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Stealing From The Bottom Line. Professor Bob Berry (NUBS)

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1 Stealing From The Bottom Line. Professor Bob Berry (NUBS)

2 What is “the bottom line”? 1.The phrase has come into common usage from accounting. 2.Profit After Interest and Tax (PAIT) might be a candidate. 3.An alternative, more general, current interpretation is “the most important thing”. 4.(A Jamie Oliver inspired example.) 5.So what is the true “bottom line”. Cash! 6.It is cash that pays the bills.

3 Cash and cash flow. 1.When thinking about cash “Bathtub accounting” is a useful starting point: There is a level of water in the bath, a stock of water. There is a stream of water coming into the bath from the tap, an inflow. There is a stream of water leaving the bath through the plug hole, an outflow. 2.For “water” read cash: Cash(t)+CashInflow -CashOutflow = Cash(t+1)

4 Profit and cash flow. 1.Profit flows into the business. It is revenue less costs, a net flow, inflow less outflow. 2.In the same way we can define a net cash flow into the business. 3.PAIT less ∆Net Working Capital less Net Investment less Dividends plus Net New Equity plus Net New Debt equals ∆Stock of Cash. 4.(Here the definition of net working capital excludes cash.) 5.The rules of basic arithmetic allow you to rearrange this equation in a variety of ways.

5 Forecasting cash flow. 1.There seems widespread agreement that businesses need to forecast cash flow. 2.The logic is straightforward. Make sure you have (a stock of) cash available to pay your bills. 3.Where can cash come from? It may be generated internally or come from additional equity or debt. 4.The basic forecasting task is usually defined as forecasting internally generated net cash flow to allow impact on the stock of cash to be understood. 5.This allows identification of when and how much new external cash will have to be raised.

6 What about commercial cash flow forecasting systems ? 1.Many are no more than formatted spreadsheets into which you can enter your forecasts. 2.If you have basic spreadsheet skills and a copy of e.g. Excel, you don’t need to buy a commercial system. 3. Some systems add statistical forecasting tools. These can be useful if you know what you are doing, but most business forecasts are subjective. 4.An objective forecast uses a system that can be written down and replicated, 5.A subjective forecast is produced by a system “inside your head”, unwritten and not duplicable.

7 Be clear…. 1.Whatever the forecasting method, ALL forecasts are the consequences of ASSUMPTIONS. 2.Examples: If I get that new customer than sales will increase by £10,000. If next period is like this period then …. If I make that amount of profit next year I’ll have to pay a specific amount of tax. 3.As the last item makes clear some forecasts depend on other forecasts. These “automatic consequence” forecasts are easy. 4.What about the others? They can be difficult!

8 Start by deciding what to forecast? 1.What are the important items? 2.What is the right level of detail? Total sales (or sales by customer). Time unit (days, weeks, months, years) Forecast horizon (1 week or 15 years). 3.A forecast isn’t an end in itself. A forecast is information for a manager, and if the manager can’t make use of it, then what use is it? 4.So, what should a forecast look like? That depends on how we view the future!

9 Certainty, Risk, and Uncertainty, 1.Only one thing that can happen and it will happen. (certainty) 2.Several things can happen. Outcomes and probabilities can be listed. Only one thing will happen. (risk) 3.Several things can happen. Outcomes and/or their probabilities can’t be listed. Only one thing will happen. (uncertainty) 4.What is your forecast if: Outcome 5 with Probability 0.3. Outcome 20 with Probability 0.4. Outcome 25 with Probability 0.3?

10 Forecasts and Expectations. 1.A very common forecast is “20, the thing most likely to happen”. 2.BUT “not 20” is more likely to happen than “20”. 3.A statistician/economist/ etc is likely to say: 5x x x0.3 = 17 is my forecast. This is the “expected value”. You can think of this as a “hedging your bets” type of forecast. 4.Be aware that the word “forecast” can mean different things to different people. 5.Forecasts produced under risk or uncertainty aren’t usefully represented by a single number forecast.

11 “A good forecast tells me exactly what will happen and exactly when.” 1.You should be so unlucky! Wouldn’t you prefer, “This disaster will happen unless you do something.” 2.Risk and uncertainty suggest a good forecast can be imprecise, but still useful: This might happen unless you do something about it. This might happen and you could put yourself in a position to take advantage of it. 3.Forecasting identifies what might happen which is always good.

12 Emphasise the range of possible outcomes 1.Explore the future using sensitivity analysis or scenario analysis. 2.BUT don’t make this a mechanical exercise. What are the interesting assumptions to explore. 3.The worst thing that has happened isn’t the worst thing that can happen. 4.Develop contingency plans. 5.BUT look for pattern in history and forecasts as well. 6.If the pattern is bad try to understand why it exists and see if something can be done about it.

13 Case 1: Travel Costs. 1.A computer support/repair operation operates an “on site, within a specified time” service supported by mobile engineers in vans well stocked with spares. Various cash flow items are forecast on a monthly basis for a year and on an annual basis for two more years. The monthly travel cost bill has exhibited a highly seasonal pattern with one month significantly worse than all others. 2.This makes it easy to forecast next year’s travel cost, but why is that month so bad. Can anything be done? 3.Management action: Shoot the auditors.

14 Case 2: Borrowing Costs. 1.The Treasury function in a large multi divisional business had to ensure that divisional shortfalls in cash were met. Demands for cash tended to be “last minute” forcing Treasury staff to use short term, expensive, financing sources. 2.The initial definition of the forecasting task was to produce a forecast with a lead time sufficient to allow Treasury staff to make a more considered choice of financing options. 3.Investigation showed that some divisions were always asking for cash and others never asked. 4.Management solution: Introduce a charging system and change bonus arrangements.

15 Case 3: Demand Levels. 1.A large producer of processed turkey was experiencing difficulty in forecasting demand level, leading to periodically unhappy customers (supermarkets etc) and periodically high levels of returned products. 2.The “owner” of the business, a well known public figure, was widely described as “the man who made turkey an all the year round food”. 3.Investigation showed a highly seasonal pattern of under supply in December and oversupply in January. (This is a very simplified analysis.) 4.Management solution: Hire a forecasting expert.

16 Improving Forecasts. 1.So, making good use of forecasts is critically important. BUT improving forecasts can’t hurt.) 2.Forecasts, even when they involve an objective technique, still involve a forecaster. In your business the forecaster will often be you. 3.Behavioural finance says, you are: Biased (optimistic, overconfident, tend to ignore contradictory info, under the illusion you are in control) Use poor heuristics (rules of thumb). Suffer from framing effects. 4.You are in danger of seeing the future as less risky than it is. Your forecasts need to be challenged (outsider view).

17 Case 4: Some business I didn’t get! 1.Potential client was the forecasting group within a major bank. 2.Task was forecasting a particular financial series. 3.My approach was, next time you circulate your forecast attach a little questionnaire: Do you make use of this forecast? If not, is it because:  it isn’t important.  It isn’t detailed enough.  It arrives too late to be useful. 4.Theirs was, use a more sophisticated method.

18 Conclusions. 1.Forecasting cash flow is important. 2.It often helps to forecast underpinnings to generate understanding. 3.Forecasts generally identify what might happen, not what will happen. 4.Forecasts should be designed to support management action. 5.They need to be appropriate (level of disaggregation, time unit) and timely. 6.They need to be challenged and survive to be useful.


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