A value chain analysis of tuna industry in Ghana

Every can of tuna purchased by the consumer has taken a long journey before reaching the supermarket shelves. For each can bought there is a lengthy process from sea to shelf. A large proportion of the tuna cans purchased in the European Union come all the way from West Africa; a developing region with a high dependency on fisheries. Amidst an ever-increasing demand for tuna products the global tuna fisheries are set to continue expanding, apparently one of the last natural resource based industries fit to do so in West Africa. Tuna is the biggest fisheries export and dominates the fisheries sector in Ghana, a country situated in West Africa. This thesis aims to understand how this globally important industrial fisheries functions in terms of procedures, practices, Governance and finance. Socioeconomic influences, in the setting of a developing country, were also examined. For these purposes a Value Chain Analysis was employed. A Value Chain Analysis is a tool commonly used to understand how different companies and organizations participate in a domestic policy environment, which directs conclusion in the global economy. This analysis has the potential to allow researchers to fully understand a commodity chain and hence identify realistic opportunities for consequential improvements. Interviews and questionnaires were employed in-field Ghana along with secondary data collection techniques. It was found that the fisheries functions at the production level under influences from large multinational companies and tends to operate with a certain degree of lawlessness. Governance over the value chain is well defined, however implementation is poor or non-existent. The processors, whom are also dominated by multinationals, exert some control over the producers and their sales, however the high value links which are highlighted occur at the retail stage. Socioeconomic dynamics acting in the chain included the lack of communication between the public and private sector, power imbalances amongst players at production, the role of local businesswomen as actors in the chain and the general characteristics of the workers in the industry. Value addition and upgrading are needed the most in Governance over the chain, especially within Monitoring, Control and Surveillance. The results of the study provide a wealth of material about the components of a cost-heavy fishing industry in a developing country; an industry on which many eyes have recently turned due to illegal fishing activities. It highlights clearly where funding and future focus are needed. This value chain can be used as a guide for those that need to comprehend the financial complexities and real life dynamics of the Ghanaian tuna fishing industry today.

Big data warehouse framework for smart revenue management

Revenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Management