Financial benchmarking analysis: Northern Cape farmers

English: The primary objective of the study was to develop a financial measurement-based benchmarking system for GWK on the financial status of their producers. Financial benchmarking systems provide the producers with opportunities to evaluate their past and current financial performance, not only to their own financial status but also to other producers that are included in the benchmarking data. To have a better understanding of the performance of the farm’s financial status and to have a possible explanation of why certain changes had occurred, the first secondary objective was to compare trends of the whole agriculture sector of South Africa with those that occurred to the GWK producers in the study. It was found that the GWK producers had followed more or less the same trends than those experienced by the South African agricultural sector. After the trends were compared between GWK producers and the South African agricultural sector, the limited financial statements obtained from GWK, were analyzed by calculating the financial measurements for each farm over the five years. This was done in order to determine the border values that can be used to divide each measurement into three performance groups. These groups will be used to determine the position for each measurement of a farm relevant to the other farms in the benchmarking system. When the producer has seen the indication that a certain measurement is in the midpoint of bottom performance groups, he knows there are other options available to improve that position, as is already being done by other producers. This leads to another secondary objective that was identified and analyzed. When a producer wants to improve one or even more than one financial measurement, certain changes have to be made that will influence the income statement and balance sheet. As these statements are interactive and a change in one area of the statement will have an influence on the overall results, it is necessary to have an idea or indication of what these influences can be. To provide some background on what the possible outcomes can be the correlation between the measurements and their determinants were determined. These correlations will provide important information on what the possible results of a certain change by a producer on a farm can be. As the financial market is ever-changing, the changes cannot always be hundred percent predictable, but one can at least provide an idea of what can be expected. The last secondary objective is to rank the farms according to their operating efficiency for each enterprise, using DEA. As results indicated, this method of benchmarking can be used in coordination with the border measurement benchmarking system. The difference that exists is that the DEA benchmarking system only divides the farms into two groups as being efficient and inefficient. These two groups can be compared to the results obtained from the border measurement benchmarking system; the farms identified as being the efficient ones are mostly the farms that had most of their financial measurements in the top performance and the top half of the midpoint performance groups. The opposite is also true for the farms identified as being inefficient. Conclusions and recommendations from the study include that a benchmarking system can provide very important information to producers on the performance of their farms, not only to past performance, but also with regard to their rivals. When certain adjustments have to be made to improve the performance of the farm, it is important to remember the possible correlation that exists between the financial measurements and what the possible outcomes can be. The correlation between the measurements is also a point that is available for future research. Lastly, it is recommended that when a farm’s financial position is benchmarked to other competitors, more than one benchmarking system is to be used. This will provide more accurate information to the actual performance of the farm, as a wider spectrum is covered by using for example the border measurement and DEA benchmarking systems.