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A common problem faced by managers is that of project selection, to decide which project out of the lots should be undertaken. This paper aims at comparing results of the application of two approaches -respectively regression analysis, a parametric method and artificial neural network, a non-parametric technique. To demonstrate these methods, the models were illustrated using Oral, Kettani and Lang's data on 37 R&D projects for their success. From statistical analysis, it was discovered that artificial neural network showed superiority to deciding how projects should be ranked and selected.

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Total Productive Manufacturing (TPM) also addresses planned maintenance as well as autonomous maintenance that determine the maintenance requirement of machines in their operating context. This paper focuses on the effect of an integrated human resource strategy on Total Productive Manufacturing (TPM) and assesses how the combination of the two can increase a firm's productivity. Some results of a South African manufacturing case study company in a unique environment are presented. The main conclusion reached in this paper is that when innovative human resources practices are applied properly, they will promote total machine system efficiency and hence increase the productivity of the company.As result TPM that does not normally focus on HR is taken to a higher level, whereby the additional human factor is taken into consideration. It suggests that operators should be nurtured and be well trained as part of the TPM focus, because they are the ones who are operating those machines with intimate knowledge. The limited research presented here as part of a doctoral thesis highlights some element of human resources practice that can be applied to enable operators to be efficient and productive as part of the TPM machine system.

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Retracted at the request of the authors for duplicate publication.