Search Results (48)

Addressing the persistent challenges of low resource utilization efficiency and the difficulty in quantifying hidden costs within the beef cattle sector, this study proposes an integrated diagnostic methodology that couples Material Flow Cost Accounting (MFCA) with Value Stream Mapping (VSM). Using a cohort of 1623 beef cattle finished in 2024 at the case study farm in Heilongjiang Province, China, the full life-cycle accounting reveals that hidden costs constitute 6.43% of total inputs. Attribution analysis further pinpoints two critical nodes: feed loss and bedding consumption, which account for 33.14% and 35.77% of negative product costs, respectively. Based on these diagnostics, two optimization strategies were devised: refined feed supply chain management and a recycled bedding system centered on the aerobic fermentation of cattle manure. Empirical estimates indicate that upgrading hardware facilities could reduce the feed loss rate to under 2%, yielding annual savings of ¥485,200. Furthermore, the bedding recycling system not only achieves zero waste discharge but also generates an average annual displacement income of ¥3.504 million, with an investment payback period of just 0.54 years. These findings demonstrate the efficacy of the coupled MFCA-VSM model in identifying environmental costs and unlocking economic potential, thereby providing an actionable pathway for the livestock industry’s transition toward more intensive and circular practices. Full article

This study proposes a real-time data-based Digital Value Stream Mapping (Digital VSM) framework that integrates Artificial Intelligence (AI) feature selection and discrete-event simulation validation to enhance production system performance. Unlike conventional VSM approaches that rely on static, manually aggregated data, the proposed framework uses real-time operational data to dynamically quantify Value Added (VA), Non-Value Added (NVA), and Necessary Non-Value Added (NNVA) activities. To improve decision accuracy, an Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) feature selection is employed to identify dominant production variables influencing lead time and line imbalance. Furthermore, Ranked Positional Weight (RPW) optimization results are validated through Tecnomatix Plant Simulation to ensure robustness before physical implementation. The proposed framework was applied to a discrete manufacturing line, resulting in a reduction of total lead time from 8755 s to 6400 s and an increase in process ratio from 33.64% to 45.91%, with line efficiency reaching 91.7%. The findings demonstrate that integrating Digital VSM with AI-driven feature selection and simulation validation transforms Lean analysis from a descriptive tool into a predictive and validated decision-support system suitable for Industry 4.0 environments. Full article

As manufacturing organisations work to align their operations with global sustainability expectations, the integration of the United Nations (UN) Sustainable Development Goals (SDGs) into production systems has become increasingly important and a critical pathway toward Industry 5.0. Sustainable Value Stream Mapping (Sus-VSM) provides a structured approach for assessing economic, environmental, and social performance, but its practical adoption remains limited, and it has not been systematically aligned with SDG Targets. These limitations are particularly evident among small and medium-sized enterprises (SMEs) which often lack the resources needed to implement extensive sustainability indicator sets. This study combines a systematic literature review with industrial evidence to identify Sus-VSM indicators and examine their use in practice. A consolidated set of 18 economic, 22 environmental, and 18 social indicators is derived and mapped to 16 SDG Targets relevant to manufacturing by means of a Delphi-based expert assessment. The mapping results are analysed to evaluate indicator usefulness and expert agreement and are complemented by an industrial verification from 30 companies across different sectors that rates each indicator in terms of relevance to SDGs and applicability in real factory contexts. The results show that economic indicators are most aligned with SDG 8, environmental indicators with SDG 9, SDG 12, and SDG 13, and social indicators with SDG 3 and SDG 8, while gaps persist for water, energy transition, and some climate-related Targets. A prioritised indicator set is proposed that maintains coverage of the selected SDG Targets and remains feasible for SMEs, providing a practically oriented basis for embedding SDG-aligned sustainability assessment within Sus-VSM. Full article

