CONCEPT OF MANPOWER FORECASTS
Need for Manpower Forecasts
Types of Manpower Forecasts
MICRO LEVEL MANPOWER DEMAND FORECASTING
METHODS AND TECHNIQUES OF MANPOWER DEMAND FORECASTING
Forecasting Using Quantitative Tools
Work Study Technique
Time Series Method of Forecasting
Issues in Manpower Demand Forecasting Using Quantitative Tools
Forecasting Using Qualitative Tools
Nominal Group Method
DISCUSSION AND QUESTIONS
Manpower Forecasting is the first step of the entire manpower planning activity. The HR manager foresees the demand and supply of different types of manpower resources in the firm. The basic idea is to look into in which department; unit or level there is a shortage or surplus of human requirements. Forecasting is the process of making judgments about accrued events whose actual outcomes have not been seen. Successful manpower planning involves only two critical steps .First one is estimating the Demand Forecast for manpower accurately and the second one is ensuring the Supply Forecast for manpower to meet the Demand Forecast. Demand forecasting and Supply Forecasting go hand in hand. The job of the manpower planner is to take suitable steps to bridge the gap between Demand and Supply by understanding the trends that occurs due to dynamic business environment and estimating intensively the future needs of an enterprise. In the absence of any systematic work in this direction, there is a high probability that an organisation may face many surprises in terms of human resource requirements and therefore be unable to cope up with the future challenges. The absence of the right persons at the right time may prohibit the fullest accomplishment of corporate plans. It may also lead to incur losses because of the organisations’ inability to cash in the opportunities which necessitate both Demand Forecasting as well as Supply Forecasting.
CONCEPT OF MANPOWER FORECASTS
In the context of manpower forecasting, there is a need to understand the distinction between the different concepts like ‘projections’, ‘estimates’ and ‘forecasts’. ‘Projections’ are predictions of outcome at the responses of spontaneous forces. The outcome which is expected to happen in the normal course of events with the absence of external stimulus is projection. They are mathematical extensions of existing manpower data into the future. On the other hand Estimates’ are educated guesses. Estimates are calculated approximately based on experiences and opinion of experts.
Forecasts refer to predictions of outcome when normal course of events are influenced and altered by extraneous factors. Forecasts usually take into account both projections and the estimates. There could be different types of Forecasts depending upon the purpose for which forecasts are made. Some of the major types of forecasts are described below. HR plans depend heavily on forecasts, expectations, and anticipation of future events, to which manpower requirements in terms of quality and quantity are directly linked. Moreover, uncertainty adds complexity to forecasting.
Manpower Forecasting is defined as, "the prediction of future levels of demand for and supply of workers and skills at organizational level, or at regional level, or may be at national level. A variety of techniques are used in manpower forecasting. It includes the statistical analysis of current trends and the use of mathematical models. At national level, analysis of census statistics is included. At organizational level, sales and production figures is the base line upon which projections of future manpower requirements are made".
NEED FOR MANPOWER FORECASTS
The basic rationality of manpower forecasts is the long gestation lags in the production of skilled professional people. Manpower forecasts well in advance facilitate planning of education and training. It is an effort to ensure that manpower required both in terms of quality and quantities are available at the time when they are needed.
The second major reason of manpower forecast is the imperfections in the labour market. Markets for manpower with long lead time for production are characterised by long lags in the supply side and short lags, on the demand side. Therefore supply is to be planned in order to meet the demand. If that does not happen, there is a high probability that the labour market may lead to distortions in occupation-education correspondence. As a result of which there could be a huge educated unemployment or with people taking up occupations for which they are not adequately trained or both. Manpower Forecasts are expected to facilitate correction of labour market distortions.
The third major reason of man power forecast is that, in the short-run, elasticities of substitution among various skills have been observed to be either zero or near zero. Therefore, production of goods and services requires different categories of skilled manpower in fixed proportion. In such a situation shortages of any skilled category of manpower, would adversely affect the production of goods and services within the economy. Manpower Forecasts would help in avoiding such types of situation by facilitating anticipation of skill shortages and planning skill supplies accordingly.
