Search Results (102)

This study addresses a critical gap in understanding how Lean Manufacturing (LM) practices, particularly people-centered approaches, can enhance operational performance within the unique industrial context of Saudi Arabia’s Vision 2030 economic transformation. The concept of Lean Manufacturing involves a systematic approach and principles aimed at enhancing efficiency, minimizing inefficiencies, and boosting output in manufacturing operations. While LM principles are well-established globally, their application in Gulf Cooperation Council (GCC) economies remains understudied, particularly regarding the central role of workforce engagement in successful implementation. The main objective of the study is to investigate the implications of LM on the productivity of the industry sector. Specifically, this research examines how the integration of people-centered practices with traditional LM constructs (Just-in-Time, Jidoka, Stability and Standardization) influences operational outcomes in Saudi manufacturing firms. A survey was conducted among specific private and public enterprises to collect data, yielding a 55.8% response rate and 67 valuable responses from a pool of 120 contacted companies. The sample encompassed small, medium, and large enterprises across seven manufacturing sectors. SmartPLS 3 and SPSS were used to assess the structural and measurement models. Common method bias was evaluated using Harman’s single-factor test. The findings demonstrate that implementing the recommended LM structural model significantly enhances operational performance. Notably, people integration exhibited the strongest influence on operational performance (β = 0.361), suggesting that human-centered approaches may be particularly salient in the Saudi context. These findings offer practical guidance for manufacturing firms seeking to align lean initiatives with Vision 2030 objectives. Full article

Despite significant research interest, the understanding of how to systematically implement Lean practices in supply chains remains limited. Therefore, this study analyzes the impact of blockchain technology on implementing Lean principles within supply chain networks. A theoretical model was developed based on a comprehensive literature review, utilizing innovation diffusion theory, agency theory, and transaction cost economics. The LDA topic modeling, based on big data from the past decade, was employed to explore key areas and essential industry practices related to blockchain technology. By cross-validating big data analysis and survey results, we also developed reliable metrics that can be used to study blockchain utilization in SCM. The hypotheses were empirically tested using survey data from 219 US enterprises that have adopted blockchain technology. The empirical results revealed that blockchain adoption significantly improved Lean management practices within supply chain networks. Furthermore, research has shown that blockchain can significantly enhance operational performance, including cost reduction, quality improvement, delivery capacity, and greater flexibility. These compelling results suggest that blockchain has the potential to serve as a powerful platform for systematically integrating and orchestrating Lean management practices across the entire supply chain network, thereby achieving operational excellence. An in-depth discussion of the study’s practical implications and theoretical contributions is presented. Full article

This research examines the drivers of lean implementation in sustainable energy enterprises (SEEs) to balance efficiency, sustainability, and competitiveness. This research investigates the interdependence among lean drivers and classifies them by driving power and dependence. This study followed a novel mixed-method approach combining a systematic literature review for driver identification, interviews with entrepreneurs for expert consensus, and analysis using total interpretive structural modelling (TISM), cross-impact matrix multiplication applied to classification (MICMAC), and a graph-theoretic approach (GTA). The result indicated that leadership commitment, teamwork and collaboration, and time management are high drivers; cost reduction, resource optimization, and continuous improvement are linkage drivers; and customer focus and flexibility are found as dependent drivers, revealing the sustainable outcome. This provides a structured pathway for the SEEs for the lean implementation drivers, where prioritization is required. The exploration adds to the Resource-Based View, dynamic capability theory, system theory, etc. The study calls for policymakers’ interventions in designing capacity-building programmes, leadership training, and collaborations. This research incorporated the antecedents–decisions–outcomes (ADO) framework for highlighting the antecedents, leading to decisions, and the outcomes of the choices, with future research questions connecting with multiple sustainable development goals (SDGs), such as SDG7, SDG9, SDG12, and SDG13. Full article

Peru’s highly competitive garment sector, where microenterprises account for 88.4% of all enterprises, the main barrier to sustainability is low productivity, driven by high rework rates and operational errors. To address this problem, this research proposes an improvement model that combines Standardized Work to unify processes with a Poka Yoke technological solution using artificial vision for real-time defect prevention. This dual approach addresses the root causes of inefficiency, merging a core principle of Lean Manufacturing with an accessible Industry 4.0 tool designed for implementation in resource-constrained environments. The validation of the model demonstrated its remarkable effectiveness, achieving transformative results: the sewing rework rate was drastically reduced from 28.43% to 8.94%, the labeling rework rate were reduced from 18.02% to 3.88%, the production cycle time was optimized from 23.74 to 16.54 min per garment, with a 173.74% increase in productivity. These results not only confirm the validity of the model, but, due to its basis in universal principles and scalable technology, they also guarantee high applicability and replicability in other micro and small companies in the sector, offering a clear path towards a qualitative leap in productivity and competitiveness. Full article

