Sanchay
sanchay - accumulation, what is gathered and held
Forecast demand, size inventory, and reorder before the shelf empties or the batch expires.
Sanchay is a demand and inventory accelerator for distribution-heavy supply chains, built first for pharmaceutical distribution. It combines SKU-level demand forecasting, scenario planning across bull, base, and bear cases, and accuracy tracking with real-time inventory health, batch expiry tracking, and multi-location stock views. Procurement teams manage purchase orders, suppliers, and reorder recommendations, while an alert centre flags stockouts, overdue orders, and expiring batches before they become write-offs.
Workflow
Problem
Distributors forecast in spreadsheets and hold safety stock by habit, so capital sits in slow inventory while fast-moving SKUs stock out and batches quietly expire.
Outcome
SKU-level forecasting, live inventory health, and reorder recommendations that balance service level against working capital.
01
Forecast the demand
Per-SKU demand is projected and the accuracy of each forecast is tracked, so the model earns trust or loses it.
02
Model the range
Bull, base, and bear scenarios are held alongside the base plan rather than argued about separately.
03
Watch the stock
Inventory position, batch expiry, and location coverage are monitored continuously.
04
Recommend the order
Reorder points and purchase recommendations are produced against forecast, lead time, and shelf life.
Modules
SKU Demand Forecasting
Per-SKU forecasts with accuracy tracked over time rather than assumed.
Scenario Planning
Bull, base, and bear cases modelled so a plan has a range, not a single number.
Inventory Health
Stock position, batch expiry, and multi-location visibility in one view.
Reorder Intelligence
Purchase order, supplier, and reorder recommendations driven by forecast and lead time.
Representative use cases
Preventing Stockouts on Fast-Moving Pharmaceutical SKUs
A pharmaceutical distributor supplying 900 pharmacies holds inventory against a forecast maintained in spreadsheets and updated monthly.
Cutting Batch Expiry Write-Offs Across Multi-Location Inventory
A distributor holds temperature-sensitive stock across eleven regional warehouses, with batch expiry tracked separately at each site.
Scenario Planning a Demand Shock Across Bull, Base, and Bear Cases
A consumer goods distributor entering a volatile season must commit purchase orders months ahead of knowing how demand will land.
Sizing Safety Stock Against Real Supplier Lead-Time Variability
A distributor sets safety stock from nominal supplier lead times published in the ERP, which bear little relation to actual delivery performance.
Giving Procurement One Alert Centre Instead of Five Reports
A procurement team monitors stockouts, overdue purchase orders, expiring batches, and supplier delays through four separate reports run on different schedules.
Evidence
What Wayam can show for this entry
Curated solution design with an owner and a review date. No performance or production claim.
Allowed claims at this tier: Possible workflow, typical stack, prerequisites.
- Evidence tier
- T4 · Reference pattern
- Integration status
- Typical enterprise system
- Metric status
- Scenario only
- Evidence owner
- Not yet attached
- Validated
- Not yet attached
- Valid until
- —
- Content version
- 2026.09
Pending evidence attachment
This entry represents an enterprise architectural reference pattern. Customer benchmarks, run replays, and metric verifications are established during technical discovery.
Limitations
- Headline metrics are representative until a customer result is attached.
Representative metrics · Reference · scenario only
Per SKU
forecasting with tracked accuracy
Batch-level
expiry visibility across locations
These describe the intended outcome of the design. They are not measured customer results until a validated case is attached above.
Architecture & controls
Composed across the operating loop
Ingest
Connect the systems of record and read the signals the work already produces.
Covered by the platform
Reason
Ground context, score options against policy and the stated goal, draft the next step.
6 agent roles
Act
Execute an approved step in the system of record and keep the evidence.
3 agent roles
Govern
Set policy, gate consequential actions on a named approver, and audit what ran.
Covered by the platform
Pack Architecture & Agent Composition Graph
9 Composed Agents- AGT-0134
Spend Analysis Agent
Procurement
- AGT-0135
Supplier Discovery Agent
Procurement
- AGT-0136
RFx Drafting Agent
Procurement
- AGT-0157
Production Schedule Optimization Agent
Manufacturing Operations
- AGT-0201
Computer Vision Quality Inspection Agent
Manufacturing Operations
- AGT-0337
Plant Digital Twin Agent
Manufacturing Operations
- AGT-0222
Incident Triage Agent
IT Operations
- AGT-0223
Root Cause Analysis Agent
IT Operations
Control gates
- A named approver on every consequential write-back
- Evidence and audit trail kept with every run
Capability atoms
- forecasting
- system-connectors
- scenario-simulation
- alert-routing
- anomaly-detection
Typical enterprise systems
Typical stack for solution design; compatibility is validated during discovery.
- SAP Ariba
- Coupa
- Snowflake
- Dun and Bradstreet API
- Cognex VisionPro
- Siemens MES MCP
- NVIDIA Jetson
- SAP S/4HANA
Designed for
healthcare, retail-cpg, logistics
Business case
Model a Sanchay scenario with your own baseline
Three scenarios from the numbers you enter. Capacity released is time; it becomes a saving only when roles or costs are actually removed or avoided.
Improvement is seeded at the midpoint of this entry's directional band (20–40%). Source status: scenario only. Edit it to your own estimate.
| Scenario | Improvement | Capacity released | Cashable savings | Net annual | Payback |
|---|---|---|---|---|---|
| Conservative | 11% | — | — | — | — |
| Expected | 21% | — | — | — | — |
| Upside | 29% | — | — | — | — |
Enter an annual volume and a baseline cost to see figures.
Illustrative planning scenario, not a guarantee. Results depend on process baseline, adoption, data quality, integration scope, controls, and deployment costs.
Pilot this
From solution design to a bounded pilot
Step 1 · 3–4 weeks
Discovery Sprint
Validate the workflow, data, controls, baseline and business case before anything is built.
Gate: Blueprint and pilot plan signed by the business owner, technical owner and Wayam.
Step 2 · 6–10 weeks
Bounded Pilot
Prove quality and value on agreed data against agreed acceptance tests.
Gate: Acceptance thresholds met on the evaluation set; go/no-go decision recorded.
Agrani · evidence pending3 Agrani candidates in this solution: Computer Vision Quality Inspection Agent, Incident Triage Agent, Change Risk Assessment AgentPairs well with