Lean Model Research Articles

Development and validation of intraoral periapical radiography-based machine learning model for periodontal defect diagnosis.

Radiographic determination of the bone level is useful in the diagnosis and determination of the severity of the periodontal disease. Various two- and three-dimensional imaging modalities offer choices for imaging pathologic processes that affect the periodontium. In recent years, innovative computer techniques, especially artificial intelligence (AI), have begun to be used in many areas of dentistry and are helping increase treatment and diagnostic performance. This study was aimed at developing a machine-learning (ML) model and assessing the extent to which it was capable of classifying periodontal defects on 2D periapical images. Eighty-seven periapical images were examined as part of this research. The existence or absence of periodontal defects in the aforementioned images were evaluated by a human observer. The evaluations were subsequently repeated using a radiomics platform. A comparison was made of all data acquired through human observation and ML techniques by SVM analysis. According to the study findings the ability of human observers and the ML model to detect periodontal defects was significantly different in comparison to the gold standard. However, ML and human observers performed similarly for the detection of periodontal defects without a significant difference. This study reveals that the prediction of periodontal defects can be achieved by combining particular radiomic features with image variables. The proposed machine leaning model can be utilized for supporting clinical practitioners and eventually substitute evaluations conducted by human observers while enhancing future levels of performance.

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database

Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals’ carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96−18−4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.

MPharma: health-tech start-up makes medicines affordable and accessible to patients in Africa

Learning outcomes After discussing this case, students will be able to understand the challenges faced by social entrepreneurs in starting a health-tech start-up in Africa; create and evaluate lean business models of health-tech companies as a social enterprise; evaluate how health-tech start-ups were developing innovative business models and supply chain networks to make prescription drugs accessible and available in Africa; understand how inorganic growth strategies can help health-tech start-ups scale up; and evaluate what promises investors were seeing while investing in social enterprises in the health-care sector in Africa and what social wealth they were creating. Case overview/synopsis In August 2022, Gregory Rockson (Rockson), social entrepreneur and founder of for-profit health technology (health-tech) social enterprise in Ghana, mPharma, stated that he had plans to replicate the company’s business model, which provided people access to drugs and at affordable prices, to other African nations, beyond the company’s existing footprint. However, analysts pointed out that the fragmented drug supply chain and poor regulation in the health-care market across Africa could act as a challenge for mPharma to replicate its business model successfully across the African continent. People in Africa were forced to pay higher prices to buy life-saving drugs due to the continent’s fragmented drug supply chain. To add to their woes, pharmacies struggled to keep life-saving and life-sustaining medicines in stock. Often, patients traveled miles to a pharmacy only to find out that the drugs they needed were not in stock. In addition to this, the markets were flooded with counterfeit drugs. And the Covid-19 pandemic only exacerbated the situation. mPharma managed the prescription drug inventory for pharmacies and drug suppliers using its proprietary vendor management information system. By using the technology infrastructure it had built, the company connected patients, pharmacies and hospitals through a cloud-based software. The system enabled doctors to track in real-time which drugs were available and at which location, thus giving patients reliable access to medicines. Patients registering with mPharma with their prescriptions and medical history received an alert on their mobile phones notifying them where the drugs they needed were available. mPharma bought drugs from major drug manufacturers such as Novartis International AG, Pfizer Inc. (Pfizer) and Bayer AG, on behalf of the pharmacies. This enabled the pharmacies to save on the up-front costs of stocking the drugs, reduced supply constraints and ensured availability of drugs to consumers in these underserved markets. The company had a consignment model wherein member pharmacies had to pay only for what they sold. Most pharmacies forecast the number of drugs they needed and purchased them from mPharma at pre-agreed rates. The company took the inventory liability to prevent pharmacies from going out of stock. As mPharma used its purchasing power to buy drugs in large quantities from drug manufacturers and suppliers, it was able to help patients realize cost savings of 30% to 60% in the purchase of medicines. mPharma was focusing on achieving its ambitious goal of dominating the health-care market in Africa in future. However, analysts felt that the company would face challenges related to poor regulation in the health-care market, high prices of drugs and the fragmented pharmacy retail market in the continent. Complexity academic level This case is intended for use in MBA/MS level programs as part of a course on Social Entrepreneurship, Sustainability, Business Model Innovation, Disruptive Business Models, and Supply Chain Management in the Drug Industry. Supplementary materials Teaching notes are available for educators only. Subject code CSS 3: Entrepreneurship.

Design Thinking Learning Model on Digital Workspace for Sustainable Products and Services to Enhance Digital Entrepreneurs (DTLDW-SPSEDE Model)

This study aims to: (1) a design for design thinking learning model on digital workspace for sustainable products and services to enhance digital entrepreneurs; and (2) evaluate the design thinking learning model on digital workspace for sustainable products and services to enhance digital entrepreneurs. The sample is made up of ten specialists who were chosen using purposive sampling. The instrument used in the study was an evaluation form of the leaning model. The arithmetic mean and standard deviation were employed in the investigation. The results show that: (1) the design thinking learning model on digital workspace for sustainable products and services to enhance digital entrepreneurs consists of four key components: 1) Design thinking process, 2) Digital Workspace, 3) Design thinking tools and 4) Sustainability metrics. (2) All ten specialists agreed that the design thinking learning model on digital workspace for sustainable products and services to enhance digital entrepreneurs designed through this study shows the highest level of appropriateness.

