This statistic displays the results of a survey on the share of companies applying lean management methods and Industry 4.0 principles in Italy in 2015, by progress of implementation. During the survey period it was found that ** percent of the responding companies reported that they do not apply lean management principles.

This data base is in SAV version for SPSS and has 169 responses to a survey applied to Mexican maquiladora industry regarding TQM, DIRFT, Wastes and commercial benefits gained

This database contains 169 responses to a questionnaire applied to the manufacturing industry of Ciudad Juarez. It integrates 16 lean manufacturing tools that are applied to the production systems in this industrial sector and the three types of sustainability: environmental, economic, and social. In summary, the database contains a total of 143 variables that are analyzed, including some demographic variables that allow identifying the analyzed sample.

Abstract The bakery industry has great economic and social importance in the city of Medellin; most of the companies are small with high levels of informality, low value added and productivity. Lean Manufacturing has become one of the most popular paradigms of waste disposal in the industrial sector and services, with great benefits of their practice on the improvement of the quality and organizational productivity. This work evaluates the level of implementation of the lean manufacturing techniques in the micro and small enterprises in Medellin, in the food sector. It was using diagnostic and follow-up tools with questionnaire to the production chief, which include 9 techniques or tools, and a variable of administration that allow organizations to a guide, to improve the current conditions of productivity. The main results show that the outstanding Lean practices are: PokaYoke, Kaizen and visual factory. However, for which the sector is considered to be of world class, the practices must be strengthened: VSM (generation of value), JIT (Production Flow) and ADMON (Administration). In addition, organizations must increase sales least 139, 20% to increase the level of efficiency of the sector, without changing the number of employees.

Abstract The goal of this study was to evaluate the effect of the motivational factor on the results obtained after implementing a lean manufacturing system in a multinational consumer goods manufacturing company. Key performance indicator data were collected from three production lines during periods before and after lean manufacturing implementation. Unstructured interviews were conducted, and the Motivation and Work Meaning Inventory (MWMI) instrument was applied. The motivational factors were then correlated with the performance indicators. The results provide evidence to support the hypothesis, based on the literature, that the motivational factor in work teams in a lean implementation process will affect the degree of success of the process. It was also confirmed that after implementation of the program, there was a significant improvement in the lines’ operational performance.

Although numerous studies have explored the technical aspects of lean manufacturing, few have specifically examined the impact of national culture on its effectiveness. Furthermore, the relationship between national culture and operational performance resulting from the practice of lean manufacturing is likely non-linear, leading to inconsistent findings in previous research. To address this gap, this study employs multi-group invariance analyses, which do not rely on strict assumptions of linearity, to investigate how lean manufacturing practices affect operational performance across groups with varying national cultural dimensions. The author developed and tested seven models associated with five cultural dimensions, along with two control variables (type of ownership and enterprise size), using data from 271 global manufacturing plants located in Vietnam. The findings indicate that lean manufacturing is more effective in plants with the following characteristics: 1/ Small size: Smaller plants tend to benefit more from lean practices; 2/ Low power distance: Organizations valuing a flatter hierarchy and reduced power distance experience greater effectiveness; 3/ Low uncertainty avoidance: Cultures that embrace ambiguity and change are better suited for lean implementation; 4/ Feminine culture: Lean practices align well with cultures emphasizing collaboration, empathy, and work-life balance. These results partially support the practice-culture congruence perspective. The dataset below includes processed information from the 271 global manufacturing plants in Vietnam, covering details such as type of ownership, industries of operation, average years of operation, number of employees, and the Cultural Values Scale (CVSCALE), which assesses Hofstede’s five cultural dimensions.

Ethical clearance Ref Number: 2024FEBEFREC-STD-02The research project is conducted in the maintenance department at a manufacturing company in the Western Cape, South Africa. Since the implementation of world class manufacturing, lean management has become increasingly recognised as highly desirable for manufacturing organisations. Lean tools provide data to inform the company of various losses, while production and maintenance departments can utilise this to improve productivity.The research project investigates the impact of Mean Time to Repair (MTTR) in relation to the Overall Equipment Effectiveness (OEE). Organisations often turn to further investments when increasing their productivity. By proper utilisation of these lean tools, organisations can increase productivity via understanding the lean tools and with corrective actions which do not include hefty machine investmentsThe primary research objectives of this study are the following:• To determine if a common trend between MTTR and OEE within a manufacturing plant in the Western Cape• To determine whether an increase in MTTR results in a decrease in OEE within a manufacturing plant in the Western Cape.• To determine whether the MTTR trend increases positively with the reduction of the six major losses within a manufacturing plant in the Western CapeThis research aims to improve productivity through the implementation of lean tools in maintenance management.

