National Journal of Quality, Innovation, and Business Excellence

New Data Shows 40% Quality Improvement — Supply Chain Tools and Techniques

Emre Yılmaz, Aylin Demir, Okan Korkmaz — Sat, 05 Apr 2025 00:00:00 +0300

Since 2013, the digital transformation of supply chain management has changed the paradigm of how business works and is with the help of recent studies, it has a notably positive impact on the competitive performance (β1 = 0.24). They are no longer simple tracking systems, but complex solutions that bring in measurable efficiency and visibility improvements to the supply chain.Walmart and Maersk are given as examples of companies that show the great impact that the digital integration in supply chain operations can produce. Modern supply chain capabilities help you to have transparent inventory management and succeed in their success stories. Moreover, the evidence from 408 Chinese manufacturing firms is confirmed that supply chain collaboration and visibility directly strengthen resilience.The integration of IoT devices has helped a great extent in enhancing the operational availability of the devices and well as improving the efficiency through real time tracking and monitoring. On top of this, predictive analytics and AI has also picture demand forecasting and has allowed businesses to optimize its inventory levels and predictively predict the market trends. Further in this article, we will look at how advanced tools and techniques are leading to the quality improvements and changing the approach to supply chain management for the future.

Building Excellence in Education through Evidence-Based Practice

Nikola Jovanović, Maja Petrović, Marko Ilić — Wed, 09 Apr 2025 00:00:00 +0300

Did you know that only a third of the efforts make it to bring a new educational program into their roll out? In reality, most school based programs are only implemented with some fidelity, meaning less than 25-50% of school based programs are implemented in a way that achieves the intended result with regard to the student or the school (Nagy 2011). This sets the stage for why we need to a more methodical approach in putting these educational innovations into practice.The implementation science framework is exactly such a place where we began. As researchers and educators, we have identified more than 70 implementation strategies in use that will work to adopt evidence based practices in schools. Yet, it’s no quick fix – usually takes 2 to 3 years of continuous work and watching all organizational levels to succeed.We know that getting good intentions to come to fruition in the complex educational system will require more than that. And that is where this article comes in, we will look at some of the ways in which implementation science can help close the research to practice gap in education, take a deeper look at proven frameworks for success, and offer practical strategies for educators and administrators, who yearn to make lasting positive change in their school.

Quality-Driven Governance Frameworks for Reliable and Compliant AI Systems Using Data Contract Architectures

J.Karthika, K P Uvarajan — Mon, 05 May 2025 00:00:00 +0300

To guarantee the reliability/consistency and regulatory compliance of artificial intelligence systems, governance structures are needed that can impose quality constraints throughout the entire lifecycle of the data and model processes. The old forms of AI governance are based on manual inspection, compliance that is based on documentation and reactive compliance auditing which are inadequate in dynamic systems that constantly respond to real-time data streams. This paper presents a quality-centered governance model that makes use of Data Contract Architectures, programmable and enforceable interfaces among data producers, AI systems, and governance strata. Data contracts specify clear-cut quality conditions, validation conditions, compliance conditions, and operational conditions which may be automatically reviewed and implemented at the time of data ingestion, transformation and execution of model processes. The suggested framework brings together the architectural ideas of data engineering, quality assurance, and AI governance with the aim of facilitating transparent operations, responsible ones, and verifiable ones. The evaluation presented through experiments shows that there are enhanced data integrity, consistency, system stability, and traceability of compliance. This paper demonstrates that data contracts may be used to build viable and compliant AI systems and have the potential to maintain high-quality performance in response to changing regulatory and operational pressures.

