Scope 3 emissions constitute the largest and most difficult-to-manage component of the carbon footprint of the steel industry; however, they remain underexplored owing to fragmented data systems and the absence of holistic analytical approaches. This study presents an integrated, real-data-driven framework for quantifying and reducing Scope 3 emissions in a medium-scale steel supply chain in West Bengal, India. Primary operational data were collected from upstream suppliers, midstream manufacturing operations, and downstream distributors using transport logs, meter-based energy records, scrap inspection sheets, on-site walk-throughs, and structured stakeholder interviews. Environmental Value Stream Mapping (EVSM) coupled with life-cycle emission accounting was applied to six process stages (UP1, UP2, MS1, MS2, DS1, and DS2), revealing the Electric Arc Furnace (MS1) as the dominant hotspot, contributing more than 90% of the total Scope 3 emissions. Circularity metrics, namely the Scrap Quality Index (SQI) and Material Circularity Index (MCI), demonstrated that higher scrap quality and increased recycled content can significantly decrease upstream embodied emissions. A cooperative game-theoretic model quantified abatement opportunities for suppliers, the manufacturer, and distributors, showing that full coalition formation {U, M, D} generated the highest net payoff (₹1.89 million). Shapley value allocation confirmed the manufacturer as the major beneficiary (97.6%), with proportionate gains assigned to suppliers and distributors. The results highlight that collaborative governance, enhanced circularity, optimized logistics, and renewable energy integration, particularly solar-based electricity substitution, collectively offer a high-impact pathway for Scope 3 decarbonization. The proposed multi-method framework provides a transparent, equitable, and industry-ready decision support system for accelerating low-carbon transitions in the Indian steel sector.

This study explores the implementation of lean manufacturing principles, specifically focusing on line balancing, in the automotive components industry to address productivity challenges, such as inventory management, scrap reduction, and lengthy die exchange times. By optimizing the assembly line, the study aimed to reduce manpower and improve efficiency. The methodology involved calculating process cycle times, identifying nonvalue-added activities, and using tools like the standardized work combination table (SWCT), value stream mapping (VSM), and single minute exchange of die (SMED) to streamline operations. Key findings revealed that the number of operators required per part was significantly reduced, leading to a theoretical reduction of 18 operators per day. This optimization not only minimized cycle time variations and die changeover times but also enhanced overall production efficiency, resulting in annual cost savings of ₹28,08,000. The analysis demonstrated substantial improvements in line efficiency by addressing bottlenecks, balancing workloads, and implementing lean tools, such as 5S, Kaizen, and visual management. The introduction of a more balanced and efficient production process reduced nonvalue-added activities and idle times, with line efficiency (LBR) improving on balanced lines (e.g. CFT Outer U129: 73%-93%) and process efficiency (PE) in the stores, line segment increasing from 37.89% to 65.45% ([Formula: see text]72.7% relative), alongside a 60 min reduction in stock staging time at the process control (PC) zone. The case study underscores the potential of lean methodologies to significantly enhance productivity, reduce costs, and improve resource utilization in medium-scale manufacturing industries. These findings provide a valuable framework for other industries facing similar challenges, highlighting the importance of continuous improvement and sustainability in maintaining a competitive edge. The novelty of this study lies in the empirical integration of lean tools to address specific constraints within a mid-scale automotive supply chain, resulting in quantifiable manpower savings and cost reduction.

Value Stream Mapping (VSM) is a widely used lean tool that improves efficiency by identifying and eliminating wastes across various processes, including those in healthcare. In recent years, stakeholders have increasingly pressured organisations to integrate sustainability into their operations, underscoring the need to expand lean management practices by incorporating environmental and social considerations, such as the implementation of sustainable VSM. Unlike traditional VSM, which primarily focuses on customer (i.e., patient) value, sustainable VSM addresses the needs of a broader range of stakeholders, including patients, employees (healthcare staff), labour/professional unions, insurers, local communities, and regulatory bodies. This study aims to systematically identify the metrics used in VSM within the healthcare sector and propose an expanded metrics-based VSM that integrates economic, environmental, and social dimensions. A systematic literature review on sustainable VSM was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review indicates that current VSM practices in healthcare focus on metrics related to time, efficiency, capacity, cost, patient safety, and patient satisfaction. However, the healthcare sector also has significant implications for sustainability, including environmental aspects (e.g., resource consumption and healthcare waste management) and social aspects (e.g., occupational health and safety, and stakeholder satisfaction). To address these broader considerations, this study expands the existing application of VSM in healthcare by adapting multidimensional metrics commonly used in sustainable VSM in the manufacturing sector. This approach aims to achieve comprehensive outcomes by enhancing the quality of patient-centred care, while minimising resource consumption (materials, energy, and water), ensuring effective healthcare waste management, promoting health and safety, and improving the satisfaction of patients and other stakeholders. This study establishes a theoretical foundation for future research on integrating lean practices and sustainability. Additionally, it offers actionable insights for healthcare organisations seeking to implement a comprehensive approach to VSM that improves efficiency, care quality, and sustainability.

