Tensor AnalyticsTM
Selected workRepresentative patterns

Threeplanning
problemsGrepEye
wasbuilttosolve.

These are the patterns we see most often. If any of them sound familiar, GrepEye addresses the root cause, not with another dashboard, but with a connected ops platform that runs ML forecasting, S&OP, supply netting, and automation, with AI agents on top.

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Case studies

The patterns we see most often.

Each pattern is described as a representative scenario. If any of them sound familiar, GrepEye addresses the root cause, not just the symptom.

Manufacturer · 5,000 SKUs
The problem

“S&OP cycle taking three days in Excel”

The outcome

Replaced with the S&OP console end-to-end

A manufacturer running 5,000 SKUs across 12 warehouse locations was spending three days per S&OP cycle reconciling demand signals from different teams. Each planner had their own version. Consensus meant the loudest voice won. GrepEye replaced it with the S&OP console: ensemble-blended ML forecasts, demand planning grid, sequential gate enforcement (Demand → Supply → Exec), and a cycle lock that froze the plan after exec sign-off. The S&OP cycle now takes hours, not days.

  • Sequential gate enforcement, no more arguing over whose number
  • Cycle lock freezes the plan, nothing changes after sign-off
  • Meeting notes and rejection reasons captured per gate
01
5,000
SKUs planned
12
Warehouse locations
3 days → 4 hours
S&OP cycle time
Distributor · inventory team
The problem

“Safety stock set by gut feel”

The outcome

One-click supply netting + exception intelligence

A distributor was setting safety stock manually, mostly based on buyer experience and rule-of-thumb multipliers. The result was chronic stockouts on fast movers and dead stock on slow movers. GrepEye's one-click supply netting recomputed net supply from approved demand in seconds. Exception intelligence auto-raised gaps over 10% of demand as soon as netting ran, long before they became write-offs. Severity scoring told the team where to look first.

  • Net supply recomputed per SKU per period, idempotent on re-run
  • Gap exceptions auto-raised with critical/warning/minor severity
  • Reorder points tied to actual lead times
02
Per SKU
Net supply granularity
>10%
Auto-flagged gap threshold
Pre-write-off
Risk visibility
Manufacturer · planning team
The problem

“Three tools, no single source of truth”

The outcome

Consolidated into one operational data model

A planning team was running a Python forecasting model, a shared spreadsheet for consensus sign-off, and manual ERP entries for safety stock. Each handoff introduced errors, there was no audit trail, and finance was always working from a different number than operations. GrepEye replaced all three with structured datasets, an ML ensemble leaderboard, an append-only ledger for every operationally significant event, and AI agents that handled routine triage through chat, with human-in-loop approval on every write.

  • Single operational data model across the planning cycle
  • Append-only ledger with idempotency keys for reconciliation
  • AI agents handle routine ops in chat, every action audited
03
3 → 1
Tools consolidated
Full
Audit trail per action
One number
For finance + ops

All scenarios are described as representative patterns observed across engagements. Specific client names are withheld for commercial confidentiality.

The patterns

Four planning anti-patterns GrepEye replaces.

These are the structural problems we see across every engagement. Each one has a specific GrepEye response, not a feature, a workflow change.

01

Spreadsheet sprawl

The anti-pattern

Each planner maintains their own version. Consensus means the loudest voice wins. No audit trail, no version history. Cycle times balloon because every change has to be reconciled across multiple files.

GrepEye's response

Structured datasets with version-controlled records. S&OP console with sequential gate enforcement. Append-only ledger captures every override with actor and timestamp.

02

Single-model forecasting

The anti-pattern

One statistical model applied across all SKUs, regardless of demand pattern. Intermittent demand distorts exponential smoothing. Seasonal peaks missed. No way to tell which model works best for which product family.

GrepEye's response

ML ensemble (multiple model families) per SKU. Inverse-MAPE weighting blends the best performers. Accuracy leaderboard shows which model wins per product family.

03

Manual supply netting

The anti-pattern

Supply plans computed in spreadsheets, often stale by the time they're published. No visibility into gaps until they become stockouts. Manual review of every SKU every cycle.

GrepEye's response

One-click supply netting recomputes net supply from approved demand in seconds. Exception intelligence auto-raises gaps over 10% of demand with severity scoring. Idempotent on re-run.

04

Plan drift after sign-off

The anti-pattern

Planning numbers keep changing after production has started. No formal lock. Finance and operations work from different versions. New product launches and what-if scenarios disrupt the live plan.

GrepEye's response

Cycle lock freezes the plan permanently after exec sign-off. Scenario planning keeps what-ifs isolated as sparse overlays on the base version, never touching the baseline.

Sound familiar?

Let's run GrepEye on your data.

If any of these patterns sound familiar, book a 45-minute walkthrough. We'll run the full platform on a representative slice of your demand data and show you exactly what changes.

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