実績

計画を、端から端まで。

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.

Scroll to move the freight ↓

S&OP / IBPプロセス設計

月次のケイデンスを立ち上げます — 需要レビュー、供給レビュー、調整、経営S&OP — 意思決定がなされ、維持されるように。

SAP IBP & o9実装

需要、在庫、供給計画にわたるモデリング、設定、展開 — ブループリントからハイパーケアまで。

ネットワーク & 在庫最適化

サービスを枯渇させずに現金を解放する、多段階在庫、安全在庫ポリシー、ネットワーク設計。

需要計画 & 予測

統計ベースラインに本物の合意プロセスを合わせる — 測定可能な精度で、下流の火消しを減らす。

シグナル

需要パターンは、誰も気づかないうちにトレンドから外れる。

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

計画

S&OPが読みを供給とコストと突き合わせる。

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

実行

計画は発注書、アロケーション、移動になる。

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

学習

予測誤差が次のサイクルに還元される。

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

Bring the loop to your network.

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

声をかける ✦コンタクト

ロゴではなく、結果。 ↓

実績

シグナルから棚まで。

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プロセス設計

月次のケイデンスを立ち上げます — 需要レビュー、供給レビュー、調整、経営S&OP — 意思決定がなされ、維持されるように。

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実装

需要、在庫、供給計画にわたるモデリング、設定、展開 — ブループリントからハイパーケアまで。

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.

ネットワーク & 在庫最適化

サービスを枯渇させずに現金を解放する、多段階在庫、安全在庫ポリシー、ネットワーク設計。

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.

需要計画 & 予測

統計ベースラインに本物の合意プロセスを合わせる — 測定可能な精度で、下流の火消しを減らす。

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.

実績

私の言葉は信じないで — 自分でレバーを動かして。

計画のトレードオフが実際どう動くかを見せる、2つの動くツール。スライドも、ごまかしもなし。

S&OPシナリオシミュレーター

稼働中

プランナーが実際に制御するレバーを動かす — トレードオフがリアルタイムで動くのを見てください。説明用のモデルです。

需要 vs 計画0%

生産能力100%

目標サービスレベル95%

補充リードタイム4 週

予測サービスレベル90.5%

稼働率100%

在庫指数(計画 = 100)100

陳腐化リスク0%

均衡した操業点。 在庫と稼働率が健全な範囲にあり、サービスは目標近くで維持されています。

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.

在庫最適化

稼働中

自分の数値からEOQ、安全在庫、発注点を算出 — あらゆる補充ポリシーの背後にある数学です。

年間需要

発注コスト($)

単価($)

保管コスト %/年

1日平均需要

リードタイム(日)

日次需要のσ

サービスレベル %

4,767

EOQ(個数)

563

安全在庫

5,183

発注点

4,767個を、年に約25.2×回発注します。在庫が5,183に達したら発注 — 14日間のリードタイムをまたいで97%のサービスレベルを維持するため、563個の安全在庫を保有します。

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.

ビルウィップ効果ビジュアライザー

稼働中

棚での小さな揺らぎが、上流では嵐になる。需要変動が階層ごとに増幅していくのを見てください。説明用のモデルです。

需要変動(CV)20%

サプライチェーンの階層4

棚での小さな需要の揺らぎが、上流では大きな発注の揺らぎに増幅します — 階層4(地域DC)では、20%のCVが発注指数173になります。需要の可視化、リードタイムの短縮、発注の平準化がこの鞭を鎮めます。

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.

02実績

ロゴではなく、結果。

案件は匿名化しています — それぞれ状況、私の役割、やったこと、そして幅付きの結果として。

ケーススタディを読み込み中…

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