In this paper, we establish an integrated mathematical production management model based on five parts insisted of finished goods production in production sites, inventory control of finished goods in production and distribution facilities, semi-finished goods production in producer’s facilities, inventory control of raw materials in production sites and Inventory control of semi-finished products. The main goal of developed model is to minimize total cost of company in the mentioned parts. Finally, the model is validated with 7 real-world case studies from auto-industry in one of the largest Iranian car producer company, Iran Khodro co and the empirical results are illustrated at the end of the paper.

This study investigates the association between firm characteristics, corporate governance attributes and the level of corporate disclosure of listed firms in India. The research paper has been based on a sample of 60 firms listed in the Bombay Stock Exchange (BSE) / National Stock Exchange (NSE) during the study period from 2000-01 to 2009-10. The study has used the Standard & Poor (2008) model for measuring the level of corporate disclosure. To examine the association between explanatory variables and the level of corporate disclosure, multiple regression model has been used. The results suggest a positive relationship between board size, ratio of audit committee members to total board members, family control, CEO duality, firm size, profitability, liquidity and the extent of corporate disclosure. However, the degree of corporate disclosure is negatively related to board composition, leverage and age of the firm.

In financial industry, the accurate forecasting of the stock market is a major challenge to optimize and update portfolios and also to evaluate several financial derivatives. Artificial neural networks and technical analysis are becoming widely used by industry experts to predict stock market moves. In this paper, different technical analysis measures and resilient back-propagation neural networks are used to predict the price level of five major developed international stock markets, namely the US S & P500, Japanese Nikkei, UK FTSE100, German DAX, and the French CAC40. Four categories of technical analysis measures are compared. They are indicators, oscillators, stochastics, and indexes. The out-of-sample simulation results show a strong evidence of the effectiveness of the indicators category over the oscillators, stochastics, and indexes. In addition, it is found that combining all these measures lead to an increase of the prediction error. In sum, technical analysis indicators provide valuable information to predict the S & P500, Nikkei, FTSE100, DAX, and the CAC40 price level.

Web browser is one of the most important internet facilities for surfing the internet. A good web browser must incorporate literally tens of features such as integrated search engine, automatic updates, etc. Each year, ten web browsers are formally introduced as top best reviewers by some organizations. In this paper, we propose the implementation of TOPSIS technique to rank ten web browsers. The proposed model of this paper uses five criteria including speed, features, security, technical support and supported configurations. In terms of speed, Safari is the best web reviewer followed by Google Chrome and Internet Explorer while Opera is the best web reviewer when we look into 20 different features. We have also ranked these web browsers using all five categories together and the results indicate that Opera, Internet explorer, Firefox and Google Chrome are the best web browsers to be chosen.

The classic ARIMA models use the information criteria for lag selection since 1990s. The information criteria are based on the summation of two expressions: a function of Residual Sum of Squares (RSS) and a penalty for decrease of degrees of freedom. However, the information criteria have some disadvantages since these two expressions do not have the same scale, so the information criteria are mainly based on the first expression (because of its bigger scale). In this paper, we propose a hybrid ARIMA model, which uses the Data Envelopment Analysis (DEA) model to select the best lags of AR and MA process called DEA-ARIMA. DEA is a linear programming technique, which computes a comparative ratio of multiple outputs to multiple inputs for each Decision Making Unit (DMU), which is reported as the relative efficiency score. We identify inputs as the number of AR and MA terms and outputs of the model are inverse of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In fact, in our proposed model, inputs consider as resources, so we are looking for some models with fewer resources and high efficiency. The DEA unlike the information criteria may have more than one solution and all of them are efficient so to compare this two models selection the mean of best DMUs is calculated. Experimental results demonstrate DEA-ARIMA will not trap in over fitting problem in contrast to classic ARIMA models because of considering a set of efficient ARIMA models.

