ForecastingEssay Preview: ForecastingReport this essayAbstractSuccessful demand forecasting allows for firms to apply strategic planning to their operations. This paper outlines those four predominate categories of forecasting methods and elaborates on some of their techniques. In further applying them to an organization, namely the XXXX language school, not only were contrasts highlighted but also insights as to how this firm could better address the predicting of demand under conditions of uncertainty.
Successful operations result from strategic and tactical plans incorporating the efficient use of resources in producing an output of perceived value. In structuring systems to deliver this, an appreciation of capacity is essential. In anticipating demand, firms can make the necessary changes required to meet those capacity needs and continue to produce their valued products and services (Brown, 1995).
Age of uncertaintyAs the marketing coordinator for one of seven Japanese based language schools across Canada, figuring our what lay ahead makes the difference in successfully planning for capacity and formulating solutions that benefit our clientele. However, I should state on the onset, that no formal forecasting technique has been practiced within my operation. This is unfortunate and alarming given that issues such as the Asian economic crisis and the SARS epidemic may have had a lesser effect if the use of forecasting been more appropriately applied.
Our text author, (Chase, 2003) categorizes this procedure into four basic types: qualitative, time series analysis, causal relationships and simulations. Within each are numerous techniques that give an idea of how demand could be expected. As we compare some of these methods I will apply them to my schools operation so that my firms challenges can be addressed and a better contrast between the methods can be illustrated.
Gut reactionQualitative techniques, when contrasted to the others, are unique in that they are derived from experience, instinct and opinion. Those who have been deemed as authorities because of their intimate knowledge, relationship and experience configure estimates from which future demand can be derived.
Developed by the Rand Corporation in the 1950s, the Delphi Method is one such example that compiles such forecasting insights via a questionnaire. Evolving from the shortcomings inherent within its predecessor, the panel consensus, Delphi allows for a variety of insights to be asserted. Unlike the earlier method, intimidation, prejudice, or favoritism from the presence of higher management is averted by anonymity given to those who submit to the study (Chase, 2003).
In emulating such an exercise, the following five steps could be applied to my organization.1. A variety of experts (sales, marketing, academic planning, etc.) from certain geographic school locations (Japan, Canada, United States, Oceana, Europe, and Latin America) would be chosen to participate.
2 Through a questionnaire (or e-mail), forecasts, including any premises or qualifications, could be collected from all the participants.3 A summary of the results followed by a redistribution of appropriate new questions would again be issued out.4 A final summary refining the forecasts and conditions would then be used again to develop new questions.5 Step four would be repeated if it were necessary followed by the distribution of the final results to all participants (Chase, 2003).Those insights collected under Delphi could be redistributed via tools such as the affinity diagram or KJ method (Anderson, 1995). Results from such an exchange are illustrated in the following sample affinity diagram.
What goes around comes aroundMuch like the subjective attempt at prediction just mentioned, the time series methods derive an idea of what to expect in the future from what happened in the past. However, unlike the above approach, these techniques are grounded on recorded and measured data rather than intuitive speculation. It is interesting to note the discrepancy in the required time period in each technique within this category. Data can be vast and entail statistics from very dated results or simpler in composition from more up to date incidences.
The latter seems valid in most applications where “the most recent occurrences are more indicative of the future than those in the more distant past” (Chase, 2003, p. 475). Subsequently, because of its short preparation time, slight sophistication, and rather sparse historical data requirement, exponential smoothing has become one of the most popular of forecasting techniques (Reynolds, 2001).
By using the most recent forecast of a chosen time period, its actual demand, and an appropriate smoothing alpha constant (α), a formula can be developed to give us an idea of what to expect (Chase, 2003).
Ft = Ft−1 + α (At−1 − Ft−1)WhereFt = The exponentially smoothed forecast for period tFt−1 = The exponentially smoothed forecast made for the prior periodAt−1 = The actual demand in the prior periodα = The desired response rate, or smoothing constant (Chase, 2003, p.476).(Chase, 2003, p.477).In addition to adjustments made to the alpha, a trend formula utilizing a smoothing constant delta (δ) is put to use.FITt = Ft + TtFt = FITt−1 + α(At−1 − FITt−1)Tt = Tt−1 + δ(Ft − FITt−1)WhereFt = The exponentially smoothed forecast for period tTt = The exponentially smoothed trend for period tFITt = The forecast including trend for
To get a smooth trend, one can use a smoothing constant dt ψ
This is the average trend, with the slope equal to the slope of the baseline value over the 3 periods.
This is the number of smoothing periods in which the expected trends are not shown as a range. This can be useful for modeling data on the frequency dependence of changes in the number of smoothing periods that result from a change in the baseline-derived trends.
All the smoothing periods are rounded, meaning the smoothing curve on a line is at its maximum phase while the curve on the right edge is at its minimum phase.
All the periods are rounded, meaning the smoothing curve on a line is at its maximum phase while the curve on the right edge is at its minimum phase.
Table 2. Estimate the T-test over the three periods At−1 = Age = Tt;1 = Height = Tt;1 = Weight = Tt;1 = BMI = Tt;1 = Wearing clothing = Tt;1 = Standing in public = Tt;1 = Sitting in public = Tt;
In Table 2, we can compare the results between the periods with two different values: time for periods of tT = The increase in the baseline value over the 3 periods. We see the trend curve with a slope slope of ±0.2 degrees (Mt, Age), as defined by the T-test.
3. Estimate the T-test and T-test at the baseline Tt = Age = Tt;1 = Height = Tt;1 = Weight = Tt;1 = BMI = Tt;1 = Wearing clothing = Tt;1 = Standing in public = Tt;
Table 3. The T-Test In Table 3, we can conclude that the baseline Tt, which is at Age = Tt, would not be affected by the shift to older clothing. Table 4 shows how the baseline trends (as determined by the T-test) are obtained by averaging the T-test results for 4 of the three period periods.The T-