What is the purpose of forecasting?

1. To Plan for long term
   • Types of products and services to offer.
   • What facilities and equipment to have.
   • Where to locate.

2. To Plan for short term
   • Budgeting -- What is the top line in the budget???
   • Inventory levels
   • Workforce levels
   • Purchasing
   • Production
   • Scheduling

What do we want to forecast?

Just about anything.

Number of customers

Unit Sales

Dollar Sales

Market Share (you should know what this is by now.)

Rate of adoption

Educated guessing

Do not be lulled into thinking that just because this method has worked pretty well for a few periods that it will continue to work well. It may assume a state of the world that hasn't changed during the time that it has worked, but which will not be reflected in forecasts if it does change.

A second thing to remember about forecasts is that all forecasts should be subjected to a sensitivity analysis where changes in underlying assumptions are made and the resulting changes in forecasts compared.

Note that Sales forecasts are the base number used for budgeting by accounting. Accountants should remember that these things are at best good guesses, and should do sensitivity analysis on their budgets based on different actual sales results.

Educated guessing -- Heuristic methods

For total demand

Total $ales = #buyers X quan X price

Chain ratio method (adjusting percent)

For areas (if whole country, total)

Market Buildup (often called SIC)

Examples of NAISC (SIC)Codes   

Product Names: Apparel and similar products(230000);
Men's and boys' jeans(232549);
Women's, misses' and juniors' jeans and pants(233952);
Apparel and accessory stores(560000)

NAICS Codes

Market Factor Index Methods (e.g. Buying Power Index or BPI from Sales and Marketing Management Magazine)

Chain ratio method a heuristic

Example of Chain ratio method

(remember it is only as good as its links)

Estimating the number of

potential female romantic partners for a Freshmen male

last updated Fall 2001

12,409 students at WWU

x .56 female

leaves 6,969 females

x .8 not married

leaves 5,575 females eligible for marriage

x .5 already attached at any given time

leaves

2,788 potential romantic partners

x .96 potentially interested

leaves

2,676 potential romantic partners

But!!!

Only .01 of those are actually interested in freshmen males

leaves 26 or 27 Western women actually interested in freshmen males

(this might call for a sales philosophy --- marketing)

Market factor index methods

BPI Buying Power Index

BPI is a Percentage of national buying power =

.5 x percent nat. effective buying income in the area
+

.3 x percent nat. retail sales in the area
+

.2 x percent nat. population in the area

Caveats:

applies only to retail operations and mass market consumer goods

assumes potential sales are a function of purchasing power

assumes that people purchase the goods in question in the same ratio everywhere

Subjective methods

Jury of Experts

Leading indicators

Salesforce Composite (popular)

   - salesmen have other motives

   - may not know

Buyer intention

   - intentions don't always lead to sales

Leading Indicators
-- changes in these happen first in a dynamic system

• prices of 500 common stocks
• new orders of durable goods
• ratio of price to unit labor cost
• nonagricultural placements
• index of net business formations
• corporate profits after taxes
• new building permits (private)
• industrial materials prices
• average work week manufacturing
• change in manufacturing inventories
• contracts and orders for plant & eqpt
• change in consumer installment debt

An undervalued leading indicator is the Consumer Confidence Index. published monthly by the Conference Board

Leading Indicators can predict turns in a series.

Every issue of Business Week has a listing of leading indicators.

Objective (aka Quantitative) methods of Forecasting

Theory based multiple regression

fit a model with theoretically relevant variables (cross sectional data - one point in time)

Time series based methods

• Average or Constant Demand -- the baseline
• Trend -- up or down change in demand over time
• Seasonality -- variation based on season or day of week
• Cyclical Factors -- variation with long term business cycle
• Irregular Variations -- non-recurring variations caused by identifiable external events
• Random Variations -- non-recurring variations of unknown origin

Correction for seasonality

Nearly all businesses are seasonal.

Therefore, All forecasts should be made with adjustments for seasonality. There is more than one way to approach seasonality, but the most robust is outlined below.

1) Deseasonalize the data.

• Makes the lumps smooth out
• Preserves any trend changes
• Each de-seasonalized point is the ratio (actual data / index)
•    where the index is (average of this particular period / grand average of all relevant periods)

2) Make a forecast in deseasonalized terms

Choose one or more methods.

3) Reseasonalize the forecast to an actual expected number given the period.

Naive methods

    same value as last time

    same change as last change ( Naive or simple Trend)

    change can be either a percentage change, or an absolute one

Moving averages

An average of a fixed number of prior periods.
To forecast more than one period into the future, use forecasts for interim periods as data. [n.b. always use the deseasonalized data until the final reseasonalizing step.]

Exponential smoothing -- weighted moving averages (Operations Management 360)

Box-Jenkins -- input output models

Time-series (econometric) multiple regression. (Econ 309)

---

Factors to consider in choosing forecasting method.

-time available

-cost

-pattern of the data

-accuracy

-ability to predict turning points

-ease of intrepretation

Remember:

1. Always use more than one forecasting method.
   
2. Always do a sensitivity analysis that tests the effects of changes in the major assumptions of your method.
3. Try to forecast within a range so contingency plans can be made.