Types of Forecasts
Short-term forecasts are usually made for a period not exceeding two years. Short-term forecasts are very useful at the micro-level i.e company level or department level.
The horizon for planning for medium term forecasts is generally about two to five years. Medium term forecasts are useful for those offices which are concerned with advising ministers or preparing contingency plans to meet the ‘twists and turns of economic circumstances or international events’.
Forecasts for a period of more than five years are considered as long-term forecasts. Long-term manpower forecasts are useful in educational planning. Planning for requirements of highly skilled professional categories of manpower are forecasted on long term basis. Long term forecasts are very useful in the preparation of corporate plans incorporating productivity changes, technological changes and major organisational developments.
Policy Conditional Forecasts
Policy conditional manpower forecasts are determined by the man power policy factors which influence the demand for manpower. Such types of manpower forecasts are based upon a rule of thumb. Sometimes professional judgement or an explicitly specified model is used for policy conditional forecasts.
An onlooker manpower forecasts are those which are derived by assuming that the factors influencing future manpower demand behave in similar way as they did in the past. Like policy conditional forecasts, onlookers’ forecasts are also obtained with the help of a rule of thumb, or professional judgement, or an explicitly specified model, or any combination of the three.
Optimising manpower forecasts are done within the framework of an optimisation model in which numbers demanded of various categories of manpower are so determined that either the end benefits are maximised, or cost of resources used in achieving a pre-determined end objective is minimised.
Macro and Micro Forecasts
There is a clear distinction between macro and micro forecasts, primarily because of two reasons. First, the end purposes of the two types of forecasts are different. Second, the methodologies employed and data base used are different. It is, however, possible that micro forecasts, if properly planned, might ultimately lead to macro forecasts but not vice-versa. Macro forecasts are done usually at the national, industry/sector and region/state levels. They are primarily used in:
Planning education and training facilities.
Decision making for the choice of industries for development of economy.
Choice of location, technology, and size of organisation among selected industries.
Determining order of priorities for creating and expanding economic and social infrastructure.
Micro forecasts have relevance at the enterprise or department level. Micro manpower forecasts are needed primarily for planning, recruitment and selection, promotion, training and counselling purposes in order to meet the plan for the development of an enterprise or department concerned. Therefore details and precise forecasts are very much required at this level. The micro forecasts are usually expressed in terms of numbers required for each occupation. It also takes into account the source and stage of recruitment, scheduling of training and so on and so forth. This chapter deals with micro level demand forecasting in details.
MICRO LEVEL MANPOWER DEMAND FORECASTING
A key component of HRP is forecasting the number and type of people needed to meet organizational objectives. This step results in estimation of both short term and long term staffing requirement of an organisation. Therefore, forecasting is the foundation of the planning activity. Demand Forecasting is concerned with the process of evaluating the quality and quantity of employees an organization requires to meet its future manpower needs. A forecast could be a long-term or a short-term plan depending on the activity levels for each function and department. There are several internal and external factors to be taken into account in Demand Forecasting. Internal factors include budget constraints, production levels, new products and services. External factors include competition from other firms. It could be from the domestic or international firms, their economic value, changes in technology etc.
It is very pertinent to note at this point that Demand Forecasting is not a very accurate exercise over a long-term period. For short range planning of less than a year, a fairly accurate forecast is perhaps possible. No processes or techniques can take into account all the parameters and circumstances required for accurate long-term estimation of manpower needs. Dynamic business circumstances, rapidly changing technologies and their impact on products and production methods, political and social changes and ever increasing competition that keep changing the set of circumstances assumed at the time of forecast.
