Quality Function Deployment (QFD) has been traditionally used as a planning tool primarily for product development and quality improvement. In this context, many people have used QFD for making Decisions on how to prioritize critical product areas from a customer perspective.However, it is the position of the author that the QFD process can be viewed as a Decision Support System that would encompass multiple facets of information to enhance the quality and output of the Decision making process in the course of product development. In this paper, the author submits the QFD process in combination with other engineering Decision making tools such as the Pugh concept selection and the Kepner-Tregoe analysis technique as a Decision making and modeling process that is as old as management science itself. Specifically, the attempt is made to illustrate how QFD provides an iterative sequence of steps involved in Decision making, from the initial problem identification to a proposal for the actual implementation plan. To make this position more applicable to the reader, the subject has been approached from an actual business environment.

Introduction: Osteoporosis is a common disease in women. Osteoporosis fractures may cause irreparable damages; therefore, early diagnosis and treatment before fractures is an important issue. The ojectiveof this study was to develop a Decision Support System for diagnosing osteoporosis using artificial neural networks. Method: This developmental study has been done in second half of 2017 based on crossectional method. In present study initially Osteoporosis affecting clinical factors were identified and the most significant clinical factors were selected through incorporation of a questionnaire-based survey. Subsequently, information of 256 female participants and their BMD test results, five years after initial data entry were used to train neural network. The information was obtained fromwomen who refered to department of Bone Mineral Densityof oushehr university of medical sciences. In order to identify the best network, trial and error method was used and neural networks with different topologies were trained using Scaled Conjugate Gradient and Levenberg-Marquardt algorithms. Confusion matrix was used to evaluate the network’ s accuracy, sensitivity and specificity. Results: In the first stage, out of 15 essential variables, 12 variables were selected as the most important risk factors. Multilayer perceptron neural network was designed. Results showed that the best structure of network was due to Scaled Conjugate Gradient algorithm with 10 neurons and Levenberg-Marquardt algorithm with 12 neurons in hidden layer. Accuracy comparison was showed that generally Levenberg-Marquardt algorithm had better result. The best sensitivity, specificity, and accuracy was 83. 1%, 89. 4%, and 86. 3% respectively. Conclusion: In this study developed a diagnostic tool based on local data that could be effective in tracking osteoporosis. Utilizing such a diagnostic tool as a timely referral of individuals and initiating therapy as soon as possible may prevent fractures from occurring and help avoiding the frequent complications of osteoporosis.

Providing a plan for any corporate or firm at macro level, as an organization or enterprise resource planning has particular importance nowadays. To meet the enterprise resource planning needs applications software packages provide a set of uniform pre-prepared and pre-designed that covers all business process throughout an organization. To achieve maximum efficiency in the implementation of these packages and their synchronization with the needs, organizations face a very complex problem. In this paper, a Decision Support System to be used for planning in various economic firms is presented. By using this System, the managers can investigate their corporate in the past and then they can make a plan for future. We implemented the proposed Decision Support System for a company in Iran. This company has two production lines subject to exchange rates. In the evaluation, four alternatives to make the plan for future are applied. The experimental results show the optimizing the exchange rate allocated to production lines provides the best solution for this company.

Decisions relating to portfolio selection have been a main concern of multi business companies' managers. Selecting industries for long-term success of the business strategy and strategic planning is essential. The set of industries in which a firm operates on, affect the ability of that firm in sharing resources and creating synergy among subsidiaries. For determining the Target industries of bank's holding company, we should differentiate available industries in the holding and new industries that the holding can be inserted into them. In this research, after determining the relations between criteria with dematel, we use analytical network process to prioritizing criteria and different industries. Finally, we use linear goal programming to designing an effective mechanism for determining the quantity of each industry. After solving the model with Lingo software, the results show the compatibility of the investment portfolio with the bank's policies and multi business holding companies in our country. The attained model is the first model that solves portfolio selection problem in multi business companies and can be developed.

The Trips selection will become even more important nowadays especially as online trips in large trips databases can be used to give online travelers a better choice of trips than can be found off-line.The process of selecting a trip is considered as one of the most difficult stages a traveler goes through, especially with tens of trips of similar goals, different prices and detailed characteristics in the market.This paper is interested in proposing a Decision Support System to select the optimal Trip that meets each traveler's preferences; using Case- Based Reasoning techniques ' CBR', and calculating similarity between the traveler's preferences and the trip characteristics.