Additive Manufacturing (AM) processes enable the validation of design variants, and the manufacturing of low volume specialty components. Slow fabrication times are an issue for larger production volumes, but for the directed energy deposition (DED) and hybrid manufacturing (where additive and machining operations are interwoven), new process planning scenarios can be explored for both low and medium volume production levels, which aligns well with addressing on-demand service and out of production components. DED AM is a material deposition based process. Wire filament or powder is melted by a heat source, and multi-axis tool paths can be employed to deposit the material. Large freeform components can be fabricated without support material; however, production volume scalability is an issue. Prior to exploring multi-function or reconfigurable machines and dynamic layouts, a framework for defining nomenclature for DED AM precedence diagrams and value stream maps, and insights for systematically decomposing components for macro and micro level process planning needs to be developed. The goal of this research is to provide a foundation for DED and hybrid manufacturing for low volume production (100 – 2000 pcs) for short planning horizons (1 week to 1 month) which would align to ‘medium volume’ production levels. This specific paper will present research performed to date on addressing these challenges.

The U.S. agricultural sector is undergoing a paradigm shift driven by the convergence of smart agriculture practices and Industry 4.0 technologies. Rising demands for food security, sustainability, and resource efficiency are compelling stakeholders to adopt advanced tools that integrate data-driven decision-making with traditional agricultural management. This paper explores how industrial engineering tools, such as process optimization, lean methodologies, predictive analytics, and systems modeling, can be combined with smart agriculture and Industry 4.0 technologies to significantly improve agricultural productivity in the United States. Key enabling technologies include the Internet of Things (IoT), robotics, artificial intelligence (AI), big data analytics, blockchain, and cyber-physical systems, which collectively allow for real-time monitoring, precision farming, predictive maintenance of agricultural machinery, and supply chain optimization. By applying industrial engineering methods such as value stream mapping, simulation modeling, and queuing theory, agricultural operations can be systematically streamlined to minimize waste, reduce downtime, and optimize input usage (e.g., water, fertilizer, energy). Case studies and simulation results presented in this paper demonstrate that integrating Industry 4.0 frameworks with industrial engineering tools in U.S. farms can increase crop yields by up to 18%, reduce resource wastage by 25%, and enhance overall operational efficiency by 20%. Furthermore, the adoption of smart agriculture practices supported by data-driven MIS (Management Information Systems) can improve resilience to climate variability and labor shortages. While challenges remain in terms of high upfront costs, interoperability of digital platforms, and farmer training, the proposed framework offers a structured roadmap for modernizing U.S. agriculture and enhancing food security. The findings contribute to the growing body of knowledge on agricultural digital transformation and highlight the critical role of industrial engineering tools in accelerating smart agriculture adoption.

Background: The medication service at the Outpatient Pharmacy of RSUD Drs. H. Amri Tambunan experiences long waiting times, especially for BPJS guarantee prescriptions, which contradicts the established service time standards, i.e., dispensing medications ? 30 minutes and preparing compounded medications ? 60 minutes, in accordance with health service regulations. Research Objective: This study aims to analyze the waiting times for medication services at the Outpatient Pharmacy, identify types of waste within the service process flow, and identify factors that hinder service efficiency. Additionally, the study aims to provide recommendations for improvement to reduce waiting times and enhance service quality. Research Method: This qualitative study uses Value Stream Mapping (VSM) to analyze the medication service process and identify waste in each service stage. Additionally, a fishbone diagram is used to identify root causes affecting waiting times. Data is collected through interviews with pharmacy staff, the head of the pharmacy installation, and direct field observations. Research Results: This study found various types of waste in each stage of the medication service, including waiting, motion, defects, and overproduction. Factors hindering service time include patient congestion during peak hours, medication unavailability, and prescription inconsistencies with the formularium. Suboptimal use of information systems and a shortage of pharmacists were also identified as major hindrances. Conclusion: Improvements in process flow, coordination between departments, routine training, and the implementation of an integrated hospital management information system are needed to minimize waste and expedite service times at the Outpatient Pharmacy of RSUD Drs. H. Amri Tambunan.

