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Validated quantitative models for lean supply chain planning (LSCP) are still scarce in the literature, particularly because conventional push systems have not been widely integrated and tested with pull systems in sustainable and resilient environments in the Industry 4.0 context. Hence the main contribution of this paper is to develop an optimisation model that is able to contribute to the LSCP with the combination of push and pull strategies. Here we present an integrated just-in-time (JIT) production system with material requirement planning (MRP) for a SC that takes a traditional five-level structure based on a mixed-integer linear programming model (MILP) dubbed as LSCP 4.0. The model is able to simultaneously plan the production and inventory of materials and finished goods to satisfy demand from forecasts and firm orders. The selection of alternative suppliers as a proactive measure to face disruptive events is also considered. Furthermore, sustainable practices are included in the objective function for profit maximisation by considering CO2 emissions. This proposal is tested in the footwear sector. The results demonstrate that the combined use of JIT and MRP through a quantitative approach improve performance in leanness, sustainability and resilience by decreasing the bullwhip effect at different SC levels. [ABSTRACT FROM AUTHOR]

Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the operations that do not add value to the product or processes to increase the value of each performed activity. Here the main motivation is to study how quantitative modelling approaches can support LM tools even under system and environment uncertainties. The main contributions of the article are: (i) providing a systematic literature review of 99 works related to the modelling of uncertainty in LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifying LM works under uncertainty; and (iv) identify quantitative models and their solution to deal with uncertainty in LM environments by identifying the main variables involved. Hence this article provides a conceptual framework for future LM quantitative modelling under uncertainty as a guide for academics, researchers and industrial practitioners. The main findings identify that LM under uncertainty has been empirically investigated mainly in the US, India and the UK in the automotive and aerospace manufacturing sectors using analytical and simulation models to minimise time and cost. Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce waste in a context of system uncertainty. [ABSTRACT FROM AUTHOR]

Modern manufacturing is the world's largest and most automated industrial sector. The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning has enabled a better connection with machines and factory systems. Data harvesting allowed for a more seamless and comprehensive implementation of the knowledge-based decision-making process. New models that provide a competitive edge must be created by combining the Lean paradigm with the new technologies of Industry 4.0. This paper presents novel computer-based vision models for automated detection and classification of damaged packages from intact packages. In high-volume production environments, the package manual inspection process through the human eye consumes inordinate amounts of time poring over physical packages. Our proposed three different computer-based vision approaches detect damaged packages to prevent them from moving to shipping operations that would otherwise incur waste in the form of wasted operating hours, wasted resources and lost customer satisfaction. The proposed approaches were carried out on a data set consisting of package images and achieved high precision, accuracy, and recall values during the training and validation stage, with the resultant trained YOLO v7 model. [ABSTRACT FROM AUTHOR]

In modern manufacturing systems, operators' role is evolving continuously in relation to the main production paradigms affecting how companies approach their manufacturing and logistics operations. If the introduction of Lean Manufacturing principles has affected workers' well-being directly, the adoption of digital technologies is modifying how factory work is organised and performed. As a result, digitalised and Lean Manufacturing contexts require operators to enhance their skills increasingly to perform several tasks and functions. This paper examines the evolution of operators' role in production by recalling two main work design paradigms that originated in the 1960s: Job Enlargement and Job Enrichment. Indeed, through a literature review and development of a causal loop diagram (CLD), this research points out how the main features of Industry 4.0 and Lean Manufacturing jointly affect the Job Enlargement and Job Enrichment concepts. The study significantly helps reshape work design in next-generation production systems, shedding light on the implications and relationships of the most widespread manufacturing paradigm, i.e. Lean Manufacturing and Industry 4.0, using Job Enrichment and Job Enlargement strategies. A CLD also can be a helpful tool for practitioners to understand what factors to leverage to increase efficiency and worker satisfaction. [ABSTRACT FROM AUTHOR]

