Posts Tagged ‘scenario writing’

Forecast Friday Topic: Other Judgmental Forecasting Methods

March 3, 2011

(Thirty-ninth in a series)

Over the last several weeks, we discussed a series of different non-quantitative forecasting methods: Delphi Method, Jury of Executive Opinion, Sales Force Composite Forecasts, and Surveys of Expectations. In today’s brief post, we’ll finish with a brief discussion of three more judgmental forecasting methods: Scenario Writing, La Prospective, and Cross-Impact Analysis.

Scenario Writing

When a company’s or industry’s long-term future is far too difficult to predict (whose isn’t!), it is common for experts in that company or industry to ponder over possible situations in which the company or industry may find itself in the distant future. The documentation of these situations – scenarios – is known as scenario writing. Scenario writing seeks to get managers thinking in terms of possible outcomes at a future time where quantitative forecasting methods may be inadequate for forecasting. Unfortunately, much literature on this approach suggests that writing multiple scenarios does not have much better quality over any of the other judgmental forecasting methods we’ve discussed to date.

La Prospective

Developed in France, La Prospective eschews quantitative models and emphasizes several potential futures that may result from the activities of individuals. Interaction among several events, many of which can be, and indeed are, dynamic in structure and constantly evolving, are studied and their impacts are cross-analyzed, and their effect on the future is assessed. La Prospective devotes considerable attention to the power, strategies, and resources of the individual “agents” whose actions will influence the future. Because the different components being analyzed can be dynamic, the forecasting process for La Prospective is often not linear; stages can progress in different or simultaneous order. And the company doing the forecasting may also be one of the influential agents involved. This helps companies assess the value of any actions the company might take. After the La Prospective process is complete, scenarios of the future are written, from which the company can formulate strategies.

Cross-Impact Analysis

Cross-Impact analysis seeks to account for the interdependence of uncertain future events. Quite often, a future event occurring can be caused or determined by the occurrence of another event. And often, an analyst may have strong knowledge of one event, and little or no knowledge about the others. For example, in trying to predict the future price of tissue, experts at companies like Kimberly-Clark, along with resource economists, forest experts, and conservationists may all have useful views. If a country that has vast acreages of timber imposes more stringent regulations on the cutting down of trees, that can result in sharp increases in the price of tissue. Moreover, if there is a major increase, or even a sharp reduction, in the incidence of influenza or of the common cold – the realm of epidemiologists – that too can influence the price of tissue. And even the current tensions in the Middle East – the realm of foreign policy experts – can affect the price of tissue. If tensions in the Middle East exacerbate, the price of oil shoots up, driving up the price of the energy required to convert the timber into paper, and also the price of gas to transport the timber to the paper mill and the tissue to the wholesalers and to the retailer. Cross-impact analysis measures the likelihood that each of these events will occur and attempts to assess the impact they will have on the future of the event of interest.

Next Forecast Friday Topic: Judgmental Bias in Forecasting

Now that we have discussed several of the judgmental forecasting techniques available to analysts, it is obvious that, unlike quantitative methods, these techniques are not objective. Because, as their name implies, judgmental forecasting methods are based on judgment, they are highly susceptible to biases. Next week’s Forecast Friday post will discuss some of the biases that can result from judgmental forecasting methods.


Objective and Subjective Forecasting Approaches

May 3, 2010

(second in a series)

Today we discuss the various categories of forecasting methods that are available to businesses.  Forecasting methods can be either objective (using quantitative approaches) or subjective (using more intuitive or qualitative approaches), depending on what data is available and the distance into the future for which a forecast is desired.  Forecasting approaches will typically be more objective for nearer term forecasting horizons and for events where there is plenty of quantitative data available.  More distant time periods, or events with a lack of historical quantitative data will often call for more subjective approaches.  We will discuss these two classes of forecasting methods, and the categories within each.

Objective Forecasting Approaches  

Objective forecasting approaches are quantitative in nature and lend themselves well to an abundance of data.  There are three categories of objective forecasting methods: time series, causal/econometric,  and artificial intelligence.  AI approaches are outside my experience, so I won’t be covering them in this series, but mention them as another alternative, in case you wish to investigate them on your own. 

Time Series Methods

Time series methods attempt to estimate future outcomes on the basis of historical data.  In many cases, prior sales of a product can be a good predictor of upcoming sales because of prior period marketing efforts, repeat business, brand awareness, and other factors.  When an analyst employs time series methods, he/she is assuming that the future will continue to look like the past.  In rapidly changing industries or environments, time series forecasts are not ideal, and may be useless.

Because time series data are historical, they exhibit four components that emerge over time: trend, seasonal, cyclical, and random (or irregular).  Before any forecasting is done on time series data, the data must be adjusted for each of these components.  Decomposing time series data will be discussed later in this series. 

The most common time series methods include moving average (both straight and weighted), exponential smoothing, and regression analysis.  Each of these approaches will be discussed later in the series.

Causal/Econometric Methods

Causal or econometric forecasting methods attempt to predict outcomes based on changes in factors that are known – or believed – to impact those outcomes.  For example, temperature may be used to forecast sales of ice cream; advertising expenditures may be used to predict sales; or the unemployment rate might be used to forecast the incidence of crime in a neighborhood.  It is important to note, however, that just because a model finds two events that are correlated (e.g., occur together), it does not necessarily mean that one event has caused the other.

Regression analysis also falls under the causal/econometric umbrella, as it can be used to predict an outcome based on changes in other factors (e.g., SAT score may be used to measure likelihood of being accepted to a college).  Econometric forecasting methods include  Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models.  ARIMA was previously known as Box-Jenkins.  ARMA and ARIMA models are used in certain cases, but most of the time are unnecessary.  Although these two methods won’t be covered in much depth later in the series, there will be a brief description of them and when they are needed.

Subjective Forecasting Approaches

Subjective forecasts are more qualitative.  These approaches rely most heavily on judgment and educated guesses, since there is little data available for forecasting.  This is especially the case in long-range forecasting.  It’s easy to forecast next week’s sales of ice cream – and possibly even of individual flavors, since you’ll likely have months or years of past weekly ice cream sales data.  However, if you’re trying to get an idea of what ice cream consumption or flavor preferences will be 10 years from now, quantitative approaches will be of little use.  Changes in tastes, technology, and political, economic, and social factors occur and can dramatically alter the course of trends.  Hence, the opinion of subject matter experts is often called upon.  There is essentially only one category of subjective forecast approaches – and it is rightly called “Judgmental” forecasts.

Judgmental Methods

Judgmental forecasting methods rely much on expert opinion and educated guesses.  But just because they have little quantitative or objective basis doesn’t mean they should be dismissed or not measured for accuracy.  The most common types of of judgmental forecasting methods are composite forecasts, extrapolation, surveys, Delphi method, scenario writing, and simulation.  Each of these methods will be discussed in detail later in the series. 

Introducing “Forecast Fridays” – ON THURSDAYS!!!

Beginning with part 3, which will discuss moving average forecasts, the forecasting series will begin posting weekly so that the remaining days of the week can still be devoted to other topics in the marketing research and analytics field.  The weekly post will be called “Forecast Friday.”  However, it will be posted every Thursday!  Why?  Find out in tomorrow’s post!