The findings suggest that AI-enhanced assistance systems can significantly strengthen organizational learning capabilities in SMEs, provided that they are embedded within structured quality management frameworks.
Abstract
Manufacturing small and medium-sized enterprises (SMEs) are under increasing pressure to detect, analyse, and eliminate quality-related failures faster and more systematically, while operating with limited personnel, fragmented information structures, and heterogeneous IT environments. Although standards such as ISO 9001 require structured corrective action and documented organizational learning, practical failure management in many SMEs remains reactive, media-discontinuous, and weakly connected to reusable knowledge. This paper presents the design and prototypical implementation of a modular, knowledge-based assistance system that combines established quality engineering methods with natural language processing (NLP), machine learning, and large language models (LLMs) to support the entire problem-solving cycle. The research follows a design science research approach. First, requirements were derived from normative sources, the state of research, and an empirical industry survey with 104 valid company responses. The resulting requirements were structured into seven functional domains. On this basis, a modular reference architecture was developed that integrates structured failure capture, historical document analysis, method-guided problem solving, an explainable knowledge base, and feedback-driven learning loops. The prototype was implemented as a containerized full-stack web application using open-source technologies, including Flask, PostgreSQL, Docker, HTML/CSS/Bootstrap/JavaScript, and Llama 3- and Rasa-based conversational services. Transformer-based subcomponents for root-cause classification and guided 5-Why questioning complement the LLM-supported retrieval-augmented generation (RAG) assistant. The system was prototypically validated using historical failure documentation from manufacturing case studies. Evaluation results indicate improvements in knowledge accessibility, reduction in analysis time, increased consistency in root cause identification, and enhanced standardization of corrective action documentation. The findings suggest that AI-enhanced assistance systems can significantly strengthen organizational learning capabilities in SMEs, provided that they are embedded within structured quality management frameworks. The paper contributes to research in digital quality management by (1) providing a structured requirement-based reference architecture for AI-supported failure management systems, (2) proposing a systematic mapping between data mining methods and problem-solving phases, and (3) demonstrating a practical integration approach for LLMs in industrial quality environments. It bridges the gap between classical quality engineering and modern AI-based knowledge systems, offering a scalable pathway toward intelligent, learning-oriented failure management in manufacturing.
: Small and medium-sized enterprises (SMEs) face growing pressure to automate their business processes but often lack the resources to evaluate automation initiatives systematically. Robotic Process Automation (RPA) addresses rule-based, structured processes, whereas Intelligent Process Automation (IPA) combines RPA with artificial intelligence (AI) to handle more complex tasks. However, a systematic framework that helps SMEs decide whether a process is suitable for RPA, IPA, or neither is still missing. This paper addresses that gap by presenting a prototype decision support system (DSS) called “ Automation Fit ” , developed following a design science research approach. Based on a literature review, 32 criteria across multiple dimensions were identified, from which a questionnaire and a scoring model were derived. Expert validation through eight semi-structured interviews provided preliminary support for the relevance and practical feasibility of the assessment approach. The resulting prototype was evaluated in two qualitative interviews focusing on user experience and practical applicability in an SME context. The findings suggest that the prototype can support the initial stages of automation assessments, while identifying areas for improvement relating to interface terminology and the clarity of technically complex questions.
Ann-Kathrin Holaj, Sascha Alpers· Proceedings of the 15th Inte...· 0 citations
Engineering organizations progressively generate large volumes of heterogeneous quality data through inspection records, laboratory tests, production logs, non-conformance reports, statistical audits, and project documentation. However, these data are commonly processed through disconnected spreadsheets, isolated statistical utilities, and manually interpreted dashboards. Such fragmented workflows weaken traceability, delay corrective action, and limit the reuse of engineering knowledge across projects. This paper presents the design and implementation of the Digital Quality Intelligence Platform (DQIP), a modular decision-support software framework for engineering quality intelligence. DQIP integrates data ingestion, preprocessing, statistical process control, Six Sigma quality assessment, rule-based reasoning, visualization, reporting, and recommendation generation within a layered software architecture. The framework separates domain-independent services from configurable domain knowledge so that the same platform can support multiple engineering quality scenarios without redesigning the core application. The proposed architecture is organized into presentation, application, analytics, reasoning, data management, and infrastructure layers, with service interfaces that support maintainability, extensibility, and independent module evolution. A prototype implementation using Python-based open-source components demonstrates the feasibility of combining engineering analytics and explainable decision support in a lightweight deployable platform. The evaluation focuses on functional adequacy, modularity, maintainability, responsiveness, interoperability, and cross-domain adaptability. Results indicate that DQIP offers a coherent software engineering solution for transforming raw engineering quality records into actionable intelligence while reducing manual workflow dependency. The platform establishes a reusable foundation for future integration with predictive models, digital twins, cloud services, and enterprise quality-management systems.
Unknown authors· International Journal of Adv...· 0 citations
A Data-Driven Operations Synchronization Stack is proposed that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture in high-throughput manufacturing contexts.
