Design for Limit Variability in Quality of Industrial Products: A Case Study of Cutix Cable Manufacturing Company, Nnewi, Nigeria

This paper presents numerical approaches to assessment of process capability and product quality standards for production processes. Onehundred cables produced by cable production process of CUTIX factorywere sampled in order to access their insulation quality. Twenty-five samples were used from bathes in stock and each of the samples has sample size of four. The statistical method was used to establish the mean of distribution, the average standard deviation of samples and the average range of samples to establish the process control limits. The distribution of means of samples was normalized in order to ascertain the conformity of the distribution to normal distribution. The distribution was confirmed normal and transformed to standard normal distribution so that the area under the normal curve applies in the analysis of the process distribution. The area under the normal curve of the distribution was evaluated as 0.94 falling within acceptable limit for processes in control. Process control was established using classical relations and analogies to establish process capability and Process capability index. Control charts were developed for the mean and range of samples for the process monitoring period prior to the evaluation of the process population mean as 5.2, the average standard deviation of the process as 0.60, action limits 6.1 and 4.3 for the mean and for the range 2.3 and 0.0. The coefficient of variation was found to be 11% (0.11), indicating low variability of process. The CUTIX process for cable production thereforeproduces within specification. Above all, the probability of any sampleobservation being in the warning limit is 0.76 while the probability of anysample observation being within the control limit is 0.94, showing that theprocess in control.Keywords: limit variability, quality of products, control limits, processcapabilit

Limit Stress Spline Models for GRP Composites

This paper focuses on the use of Spline functions in modelling the critical stress - strain responses of polyester matrix GRP Composites. Spline functions were established on the assumption of three intervals and fitting of quadratic and cubic splines to critical stress-strain responses data. Quadratic and Cubic spline models for three intervals of data points 0.024 £ x£ 0.036, 0.036 £ x £ 0.061and 0.061 £ x £ O. 12 were established. The optimization of quadratic and cubic models by gradient search optimization gave the critical strain as 0.024, which resulted to strength of approximately 26 MPa. Strain hardening was observed to occurr within a strain range of 0.03 to 0.12 leading to strength of about 62 MPa predicted by Cubic spline. Splines were found to accurately predict the functional values at subinterval, 0.024 £ x £ 0.036 of data points. Spline model is therefore recommended as it evaluates the function at subintervals, eliminating the error associated with wide range interpolation

Multivariate Time Series Analysis for Optimum Production Forecast: A Case Study of 7up Soft Drink Company in Nigeria

This study focuses on the establishment of an optimum forecast model that predicts future production trends of 7UP Bottling company. Sixty (60)months time series data of 7UP bottling company were used after ascertaining the presence of seasonal variation and trend components of the data to establish the multidimensional forecast model. Predictive Production rate model was developed using a general multivariate regression equation form. The monitoring schemes show values of MSE and MAD as 0.0177 and 0.0658 respectively giving a tracking signal of 0.0. These values established the multivariate forecast model as optimum approach in tracking demand and production trends in a production setup. The value of the standard deviation of distribution of errors of 0.0823 estimated with MAD also confirms the authenticity of this model. The responses shown in the graphics of this study clearly explains the mixed time series which definitely contains seasonal variation and trend components as established in this study. Also the coefficient of determination of 0.957956 explains about 97% fitness of the established model to production data. The trend component associated with time variable (Mtncod) causes production to increase by 0.002579KG/Month. Finally, this work adds to the growing body of literature on data-driven production and inventory management by utilizing historical data in the development of useful forecasting mathematical model.Keywords: production model, inventory management, multivariate timeseries, production forecas