A review on the selection of lean production tools and techniques

There has been numerous published literature related to lean production. However, very limited studies have been found focussing on the selection of lean production tools and techniques especially for Malaysian context. The review is based on the contemporary literature that published between year 2004 to 2014. The main databases used were Science Direct, Scopus and Emerald. The review gives general pictures of the selection and implementation of lean tools and techniques in various industries and the factors that affect the selection process. The analysis showed that there was no study yet on the selection of lean production tools and techniques specifically in Malaysia by using rational decision making process. Therefore, this gap requires further research on the selection of appropriate lean production tools and techniques by considering several critical decision criteria

A review on the selection of lean production tools and techniques

There has been numerous published literature related to lean production. However, very limited studies have been found focussing on the selection of lean production tools and techniques especially for Malaysian context. The review is based on the contemporary literature that published between year 2004 to 2014. The main databases used were Science Direct, Scopus and Emerald. The review gives general pictures of the selection and implementation of lean tools and techniques in various industries and the factors that affect the selection process. The analysis showed that there was no study yet on the selection of lean production tools and techniques specifically in Malaysia by using rational decision making process. Therefore, this gap requires further research on the selection of appropriate lean production tools and techniques by considering several critical decision criteria

Penguasaan matematik memberi input terhadap pencapaian akademik: satu tinjauan di kalangan pelajar tahun 1 diploma kejuruteraan mekanikal KUiTTHO

Kajian ini bertujuan untuk melihat hubungkait di antara penguasaan Matematik SPM dengan pencapaian akademik di kalangan pelajar Tahun 1 Diploma Kejuruteraan Mekanikal di KUiTTBO. Seramai 96 orang pelajar telah dipilih sebagai sampel untuk kajian ini. Maklumat senarai subjek yang ditawarkan untuk Tahun 1 Diploma Kejuruteraan Mekanikal, keputusan Matematik SPM sampel dan keputusan subjek�subjek yang diambil sepanjang Tahun 1 diperolehi daripada Unit Kemasukan dan Rekod Pelajar dan Pusat Komputer KUiTTBO. Data-data dianalisa dengan menggunakan pakej perisian SPSS (Statistical Packages for Social Sciences) versi ] 0 bagi mendapatkan nilai taburan, peratusan dan kolerasi Pearson. Basil kajian dipersembahkan dalam bentuk jadual, carta dan rajah. Basil kajian menunjukkan bahawa penguasaan Matematik di peringkat SPM mempengaruhi keputusan pelajar untuk subjek-subjek teras kursus ini. Beberapa cadangan dikemukakan untuk menjadi panduan untuk kajian selanjutny

Polyethersulfone/HFO mixed matrix membrane for enhanced oily wastewater rejection

The recent growth of oil and gas industry has led to the increase of oily wastewater release. Membrane technology has been in the spotlight in recent advancement to treat the oily wastewater. Fouling due to surfactant adsorption and/or oil droplets plugging the pore has become one of the major hindrances in most of the research on oily wastewater treatment. In this work, self-synthesized hydrous ferric dioxide nanoparticles (HFO NPs) via chemical precipitation method were incorporated in polyethersulfone (PES) to fabricate a novel nanocomposite mixed matrix membranes (MMMs) for ultrafiltration (UF). The morphologies and physicochemical properties of prepared HFO NPs and MMMs were characterized using Scanning Electron Microscopy (SEM) and Transmission electron microscope (TEM), contact angle goniometer, before further subjected to water permeation test and oil rejection test. It was found that contact angle of membrane decreased remarkably with an increase in HFO nanoparticle loading from 70° to 38° at which proved its improved hydrophilicity which led to a significant rise in permeate flux, achieving 168.06 L/m2h bar in comparison to 63.67 L/m2h bar shown by the plain PES membrane. Total rejection of oil (100% rejection) demonstrated by the MMMs has confirmed the superior potential of PES/HFO UF membrane for total purification of oily wastewater especially to be reused in oilfield and refinery processes as well as to be released to the environment

