Abstract

One of the most important segments in management of Universal Service Providers (USPs) is reaching the decisions concerning changes in the postal network infrastructure. USPs decide on such matters based on an analysis of financial indicators and defined qualitative parameters in accordance with the international regulations and obligations imposed by a competent regulatory agency. In this paper, the previously known method to analyse the existing postal network and define the minimal number of Postal Network Units (PNU) is implemented and upgraded by a new approach based on Data Envelopment Analysis (DEA) and fuzzy logic. The final aim of the proposed new approach is to determine which of the considered PNU should be closed or reorganized having in mind the minimization of negative effects, both financial and social. The proposed model gives the indices for all considered postal branches, which allows the decision-maker to rank the importance of each unit. The proposed model is a business intelligence tool, which replaces a multidisciplinary team composed from managers of the company and policymakers from both the postal sector as well as a sustainable rural development sector in reaching an important decision on changing the postal network. This decision may be considered as extremely complex since it should sublimate the opposed criteria that relate to the business success of the company, state regulations and sustainability of the local community. The indices obtained in the proposed method exactly include the mentioned three categories. The authors demonstrate the applicability of the suggested methodology based on the real data acquired in a district of the Serbia, i.e. in a regional organizational entity of the USP and provide the analysis of the results reached for the rural delivery post offices.

Highlights

  • The Universal Postal Service (UPS) comprises of the postal services that are constantly offered to all customers by the same conditions, fulfilling the principles of predefined quality and affordable prices

  • Since the policy of postal network expanding in the rural areas is unsustainable from the standpoint of profit, the model proposed in this paper offers a solution for downsizing or restructuring the post office network, having in mind both profitability and social factor in the decision-making process

  • This paper introduces a fuzzy logic system, which the authors have developed as a business intelligence tool for deciding which Postal Network Units (PNU) in rural areas should be shut down or reorganized while taking into consideration the financial, but the social criterion

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Summary

Introduction

The Universal Postal Service (UPS) comprises of the postal services that are constantly offered to all customers by the same conditions, fulfilling the principles of predefined quality and affordable prices. Since the policy of postal network expanding in the rural areas is unsustainable from the standpoint of profit, the model proposed in this paper offers a solution for downsizing or restructuring the post office network, having in mind both profitability and social factor in the decision-making process It takes into consideration the most sensitive groups that would be affected by such decisions. Authors measure levels of community cohesion in selected rural parishes between two points of time – 2000 and 2010 – using an index of indicators based on the presence or absence of retailers and amenities Results of this analysis provide empirical evidence that the presence of facilities and services has a considerable impact on residents in rural areas, suggesting a significant relationship between the presence of small retailers and social engagement in the English countryside. These have to include financial parameters, but on the other, the most sensitive groups of people with limited mobility that would be affected should be taken into account

The current criteria for defining the minimal number of PNUs
The proposed model for reconfiguration of the postal network
Input and output variables
Input variable x1 – efficiency
Input variable x2 – distance
Input variable x3 – legal entities
Input variable x4 – social criterion
Output variable y – preference
Generating fuzzy rules based on empirical data
General information
Obtained results
Findings
Conclusions
Full Text
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