Journal of Advances in Industrial Engineering
Volume:44 Issue: 2, 2011

In recent decade, several methodologies and tools have been developed to undertake a risk analysis in order to prevent accidents in the process industries. Attempts have been made to study and compare these methods. However, yet no method has been proposed to aid managers in a scientific selection of a proper risk analysis method. In this paper, we have presented a Multi Criteria Decision Making for selecting a risk analysis technique in process industry. First, we introduce different risk analysis techniques for process units are first introduced. Then, information about important indicators like cost, time, required input data for the analysis, method output, and method applicability for different stages of process life cycle are provided in order to ease selection of the most proper method. This introduction is followed by steps for selection of a proper method. In this regard, a model is proposed based on the Fuzzy TOPSIS technique which is a multiple criteria decision making technique. Thus, first the required indicators for method comparison are selected, then they are weighted and the resulting simple matrix is solved to acquire a final decision making matrix which in turn specifies the most proper risk analysis method according to requirements of the organization.

During implementation of Business Process Reengineering (BPR) in many organizations, it will be less attention to previous activities and capabilities which they extract from other improvement projects in the past and this would include the costs for doing this effort and activities. Considering these issues are important for BPR projects. Required capabilities before implementing of BPR projects have been introduced for achieving success. The literature has suggested required resources for BPR and various approaches to assessing readiness and implementation of BPR projects. Therefore in this research, a practical measurement tool for required capabilities is proposed to enhance the chances for successful implementation of BPR by mapping these capabilities on preparation step of BPR projects. In addition, the relations between these capabilities created organizational capabilities from quality management systems (COC-QMs) are identified. In conclusion, this research has examined and established two types of operational and special capabilities related to preparation step of BPR and QMS projects, in addition to exploring and describing the relationship between these two types of capabilities using the concepts of building capabilities. The results show that every organization should measure the capabilities such as “Planning and monitoring of business plans”, “People’s abilities in recognizing and understanding customers and stakeholders needs”, “Transferring of managerial and working experiences and knowledge”, “Team working”, “Managers’ involvement in organization’s changing process”, and “Loyalty to total organizational growth within managers and people” prior to implementing BPR. Considering the results of this research, the affect of COC-QMs on DOCs as BPR enablers has been proposed as a conceptual model.

In order to survive and sustain the competitive advantage in the competitive markets, many organizations have turned to relationship marketing by focusing on maximizing the life time value of their customers and churn management. In fact, more and more companies do realize that their most precious asset is the existing customer base. Customer retention is a valuable strategy to ensure long term profitability and success of the company. This research studies the problem of customer churn prediction in one of the largest Iranian heavy equipment production companies in a non-contractual setting. Using random forests and boosted trees as classification techniques, a model is build to predict the valuable customers who tend to churn. The results show that past behavioral variables, more specifically period of customer relationship, variance of inter-purchase time, relative frequency and monetary value are the best predictors of customer churn. The results of this study could be very useful and effective for managers in churn management and marketing strategies planning.

Consulting companies are encountered with many problems when they are supposed to generate a new document, e.g. a new Request For Proposal (RFP). They already have the building blocks of these new requests but they are buried as implicit knowledge in the unorganized documents of the company. In this paper it is shown how one can use semantic technology in knowledge and content management in order to achieve proper reports for consulting companies. First, the documents are identified and categorized based on their commitments and their chain values in producing organizational knowledge. A new semantic content management model is established based on the ontology of the contents of the documents. Then by using the proposed model the categorized documents of a consulting company are represented on World Wide Web. These representations of the documents provide a clear view of them which enables the managers to compose new reports by combination of many current materials. A software tool is provided and tested for implementation of the idea.

Nowadays considering suppliers benefits in the supply chain management strategies is one of the critical success factors for forming a long time and strategic relationship with suppliers. It is known as a "win-win strategy" that can be formulated by supply chain coordination models. Because of a leading role for suppliers in developing new products and improving current products, they are being active participants in organizations. So it is necessary to consider suppliers’ benefits in purchasing decisions. By generalizing this concept to supplier selection problem, it is needed to consider whole supply chain benefits consist of suppliers and buyer benefits. In this paper, a multi-objective model, including cost, quality and delivery objectives, is proposed to coordinate between buyer and suppliers in supplier selection problem. Maximizing the number of received perfect products is the quality objective and minimizing the number of delays is delivery objective. The total cost of supply chain consist of buyer and suppliers costs, includes the costs of ordering, purchasing, inventory holding and compensation for delays and defects in products. The proposed mathematical model is formulated as a mixed integer nonlinear problem and its efficient solutions are obtained by using a heuristic algorithm. The results are showed in a supplier selection problem using a coordinated model has more benefits for supply chain than usual models.