Lean construction is considered a new methodology for minimizing the causes of waste that hinder the achievement of green building (GB) goals. The main aim of this study is to develop a lean model using fuzzy logic technique to mitigate causes of waste effect in GB projects and to determine the most appropriate lean tools affecting these causes. The inputs of this model include GB waste and four lean tools, comprising Quality Function Deployment (QFD), Last Planner System (LPS), Value Stream Mapping (VSM), and 5S, while the outputs include four improvement level indices based on the lean tools. The model uses various logical rules to achieve several relations among the inputs and outputs, and it is applied and verified using data related to several causes of waste categorized under five groups. The strongest correlation is found between VSM and 5S indices, while an adverse relationship is observed between QFD and 5S indices. The results indicate that a cause of waste that refers to poor assessment of site conditions is considered the most substantial one due to its high improvement level indices across all lean tools. The most significant waste group is related to GB stakeholders, which contains 38% of key causes of waste. The improvement using QFD increases by 10% compared to VSM and 28.20% compared to 5S. QFD and LPS are measured as the most suitable lean tools to mitigate the causes of waste effects due to their high impact and high improvement level indices. Full article

The construction sector, facing a persistent productivity gap compared to other industries, is hindered by fragmented value streams, inconsistent performance metrics, and the limited scalability of process improvements. We introduce a pioneering, four-tiered hierarchical productivity framework to respond to these challenges. This innovative approach integrates operational, tactical, strategic, and normative layers. At its core, the framework applies standardised, repeatable process steps—mapped using Value Stream Mapping (VSM)—to capture key indicators such as input efficiency, output effectiveness, and First-Time Quality (FTQ). These are then aggregated through takt time compliance, schedule reliability, and workload balance to evaluate trade synchronisation and flow stability. Higher-level metrics—flow efficiency, multi-resource utilisation, and ESG-linked performance—are integrated into an Overall Productivity Index (OPI). Building on a modular production model, the proposed framework supports real-time sensing, AI-driven monitoring, and intelligent process control, as demonstrated through an empirical case study of continuous process monitoring for Kelly drilling operations. This validation illustrates how sensor-equipped machinery and machine learning algorithms can automate data capture, map observed activities to standardised process steps, and detect productivity deviations in situ. This paper contributes to a multi-scalar measurement architecture that links micro-level execution with macro-level decision-making. It provides a foundation for real-time monitoring, performance-based coordination, and data-driven innovation. The framework is applicable across modular construction, digital twins, and platform-based delivery models, offering benefits beyond specialised foundation work to all construction trades. Grounded in over a century of productivity research, the approach demonstrates how emerging technologies can deliver measurable and scalable improvements. Framing productivity as an integrative, actionable metric enables sector-wide performance gains. The framework supports construction firms, technology providers, and policymakers in advancing robust, outcome-oriented innovation strategies. Full article

Discrete manufacturing environments face increasing challenges in managing work-in-process (WIP) inventory due to growing product customization and demand volatility. While Value Stream Mapping (VSM) has been widely used for process improvement, traditional approaches lack the ability to dynamically control WIP levels while optimizing multiple performance dimensions simultaneously. This research addresses this gap by developing an integrated framework that synergizes Multi-Dimensional Value Stream Mapping (MD-VSM) with multi-objective optimization, functioning as a specialized digital twin for dynamic WIP control. The framework employs a four-layer architecture that connects real-time data collection, multi-dimensional modeling, dynamic WIP monitoring, and execution control through closed-loop feedback mechanisms. A mixed-integer optimization model is used to balance time, cost, and quality objectives. Validation using a high-fidelity simulation, parameterized with real-world industrial data, demonstrates that the proposed approach yielded up to a 31% reduction in inventory costs while maintaining production throughput and showed a 42% faster recovery from equipment failures compared to traditional methods. Furthermore, a comprehensive sensitivity analysis confirms the framework’s robustness. The system demonstrated stable performance even when key operational parameters, such as WIP upper limits and buffer capacity coefficients, were varied by up to ±30%, underscoring its reliability for real-world deployment. These findings provide manufacturers with a validated methodology for enhancing operational efficiency and production flexibility, advancing the integration of lean principles with data-driven, digital twin-based control systems. Full article