Data for paper
Lean manufacturing tools associated with human factors for social sustainability 1. Model in prj extension to be open with WarpPLS 2. Raw data 3. Model outputs 4. T ratios 5. Model fit and quality index 6. Reliabilities 7. Discriminant validity

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Context: there is little empirical evidence of the relationship between the implementation of lean techniques (such as the pull system) and their real effect on supply chain performance. Objective: the purpose of this paper is to describe the process of implementing the pull production logic in the supply chain, reporting the historical evolution of indicators, such as inventory levels and lead times over 23 months of intervention. Methods: an action research project was carried out describing chain intervention steps in 2017-2019, divided into phases as follows: planning, data collection, implementation of the action, analysis and evaluation of the results. Results: the main contribution was to demonstrate that the production shift from push to pull had a positive impact on lead time, inventory, and planning routines indicators. Inventory levels were reduced by more than 30% and lead times were down approximately 40%. In addition, sales forecast assertiveness increased. Conclusion: this paper may provide a reference for organizations that want to make similar changes in their supply chains and significantly change the planning routine of their suppliers and distributors by implementing the pull logic.

Find out details of Wuxi Lean Manufacturing Co Ltd exporting to United States.Shipments data from Global bill of Lading.

This data set contains two files: 1. An Excel file with several sheets reporting the raw data and results. 2. The equations for the system dynamic model containing the survey used.

Abstract—This article presents the lean methodology for modernizing garment manufacturing, focusing onlean thinking, lean practices, automation development, VSM, and CRP, and how to integrate them effectively.While isolated automation of specific operations can improve efficiency and reduce cycle time, it does notnecessarily enhance overall garment output and efficiency. To achieve these broader improvements, it isessential to consider the entire production line and process using VSM and CRP to optimize production andcenter balance. This approach can increase efficiency, and reduce manufacturing costs, labor time, and leadtime, ultimately adding value to the company and factory.

Explore the progression of average salaries for graduates in Automotive Lean Manufacturing Experience from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Automotive Lean Manufacturing Experience relative to other fields. This data is essential for students assessing the return on investment of their education in Automotive Lean Manufacturing Experience, providing a clear picture of financial prospects post-graduation.

This section presents a discussion of the research data. The data was received as secondary data however, it was originally collected using the time study techniques. Data validation is a crucial step in the data analysis process to ensure that the data is accurate, complete, and reliable. Descriptive statistics was used to validate the data. The mean, mode, standard deviation, variance and range determined provides a summary of the data distribution and assists in identifying outliers or unusual patterns. The data presented in the dataset show the measures of central tendency which includes the mean, median and the mode. The mean signifies the average value of each of the factors presented in the tables. This is the balance point of the dataset, the typical value and behaviour of the dataset. The median is the middle value of the dataset for each of the factors presented. This is the point where the dataset is divided into two parts, half of the values lie below this value and the other half lie above this value. This is important for skewed distributions. The mode shows the most common value in the dataset. It was used to describe the most typical observation. These values are important as they describe the central value around which the data is distributed. The mean, mode and median give an indication of a skewed distribution as they are not similar nor are they close to one another. In the dataset, the results and discussion of the results is also presented. This section focuses on the customisation of the DMAIC (Define, Measure, Analyse, Improve, Control) framework to address the specific concerns outlined in the problem statement. To gain a comprehensive understanding of the current process, value stream mapping was employed, which is further enhanced by measuring the factors that contribute to inefficiencies. These factors are then analysed and ranked based on their impact, utilising factor analysis. To mitigate the impact of the most influential factor on project inefficiencies, a solution is proposed using the EOQ (Economic Order Quantity) model. The implementation of the 'CiteOps' software facilitates improved scheduling, monitoring, and task delegation in the construction project through digitalisation. Furthermore, project progress and efficiency are monitored remotely and in real time. In summary, the DMAIC framework was tailored to suit the requirements of the specific project, incorporating techniques from inventory management, project management, and statistics to effectively minimise inefficiencies within the construction project.

