The main idea of the current research is to apply customer satisfaction level Key Performance Indicators (KPIs) for supply chain reliability improvement. The Supply Chain Operations Reference (SCOR) model-based KPI metrics increase the quality of product/service by monitoring, visualising, and digitalising directly involved processes. In the long run, the solution will ultimately help reduce/eliminate the number of customer reclamations in the supply chain. An industry-oriented performance measurement model based on SCOR can be easily adapted for different sectors. The approach proposed in the current research is based on identifying key factors of supply chain performance of the SCOR model connected with the predictive and diagnostic capability of Bayesian Believe Networks. The difference in performance can be reached via applying the best practices to processes, affecting the performance on a larger scale.
This paper aims to rank strategic objectives in a strategy map to improve the efficiency of strategy implementation. Objectives are ranked based on strategic destinations using the combination of Logarithmic Fuzzy Preference Programming (LFPP) and similarity method. In the first step, the weight of strategic destinations is obtained using LFPP technique; then objectives are ranked by similarity method. Similarity method uses the concept of alternative gradient and magnitude for effectively solving the general multi-criteria analysis problem. Finally, objectives are ranked in an actual strategy map. As a practical and efficient tool, the proposed approach can assist managers and decision-makers in drawing more efficient output from strategy maps.
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