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Cash Forecasting - Current Solutions & Workarounds

Tools & Systems

Informal Trust Assessment

  • What it is: Gut feel and experience-based trust in forecasts
  • How they use it: Treasury teams develop intuition about when forecasts are reliable
  • Limitations: Not explainable to stakeholders, not systematic
  • Used by: ON

Peer Validation

  • What it is: Talk to other treasury teams (e.g., HelloFresh) about forecast reliability
  • How they use it: Shared experience builds confidence when no formal metrics exist
  • Limitations: Anecdotal, not data-driven
  • Source: ON (2025-11-11)

Conservative Assumptions

  • What it is: Apply manual adjustments or use conservative scenarios when trust is low
  • How they use it: Can't fully rely on model output without understanding it
  • Source: ON (2025-11-11)

Excel

  • What it is: Spreadsheet software
  • How they use it: Primary forecasting tool, manual aggregation of entity data
  • Limitations: Manual, no real-time updates, difficult to maintain across entities
  • Used by: Euroports

Cash Analytics

  • What it is: Treasury Management System
  • How they use it: Cash visibility, but manual input only (no ERP integration)
  • Limitations: Not designed for forecasting, limited analytical capabilities
  • Used by: Euroports

ERP Systems

  • What it is: Enterprise resource planning systems
  • How they use it: Source of AR/AP data for forecast inputs
  • Limitations: Only updated monthly, not useful for real-time forecasting
  • Used by: Euroports

SAP

  • What it is: Enterprise resource planning system
  • How they use it: Common ERP across ~40 entities; cash management module built in 2016; source of AP/AR data
  • Limitations: Not designed for treasury forecasting; need to extract data by due date range
  • Key insight: Standardization seen as critical - "I've seen big companies try to do forecasting with six or seven different ERPs and I don't think it really works so well"
  • Used by: Volvo Cars

Quantum

  • What it is: Treasury Management System
  • How they use it: Entities upload Excel forecasts; central Treasury uses for cash positioning and weekly/monthly forecasts
  • Limitations: Relies on manual Excel uploads; overwriting forecasts loses variance history
  • Used by: Volvo Cars

TIS Payments + Integrity TMS

  • What it is: Payment hub (TIS) + Treasury Management System (Integrity), connected via SFTP
  • How they use it: TIS for bank connectivity (fast setup, ~1 bank/month); Integrity for target balancing and cash pooling logic
  • Limitations: Requires manual payment run requests; not fully automated zero-balancing (since different banks)
  • Key insight: TMS-based pooling as interim solution when traditional bank cash pool not feasible
  • Used by: Live Events (2025-04-01)

Kyriba

  • What it is: Treasury Management System with cash forecasting module
  • How they use it: Open AR + Open AP + manual forecasts (salaries, taxes, significant payments) = forecast
  • Limitations:
  • "Nothing super smart, nothing of AI" - just open items and manual inputs
  • No category-level forecast views (can't see development of salaries, Capex separately)
  • No variance analysis capability ("Variance is not there. No comparison.")
  • Cash pool accounts show misleading negative balances
  • Forecasting module can't import external data - cash flows must be generated within Kyriba (IHG)
  • Cash Position Worksheets limited: single currency only, not customizable, changes disappear (IHG)
  • Liquidity Planning module (can import) costs extra (IHG)
  • Module inconsistency: different capabilities in different modules (IHG)
  • Used by: ON, IHG

HighRadius

  • What it is: AR/Collections management platform
  • How they use it: Customer payment pattern recognition, "cash application" (matching payments to invoices)
  • Limitations:
  • Customer behavior data doesn't flow to cash forecasting
  • Knows customer patterns (e.g., "Foot Locker always pays on the 20th") but this intelligence is siloed
  • Used by: ON

Dynamics

  • What it is: Microsoft ERP system
  • How they use it: Source of open AR/AP data for forecasts
  • Limitations:
  • Timing issues: paid items still show as open until accounting closes them
  • Causes duplicate forecasts when items are paid but not yet settled
  • Used by: ON

Manual Workarounds

Entity-by-Entity Excel Collection

  • What they do: Collect forecasts from each entity via email/spreadsheets, manually consolidate
  • Why: No centralized system supports decentralized input with group consolidation
  • Sources: Euroports (2025-10-27), Volvo Cars (2025-03-27), IHG (2025-04-07) - India service center collates spreadsheets

