ISSN: 3108-0723 (Online)

International Journal of Technology, Health and Sustainability

IF: 2.64

as on 30st September, 2026.

International Journal of Technology, Health and Sustainability,

A Conceptual Framework for Using Power BI Analytics to Improve Vendor Performance Management and Supply Forecasting

Kazeem Abdulrazaq1, Habeeb Damilola Yusuf2, Sylviastella Favour Peteranaba3, Helen Ekwi Osinem4, Florence Eribenne5

1 Department of Public Health Sciences, New Mexico State University, Las Cruces, New Mexico, USA.

2 Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee, USA.

3 University of New Haven, West Haven, CT, USA. 

4 Jos University Teaching Hospital (JUTH), Nursing Services Department, Jos, Nigeria. 

5 Bay Cove Human Services, Boston, MA, USA. 

Abstract

Procurement and supply chain functions generate large volumes of transactional data, yet many organisations still evaluate vendors through retrospective spreadsheets and forecast supply through judgement-heavy, error-prone processes. Self-service business intelligence platforms, and Microsoft Power BI in particular, offer an accessible route to closing this gap, but the literature provides limited theoretical guidance on how such tools should be structured to produce measurable improvements in vendor performance and forecast reliability. This paper develops an integrated conceptual framework that specifies how Power BI analytics capabilities can be organised to strengthen both vendor performance management and supply forecasting. The framework is derived through a narrative synthesis of literature on supplier evaluation, demand and supply forecasting, business intelligence value creation, data governance, and technology adoption, and is grounded in the resource-based view, the dynamic capabilities perspective, the technology-organisation-environment framework, and the DeLone and McLean information systems success model. The proposed framework comprises five interacting layers: data acquisition and integration, semantic modelling and governance, analytical intelligence, visualisation and decision support, and action and feedback. Two coupled subsystems are specified in detail: a vendor performance measurement subsystem built around a weighted, multi-criteria scorecard, and a supply forecasting subsystem that combines time series methods with supplier reliability signals derived from the first subsystem. Seven propositions are advanced linking framework constructs to intermediate outcomes such as decision latency and forecast accuracy, and to organisational outcomes such as stockout frequency, expediting cost, and working capital efficiency. A phased implementation roadmap and a mixed-method validation strategy are outlined. The paper contributes a theoretically grounded, operationally specific artefact that researchers can test empirically and that practitioners can adapt for deployment.     

Keywords: Power BI, Business intelligence, Vendor performance management, Supplier scorecard, Supply forecasting, Demand planning, Conceptual framework, Supply chain analytics, Self-service analytics

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Modeling Groundwater Velocity Response to Permeability and Storage Coefficient Variations in Confined Gravel Aquifers of Nigeria

Rivers State University, NIGERIA.

Sustainable Development and the Role of the Indian Judiciary in Promoting It with Special Reference to the Precautionary Principle and the Polluter Pays Principle

Durham University, Durham, UNITED KINGDOM (UK).

Analyzing the Spread and Impact of 5G-Corona Misinformation on Twitter: A Statistical Approach

Kampala International University, UGANDA; Rivers State University, NIGERIA.

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