This paper presents a concept for a Learning Factory (LF) designed for interdisciplinary engineering education. Learning factories are experiential learning environments that bridge the gap between theory and practice while supporting the demands of digital transformation. The proposed LF concept was developed using an integrated approach that assessed stakeholder needs and reviewed institutional infrastructure and capacity. These inputs were triangulated into a concept consisting of five core thematic components: Lean processes as an educational anchor, Enterprise Resource Planning (ERP) systems, Internet of Things (IoT)-based integration, simulation, and physical prototyping. Validation workshops with Small- and Medium-sized Enterprise (SME) managers, academic experts, and students confirmed the perceived relevance of this structure and its potential. The resulting concept focuses on practice-orientated, team-based learning methods that are in line with the principles of Education 4.0. The design sets goals in four key dimensions: educational integration, technological readiness, industrial relevance with SME orientation and flexibility and scalability. These design principles and practical insights can be utilized for future academic implementations of learning factories. Full article

Enterprises play a key role in achieving sustainable development goals because they affect them to a greater or lesser extent, both in a positive and negative way. The aim of the study presented in the article is to answer the question concerning the level of application of the best practices in enterprises related to the implementation of sustainable development strategy. As recognized means of operational activities in organizations, they are a guarantee of the effective achievement of their goals. The method employed in the research procedure was the Human Lean Green method. Thus, best practices applied in the enterprises analyzed became the basis for measuring their organizational maturity in three areas of sustainable development, i.e., social (Human), economic (Lean) and environmental (Green). The study was conducted in 20 enterprises (manufacturing or service enterprises). The results of the research show, among others, that the popularity of using practices from the Human area is greater than Lean Green practices. Full article

While Lean methodologies have been widely adopted in large enterprises, their application in small and medium enterprises remains an area requiring further exploration. This study aims at a hybrid approach combining a systematic literature review based on the PEO framework and PRISMA methodology with comprehensive data analysis from 780 respondents to examine the direct relation between Lean and SMEs. Various statistical methods were used to identify different patterns and themes. The findings suggest that while resource constraints pose significant challenges for SMEs in adopting Lean, tailored approaches with a significant reliance on leadership commitment can considerably enhance operational efficiency, resilience, costs, and waste reduction. Insights from the analysis further revealed a preference for operational methods with limited adoption of complex and strategic methods of Lean across SMEs. The paper concludes with practical recommendations for SMEs and lays a foundation for future directions in terms of integration with ESG and sustainability. Full article

In the industrial internet environment, the operation and control of production equipment have become increasingly complex, and their performance directly affects the efficiency, benefits and sustainable development of manufacturing enterprises. From the three-dimensional perspective of “asset-application-maintenance”, this paper constructs a performance analysis framework for the operation and control of production equipment, systematically identifies the combination of core factors affecting performance, and fills the research gap in the current lack of empirical analysis from the configuration perspective in this field. On the basis of data from 82 manufacturing enterprises, the fsQCA method was used to identify three performance improvement paths: the high-load output mode, the lean management and control mode, and the low-failure operation mode. These paths clarify the equivalent approaches to achieve high performance in the operation and control of production equipment under the interaction of multiple factors. On this basis, the study demonstrates the operability and effectiveness of the proposed strategies in actual industrial scenarios through empirical verification in a manufacturing workshop of aero-engine transmission units. In contrast to existing studies, this study introduces the fsQCA method in the field of industrial equipment management and control for the first time to reveal the influencing paths; its originality and methodology have significant innovative significance. The research results provide new ideas and methodological guidance for enterprise managers to improve the performance of production equipment operations and controls in the industrial internet environment, which helps to enhance the sustainable development capability of manufacturing enterprises. Full article

