Business Analysis

People, Tools & Issues

Who's involved in the wool trading operation, what systems they work with, and where the pain points sit.

Who's involved

Personas

The key roles in Standard Wool's trading operation. Click a persona to see their responsibilities, pain points, and opportunities.

Trading Team
Create forward sales contracts, procure greasy wool, manage blends and stock position, generate invoices.
Trading
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Finance Team
Financial oversight. Ledger postings, margin reporting, currency forward contracts, and cash flow monitoring.
Finance
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Warehouse Operations
Physical stock management. Receives greasy wool, tracks bales at individual weight level, picks and dispatches against delivery instructions.
Warehouse
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Scouring (Chadwicks)
External scouring processor. Manages weekly capacity, calls in raw wool, scours in rounds, packs output bales, sends packing lists and processing invoices.
Chadwicks
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Shipping & Export
Overseas container shipping and export documentation. Arranges containers, bills of lading, and health certificates for international customers.
Shipping
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Trading
Trading Team
Manages the full trading lifecycle - sales, procurement, blending, invoicing
Jobs to be done
  • Create forward sales contracts with customers before wool is produced
  • Procure greasy wool from auctions, UK farms, and global suppliers
  • Create and manage blend recipes for scouring
  • Manage stock position - long/short against forward commitments
  • Negotiate weekly scouring capacity with Chadwicks
  • Generate sales invoices and packing lists
  • Monitor margin per contract and per lot
Systems and databases
WTS - core trading platform British Wool - auction purchasing Email / PDFs - document distribution
Pain points
PP1: No formal recipe storage. Blend recipes copied from memory - can't query historical blends per client.
PP2: Product code grouping unreliable. Different blends lumped under same group code. Described as "airy fairy".
PP3: Bale weights manually re-entered to create packing lists - duplication and error risk.
PP4: Invoices manually generated as PDFs and emailed - no automation or audit trail.
PP5: No visibility into Chadwicks' production capacity. Weekly verbal updates only.
PP6: WTS requires stock receipt before allocation, but traders need to allocate before wool arrives.
PP7: Can't accurately determine long/short position because product grouping is broken (linked to PP2).
Opportunities
Derive average recipes from historical blend data to support consistent quality and pricing.
Automated position reporting from accurate product taxonomy.
Digital blend instructions with capacity scheduling.
Finance
Finance Team
Margin reporting, GL postings, currency management
Jobs to be done
  • Financial reporting - P&L, balance sheet, cash flow
  • Margin analysis per invoice and per lot
  • Currency forward contract management and hedging
  • GL postings from invoice batches (WTS to Dynamics GP)
  • Aged debtor/creditor management
  • Stock valuation and costing
Systems and databases
SWB (Dynamics GP) - general ledger, AP/AR SUN - account codes and GL structure WTS - invoice batch postings
Pain points
PP8: Margin calculated at invoice level via stored procedure - no real-time visibility during trading.
PP9: Currency forward contracts tracked in WTS but allocation to specific trades is manual.
PP10: Stock valuation depends on lot-level cost roll-up through blend history - fragile if data quality slips.
Opportunities
Real-time daily margin dashboard replacing monthly batch process.
Automated currency exposure reporting linked to forward contracts.
Warehouse
Warehouse Operations
Physical stock - receiving, storage, bale tracking, dispatch
Jobs to be done
  • Receive greasy wool shipments and log bale-level weights
  • Track individual bales (gross, tare, nett weights)
  • Pick bales against delivery instructions
  • Manage stock across multiple warehouse locations
  • Coordinate with Chadwicks on stock call-ins for scouring
Systems and databases
DEWSDB0 - warehouse management (stockLot, stockBale, stockDelivery)
Pain points
PP11: Warehouse system uses different lot prefixes (C, T, A) than the trading system (H, SS). Cross-system mapping via CustomerRef is fragile.
PP12: Bale pick reports communicated verbally or on paper - no digital handoff to trading system.
Opportunities
Unified lot identity across trading and warehouse systems, eliminating manual cross-referencing.
Digital bale pick and dispatch confirmations replacing verbal/paper handoffs.
Chadwicks
Scouring (Chadwicks)
External scouring processor - Standard Wool is ~90% of their volume
Jobs to be done
  • Manage weekly scouring capacity across multiple clients
  • Call in raw wool from warehouses based on blend instructions
  • Scour wool in rounds across multiple days
  • Pack output into bales with weight records
  • Send packing lists and processing invoices back to Standard Wool
  • Monitor scouring efficiency, batch temps, detergent usage, effluent
Systems and databases
dbScour - scouring process control DEWSDB0 - warehouse stock at Chadwicks site
Pain points
PP5: Capacity scheduling is a weekly verbal negotiation. No digital visibility for Standard Wool into upcoming slots or queue position.
PP13: Packing lists come back as paper documents, requiring manual re-entry into WTS.
Opportunities
Shared digital capacity view between Standard Wool and Chadwicks - 2-3 weeks forward visibility.
Digital packing list exchange eliminating manual re-entry.
Shipping
Shipping & Export
Export logistics - containers, documentation, international compliance
Jobs to be done
  • Arrange container shipping for export customers
  • Produce bills of lading and health certificates
  • Coordinate with hauliers for container collection
  • Manage export documentation and compliance
Systems and databases
Manual documents - PDFs for shipping docs Email - haulier and customer coordination
Pain points
PP4: All shipping documents manually assembled and emailed as PDFs. No automated document generation.
Opportunities
Automated document assembly - invoice, packing list, bill of lading generated from a single dispatch action.
Technology landscape

Systems & Tools

The systems that run Standard Wool's trading operation. A mix of bespoke SQL Server applications, an ERP, and cloud reporting.

