Lean Paradigm Research Articles

Deploying Convolutional Neural Network to reduce waste in production system

The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning (ML) has enabled a better connection with machines and factory systems. Data collection made it possible to apply the knowledge-based decision-making process more effectively and thoroughly. The Lean paradigm and the new technologies of Industry 4.0 must be used to build new models that give a competitive edge. This paper presents a new computer-based vision model for automated detection and classification of damaged packages from intact ones. 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 computer-based vision approach detects 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 approach was carried out on a data set consisting of 200 pairs of package images and has achieved high precision, accuracy, and recall values during the training and validation stage, with the resultant trained YOLO v7 model detecting and classifying damaged packages with an average testing precision of 75%.

Waste reduction via image classification algorithms: beyond the human eye with an AI-based vision

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.

Towards a smart lean green production paradigm to improve operational performance

Over the past decades more and more production companies have adopted the lean paradigm to get operational performance improvements. More recently, the competitive environment is requiring an increasing attention towards sustainability issues leading to the implementation of practices to also improve sustainability performance and, in particular, the environmental impacts. The relationships between lean and green approaches in terms of their mutual influence in achieving target performance have been investigated in several studies (see for example (Dieste et al., 2019)– (Henao et al., 2019)). Most of related studies identify a supportive relationship between the two paradigms, and only few negative effects have been identified. Whilst the impact on performance in lean-green contexts has become a field of research in its own, little has been said in literature about how the new Industry 4.0 (smart manufacturing) paradigm and the emerging technologies, impacting in every area of a production company, can be of support to further strengthen the lean-green binomial (an exception is represented by (Duarte and Cruz-Machado, 2017)).This paper aims at proposing a framework to analyse how a smart environment relying on the Industry 4.0 tools and technologies can support the evolution of a lean-green company towards higher levels of operational performance. The final goal is to support practitioners in the strategic choice of what smart technologies to adopt in combination with selected lean and green practices to create an impact on selected operations strategy indicators or to identify initiatives to fill operational gaps in the context of a continuous improvement and radical innovations plans. Such approach may serve as a signpost for the shaping of operations strategies, especially in relation to technology management. In order to achieve the above-mentioned goal, a four-step methodology has been adopted to review the existing literature and to organize it in a way that allows highlighting the impacts of smart technologies, lean and green practices on performance indicators and to identify possible reciprocal influences. Eventually, to get a validation of the proposed approach, a semi-structured interview has been shared with four manufacturing small and medium-sized enterprises (SMEs) to get their perspective on framework potential benefits.

Lean Engineering – Identifying waste in engineering chains

Data and information in production systems engineering are the counterpart of material in production. To increase effectiveness and efficiency, waste in data and information flow must be defined in analogy to lean paradigm. For this purpose, the known eight types of waste are transferred to the data and information flow of production systems engineering, considering engineering activities and chain, to achieve overall process optimum. The validation of the defined types of waste using six case studies confirms their applicability and significance in terms of continuous engineering process improvement.

Beyond efficiency: the role of lean practices and cultures in developing dynamic capabilities microfoundations

PurposeThe purpose of this study is to theorize and test the relationships among lean operations and lean supply chain practices, learning- and innovation-oriented lean cultures and dynamic capabilities (DCs) microfoundations. Further, this study aims to assess the association of DCs microfoundations with process innovation.Design/methodology/approach The researchers combine primary data collected from 153 manufacturing firms located in five continents using a survey designed for the purpose of this study with archival data downloaded from the Bureau Van Dijk Orbis database and test the hypothesized relationships using structural equation modelling.FindingsResults support the contribution of lean operations and lean supply chain practices to the development of DCs microfoundations, which further lead to greater process innovation. Additionally, while a learning-oriented lean culture positively moderates the relationships between both lean operations and lean supply chain practices and DCs microfoundations, an innovation-oriented lean culture only moderates the relationship between lean operations practices and DCs microfoundations.Practical implicationsThis study identifies DCs microfoundations as the key mechanisms for firms implementing lean practices to achieve greater levels of process innovation and the important role played by lean cultures. This study provides direction for managers to put in place DCs through lean implementations, enabling their firms to be ready to respond to challenges and opportunities generated by environmental changes.Originality/valueWhile previous research has confirmed the positive effects of lean practices on efficiency, the role of lean practices and cultures in developing capabilities for reacting to environmental dynamism has received little attention. This study offers an empirically supported framework that highlights the potential of lean to adapt processes in response to environmental dynamics, thereby extending the lean paradigm beyond the traditional focus on operational efficiency.

