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World Journal of Engineering
Research and Technology

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

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ICV : 79.45

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Indexing

Abstract

SELF-ASSESSMENT PATTERN IN THE SUGGESTIONS SYSTEM USING INTERVAL DATA ENVELOPMENT ANALYSIS

Dr. Ali Soltani*

ABSTRACT

Diagnosis and improvement of suggestions system, quantitative targeting and survey of the goals and ultimately planning and budgeting develop the last three steps of the chain in the processes of suggestions system. Paying attention to these three processes and providing practical conditions for their implementation have been regarded as the necessary conditions for success of suggestions system within an organization. Three specific tasks are fulfilled in the diagnosis process, i.e. survey of stakeholders, benchmarking and reviewing and analyzing. With regard to the findings from these stages, targeting and planning are fulfilled. Creation of practical structure guarantees the cycle of constant improvements in this system. The present research proposes a practical rule for diagnosis using Interval Data Envelopment Analysis. In this study, Interval Data Envelopment Analysis is used to proposed practical approach for self-assessment and optimization and a development is proposed to perform this important process in the recommendation system. This pattern inspired of Business excellence model has developed from the enablers criteria which reflect the results from implementation of this system. These criteria together with the factors such as the facilities used in any organization develop the input and output data in the evaluation problem based on Interval Data Envelopment Analysis. In this study, excellence of Data Envelopment Analysis to measure performance of suggestions system with self-assessment approach for 20 organizational units has been displayed. Further, the effective capabilities of Data Envelopment Analysis in measuring the performance and identification of defects and the approaches to resolve them as well as the extent of improvement for inefficient data and outputs are taken into consideration.

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