METHODS AND TECHNIQUES OF MANPOWER DEMAND FORECASTING
Forecasting human resource demand is the process of estimating the future human resource requirement of right quality and right number. It is an educated guess of how much manpower will be required and utilized optimally by an organization. Potential human resource requirement is to be estimated keeping in view the organisation's plans over a given period of time. Analysis of employment trends, replacement needs of employees due to death, resignations, retirement termination, productivity of employees, growth and expansion of organisation, absenteeism and labour turnover are the relevant factors for human resource forecasting. Demand forecasting is affected by a number of external and internal factors. Job Analysis and forecasting about the quality of potential human resources facilitates demand forecasting. Therefore, existing job design must be thoroughly evaluated with due cognizance of the future capabilities of the present employees. There is no right way of forecasting, but there are many different types of forecasting methods. Broadly, two approaches used in forecasting the demand for human resources are quantitative and qualitative. The quantitative approach uses statistical and mathematical techniques. The qualitative approaches to HR planning use expert opinion. It includes Delphi Technique and Nominal Group Method.
FORECASTING USING QUANTITATIVE TOOLS
Quantitative methods are based on the assumption that the future is an extrapolation from the past. The followings are different methods of manpower demand forecasting using quantitative tools.
Work Study Technique
Work-study techniques can be used, work measurement technique can be applied to calculate the length of operations and the amount of labour required for completion of a total job. The starting point of work study technique in a manufacturing company is the production budget, prepared in terms of volumes of products to be sold for the company as a whole, or volumes of output for individual departments. Forecasting Manpower Demand by work study technique can be done by:
Work-load Analysis is a suitable technique when the estimated work-load is easily measureable. Workload Analysis is series of processes to calculate the workload of a position, sub position, and also needs the number of people to fill the position and sub position. Workload Analysis is very important to calculate exactly how many employees needed to complete all of tasks in a section or department. This method estimates total production and activities for a specific period of time in future. Then this information is translated into number of man-hours required to produce per unit taking into consideration the capability of the workforces. Past-experience of the management can help in this direction in translating the work-loads into number of man-hours required. Thus, demand for human resources is forecasted on the basis of estimated total production and contribution of each employee in producing each unit of items. Both quantitative and qualitative techniques are utilized for accurate results. The example of this technique is given below.
For example if the estimated production of an organisation is 3, 00,000 units. The standard man-hours required to produce each unit are 2 hours and the work ability of each employee as calculated by the past experience is 1500 man hours per annum. The work-load and demand of human resources can be calculated as follows:
Estimated total annual production = 30,0000 units
Standard man-hours needed to produce each unit = 2 hrs
Estimated man-hours needed to meet estimated annual production (i x ii) = 600000 hrs
Work ability/contribution per employee in terms of man-hour = 1500 units
Estimated no. of workers needed (iii / iv) = 600000/1500 = 400
The above example clearly shows that 400 workers are needed for the year in order to produce 30,000 units. Further, absenteeism rate, rate of labour turnover, resignations, deaths, machine break-down, strikes, power-failure etc. are given due consideration while estimating future demand of human resources by Workload Analysis method.
Workforce Analysis is the foundation of workforce plan and determines the rate of influx and outflow of employees. Workforce Analysis provides sufficient margin for absenteeism, labour turnover and idle time for the completion of the total job at hand undertaken by an organization on the basis of past experiences. The organization needs to make reasonable prediction of labour turnover or absenteeism. However, if the actual labour turnover or absenteeism exceeds the predicted value, then it puts the business under loss. Therefore the Workforce Analysis has to be done with a lot of caution by experienced persons with validation of past periods data. Any seasonal variations and special events that are likely to occur need to be incorporated for the predicted period in order to ensure a realistic demand forecasts. Moreover, a reasonable degree of buffer must be built in while doing workforce analysis in order to sustain any deviations.
In addition to Work Load Analysis and Workforce Analysis, Job Analysis also facilitates manpower demand forecasting. It helps in finding out the abilities or skills required to do the jobs efficiently. A detailed study of jobs is usually made to identify the qualifications and experiences required for optimal performance of it. Broadly, Job Analysis is bifurcated into job Description and job Specification. Job Description states the fact of the duties and responsibilities of a specific job. It entails, what is to be done and how it is to be done as well as why it is to be done. Job Specification provides necessary information on the human attributes in terms of education, skills, aptitudes and experience to perform a job effectively.