The global industrial visual management systems market is experiencing robust growth, driven by the increasing need for enhanced operational efficiency and improved communication within manufacturing and industrial settings. The market's expansion is fueled by several key factors. Firstly, the rising adoption of Industry 4.0 technologies, including IoT and digital twins, necessitates effective visual management tools for real-time data visualization and control. Secondly, the demand for lean manufacturing practices and just-in-time inventory management is prompting businesses to adopt visual management systems to optimize workflows and reduce waste. Thirdly, the growing focus on workplace safety and risk mitigation is leading to the adoption of visual management systems for clear communication of safety protocols and hazard identification. The market is segmented by type (hardware and software) and application (manufacturing, logistics, and others). While precise market sizing data is absent from the provided information, a reasonable estimate, considering average CAGR in related tech sectors, could place the 2025 market size at approximately $2 Billion, with a projected CAGR of 8-10% throughout the forecast period (2025-2033). Key players like Red Lion Controls, Seiki Systems, TXM, Visual Management Systems, and Visual Management Technology are contributing to market growth through continuous innovation and strategic partnerships. However, the market faces certain restraints. The high initial investment cost of implementing these systems can be a barrier for smaller companies. Furthermore, the complexity of integrating these systems with existing infrastructure and the need for specialized training can pose challenges. Despite these hurdles, the long-term benefits of improved efficiency, reduced operational costs, and enhanced safety are expected to drive the market's continued expansion, particularly in regions such as North America and Asia Pacific, which are poised for significant growth due to strong industrial production and technological adoption. The market's future growth will be shaped by advancements in software capabilities, increased integration with other industrial technologies, and the emergence of cloud-based visual management solutions.

Integrating Lean and Industry 4.0 (I4.0) has been widely acknowledged as an effective approach to enhancing manufacturing operations. However, existing studies primarily focused on optimizing the material flow, leaving information flow—a critical aspect of modern manufacturing—unexplored. To fill this gap, this study presents a comprehensive framework that: 1) categorizes key forms of information flow waste in modern manufacturing and their impacts on material flow waste; 2) adapts Lean principles and practices for optimizing the information flow and identifies the main types of information flow waste they target; and 3) integrates these adapted Lean principles and practices with the I4.0 technologies for successful implementation. The framework is developed using a systematic bibliographic research approach, synthesizing 56 existing studies on Lean–I4.0 integration for information flow optimization. It allows manufacturing companies to more efficiently and effectively identify information flow issues and develop and implement targeted optimizations. The proposed framework is validated at an automotive parts manufacturer in China, where it identifies key inefficiencies in information flow and their impacts and provides targeted solutions by integrating Lean practices with I4.0 technologies.

Abstract copyright UK Data Service and data collection copyright owner. Additional Scales on High Involvement Management, Family-Friendly Management and Lean Production from the Workplace Employee Relations Survey, 2004 investigates how the implementation of such employment approaches can be linked to employee well-being and organisational performance. High involvement management (HIM) involves such organisational practices as team-working, flexible job descriptions and other arrangements which encourage greater flexibility and collaboration along with acquiring the skills and knowledge required to optimise working in teams. Lean production entails practices which facilitate the timely and efficient procurement and delivery of goods and services. Family-friendly policies are aimed at helping employees by providing flexitime working and access to facilities which support the achievement of an optimal work-life balance. This study used data from Workplace Employee Relations Survey, 2004 (WERS 2004) (available from the UK Data Archive under SN 5294). Analysis procedures involved state-of-the-art statistical analysis, including latent variable analysis, multi-level modelling, path analysis and configuration analysis. The dataset deposited at the Archive includes four variables; one unique reference number which can be matched against the WERS 2004 data and 3 trait score variables. Further information can be found on the ESRC High Involvement Management, Employee Well-Being and Organisational Performance award web page. Main Topics: The dataset includes the following four variables:serno - WERS2004 unique reference number highinvolvmgt - high involvement management latent trait score (de Menezes, Wood and Dritsaki, 2009)jobdesign - job design latent trait scorefamilyfriendly - family-friendly management latent trait scoreThe documentation includes more details about these variables. One-stage stratified or systematic random sample Self-completion Compilation or synthesis of existing material Original data from the WERS 2004 Survey of Managers