This paper examines how sustainable leadership and strategic sustainability integration are framed and supported for SMEs in the EU. We apply comparative document analysis (CDA) to 35 policy, industry, and NGO reports published in 2020–2025 for Germany, Sweden, Poland, and Spain. Multi-level materials (EU, national, industry/NGO) were thematically coded, and the synthesis is presented in a multi-level conceptual framework linking policies, leadership, strategy, barriers, and transferable practices. The analysis indicates systematic differences in institutional maturity: Sweden and Germany display denser, more navigable support ecosystems and clearer leadership narratives, whereas Poland and Spain exhibit greater fragmentation and a more compliance-oriented framing. Instrument menus are broadly similar (grants/co-funding, concessional finance, advisory vouchers, training, standards/toolkits, green public procurement), yet accessibility and measurement strength diverge; outcome tracking (e.g., energy savings, CO₂e avoided) is more consistent in Sweden/Germany than in Poland/Spain. Green–digital coupling is pivotal: sequencing “on-ramps” (advisory/vouchers) into innovation finance accelerates adoption; where such on-ramps are thin, uptake concentrates among already prepared firms. Implications follow for policy design and practice: prioritize simple entry points for micro- and small enterprises, strengthen monitoring with meaningful KPIs, and ensure regional parity in access to finance and advisory. For SME leaders, role-modeling, employee development, and experimentation help embed sustainability when formal structures are lean. Beyond mapping patterns, this study provides an auditable operationalization of sustainable leadership for document analysis and a transferable framework to compare policy mixes and ecosystem readiness across countries. Full article

This study assesses De Lone and McLean’s Information System (D&M IS) Success Model concerning DAS throughout small and medium enterprises (SMEs) in Saudi Arabia (SA). The present work mainly sought to evaluate the impact of information quality (IQ), system quality (SysQ), service quality (SrvQ) serving, system utilization, and user satisfaction (Usat) on the usage of the Digital Accounting System (DAS), which is posited to ultimately improve the quality of sustainable decision-making. The research utilized a quantitative methodology, employing a self-administered questionnaire to collect data from 328 decision-makers who are knowledgeable about actual DAS usage by SMEs in SA. Subsequent to gathering data, validation was conducted via Structural Equation Modeling (SEM) by utilizing smart-PLS software. The findings indicate that SysQ and IQ significantly influenced system utilization, although SrvQ did not. DAS was determined to significantly influence user happiness. Moreover, system utilization and user satisfaction positively influenced DAS, thereby affecting the sustainability of decision-making and reflecting the overall benefits of DAS. This work enhances the current IS literature by identifying the characteristics that affect the net advantages of DAS, with the suggested model evaluated in SMEs in SA utilizing DAS. This study serves as a reference to elucidate the significance of DAS and offers consequences, limitations, and prospects for further research. Full article

This paper explores how Lean Accounting (LA) is implemented in manufacturing firms by identifying tool packages and the typologies of companies applying them. Despite growing interest in LA, prior research has focused primarily on individual tools or case studies, leaving the configuration of management accounting practices across organizations underexplored. The study aims to identify coherent packages of Lean Accounting tools used in practice and to determine how these packages correspond to different levels of implementation among manufacturing firms. An online questionnaire was used to collect data from 128 enterprises. Exploratory factor analysis was applied to identify tool groupings, followed by clustering to classify firms by their LA adoption profiles. The analysis resulted in three tool packages and three types of firms that differ in the advancement of LA implementation. The results show that firms at higher stages of implementation report greater awareness of cultural and technical barriers to LA transformation. This paper contributes to the literature by empirically identifying Lean Accounting tool packages and by proposing a typology of firms based on their implementation profiles. The novelty of this study lies in combining factor- and cluster-based approaches to explore management accounting practice configurations within the Lean paradigm—an area previously dominated by case studies. The findings enrich current knowledge on how different firms adopt Lean Accounting and how they perceive the challenges of its implementation. Full article

In a dynamic and uncertain environment, maintaining a high level of business process (BP) reliability represents a key long-term objective for organizations. The manufacturing process, as the most critical business process in manufacturing enterprises, is emphasized due to its potential to cause significant disruptions across other BPs if it fails. This paper proposes a two-stage model. In the first stage, failures leading to lean waste are evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) combined with interval-valued intuitionistic fuzzy numbers (IVIFNs), referred to as IF-TOPSIS. The model is grounded in the Failure Mode and Effect Analysis (FMEA) framework. In the second stage, a modified fuzzy logic system with IVIFN-based rules is applied to determine the risk level of the manufacturing process. This approach is based on the property of symmetry in the decision-making process, ensuring that criteria are treated in a balanced manner and inference rules are applied consistently. A case study based on real-life data demonstrates that the obtained results identify measures that can enhance business strategy and reduce failure rates. Thus, the model is validated and shown to contribute to lean waste reduction. It can be concluded that the proposed methodology provides clear and practical guidance to enterprise management, as well as to all sectors and individuals involved in ensuring a reliable manufacturing process, for defining failure priorities and implementing preventive measures. Full article