Trend analysis incorporates certain business factors (units produced, revenues) and productivity ratio (employees per unit produced). Demand for manpower is also estimated on the basis of ratio of production level and number of workers available. This is the quickest forecasting technique. The technique involves studying past ratios between the number of workers and sales in an organisation and forecasting future ratios. This ratio will be used to estimate demand of human resources. For example
Estimated production for next year = 1, 60,000 units
Estimated no. of workers needed
(On the basis of ratio-trend of 1: 200) will be = 800
These models are based on mathematical and statistical techniques for estimating future demand. Under these models sales, total production, work-load etc, are taken as independent variables and human resource requirements are taken as dependent variable. Using these models relationship is established between the dependent variable to be predicted and the independent variables. Thereby estimated demand of human resources can be predicted.
It uses information from the past relationship between the organisation’s employment level and some important success criterion known to be related to employment. For example, companies can establish a statistical relationship between sales or work output and level of employment by considering influence of learning curve which assumes that average number of units produced per employee will increase as more units are produced. Accuracy of more complicated quantitative methods can be improved by incorporating operational constraints.
Time series method of forecasting
A time series is a chronological sequence of observations on a particular variable. Usually the observations are taken at regular intervals (days, months, years), but the sampling could be irregular too. Time-series methods make forecasts based solely on historical patterns in the data. This methods use time as independent variable to produce demand. In a time series, measurements are taken at successive points or over successive periods. The measurements may be taken every hour, day, week, month, or year, or at any other regular or irregular interval. A first step in using time-series approach is to gather historical data. The historical data is representative of the conditions expected in the future. Time-series models are adequate forecasting tools if demand has shown a consistent pattern in the past that is expected to recur in the future. Time series models are characterized of four components:
Trend is the 'long term' movement in a time series and is a reflection of the underlying level. Trend is important characteristics of time series models. Although times series may display trend, there might be data points lying above or below trend line. Any recurring sequence of data points above and below the trend line that last for more than a year is considered to constitute the cyclical component of the time series data. These observations are deviations from the time series trend due to fluctuations. Cyclical behaviour of a time series data can be very well understood from the real Gross Domestics Product (GDP).
That particular component of the time series that captures the variability in the data point that occur due to seasonal fluctuations is called the seasonal component. The effects of seasonal component of a time series data are reasonably stable with respect to timing, direction and magnitude. It is similar to the cyclical component in the sense that both are referred to some regular fluctuations in a time series. Seasonal components of time series data capture the regular pattern of variability that happen within one-year period. The sales figures of seasonal commodities like air conditioner during summer are the best example of seasonal component of time series data.
The irregular component sometimes is also referred as the residual. It is what remains after the seasonal and trend components of a time series have been estimated and removed. It results from short term fluctuations in the series which are neither systematic nor predictable. In a highly irregular series, these fluctuations can dominate movements by putting a mask on the trend and seasonality component. Random variations in times series is represented by the irregular component.
Smoothing methods (stable series) are appropriate when a time series displays no significant effects of trend, cyclical, or seasonal components. In such a case, the goal is to smooth out the irregular component of the time series by using an averaging process. The moving averages method is the most widely used smoothing technique. In this method, the forecast is the average of the last "x" number of observations, where "x" is some suitable number. Suppose a forecaster wants to generate three-period moving averages. In the three-period example, the moving averages method would use the average of the most recent three observations of data in the time series as the forecast for the next period. This forecasted value for the next period, in conjunction with the last two observations of the time series, would give an average. That can be used as the forecasted value for the second period in the future. The calculation of a three-period moving average is illustrated in following table.
Manpower /Data in nos.
