This work investigates the quantitative analysis of some factors that affect the performance of manufacturing workers in industries in southern Eastern Nigeria. Experiments were designed, and conducted through the use of work measurements technique and Tests Studies. These selected specific factors (maintenance, equipment, Power/Energy, technology, safety and Training.) used for the study, were investigated using eighty-two manufacturing workers from thirteen manufacturing companies. Data were collected for analyses. The software used was SPSS. Software tools used for various analyses in the study are: statistics, correlation, multi linear regression, response surface regression and multi- co linearity diagnoses, while t – value, F- ratio, p – values and variance of inflation factors (VIF) were used to test the hypotheses. The various statistical analyses performed were presented, studied and interpreted. The correlation coefficients are positive and in descending order of maintenance, equipment, Power/Energy, technology, safety and Training. The coefficient of determination, R2 and the variance ratio (VR); and F- value and t –coefficient values were also determined for strong inference. Curves were generated to observe the behavioral patterns of the relationship between manufacturing workers’ factors influence on performance. Results showed that the identified factors affected the performance of manufacturing workers in the manufacturing industries, in such a manner that the Companies productivity is affected positively by some factors and negatively by some others. Therefore, in the general consideration, the factorial indices that predicted the manufacturing workers performance of the selected factors: motivation, power, safety, maintenance, training, equipment and technology are found to be 0.877, 0.48, 0.614, -1.36, 0.789, 1.421 and – 0.495 respectively. These factorial indices are valid in controlling problems arising from manufacturing industries.
Keywords: Analysis, Company., Factors, Manufacturing Workers, Performance Industry, quantitative
