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TECHNIQUES IN FORECASTING

1. QUALITATIVE TECHNIQUES
2. QUANTITATIVE TECHNIQUES

 

QUALITATIVE TECHNIQUES:

It is one of the techniques in forecasting, which is an estimation methodology that uses expert judgement rather than numerical analysis. This technique relies upon the knowledge of highly experienced employees and consultants to provide insights into future outcomes.

 

• Educated Guess:

  When one person uses his or her intuition and experience to estimate a forecast.

 

• Executive Committee Consensus:

  It is a group decision making process in which group member develop and agree to support a decision in the best interest of the whole.

 

• Delphi Method:

  In this method a panel of outside experts is identified. They are given a series of structured questionnaires. The answer of each questionnaire are used as input for the design of the next questionnaire. The identity of experts is not disclosed . This is for the purpose that nobody should influence the opinion of others. The coordinator of the project prepares the statistical summary of responses. This along with the support for the responses is provided to the experts in the next round. The participants are asked if they want to modify this previous response. In this way after few rounds of questionnaires the final forecast is derived. It is believed that during this process the range of answers will decrease and the group will converge towards the correct answer.

 

• • Advantages:

1. 1. 1. It is effective when past data is absent.
      2. It does not require experts to meet in person.
      3. It is extremely useful for the forecast of new technology or new product.

 

• • Limitations:

1. 1. 1. It is a time consuming process. During this the expert may change their perception. Sometimes, the very need of forecasting loses its significance due to the delay.
      2. If the questionnaires are poorly designed, Delphi method would be ineffective.
      3. As experts are not accountable, their response may be less meaningful.
      4. Accuracy or reliability of forecast is relatively poor in Delphi. Therefore, it should only be used when past trend is absent and quantitative models are difficult to use.

 

• Sales Force Survey:
  In this method, the forecasting is done by the sales force. Each salesman develops the forecast of his respective territory, the territory wise forecasts are consolidated at each branch area/regional level and the aggregate of al these forecasts is taken as the corporate forecast. It is a Grass-root method.

 

• • Advantages:

1. 1. 1. Salesman are closest to the customers and are able to judge their minds and thus the market more accurately.
      2. Forecasts developed by this method have greater stability and reliability because of the largeness of the sample.
      3. Forecast derived by this method could be easily and meaningfully broken down territory-wise, product-wise, customer-type wise and month-wise etc.
      4. Coordination by the field sales management becomes more meaningful when the forecasts are made by the sales force and integrated by the filed sales management.

 

• • Limitations:

1. 1. 1. Salesmen are not experts in forecasting, so they cannot use sophisticated techniques.
      2. Since their sales quotas are to be based on their forecasts, they may tend to underestimate demand and play it safe.
      3. They may not know the changes taking place in the economy and the given industry which may be necessary to predict the future.

 

• Customer Survey:
  In this method of forecasting, actual users of the forecast are directly contacted by the investigators and their preferences and attitude towards the product is consolidated from this data. This method is particular suited for forecasting the sales of industrial products.

 

• • Advantages:

1. 1. 1. Forecasts comes straight from the customer himself and hence more reliable.
      2. It gives ready made forecast user-wise and user industry-wise.

 

• • Limitations:

1. 1. 1. Respondent may not themselves be quite clear about their consumption pattern or buying plans or they may be unwilling to discuss their plan frankly.

 

• Market Research:
  In this technique, the salesmen sell the product in apart of the market for some time and evaluate the sales for the full market on the basis of the results of the test sales. This technique is appropriate when the product is quite new in the market or good estimators are not available or when the buyers have note prepared their purchase plan.

 

• • Advantages:

1. 1. 1. This method is most suitable for introducing a new product in the market.
      2. Any defect in the product comes to light and can be removed immediately to make the product successful in the market.
      3. Sales forecasts are more reliable because they are based on actual results.

 

• • Limitations:

1. 1. 1. It takes a long time to test the market.
      2. The sales forecasts data are planned on the results of just one part of the market. As all the parts of the market are not homogenous, these forecasts may not be reliable.

 

• Historical Analogy:
  It is used when the new product or new technology is similar to an established product whose demand data is known. This approach is effective for medium to long range forecast and it is quite effective.

 

 

QUANTITATIVE TECHNIQUES:

It is one of the techniques in forecasting, which is a statistical technique for making projections about the future which uses numerical facts and prior experience to predict the upcoming events.

 

• Moving Average:
  It is an average of some fixed or predetermined number of observations in a time series which moves through the series by dropping the top item of the previous averaged group and adding the next item below in each successive average.

Example: Forecast for 3 month by simple moving average

Week	Demand for the week (units)	Cumulative Demad	3 week average demand
10	105
11	110
12	107
13	118	322/3	107.33
14	120	335/3	111.67
15	101	345	115
16	106	339	113

 

• • Advantages:

1. 1. 1. It provides a simple an god estimate. In this method equal weightage is assigned to all the periods chosen for averaging.
      2. The process of averaging lessens influence of the fluctuations.
      3. More accurate than graphical method as it s based on mathematical calculations.

 

• • Limitations:

1. 1. 1. Records of the demand data have to be retained over a fairly long period.
      2. If demand series depicts trends as against the stationary level, the moving average method would provide forecasts that lags the original series.
      3. Choice of period of moving average is difficult.
      4. Cannot be applied if some observations are missing.

 

• Weighted Moving Average:
  This method is based on the principle that more weightage is given to relatively newer data.

 

• • Calculation of weightage:

1. 1. 1. Find the sum of ‘n’ natural numbers, Σn = n [frac up=”(n+1)” down=”2″]
      2. Arrange them in decreasing order of weight as
         • [frac up=”n” down=”Σn”], [frac up=”(n-1)” down=”Σn”] , [frac up=”(n-2)” down=”Σn”] , ……………………… [frac up=”1″ down=”Σn”]

 

Example: Generate the forecast of the time period using Simple Moving Average(SMA) for n = 3 and Weighted Moving Average(WMA) for n = 4 period, also find the forecast for 9th period.

 

PERIOD	DEMAND	SMA, n = 3	WMA, n = 4
1	340
2	460
3	490
4	640	430
5	310	530	529
6	580	480	460
7	790	510	502
8	610	560	616
9		660	628

 

Σn = 4 [frac up=”(4+1)” down=”2″]

[frac up=”4″ down=”10″] , [frac up=”3″ down=”10″] , [frac up=”2″ down=”10″] , [frac up=”1″ down=”10″]

 

WMA = (340 x [frac up=”1″ down=”10″]) + (460 x [frac up=”2″ down=”10″]) + (490 x [frac up=”3″ down=”10″]) + (640 x [frac up=”4″ down=”10″]) = 529

 

WMA = (460 x [frac up=”1″ down=”10″]) + (490 x  [frac up=”2″ down=”10″]) + (640 x [frac up=”3″ down=”10″]) + (310 x [frac up=”4″ down=”10″]) = 460

 

WMA = (490 x [frac up=”1″ down=”10″]) + (640 x [frac up=”2″ down=”10″]) + (310 x [frac up=”3″ down=”10″]) + (580 x [frac up=”4″ down=”10″]) = 502

 

WMA = (640 x [frac up=”1″ down=”10″]) + (310 x [frac up=”2″ down=”10″]) + (580 x [frac up=”3″ down=”10″]) + (790 x [frac up=”4″ down=”10″]) = 616

 

WMA = (310 x [frac up=”1″ down=”10″]) + (580 x [frac up=”2″ down=”10″]) + (790 x [frac up=”3″ down=”10″]) + (610 x [frac up=”4″ down=”10″]) = 628

 

 

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