Lean Model Research Articles

An integrated sustainable lean approach for the SMEs in India: A multi-level conceptual frame work

Sustainability has become the prime focus in most of the manufacturing industries in and around the world due to high competitive demand, recent alleviation of change in global climate and government policies. Lean Manufacturing (LM), on the other hand have been globally accepted and it is now the heart of production in the industrial sectors. Researches depict that the lean philosophy in most of the SMEs are not properly carried out in a progressive and continuous manner and are getting declined in course of time. Also the limited number of researches done in the triple bottom line (TBL) of sustainability adds to the de-escalation of lean philosophy in the industries. In short, lean is not getting sustained. In order to make a sustainable lean system, a multi-level model, which identifies the enablers and inhibitors of sustainability, has been developed. The details of which is included in this paper. 39 enablers and 27 inhibitors have been identified from different literatures and expert interactions. Incorporating the enablers and inhibitors in the sustainability pillars (Environment, Economic & Social), and lean ideologies, multi-level sustainable- lean (Sus-Lean) enabler model and inhibitor model has been carved out in this paper. This will give an insight on how to make a sustainable lean approach in the SMEs of India. Also, the future research orientations in this regard are proposed based on the models.

Challenges and opportunities in a lean automation model implementation: Characterizing micro, small and medium-sized enterprises (MSMEs) of developing countries

Challenges and opportunities in a lean automation model implementation: Characterizing micro, small and medium-sized enterprises (MSMEs) of developing countries

Engineering Functional Vascularized Beige Adipose Tissue from Microvascular Fragments of Models of Healthy and Type II Diabetes Conditions

Engineered beige adipose tissues could be used for screening therapeutic strategies or as a direct treatment for obesity and metabolic disease. Microvascular fragments are vessel structures that can be directly isolated from adipose tissue and may contain cells capable of differentiation into thermogenic, or beige, adipocytes. In this study, culture conditions were investigated to engineer three-dimensional, vascularized functional beige adipose tissue using microvascular fragments isolated from both healthy animals and a model of type II diabetes (T2D). Vascularized beige adipose tissues were engineered and exhibited increased expression of beige adipose markers, enhanced function, and improved cellular respiration. While microvascular fragments isolated from both lean and diabetic models were able to generate functional tissues, differences were observed in regard to vessel assembly and tissue function. This study introduces an approach that could be employed to engineer vascularized beige adipose tissues from a single, potentially autologous source of cells.

Identification and prioritisation of factors affecting lean leadership model

Identification and prioritisation of factors affecting lean leadership model

Towards the factory 4.0 Convergence and divergence of lean models in Italian automotive plants

This paper studies the interplay in terms of techno-organisational change between the adoption of I4.0 technologies and lean production systems. Leveraging on the results of two field-work analyses conducted under a collaboration with the Sabattini Foundation and the metal workers trade union FIOM in the period 2016-2018, we compare an ensemble of factories producing both high-end/highly customised and low-end products. Emerging patterns of convergence and divergence in the techno-organisational configurations of these factories confirm that this wave of technological innovation is far from leading to total automation or the digital revolution. On the contrary, it appears to be integrated into the historical trend of 'leanification' of production processes in the automotive sector, despite the organisational variety shaped by the actual implementation of this production model.

FPGA Implementation of Deep Leaning Model for Video Analytics

In recent years, deep neural networks have become a fascinating and influential research subject, and they play a critical role in video processing and analytics. Since, video analytics are predominantly hardware centric, exploration of implementing the deep neural networks in the hardware needs its brighter light of research. However, the computational complexity and resource constraints of deep neural networks are increasing exponentially by time. Convolutional neural networks are one of the most popular deep learning architecture especially for image classification and video analytics. But these algorithms need an efficient implement strategy for incorporating more real time computations in terms of handling the videos in the hardware. Field programmable Gate arrays (FPGA) is thought to be more advantageous in implementing the convolutional neural networks when compared to Graphics Processing Unit (GPU) in terms of energy efficient and low computational complexity. But still, an intelligent architecture is required for implementing the CNN in FPGA for processing the videos. This paper introduces a modern high-performance, energy-efficient Bat Pruned Ensembled Convolutional networks (BPEC-CNN) for processing the video in the hardware. The system integrates the Bat Evolutionary Pruned layers for CNN and implements the new shared Distributed Filtering Structures (DFS) for handing the filter layers in CNN with pipelined data-path in FPGA. In addition, the proposed system adopts the hardware-software co-design methodology for an energy efficiency and less computational complexity. The extensive experimentations are carried out using CASIA video datasets with ARTIX-7 FPGA boards (number) and various algorithms centric parameters such as accuracy, sensitivity, specificity and architecture centric parameters such as the power, area and throughput are analyzed. These results are then compared with the existing pruned CNN architectures such as CNN-Prunner in which the proposed architecture has been shown 25% better performance than the existing architectures.

