Predicting failure in the commercial banking industry

The ability to predict bank failure has become much more important since the mortgage foreclosure crisis began in 2007. The model proposed in this study uses proxies for the regulatory standards embodied in the so-called CAMELS rating system, as well as several local or national economic variables to produce a model that is robust enough to forecast bank failure for the entire commercial bank industry in the United States. This model is able to predict failure (survival) accurately for commercial banks during both the Savings and Loan crisis and the mortgage foreclosure crisis. Other important results include the insignificance of several factors proposed in the literature, including total assets, real price of energy, currency ratio and the interest rate spread.bank failure; banking crises; CAMELS ratings

Risk-Smoothing Across Time and the Demand for Inventories: A Mean-Variance Approach

The standard production smoothing model of inventory demand cannot represent the added incentives for smoothing risks or explain the impact of market shocks that independently affect expectations and uncertainty. Those limitations are overcome by modeling inventory demand as a problem in deterministic optimal control, with the risk-averse firm maximizing utility that is a separable function of the mean and variance of returns and the firm controlling on two decision variables, production and inventory investment. Support for the mean-variance approach comes from regressions using Survey of Professional Forecasters data to show how changes in the mean forecasts of the GDP price deflator and changes in the disagreement among deflator forecasts can explain changes in aggregate inventory investment over time. Further support comes from the ability of the model to explain the excess volatility of industry output over sales—a fact at odds with the production smoothing theory.

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

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

A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

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

Demand forecasting: a case study in the food industry

The use of forecasting methods is nowadays regarded as a business ally since it supports both the operational and the strategic decision-making processes. This paper is based on a research project aiming the development of demand forecasting models for a company (designated here by PR) that operates in the food business, more specifically in the delicatessen segment. In particular, we focused on demand forecasting models that can serve as a tool to support production planning and inventory management at the company. The analysis of the company’s operations led to the development of a new demand forecasting tool based on a combination of forecasts, which is now being used and tested by the company.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201