Forecast Combination

Actualmente existen diversas metodologías de pronóstico, que van desde el conocimiento empírico hasta métodos innovadores, individuales o combinados, que demuestran resultados óptimos. Este documento se deriva de un proceso de investigación y presenta alternativas relacionadas con las combinaciones de pronósticos, utilizando metaheurísticas, por ejemplo, mediante la búsqueda tabú y la programación evolutiva para optimizar el pronóstico. El documento presenta pronósticos combinados basados en la programación evolutiva utilizando mezclas de modelos de regresión bayesiana y modelos de regresión lineal clásico, el modelo de media móvil integrado autorregresivo, el suavizado exponencial y la regresión bayesiana. El documento presenta dos artículos derivados de investigación, la primera compara el algoritmo combinado con los resultados individuales de estos modelos individuales y con la combinación de Bates y Granger utilizando un indicador de error y el valor simétrico de error absoluto medio. Esos modelos y la combinación se aplicaron a la simulación de series temporales y a un caso real de ventas de productos lácteos, generando así pronósticos combinados multiproductos tanto para la simulación como para el caso real. La nueva combinación combinada con la metaheurística evolutiva mostró mejores resultados que los de los otros que se utilizaron. La segunda investigación utiliza series de tiempo simuladas, diseñando dos metaheurísticas basadas en la lista Tabú, que aprenden de los datos con base en el comportamiento estadístico de éstos, como el cluster, así como del mismo valor optimizado del error de ajuste, y se comparan las combinaciones de pronósticos con resultados de modelos individuales a tres tipos de series de tiempo.Currently diverse forecasting methodologies exists, going from the empirical knowledge to the innovative methods, individual or combined, demonstrating optimal results. This document is derived from a research process, and presents alternatives related to forecast combinations, using metaheuristics, for example, by using Tabu search and Evolutive programing to optimize forecasting. One of the designed process consists of creating combination forecasts based on evolutionary programming using, first, a mixture of Bayesian regression models and, second, a mixture of the classical linear regression model, the autoregressive integrated moving average model, exponential smoothing and Bayesian regression. The first research compares the novel combined algorithm with the individual results of these individual models and with the Bates and Granger combination using an error indicator and the symmetrical mean absolute error value. Those models and the novel design were applied to time series simulation and to a real case of dairy products sales, thus generating multiproduct combination forecasts for both the simulation and the real case. The novel combination combined with the evolutionary metaheuristic showed better results than those of the others that were used. The second research uses simulated time series and other metaheuristic that learns from the data an statistical behavior.Tecnológico de Antioquia, Universidad Nacional de Colombia.Doctorad

Combining forecasts

To improve forecasting accuracy, combine forecasts derived from methods that differ substantially and draw from different sources of information. When feasible, use five or more methods. Use formal procedures to combine forecasts: An equal-weights rule offers a reasonable starting point, and a trimmed mean is desirable if you combine forecasts resulting from five or more methods. Use different weights if you have good domain knowledge or information on which method should be most accurate. Combining forecasts is especially useful when you are uncertain about the situation, uncertain about which method is most accurate, and when you want to avoid large errors. Compared with errors of the typical individual forecast, combining reduces errors. In 30 empirical comparisons, the reduction in ex ante errors for equally weighted combined forecasts averaged about 12.5% and ranged from 3 to 24 percent. Under ideal conditions, combined forecasts were sometimes more accurate than their most accurate components

A comparison of AdaBoost algorithms for time series forecast combination

Recently, combination algorithms from machine learning classification have been extended to time series regression, most notably seven variants of the popular AdaBoost algorithm. Despite their theoretical promise their empirical accuracy in forecasting has not yet been assessed, either against each other or against any established approaches of forecast combination, model selection, or statistical benchmark algorithms. Also, none of the algorithms have been assessed on a representative set of empirical data, using only few synthetic time series. We remedy this omission by conducting a rigorous empirical evaluation using a representative set of 111 industry time series and a valid and reliable experimental design. We develop a full-factorial design over derived Boosting meta-parameters, creating 42 novel Boosting variants, and create a further 47 novel Boosting variants using research insights from forecast combination. Experiments show that only few Boosting meta-parameters increase accuracy, while meta-parameters derived from forecast combination research outperform others

Forecasting air passenger traffic flows in Canada : an evaluation of time series models and combination methods

