Fix Manual Working-Capital Assumptions with Claude-Powered Forecasting
Finance teams still rely on manual DSO, DPO and inventory assumptions that barely change from quarter to quarter. This breaks the link between real payment behavior and cash forecasting, and undermines liquidity planning. This guide shows how to use Claude to turn raw ERP and treasury data into dynamic, data-driven working-capital assumptions and more reliable cash forecasts.
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The Challenge: Manual Working-Capital Assumptions
Most finance teams still build their cash forecasts on static, manual assumptions for DSO, DPO and inventory. A few parameters get set in a spreadsheet at budget time, maybe refreshed once per quarter, and then reused across cost centers and business units. Meanwhile, customer payment behavior, supplier terms and logistics constraints are changing weekly. The result: your working-capital drivers are frozen in time while your cash position moves in real life.
Traditional approaches were designed for a world of limited data and long planning cycles. Finance analysts aggregate history in Excel, calculate average DSO and DPO over 12 months, then manually tweak for seasonality or one-off events. This is slow, error-prone and blind to emerging patterns such as a new customer cohort paying later, a deteriorating collection performance in one region, or suppliers quietly tightening payment terms. Even if your ERP and treasury systems contain rich detail, that information rarely flows into the actual forecast logic.
The business impact is substantial. Systematic over- or underestimation of future cash leads to weak liquidity planning, unnecessary credit lines, suboptimal investment timing and a higher risk of shortfalls. You either sit on excess cash "just in case" or scramble to plug unexpected gaps with expensive short-term financing. Management loses confidence in the forecast, and finance loses the strategic seat at the table, because stakeholders know the model cannot capture reality at the speed the business moves.
This challenge is real, but it is solvable. Modern AI tools like Claude can process granular ERP exports, detect subtle shifts in payment and inventory patterns, and continuously update your DSO/DPO and inventory assumptions. At Reruption, we have hands-on experience building AI-first planning and analytics workflows that unlock the value of existing data without ripping out your core systems. In the sections below, you will find practical guidance to move from manual, static assumptions to dynamic, data-driven working-capital forecasting with Claude.
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Our Assessment
A strategic assessment of the challenge and high-level tips how to tackle it.
From Reruption's perspective, the core opportunity is to shift from opinion-based assumptions to data-driven working-capital drivers that update as your business evolves. We have implemented AI solutions in finance and adjacent data-heavy functions, and see Claude as a powerful layer on top of your ERP and treasury systems: it can digest large exports, surface hidden patterns in DSO, DPO and inventory, and help finance teams design more intelligent forecasting templates without waiting for a full system upgrade.
Treat Working Capital as a Dynamic System, Not a Static Parameter
Before deploying Claude, it is crucial to change how your team thinks about DSO, DPO and inventory. These are not fixed parameters to be set once a year; they are dynamic outcomes of behavior across sales, collections, procurement, logistics and operations. A strategic use of Claude starts with this mental shift: you are building a living model that reacts to real-world signals, not a one-off spreadsheet tweak.
At the strategic level, this means defining clear ownership: who is responsible for interpreting changes in AI-derived working-capital metrics and translating them into decisions on credit terms, collection strategies, or supplier negotiations? Without that governance, even the best insights from Claude will sit unused. Align executive stakeholders early that working-capital optimization with AI is a cross-functional initiative, not just a finance experiment.
Start with High-Impact Segments, Not the Entire Ledger
A common mistake is to aim for a full, AI-enhanced working-capital model across all customers, suppliers and SKUs from day one. Strategically, it is far more effective to identify a few high-impact segments where manual assumptions are clearly wrong or highly uncertain: for example, new customer cohorts, a specific region with rising overdues, or suppliers with volatile delivery performance.
Use Claude first on these segments by feeding it detailed transaction histories, payment terms and aging reports. This creates quick wins and tangible improvements in cash forecasting accuracy while limiting change management risk. Once stakeholders see the impact, you can expand coverage progressively, using lessons learned to refine your data model, prompts and validation process.
Design for Human-in-the-Loop, Not Full Automation
For something as sensitive as liquidity planning, full automation is rarely the right first step. Strategically, you want a human-in-the-loop AI setup where Claude proposes updated DSO/DPO and inventory drivers, and finance experts review, challenge and approve them before they feed into official forecasts.
