Abstract
In order to adapt to ever-changing customer needs and satisfy them, good Business Process Management (BPM) in Small and Medium-sized Enterprises (SMEs) is crucial. The target group of this research is production SMEs whose BPM can be monitored respecting the values of key performance indicators (KPIs). This paper shows how improving the performance of the observed business processes can improve the level of customer satisfaction. This improvement should lead to the sustainability of SMEs in the market. In this paper, evaluation of business processes performance is defined as a multi-criteria decision problem. The relative importance of considered KPIs and their imprecise values are described by linguistic expressions, which are then modeled by triangular intuitionistic fuzzy numbers (TIFNs). Calculation of KPI weights is done by using the fuzzy analytic hierarchy process (FAHP). Evaluation of BPM success is conducted respecting the obtained KPI weights and KPI values. An optimal solution for BPM success improvement, respecting customer satisfaction indicators, is calculated using the Artificial Neural Network (ANN) and Genetic Algorithm (GA) approaches. By applying the proposed model, managers of production SMEs can determine the management initiatives that will improve their business and the sustainability of their companies.
Highlights
Received: 26 October 2021Small and Medium-sized Enterprises (SMEs) present a driving force of economic development in many countries
In order to develop their business and survive in the market, production SMEs must constantly satisfy the customers of their products
Increasing customer satisfaction may lead to increased profits, which is important for the sustainability of SMEs
Summary
Small and Medium-sized Enterprises (SMEs) present a driving force of economic development in many countries. The proposed model is used to find out whether it is possible to integrate ANN and GA to determine optimal business performance values that would result in improved customer satisfaction in certain business conditions. ANN for prediction and GAs to optimize business performance allows companies to improve business process management while reducing costs and increasing customer satisfaction. This approach gives SME managers important information regarding process parameter values, which are needed under different business conditions and different stages of the process to obtain the desired level of customer satisfaction indicators’ values.
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