Implementing enterprise resource planning for lean waste minimisation: challenges and proposed strategies

PurposeIn a conventional sense, information technology has frequently been considered a source of Lean waste management. However, as the corporate world evolves, new models that provide a competitive edge by merging technical breakthroughs with the Lean paradigm must be developed. Enterprise resource planning (ERP), which is such technological advancement, is found to be highly influential for Lean implementation. However, there is a dearth of literature on the adaptability of ERP to minimise Lean waste in the construction industry. This paper, therefore, aims to investigate the possibility of applying ERP to minimise Lean waste in the construction industry.Design/methodology/approachThe study used a qualitative approach, consisting of fifteen (15) expert interviews and code-based content analysis was used to analyse the empirical data.FindingsThe findings revealed the challenges faced when applying ERP with the Lean concept and the strategies that would help overcome the challenges. Most of the challenges could be overcome through training and awareness programmes and proper team management. The study also found that ERP could be applied with Lean to eliminate waste generation in the construction industry.Originality/valueThis paper contributes to the theory by providing an evaluation of the possibility of adopting ERP to eliminate Lean waste in the construction industry. The study will contribute to new knowledge related to strategies for proper use of ERP for Lean waste minimisation, which will be useful for future researchers in the area.

Lean, agile, resilient, and green human resource management: the impact on organizational innovation and organizational performance.

There are four paradigms of lean, agile, resilient, and green (LARG) which can promote human resource culture to create novel ideas and increase performance in organizations. This study aims to conceptualize, develop, and validate four lean, agile, resilient, and green paradigms in human resource management (HRM) context and investigates how different LARG HRM elements can affect organizational innovation and performance. In this way, a conceptual model for investigating the LARG concept in HRM is proposed. A new tool to measure lean, agile, resilient, and green indicators in service industry has been developed. Using convenience sampling method, an online survey questionnaire is managed to collect data from 102 service sector organizations, including banking and financial services, transportation, hotel, telecom, and insurance, having more than 50 employees in Iran. The collected data are analyzed by partial least squares-structural equation modeling (PLS-SEM). The results indicate that the LARG HRM significantly and positively influences organizational performance. In addition, the LARG HRM indirectly affects organizational performance through organizational innovation. The findings also showed that employee's ability to perform several different jobs from the lean paradigm, paying attention to employee's ideas in decision-making from the agile paradigm, increasing the ability of staff to change rules in different situations from the resilient paradigm, and having employees with a full understanding of environmental policies from the green paradigm, is the most effective elements among the LARG paradigms factors. This study provides valuable insights into recognizing the most effective LARG elements and factors for implementing the LARG HRM in organizations and how it contributes to enhancing organizational performance and organizational innovation in order to achieve competitive advantage.

Towards expediting the implementation of sustainable and successful lean paradigm for construction projects: a hybrid of DEMATEL and SNA approach

PurposeProper identification of the key motivating factors (or key drivers) is needed to ensure successful adaption and implementation of the lean concept for construction projects. However, there lacks a study investigating the complex interrelationships existing among the key drivers contributing to Sustainable and Successful Lean Construction (SSLC) implementation for such projects. To address this shortcoming, this study aims to uncover the main critical key drivers towards the implementation of SSLC for the very first time by capturing the complexity of this vexing problem.Design/methodology/approachIn this study, a new hybrid framework is developed through the integration of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Social Network Analysis (SNA). The novel developed framework is called the DSNA approach.FindingsConsidering the case of Malaysian construction projects, the developed DSNA gives the following major outcomes: (1) Most important critical key drivers are seen to be optimization, continuous improvement, and, improve company culture, and (2) For SSLC adoption, the critical drivers impacting other key drivers are seen to be “improve teamwork”, “reduce leadership conflict”, and “improve company culture”, thereby demanding more attention.Practical implicationsThe outcomes of this study give insight for decisions and policymakers in the construction industry regarding critical key drivers and their complex interrelationships towards the further adoption of SSLC, promoting the sustainability paradigm within the respective sector.Originality/valueThis paper not only presents a list of critical drivers and the corresponding association among them towards SSLC adoption, but also proposes DSNA as a novel approach for uncovering the complex interrelationship existing in an intricate problem, improving the intricate process of decision-making.

(PT-BR) - Processos puxados em saúde: uma revisão sistemática da literatura

Abstract: The lean production paradigm was first adopted by the automobile industry and has been progressively adapted and adopted by several other sectors. Health services are, in general, co-created by users. This poses a logical challenge when applying one of the principles of the lean production paradigm: the establishment of pulled processes. This article investigates how pull processes have been adapted for lean production in healthcare (lean healthcare). A bibliometric analysis of 267 articles on lean healthcare research, published between 2004 and 2021, is presented in this article. A qualitative review of 233 articles is also presented. Most articles presented in this study were published after 2012, and publications and citations are primarily from the US, Brazil, and the UK. 39 articles pertain to an application of the concept, but most of the articles refer to pulling intermediate products, which are not subject to the aforementioned logical challenge. Only 8 articles effectively discuss the pulling of the flow of users. The main conclusion is that pulling has a distinct objective when applied to health care: the goal is not exactly the elimination of queues (the equivalents of intermediate stocks) but the elimination of idleness in downstream resources with limited capacity.