This approach reduces risk and builds trust. Analysts remain accountable for the numbers but are augmented by Claude's ability to spot trends across millions of rows of data. Over time, as your team gains confidence and validates AI outputs against actual cash behavior, you can selectively increase the level of automation for low-risk segments or short-term horizons.
Invest in Data Readiness and Conceptual Clarity
Claude is powerful, but it cannot fix poor data definitions or conceptual confusion. Strategically, you need to clarify how your organization defines DSO, DPO and inventory turns for forecasting purposes. Are you using invoice date or due date? How do you treat disputed invoices, credit notes or consignment stock? These choices materially impact the AI's outputs.
Before scaling, run a data readiness and definition workshop between finance, controlling, IT and data teams. At Reruption, we often see that half the forecasting "error" comes from inconsistent logic rather than complex algorithms. Once the key concepts and data fields are harmonized, Claude can reliably calculate and explain working-capital drivers that everyone understands.
Plan Governance and Risk Controls from Day One
Introducing AI into liquidity and working-capital planning raises legitimate risk questions: what if the model misreads a temporary spike in overdue receivables, or overreacts to one-off supply issues? Strategically, you should design governance and controls alongside the AI implementation, not as an afterthought.
Define thresholds for when Claude's suggested changes in DSO/DPO or inventory assumptions trigger review, escalation, or simulation of alternative scenarios. Establish a cadence (e.g., monthly) where finance reviews model performance versus realized cash flows. This structured oversight ensures that AI-enhanced forecasting becomes a controlled, auditable process that can stand up to internal audit and external stakeholders, rather than an opaque black box.
Used strategically, Claude turns manual working-capital assumptions into responsive, data-driven drivers that reflect real customer payments, supplier behavior and inventory dynamics. The key is not just feeding data into a model, but designing governance, scope and human-in-the-loop review so finance can confidently act on the insights. Reruption combines deep AI engineering with a hands-on, Co-Preneur mindset to help you move from static spreadsheets to living cash forecasts that your CFO can trust; if you want to explore what this would look like in your environment, we are happy to discuss a concrete, scoped implementation rather than theory.
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Real-World Case Studies
From Banking to Agriculture: Learn how companies successfully use Claude.
Best Practices
Successful implementations follow proven patterns. Have a look at our tactical advice to get started.
Use Claude to Derive Segmented DSO and DPO from Raw ERP Exports
Start by replacing single, global DSO/DPO assumptions with segmented, data-driven drivers. Export detailed invoice data from your ERP: invoice date, due date, payment date, customer or supplier ID, region, business unit, payment terms and flags for disputes or credit notes. Feed this data into Claude in manageable batches (e.g., by region or business unit).
Ask Claude to calculate realized DSO and DPO for different segments and time windows, and to highlight where recent behavior deviates from your current planning assumptions. Use targeted prompts to capture both the numbers and the narrative behind them.
Example prompt for Claude:
You are a senior finance analyst.
I will provide you with invoice-level data for the last 18 months.
Columns include: invoice_id, customer_id, region, business_unit,
invoice_date, due_date, payment_date, amount, payment_terms,
credit_note_flag, disputed_flag.
Tasks:
1) Calculate realized DSO by month, region and business_unit
for the last 12 months.
2) Compare these values to the following planning assumptions:
- Global DSO: 42 days
3) Identify segments where realized DSO deviates by more than
+/- 5 days from our assumption.
4) Provide a concise narrative on possible reasons based on
patterns in the data (e.g. specific customer cohorts, terms).
5) Suggest updated DSO planning values for each segment.Expected outcome: instead of one DSO number, you get a table of segment-specific drivers plus explanations that finance can challenge and refine before updating the forecast model.
Build a Claude-Assisted Working-Capital Forecasting Template
Once you have better drivers, embed them into a repeatable forecasting template. Structure your cash forecast spreadsheet or planning tool so that DSO, DPO and inventory turns are clearly parameterized by segment (e.g., region, customer tier, product category). Then use Claude to generate and update these parameters on a recurring basis.