Towards Factory 4.0 Convergence and divergence of lean models in Italian automotive plants

This paper studies the interplay in terms of techno-organisational change between the adoption of I4.0 technologies and lean production systems. Leveraging on the results of two field-work analyses conducted under a collaboration with the Sabattini Foundation and the metal workers trade union FIOM in the period 2016-2018, we compare an ensemble of factories producing both high-end/highly customised and low-end products. Emerging patterns of convergence and divergence in the techno-organisational configurations of these factories confirm that this wave of technological innovation is far from leading to total automation or the digital revolution. On the contrary, it appears to be integrated into the historical trend of 'leanification' of production processes in the automotive sector, despite the organisational variety shaped by the actual implementation of this production model.

A lean construction and BIM interaction model for the construction industry

An Analytical Network Process (ANP) was created to test the Lean and BIM concepts with data collected from U.S. companies to find the success factors of the Lean/BIM framework. After an extensive literature review, a total of 17 sub-categories for Lean/BIM are classified into three clusters, namely Communication, Production, and Visualization. An ANP network is then established to station the links between the attributes of the framework while computing their importance weights. Eight experienced civil engineers took part in the questionnaire study to assess the relations between the attributes. The main purpose of this study is to reveal the synergy between Lean and BIM with different components reflecting this synergy and present the Lean and BIM synergy on a comprehensive model. The results indicate that Production is the prominent cluster and Production Control, Standardization and Information accuracy are the most important factors in the Lean/BIM synergy. To validate the model, five construction projects were selected to test and observe the results accordingly. The study is expected to help construction industry leaders set their priorities, benefit more from the interaction between Lean and BIM, and revise their strategies accordingly. This study identifies Lean/BIM categories and subcategories as a roadmap for research and implementation. In this context, the study reveals the relationship between the categories/subcategories along with the weights and most and less important categories for Lean/BIM implementation and research.

Qualification of the results of aggregated lean KPIs along fuzzy logic

The application of lean management has become a fundamental competitive criterion in the operation of industrial enterprises in the 21st century. The use of tools and methods related to different lean management appears in a significant part of corporate functions. However, it is also important to mention that these lean tools and methods are used in addition to the mixed use of many other tools and methods that support non-lean goals. This problem is also a challenge for controlling. Because lean accounting methods do not necessarily measure the extent of lean effectively in such an environment, a number of other methods are used to monitor lean in both academic and business practice. Of these, lean evaluation models based on fuzzy logic should be highlighted. With the help of these models, it is possible to determine the lean performance of a company primarily along financial data. The disadvantage of the model, however, is that lean management also appears in a number of functional areas that cannot be assessed along financial ratios, but they clearly influence the operation of lean management.
 In our research, we illustrated a complex set of lean controlling methods through an instrumental case study, which allows companies to evaluate the performance of lean management and to explore different points of intervention. Our research results make it possible to calculate the lean fuzzy index not only along financial data, and thus clarify the information content of the index. Along the methods and indicators formulated in our research, the effectiveness of lean methods and tools, as well as the processes and methods supporting lean goals, can be separated and measured. Our research goal is to create a generally applicable lean controlling conceptual model.

Strategic Formulation Analysis of Semi-Permanent Building Provider from Containers, "I-Cont."

PT. Karya Inti Abadi Kontainer, trademark ‘I-CONT,’ is a container modification company creating business stalls with modern, practical and economical containers. We are the solution for MSME business players, which is focusing on the food and beverage industry. Aside from that, we also provide services in membership from consultation, modification containers, delivery, maintenance, service until reselling and remodification. The strategy method uses the Value Proposition Canvas, IFE, EFE, VRIO, CPM, SWOT, IE, QSPM, Porter's Generic Strategy and Lean Business Canvas Model. The results of this analysis can help to determine a suitable business model for PT. Karya Inti Abadi. Keywords: Value Proposition Canvas, Porter's Generic Strategy, Lean Business Model Canvas, containers.