As aviation Maintenance, Repair, and Overhaul (MRO) operations become increasingly complex, Lean Manufacturing offers strategic benefits in improving efficiency and sustaining performance. However, its adaptation in service-oriented MRO settings, particularly in developing countries, remains insufficiently studied. This research investigates lean implementation across three Philippine aviation contexts: a commercial MRO (Lufthansa Technik Philippines), a general aviation center (Omni Aviation), and an academic training facility (Philippine State College of Aeronautics, PhilSCA). Using a mixed-methods case study design, data were collected from 30 semi-structured interviews, direct observations, and operational metrics, including turnaround time (TAT), rework frequency, and technician productivity. Findings indicate that lean tools, such as 5S, Kaizen, and Value Stream Mapping, improve workflow efficiency and reduce errors when adapted to institutional conditions. At PhilSCA, lean functions as a pedagogical framework, aligning instruction with industry needs. The study presents an integrated analytical model that combines the Human Factors Analysis and Classification System (HFACS) with the Maintenance Process Cycle to examine both technical and human factors. While grounded in the Philippine MRO sector, the findings offer relevant insights for similar settings in emerging economies, emphasizing the importance of leadership, contextual adaptation, and the need for training reform to sustain lean transformation.

This study aims to evaluate how the combined use of Industry 4.0 technologies with lean healthcare tools can improve healthcare organizations' sustainability. Sustainability will be assessed from the Triple Bottom Line (TBL), which includes three perspectives: economic, social and environmental. A systematic literature review (RSL) was performed and, after identifying 987 studies and applying the selection criteria, 43 articles published between 2011 and 2022 were analyzed, exploring the combination of I4.0 technologies with lean tools. The most prominent combination identified in the literature was the use of simulation technologies integrated with the value stream mapping (VSM) tool, a core element of the lean methodology. This pairing was primarily applied to enhance the service level indicator. The findings suggest that such combinations are particularly effective in improving efficiency, resilience and internal processes in healthcare organizations. These insights are especially relevant for the development and adaptation of I4.0 technologies to the healthcare context, offering strategic value during periods of instability and uncertainty, such as those experienced during the COVID-19 pandemic. The study presents several correlations worked by the articles, however, when there are two different combinations of technologies and tools for a single TBL indicator, it is not possible to measure the strength of these relationships and, therefore, to infer which one contributes most to performance improvement. This represents the main limitation of the study. Furthermore, a limitation of the study is the exclusion of 13 articles due to the unavailability of full-text access, even through the Portal CAPES (a Brazilian platform with more than 455 research bases). The results of this research can guide hospital managers in identifying combinations of I4.0 technologies and lean tools with the greatest potential to contribute to improving business sustainability, as measured by the TBL. Thus, through this combined use of technologies and tools, health organizations are expected to achieve better performance, offering high quality services to society. The TBL encourages companies and organizations to take responsibility not only for financial profit but also for the social and environmental impact of their activities. This aspect promotes equity, diversity, employee safety, and engagement with the local community and other stakeholders. It is necessary to highlight that the combination of I4.0 technologies with lean tools and the TBL has the ability to mitigate process waste in the healthcare sector. A similar study was found in the manufacturing area and another in the healthcare area focused on evaluating only the simulation technology. Thus, the originality of the research focuses on evaluating the contribution of the combined use of I4.0 technologies (in addition to simulation) with lean tools for business sustainability, measured through TBL, specifically in the health context.