Among many fallouts of the global pandemic, firms' supply chains have been severely impacted at the global scale. Such global impact can be mainly attributed to companies' current heavy focus on lean manufacturing, resulting in a lack of preparedness and flexibility, and therefore weakening their supply chain links. At the same time, firms face severe financial distress and constraints if their investments in future risks turn out to be of no value. Facing such dilemmas, many firms are unsure of how to appropriately configure lean and risk management at their operational and supply chain levels. This study aims to understand the various configurations of lean manufacturing and supply chain risk management. Also, this study pays attention to buffer capacity associated with lean and risk management. In a dataset collected from 198 US companies, cluster analyses discover the manufacturers' configurations of the varying emphases on lean management and supply chain management. Our results indicate that firms achieve the highest levels of buffer capacity and firm performance if they implement lean practices and risk management at a higher level than others. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study successfully employed lean manufacturing techniques in conjunction with the DMAIC methodology to enhance the efficiency of a sweet manufacturing production line. By using key performance indicators (KPIs) and value stream mapping, we were able to quantify the improvements achieved. A notable 36% increase in Overall Equipment Effectiveness (OEE) was realized, driven by a 19.4% improvement in performance and a 17.58% increase in quality. While availability experienced a minor decline, the overall gains in performance and quality outweighed this effect. Value stream mapping revealed a significant reduction in rework from 388 kg to 273 kg per production shift, representing a substantial 30% decrease. This reduction directly contributed to a 18.8% increase in production yield, from 612 kg to 727 kg. These improvements were facilitated by the implementation of a Poka-Yoke device and targeted bottleneck analysis. The achievement of the present work encourages to extend the implementation of lean and DMAIC combined methodology to other production lines of the company. [ABSTRACT FROM AUTHOR]

This article presents a case study on improving process efficiency in a mining equipment part fabrication company. The company was facing issues concerning communication, organisation, and workflow processes. This study investigated that ineffective communication among departments was the major weakness which was responsible for the long lead or idle time. This lead time was a waste that affected the company’s productivity. A great amount of time was spent on non-value-added processes. The Kanban Centralised Communication System was implemented. Time study and value stream mapping were also used. A significant improvement in process efficiency from 34% to 85% was achieved by reducing lead time from 4200 minutes to 1680 minutes after streamlining the communication in the company using Kanban. [ABSTRACT FROM AUTHOR]

Players in the automotive industry have been adopting different strategies to remain competitive, including product and process innovation, Lean Manufacturing, and, more recently, Green Manufacturing. In this paper, we seek to explore the integration of Lean and Green manufacturing activities from the perspective of Operations Management and Operations Strategy. We carried out case studies at four Original Equipment Manufacturers and two first-tier suppliers in the automotive industry, examining their operations strategies, their Lean practices and their Green practices to discuss whether Lean and Green practices were integrated and aligned with companies' operations priorities. We found that companies have adopted different Operations Strategies considering operations competitive priorities, decision areas subjected to changes, and Lean and Green practices implemented. Cases analysed show different levels of Lean and Green Manufacturing adoption and different levels of integration of Lean practices and Green practices. The study indicates that the implementation of an operations strategy, with the addition of the Environment to the set of competitive priorities and involving the implementation of Lean and Green practices – or the implementation of Green practices in production systems that have already adopted Lean practices –, is a complex management task. [ABSTRACT FROM AUTHOR]

