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Is capacity utilisation overrated?




Is capacity utilisation overrated?

HARARE – It is that month of the year when the Confederation of Zimbabwe Industries announces results of its annual manufacturing sector survey. And, of all the variables that the survey touches on, the spotlight is going to be on capacity utilization, which is a measure of installed capacities of manufacturing plants being employed.

For instance, if a milling plant has an installed capacity of 500 tonnes per given period and only 300 tonnes are produced during that period, then capacity utilization will be 60 %. What CZI does is to aggregate the capacity utilisation figures of companies from different sub-sectors, which are then weighted to come up with an average capacity utilization figure.

Since the year 2011, industrial capacity utilization has been declining from high levels of 57.2% in 2011 to 34.3% registered last year. The statistic is used by policy makers for planning and reviewing of policies, as well as other stakeholders. The graph below shows the targeted and actual performance of capacity utilization between 2011 and 2016.

Source: Author’s computation

The red line shows the levels of capacity utilization targeted by the policy makers, while the blue line shows the actual levels realized. The Industrialization Development Policy (IDP), launched five years ago, was targeting to grow capacity utilization from 57,2 % to 80 % by end of this year. However, instead of growing, capacity utilization has fallen down to 34,3 % by last year, with no change expected this year. Judging by capacity utilization only, the IDP can therefore be said to have failed.

But the question worth asking is: Is capacity utilization not an overrated indicator for judging the health of the manufacturing industry? The other side of the capacity utilization coin should therefore be vigorously interrogated, to understand this question.

What should be noted is that ccapacity utilization can remain constant, or it can actually decline, as industrial production increases. We will need an example to explain that. Let us say a milling company has one grinding machine with an installed capacity of 1,000kg of maize meal. Assuming that the grinding machine is only producing 500kg, capacity utilization would be 50%. Suppose, in the second year, the same company introduces a second machine with the same installed capacity as the first, and also producing 500 tonnes. The new production levels for the company’s two machines would be 1,000 tonnes, and capacity utilization will, interestingly, remain unchanged at 50% (1000/2000×100%). Here we see that production has actually increased but capacity utilization has remained unchanged.

We get another interesting scenario if, in the third year of its operation, the same company introduces a third grinding machine, with the same installed capacity as the first and second, but producing only 200 tonnes of maize meal. Total production will increase to a record 1200tonnes but capacity utilization will actually fall from 50% to 40% (1200/3000×100%).

Judging by the above scenarios, can we safely say capacity utilisation is a perfect measure of the actual manufacturing sector performance? You may want to agree with me that it is not. Statistics sometimes conceal more than they reveal. Capacity utilization may have fallen, as was the case between 2011 and 2015 in Zimbabwe’s manufacturing sector, but production may have actually increased. Taking the last recorded capacity utilization of 34.3%, let us assume that 1 000 new manufacturing companies are established today, but all operating their different plants at 34.3% capacity utilization, do you know that capacity utilisation, when calculated, will remain the same?

So why do analysts always want to single out capacity utilization when assessing the performance of the industry? Why do policy makers only use capacity utilization to judge the success or failure of manufacturing policies, when it is fraught with such shortcomings? I would say, ask them!

A better indicator that should be actually paid particular attention to, when measuring manufacturing performance, is the volume of production. If average production has increased, then that’s a good thing, even if capacity utilization has declined.

There is one last thing to consider when looking at volume of production. Production can be measured in terms of actual quantity or value. And the two can give different interpretations over different comparable periods. For instance, a company may produce 1,000kg of a product that is worth $1/kg, which translates to a $1,000. The second year, production might increase to 5,000kg of the same product but price falls to $0,20/kg, meaning that the actually quantity in value would still be $1,000. Then the last scenario is when there has been a price increase. Let’s say in the third year only 300kg is produced and price has risen to $5/kg; the quantity in value terms would be $1,500.

All the above scenarios point to how intricate it can be to analyze manufacturing survey results and how relying on one indicator may not be ideal. It is always good to explore all the indicators to come to an objective deduction.


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