Forecasted Data in nos
Example: Three-period moving averages
In calculating moving averages to generate forecasts, the forecaster may experiment with different-length moving averages. The forecaster will choose the length that yields the highest accuracy for the forecasts generated. Weighted moving averages method is a variant of moving average approach. In moving averages method, each observation of data receives the same weight. In the weighted moving averages method, different weights are assigned to different observations on data that are used in calculating the moving averages. Suppose, once again, that a forecaster wants to generate three-period moving averages. Under the weighted moving averages method, the three data points would receive different weights before the average is calculated. Generally, the most recent observation receives the maximum weight, with the weight assigned decreasing for older data values.
Actual manpower Data in no
Example: Weighted three-period moving averages method
A more complex form of weighted moving average is exponential smoothing. In this method the weights fall off exponentially as the data ages. Exponential smoothing takes the previous period’s forecast and adjusts it by a predetermined smoothing constant, ά (alpha) for future forecasts. The value for alpha is less than one. Alpha (ά) multiplied by the difference in the previous forecast and the demand that actually occurred during the previously forecasted period is called forecast error. Exponential smoothing is mathematically represented as follows: New forecast = previous forecast + alpha (actual demand − previous forecast) or can be formulated as:
Fm = Ft-1 + ά (At-1 – Ft-1)
F m =Forecasted manpower
Ft-1 = forecasted demand for the previous period
ά = Smoothing constant
At-1 = Actual manpower demand for the previous period
Actual manpower Data in no
Example: Exponential smoothing method
Other time-series forecasting methods are, forecasting using trend projection, forecasting using trend and seasonal components and causal method of forecasting. Trend projection methods use the underlying long-term factors of trend of time series data to forecast its future values. Trend and seasonal components method considers both seasonal component of a time series as well as the trend component. Causal methods use the cause-and-effect relationship between the variables whose future values are being forecasted in relation to other related variables or factors. The widely known causal method is Regression analysis. It is a statistical technique used to develop a mathematical model showing how a set of variables are related. This mathematical relationship can be used to generate forecasts. There are more complex time-series techniques like ARIMA (Auto-Regressive Moving Average) and Box-Jenkins models. These techniques are heavier due to complex statistical routines to cope with the data, data trends and the seasonality associated with it.
LIMITATIONS OF DEMAND FORECASTING USING QUANTITATIVE TOOLS
Productivity data is the base upon which manpower requirements is considered. Whereas productivity rise cannot be attributable to increased human effort always. Increase in productivity may be due to changes in technology or the sum total of managerial and operational efficiency or some other factors which need to be considered while doing manpower forecasts.
It is difficult to get units of output in the same form for all jobs. For example maintenance jobs, customer relationship job are difficult to quantify as these types of job are time independent.
Relationship between output and manpower is not always straightforward. Rise in productivity may arise out of economies of scale and resultant cost efficiency, which may not be attributable to manpower productivity.
Effect of factors like new technology adoption, incentive schemes etc to improve productivity may not be consistent over time. Therefore, projecting manpower requirement considering effect of such factors may be inaccurate.
Effect of different factors may not always linear. Although availability of statistical techniques like multiple regression and factor analysis are there to deal with such complex data, interrelationship of different factors complicates forecasting of manpower.
Uncertainty about the future is again a major problem for the manpower planner. Thus extrapolating on past data may lead to a major inaccuracy in manpower estimation.
Data on past workload factors may not be available, creating difficulty in emulating the same.
Integration of manpower planning with corporate plans may not exist in an organisation, creating problems for enterprise wide manpower plans.
Employees cannot always be related to output in a direct way.
Human resource information system (HRIS) may not exist in an organisation. Lack of proper information support system leads to inaccurate estimation of manpower.
QUALITATIVE DEMAND FORECASTING
In contrast to quantitative approaches, qualitative approaches to forecasting are less statistical, attempting to reconcile the interests, abilities, and aspirations of individual employees with the current and future staffing needs of an organization. In both large and small organizations, HR planners may rely on experts who assist in preparing forecasts to anticipate staffing requirements. The followings are different types of qualitative Manpower Demand Forecasting.