The process scheduling is still considered a crucial subject for manufacturing industry, due to the ever-changing circumstances dictated by the nowadays product demand and customer trends. These conditions are often associated with increasing costs and energy consumption, considerably affecting the long-term sustainability of manufacturing plants. To mitigate that effect, one should create an effective strategy tailoring integrated operations and processes to the customer demand and trends faced by the nowadays industry. A well-known approach to this matter is the technologies introduced by manufacturing paradigms, e.g., Industry 4.0 and smart manufacturing. As suggested in literature, these technologies are capable of helping decision-makers by continuously gathering significant information about the state of machinery and manufactured goods. This information is thereafter utilized to identify weaknesses and strengths demonstrated within manufacturing plants. To this end, the present paper presents a process optimization framework implemented in a three-stage production line prone to systematic degradation faults. Aiming at strengthening profitability, the framework engages reinforcement learning with ad-hoc manufacturing/maintenance control in decision-making carried out in implemented machines. Simulation experiments showed improved process planning and inventory management enabling cost-effective green and sustainable manufacturing in manufacturing plants. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to explore the relationship between Lean manufacturing and Industry 4.0 for small and medium size of enterprise in Japan and Brazil. Design/methodology/approach: The authors conducted a quantitative survey (20 companies in Japan and 30 companies in Brazil) combined with a qualitative interview (2 companies in Japan and 15 companies in Brazil). Findings: According to the quantitative study, 90% of them practice Lean manufacturing and 40% of them practice Industry 4.0. In the qualitative study in Brazil, four managers responded that the Lean manufacturing is a prerequisite for Industry 4.0 since any production process with waste cannot be productive, even with sophisticated digitalization technology. Originality/value: The authors explored further the relationship between "defensive Digital Transformation (DX)," which is based mainly on Lean manufacturing, and "offensive DX," which relates to customer value creation through Industry 4.0. This study clarifies the relationship and plays as a roadmap to develop better the manufacturing from current status to the vision of Industry 4.0. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to analyse the current state of research to identify the link between Lean Manufacturing and Industry 4.0 (I4.0) technologies to map out different research themes, to uncover research gaps and propose key recommendations for future research, including lessons to be learnt from the integration of lean and I4.0. Design/methodology/approach: A systematic literature review (SLR) is conducted to thematically analyse and synthesise existing literature on Lean Manufacturing–I4.0 integration. The review analysed 60 papers in peer-reviewed journals. Findings: In total, five main research themes were identified, and a thematic map was created to explore the following: the relationship between Lean Manufacturing and I4.0; Lean Manufacturing and I4.0 implication on performance; Lean Manufacturing and I4.0 framework; Lean Manufacturing and I4.0 integration with other methodologies; and application of I4.0 technologies in Lean Manufacturing. Furthermore, various gaps in the literature were identified, and key recommendations for future directions were proposed. Research limitations/implications: The integration of Lean Manufacturing and I4.0 will eventually bring many benefits and offers superior and long-term competitive advantages. This research reveals the need for more analysis to thoroughly examine how this can be achieved in real life and promote operational changes that ensure enterprises run more sustainably. Originality/value: The development of Lean Manufacturing and I4.0 integration is still in its infancy, with most articles in this field published in the past two years. The five main research themes identified through thematic synthesis are provided in the original contribution. This provides scholars better insight into the existing literature related to Lean Manufacturing and I4.0, further contributing to defining clear topics for future research opportunities. It also has important implications for industrialists, who can develop more profound and richer knowledge than Lean and I4.0, which would, in turn, help them develop more effective deployment strategies and have a positive commercial impact. [ABSTRACT FROM AUTHOR]

Lean and ergonomics are two disciplines that shared common goals to improve manufacturing production systems. However, there are limited studies that specifically link ergonomics with Lean's 3 M concept of Muda (waste), Muri (overburden), and Mura (inconsistency). The study employed semi-structured interview with 10 experienced practitioners on past lean projects to establish potential linkage between ergonomics and Lean's 3 M. A conceptual framework integrating ergonomics components into the Lean's 3 M components was then generated through group discussion, and validated using Fuzzy Delphi Method analysis. The interviews revealed four themes linking ergonomics to Lean's 3 M: (1) Human energy waste, (2) Uneven workload distribution, (3) Overburden of worker's capacity, and (4) Worker performance affects work performance. A conceptual framework integrating ergonomics components into the Lean's 3 M components was then proposed, which redefines several components of Lean's 3 M from ergonomics perspective. Fuzzy Delphi Method analysis on 12 practitioners showed overall agreement with the content, structure, and applicability of the proposed framework (threshold (d) value ≤0.2, expert consensus ≥75%, and fuzzy score ≥0.5). The findings may provide platform for lean and ergonomics practitioners to communicate and bridge the gaps between the two disciplines. [ABSTRACT FROM AUTHOR]

This article presents a case study on improving process efficiency in a mining equipment part fabrication company. The company was facing issues concerning communication, organisation, and workflow processes. This study investigated that ineffective communication among departments was the major weakness which was responsible for the long lead or idle time. This lead time was a waste that affected the company’s productivity. A great amount of time was spent on non-value-added processes. The Kanban Centralised Communication System was implemented. Time study and value stream mapping were also used. A significant improvement in process efficiency from 34% to 85% was achieved by reducing lead time from 4200 minutes to 1680 minutes after streamlining the communication in the company using Kanban.