This study aims to determine the level of waste that occurs in the warehousing process at PT. XYZ through the implementation of lean warehousing. Data analysis in this study uses the lean warehousing method, namely Value Stream Mapping (VSM) to find critical waste in the warehousing process using the Waste Assessment Model (WAM) which then identifies the root causes of waste using a fishbone diagram and analyzes improvement recommendations using the FMEA method. Based on the results of the study, it is known that the levels of critical waste from the highest to the lowest, respectively, are waste waiting, waste unnecessary motion, waste over-processing, and waste defect. Based on the fishbone diagram analysis, there are 6 factors that cause the four critical wastes, namely human factors, method, material, environment, measurement, and machine. The proposed improvements that are the result of the analysis using FMEA are implementing the 5S concept in the warehouse, implementing a FIFO system for placing goods in the warehouse, increasing the number of workers, optimizing employee placement, and implementing a warehouse management system.

BackgroundDuring the COVID-19 outbreak in May 2021, our hospital—designated as a specialized facility for severely infected patients—faced critical staff and resource shortages. The urgent need for efficient bed management to ensure timely admissions underscored the inefficiencies of the manual, phone-based allocation process, which averaged 454 seconds per query. Traditional IT solutions were not feasible due to time and cost constraints.ObjectiveThis study aims to design and implement a rapid, zero-cost Quick Isolation Bed Inquiry System that provides real-time bed information and enables timely admissions without requiring additional workforce or expense.MethodsWe conducted a 3-cycle pre-post quality improvement study guided by the Toyota business practice (TBP), an 8-step problem-solving framework. After clarifying the problem and constructing a value stream map, we identified bottlenecks. A user-centered solution was developed by leveraging an underutilized data export function in the hospital’s bed inquiry platform. Using Microsoft Excel Visual Basic for Applications, we automated the filtering and display of relevant bed information. The primary outcomes were process time and number of steps; secondary outcomes included staff time savings and system accuracy.ResultsThe baseline manual process required 25 steps and 454 seconds to complete a query. The new system reduced this to 3 steps and 12 seconds, representing a 97.4% gain in efficiency. Single-click execution generated 3 outputs (administrative PDF, large-screen display, and mobile version) in 4 seconds, with distribution to the hospital LINE group completed in 7 seconds. Reliability reached 100%, with continuous availability through virtual private network access. Development and debugging were completed within 3 days using only existing resources. Postpandemic, the system was adapted for general ward management with minimal modifications.ConclusionsApplying TBP enabled the rapid development of a user-centered, zero-cost bed management tool by repurposing existing digital assets. The intervention markedly improved efficiency, reliability, and usability without additional staffing or expenditure, providing a scalable model for agile health care systems operating under resource constraints. Future work will focus on deeper automation, such as application programming interface–based real-time updates, and on evaluating downstream impacts on patient flow and bed turnover.

This study presents a systematic review of Lean and Six Sigma (LSS) implementation in the maritime sector for the period 2016–2025, encompassing the contexts of ports, shipyards, logistics, the LNG industry, fisheries, and ship design. The analysis reveals a shift in focus from initial case studies to strategic implementation integrated with digital transformation, supply chain resilience, and sustainability. DMAIC and Value Stream Mapping methodologies dominate as the primary framework, supported by the integration of Industry 4.0 technologies and data analytics to maximize process efficiency. Quantitative evidence demonstrates the reduction in process time, reduction in defect rates, and significant operational cost savings. However, the success of LSS implementation is highly influenced by management support, human resource readiness, and the availability of reliable data, with the main obstacles being cultural resistance, limited initial investment, and lack of technical training. This study confirms that the future of LSS in the maritime sector is moving towards a hybrid model that combines Lean, Six Sigma, digitalization, and sustainability principles, which has the potential to become a global best practice in increasing the competitiveness of the maritime industry in an era of competition and uncertainty in the global supply chain.

The emergence of Generative Artificial Intelligence (GenAI) is reshaping logistics and supply chain operations, offering new opportunities to improve efficiency, accuracy, and responsiveness. In the automotive manufacturing sector, where high-volume throughput and precision are critical, the integration of AI technologies into warehouse management represents a strategic advancement. This study presents a case analysis of the implementation of AI-driven reception processes at an Automotive facility in Blaj, Romania. The research focuses on the transition from manual operations to automated recognition using industrial-grade imaging systems integrated with enterprise resource planning platforms. The integrated approach used combines Value Stream Mapping, quantitative performance analysis, and statistical validation using the Wilcoxon Signed-Rank Test. The results reveal a substantial reduction in reception time up to 79% and significant cost savings across various operational scales with improved data accuracy and minimized logistics failures. To support broader industry adoption, the study proposes a Cleaner Logistics and Supply Chain Model, incorporating principles of sustainability, ethical compliance, and continuous improvement. This model serves as a strategic framework for organizations seeking to align AI adoption with long-term operational resilience and environmental responsibility. The findings validate the operational and financial advantages of AI-enabled warehousing management in achieving sustainable digital transformation in logistics.

