Journal of Quality Engineering and Management

Abstract Purpose: Effective management of organizational processes and facing challenges is crucial for organizations such as the Cooperative Investment Guarantee Fund to achieve their goals in today's competitive world. This issue is doubly important for this fund, given its wide range of stakeholders and its key role in supporting the cooperative sector. Therefore, the present study aimed to present challenges and solutions for improvement in the processes of issuing cooperative development guarantee credit insurance policies.
Methodology: This research is based on the principles of quality management and a process approach to ensure the scientific and practical validity and reliability of the results. In-depth analysis of the challenges and their prioritization was carried out using the Shannon entropy method, TOPSIS technique, and Nominal Group Approach (NGT).
Findings: The research findings showed that most of the fund's problems are concentrated in the process and strategy sections; therefore, in accordance with the extracted priorities, optimization solutions were presented and Key Performance Indicators (KPIs) were developed for continuous monitoring.
Originality/Value: In addition to creating process transparency, the final results of this research, by providing an operational and scientific roadmap, provided a basis for focusing resources on key points of success, which is a pivotal step towards reducing time and cost, improving effectiveness, and achieving strategic goals in the cooperative sector.

Abstract In this research, a hybrid approach based on fuzzy logic and artificial neural network is presented to predict the failure of machines in order to increase productivity. The subject of this research is one of the factories of the automobile industry named Diaco Ide Aria, which operates in the field of automobile parts production. Preventive maintenance requires correct prediction of breakdowns and accidents, equipment and machines so that productivity can be increased by timely and correct maintenance of machines as well as fixing defects and breakdowns. To model the multi-layer perceptron fuzzy-neural network (MLP), first, 100 failures and stops were collected in a period of 15 months and then entered into MATLAB software. The obtained results show that the implementation of fuzzy-neural network and the prediction of machine failure time has reduced the duration and cost of repairs. Therefore, the working time and accessibility of the machines increased and ultimately increased the productivity by 57%, also, the accuracy of the developed neural-fuzzy model was estimated at 94%.