Capacity planning and control

:2pt;margin-bottom:2pt;margin-left:0pt;margin-right:0pt;text-align:left;text-indent:0pt;line-height:30pt">2. to identify the alternative capacity plans which could be adopted in response to the demand fluctuations.

 

3. to choose the most appropriate capacity plan for their circumstances.

 

MEASURING DEMAND AND CAPACITY

 

Forecasting demand fluctuations

 

Demand forecasting is usually the responsibility of the sales and/or marketing functions

 

But it is a very important input into the capacity planning and control decision.

 

After all, without an estimate of future demand it is not possible to plan effectively for future events, only to react to them.

 

It is therefore important to understand the basis and rationale for these demand forecasts. (See the supplement on forecasting after Chapter 6.)

 

3 REQUIREMENTS FROM A DEMAND FORECAST

 

1. It is expressed in terms which are useful for capacity planning and control

 

If forecasts are expressed only in money  terms,

 

they will need to be translated into realistic expectations of demand,

 

expressed in the same units as the capacity (for example, machine hours per year, operatives required, space, etc.).

 

 

2. IT IS AS ACCURATE AS POSSIBLE

 

 

Demand can change instantaneously

 

There is a lag between deciding to change capacity and the change taking effect.

 

Thus many operations managers are faced  with a dilemma.

 

In order to attempt to meet demand,  they must often decide output in advance,  based on a forecast which might change  before the demand occurs, or worse, prove  not to reflect actual demand at all.

 

3. It gives an indication of relative uncertainty

 

Decisions to operate extra hours and  recruit extra staff are usually based  on forecast levels of demand

 

They could in practice differ considerably from actual demand, leading to unnecessary costs or unsatisfactory customer service.

 

A forecast of demand levels in a supermarket may show initially slow business

 

that builds up to a lunchtime rush.

 

After this, demand slows, only to build up again for the early evening rush before it falls again at the end of trading.

 

The supermarket manager can use this forecast to adjust (say) checkout capacity throughout the day.

 

But although this may be an accurate  average demand forecast, no single day  will exactly conform to this pattern. 

 

Of equal importance is an estimate of  how much actual demand could differ from  the average. 

 

This can be found by examining demand  statistics to build up a distribution  of demand at each point in the day. 

 

So we know when it will be important to have reserve staff, perhaps filling shelves, but on call to staff the checkouts should demand warrant it.

 

 

Probabilistic forecasts allow to make a judgement between

 

• possible plans that would guarantee ability to meet actual demand

 

• and plans that minimize costs.

 

Ideally, this judgement should be influenced  by the nature of the way the business  wins orders: 

 

• price-sensitive markets may require a risk-avoiding cost minimization plan that does not always satisfy peak demand,

 

• markets that value responsiveness and service quality may justify a more generous provision of operational capacity.