In several countries around the world, grain commodities are traded as assets on stock exchanges. This indicate that the market and effectively the prices of the grain commodities in such countries, are controlled by several local and international economic, political and social factors that are rapidly changing. As a result, the prices of some grain commodities are volatile and trading in such commodities are prone to price-related risks. There are different trading strategies for minimising price-related risks and maximising profits. But empirical research suggests that making the right decision for effective grain commodities trading has been a difficult task for stakeholders due to high volatility of grain commodities prices. Studies have shown that this is more challenging among grain commodities farmers because of their lack of skills and the time to sift through and make sense of the datasets on the plethora of factors that influence the grain commodities market. This thesis focused on providing an answer for the main research problem that grain farmers in South Africa do not take full advantage of all the available strategies for trading their grain commodities because of the complexities associated with monitoring the large datasets that influence the grain commodities market. The main objective set by this study is to design a framework that can be followed to collect, integrate and analyse datasets that influence trading decisions of grain farmers in South Africa about grain commodities. This study takes advantage of the developments in Big Data and Data Science to achieve the set objective using the Design Science Research (DSR) methodology. The prediction of future prices of grain commodities for the different trading strategies was identified as an important factor for making better decisions when trading grain commodities and the key factors that influence the prices were identified. This was followed by a critical review of the literature to determine how the concepts of Big Data and Data Science can be leveraged for an effective grain commodities trading decision support. This resulted in a proposed framework for grain commodities trading. The proposed framework suggested an investigation of the factors that influence the prices of grain commodities as the basis for acquiring the relevant datasets. The proposed framework suggested the adoption of the Big Data approach in acquiring, preparing and integrating relevant datasets from several sources. Furthermore, it was suggested that algorithmic models for predicting grain commodities prices can be developed on top of the data layer of the proposed framework to provide real-time decision support. The proposed framework suggests the need for a carefully designed visualisation of the result and the collected data that promotes user experience. Lastly, the proposed framework included a technology consideration component to support the Big Data and Data Science approach of the framework. To demonstrate that the proposed framework addressed the main problem of this research, datasets from several sources on trading white maize in South Africa and the factors that influence market were streamed, integrated and analysed. Backpropagation Neural Network algorithm was used for modelling the prices of white maize for spot and futures trading strategies were predicted. There are other modelling techniques such as the Box-Jenkins statistical time series analysis methodology. But, Neural Networks was identified as more suitable for time series data with complex patterns and relationships. A demonstration system was setup to provide effective decision support by using near real-time data to provide a dynamic predictive analytics for the spot and December futures contract prices of white maize in South Africa. Comparative analysis of predictions made using the model from the proposed framework to actual data indicated a significant degree of accuracy. A further evaluation was carried out by asking experienced traders to make predictions for the spot and December futures contract prices of white maize. The result of the exercise indicated that the predictions from the developed model were much closer to the actual prices. This indicated that the proposed framework is technically capable and generally useful. It also shows that the proposed framework can be used to provide decision support about trading grain commodities to stakeholders with lesser skills, experience and resources. The practical contribution of this thesis is that relevant datasets from several sources can be streamed into an integrated data source in real-time, which can be used as input for a real-time learning algorithmic model for predicting grain commodities prices. This will make it possible for a predictive analytics that responds to market volatility thereby providing an effective decision support for grain commodities trading. Another practical contribution of this thesis is a proposed framework that can be followed for developing a Decision Support System for trading in grain commodities. This thesis made theoretical contributions by building on the information processing theory and the decision making theory. The theoretical contribution of this thesis consists of the identification of Big Data approach, tools and techniques for eradicating uncertainty and equivocality in grain commodities trading decision making process.