An empirical research on achieving success with Lean

Several companies apply lean manufacturing however the implementation in organizations is not always successful. One of the difficulties is deciding which of the lean approach to avoid the failure; therefore this paper reviews the key concepts and attributes of the lean paradigm, and identifies how they can be applied to ETP’s current operational practices. The study results show a successful lean transformation case study. Practically, this is a comprehensive guideline for practitioners to implement Lean in their organizations to make better decisions.

Selection and performance estimation of Green Lean Six Sigma Projects: a hybrid approach of technology readiness level, data envelopment analysis, and ANFIS.

Nowadays budget and schedule constraints have forced organizations to select six sigma projects based on pre-defined success criteria. Also, progressive approaches based on green and lean paradigm are vital for companies to enhance their social and environmental performance. Then, Green Lean Six Sigma (GLS) projects play the main role in improving the performance of an organization while augmenting its sustainability. Accordingly in this paper, past studies were reviewed, and GLS projects' indicators and performance evaluation criteria were identified. Data envelopment analysis (DEA) was employed for the appropriate selection of GLS projects. Next, the ranking and performance weight of each project were investigated, and also the projects were categorized based on the technology readiness level (TRL). Additionally, an adaptive neuro-fuzzy inference system (ANFIS) method was applied for the successful prediction of selected GLS projects. Twenty-eight inputs and 9 outputs for the first project category (with TRL 9) and 28 inputs and 6 outputs for the second project category (with TRL 8) were entered into the model. The statistical assessment measures such as Nash-Sutcliffe efficiency (NSE), root mean squared of error (RMSE), mean absolute error (MAE), and R2 were employed for capability appraisal of ANFIS model. Results of NSE and R2 indicators for both project categories were 1.00 that proved the efficiency of the ANFIS model for success prediction of GLS projects. Also, RMSE and MAE indicators for category 1 were 0.01 and 0.02 respectively. Similarly, these measures for category 2 were 0.02 and 0.02. The results advocate a proper approximation for observed values by the ANFIS model. Also, the results indicated that TRL as an important enabler of the GLS project has a meaningful role in the performance of GLS projects.

The most innovative lean practices deployed in transportation and their effects on the financial and operational performance

Abstract The new lean paradigm defines the necessary management tools and techniques that create more value for the customers using lean resources. By reducing the non-value activities in one hand, and emphasizing on continuous improvement on the other hand, the lean practices implemented open the path for superior level of financial and operational performance. The purpose of this paper is to highlighting the strong correlation between the combination of the TQM, JIT, TMS, TPM and RFID as the most important lean practices employed by the transportation companies surveyed and its financial and operational performance. The correlation between the lean practices deployed within the transportation companies analyzed and their financial or operational performance has been established due to applying the SPSS quantitative methods, factor analysis and linear regression.

An Operational approach to gauge the lean performance in bias tyre manufacturing: a case study

Tyre manufacturing firms are facing a host of challenges such as global competition, pricing strategies and complex production process. Due to the complexity of tyre manufacturing process, it is prone to waste which result in non-value added activities across the entire value stream. The study is undertaken in a bias tyre manufacturing firm which is based in the South India. The top management of the case firm is concerned about the increasing non-value added activities. The aim of the study is to devise a template to measure the leanness index of the bias tyre manufacturing firm. The proposed leanness template incorporates the eight types of wastes defined in lean manufacturing paradigm viz. overproduction, unnecessary inventory, waiting, transportation, processing, unnecessary motion, defects and underutilisation of human resource. This will facilitate the people at the operational level to take appropriate decisions. The results of the study indicate that the HR underutilisation and motion are most detrimental wastes in bias tyre manufacturing, which is due to the high rate of absenteeism among workers in all the three shifts.

Implementing lean paradigm in an Indian foundry facility: a case study

Implementing lean paradigm in an Indian foundry facility: a case study

Implementing lean paradigm in an Indian foundry facility: a case study

This article seeks to establish the effectiveness of lean manufacturing in eliminating the hidden wastes, maintaining better inventory control and reducing the total production time. A large Indian foundry facility is utilised to illustrate the adopted approach. Firstly, current state mapping is prepared to have the knowledge of the existing procedure followed in the production of castings. In addition, relevant data such as, cycle time, changeover time, percentage of defects, percentage downtime and inventory in between the processes are collected for each process. Further, the future state map is created, after amending the entire process, by adopting the methodology proposed by Rother and Shook (1999). Additionally, the required buffer size (number of Kanbans) at each stage and the sequence of production are also estimated by adopting the method advocated by the Smalley (2004). Finally, improvement is accounted in terms of reduced lead time of the process.