Lean production is a set of methods aimed at increasing efficiency and flexibility within a firm by eliminating waste and accurately defining value. For this reason, it is interested in eliminating activities that do not add value to the product to reduce costs. Within the scope of lean production, different methods are used based on certain priorities and simultaneously. In this study, the Lean Production Leveling-Heijunka approach, a lean production method applied in a mold sponge manufacturing firm, is discussed. In the accessible literature, it has been seen that the heijunka method has been studied little before and no linear model has been found for its application. Therefore, within the scope of this study, a multi-objective linear model that includes production leveling, sequencing and job stability elements is proposed. As the proposed model is multi-objective, the weighted sum method was used as the aim aggregation method to solve it. The problem of the firm was solved using the proposed model in GAMS/CPLEX. To evaluate the performance of the model, first a small-size sample problem was solved and then the main problem of the firm where the application was used was solved. The effectiveness of the model was evaluated by comparing the results obtained after solving the firm problem with the current case. In addition, Heijunka values for both cases were calculated to demonstrate the success of the proposed model's solution. According to the calculations, the Heijunka ratio was four in the solution obtained by applying the proposed model, while it was two in the current situation. These results demonstrate that a successful Heijunka method was implemented in the firm where the application was conducted, and the production process was made more balanced. [ABSTRACT FROM AUTHOR]

The article deals with the use of the 5S methodology in an organization. It focuses on the implementation of 5S in an organization involved in the production and processing of metal components for the automotive industry. 5S is a Japanese methodology that was first applied in the automotive industry to improve productivity. The article is dedicated to the basic principles of lean manufacturing.The practical part analyses the state of the workplaces before the introduction of 5S and the actual implementation of this methodology. The implementation of 5S brings a number of benefits to the organization, including: - reducing time wasted searching for tools, - improving safety, - boosting employee morale, - building the culture of the organization. Keeping the workspace organized with visual aids helps to increase productivity and reduce waste. The ultimate goal of 5S is continuous improvement in the workplace. The final section evaluates the benefits to the organization after the implementation of 5S. By implementing this strategy, the workplace becomes clean and standardized, allowing employees to work as efficiently as possible. The 5S method creates a more efficient work environment by improving the organization and optimizing the system of operation. [ABSTRACT FROM AUTHOR]

The article analyzes the impact of measures to improve labor productivity within the framework of the national project “Labor Productivity” at enterprises – small businesses of the Rostov region with revenues from 100 to 400 million rubles for the period from 2019 to 2022 inclusive on the aggregate financial performance of companies involved in the project implementation. Based on the analysis of accounting data for the studied set of enterprises, we can conclude about correlation between the measures to improve labor productivity and financial performance within each specific enterprise, as well as in terms of the positive impact on total deductions to the budgets of various levels. It has been shown that total dynamics of payments to the budget for the analyzed period for the studied aggregate increased by 2.2 times, property status of the production complex – by 3.6 times, and profitability indicators – by more than 3 times. Positive influence of the national project “Labor Productivity” on the economy of Rostov region has been proved. The study results can be used as a basis for adjusting further trajectory of the project implementation in the region, which determines the relevance of the study.

Globally, the textile industry plays a significant role in production but faces many challenges that affect its operation and sustainability. Product quality remains an essential concern despite technological advancements and quality control measures. Raw materials, production methods, and skills differ, resulting in customer dissatisfaction and damage to reputation. A quality management strategy is necessary to overcome these problems and promote quality improvement. This includes the implementation of the Lean Six Sigma approach. This article focuses on improving textile quality by developing the Six Sigma DMAIC method. An analysis of three months' data from a textile company shows that an increase in the Outgoing Quality Level (OQL) from 3% to 6% resulted in significant economic losses, amounting to PKR-240,000 due to rework, residual costs, customer returns, and lost sales time. By utilizing Six Sigma tools, the company reduced OQL from 6% to 4%, thus lowering product losses and increasing operational efficiency. As a result of these findings, the textile industry can improve quality and profitability through a Lean Six Sigma approach.

The paper discusses the concept of Kaizen, a Japanese philosophy of continuous improvement, and highlights its historical significance and widespread adoption in various industries worldwide. This paper is based on literature review of 98 research articles published related kaizen's evolution, industry application, and limitations during 2000-2022. The study employed a comprehensive search strategy across multiple databases like Scopus, Google Scholar, and others to gather relevant systematic reviews on kaizen. It references established guidelines like PRISMA and it mentions the planned thematic analysis using NVivo software, highlighting its applicability in identifying patterns and themes across diverse data sources. The review showed that Kaizen adoption is a useful tool for organizations looking to achieve continuous performance improvement and sustainability over time.