Managerial judgement technique is very common technique of Demand Forecasting. This technique is very simple. In this method, managers sit together, discuss and arrive at a figure, which would be the future demand for labour. This approach is applied by small as well as large scale organisations. This technique involves two types of approaches. One is 'bottom-up approach' and the other is 'top-down approach'. Under the 'bottom-up approach', line mangers request their departmental requirement of human resources to top management. Top management ultimately forecasts the human resource requirements for the overall organisation on the basis of proposals of different departmental heads. Under the ‘Top-down approach', top management forecasts the human resource requirements for the entire organisation and for various departments. This information is supplied to various departmental heads for their review and approval. Neither of these approaches forecasts accurately. However, a combination of both the approaches i.e. 'Participative Approach' should be applied for effective Demand Forecasting. Under this approach, top management and departmental heads meet, discuss among each other and reach at a consensus to decide about the future human resource requirements for the department or for the organisation. Therefore demand of Human Resources can be forecasted with unanimity under this approach.
The development of Delphi method goes back to 1950s by the RAND Corporation, Santa Monica, California. This approach consists of a survey conducted in two or more rounds and provides the participants in the second round with the results of the first so that they can alter the original assessments if they want to, or stick to their previous opinion. Nobody ‘looses face’ because the survey is done anonymously using a questionnaire. It is commonly assumed that the method makes better use of group interaction (Rowe et al. 1991) whereby the questionnaire is the medium of interaction (Martino 1983). The Delphi Method "is effective in improving and clarifying the collective judgment of experts" (Cornish, pg. 67). Any expert around the world can be included in the Delphi Method (Cornish, pg. 67).
The Delphi Technique begins with the development of a set of open-ended questions on a specific issue. These questions are then distributed to various ‘experts’. The responses of the experts to these questions are summarised and consolidated. Basing upon this a second set of questions is formulated and distributed to the same group of ‘experts’ putting emphasis to clarify on the areas of agreement and disagreement. Delphi technique is subjective in approach. For this very reason it is often questioned by members of quantitative school. The objective of Delphi technique is to predict future situations by integrating independent opinion of experts. A major goal of Delphi technique is to avoid face to face confrontation of experts. Since some individuals may be unduly influenced by others, resulting in compromise of relevant ideas. Delphi technique is facilitated by an intermediary, who provides the experts with a sequential series of questionnaires concerning the forecasting along with the controlled written feedback to each expert. During each round of written interrogation, each expert making forecast independently specifies the assumptions concerning the problem, identifies source materials that would be helpful in revising the forecast estimates, and is also provided with the same kind of information developed by each of the other experts whose names are not associated with these data.
The intermediaries gather data of requests of the experts and summarize them along with the experts answer to the primary questions. The developers of the Delphi argue that the procedures are more conducive to independent thought and allow more gradual formulation to a considered opinion. Successive revisions of these procedures are continued until a composite forecast is obtained.
These rounds of information and decision making provide each expert with an iterative or step by step feedback loop in which the experts receives successive rounds of reactions of others which may be helpful in providing a viable composite forecast forecasting. Such successive rounds usually result in the opinions of experts converging and thereby providing a viable composite forecast.
Advantages of Delphi Technique.
The technique is conducted in writing and does not require face-to-face meetings.
It is more convenient for the participants to put their responses.
Individuals from diverse background work together on the same problems.
It is relatively free from social pressure, personality influence, and individual dominance.
The procedure foster independent thinking and gradual formulation of reliable judgments leading towards accurate forecasting results.
This technique is also helpful in generating consensus out of the divergent opinions of hostile groups.
It is also helpful to keep focus directly on the issue.
It allows broad range of views to be captured from a number of experts.
It allows sharing of information and reasoning among participants.
Iteration enables participants to review, re-evaluate and revise all their previous statements in line with the compliments made by their peers.
Disadvantages of Delphi Technique:
Information is captured from a selected group of people and may not be the proper representative.