A. Y. I. ElGabroni, Paulo Peças· Systems· 0 citations
The contemporary business environment is characterized by rapid technological change and an increasing reliance on data-driven decision-making, making artificial intelligence (AI) a key driver of digital transformation and organizational efficiency. However, AI implementation is particularly challenging for small and medium-sized enterprises (SMEs) due to limited resources, a lack of skilled personnel, and various technical, financial, organizational, legal, and ethical risks associated with the process of digital transformation. Building on these challenges, the main aim of this paper is to identify the key risks associated with AI implementation in SMEs and to formulate appropriate strategies for their management and mitigation. The paper seeks to offer a systematic approach to risk management in AI projects, drawing on the principles of contemporary risk management standards and the specific characteristics of SME operations. It is based on the premise that digital transformation, including the adoption of artificial intelligence, is inevitable in the modern business environment, yet its implementation entails significant and multifaceted risks. At the same time, SMEs, due to their limited resources and specific organizational structures, occupy a particularly vulnerable position, which requires a systematic, thoughtful, and strategic approach to managing the risks associated with AI projects.
Milena Šović· Ekonomija: teorija i praksa· 0 citations
Retrieval-augmented generation (RAG) with large language models (LLMs) provides a new technical pathway for integrating industrial-domain knowledge with the reasoning capabilities of LLMs and has substantial application potential in sustainable industrial decision support. However, existing RAG-based decision-support methods focus mainly on enhancing semantic relevance and remain inadequate in industrial knowledge governance, the participation of retrieved evidence throughout the decision process, and the validation of decision results and feedback before implementation, thereby limiting the accuracy, traceability, and reliability of decision outcomes. To address these problems, this study proposes a RAG–LLM framework for sustainable industrial decision support. The framework generates governed knowledge units with explicit evidence boundaries from multi-source heterogeneous industrial data and combines hybrid retrieval, evidence packaging, and validation and feedback mechanisms to enable reliable industrial-knowledge retrieval and controllable decision generation. Simulated experiments were conducted in sustainable industrial engineering and management decision scenarios covering four representative tasks: equipment-fault diagnosis, quality-anomaly analysis, energy optimization, and wastewater-compliance management. The results show that the proposed method effectively improves evidence-retrieval performance and content coverage. With the hybrid retrieval strategy, mean Recall@6 increased from 0.7917 for BM25 to 1.0000, mean nDCG@6 increased from 0.8398 to 1.0000, and mean GVR@6 decreased from 0.1667 to 0. The enhanced RAG–LLM maintained high gold-standard source coverage across different LLMs, with mean evidence source-ID coverage values of 1.0000, 0.9917, and 0.9750 for the ChatGPT 5.5, Gemini 3.5 Flash, and DeepSeek V4 Flash combinations, respectively. After feedback regulation before decision implementation was introduced, the mean evidence source-ID coverage for representative task responses increased from 0.4167 to 1.0000. The mechanism that binds source-labeled evidence throughout the decision process further improved the verifiability of generated results and the traceability of decision evidence, indicating that the enhanced RAG–LLM framework can provide reliable decision support for sustainable industrial engineering and management. Future research will further explore real-time multimodal data integration, privacy-preserving deployment, and long-term validation in real industrial environments.
Yi Zhang, Jing Gu, Yinghui Lang et al.· Sustainability· 0 citations
The rapid developments in artificial intelligence (AI), intelligent automation, and data-driven decision-making have changed the way that competitive dynamics play out across industries and businesses, forcing them to rethink and reimagine their traditional working methods and key strategies. While there's an increase in investments in digital technologies, many organizations are still experiencing disparate implementations, legacy systems, organizational barriers, and inadequate data capabilities that lead to inconsistent transformation results. This study provides a systematic review of the academic sources to explore the role of the combination of AI, automation, and data-driven strategies in supporting the metamorphosis of conventional businesses and enhancing their operational efficiency, organizational agility, and long-term competitive advantage. A methodical literature review approach was used to present and synthesize peer-reviewed studies from the main academic databases according to specific inclusion and exclusion criteria to guarantee methodological rigor and transparency. The review brings together insights from various industries, including manufacturing, retail, healthcare, finance, logistics, and small and medium-sized businesses, to find out what technological capabilities all have in common in these industries, what challenges they encounter when implementing them, what enablers they require at the organizational level and what measurable business outcomes they achieve. Evidence synthesized suggests that digital transformation is not just about technology, but also about investing in complementary aspects such as organizational capabilities, leadership commitment, workforce reskilling, process redesign, and strong data governance. The adoption and integration of AI into intelligent automation and evidence-based decision-making consistently leads to increased productivity, cost savings, customer satisfaction, operational resilience, and innovation capabilities - which, however, is heavily dependent on the ability of the organization to implement AI and become digitally mature. From these insights, this review suggests an integrated conceptual model linking technological, organizational and data capabilities and business transformation outcomes. The study advances the digital transformation literature by offering a comprehensive, evidence-based synthesis that is able to bridge between the fragmented research streams and provide recommendations for managers, policy makers, and researchers aiming at accelerating sustainable transformation using AI in traditional business settings.
Gurpreet Kaur· The American Journal of Mana...· 0 citations
