These shortcomings increase the confounding effects of the productivity variables measured and analyzed.
The figures calculated for each of the variables of productivity (factor productivity ratios) over a period of time can be systematically plotted to identify and predict a trend that shows change in productivity. The figures for each variable can be monitored individually or collectively with other variables. To monitor productivity over a period of one year productivity metrics for each month have to be calculated. The change in productivity (trend) between one month and another or across the 12 months can be identified by simply calculating the difference in the productivity metrics. The trend can be graphically represented by plotting the productivity metric over a period of year. Fig 1 shows a trend for multifactor (labor and capital) productivity ratio calculated for the months of January is 0.560, February 0.585, March 0.615, April 0.616, May 0.610, June 0.623, July 0.623, August 0.634, September 0.598, October 0.590, November 0.589 and December 0.590. Also for the trend identified to be more meaningful, the variable(s) plotted must be compared with productivity of other firms with factors similar to the one in question. This can be done by comparing the productivity metrics with external benchmarks. The last bar in Fig 1 represents the benchmark for similar (small catering/restaurants service). The operation manager of the food service unit can therefore comparatively monitor performance of the unit. The work of the operation manager can be made simpler if s/he uses Microsoft Project application to measure and monitor schedule performance of staff and inputs (monetized resources). This application will allow the manager to handle large variety of data and to (intra) extrapolate outcomes.
The operation managers can use the information (factor