Offshore Service Center for Forecast Collation

  • What they do: Shared service center (e.g., India) sends spreadsheets of subsidiary cash flows; Treasury dealer collates into master file
  • Why: Centralized Treasury team doesn't have direct access to subsidiary systems; need human verification
  • Limitations: Manual, prone to errors (payroll runs missed), time-consuming
  • Source: IHG (2025-04-07) - "Sometimes the guys in India will just miss, they'll just forget to add something... even like a payroll run has been missed off"

Day Sweeps for Cash Pooling

  • What they do: Automatic daily sweeps; entities don't hold cash; central Treasury covers negative balances
  • Why: Centralized cash management; entities focus on forecasting not funding
  • Source: Volvo Cars (2025-03-27)

Annual Budget as Forecast Baseline

  • What they do: Use annual budget as starting point, make manual adjustments
  • Why: Only 1-2 formal forecast cycles per year
  • Source: Euroports (2025-10-27)

Kyriba + Google Sheets Split

  • What they do: Use Kyriba for actuals only; all forecasting in Google Sheets with quarterly version saves
  • Why: Kyriba lacks forecasting capabilities; need to track forecast accuracy over time
  • Source: Sonder (2024-10-03) - "We don't do any forecasting in Kyriba really"

Quarterly Forecast Version Saving

  • What they do: Save forecast versions each quarter; compare original forecasts to actuals over time
  • Why: Need to measure forecast accuracy and learn from variances
  • Source: Sonder (2024-10-03) - "We can go back and say, well, how accurate were we forecasting the end of October at the start of October?"

Starting Balance + ERP + Manual Inputs

  • What they do: Simple forecast formula: starting balance + AP data from ERP + manual inputs (tax, salary estimates from stakeholders)
  • Why: Foundation of trustworthy short-term forecast based on booked data
  • Source: ON (2024-11-19)

Conservative Buffer Calculation

  • What they do: Calculate buffer based on historical variance; more unforeseen events = bigger buffer; "be pessimistic about cash-ins"
  • Why: You have to pay on time but won't receive on time; need cushion for forecast uncertainty
  • Source: ON (2024-11-19)

13-Week Rolling Daily Forecast

  • What they do: Maintain rolling 13-week forecast updated daily in ~15 minutes; actuals replace forecasts with variance analysis
  • Why: Standard process for daily cash management; balance efficiency with accuracy
  • Source: Sonder (2024-11-19)

Multi-Channel Stakeholder Inputs

  • What they do: Different touchpoints per team - calls with real estate, Google Sheets with tax, automated reports from payroll
  • Why: Different teams have different data availability and reliability levels; adapt collection method to each
  • Source: Sonder (2024-11-19)

Conservative Inflow Forecasting

  • What they do: Forecast lower on collections; "if you sign 8, put 6" - upside surprises are better than shortfalls
  • Why: Cash shortfalls cause operational problems; excess cash is easily deployed
  • Source: Sonder (2024-11-19) - "For cash flows, if you want to be conservative on the inflow... You'd rather miss a little too low than too high"

Industry Competitive Landscape (Treasury Dragons, 2025-12-09)

TIS

  • Positioning: Enterprise multinationals with geographic/entity complexity
  • Approach: Data integration first ("walk, run, fly" implementation), Working Capital Insights module (DSO/DPO)
  • AI use: Customer payment pattern recognition, natural language prompts for configuration, agentic AI planned for 2026
  • Differentiator: Strong entity collaboration workflows, two-way communication with subsidiaries

Cobase

  • Positioning: Bank connectivity first (origin as multibank payment platform)
  • Approach: Connect all banks and data sources → build treasury modules on top
  • AI use: Transaction categorization
  • Differentiator: Native multibank platform, workflow/approval flows for central treasury control

Panax

  • Positioning: AI native for mid-market (complex needs, lean teams)
  • Approach: Fast implementation (3-8 weeks), cleaning fragmented data
  • AI use: AI in every layer - data monitoring, categorization, ERP mapping, LLM-powered insights chat
  • Differentiator: Speed to value, built for lean teams without dedicated IT support