Background/Objectives: Colorectal cancer (CRC) ranks third in the Western world in cancer incidence and second as the cause of cancer-related deaths. Despite advances in perioperative care, minimizing postoperative morbidity is crucial in clinical practice. Digitalization of the healthcare process plays a key role in genuinely and effectively engaging patients. Our aim was to evaluate a digital solution for remote monitoring of patients with CRC, from surgery indication to postoperative discharge. Methods: We developed a digital solution using Value Stream Mapping (VSM) to identify patient care flow and Lean Sigma for optimization and efficiency. We incorporated the Enhanced Recovery After Surgery (ERAS)/RICA pentamodal recommendations to create a program with an individualized schedule for each patient, who received tailored educational, medical, and practical information at every stage of the process. Results: A total of 193 patients used the digital solution, with >75% adhering to ERAS recommendations. The median length of hospital stay was 5 days, with low adherence leading to 3.4 (p = 0.628) or 3.27 (p = 0.642) extra days in the hospital compared to patients with intermediate and high adherence, respectively. The mean comprehensive complication index (CCI) was 9.1/100, which was higher in patients with low adherence (15) versus intermediate (8.17; p = 0.027) and high (7.42; p = 0.011) adherence. An increase in self-perception of quality of life by 9.2% was identified at the end of the process compared to the outcome at the beginning (p = 0.09), and 80% rated their overall satisfaction with the care process as 8 or higher out of 10. Conclusions: The digital solution facilitates the monitoring of CRC care and implementation and adherence to ERAS recommendations, improving patient engagement and satisfaction. Full article

We developed an optimization method with value stream mapping (VSM), dynamic system simulation, and processing time prediction. First, VSM was used to quantitatively analyze the production process, identify value-added and non-value-added activities, and build a mathematical model to describe the flow of resources and waste at various stages. Then, a discrete event simulation (DES) was applied to simulate changes in the production process under different improvement conditions and to assess the effect of improved production efficiency using stochastic event modeling. As a result, we identified potential bottlenecks based on the flow of resources and waste sources throughout the production process and proposed improvement solutions for higher efficiency based on production simulations by predicting processing times for stability of production plans. Full article

Although Thailand is one of the world’s leading exporters of frozen shrimp, the production process and management of the production line remain problematic, due to high operation costs, which may make it difficult for Thailand to compete with other export countries. The aim of this research was therefore to improve the production process for frozen shrimp. Value stream mapping (VSM) was utilized to identify the activity processes, from raw material to the customer, and line balancing (LB) was employed to arrange the production line to achieve process improvements. The ECRS (Eliminate, Combine, Rearrange, Simplify) technique was applied to manage and ameliorate the production process. The result was a suitable production process for frozen shrimp in which the profitability to entrepreneurs can be increased through lean improvement techniques. VSM revealed that the efficiency of the total cycle time could be decreased by approximately 61.72%, and that the lead time could be reduced by about 48.8%. Improvements to the frozen shrimp process through LB could yield an accuracy of up to 90.50%. The ECRS technique helped in arranging new processing to achieve improvements; value-added (VA), non-value-added (NVA), and necessary non-value-added (NNVA) tasks showed that the efficiency of the production process could rise to 46.37%, 25%, and 92.85%, respectively. Entrepreneurs will be able to run their manufacturing processes and achieve high production efficiency in the future using the methodologies and management practices described here. Full article

The petroleum industry has been a key driver in the development of the world economy yet continues to attract increasing criticism due to its negative environmental impact via greenhouse gas emissions, resource inefficiency, and waste. Integrating lean and green practices is fast becoming a transformative approach to tackling these issues as it integrates process optimization with sustainability principles. This review paper examined the existing literature on lean and green integration, highlighting its benefits, models, critical success factors, and a roadmap for its implementation. Also, it identified sustainability challenges and offered strategic solutions. The findings showed that integrating lean and green offers potential for both process optimization and waste and carbon footprint reduction, particularly for the petroleum industry in Sub-Saharan Africa (SSA). This can be achieved by using appropriate tools and models. Sustainable value stream mapping (Sus-VSM) is a strategic tool that highlights the importance of sustainability metrics. These sustainability metrics address the implementation challenges of the convectional value stream mapping tool (VSM). This paper is one of the few initiatives to promote lean–green integration within the petroleum industry. Full article

