Value chain diversification in the sugar industry using quantitative economic forecasting models

Master Thesis


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The South African sugar industry is facing increasing pressure from global sugar markets where the price of sugar is significantly lower than in domestic markets, as well as from the implementation of the health levy which has resulted in beverage manufacturers replacing sugar with non-taxable sweeteners. To maintain the industry infrastructure and to increase the demand for sugar, a diversification route for sucrose is needed. Most of the studies focused on identifying a diversification solution for bioproducts are survey or experienced based and so, one of the main aims of this study was to use mathematical modelling of industrial manufacturing data to identify one single industry to explore sucrosebased chemicals. Datasets published by Statistics South Africa, The World Bank, Trading Economics and by the Organization for Economic Cooperation and Development were considered, from which the monthly manufacturing industries' sales data published by Statistics South Africa was selected for model building. Seven different types of models were considered, including the Naïve method, simple and weighted moving averages, simple exponential smoothing, Holt's method, Holt-Winters' method and Auto-Regressive Integrated Moving Average (ARIMA) models. Each type of model was analysed in the context of the eight industries' data, from which ARIMA models were identified as those which were broad enough to cater for the varying degrees of trends and seasonality in the data without oversimplifying the data's behaviour. The other seven were not suitable either because their narrow applicability was not suitable to most of the datasets at hand or because they would provide an oversimplified model which would not be robust for future datapoints. The models were then built using training and test data splits with the auto.arima function in R Studio. From these, selection matrices were constructed to evaluate the industries' forecasts on sales growth and revenue generating potential, the results of which identified the beverages' industry to the best option for investment. One of the objectives of the study was to identify a sucrose-based chemical for investment that is not highly commercialized in order to widen the range of investment options available. To this end, only four of the less commercialized chemicals explored showed significant advancement based on published research and patents, namely caprolactam, dodecanedioic acid, adipic acid and muconic acid. However, all four chemicals would feature mainly in the textiles industry, which the model identifies as not being a high growth industry and thus would limit the revenue generating potential. The main beverage constituents of common drinks were then explored, from which nonnutritive sweeteners were chosen based on their wide applicability. From the six sweeteners considered, sucralose is the most widely used sweetener with the least number of reported serious health risks; this is thought to compensate for sucralose being a mid-price range product. Sucralose would also allow the sugar industry to leverage beverage manufacturers' replacement of sugar with sweeteners to comply with the Health Protection Levy. The techno-economic analysis performed for the selected synthetic sucralose production process proved profitable in the first year of operation, as did a refined configuration using a lower ethyl acetate flow rate. This is largely due to the retail price of sucralose being close to 8 times the purchase cost of the most expensive raw material used. Although this profitability analysis is promising, further investigation into the fixed capital costs involved should be done prior to the sugar industry investing in sucralose. Recommendations for further work to improve the profitability of this scenario include the consideration of forming a strategic partnership with key players in the beverages' industry, exploring alternative production routes, and using other time series models to validate the results achieved here.