheld under NDA.

find me elsewhere.

what ido.

01S&OP / IBP Process Design+s&op · ibp · demand review · supply review · consensus cadence

02SAP IBP & o9 Implementation+sap ibp · o9 · configuration · deployment · hypercare

03Network & Inventory Optimization+multi-echelon · safety stock · network design · service levels

04Demand Planning & Forecasting+statistical baseline · consensus process · forecast bias · accuracy tracking

Stockout

The Beer Game

S&OP Scenario Simulator

Inventory Optimization

Bullwhip Visualizer

01Global CPG manufacturer (anonymised)Solution lead — S&OP process design + planning-tool rolloutread the case →

02Pharmaceutical manufacturer (anonymised)Functional lead — inventory & safety-stock policyread the case →

03Industrial manufacturer (anonymised)PMO lead + functional architectread the case →

Planning, end to end.

S&OP / IBP Process Design

SAP IBP & o9 Implementation

Network & Inventory Optimization

Demand Planning & Forecasting

Signal

Plan

Execute

Learn

That is the loop.

S&OP / IBP Process Design

SAP IBP & o9 Implementation

Network & Inventory Optimization

Demand Planning & Forecasting

Signal

Plan

Execute

Learn

That is the loop.

what ido.

01S&OP / IBP Process Design+s&op · ibp · demand review · supply review · consensus cadence

02SAP IBP & o9 Implementation+sap ibp · o9 · configuration · deployment · hypercare

03Network & Inventory Optimization+multi-echelon · safety stock · network design · service levels

04Demand Planning & Forecasting+statistical baseline · consensus process · forecast bias · accuracy tracking

Stockout

The Beer Game

S&OP Scenario Simulator

Inventory Optimization

Bullwhip Visualizer

01Global CPG manufacturer (anonymised)Solution lead — S&OP process design + planning-tool rolloutread the case →

02Pharmaceutical manufacturer (anonymised)Functional lead — inventory & safety-stock policyread the case →

03Industrial manufacturer (anonymised)PMO lead + functional architectread the case →

Planning, end to end.

S&OP / IBP Process Design

SAP IBP & o9 Implementation

Network & Inventory Optimization

Demand Planning & Forecasting

Signal

Plan

Execute

Learn

That is the loop.

S&OP / IBP Process Design

SAP IBP & o9 Implementation

Network & Inventory Optimization

Demand Planning & Forecasting

Signal

Plan

Execute

Learn

That is the loop.