Ces quinze dernières années, le transport aérien a connu une expansion sans précédent au Canada. Cette étude fournit des prévisions de court et moyen terme du nombre de passagers embarqués\débarqués au Canada en utilisant divers modèles de séries chronologiques : la régression harmonique, le lissage exponentiel de Holt-Winters et les approches dynamiques ARIMA et SARIMA. De plus, elle examine si la combinaison des prévisions issues de ces modèles permet d’obtenir une meilleure performance prévisionnelle. Cette dernière partie de l’étude se fait à l’aide de deux techniques de combinaison : la moyenne simple et la méthode de variance-covariance. Nos résultats indiquent que les modèles étudiés offrent tous une bonne performance prévisionnelle, avec des indicateurs MAPE et RMSPE inférieurs à 10% en général. De plus, ils capturent adéquatement les principales caractéristiques statistiques des séries de passagers. Les prévisions issues de la combinaison des prévisions des modèles particuliers sont toujours plus précises que celles du modèle individuel le moins performant. Les prévisions combinées se révèlent parfois plus précises que les meilleures prévisions obtenues à partir d’un seul modèle. Ces résultats devraient inciter le gouvernement canadien, les autorités aéroportuaires et les compagnies aériennes opérant au Canada à utiliser des combinaisons de prévisions pour mieux anticiper l’évolution du traffic de passager à court et moyen terme. Mots-Clés : Passsagers aériens, Combinaisons de prévisions, Séries temporelles, ARIMA, SARIMA, Canada.This master’s thesis studies the Canadian air transportation sector, which has experienced significant growth over the past fifteen years. It provides short and medium term forecasts of the number of enplaned/ deplaned air passengers in Canada for three geographical subdivisions of the market: domestic, transborder (US) and international flights. It uses various time series forecasting models: harmonic regression, Holt-Winters exponential smoothing, autoregressive-integrated-moving average (ARIMA) and seasonal autoregressive-integrated-moving average (SARIMA) regressions. In addition, it examines whether or not combining forecasts from each single model helps to improve forecasting accuracy. This last part of the study is done by applying two forecasting combination techniques: simple averaging and a variety of variance-covariance methods. Our results indicate that all models provide accurate forecasts, with MAPE and RMSPE scores below 10% on average. All adequately capture the main statistical characteristics of the Canadian air passenger series. Furthermore, combined forecasts from the single models always outperform those obtained from the single worst model. In some instances, they even dominate the forecasts from the single best model. Finally, these results should encourage the Canadian government, air transport authorities, and the airlines operating in Canada to use combination techniques to improve their short and medium term forecasts of passenger flows. Key Words: Air passengers, Forecast combinations, Time Series, ARIMA, SARIMA, Canada

Combinação ótima de métodos de previsão segundo o critério Payoff-Jolliffe fatorial: uma abordagem multivariada para a estimação de demanda de gás natural

This study presents a nonlinear multi-objective optimization method for defining optimal weights for combining time series forecasting methods used to estimate annual natural gas demands. The weight allocation approach employs mixed experimental arrangements to model the relationship between various predictive performance metrics, and the weights assigned to the prediction residuals of the individual time series methods chosen for the combinations. The Double Exponential Smoothing (DES) method, the Holt-Winters additive (WA) method, and the multiplicative (WM) method were used in this study. Various performance metrics related to location, dispersion, and diversity were modeled using canonical polynomials for mixtures, which were then individually optimized to form a Payoff matrix for the individual solutions. These were then grouped according to the minimum distance between optimal points and the Jolliffe criterion, defined by the Principal Component Analysis (PCA), and applied to each group identified for non-redundant metric first selection (Payoff-Jolliffe Criteria). Factor analysis (FA) was applied to the remaining metrics, via principal component extraction and varimax rotation, storing the rotated factor scores. After modeling these scores with the same canonical polynomial mixture class, the Normal Boundary Intersection (NBI) optimization method was used, modified by adding an auxiliary elliptic constraint class. The set of results was compared with results from the best individual forecasting methods, results from traditional combination methods, results from the FA-NBI method, and its variants according to the 3 applied Jolliffe rules, in order to verify the reasonableness of the data treatment. The results for all methods were compared with a test set not used in the modeling and optimizing stages, i.e., an out-of-sample set, which verified the remarkable efficiency of the method proposed in this paper, relative to the other methods. Although the results are limited to the studied series alone, the adequacy of the methods presupposes that all other types of time series, or combinations of methods, might result in similar significant improvements in forecast assertiveness.Este trabalho apresenta um método de otimização multiobjetivo não-linear para definir pesos ótimos para a combinação de métodos de previsões de séries temporais usadas para a estimação de demanda anual de gás natural. A abordagem de alocação de pesos emprega um arranjo de experimentos de misturas para modelar a relação entre diversas métricas de desempenho de previsão e os pesos atribuídos aos resíduos de previsão dos métodos de séries temporais individuais escolhidos para formar a combinação. Nesta pesquisa, foram considerados os métodos de Alisamento Exponencial Duplo (DES) e os de Holt-Winters aditivo (WA) e multiplicativo (WM). As diversas métricas de desempenho relacionadas à localização, à dispersão e à diversidade foram modeladas por polinômios canônicos de misturas e, posteriormente, otimizadas individualmente, formando uma matriz Payoff das soluções individuais. Estas foram, então, agrupadas de acordo com a mínima distância entre os pontos de ótimo e os critérios de Jolliffe, definidos segundo uma análise de componentes principais (PCA) e aplicados a cada grupo identificado para uma primeira seleção das métricas nãoredundantes (Critérios Payoff-Jolliffe). Às métricas remanescentes aplicou-se uma análise fatorial (FA) com extração por componentes principais e rotação varimax, armazenando-se os escores rotacionados dos fatores obtidos. Após a modelagem desses escores pela mesma classe de polinômio canônicos de misturas, aplicou-se o método de otimização NBI (Normal Boundary Intersection), modificado pela adição de uma classe de restrições elípticas auxiliares. Para se verificar a razoabilidade da tratativa, confrontou-se o conjunto de resultados obtidos com aqueles proporcionados pelos melhores métodos individuais de previsão, por métodos de combinação tradicionais, pelo método FA-NBI e suas variantes obtidas segundo a aplicação das 3 regras de Jolliffe. Comparando-se, também, os resultados da aplicação de todos os métodos a um conjunto de teste não utilizado nas etapas de modelagem e de otimização (out-of-sample), constatou-se uma destacada eficiência do método proposto nesta tese em relação aos demais. Embora os resultados obtidos circunscrevam-se apenas à série estudada, sua adequabilidade pressupõe que quaisquer outros tipos de séries temporais ou combinações de métodos poderiam experimentar melhorias significativas similares quanto à assertividade das previsões geradas