ABSTRACTWith electromagnetic (EM) pollution on the rise, there is a growing need for high‐performance absorbing materials. Current absorbers often fall short, failing to balance strong absorption with a wide range of frequencies. In the current work, the reduced‐graphene oxide@ carbon nanotube@ magnetic iron oxide@ poly (aniline‐co‐Melamine) (rGO@CNT@Fe3O4@ poly [An‐co‐M]) as a novel EM wave absorbing nanofiller was prepared. The analysis of the prepared nanofillers at various stages of synthesis, including rGO@CNT, rGO@CNT@Fe3O4, and rGO@CNT@Fe3O4@ poly (An‐co‐M), was conducted using several characterization methods such as Fourier transform infrared spectroscopy (FTIR), x‐ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM), and value stream mapping (VSM). Different nanofillers were utilized to construct epoxy‐based nanocomposites, and their EM absorber performances, including complex permittivity (εr), permeability (μr), and reflection loss (RL), were evaluated. Next, the effect of various parameters, such as nanofiller concentration, EM absorber type, and the molar ratio of aniline to melamine (An:M) in the poly(An‐co‐M) shell, on the EM absorption properties of epoxy‐based nanocomposites was studied. The optimal An: M molar ratio in the poly(An‐co‐M) shell was determined to be 3:1 with a nanofiller content of 5.0 wt%. According to the vector network analyzer (VNA) test results, among the pure epoxy and other prepared epoxy‐based nanocomposites, the epoxy‐based nanocomposite modified with rGO@CNT@Fe3O4@ poly (An‐co‐M) nanofiller exhibits better absorption properties, which is attributed to the combination of dielectric and magnetic losses of the nanofiller. The study indicated that the rGO@CNT@Fe3O4@ poly (An‐co‐M) epoxy‐based nanocomposite, referred to as the S‐4 sample, achieved an optimum RL of −17.0 dB and an absorption bandwidth of > 3 GHz at a matched thickness of 5.0 mm. Taking into account the more excellent EM absorption behavior of rGO@CNT@Fe3O4@ poly (An‐co‐M) incorporated epoxy nanocomposite, the poly(An‐co‐M) coating on the rGO@CNT@Fe3O4 nanofiller is an effective strategy for constructing a high‐efficiency EM absorber.

Abstract This study explores the integration of Lean methodologies with occupational safety enhancement in an industrial context. Conducted in a renown Rope factory, the research utilized an Action Research methodology guided by a novel Lean-Based Safety Leadership Framework. Through the implementation of Lean tools such as SMED, 5S, Poka-Yoke, and Visual Management, it is possibe to improve both operational efficiency and occupational safety. A Safety Efficiency Value Stream Mapping (SEVSM) approach was employed to diagnose risk points and inefficiencies within one of the factory’s sectors. The intervention led to a significant reduction in operational risk and improvements in productivity, validating the framework’s effectiveness as a dual-propose tool for safety and efficiency.

This community service program was carried out in collaboration with the Forum Komunikasi (Forkom) UMKM Ngemplak Sleman, which faces challenges in production and business management. The objective of the program was to enhance business capacity through the application of Value Stream Mapping (VSM), financial management training, provision of production equipment, and assistance in recording cost of goods sold (COGS) and preparing simple income statements. The methods employed included community education, training in production and management, diffusion of science and technology through manual bookkeeping, as well as ongoing mentoring and evaluation. The results indicate an improvement in participants’ understanding of financial management and digital business. The provision of production equipment proved to increase efficiency, capacity, and product quality consistency. In addition, training in COGS and simple income statements enabled business actors to understand cost structures and assess financial performance. Overall, this program contributed to strengthening the competitiveness of Ngemplak culinary MSMEs and supporting business sustainability.