This research investigates the synergistic relationship between Lean Manufacturing and Industry 4.0, crucial paradigms in modern industry, through a literature review of 60 scientific articles published from January 2013 to November 2023. The study reveals a tripartite relationship: Industry 4.0 technologies amplify Lean Manufacturing’s efficiency; Lean Manufacturing principles facilitate Industry 4.0 adoption; and their interaction fosters mutual enhancement, indicating a dynamic interplay. The results show three main trends: combining Industry 4.0 technologies, especially the Internet of Things, with Lean Manufacturing principles to make things more efficient and productive; highlighting the important role of Lean Manufacturing and Industry 4.0 in tackling sustainability issues and encouraging eco-friendly practices; and combining Lean principles with Industry 4.0 as a step toward Industry 5.0. Challenges include the need for a cultural transformation to align Lean’s simplicity with Industry 4.0’s complexity, resolve compatibility issues between legacy systems and new technologies, enhance data management and cybersecurity, address skill deficiencies through staff training, and ensure supplier and partner alignment. This research enriches academic and industrial discourse by presenting a novel outlook on the convergence of lean manufacturing and Industry 4.0, highlighting its significance for future works.

Green tea production activities at PT Candi Loka indicated a time-consuming, which caused process efficiency to be less than optimal. Process efficiency improvements must be made to increase company competitiveness and consumer satisfaction. This research aimed to analyze the efficiency of green tea production time at PT Candi Loka by identifying the processing time required in each production activity, including the time consumption and formulating recommendations for improvement using a lean manufacturing approach using the VSM (Value Stream Mapping) method. There were 30 activities in 6 processes consisting of receiving raw materials, withering, rolling, drying, and packaging. Identification of value streams using the PAM (Process Activity Mapping) tool produced 9 Value added (VA) activities, 19 Necessary but Non-Value Added (NNVA) activities, and 2 Non-Value Added (NVA) activities in the form of delays in the withering and drying processes. In the current state map analysis, the production lead time value was 315.286 seconds. Recommended improvements to eliminate delays in the withering process were to set up the rotary panner machine earlier and increase the number of supervisors, while to eliminate delays in the drying process were to implement a piece rate system. Increasing efficiency was also carried out by improving NNVA activities by adding weighbridge facilities and using a conveyor system. The future state map proposed by implementing recommended improvements resulted in an increase in production time efficiency to 222.356 seconds and an increase in the process cycle efficiency value from 58.95% to 84.85%.

Value stream mapping is one of the basic tools used in the processes improvement or design – in accordance with the Lean concept. This paper presents a model of designing production processes and introducing new products to the market based on the analysis of the value streams flow in the production process. Particular attention was paid to the importance of input data necessary to plan a properly functioning production process, which results in a high-value product (from the customer’s point of view). In the paper also the list of all the most important tools supporting all stages of designing new production processes or improving existing processes has been presented. Also the advantages of big pictures analysis using was highlighted. In this case value stream mapping and big picture analysis were oriented on quality in the product development process and contribute to: creating value (based on customer requirements) while maintaining timely deliveries and constant costs (Value Stream Mapping), sustainable use of tangible and intangible resources (removing waste at the design stage). However, from the company point of view, and particular in the cost level, the product can gain value because a lower input is required for its production. After improvement, the process lead time has been shortened in an area where value was not added, but the consequence of implementing the change is an increase in the production volume while maintaining the process transition time – therefore, unit costs of production will change.

Integrating Lean Manufacturing tools with artificial intelligence (AI) is emerging as a revolutionary approach to optimize production processes, reduce waste, and enhance efficiency. Traditional Lean practices focus on waste reduction and process improvement, often relying on human expertise for problem identification and resolution. AI algorithms, on the other hand, excel in pattern recognition, data analysis, and decision-making. Lean tools and AI can offer more precise, data-driven solutions for common manufacturing challenges when integrated. AI algorithms can automate and refine Lean techniques like value stream mapping, Kanban, and 5S by providing real-time, actionable insights drawn from big data. This fusion of Lean and AI aids in predictive maintenance, quality control, and optimization, enhancing the efficiency and responsiveness of the manufacturing process. Moreover, AI's capability for machine learning allows the system to adapt and improve autonomously over time, further aligning with Lean's continuous improvement ethos. Seven case studies were conducted to show how this alignment might aid Lean Manufacturing. However, successful implementation necessitates overcoming data quality and algorithmic bias challenges. Despite these hurdles, integrating Lean tools and AI can redefine best practices in manufacturing, setting new standards for operational excellence. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Using Lean Manufacturing (LM) tools in production processes is crucial for companies' economic, environmental, and social sustainability success. This study shows a structural equation model (SEM) that shows the relationship between LM Tools like Kaizen (KAI), Gemba (GEM), Value Stream Mapping (VSM) and Key Performance Indicator (KPI) with Economic Sustainability (ECS). Seven hypotheses were evaluated with data from 179 responses to a questionnaire about the Mexican maquiladora industry, showing that these variables are linked. At a 95% confidence level, the model was evaluated using the partial least squares method. The findings indicate that the relationships between KAI and GEM and KAI and VSM have the strongest relationship, followed by VSM and ECS; however, VSM has the strongest effect on ECS. Based on these findings, it is recommended that managers adopt a continuous improvement (KAI) approach based on working directly on the shop floor (GEM and VSM) to support their decisions regarding economic growth (ECS). [ABSTRACT FROM AUTHOR]