Thesis (MBA (Business Management))--Stellenbosch University, 2008. / ENGLISH ABSTRACT: This research studies the relationship between economic growth and commodity-market volatility in South Africa. The mining industry, largely supported by gold, diamonds, coal, iron ore and platinum-group metals, has played a central role in South Africa's economic development. The commodities that were selected for the study are the five major minerals, namely gold, coal, iron are, platinum-group metals and diamonds. It investigates two central questions, the first of which is whether the mining of the above commodities still makes a significant contribution to the South African economy in terms of employment, revenue and foreign-currency earning. The second is whether there is a link that reflects a statistically and economically significant association between commodity-price volatility and economic growth in South Africa. The economic environment in South Africa has been extremely positive, with a growth averaging around 5% for the period 2004-2006. An important contributing factor to this favourable environment has been the behaviour of mineral commodity prices. Mining makes a direct and indirect contribution of approximately 15% to GOP, accounts for around 50% of merchandise exports (including primary and beneficiated mineral exports), 12% of fixed investment, 30% of the market value of the JSE limited and 20% of formal-sector employment. Therefore, mining remains a key foundation of the South African economy. Time series data analysis confirms that the volatility of the major foreign currency-earning commodities - gold, platinum, coal, diamonds and iron ore - are negatively or weakly related. This relationship actually reflects the harmful effect of the volatility of these commodities on economic growth. Until recently, South Africa was heavily dependent on exports of primary commodities. Since the commodity prices are highly volatile, South Africa has to cope with large shocks, both positive and negative. Commodity cycles used to be determined by the growth cycle in the United States, but more recently, with the emergence of the Asian economies and China, in particular, the dominant influence of the United States economy on the commodity cycle has waned. The continuing instability in commodity prices and export earnings of South Africa has to be addressed by diversifying the exports towards more dynamic products; particularly manufactured goods and services. / AFRIKAANSE OPSOMMING: Die verwantskap tussen ekonomiese groei in Suid-Afrika en die mynbedryf, wat hoofsaaklik ondersteun word deur goud, diamante, steenkool, ystererts en die platinumgroepmetale, het 'n sentrale rol in Suid-Afrika se ekonomiese ontwikkeling gespeel. Die kommoditeite wat vir hierdie navorsing gebruik word, is die vyf belangrikste minerale, naamlik goud, steenkool, ystererts, die platinumgroepmetale en diamante. Twee sleutelvraagstukke word hier ondersoek, waarvan die eerste dit bevraagteken of die ontginning van bogenoemde kommoditeite nog steeds 'n belangrike bydrae tot die Suid-Afrikaanse ekonomie lewer wat indiensneming, inkomste en buitelandse valuta betref. Tweedens word daar ondersoek of daar enige skakel is wat 'n statistiese en ekonomies betekenisvolle verwantskap tussen kommoditeitsprysonbestendigheid en die ekonomiese groei van Suid-Afrika weerspieel. Die ekonomiese omgewing in Suid-Afrika was besonder positief, met 'n groeikoers van ongeveer 5% gedurende die 2004-2006-tydperk. Die gedrag van mineraalkommoditeitspryse het 'n belangrike bydrae tot die gunstige ekonomiese omgewing gelewer. Mynwese lewer 'n direkte en indirekte bydrae van ongeveer 15% tot die algemene binnelandse produk, is verantwoordelik vir ongeveer 50% van die uitvoer van handelsware (insluitend primere en veredelde mineraaluitvoere), 12% van vaste beleggings, 30% van die markwaarde van die Johannesburgse Aandelebeurs en 20% van die werksgeleenthede in die formele sektor. Daarom is mynwese 'n sentrale deel van die Suid-Afrikaanse ekonomie. Die ontleding van tydreeksdata bevestig dat die onbestendigheid van die belangrikste kommoditeite wat buitelandse valuta verdien, naamlik goud, platinum, steenkool, diamante en ystererts, negatief of swak verwant is. Hierdie verwantskap weerspieel eerder die skadelike uitwerking van hierdie kommoditeite se onbestendigheid op ekonomiese groei. Tot onlangs was Suid-Afrika grootliks afhanklik van die uitvoer van primere kommoditeite en die pryse van hierdie kommoditeite is baie onbestendig. Suid-Afrika moes dus groot skokke, positief sowel as negatief, die hoof bied. Die groeisiklus in Amerika het in die verlede die kommoditeitsiklusse bepaal, maar meer onlangs het die Asiatiese ekonomiee en veral China die dominante invloed van ekonomiese Amerika laat afneem. Die voortdurende onstabiliteit in kommoditeitspryse en buitelandse inkomste vir Suid-Afrika moet meer aandag geniet deur uitvoere te diversifiseer na meer dinamiese produkte, veral vervaardigde produkte en dienslewering.