WTS (Wool Trading System)
SQL Server - JHP database
Core trading platform. Handles sales contracts, purchase orders, blend management, stock allocation, invoicing, and margin calculation. Named after predecessor business Jacomb Hoare Partnership. A bespoke system with ~3GB codebase.
Traders Finance
DEWSDB0
SQL Server
Warehouse management system. Tracks physical stock at bale level - individual bale weights (gross, tare, nett), lot assignments, delivery notes, and warehouse locations. Uses different lot prefixes (C, T, A) than the trading system.
Warehouse Chadwicks
dbScour
SQL Server
Scouring process control system. Monitors batch temperatures, detergent usage, scouring efficiency, and effluent levels at the Chadwicks plant. Quality management data for the scouring operation.
Chadwicks
SWB (Dynamics GP)
Microsoft Dynamics GP - SQL Server
Financial ledger system. General ledger, accounts payable and receivable. Receives invoice batch postings from WTS.
Finance
SUN (SunSystems)
SunSystems
Accounting system providing the account code structure and account names referenced by WTS for GL postings. Works alongside Dynamics GP for the full financial picture.
Finance
British Wool Auction
External platform
External auction platform for purchasing UK wool. Provides test results (yield, microns) for auction-sourced lots. One of three procurement channels alongside UK farms and global suppliers.
Traders
Systemic challenges

Cross-cutting Issues

Issues that span multiple stages and affect multiple people. These are the structural problems that a new platform needs to solve.

Broken product taxonomy
PP2 + PP7 - affects position reporting, financial analysis, and blend traceability

Product code grouping (WOOL_CLASS) is unreliable. Different blend recipes and types are lumped under the same product group, and the decision to create a new code versus reuse an existing one is inconsistent. Described internally as "airy fairy".

Impact
Financial reporting on product groups is inaccurate
Position tracking (long/short by product) is unreliable - traders can't see true exposure
Buying decisions made on gut feel rather than data
Blocks any meaningful analytical reporting until resolved
Resolution path
Define a new product taxonomy with consistent classification rules. The product taxonomy workshop (12 Mar) started this work.
No recipe formalisation
PP1 - affects blend consistency, analytical reporting, and knowledge retention

Blend recipes aren't formally stored. Traders copy and modify previous blends from memory. Historical blend data exists in the database (BLEND, BLEND_RESULTS, BLEND_INV_OUTPUT tables) but isn't queryable as recipes.

Impact
Can't answer "what did we typically blend for this client?" without manual work
Blocks recipe derivation - average recipes per client/product can't be calculated
Knowledge locked in traders' heads, not in the system
New traders have no historical reference for blend decisions
Resolution path
Derive average recipes from historical blend data. The data exists - it needs extraction and formalisation into a queryable recipe structure.
Manual document distribution
PP3 + PP4 - affects invoicing, packing lists, and shipping documentation

Sales invoices, packing lists, and shipping documents are all manually assembled as PDFs and emailed to customers. Bale weights are re-entered by hand from warehouse information to create packing lists - double handling of data that already exists digitally.

Impact
Duplication of effort - bale weights entered twice (PP3)
Risk of transcription errors in packing lists
No audit trail for document distribution
No automated document assembly across invoice, packing list, and shipping docs
Resolution path
Automated document generation pulling bale weights directly from warehouse data. Single-click invoice and packing list creation with email distribution and audit trail.
Processor visibility gap
PP5 - affects production planning and customer delivery commitments

Standard Wool has no visibility into Chadwicks' production capacity or scheduling. Capacity is negotiated weekly through verbal discussions. Standard Wool is ~90% of Chadwicks' volume but can only plan 5-10 days ahead when ideally they need 2-3 weeks forward visibility.

Impact
Can't reliably commit to customer delivery dates
No digital record of capacity allocation or scheduling
Blend prioritisation decisions made reactively rather than strategically
Resolution path
Shared digital capacity view between Standard Wool and Chadwicks. Given Standard Wool is 90% of volume, this is a reasonable integration to build.
Stock allocation timing mismatch
PP6 - affects blend planning and system accuracy

WTS requires stock to be formally received before it can be allocated to blends. But traders need to plan blends and allocate wool before it physically arrives. The workaround is either allocating outside the system and reconciling later, or forcing a receipt on paper before the wool arrives.

Impact
System state doesn't reflect actual trading intent
Workarounds create data quality issues
Receipting wool on paper before arrival creates phantom stock
Resolution path
Support provisional allocation against in-transit stock, with reconciliation on physical receipt. The system should match how traders actually work.
Cross-system lot identity
Structural - affects traceability from purchase to sale across all systems

The trading system (JHP), warehouse system (DEWSDB0), and scouring system (dbScour) each use different lot identification schemes. JHP uses H/M/SS prefixes. The warehouse uses C/T/A prefixes with cross-referencing via a CustomerRef field. Only 1 SS lot exists in the warehouse system.

Impact
Full lot traceability requires manual cross-referencing between systems
C-prefix mapping to SS lots through CustomerRef is fragile and undocumented
~51% of warehouse lots use prefixes not present in the trading system
Resolution path
Unified lot identity across all systems, or at minimum a documented and automated cross-reference table. The data model work addresses this at the design level.