Under this method extreme positions are always eliminated and participants choose middle path in order to arrive at the consensus.
This is an expensive technique and requires requisite skill in written communication.
It necessitates adequate time and participants’ commitment.
Nominal Group Method
Nominal group method is otherwise known as NGT technique. It is "a structured method of group brainstorming that encourages contributions from every member". Like Delphi method, Nominal Group Method also involves panel of experts. However, the major difference between the two is that, under Delphi technique, experts are not allowed to discuss among themselves. Under nominal group method, experts join a conference table, independently list their ideas. While writing they are allowed to discuss among themselves to assess the questions. Under this method the co-ordinator assumes the role of a facilitator, allowing the experts to sit together and discuss their ideas. The records of such discussions are made on a flip chart. This process encourages the more passive group members to participate resulting in a set of prioritized solutions or recommendations. After this discussion, experts are asked to rank their ideas according to their perceived priority. The group consensus is then derived mathematically in terms of individual rankings. The process, therefore afford creativity and facilitate scientific group consensus as the coordinator ultimately decides the best course of action.
As, there are advantages and disadvantages associated with any technique. Nominal Group Method is no exception. The most obvious advantage of this method is, it provides opportunities for equal participation of group members to respond to and clarify their ideas. As to disadvantages, opinions of experts may not converge, constraining cross-fertilization of ideas resulting into a mechanical process.
The other techniques of Human Resource demand forecasting are specified as under:
Simply following the techniques of demand forecasting of human resources used by other similar organisations
Estimation based on techniques of production
Estimation based on past records
Statistical techniques e.g. co-relation and regression analysis
In this method a mathematical model is developed for personnel forecasting.
En = (Lagg+G)1/xY
En – is estimated level of manpower demand in n planning periods
Lagg- is overall turnover or aggregate level of current business activity in rupees during n period.
G- is the total anticipated growth in business activity during period n in terms of rupees.
X- is the average improvement in productivity
Y- is conversion figure relating to today’s overall activity
Computer Analysis/ MANPLAN
Computer Analysis is otherwise known as MANPLAN. It was developed by "General Electric" to overcome Human Resource Modelling problems such as the overwhelming mathematical complexity of the data. Computer analysis can help in this direction. One final merit of MANPLAN is that running the computer model is relatively inexpensive. It also provides for ranges of possible human resource needs for any period.
BENEFITS OF MANPOWR DEMAND FORECASTING:
The end-result is much relevant, if forecasting is accurate. An accurate forecast may improve likely hood of achieving most of the organizational goals for the planning year. It can help to identify risks, clarify what needs to be done and sets fair expectations.
The process of forecasting makes managers sensitive to change. It helps them to curtail their flamboyant decision on the manpower expenditure. Thereby helps in focusing more on achieving the business goals.
Manpower Demand forecasting is the process of estimating the future human resource requirements of right quality and right number. Human Resource Planner should include both quantitative and qualitative approaches while doing Manpower Demand Forecasting. In combination, both the two approaches complement to each other. Thus provides a more complete forecast by bringing together the contributions of both theoreticians and practitioners. However, since, the best business plans are subject to change in today’s dynamic world and no matter how well a planner considers the various contributing factors, there is always existence of a certain amount of uncertainty and chance. Therefore, instead of attempting to forecast the precise number of people required by an organisation, the trends be studied, in order to understand the possible changes in the business and evolve an appropriate strategy to cope with the emerging scenario.
What is Manpower Forecasting? Differentiate between projections, estimates and forecasts. Elaborate the need for Manpower Forecasts.
Elaborate different types of Manpower Forecasts. Differentiate between Macro Forecasts and Micro Forecasts.
Analyse Work Study technique of Manpower Demand Forecasting?
Explain Time Series method of Manpower Demand Forecasting.
What is qualitative method of Manpower Demand Forecasting?
Write short notes on---
Quantitative method of Forecasting Vs Qualitative Method of Forecasting
Ratio trend Analysis
Nominal Group Method