فصلنامه مطالعات مدیریت صنعتی
پیاپی 22 (پاییز 1390)

By the new developments occurring in technology, management theories, manufacturing and production, and as a result, improvement in products variety, productivity and innovation, all companies and organizations are trying to handle and manage their product in a efficient manner. By identifying new methods in purchasing, warehousing, handling and ordering, all classical processes have lost their applications. These new methods such as Artificial Intelligence (AI) have provided the necessary tools that we need for decision making in specific situations. In this paper, some new defenitions in re order point (ROP) have been discussed, in addition we have used Artificial Neural Networks (ANN) for determining re order point for purchasing goods in commercial organizations, specially import companies. For this matter, ANN has been used for decision making in purchasing, and also normal curve has been used for calculating order lead times. In the conclusion, we agreed that our new model has many priorities to its classical kinds.

Nowadays the most important issues considered by the managers of industry is production planning and in this area, managers are faced with several goals that in many cases are in conflict with each other. Operations research techniques, with modeling, while the existing constraints consider, optimize organizational goals. Objectives of all these techniques are raising productivity in the organization. Mathematical model for this research that is made for production planning in Khavar-E-Miane Furniture Co is multiple Objective fractional programming. One of the difficulties in solving the multiple Objective fractional problems is computational problem that arise of variability, in the example Charnes and Cooper and Gillmore and Gomory methods there. Hence this research was used fuzzy approach to solve multiple Objective fractional mathematical model of Khavar- E-Miane Furniture Company. Thereby also overcome the computational problems of variability in the previous methods, the relevant officials will be able to optimize their production systems. In this context the first phase, Pal's method was used that using fuzzy goal programming was solved the multiple Objective fractional mathematical model. More using Dutta fuzzy method was determined that the result using both methods is identical and membership functions are equal to 0.701 1   and 1 2  . It should be noted that other companies also changed slightly in the proposed mathematical model will be able to optimize the production planning.

In new era systems managers should focus on managing the flow of production through all the steps that add value to the final product. Lean production as an efficient approach has presented in literature of production management for increasing the level of product's quality and decreasing cost of production. Although this approach is introduced and studied in many of works but there are some challenges in applying it. It means that there is not a comprehensive model for assessing and analyzing the lean production in manufacturing firms. This paper tries to present a model able to accuracy analyze and measure the leanness degree of firms. In this study, first the literature of lean production and, specifically, existing models that identify the variables and component elements of lean production firms was studied. In the next step a hierarchical model for measuring the leanness degree was developed and its dimensions and criteria's were defined. At final step for assessment phase formulas and relations were developed and applied in Yazd Tile and Ceramics industries.

Industry competitiveness has positive impact on productivity and public wealth. In this research, we try to draw a proper competitiveness model for Iranian manufacturing industries through recognizing influential factors. We reviewed 23 manufacturing industries in Iran from 1373 to 1386. The result shows that “Industry size” is the most important factor among others that impact on competitiveness of manufacturing industries in Iran. “Labor productivity” has low impact on Industry competitiveness. Besides, government has a central role on competitiveness success in Iranian manufacturing industries.

Using e-government services makes several advantages like reducing production costs, decreasing waiting time, acquiring and sharing information to small and medium sized enterprises (SMEs). Besides these advantages, the use of e-government services in SMEs is less than the expectation. The aim of this paper is identifying influential factors affecting the acceptance of e-government services by SMEs. In this regard, these factors are identified and extracted from relevant literature. Then a conceptual model including eight major factors was designed based on technology-organization and environment framework. This model has been tested in a field study with 98 samples from the Iranian SMEs. The data analysis showed a highly significant relation between direct benefit, indirect benefit, complexity, top management support, information technology expertise, government pressure with electronic services acceptance independent variable. There was not any significant relation between industry pressure and consistency and independent variable. However, all factors have positive relation and just factor of complexity has negative relation with independent variable. These analyses were done by structural equation modeling and LISREL software.

Key Performance Indicators, help an organization to define and measure the progress of organization toward organizational goals. Key Planned Performance Indicators (KPPI) are the tools for measure the progress of organization toward goals and strategic. Since the Decision Makers are concerned with these attributes and indices in uncertain environments, selection of these indices is a Multiple-Attribute Decision-Making problem. In the past, several methods such as the linear weighting methods, AHP, TOPSIS, Fuzzy Logic and Mathematical programming have been used to solve the indices selection problem. In this thesis, we give a new grey-based approach to deal with the indices selection problem with regards to organizational strategic plans. Firstly, the weights and ratings of strategicbase attributes for all alternatives are described by linguistic variables that can be expressed in grey numbers. Secondly, using a Grey Possibility Degree (GPD), the ranking order of all alternatives is determined. Finally, an example of indices selection for instruction and research department of IRIB is used to illustrate the proposed approach.

The importance of industrial development in developing countries has lead many countries to establish and strengthen small and medium enterprises (SMEs) and to benefit from it as a job-maker strategy for improving competitive ability and increasing the exports. Because over 90% of enterprises in Iran are (SMEs), they should be highly paid attention. In this paper, the plans of improvement and growth of SMEs among twenty countries of the world (most of them are developed countries) are assessed and these factors are categorized in six main dimensions. The selected factors are presented to over 100 managers, experts and pundits of country's industrial cities in the form of questionnaire and the plans of improvement and growth of SMEs are extracted. Finally, the plans of improvement and growth of SMEs in terms of expert's opinions are divided and examined into four categories: obstacles, correction, requirement and deletion. Paper concludes that the most important obstacle of achievement of SMEs in terms of expert's opinions is chain and network obstacle.

Often, the nature of many real life processes, especially in management and business fields are nonlinear. Forecasting the behavior of these processes requires accurate and effective forecasting tools. Shortages of such processes are removable by artificial neural network as an important modeling tool in business forecasting problems. In a comparing analyze, this paper shows the excellent performance of neural network in forecasting nonlinear processes rather than other forecasting models. For this, production, import and import value (dollar) data, related to wood industry of Iran, from 1961 to 2007 are studied. First, applying this data to neural network model and nonlinear models obtained from MATLAB software, the Iran wood industry was forecasted and then based on MAPE 1, yielded outcomes from both models compared. Study findings show that in all cases neural network has more successful performance than models from MATLAB.

In this paper we developed an inventory model in mixed imprecise and uncertain environment. Presented model is developed form of (r,Q) and is a multi-items model with two objectives as minimizing costs (holding & shortage) and risk level under constraints including available budgetary, the least service level, storage spaces & allowable quantities of shortage. Demand distribution functions are assumed to be exponential and extra demands are supposed in two situations as lost sales and backlogging. At first we develop crisp model then fuzzy stochastic model with fuzzy budgetary, allowable quantities of shortage and shortage spaces (i.e. stochastic with normal distribution function) parameter. All of fuzzy numbers are triangular type. In methodology of solution we change model to a crisp multiobjectiveby using difuzzification of fuzzy constraints and fuzzy chance-constrained programming methods, and then solve it by fuzzy logic method. Finally an illustrated example is taken and solved using LINGO package.