Monte Carlo Simulation is a standard technique for project risk analysis. However, some assumptions of this technique are not reasonable in real world. When a project falls behind the schedule, managers take actions to improve the performance. Nevertheless, most of the simulation models as Monte Carlo omit these reactions in their analysis, which results in unreasonable wide distributions or even very optimistic ones. More ever, it is not adequate to simply formulate direct results of these control actions because they have secondary and territory side effects. System dynamics models of projects can capture these reactions and their direct and indirect impacts through feedback loops and casual relationships. In this paper, a system dynamics model has been developed to perform Monte Carlo simulation. Vensim professional software is used to develop the SD model and formulating project operational structures. Comparison of results of system dynamics model and original Monte Carlo analysis is presented by an example. Simulation results shows that SD and Monte Carlo both achieve an almost same average project completion time, however the shape of distribution for project completion time is different in two methods. In fact, modeling of dynamic complexities of projects in SD results in a more reasonable distribution for project completion time.

Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Forecasting accuracy is one of the most important features of forecasting models. Nowadays, despite the numerous time series forecasting models which have been proposed in several past decades, it is widely recognized that financial markets are extremely difficult to forecast. Artificial Neural Networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply them blindly to any type of data. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Both theoretical and empirical findings have suggested that integration of different models can be an effective method of improving upon their predictive performance, especially when the models in the ensemble are quite different. This is the reason that hybrid methodologies combining the linear models such as ARIMA and nonlinear models such as ANNs have been proposed in the literature of time series forecasting. These hybrid techniques decompose a time series into its linear and nonlinear form in order to use the unique advantages of linear and nonlinear modeling methods and are one of the most popular hybrid models, which have recently been shown to be successful for single models. In this paper, an improved version of the hybrid ANNs/ARIMA models is proposed for time series forecasting. In the proposed model, the performance of the hybrid ANNs/ARIMA models is improved using diagnosing the trend of residuals by Probabilistic Neural Networks (PNNs). Empirical results of exchange rate forecasting indicate that the proposed model is more satisfactory than ANNs/ARIMA models.

Today, the shortage of information about the different sections of organization is not the main challenges of managers and organizations. But, the ways to handle the huge amount of generated information for solving different problems of organization and decision making are main difficulties. Information technology (IT) can provide effective tools that help organizations to address such problem. In today’s competitive business environment, significant investment in IT is becoming an important source of competitive advantage and operational efficiency. Companies are implementing tens of millions of dollars at a time to implement Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM) technologies in hopes of achieving dramatic improvements in an organization’s efficiency. By using information systems, managers will have the capability to effectively manage different aspects of supply chain. And also the capabilities of E-commerce will be improved. Information systems software packages like MRPII and ERP help companies to streamline their processes and empower them with a better control of data. The main advantages of enterprise systems are improved coordination across functional departments and increased efficiencies of business processes. Despite numerous advantages of these systems, many organizations face several difficulties in implementation process. With regards to these problems in this research by reviewing most common Critical Success Factors (CSFs), and challenges in literature of ERP and other information systems like MRPII, an appropriate method by combining Analytical Network Process (ANP) and DEMATEL technique under fuzzy environment, is proposed to evaluate and ranking of these CSFs. The proposed methodology is applied in an real case and results show that Project Team, Top Management Support and Vendor Selection are the three top CSF in case study.

Today, the country's banking system, according to the discussions to join the World Trade Organization is encountered with new challenges such as entry of foreign banks, emerging private banks and increasing the activities of financial and credit institutions. Therefore, in order to survive and compete in this dynamic environment banking organizations require paying more attention to human resources as one of their main pillars. Human resources function for any organization is that which can lead to survival or destruction of that organization. Therefore, in this paper affecting factors on human resource performance are investigated. Then Data Envelopment Analysis method (DEA), which is a common method of performance evaluation in different fields, is used to determine efficiency, rank, surplus of input parameters, deficiency of output values and critical indicators in banks. Since in some real world cases, the criteria for evaluating the operations of employees cannot be precisely measured, a FDEA is proposed to consider the uncertainty existent within qualitative indicators. Then efficiency and rank of banks are determined. Moreover, the ranking results obtained by FDEA with crisp inputs have been compared with DEA using Spearman correlation technique in which a relatively high degree of correlation has been identified between DEA and FDEA with crisp inputs. The proposed approach in this paper can be utilized for ranking, and investigation of human resources efficiency and suggestion of optimum human resources operation in any banking organization.

This paper addresses the problem of minimizing the sum of maximum earliness and tardiness on identical parallel machines scheduling problem. Each job has a processing time and a due date. Since this problem is trying to minimize and diminish the values of earliness and tardiness, the results can be useful for just–in-time production systems. It is shown that the problem is NP-hard. Using efficient lower and upper bounds and dominance rules based on adjacent pair-wise interchanges, a branch-and-bound algorithm is proposed. Then, large sizes problems were solved by two evolutionary meta-heuristic algorithms, genetic Algorithm and particle Swarm Optimization based on permutation and priority approaches. In order to evaluate the efficiency of the branch and bound algorithm, 1920 instances were randomly generated in small and medium sizes. Also, the proposed heuristic and metaheuristic algorithms are then tested on 4880 randomly generated problems varying from small to large sizes. The results indicate that the branch & bound method is efficient in solving small and medium sized problems optimally with up to 20 jobs and 5 machines, and also the presented genetic algorithm is efficient in tackling problems of any size.