Cash Flow Forecasting: Creating a Solid Foundation

1. Cash Flow Forecasting: Creating a Solid Foundation Ken Parkinson Treasury Info. Services tisconsulting.com & tisbooks.com

2. Forecasting Setting • Investors (companies) pay [far] less attention to forecasting than they would if they borrowed. • Why is this usually the case? • So what to do if you’re an investor? • How is your cash forecast used or regarded? • By the sources of data • By management users • Does anyone seem to care?

3. Forecasting Variables • Business maturity: more mature, easier to forecast • Historical predictability: similar to previous • Cash flow decentralization: hard to get data, but … • Forecasting period: smaller ones - more accuracy • Accounting influence: the less, the better • Ease of data collection: harder to get – more errors • Org. level of source: too high, bad estimate

4. The Forecasting Cycle

5. Forecast Horizons • Daily – rolling 5-days; a necessary part • Weekly – rolling 4-6 weeks; often ignored • Monthly – rolling 12 months; a “staple” • Quarterly – rolling 4-6 quarters; necessary? • Annual – rolling 5 years; a different context

6. The Problem with Forecasting The actual nature of your cash flow forecasting “challenge” How your boss sees it – “Just push the button.” How do you reconcile this?

7. So Much Unhappiness! • 75-80% lf senior execs do not consider their cash flow forecasts reliable. • Why is this? • Exactly what do they dislike? • Do firms evaluate forecasting properly? • Or do they try quick fixes? • Or do they give up? • Why don’t many cash flow forecasting systems work?

8. Managing Expectations • Whose forecast is it? • Senior management? Sources? How about – YOURS? • What is in your forecast? • Compilation of data • Best guess • Worst case • What are you forecasting? • Cash position • Cash flow (and cash balances) • Why are you forecasting? • For senior management • For strategic planning, esp. financing

9. Managing Expectations • How often do you need to forecast? • Daily? Weekly? Monthly? Annually? • How often canyou forecast? • Different ways to forecast? • How much do you already know? • Financial transactions? • Large operational cash flow items? • Repetitive customer (i.e., “regulars”) payments • How much don’t you know? • Surprises • Variances • Do you rate the sources?

10. What Bad Forecasts Do to You • Create severe constraints on working capital. • Shorten investment maturities ( = lower yields) or causes investment liquidity risks (bad guesses). • Increase inefficient short-term borrowing • Overuse credit facilities. • Lines that are never used. • Create excess fund situations in local accounts. • Cause inefficient planning for working capital management and long-term financing. • Lost opportunities and/or more exposure to ST rate changes

11. What’s the Best Structure? • Should mirror organization’s cash flows • Centralized vs. decentralized • Can’t ignore potential data sources • Benefits of decentralized structure • Many data points reduce dependence on few • Isolate problems at operating levels • Possible benefit from offsetting errors • Benefits of centralized structure • Fewer points mean easier data gathering • Better control over data, submissions • Ignore local politics

12. Which Structure? • Top-down • Work from financials • What’s the “top” for daily-weekly-monthly? • Probably better for LT models; maybe monthly • May be statistical model • Bottom-up • Nuts + bolts; no statistical models • More [real] cash flow oriented • Perhaps easier to grasp and use

13. Top-Down & Bottom-Up Forecasts • Top Down: Start with sales • Receivables, payables, payroll, inventory, cash all can be related to sales • Use historical % of sales to project, based on a good sales forecast • Bottom Up: Start with detailed projections • Get estimates of components (receipts, disbursements, etc.) • Roll up into total forecast Which is better?

14. Forecasting Approaches • Top down – for medium-term (i.e., monthly and up) • Bottom up – for everything up to annual • Is it either-or … or both? • The case for either: simplified system; pick most accurate, cost-effective • The case for both – redundancy can catch errors • Forecasts should be ‘rolling’

15. Cash Flow Forecasting System Source: Cash Flow Forecasting: A Hands-on Approach

16. Integrate Your Forecasting • Shorter forecasts link to longer ones • Daily should reconcile with the 1st week of the weekly • Weekly should reconcile with 1st month of monthly • Monthly should reconcile with 1st year of annual

17. Types of Sources • People • Managers, et al. close to activity • Should be “experts” in expert judgment systems • Other company systems • Key advantage: Minimal human intervention • ERP systems • Transaction-oriented systems • Independent estimates • Financial models, etc.

18. Operating Flows Possibly many sources = hard to get Recurring payments means history useful New, identified flows Planned budget can give some idea Base levels may be relatively easy to estimate, reducing difficulty Financial Flows Reactive flows, depending on operating flows Often has lead time Fewer sources History can be guide, such as rollover levels LT planning can provide estimates Operating and Financial Flows Do you try to estimate each one separately? Why or why not?

19. History and Predictability • History can be lifesaver • Use in emergencies • Learn from it • Predict patterns from it • Some things are easy to predict • Payroll, taxes, major cash flows, debt levels • Some are not so easy • Payment timing, capex flows, inventory usage • You won’t be able to predict everything.

20. Whole > ∑Parts? Note!

21. Techniques • Don’t expect to use many “sophisticated” techniques. • Simple = better? • “Heavier” ones don’t work because … • Think this way • Short-term (up to 1 yr): data manipulation (management) • Longer-term (1 yr+): more ‘real’ forecasting • Most statistical forecasting techniques are tough. • Lots (and lots) of clean data • Predictability of at least one main factor (usually sales) • Regular “maintenance” and re-testing

22. Methods to Consider

23. Application of Distribution Method • Receivables or payables projection models • Use history to predict clearance factors • Influenced by Pareto’s 80/20 rule “clearance” factors applied AR or AP ageing Estimates by period

24. Analyzing Roll-over Rates • Track your liquidity “balances” regularly. • A spreadsheet will do. • Go back and find history if necessary. • Compute the average roll-over rates by month or most reliable period for ST investments and debt separately (if both present). • Look for changes in net position (indicators of cash flow movements. • Are they recurring? How predictable are they? • Look for the minimum and maximum. • Incorporate into your forecasting system and predict usage of lines or potential excess cash situations. • Track continuously. • Monitor and modify as necessary.

25. Roll-Over Rates max $M min min min max Time

26. Per cent of Sales %-of-sales Assumed “Freeze” Notes to compute new debt figure Projected financials

27. Master Maturity Schedule Details (actuals) Summaries (links) links

28. Variances • Measuring is not enough: You need reasons. • Typically measure latest estimate (day, week, month) • Should be best one • Why is this insufficient? • Measure several variances • Several intervals • Gives you a better picture • How to “fix” variances • Communications • Adjustments to current and/or future forecasts

29. Charting Variances Net cash flow for 4 mos. Note change in direction. $ Note shift here.

30. Variances for Intervals Net cash flows

31. Variance vs. History

32. Conclusions • Many items may lower your overall error rate, but … • How are you doing individually? • What do you do? • Once you receive forecasts from sources – the forecast becomes yours • Act accordingly • Watch out for statistical methods. • They are suited to LT forecasts. • You won't be able to predict everything. • But you want to be in the ball park. • And remember -- it’s just a forecast!

33. Any Questions? Thanks!