Capacity Measurement and Prediction

In case of homogeneous tangible products which can be counted, the capacity is measured in terms of units of output per period of time. (e.g. electricity generation is measured in terms of MW, transistor radio in terms of number of sets per day, steel plant in terms of millions of tons per year)

There are however complex situations. There could be multiple products, sharing common facilities. Job shop is an extreme example. In all these cases, capacity is very difficult to express. Here, then, we express capacity in terms of the constraints. For example, a job shop has maximum these many labor hours available per month could be a measure of its capacity. For hospitals, capacity is bed days/month.

Service capacity

For countable homogeneous units of service like insurances policies, the capacity expression becomes number of policies serviced per year. In case of heterogeneous units, as explained above, capacity is expressed by referring to the constraints — man hours per month for a bank branch etc. Service output is not storable planning capacity measurement is always a challenging task. There are periods of peak demand (for plane seats, for banking services, for hotel rooms) and periods of average demand, which is much less than peak demand period. So most of these units produce an output on an average, which is less than its capacity. The problem of capacity utilization on the lower side coupled with a problem of productivity deserve the attention of the business planning department.

Future capacity requirement prediction

Capacity plans are heavily dependent upon demand forecasting for outputs. Long term forecasts of demand are so difficult to compute. Though there is secular trend and cyclical effect there are contingencies difficult to foresee which affect the demand — these contingencies could be acts of God like drought and floods or man made like wars, technological breakthrough, and oil embargoes. As a rule, however, mature products are subject to better prediction than the recent either in introductory or growth stages of PLC.

Multiple outputs

There could be many products for which demand is to be forecast, and each product could be at different stages of PLC. The cumulative demand for all outputs might not fluctuate much but individual outputs having different growth rates show demand fluctuations. In the long run, the output mix of the organization may change.

Multiple outputs insure us against uncertainties in environment, especially if each output were to have an independent demand. Segmentation of markets resulting in different in different brands for each segment results in a better planning of total capacity requirements.

In other words, demand forecasting for a flexible output is easier than that that of a specific output of continuous process or process industries.

“What? Gaming in the workplace? No way!” This is something that we hear from Corporate
Closely tied to the question of how much capacity should be provided to meet forecasted
The notion of focus naturally, almost inevitably from the concept of fit. Just as a
At its heart a capacity strategy suggests how the amount and timing of capacity changes
However, as with most strategic decisions, the issue is more complex than it first appears.

  • ganeshraj

    Good. It could have specific examples.

    Example—- For Paper Industry, the capacity is expreseed in terms “Normal Achievable Equivalent production per annum in MT”

    Consideration – no of days in a year on 24 hrs basis, gsm of a representative Paper, Machine production rate MT per hour, Finishing loss.

    Example—- Normal Achievable Production of 70 gsm Paper in MT per year = 330 days x 24 hrs x production rate say 10 MT / hr x (1 minus Finishibg loss say 10%) = 71280

    Additionally the Maximum production is also assessed.