In this paper, we present a Bayesian analysis of a data set selected from a Brazilian food company. This data set represents the times taken for different quality control analysts to test manufactured products arriving at the company’s quality control department. The samples selected from each batch contain mixtures of different products, which may be submitted to quality testing taking different times. From preliminary analysis of the data, it was observed that the histograms presented two clusters, indicating a mixture of distributions. A mixture of parametrical distributions was thus assumed in the presence of a covariate in order to analyze the data set and to establish standards to be used by the company for the times taken by the analysts. Inferences and predictions are obtained using a Bayesian approach with standard existing Markov Chain Monte Carlo (MCMC) methods.

A number of factors, e.g. cutting speed and feed rate, affect the surface roughness in machining process. In this paper, an Artificial Neural Network model was used to forecast surface roughness with related inputs, including cutting speed and feed rate. The output of the ANN model input parameters related to the machined surface roughness parameters. In this research, twelve samples of experimental data were used to train the network. Moreover, four other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation of Particulate Reinforced Aluminum Matrix Composites (PAMCs) specimens with 0%, 5%, 10% and 15% filler. The aim of this work is to decrease the production cost and consequently increase the production rate of these materials for industry without any trial and error method procedure.

During the past two decades, there have been significant damages on environment such as ozone layer depletion, global warming effects, etc. and people are getting more concerned about taking necessary actions to help environment. The purpose of this paper is to study the effect of green marketing on consumers’ purchasing intention in dairy industry. The proposed study designs a questionnaire and distributes it among 154 randomly selected people who purchase dairy products in four different regions of city of Babol, located in north region of Iran. Using structural equation modeling, the study has detected that green marketing influences on consumers’ purchasing intention, positively.

This paper presents a new search methodology for different sizes of 0-1 Knapsack Problem (KP). The proposed methodology uses a modified scatter search as a meta-heuristic algorithm. Moreover, rough set theory is implemented to improve the initial features of scatter search. Thereby, the preliminary results of applying the proposed approach on some benchmark dataset appear that the proposed method was capable of providing better results in terms of time and quality of solutions.

This paper presents an empirical study to investigate the effect of conflict management strategies on development of cooperative culture in one of Iranian auto industries. The proposed study adopts a Putnam and Wilson’s questionnaire (1982) [Putnam, L. L., & Wilson, C. E. (1982). Communication strategies in organizational conflicts: Reliability and validity of a measurement scale. Communication Yearbook, 6, 629-652.] for measuring different components of conflict management. In addition, the study also designs a questionnaire for measuring cooperative culture. Cronbach alphas are calculated for conflict management and cooperative cultures as 0.76 and 0.79, respectively. Using Spearman correlation ratio as well as regression technique, the study has determined that three components of conflict management strategies including cooperation, competition and compromise could significantly contribute of development of cooperative culture.

The present work deals with the cutting forces and cutting temperature produced during turning of titanium alloy Ti-6Al-4V with PVD TiN coated tungsten carbide inserts under dry environment. The 1st order mathematical models are developed using multiple regression analysis and optimized the process parameters using contour plots. The model presented high determination coefficient (R2 = 0.964 and 0.989 explaining 96.4 % and 98.9 % of the variability in the cutting force and cutting temperature, which indicates the goodness of fit for the model and high significance of the model. The developed mathematical model correlates the relationship of the cutting force and temperature with the process parameters with good degree of approximation. From the contour plots, the optimal parametric combination for lowest cutting force is v 3 (75 m/min) – f 1 (0.25 mm/rev). Similarly, the optimal parametric combination for minimum temperature is v 1 (45 m/min) – f 1 (0.25 mm/rev). Cutting speed is found to be the most significance parameter on cutting forces followed by feed. Similarly, for cutting temperature, feed is found to be the most influencing parameter followed by cutting speed.

The primary objective of this paper is to detect factors influencing the alignment of accounting information systems for firms in manufacturing sector listed on Tehran Stock Exchange. The concept of alignment has been investigated for many years, and strategic alignment plays essential role in increasing company performance. This paper investigates different levels of alignment and studies the factors, which influence alignment. More specifically, the work concentrates on the alignment between the requirements for accounting information (AIS requirements) and the capacity of accounting systems (AIS capacity) to build the information, in the specific context of manufacturing in Iran. The research sample consists of 216 companies over the period 2011-2007. The fit between these two sets was explored based on the moderation method and evidences indicate that AIS alignment in some firms was high. In addition, the relationship between the dependent variable and independent variables through multiple regressions yields a positive relationship between these variables.

This paper deals with the development of an inventory model for time varying demand and constant demand; and time dependent holding cost and constant holding cost for case 1 and case 2 respectively. Previous models incorporating that the holding cost is constant for the entire inventory cycle. Mathematical model has been developed for determining the optimal order quantity, the optimal cycle time and optimal total inventory cost for both cases. Differential calculus is used for finding optimal solution. Numerical examples are given for both cases to validate the proposed model. Sensitivity analysis is carried out to analyze the effect of changes in the optimal solution with respect to change in various parameters.

This paper investigates the effect of market orientation on small businesses active in fast food industry. Using a simple random method, the study selects a sample 278 fast food units out of approximately 1000 existing restaurants in city of Tehran, Iran. The proposed study designs a questionnaire in Likert scale and distributes it among regular fast food consumers. Using structural equation modeling, the study has detected that there were positive and meaningful relationships between continuous changes on consumer preferences on products and services on one side and market orientation on the other side.

Capacity waste management is highly essential because under utilization of capacity is often referred to as a major reason for lower productivity among industries around the world. For better estimation of capacity and its utilization and then for its improved management; newer techniques are being devised in industrial sector. The current case of capacity waste problem has been taken up as a Six Sigma project, where we try to analyze critical factors responsible for the capacity waste. Decisions on critical factor selection in analysis phase of Six Sigma are always very crucial. The paper discusses an approach for selection of capacity waste factors at an automotive industry using fuzzy logic based AHP method. The fuzzy AHP is a well recognized tool to undertake the fuzziness of the data involved in choosing the preferences of the different decision variables engaged in the process of capacity waste factors selection. In this context, we have explored six crucial parameters for selection of capacity waste factors. Final ranking is calculated through priority vector thus obtained and it is seen that conveyor malfunction is found to be the key factor for capacity waste among all alternatives at the selected site.