Implementing lean manufacturing practices in the systematic layout planning (SLP) process for improving facility layouts in various industries is essential. This approach enhances material handling processes and effectively manages space utilization. An efficient plant employing systematic layout planning leads to improved resource utilization and facilitates the integration of lean manufacturing techniques such as value stream mapping, 5 S, Ohno's 7 wastes, and cellular manufacturing. This research article presents a case study on a steel processing plant in Dubai with an area of 160 m × 160 m, where systematic layout planning and lean manufacturing techniques were applied. The factors considered included space and material flow optimization, incorporating layout alternatives based on systematic layout planning principles along with lean manufacturing techniques. The study revealed a 34% reduction in material flow and an efficient overall space utilization improvement of 26%. Additionally, cost analysis validation was conducted. [ABSTRACT FROM AUTHOR]

Critical for current situation analysis in lean initiatives, establishing standardised work can be challenging due to the intricacies involved in documenting complex routine work, mainly characterised by nested-loop work sequences. This study aims to extend the existing methods of establishing standardised work and analysing a production line for complex routine work. Novel concepts and terminologies are introduced, combining the existing methods into a modelling approach capable of addressing all complex features simultaneously. A standardised work sequence diagram is defined as a fundamental representation, forming the basis for a new simulation-based modelling approach. Described, validated, and applied through an industrial case study, the proposed approach is confirmed effective, achieving an average relative combined cycle time of 0.99, compared to the expected value of 1. The resulting redesign of standardised work significantly improves line efficiency from 53% to 79%, with a manpower reduction from 8 to 4 workers. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Exact Sciences is the property of Master Editora and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The advent of Industry-4.0 has completely changed the manufacturing environment in recent years, bringing new opportunities and difficulties for companies looking to maximise their efficiency. Simultaneously, lean manufacturing techniques have become well-known for their capacity to reduce waste and streamline operations. The goal of this review paper is to present a thorough examination of how lean manufacturing principles and Industry 4.0 technology are integrated. This article investigates the various A comprehensive literature review of Industry 4.0 technologies and their potential to improve lean manufacturing. This study highlights the benefits, challenges, and future research objectives in leveraging Industry 4.0 to promote lean techniques in the food processing industries by looking at case studies, best practices, and existing research. This study examines how lean manufacturing concepts may be supported by Industry 4.0 technologies like big data analytics, AI and IoT. These technologies can also be utilised to enhance manufacturing operations by increasing value, flow, pull, and perfection. Businesses can achieve operational excellence and build smart factories that are far more lucrative, productive, and efficient by utilising these technologies. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to understand how integrating lean principles with human factors can contribute to a more productive, satisfied, and motivated workforce. Additionally, the paper investigated the roles of employee empowerment, continuous improvement, and organizational support in creating a human-centered lean manufacturing environment. Design/methodology/approach: The authors conducted a detailed analysis of the literature. This theoretical analysis enabled to preparation of a comprehensive and detailed examination using a survey questionnaire, providing in-depth insights into the research statements. This data was extended due to the participant observation which allowed to immerse in the day-to-day activities of the workers. Findings: Lean Manufacturing positively impacted the humanization of work, with effective team cooperation and communication boosting productivity. However, some employees felt excluded from joint decision-making and feedback processes, which could harm motivation and organizational effectiveness. Enhancing training opportunities could help employees better adapt their skills to their tasks. Research limitations/implications: The limitation of the research is the pilot study which was conducted among 15 respondents from one company. In the future, it is planned to extend it to other production companies. Originality/value: The paper makes a contribution to the field by linking Lean Manufacturing and human aspects in a Polish work environment. Instead of exploring the traditional benefits of Lean Manufacturing in enhancing operational efficiency and reducing waste, we focused on its impact on the workforce, workplace culture, and employee engagement. [ABSTRACT FROM AUTHOR]