Author and institution citation analysis of the lean literature

The lean paradigm is now firmly established within the field of operations and SCM. However, even though a large number of narrative reviews exist on this topical body of material, no citation analysis (CA) has yet been conducted. This paper presents the first CA of the lean literature, and also represents the most extensive known CA study within the wider field. It uses ten search phrases over a source population of over 15,000 Google Scholar publications. These are filtered into a dataset of the 241 most highly cited publications on lean that account for an aggregated total of 98,829 citations. The research reveals a surprising degree of influence of academics within the supposedly a theoretical lean literature. US institutions dominate, with MIT being the most influential. Both practitioners and management consultants almost exclusively co-authoring with their own type. Japan has the second largest number of management consultant authors after the USA.

Development of multivariate framework for lean and green process

The supply and demand on manufacturing industry has always been contributing significantly towards global economic growth. With the criticality of global warming and climate change, the manufacturing industry has suffered from being accused as the main contributor to environmental pollution. The volatility of global economic and competition has also hit the manufacturing industry badly. The urge to shift the manufacturing industry towards lean and green paradigm is inevitable. A sustainable process is favourable to cope with global supply demand. Lean and green process can act as a strategy and solution for developing country to achieve sustainable manufacturing. The development of multivariate framework for lean and green process covers five major areas (e.g. manpower, machine, money, material and environment) that will enhance the industry to be more sustainable through lean and green approach. The multivariate framework aims to establish the relationship between five major areas to identify the potential debottlenecking possibilities. This framework evaluates the lean and green factor to form a balance solution to the operation. Lean and green framework is expected to act as an effective tool in assisting the industry to strive for greater sustainability.

Implementation of the LPS using an excel spreadsheet

PurposeThe purpose of this paper is to investigate the implementation challenges of one of lean construction’s recent tools, the last planner system (LPS), by exploring issues in the New Zealand construction sector to identify potential areas for improvement. To achieve this aim, the study formulated two objectives: to present the challenges in LPS use and to explore solutions by using an Excel spreadsheet for facilitating LPS applications.Design/methodology/approachThe study drew primarily upon a case study approach. A fieldwork study and document analysis of a New Zealand construction project were conducted with an extensive literature review undertaken on the LPS concept.FindingsThe findings revealed that although an automated spreadsheet could be a simple and inexpensive option for using the LPS, data collection, storage and transfer into the spreadsheet could significantly influence the reliability of the LPS outcomes. Most data utilisation challenges were found to occur around the three data sets included in the weekly work plan (WWP). The study presented several automation solutions which had been applied to overcome data utilisation challenges.Originality/valueAmong the first of its kind in the construction industry, this study, with its first-hand account of an organisation which uses the lean paradigm, provides an in-depth insight into LPS tool implementation. The study extends the current body of knowledge by unearthing the challenges of LPS integration into construction activities and presenting efforts undertaken in a construction case project to overcome relevant issues. This adds value by enhancing the reliability of the LPS and, consequently, the effectiveness of its implementation in practical terms.

A Lean based process planning for aircraft disassembly

Abstract Each year, 300 aircraft are taken out of service, and because of the development of the aerospace industry, this number is constantly increasing (approximately 12,000 airplanes which are currently in service for different purposes will be removed from service in the next 20 years). Otherwise, the increased demands for recycling, reusing and remanufacturing or refurbishing attract the interest of researchers to improve the aircraft end of life treatment. This article present a process planning with Lean paradigm perspective to improve the recovery of parts and the maintenance of aircraft on which the parts are reused showing the approach for the optimal disassembling and dismantling based solely on the Aircraft Maintenance Manual (AMM).

How Lean transformation affects scheduling

The Lean paradigm transforms a production company from utilisation-centric planning into a system in which other operating conditions such as short flow times, local control, reduction in variation, and first-in-first-out control are weighted as well. This paper studies how the scheduling of production changes when the above four conditions are implemented. Their effects are studied by constructing an optimisation model for the scheduling of a flow shop. The optimisation model is based on the following ideas. First, when the flow time is emphasised, the objective of the scheduling changes from utilisation to a short flow time. Second, if local control is used, it means that the optimisation is performed locally, i.e. individually at each station, and it concerns the makespan at the station. Third, if the variation is reduced, the processing times and arrival times have less variation and, fourth, the scheduling can force the flow times to have less variation by using first-in-first-out (FIFO) sequencing. The experimental results achieved using the model describe how and in which order the operating conditions under study should be implemented in the scheduling. For example, if utilisation is important, local control and FIFO should not be used before variation is reduced. Lean focuses on short flow time, local control, variation reduction and FIFO control.Paper studies on how these conditions should be taken into account in scheduling..An optimisation model is constructed to study the effects of conditions.Experiments give insights about the order in which conditions should be used.