The purpose of the present study is to introduce a model for competitiveness of suppliers in supply chain through game theory approach in one of the automobile companies of Iran. In this study, the game is based on price and non-price factors and this company is going to estimate the real profit obtained from collaboration with each of supply chain members. This happens by considering the governing competitive condition based on game theory before entering a bit for purchase of ? piece as spare part among 8 companies supplying this piece as the supply chain members. According to experts in this industry, the quality is the main non-price competitiveness factor after price. In the current research models, the model introduced by Lu and Tsao (2011) [Lu, J.C., Tsao, Y.C., & Charoensiriwath, C. (2011). Competition Under manufacturer Service and retail price. Economic Modeling, 28,1256-1264.] with two manufacturers- one distributer, being appropriate for the research data, has been considered as the basis and implemented for case study and then it has been extended to n-manufacturers-one common retailer. Following price elasticity of demand, potential size of market or maximum product demand, retailer price, production price, wholesale price, demand amount, manufacturer and retailer profit are estimated under three scenario of manufacturer Stackelberg, Retailer Sackelberg and Vertical Nash. Therefore, by comparing them, price balance points and optimum level of services are specified and the better optimum scenario can be determined. Sensitivity analysis is performed for new model and manufacturers are ranked based on manufacture profit, Retailer profit and customer satisfaction. Finally, in this research in addition to introducing-person game model, customer satisfaction, which has been presented in the previous models as a missed circle are analyzed.

Data Envelopment Analysis (DEA) is one of the most popular techniques for measuring the relative efficiencies of a set of decision making units (DMUs), which use different inputs producing various outputs. Ranking of efficient DMUs is one of the most interesting DEA perspectives. However, there are cases where we see some limitations on available resources and the proposed model of this paper is associated with Indicator with Limited Sources (ILS), which affects ranking methods. The ILS exists as fixed amount in a community and the DMUs can own it with their abilities. When a DMU loses the same amount of the indicator, the rest of the DMUs are able to own some without even changing their capacities of other indicators and or vice versa. If a DMU looks for more of the same amount of the indicator, the rest of the DMUs have to supply it without even changing their capacity of other indicators. This paper develops a ranking method based on the ILS for the efficient DMUs, when there is changes either in inputs/ outputs ILS. The implementation of the proposed model is applied for a case study of banking system.

Bombardier Aerospace’s high performance aircrafts and services set the utmost standard for the Aerospace industry. A case study in collaboration with Bombardier Aerospace is conducted in order to estimate the target cost of a landing gear. More precisely, the study uses both parametric model and neural network models to estimate the cost of main landing gears, a major aircraft commodity. A comparative analysis between the parametric based model and those upon neural networks model will be considered in order to determine the most accurate method to predict the cost of a main landing gear. Several trials are presented for the design and use of the neural network model. The analysis for the case under study shows the flexibility in the design of the neural network model. Furthermore, the performance of the neural network model is deemed superior to the parametric models for this case study.

In today’s global and dynamic business environment, manufacturing organizations face the tremendous challenge of expanding markets and meeting the customer expectations. It compels them to lower total cost in the entire supply chain, shorten throughput time, reduce inventory, expand product choice, provide more reliable delivery dates and better customer service, improve quality, and efficiently coordinate demand, supply and production. In order to accomplish these objectives, the manufacturing organizations are turning to enterprise resource planning (ERP) system, which is an enterprise-wide information system to interlace all the necessary business functions, such as product planning, purchasing, inventory control, sales, financial and human resources into a single system having a shared database. Thus to survive in the global competitive environment, implementation of a suitable ERP system is mandatory. However, selecting a wrong ERP system may adversely affect the manufacturing organization’s overall performance. Due to limitations in available resources, complexity of ERP systems and diversity of alternatives, it is often difficult for a manufacturing organization to select and install the most suitable ERP system. In this paper, two ERP system selection problems are solved using fuzzy multi-objective optimization on the basis of ratio analysis (MOORA) method and it is observed that in both the cases, SAP is the best solution.

Product selection is always one of the troubles that decision makers are facing with it. Correct selection requires having suitable method for this important issue. In this article, we concern to introduce an approach of fuzzy decision making for selection to decision makers. The nature of decision making is usually complex and without structure. Totally, most of qualitative and quantitative factors such as quality, price, and flexibility should be concerned for determining a suitable product. In this study, it is attempted to use recent advances in ranking methods for product selection. The proposed study uses oral preferences language shown in terms of triangular and trapezoid fuzzy numbers. Then, a multi criteria hierarchical decision making is suggested on the basis of fuzzy collection theory for product selection where the proposed fuzzy VIKOR uses different qualitative and quantitative criteria.