LEAN maintenance model based on change management allowing the reduction of delays in the production line of textile SMEs in Peru

This article examines the problem of production line delays in a textile small- and medium-sized enterprise (SME) that produces polyester fibre from recycled bottles, based on orders. Factors that have resulted in production line delays include prolonged unscheduled maintenance time, and preparations and adjustments prior to operating the equipment. To address the problem, a model was developed applying lean manufacturing tools through change management, with the aim of increasing equipment availability and useful life. To validate the model, a pilot was developed to determine how the increase in equipment availability helps reduce delays in the production line, which eventually improves completion of customer orders

Detecting periodicity in experimental data using linear modeling techniques

Fourier spectral estimates and, to a lesser extent, the autocorrelation function are the primary tools to detect periodicities in experimental data in the physical and biological sciences. We propose a new method which is more reliable than traditional techniques, and is able to make clear identification of periodic behavior when traditional techniques do not. This technique is based on an information theoretic reduction of linear (autoregressive) models so that only the essential features of an autoregressive model are retained. These models we call reduced autoregressive models (RARM). The essential features of reduced autoregressive models include any periodicity present in the data. We provide theoretical and numerical evidence from both experimental and artificial data, to demonstrate that this technique will reliably detect periodicities if and only if they are present in the data. There are strong information theoretic arguments to support the statement that RARM detects periodicities if they are present. Surrogate data techniques are used to ensure the converse. Furthermore, our calculations demonstrate that RARM is more robust, more accurate, and more sensitive, than traditional spectral techniques.Comment: 10 pages (revtex) and 6 figures. To appear in Phys Rev E. Modified styl