| Summary: | There are a myriad of bottleneck variables that constrict the overall manufacturing capacity and make improvement decisions complex. Consequently, this study aims to identify and analyse the copious variables to pinpoint the key variable factors that influence and turn the manufacturing elements into bottleneck problem to prioritize process improvement effort. The study is limited to identifying and analysing the numerous bottleneck variables to gain insight into how much each of the variables influences the process output via the manufacturing elements. The 76-bottleneck variables abstracted from the literature were used to craft a structured questionnaire that were administered to respondents in the manufacturing industry whose size was determined at a 95% confidence level and 5% error margin respectively. The 95% confidence level is chosen to ensure adequate representation of the population and to validate the data for the study. The respondents' scores were collated into (m x n) data matrix which served as input variable into the factor analysis model. StatistiXL software was then employed to evaluate the data matrix. The trivial variables were discarded and 19 factors with eigenvalues (λ ˃ 1) were extracted and creatively labelled for interpretation. The result established that the “Process capability index” is the principal bottleneck factor that loaded 25% of the variables studied. The principal variables in the cluster include Equipment failure = -0.832, Operations = -0.780, Material unavailability = -0.811, and Market demand = -0.739 among others. Similarly, Manufacturing process restraint, Resources, Weather, Communication, Logistics, and Line dedication are other key factors by the magnitude of their respective variables’ factor loadings such as Random event = 0.812, Raw materials flow = -0.834, Process technology = 0.878, and Random environmental factors among other variables. Although bottleneck problems vary from one manufacturing system to another, the problems identified and the solutions presented in this study are generic and the improvement effort should focus on addressing the principal variables while not neglecting the middling and weakling variables.
|
|---|