Each forecasting cycle, export the latest data, paste key aggregates into Claude, and ask it to produce a concise parameter sheet ready for import back into your planning template.
Example prompt for Claude:
You are helping me prepare updated working-capital drivers
for our rolling 13-week cash forecast template.
Input data below: [paste summarized tables, or link description]
Please:
1) Propose DSO, DPO and inventory-days assumptions by region
and business_unit for the next quarter.
2) Present them in a clean table with columns:
region, business_unit, DSO_days, DPO_days, inventory_days.
3) Highlight where your proposed values differ more than
+/- 3 days from last quarter's planning values
(which I'll provide below).
4) Add 3-5 bullet points of commentary explaining the main
changes driving your recommendations.Expected outcome: a ready-to-use parameter table that plugs into your cash forecast model, plus commentary you can include in management reporting.
Run Sensitivity Analyses on Working-Capital Levers with Claude
Manual sensitivity analysis is tedious, so it rarely gets done beyond a few coarse scenarios. Claude can automate this by quickly simulating cash impact from changes in DSO, DPO or inventory turns across large transaction sets. Prepare a simple model where cash impact is a function of these drivers, then ask Claude to generate and summarize scenarios.
Use prompts that focus on decisions, not just numbers. For instance, simulate the effect of tightening customer payment terms for a specific segment, or extending DPO with a supplier group, and have Claude translate this into weekly or monthly cash deltas.
Example prompt for Claude:
You are a treasury and working-capital specialist.
We have the following baseline:
- Current revenue and cost run-rate by month for the next 6 months
- Current assumptions: DSO = 45 days, DPO = 35 days, inventory_days = 50
1) Build a simple scenario model to estimate monthly cash position
under these baseline assumptions.
2) Create 3 scenarios:
A) DSO improves by 5 days over the next 3 months
B) DPO extends by 7 days starting next month
C) Inventory_days reduce gradually from 50 to 40 over 4 months
3) Estimate the incremental cash impact of each scenario by month
vs. baseline.
4) Summarize findings in a short management-ready narrative
(max 300 words).Expected outcome: a clear view of which levers matter most, with concrete euro impacts per month that support prioritization of working-capital initiatives.
Use Claude to Detect Emerging Anomalies in Payment Behavior
Beyond averages, Claude can help you monitor for early warning signs in customer and supplier behavior that your standard DSO/DPO metrics might miss. Regularly feed it aged receivables and payables reports, highlighted overdue statuses, and any changes to payment terms.
Ask Claude to detect anomalous patterns: customers whose payment behavior has recently deteriorated, clusters of invoices repeatedly paid just after due date, or suppliers who systematically change agreed terms. Combine this with narrative explanations and suggested actions for collections or procurement.
Example prompt for Claude:
You are an accounts receivable risk analyst.
Here is our latest aged receivables report with
customer_id, region, terms, amount, days_past_due,
payment_history_summary.
Please:
1) Identify customers whose payment behavior worsened
over the last 3 months vs. prior 9 months.
2) Flag those with a material impact (> 200k EUR exposure).
3) Suggest how this should influence our short-term DSO
planning assumptions.
4) Provide 5 practical recommendations for our collections
team to reduce risk in the next 4-8 weeks.Expected outcome: actionable lists of risk hotspots and guidance on how to adjust near-term working-capital assumptions and collection priorities.
Generate Clear Liquidity Narratives for Management and Banks
Numbers alone do not build confidence; stakeholders need to understand why working-capital assumptions changed. Claude is strong at structuring complex quantitative inputs into clear narratives. After you finalize your updated DSO/DPO and inventory drivers, use Claude to draft executive summaries, board slides, and explanations for lenders.
Provide Claude with the key parameter changes, main drivers (e.g., customer cohort shifts, term renegotiations, process improvements), and actuals vs forecast comparisons. Ask it to write concise, non-technical explanations that connect these drivers to overall liquidity and risk.
Example prompt for Claude:
You are supporting a CFO in preparing a liquidity update
for the board and our main bank.