This study evaluates the degree to which small and medium sized enterprises (SMEs) are prepared to adopt smart manufacturing in contrast to large enterprises, a transition that depends on the effective use of the Internet of Things, artificial intelligence (AI), and advanced analytics. While many large multinational companies have already integrated such technologies, smaller firms still struggle because of tight budgets, limited technical expertise, and difficulties in scaling new systems. To capture these realities, the investigation refines the Initiative Mittelstand-Digital für Produktionsunternehmen und Logistik-Systeme (IMPULS) Industry 4.0 readiness model, which was initially developed to help German SMEs, so that it aligns with the circumstances faced by smaller manufacturers. A thorough review of published work first surveys existing readiness and maturity frameworks, highlights their limitations, and guides the selection of new, SME-specific indicators. The framework gauges readiness across six dimensions: strategic planning and organizational design, smart factory infrastructure, lean operations, digital products, data-driven services, and workforce capability. Each dimension is operationalized through a questionnaire that offers clear benchmarks and actionable targets suited to the current resources of each enterprise. Weaving strategic vision, skill growth, and cooperative support, the approach offers managers a direct path to sharper competitiveness and lasting innovation within a changing industrial landscape. Additionally, a separate Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is provided for each dimension based on survey data offering decision-makers concise guidance for future investment. The proposed adaptation of the IMPULS framework, validated through empirical data from 31 SMEs, introduces a novel readiness index, diagnostic gap metrics, and actionable cluster profiles tailored to developing-country industrial ecosystems. Full article

This paper analyzed the importance of applying modern concepts and tools for monitoring production processes in order to improve effectiveness, efficiency, and sustainable manufacturing. The aim of the study was to develop and apply a stochastic model based on a modified real-time observation method to optimize production cycles in the metalworking industry. The research was conducted over several years in real industrial conditions using instantaneous observations, and the collected data were statistically analyzed using control charts and flow coefficient functions. The results showed a significant reduction in production cycle times and improved efficiency, particularly when the batch size was optimized to 10 units. The analyzed working time elements and flow coefficients enabled a comprehensive analysis and influenced trends in subsequent years, thereby improving production management. A comparative analysis of the results reveals a downward trend in average PC time per unit over the years—56.2, 37.7, 31.5, and 44.8 min from phases I to IV—until the introduction of a new operation. The corresponding flow coefficient functions are y₁ = 297.54/x + 2; y₂ = 239/x − 7.36; y₃ = 192/x + 0.65; and y₄ = 438.2/x − 11.3. These findings suggest that the optimal batch size for the enterprise under consideration is 10 units. The findings confirmed that the integration of Lean principles and Industry 4.0 methods contributes to the reduction of non-productive time and better process control. The study provided a simple and effective model for cycle time optimization that can be implemented even in small and medium-sized enterprises. Full article

This study examines how selected technological and human-centered factors affect sustainable lean performance potential (SLPP) in manufacturing enterprises within the context of Industry 4.0 transition to Industry 5.0. The relationship between SLPP and Industry 4. transition to Industry 5.0 is contextual, meaning this direct relationship is not analyzed via statistical methods. A structured survey was conducted with 128 managers (n = 128), focusing on human-centric technology design (HCTD), artificial intelligence for waste minimization (AIWM), predictive maintenance (PMAI), and IoT integration in production (IOTP). The data were analyzed using descriptive statistics, correlation analysis, linear regression analysis, Harman’s single-factor test results, multicollinearity test, and non-linear curve estimation analysis. The results show that all four independent variables are positively associated with SLPP. IoT integration and AI for waste minimization had the strongest effects, followed by predictive maintenance. Human-centric technology design showed a weaker, yet still statistically significant, relationship with SLPP. The findings support a model where digital tools contribute directly to lean and sustainable outcomes, while human-centered approaches are emerging more gradually. The research adds empirical evidence to ongoing discussions about factors affecting lean performance in the context of industrial changes. Full article