Supply Chain Performance Management Modeling Based on Information Dashboards in Private Banks

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
The purpose of this research is to model supply chain performance management based on information dashboards in private banks. The data was derived from in-depth and semi-structured interviews with 12 managers of five private banks in the country, which were based on purposeful sampling and continued until reaching theoretical saturation. The validity of the research data was checked and confirmed by going back to the participants and external auditors. Data analysis was done based on the Strauss and Corbin model in the form of open, axial, and selective coding in the Atlas TI8 software. Modeling of supply chain performance management based on information dashboard in private banks including causal factors (intensification of competition, supply chain inefficiency, banking system challenges), intervening (appropriate corrective measures, organizational strategies, banking service challenges), platforms (quality management services, facilitating elements, and action management), strategies (development strategy, partnership strategy, discovery strategy, and focus strategy) and consequences (correction of performance evaluation system, financial-administrative function, performance improvement and innovation and supply chain development). To succeed in developing and changing their business model, they must correctly recognize factors affecting the supply chain of banking services in the fourth industrial revolution and digital revolution and successfully transition to the new technological era. A supply chain refers to the flow of materials, information, funds received from customers, and services from suppliers of raw materials through factories and warehouses to final customers, and includes organizations and processes that create goods, information, and services and deliver them to intended consumers.
Language:
English
Published:
International Journal of Knowledge Processing Studies, Volume:4 Issue: 1, Winter 2024
Pages:
1 to 19
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