A System Modelling Approach Optimization Process and Machine Utilization In Casting Plant Using Lean Manufacturing Simulation Model

Perbaikan berkelanjutan, reduksi dan eliminasi waste dalam proses bisnis menjadi salah satu aspek yang dilakukan agar dapat terus memiliki daya saing yang sustainable. PT. X saat ini sedang dalam proses melakukan transformasi, reduksi dan eliminasi NVA serta perbaikan berkelanjutan di sisi proses guna meningkatkan daya saing yang sustainable. penelitian ini bertujuan dalam menganalisis dan mengajukan formulasi perbaikan proses menggunakan metode kerangka kerja pemodelan sistem dan simulasi. Temuan dari penelitian diketahui bahwa peningkatan Utilisasi Dies rata-rata 82,82 % relative meningkat 36% dibanding simulasi sebelumnya (52,9%); dengan rata-rata output 21,04 pcs/jam relative meningkat 42% dibanding simulasi sebelumnya (12,9 pcs/jam), hal ini dengan melakukan improvement proses semi auto dalam proses eject produk yang sekaligus berdampak dalam pengurangan manpower, serta mereduksi loss time akibat lama proses pendinginan dengan sistem heat transfer conveyor system

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

The present study analysed the ability for portable near infrared reflectance (NIR) and Raman spectroscopy sensors to differentiate between grass-fed and grain-fed beef. Scans were made on lean and fat surfaces of 108 beef steak samples labelled as grass-fed ( n = 54) and grain-fed ( n = 54), with partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) used to develop discrimination models which were tested on independent datasets. Furthermore, PLS-DA was used to predict visual marbling score and days on feed (DOF). The NIR spectra accurately discriminated between grass- and grain-fed beef on both fat (91.7%, n = 92) and lean (88.5%, n = 96), as did Raman (fat 95.2%, n = 82; lean 69.6%, n = 68). Fat scanning using NIR spectroscopy moderately predicted DOF (r2val = 0.53), though Raman and NIR spectroscopy lean prediction models for DOF and marbling were less precise (r2val < 0.50). It can be concluded that portable NIR and Raman spectrometers can be used successfully to differentiate grass-fed from grain-fed beef and therefore aid retail and consumer confidence.

Adaptation of the Lean 6S Methodology in an Industrial Environment under Sustainability and Industry 4.0 Criteria

Industry 4.0 paradigms have a positive influence on standard operating procedures, methodologies used in Lean Manufacturing techniques and management models with sustainability criteria. Interdependencies and correlations have been found between Lean systems and Industry 4.0. The Lean principles of avoiding waste and zero defects are related to the cloud and big data paradigms. In a current workplace, there has been an exponential increase in digital information and the need to generate direct commitments to environmental management. This situation forces us to innovate and improve the management methodologies and models used in the industrial environment. The Lean 6S methodology must adapt and respond to new demands. In this work, an update of the Lean 6S methodology is carried out to guarantee increased productivity in the workplace through the organization of industrial resources, both physical and digital. A revision of the implementation procedure is proposed, which includes activities that generate a direct commitment to sustainability and the organization of digital information, through a proposal for an organizational architecture of Industry 4.0 technologies.

Vision-Based Edge Detection System for Fruit Recognition

There are variety of fruits around the world, different types of fruits contain different types of nutrients and vitamins which could benefits our health. In order to understand which fruit can provide specific type of nutrients, we need to identify the types of fruits. However, fruits grow in a different shape, colour and texture based on the country they were planted and the environment of the land. Implementing a machine vision-based recognition on the fruits can help people recognize them easily. In this paper, an edge detection method is applied using computer vision approach to recognize different types of fruits. The fruits are classified based on the features extracted from their images. In the experiment, a total of 450 images of three types of fruit are used, which are apples, lemons and mangoes. Pre-processing steps are applied on the captured image to improve the quality of fruit details and the edge features are extracted using Canny Edge Detection method. Classification of the fruits is accomplished using two different types of learning model, the deep leaning model, Convolution Neural Network (CNN) and machine learning model, Support Vector Machines (SVM). The performance of both classifiers is compared and the model with the best performance, SVM is chosen as the model for the system. The system can achieve 86% classification accuracy with the SVM model, which is good enough for fruit recognition.

Techno-Entrepreneurship Learning Model in Higher Education

Techno-Entrepreneurship becomes an important part of higher education based on information technology. This is useful to enhance the spirit of entrepreneurship, the ability to read opportunities and build information technology-based businesses. This study aims to examine the appropriate techno-entrepreneurship learning model in higher education. The research method used is action research with practice using lean canvas models for business models and design sprint method to build product prototypes. The results of this study show how to use practice and tutorials, students can create the right business model and be able to make a prototype of a digital product that is good in implementing business ideas. The conclusion obtained in this study is that techno-entrepreneurship is very appropriate to use lean canvas to build business models and more efficiently use sprint designs to build prototypes of digital products. This model is highly recommended for entrepreneurship learning in higher education.