Previsão da procura na indústria do vestuário

Dissertação de mestrado integrado em Engenharia e Gestão IndustrialTradicionalmente, produtos de moda, designadamente na indústria do vestuário, incorrem em grandes perdas devido a quebras de stock e a stocks obsoletos, devido a dois fatores muito caraterísticos deste mercado, longos tempos de processamento dos produtos, combinado com a concentração das vendas em períodos muito curtos. Assim sendo, as previsões de vendas tem tido um papel cada vez maior na cadeia de abastecimento, e cada vez mais relevantes para a manutenção da competitividade num mercado cada vez mais globalizado e concorrencial. Nesse sentido, surge o projeto de investigação apresentado nesta dissertação, que consiste no desenvolvimento de um modelo de previsão da procura para a empresa Cruz&Areal, detentora da marca BusUrban Wear. O atual processo de previsão da procura (indutivo, sem qualquer base matemática), tem conduzido a elevados custos provenientes de excesso ou quebras de stocks. Neste sentido, o projeto de desenvolvimento de um modelo de previsão tem como objetivo atingir um valor de erro reduzido (erro percentual absoluto médio próximo de 10%), que permita a racionalização dos recursos envolvidos e a maximização da faturação proveniente da redução de stocks conjugada com a minimização das quebras. Na fase inicial do projeto, foi efetuada a revisão da literatura que incidiu na análise dos conceitos, técnicas e abordagens no processo de previsão. Esta revisão bibliográfica foi importante para uma melhor compreensão das dificuldades e desafios associados aos métodos de previsão de novos produtos e a analisar possíveis abordagens para ultrapassar estas dificuldades. A fase seguinte consistiu na aplicação das abordagens referidas na literatura no sentido de verificar a adaptabilidade das mesmas à tipologia do problema, sendo necessário recorrer a uma série de métodos para a obtenção de resultados enquadrados com o objetivo. A última fase consistiu num estudo originado pelo tratamento dos dados, que indicava uma grande oportunidade de optimizar o mostruário (grupo de peças de coleção propostas aos clientes), podendo levar a poupanças muito significativas e a um eficiente aproveitamento dos recursos.Traditionally, fashion products, particularly in the garment industry have incurred high losses due to stock outs and inventory obsolete caused by two factors very characteristic of this market, long lead times, combined with the concentration of sales in very short periods. Therefore, sales forecasts have had a growing role in the supply chain, and more and more relevant to maintaining competitiveness in an increasingly globalized and competitive market. In this regard, arises the research project presented in this dissertation, which is to develop a model for forecasting demand to the company Cruz&Areal, owner of the brand Bus Urban Wear. The current process of forecasting demand (inductive, without any mathematical foundation), has led to high costs from excess stocks or breaks. In this sense, the project of developing a forecasting model aims to achieve a low error value (mean absolute percentage error around 10%), allowing the rationalization of resources involved and the maximization of billing from the lower inventories combined with minimization of stock outs. In the initial phase of the project was made a literature review that focused on the analysis of concepts, techniques and approaches in the forecasting process. This literature review was important for a better understanding of the difficulties and challenges associated with forecasting methods of new products and analyze possible approaches to overcome these difficulties. The next step was the application of these approaches referred in the literature in order to verify the adaptability of them to the typology of the problem, being necessary use a number of methods for obtaining results framed with the objective. The final stage consisted of a study caused by the processing of data, which indicated a great opportunity to optimize the showcase (group of collection pieces offered to customers), that can lead to very significant savings and an efficient use of resources