Market needs analysis for Turnkey automation project based organisation in the Eastern Cape
- Authors: Buys, Stefan
- Date: 2014
- Subjects: Industrial marketing , Marketing research
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: vital:8927 , http://hdl.handle.net/10948/d1021170
- Description: Customer value is essentially the perceived value that the customer gains when purchasing a product. The perceived value is the trade-off between the cost of the product and the benefits it provides. If the customer perceives the benefits exceed the costs, then the customer perceives value in the product. A need is defined as a perceived lack of something. Customers across industries consider fulfilment of their unique needs as a key metric in evaluating the effectiveness of a solution. In order for an organisation to successfully provide solutions based on customer needs it is crucial for the organisation to understand the needs of their target market. Market Needs Analysis (MNA) is the investigation that intends to improve knowledge about the needs of the organisation’s target market. Project Based Organisations (PBOs) are organisations who are primarily orientated to execute once-off projects with an organisational structure specially formed for a temporary period tailored to deliver a product that meets the needs of specific customers. This research investigates the value offering of a Turnkey PBO and its alignment to the needs of its market. The organisation is privately owned, operating primarily in the South African automotive industry. The true name of the organisation that will be researched will not be disclosed for confidentiality reasons; instead it will be called My Automation Company (MAC). Until the end of 2010, the core focus of the organisation was the supply and maintenance of specialised electronic and computerised tools and services used mainly for quality assurance and production support. Towards the end of 2010 the organisation shifted its focus to providing a new product and service, Turnkey Industrial Automation Projects, to its existing market. In the rush of introducing new products and services many organisations neglect to analyse the market to ensure that they fully understand and can satisfy its needs. Understanding customer needs is crucial in order for new products to be successful thereby capitalising on the available growth potential. Turnkey Industrial Automation Projects is a new product in the organisation’s existing market. It is therefore important that the organisation investigates the customer needs for this particular product as it will differ significantly from customer needs for Service Level Agreements which the organisation is familiar with. The purpose of this research study is to advance the current understanding of the Customer Value Proposition (CVP) of Turnkey PBOs by performing a systematic analysis of the determinants of customer value. This research is an exploratory quantitative study comprised of literature- and case study components used to test proposed hypotheses. The literature study was performed on secondary sources to establish the key concepts related to the topics of PBOs, Industrial Marketing, Market Needs Analysis and CVP. The empirical study consisted of surveys (questionnaires) completed by various customers and employees of MAC. The questionnaire used in this research consisted of questions regarding demographic data and questions regarding perceived CVP and influencing factors. Descriptive statistics was used to summarise the data into a more compact form which could simplify the identification of patterns in the data. Inferential statistics was used to verify if conclusions made from the sample data can be inferred onto a larger population Recommended business practices based on the statistical analysis of the survey results were identified. It was shown that there exists a relationship between Perceived Value and Product Characteristics, Relational Characteristics, Supplier Characteristics, Benefits and Sacrifices by using Pearson’s product-moment correlation coefficient to measure the linear association between the variables. A significant difference in the perceived performance of MAC in certain aspects was found. There is however no significant difference between the perceived importance’s assigned to CVP factors by High- and Low-level Management customers. It was also found that there is a significant difference in the perceived performance of MAC by Customers and Employees in certain aspects. While there is alignment between the importance Employees and Customers place on the majority of independent variables, there is misalignment with regard to the various Supplier Characteristics. Supplier Commitment was shown to be the factor that requires the most attention as: it has the biggest influence on the perceived value gained from dealing with MAC; Customers rate the organisations performance in this regard lower than Employees do and Employees assign lower importance to this factor than Customers. This study concluded in the development of a hypothesised CVP model that indicated not only which factors influence the CVP of a Turnkey PBO in the Eastern Cape but also the effect that each of the identified factors have on perceived value.
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- Date Issued: 2014
Genetic algorithm for Artificial Neural Network training for the purpose of Automated Part Recognition
- Authors: Buys, Stefan
- Date: 2012
- Subjects: Genetic algorithms , Software architecture
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:9648 , http://hdl.handle.net/10948/d1008356 , Genetic algorithms , Software architecture
- Description: Object or part recognition is of major interest in industrial environments. Current methods implement expensive camera based solutions. There is a need for a cost effective alternative to be developed. One of the proposed methods is to overcome the hardware, camera, problem by implementing a software solution. Artificial Neural Networks (ANN) are to be used as the underlying intelligent software as they have high tolerance for noise and have the ability to generalize. A colleague has implemented a basic ANN based system comprising of an ANN and three cost effective laser distance sensors. However, the system is only able to identify 3 different parts and needed hard coding changes made by trial and error. This is not practical for industrial use in a production environment where there are a large quantity of different parts to be identified that change relatively regularly. The ability to easily train more parts is required. Difficulties associated with traditional mathematically guided training methods are discussed, which leads to the development of a Genetic Algorithm (GA) based evolutionary training method that overcomes these difficulties and makes accurate part recognition possible. An ANN hybridised with GA training is introduced and a general solution encoding scheme which is used to encode the required ANN connection weights. Experimental tests were performed in order to determine the ideal GA performance and control parameters as studies have indicated that different GA control parameters can lead to large differences in training accuracy. After performing these tests, the training accuracy was analyzed by investigation into GA performance as well as hardware based part recognition performance. This analysis identified the ideal GA control parameters when training an ANN for the purpose of part recognition and showed that the ANN generally trained well and could generalize well on data not presented to it during training.
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- Date Issued: 2012