When to Be Agile: Ratings and Version Updates in Mobile Apps

Lean and agile models of product development organize the flexible capacity to rapidly update individual products in response to customer feedback. Although agile operations have been adopted across numerous industries, neither the benefits nor the factors explaining when firms choose to become agile are validated and understood. We study these questions using data on the development of mobile apps, which occurs through the dynamic release of new versions into the mobile app marketplace, and the apps’ customer ratings. We develop a structural model estimating the dependence of product versioning on (a) market feedback in the form of customer ratings against (b) project and work-based considerations, such as development timelines, scale economies, and operational constraints. In contrast to when they actually benefit from operational agility, firms become agile when launching riskier products (in terms of uncertainty in initial customer reception) and less agile when they are able to exploit scale economies from coordinating development over a portfolio of apps. Agile operations increase firm payoffs by margins of 20% to 80%, and interestingly, partial agility is often sufficient to capture the bulk of these returns. Finally, turning to a question of marketplace design, we study how the mobile app marketplace should design the display of ratings to incentivize quality (increasing app categories’ average user satisfaction rates by as much as 22%). This paper was accepted by Jayashankar Swaminathan, operations management.

The lean canvas business model training for members of the Indonesian laundry entrepreneurs association (HIPLI)

Community Service Partners are members of the Indonesian Laundry Entrepreneurs Association (HIPLI). This Student Creativity Program (PKM) activity is intended to provide a thorough explanation of the Lean Canvas model, business opportunity theory, appropriate market strategies, and risk analysis for business owners that are members of HIPLI. The participants were trained using presentation and discussion techniques. The feedback from the participants through the questionnaires showed that they were satisfied with the training’s implementation. Finally, this PKM implementation concluded that the Lean Canvas Business Model Training for HIPLI entrepreneurs provided a better understanding of the training material such that participants are more enthusiastic about continuing their business.

Not Home Alone: Leveraging Telehealth and Informatics to Create a Lean Model for COVID-19 Patient Home Care.

In response to the emerging COVID-19 public health emergency in March 2020, the Medical University of South Carolina rapidly implemented an analytics-enhanced remote patient monitoring (RPM) program with state-wide reach for SARS-CoV-2-positive patients. Patient-reported data and other analytics were used to prioritize the sickest patients for contact by RPM nurses, enabling a small cadre of RPM nurses, with the support of ambulatory providers and urgent care video visits, to oversee 1234 patients, many of whom were older, from underserved populations, or at high risk of serious complications. Care was escalated based on prespecified criteria to primary care provider or emergency department visit, with 89% of moderate- to high-risk patients treated solely at home. The RPM nurses facilitated the continuity of care during escalation or de-escalation of care, provided much-needed emotional support to patients quarantining at home and helped find medical homes for patients with tenuous ties to health care.

Establishing an Effective Blended Learning Model: Teacher Perceptions from the United Arab Emirates

Blended learning is fast becoming the new normal and increasingly considered an integral part of program offers within higher education (HE) institutions. Although there is a general consensus about online and physical face to face components, which constitute a framework for blended learning models. There are variances on how these components should be combined, established, and delivered. Empirical evidence on teachers’ perceptions of these elements is also limited. This qualitative study used focus groups to gather teacher perspectives of blended learning models, which produced three major themes related to professional development, student success, and blended courses. A sample of teacher from this group was most relevant to the study because they were involved in the implementation, and iterations, of blended learning at their HE institution for three semesters. Their experiences and perceptions provided highly relevant insights on what constitutes an effective blended leaning model. This study confirms previous empirical claims on the importance of professional development in increasing teacher efficacy. But also reveals the role of self-learning, informal communities of practice and their impact on changing teacher perceptions, which in turn improve efficacy. The study also highlights effective communication, course materials, course design and how blended learning environments are set-up, as crucial elements of an effective blended learning model. Teachers perceived these elements as the biggest influences on student success. This study provides important insights for educational institutions seeking to establish or improve their blended learning strategy and enhance student experience.

A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

Purpose:Less contrast between lesions and skin, blurriness, darkened lesion images, presence of bubbles, hairs are the artifactsmakes the issue challenging in timely and accurate diagnosis of melanoma. In addition, huge similarity amid nevus lesions and melanoma pose complexity in investigating the melanoma even for the expert dermatologists. Method: In this work, a computer-aided diagnosis for melanoma detection (CAD-MD) system is designed and evaluated for the early and accurate detection of melanoma using thepotentials of machine, and deep learning-based transfer learning for the classification of pigmented skin lesions. The designed CAD-MD comprises of preprocessing, segmentation, feature extraction and classification. Experiments are conducted on dermoscopic images of PH2 and ISIC 2016 publicly available datasets using machine learning and deep learning-based transfer leaning models in twofold: first, with actual images, second, with augmented images. Results:Optimal results are obtained on augmented lesion images using machine learning and deep learning models on PH2 and ISIC-16 dataset. The performance of the CAD-MD system is evaluated using accuracy, sensitivity, specificity, dice coefficient, and jacquard Index. Conclusion:Empirical results show that using the potentials of deep learning-based transfer learning model VGG-16 has significantly outperformed all employed models with an accuracy of 99.1% on the PH2 dataset.