Inputs:
- Table of old vs new DSO, DPO, inventory assumptions
- Explanation of key operational drivers behind changes
- Chart of forecast vs actual cash balance last 6 months
Please draft:
1) A 1-page narrative explaining:
- What changed in our working-capital assumptions
- Why it changed (behavior, terms, processes)
- How this affects our 6-12 month liquidity profile
2) A bullet-point summary (max 10 bullets) suitable for
a slide deck, focusing on risk and mitigation.Expected outcome: consistent, understandable communication that increases trust in your cash forecasting process among executives, auditors and financing partners.
Expected Outcomes and Metrics to Track
When implemented thoughtfully, these practices typically deliver measurable improvements. Finance teams can expect a 5–15 day reduction in DSO or DPO forecast error for key segments within the first 3–6 months, and a noticeable increase in forecast accuracy for 4–13 week liquidity horizons. Manual effort for updating working-capital assumptions can often be reduced by 30–50%, freeing analysts to focus on decisions rather than data wrangling.
Track metrics such as forecast vs actual cash variance, DSO/DPO and inventory-days prediction error by segment, frequency and speed of assumption updates, and time spent per planning cycle. These KPIs make it clear whether Claude is genuinely strengthening your working-capital and liquidity planning, and where further tuning is needed.
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Frequently Asked Questions
Claude can process detailed exports from your ERP and treasury systems to calculate realized DSO, DPO and inventory turns by customer, supplier, region, business unit or product category. Instead of relying on one global parameter set once a year, you can ask Claude to derive segment-specific assumptions from recent behavior and highlight where they deviate from your current planning values.
Beyond the numbers, Claude explains the drivers behind changes (e.g., certain customer cohorts paying later, suppliers tightening terms, shifts in inventory mix). Finance teams can use these insights to update cash forecasting templates, run targeted scenarios and communicate a clear rationale for working-capital assumptions to management and financing partners.
You do not need a large data science team to benefit from Claude. The critical ingredients are: (1) a finance team that understands working-capital mechanics and can frame the right questions, (2) access to relevant data from ERP, treasury and inventory systems, and (3) someone who can prepare clean exports and basic aggregations (often a controller or BI analyst).
Claude is prompt-driven, so most of the work is in designing robust workflows and prompts rather than building complex models from scratch. Reruption typically helps clients define data requirements, design prompt templates, and set up repeatable processes so that finance analysts can operate the solution day-to-day without depending on IT for every change.
For a focused use case like replacing manual DSO/DPO assumptions, you can usually see first usable results within a few weeks. In the first 1–2 weeks, we define scope, clarify metrics, and prepare initial data exports. Within the next 1–2 weeks, Claude can generate initial segmented assumptions, scenario analyses and narratives, which your finance team validates against actuals.
Reaching a stable, repeatable process that integrates into your official cash forecasting cycle typically takes 6–10 weeks, including governance, documentation and training. The goal is to move quickly from one-off analysis to a recurring, auditable workflow that finance can run each planning cycle.
The ROI comes from three main sources: (1) better liquidity planning that reduces the need for expensive short-term funding and excess cash buffers, (2) improved working-capital performance through targeted DSO/DPO and inventory initiatives, and (3) time savings for analysts who spend less effort on manual data crunching. Even small improvements in average DSO or inventory days can free up significant cash on the balance sheet.
Claude itself is relatively cost-effective compared to traditional enterprise software, as you pay for usage rather than large upfront licenses. The main investment is in designing and implementing the workflows around it. Reruption's structured AI PoC offering at 9,900€ is designed to validate technical feasibility and business impact quickly, before you commit to larger rollouts. From there, we help you scale in a way that keeps cost per use case transparent and under control.
Reruption supports clients end-to-end with a Co-Preneur approach: we work alongside your finance, controlling and IT teams as if we were building the solution for our own P&L. We typically start with our 9,900€ AI PoC, where we scope a concrete use case (e.g. dynamic DSO/DPO assumptions for a specific business unit), assess data readiness, build a Claude-powered prototype, and measure forecasting and productivity impact.
Once the PoC proves value, we help you industrialize the solution: designing robust prompts and templates, integrating with your existing ERP/BI stack, setting up governance and controls, and training your finance team to operate and evolve the workflow. Because we combine AI engineering depth with practical finance understanding, we focus on shipping working tools that improve your liquidity and working-capital planning, not just slide decks.
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