This study investigates the transformation of e-commerce warehouse operations by integrating Lean Six Sigma tools to enhance efficiency and sustainability. Beginning with Value Stream Mapping (VSM) to identify inefficiencies, followed by a Hoshin Kanri plan to align improvement initiatives with strategic objectives, the study implemented measures such as pallet pooling, process standardization, automation in inspection and picking, layout optimization, and Kanban systems for continuous improvement. A case study of a local e-commerce warehouse specializing in medical devices and healthcare products identified 29 activities across receiving, inspection, storing, picking, packing, and shipping, highlighting inefficiencies addressed through Lean-driven initiatives. These efforts resulted in a 23% reduction in total lead time, doubled value-added time, and significant improvements in inspection, picking, packing, and automation, reducing delays, lowering costs, and enhancing workflow. The study fills a gap in the literature by integrating multiple Lean tools and utilizing the Critical to Quality (CTQ) matrix to ensure sustainable improvements in e-commerce warehousing, emphasizing the strategic value of Lean Six Sigma in creating efficient, customer-focused operations. Full article

Purpose: Addressing sustainability challenges in additive manufacturing (AM) is critical due to high energy consumption and waste. Optimising AM operations is vital for sustainability. However, the existing literature lacks practical examples for AM companies on how to enhance sustainability. To address this gap, a case study within an AM firm was conducted. Design approach: Using the analytical hierarchy process (AHP), sustainability key performance indicators (KPIs) were identified, aligned with the firm’s strategic objectives. Sustainable value stream mapping (Sus-VSM) analysed the production process for sustainability performance. A sustainability strategy map (SSM) was created based on the five perspectives of the 4th generation balanced scorecard (BSC) approach and the results from the case study. Findings: The research identified optimisation areas in the multi-jet-fusion (MJF) process, crafted tailored sustainability KPIs, and developed a SSM to seamlessly integrate sustainability initiatives into the company’s vision. Value: This contribution provides a structured methodology for selecting KPIs and offers a holistic approach to implementing Sus-VSM within the AM industry. The manuscript emphasises the need to incorporate a fifth perspective into the BSC and proposes a framework for developing a SSM. This study aims to guide AM companies toward a more environmentally responsible and socially conscious approach to manufacturing. Full article

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions. Full article

Background/Objectives: The implementation of information quality value stream maps (IQ-VSMs) in food animal production systems can increase our understanding of the opportunities and challenges when using laboratory testing for antimicrobial resistance (AMR) to support antimicrobial stewardship (AMS). Our objectives were to (1) explore the implementation of information quality value stream mapping as a continuous improvement tool to inform decisions for bovine respiratory disease (BRD) management and AMS and (2) apply the information quality dimensions to identified Kaizen opportunities for the integration of laboratory data into BRD management systems to assess the appropriateness of BRD treatment plans in western Canadian feedlot production. Methods: A ‘Current State’ IQ-VSM outlined the processes, available information, information processing steps, and control decisions contributing to BRD management and treatment in commercial western Canadian feedlots, recognizing that laboratory BRD pathogens and AMR data are typically not part of BRD management. Results: The ‘Future State’ IQ-VSM incorporated Kaizen opportunities for improvement, including (i) the strategic collection of respiratory samples from representative samples of calves for laboratory analysis, regardless of clinical BRD status, (ii) compilation of laboratory data at the pen and feedlot levels, and (iii) analysis of pen- and feedlot-level laboratory data to inform the veterinarian’s assessment of the appropriateness of current BRD treatment plans. Conclusions: The IQ-VSMs provided a valuable framework to visualize the integration of BRD pathogen and AMR laboratory data to support AMS and address any potential future testing requirements. Full article

The current market is highly competitive, and customers are increasingly demanding. In this context, organizations need to adopt tools that enhance process efficiency to ensure competitiveness. This report aims to implement Lean tools, specifically Value Stream Mapping (VSM) and complementary tools, to optimize the production process in the metal treatment industry. A case study was conducted, beginning with a brief sector and process recognition, followed by an analysis of production stages using VSM. Value-added activities, non-value-added activities, and waste were identified. The current VSM revealed a Lead Time (LT) of approximately 336 h (14 days), value-added activities (VA) of 21 h, and a process cycle efficiency (PCE) of 6.29%. Improvement actions were proposed to reduce waste and increase competitiveness. After implementation, the LT decreased to approximately 318 h (13 days), VA increased to 23 h, and process efficiency improved to 7.15%. Despite the limitations of VSM in discontinuous flows, its use increased process efficiency, demonstrating its applicability in complex industrial contexts. Full article