Production plays an important role in maintaining the quality and quantity of products until they reach consumers. The many important activities in the production process require companies to improve their performance to be effective and efficient in order to maintain trust. PT XYZ is a BUMD company engaged in the manufacturing of fertilizer and dolomite rock mining materials since 2018. In production activities, there are various activities that do not have added value or waste. This study aims to identify waste and find out the root causes of waste in production process activities. The implementation of Lean manufacturing is carried out by identifying the warehousing flow process with Value Stream Mapping (VSM), then identifying 7 wastes that occur in the warehousing process using the Waste Assessment Model (WAM) method. The results of the study showed that the waste with the highest percentage level was waste Processing (P) waste transportation (T) waste overproduction (D). From the proposed improvements and mapping of future flows, it was found that lead time was reduced to 264 minutes from 350 minutes, which means eliminating 86 minutes of activities that did not provide added value. Thus, activity increased by 22% from the initial condition.

Abstract The Gram staining technique, developed in the late 19th century, remains a key method for bacterial identification in microbiology. This procedure divides bacteria into Gram-positive and Gram-negative groups and allows for distinguishing cellular structures such as cocci, bacilli, etc. Traditional methods are prone to human error and heavily rely on the operator’s expertise. Therefore, automating the proceedings with equipment that employs technology to standardize it would optimize the process, reducing time and improving accuracy. Methods: This study utilized clinical samples and ATCC bacterial strains, processed at the National Reference Center of Valle de México. Two methods were compared: manual and automated, evaluating three main criteria:Staining times, analyzed using a Value Stream Mapping (VSM).Staining quality, assessed parameters such as homogeneity, structure definition, and the absence of precipitated reagents.Reproducibility, analyzed using 20 samples of Escherichia coli ATCC 25922, 20 samples of Enterococcus faecalis ATCC 29212, and 20 clinical samples per method, using 2x2 contingency tables to determine concordance. Results: The VSM analysis showed that the manual method requires 12.48 minutes to process 30 samples (24.6 seconds per sample), whereas the automated method reduces the time to 7.7 minutes (15 seconds per sample). This translates to a 39% reduction in process time. The automated system provided superior staining quality, with better homogeneity and clearer definition of cellular structures, as well as a reduction in pre-analytical errors. The results were reproducible and comparable for both methods, ensuring the accuracy of the automated process. Conclusion: Automating the Gram staining process offers multiple advantages, such as time savings, result standardization, and reduced human error. The automated system optimizes the staining process by reducing processing times and enhancing the quality of observed structures. While both methods are comparable in terms of reproducibility, the automated method demonstrates a clear advantage in efficiency and precision, making it ideal for laboratories with high sample volumes or those aiming to optimize time and minimize pre-analytical errors.

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.

This project investigated the impact of Total Productive Maintenance (TPM) on a Bavius 5-axis CNC machine used in aircraft floor beam production. In Fall 2024, students partnered with Spirit AeroSystems to assess and improve maintenance using 5S, OEE, and value stream mapping. This study identifies inefficiencies, evaluates process effectiveness, and offers recommendations. Early results show mixed outcomes, providing key insights into TPM’s role in boosting operational efficiency.

Abstract The BTS structure was used for the Smooth service chains coaching to support wellbeing services counties to design multiprofessional service chains and pathways for clients in vulnerable positions. The teams consisting of professionals from different services and of an expert-by-experience attended four workshops and active working periods within 12-15 months. Coaches assisted the teams to use different Lean tools to analyse the current situation (e.g. value stream map), to set SMART goals, to model an ideal service path and plan PDSAs to support designing the process and/or implementing it. Also, the teams were advised to collect data to analyse the achieved change and report their models and evaluation results in a national website that operates as a platform for innovation and co-design. A process evaluation was conducted to analyse how the coaching model was implemented, how the activities triggered change and how external factors influenced functioning of the coaching. The data was collected from two coaching rounds which had a total 18 teams participating. Of these, six development teams (n = 31) and five process owners (n = 9) attended group interviews. Also, an interview of coaches (n = 4) and the design team (n = 3) were conducted. The data also includes feedback surveys of all participants in the studied rounds (n = 97) and reporting sheets for teams’ results. The main benefits the participants experienced concerned being able to model the service chain or pilot a new solution, gaining new skills for development work, learning to use different tools, understanding how to form measurable aims and track changes, and becoming familiar with other stakeholders’ work. The challenges included how the new service chains can be implemented and sustained and how to measure the outcomes after the coaching ended. To conclude, the original functions of the Model for Improvement were present but designing and implementing a new service path within the time was challenging.