# Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector +
## 6 Conclusions and Future Work  
The analysis from current research provided valuable information on the critical factors that shape the productivity profile of each employee in the public sector. The proposed model extends the conventional theoretical framework of employee assessment and ranking, providing emphasis on the public sector. The results indicated the significant impact of work experience in the private sector compared to the public sector, and academic skills,reinforcing the opinion that age does not affect the total outcome at a significant level. The data sample was adequate to provide valuable information, but more records could be added to future research with more membership functions for ANFIS modelling. The main advantage remains the fact that the capacity factor for each employee is extracted and calculated directly from the system without taking into consideration the subjective proposals of execution times from experts. In addition, the proposed model can be deployed as a distributed system that supports decision-making processes. In case more inputs are needed, they can be incorporated into the existing modelling. Employees' performance is based only on task deliveries and job completions in such a way that the system can be treated as self-learning. It has to be noted that the availability of open data on public bodies can further enhance the prediction of the capacity factor with more accuracy.  
The model evaluates corporate aspects and advice in accordance with the objectives of the organisation. It may not only cut expenses during the phase of selection, but also minimise conflict and hidden costs during the phase of implementation. On the other hand, the limitations of the current model include primarily a shortage of the sampling range due to limited employee records. In order to have a more precise time-factor forecasting, three MFs per input would provide more accurate results, thus making the surface analysis smoother. Additional factors should be taken into consideration such as language skills, as well as soft-skill-related factors such as communication and risk management abilities. Approaches that are related to the working environment and could be quantified with IoT platforms such as weather condition measurements could modify the results even more. Nevertheless, a migration of the proposed model to an HW platform with real-time statistics is fully dependent on the HW specification and is a case study for future development.  
The next step is to formalise the findings and proceed with the evaluation and definition of the tasks that each employee can perform based on the values of the capacity factor. The deployment of an ad hoc mechanism that calculates the capacity of each node and redistributes the assigned jobs could form a new approach to HR services having as a primary goal the perseverance of flow and load regulation in every department in the public sector.  
[PERSON], D.M., A.K., C.K., and S.S. conceived of the idea, designed and performed the experiments, analysed the results, drafted the initial manuscript and revised the final manuscript. All authors have read and agreed to the published version of the manuscript.  
The publication fees of this manuscript have been financed by the Research Council of the University of Patras.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.