Data Lakes vs. Data Warehouses in Library Analytics: An Innovation Management Perspective

Prerna Dusi, F. Rahman — Tue, 06 May 2025 00:00:00 +0300

Academic libraries are already changing their service focused units to strategic data centres in which innovation, informed decision-making, and operational performance are based on strong information structures. Here, the data lakes or data warehouses debate is of paramount importance to the acquisition, handling, and utilisation of data by libraries to develop superior analytics. Within this paper, a comparative study of data lakes and data warehouse was provided on the basis of an innovation management viewpoint specific to academic libraries. It evaluates the critical quality parameters such as data integrity, accessibility, governance, scalability, and adaptability of analytics to determine how each of the architectures supports the various forms of library analytics. The research points to the fact that data lakes, which have a schema-on-reads capability and are capable of accommodating heterogeneous, large-scale, and multi-format data, offer increased flexibility to exploratory analytics, machine learning, and fast prototyping of new innovative services like personalised recommendations, predictive user engagement models. On the other hand, databases with the properties of schema-on-write design and with tightly regulated ETL operations provide a higher degree of data consistency, reliability and auditability, which are more appropriate to standardised reporting, accreditation metrics and compliance-driven analytics. Having acknowledged that libraries need to ensure innovativeness and high data quality standards at the same time, the paper suggests an Innovation Governance Framework that combines the two architectures in a complementary way. In this hybrid system, the data lake serves as an experimentation, discovery system and the data warehouse as the system of record to the validated indicators and institutional dashboards. With real-time adaptation of technical architectures to the data governance, ethical matters or considerations and continuous improvement activities, the framework facilitates more intelligent decisions made by academic libraries, strategic planning and increased contribution to the excellence of organisational data as an essential factor.

Quality-Driven Blockchain Framework for Institutional Information Governance and Innovation Management

Shaik Sadulla — Fri, 09 May 2025 00:00:00 +0300

This growing complexity of institutional governance and innovation management in higher education demands strong frameworks that provide transparency, accountability and quality adherence. The paper presents a Quality-Driven Blockchain Framework (QDBF) integrating ISO 9001:2015 quality management principles with blockchain-based smart contracts to automate the validation of the policy, audit trails, and management of the intellectual property (IP). This is to bring institutional quality assurance mechanisms and decentralized digital infrastructure in order to have continuous improvement and innovation excellence. The approach will be followed by the three-level architecture, which includes a Policy Integration Layer (ISO 9001 clauses and standards), a Blockchain Validation Layer (Hyperledger Fabric permissioned network) and an Innovation Management Layer (tracing of research and IP assets in a smart contract). The validation of the framework was conducted in a simulation study that comprised five validator nodes and two client nodes that were running on a DockerizedHyperledger Fabric environment. Commpliance Efficiency (CE), Administrative Overhead (AO) and Data Integrity Rate (DIR) were measured. The results of the experiment revealed that the compliance efficiency was 60 percent better, the administrative overhead was 45 percent less, and the rate of data integrity was 99 percent of the traditional systems. The results substantiate that the implementation of the ISO-based quality standards within blockchain infrastructure improves the institutional accountability, traceability of the processes, and performance of innovations. This study provides a verifiable and scalable governance thesis to academic institutions funding to the direction of data-driven quality improvement and sustainable innovation management.

Sentiment-Driven Business Intelligence: AI-Based Framework for Strategic Content and Customer Engagement Optimization

Rajan.C, R. Prashanth — Sat, 05 Apr 2025 00:00:00 +0300

In the modern digital economy, consumer sentiment is the key to proper brand communication and strategic decision-making. The present paper outlines the proposal of an AI-based sentiment intelligence system that helps to increase the content curation, brand positioning, and customer interaction in business intelligence systems. The system proposed incorporates ensemble learning methods which incorporate the use of logistic regression, random forest and deep sentiment embeddings to learn both syntactic and contextual information within the large textual data. Using the heterogeneous data sources, including posts on social media, reviews, and feedbacks, the model determines behavioral trends resulting from sentiment, which form the foundation of adaptive business strategies. The experimental experiments prove that the ensemble architecture is strong in multi-domain applications since the predictive accuracy is significantly higher than the baseline classifiers (31%). In addition to technical development, the paper shows the significance of sentiment-informed analytics in leading to innovation management, the quality of communication assurance, and strategic content optimization. The results develop a connection between artificial intelligence and strategic business intelligence, which helps develop a new paradigm of data-driven and sentiment-conscious decision-making in the enterprise.