Evaluation of the productivity concept

Productivity

Evaluation of productivity measures

Peters and Waterman (1982) have observed from lessons learned in America's best-run companies, that success is not so much dependent on procedures and systems, but on having caring, motivated employees, managed by people committed to maintain standards and occasionally take risks.

How can these less–quantifiable measures be devised and recounted with the more traditional productivity measures?

Smith (1967) found in studies undertaken in the USA that there was a strong relationship between entrepreneurs who achieved rapid growth in their businesses and those that were opportunistic by nature. Interestingly, these individuals had all developed adaptable organizations capable of reacting to external demands in a flexible way.

Burns and Stalker (1961) in their study of technology and organization structure showed a relationship between the type of technology and the management structure a company adopted. Of importance here was their conclusion that the companies with well-proven technologies adopted formal management structures, whilst those with newer technologies adopted less-defined structures. For example the textiles and footwear industries have been unable to adapt to meet the challenges of changing technology and markets.

The different conceptual approaches to measuring productivity fall within a range of sophistication with the simplest measures at one end and the most complex at the other. The Evaluation of Measures schematic (contained in the PDF version) shows these measures.

The less complex at the left of the diagram tend only to relate to one or two factor inputs and outputs and have a goal- or output-orients approach to the evaluation. Further to the right the approach becomes more systems-orientated as multiple measures are incorporated. Measures on the far right are the most comprehensive, taking into account all factor outputs and inputs in a system.

Whereas most companies attempt to evaluate performance over the whole range, success is dependent on two main factors:

  • The degree to which goal/output measures can be related to the whole system;
  • The data available and its quality.

Whereas performance measures can be of two kinds, (as in the Evaluation of Measures schematic contained in the PDF version) goal-based measures or systems-based measures, dependent on the degree to which they take into account the performance of the organization as a whole, there are really only three basic types of measures in common use. These are:

  1. Partial productivity measures; where total output is divided by a partial measure of input.
  2. Total productivity measures where the total inputs to a system are measured as well as the total outputs.
  3. Added value indices where unlike the other two, a total measure is converted into a partial measure, by deducting the value of raw materials and bought-out goods and services from the numerator and denominator, to give that amount of value added during the production process.

Although many productivity measures take a range of inputs into consideration, they are not, in the strict sense, total measures. However, they often do attempt to measure the productivity of the whole or part of the system. Discussion of the nature and problems arising from these types of measures has been divided into the two main types previously mentioned:

  • Goal measures, that by their nature tend to be partial measures, and
  • System-based measures that attempt to take account of context and the wider system in which they operate.

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