The United States Manufacturing sector has enjoyed revenue growth over the past five years. A diversified demand across various downstream markets contributed to this performance, with the automotive, electronics and consumer goods industries playing pivotal roles. Technological advancements, particularly in production automation, have significantly enhanced efficiency. The introduction of automated assembly lines and robotics has reduced labor costs and minimized human error. Additive manufacturing, or 3D printing, has enabled rapid prototyping and customization, catering to specific consumer needs. Lean manufacturing techniques have streamlined operations, cutting waste and improving product quality. The sector maintained positive revenue trajectories despite fluctuating commodity prices and increasing regulatory pressures. Global supply chains supported this expansion, with continued importance placed on logistics optimization. The impact of trade agreements like the United States-Mexico-Canada Agreement (USMCA), established in 2020, has also been a critical factor. Innovations like predictive maintenance and leveraging data analytics to foresee equipment failures have optimized operational performance and downtime. These developments have allowed manufacturers to adapt quickly to changing market demands. Over the past five years, the manufacturing sector has faced profit challenges despite revenue expansion, mainly because of rising purchase costs. Higher crude oil prices directly impacted raw material costs and logistics expenses. In response, companies increasingly adopted energy-efficient technologies, such as connected device networks, to control utility costs. Advanced materials like composites and lightweight alloys provided cost-effective alternatives for component manufacturing. One significant regulatory change, the 2018 Tariffs on Steel and Aluminum, increased material costs, prompting companies to seek alternative sourcing strategies. Companies focused on supply chain optimization, employing analytics for precise demand forecasting and inventory management, reducing excess costs. Investments in process automation aimed to minimize manual intervention and enhance throughput rates. The deployment of just-in-time production reduced inventory holding costs, aligning production schedules closely with demand fluctuations. Although consumer demand supported sales volumes, pricing pressures persisted amid competitive market dynamics. To address sustainability mandates, manufacturing processes integrated circular economy principles such as recycling and reuse, aligning cost savings with compliance. Technological advancements like cloud-based ERP systems improved planning and resource allocation, directly impacting financial performance. Manufacturing sector revenue has been expanding at a CAGR of 1.8% over the past five years and is expected to total $6,941.2 billion in 2025, when revenue will fall by an estimated 4.1%. The sector's revenue will exhibit moderate growth over the next five years. Innovation and technology will be crucial drivers, especially with the increased adoption of artificial intelligence and connected device ecosystems in manufacturing operations. Automation and robotics will enhance production efficiency and flexibility, addressing the complexities of modern consumer demands. Continuous developments in machine learning will improve process optimization and quality control standards. Digitalization and smart factory initiatives will transform traditional workflows, driving productivity gains through real-time data insights and transparent operations. Exploration of augmented reality tools will aid in maintenance and training processes, reducing downtime and error rates. Companies will diversify revenue streams by adopting mass customization strategies that appeal to dynamic consumer preferences. Despite these advancements, profit will remain under pressure from continued volatility in raw material costs tied to geopolitical shifts. Environmental regulations like the 2020 Clean Air Act Provisions will continue to push companies toward low-emission technologies. Global trade dynamics, including tariffs and changing consumer expectations, will influence strategic decisions and market positioning. Downstream market performance will continue to impact production planning and inventory management, emphasizing agility and responsiveness. Manufacturing sector revenue is expected to inch upward at a CAGR of 0.4% to $7,086.7 billion over the five years to 2030.

Abstract Given the current market demands, small and medium-sized enterprises (SMEs) are under pressure to change their working methods, implying changes in concepts and practices in order to improve their production systems and processes. Thus, the search for production methodologies, techniques and tools becomes urgent and imperative. One way to achieve this goal is to adopt the Lean Production (LP) methodology. This document presents the results of a survey, developed in the north of Brazil, involving 75 SMEs from the free economic zone of Manaus to assess the degree of LP implementation. The results showed a limited implementation of LP. Moreover, palliative practices (momentary relief), hopeful implementations and impediment factors are common. Attending to these results, the authors are tempted to say that LP is still a fiction for SMEs in this region.