Question

1)In the absence of data history, what is the primary approach to forecasting?

2)Why are long term forecasts important and which level of management is most interested in long term forecasts?

3)Why are short term forecasts important and which level of management is most interested in short term forecasts?

4)Briefly define a micro forecast-

5)Briefly define a macro forecast-

6)Briefly define a point forecast-

7)Briefly define an interval forecast-

8)Briefly define a density forecast-

9)What is one of the chief difficulties in developing accurate forecasts of overall economic activity?

Answer 1

1. Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date.

Four primary approach of forcasting :

i. The Delphi Technique:

The RAND Corporation developed the Delphi Technique in the late 1960s. Using this technique, a group of field experts responds to a series of questionnaires. The experts are kept apart and unaware of each other.

The results of the first questionnaire are compiled, and a second questionnaire based on the results of the first is presented to the experts, who are then asked to reevaluate their responses to the first questionnaire. This questioning, compilation and re-questioning continues until the researchers have a narrow range of opinions.

ii. Scenario Writing to Generate Different Outcomes:

In scenario writing, the forecaster generates different outcomes based on different starting criteria. The decision-maker then decides on the most likely outcome from the numerous scenarios presented. Scenario writing typically yields best, worst and middle options.

Business startups often use scenario writing to generate financial projections covering best-, likely- and worst-case income and expense scenarios, since they don’t have an established track record to present to funders and potential partners.

iii. The Subjective Forecasting Approach :

Subjective forecasting allows forecasters to predict outcomes based on their subjective thoughts and feelings. Subjective forecasting uses brainstorming sessions to generate ideas and to solve problems casually, free from criticism and peer pressure. These sessions are often used when time constraints prohibit objective forecasts. Subjective forecasts are subject to biases and should be viewed skeptically by decision-makers.

iv. Time-Series Forecasting :

Time-series forecasting is a quantitative forecasting technique. It measures data gathered over time to identify trends. The data may be taken over any interval: hourly, daily, weekly, monthly, yearly or longer. Trend, cyclical, seasonal and irregular components make up the time series.

The trend component refers to the data's gradual shifting over time. It is often shown as an upward- or downward-sloping line to represent increasing or decreasing trends, respectively. Cyclical components lie above or below the trend line and repeat for a year or longer. The business cycle illustrates a cyclical component.

Seasonal components are similar to cyclicals in their repetitive nature, but they occur in one-year periods. The annual increase in gas prices during the summer driving season and the corresponding decrease during the winter months is an example of a seasonal event. Irregular components happen randomly and cannot be predicted.

2. Long-Term Forecasting Advantages :

i. Better Production Management :

Working with your sales department to create long-term demand forecasts helps improve your production processes in a variety of ways. If you foresee seasonal spikes or large orders from one or more customers coming, you can create inventory in advance or ask clients to spread their orders over a longer time period. You’ll also be able to ensure you have enough materials, supplies and labor, take steps to efficiently warehouse and ship orders and schedule maintenance around peak times. Make quarterly or annual demand forecasting a regular part of your management activities.

ii. Improved Cash Flow :

Knowing your expense needs and income potential far enough in advance lets you manage your cash and credit better, ensuring you have adequate capital to manage irregular cash flow. When you make a large sale or receive a big order without warning, you might not have enough money to pay suppliers or your workers to produce that order. Knowing your capital needs in advance lets you maintain or increase cash reserves and arrange for loans or additional credit.

3) The Important Short Term Benefit of the Rolling Forecast :

Looking at long-term goals for a business is important, but the short-term goals and decisions can determine whether a small business succeeds or fails.

The rolling forecast puts a focus on these short-term goals. It gives an honest, sober assessment of where the business actually stands and where it’s going. Once a rolling forecast is implemented, the business owner can have those conversations with employees to analyze the implications of decisions before they are made.

This process truly helps everyone make decisions that will affect the company immediately.

4)micro forecast : We define forecasting at the micro level as doing so for specific customer segments, assumed smaller than business line items. Unlike the macro level, micro level forecasting is very important for tactical operational planning.

5)macro forecast : macro or Economic forecasting is the process of attempting to predict the future condition of the economy using a combination of important and widely followed indicators. Economic forecasting typically tries to come up with a future gross domestic product (GDP) growth rate, involving the building of statistical models with inputs of several key variables, or indicators.

6)Unlike other forms of forecasting which are directional in nature, a point forecast identifies an event in which price reverses to a statistical reference, irregardless of its current direction.

Point forecasts are commonly used in event arbitrage, where specific times are used to identify such events. A “point” would refer to a specific time at which price is expected to reverse, in such a case.

7)In statistical inference, specifically predictive inference, a interval forecast or prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis.

8) By density forecast we mean finding the entire probability

distribution of a future value of interest.

Linear models with normally distributed innovations:

the density forecast is typically normal with mean equal to

the point forecast and variance equal to that used in

computing a predicion interval.

9)The barriers to effective sales forecasting :

Aberdeen’s research offers some important pointers to the root causes of the forecast accuracy problem. It suggests that the most significant barriers to effective sales forecasting are:

i. Sales people not having sufficient knowledge of the details of specific deals, and/or (nearly as bad) failing to enter that information into the sales forecasting system

ii. A lack of personal accountability on the part of individual sales people as to their responsibilities for accurate sales forecasting

iii. Sales people displaying over-confident, conservative or sandbagging behaviours in their personal forecasting

iv. Management failure to define or enforce strict stage definitions, milestones and data entry standards

v. A general inability to understand or calculate the realistic probabilities and closing dates for current deals