What is Micro Forecasting?

Micro forecasting is concerned with the estimation of the need of the manpower for a particular workload structure. At the micro level, corporate need to realize that manpower is a expensive and a highly valuable resource to be used as effectively as possible. Scientific level HR planning at the corporate level will make national planning more realistic and effective. The need for HR planning at all levels needs no mean emphasis.

The process involved in this are:

• In due course the manning norms are evolved looking into the workload structure.
• Workloads are forecasted.
• Workloads are related to manning norms.

Evolving Manning Norms:

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In this the overall view of the work of an organization is taken into consideration and further divided into functions. The functions are further divided into tasks and after that each work group is identified which is associated with respective function.

Then in case of each work-group it is the level and number of positions at each level, the job description at each level, performance of incumbents to each position by level with respect to job expectations. All these are examined further. Based on the results the training and experience requirement of the incumbents to each position is worked out. Thus the desired manning norms are arrived at.

In course of time if any change comes up that may be because of technological reasons or better use of manpower utilization. Technological change can result in labor saving as well as money saving. Whatever may it is the technological change which causes change in manning level and norms in any organization.

Forecasting Workloads

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Factor analysis and principal component analysis have more or less the similar aim. They both use a sophisticated statistical model. In the illustration as given below there are some small number of factors which are identified and they have the potential to explain the behaviour of the number of tasks. When the behaviour of individual factors is predicted, it is made possible to arrive at forecasts of workload under each task and hence the forecast of total workload can be calculated.

The character of work in each task will not change significantly during the period of forecast, while technological change and/or manpower utilization can change the character of work in any one or more tasks.

But there are solutions where the tasks are not perfectly correlated and in that case any of the two statistical techniques could be used:

1. Principal Component Analysis (PCA).
2. Factor Analysis.

Principal component analysis involves a mathematical procedure that changes a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. These are helpful in arriving at the forecasts of workloads.

Factor analysis might incorrectly be treated as being interchangeable with principal component analysis. The two methods are related, but distinct, though factor analysis becomes essentially equivalent to principal component analysis if the “errors” in the factor analysis model can be assumed to all have the same variance. It is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables.

There are basically two types of factor analysis: exploratory and confirmatory. Exploratory factor analysis attempts to discover the nature of the constructs influencing a set of responses and confirmatory factor analysis tests whether a specified set of constructs is influencing responses in a predicted way. The primary objectives of an EFA are to determine:

• The number of common factors influencing a set of measures.
• The strength of the relationship between each factor and each observed measure.

There are seven basic steps for performing an EFA:

• Collect measurements.
• Obtain the correlation matrix.
• Select the number of factors for inclusion.
• Extract your initial set of factors.
• Rotate your factors to a final solution.
• Interpret your factor structure.
• Construct factor scores for further analysis.

Relating Workload to Manning Norms: If workload is denoted by ‘W’ and productivity of workers denoted by ‘P’, then manpower forecast in terms of numbers required in the future can be obtained as: Numbers required in future = W/P