Using deep leaning models to detect ophthalmic diseases: A comparative study.

The aim of this study was to prospectively quantify the level of agreement among the deep learning system, non-physician graders, and general ophthalmologists with different levels of clinical experience in detecting referable diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy. Deep learning systems for diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy classification, with accuracy proven through internal and external validation, were established using 210,473 fundus photographs. Five trained non-physician graders and 47 general ophthalmologists from China were chosen randomly and included in the analysis. A test set of 300 fundus photographs were randomly identified from an independent dataset of 42,388 gradable images. The grading outcomes of five retinal and five glaucoma specialists were used as the reference standard that was considered achieved when ≥50% of gradings were consistent among the included specialists. The area under receiver operator characteristic curve of different groups in relation to the reference standard was used to compare agreement for referable diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy. The test set included 45 images (15.0%) with referable diabetic retinopathy, 46 (15.3%) with age-related macular degeneration, 46 (15.3%) with glaucomatous optic neuropathy, and 163 (55.4%) without these diseases. The area under receiver operator characteristic curve for non-physician graders, ophthalmologists with 3-5 years of clinical practice, ophthalmologists with 5-10 years of clinical practice, ophthalmologists with >10 years of clinical practice, and the deep learning system for referable diabetic retinopathy were 0.984, 0.964, 0.965, 0.954, and 0.990 (p = 0.415), respectively. The results for referable age-related macular degeneration were 0.912, 0.933, 0.946, 0.958, and 0.945, respectively, (p = 0.145), and 0.675, 0.862, 0.894, 0.976, and 0.994 for referable glaucomatous optic neuropathy, respectively (p < 0.001). The findings of this study suggest that the accuracy of this deep learning system is comparable to that of trained non-physician graders and general ophthalmologists for referable diabetic retinopathy and age-related macular degeneration, but the deep learning system performance is better than that of trained non-physician graders for the detection of referable glaucomatous optic neuropathy.

Towards Lean Manufacturing in Developing Countries: Research Gaps and Directions in Tanzania

In the current knowledge-driven economy, many industries across the globe are forced to adopt manufacturing technologies such as lean manufacturing to remain competitive in the globalized world. Despite the increase in lean manufacturing implementations, the adoption rate in Tanzania is very low and estimated to be 8 percent. Likewise, the sustainability of lean manufacturing tools and practices is still questionable, which shows the need to develop lean maturity models. To date, several lean maturity models have been developed but there is a lack of maturity models that consider different levels of manufacturing industries. The study presented in this paper focused on the pre-implementation, implementation, and post-implementation phases of lean manufacturing. To achieve this aim, a systematic literature review and social network analysis using VOS viewer 1.6.18 were conducted. The literature was obtained from four academic databases: Emerald, Elsevier, Springer, and Taylor and Francis. The search covered the period from 2010 to 2022. Of the 100 publications analyzed on lean manufacturing, only 1 percent is from low-income developing countries and 5 percent of publications from Africa. The study has identified that there are few numbers of research publications on lean manufacturing in Africa and low and lower-middle-income developing countries in general, as a result there is a low rate of implementation of lean manufacturing in low and lower-middle-income developing countries. It is recommended that more studies on the drivers and barriers, critical success factors, and lean assessment be conducted in developing countries.

A review of the interrelationships and characteristics of Building Information Modeling, Integrated Project Delivery and Lean Construction maturity models

PurposeThe application of integrated project delivery (IPD) in conjunction with building information modeling (BIM) and Lean Construction (LC) as the efficient method for improving collaboration and delivering construction projects has been acknowledged by construction academics and professionals. Once organizations have fully embraced BIM, IPD and LC integration, a measurement tool such as a maturity model (MM) for benchmarking their progress and setting realistic goals for continuous improvement will be required. In the context of MMs literature, however, no comprehensive analysis of these three construction management methods has been published to reveal the current trends and common themes in which the models have approached each other.Design/methodology/approachTherefore, this study integrates systematic literature review (SLR) and thematic analysis techniques to review and categorize the related MMs; the key themes in which the interrelationship between BIM, IPD and LC MMs has been discussed and conceptualized in the attributes; the shared characteristics of the existing BIM, IPD and LC MMs, as well as their strengths and limitations. The Preferred Reporting Items for Systematic Reviews (PRISMA) method has been used as the primary procedure for article screening and reviewing published papers between 2007 and 2022.FindingsDespite the growth of BIM, IPD and LC integration publications and acknowledgment in the literature, no MM has been established that holistically measures BIM, IPD and LC integration in an organization. This study identifies five interrelated and overlapping themes indicative of the collaboration of BIM, IPD and LC in existing MMs' structure, including customer satisfaction, waste minimization, Lean practices and cultural and legal aspects. Furthermore, the MMs' common characteristics, strengths and limitations are evaluated to provide a foundation for developing future BIM, IPD and LC-related MMs.Practical implicationsThis paper examines the current status of research and the knowledge gaps around BIM, IPD and LC MMs. In addition, the highlighted major themes serve as a foundation for academics who intend to develop integrated BIM, IPD, and LC MMs. This will enable researchers to build upon these themes and establish a comprehensive list of maturity attributes fulfilling the BIM, IPD and LC requirements and principles. In addition, the MMs' BIM, IPD and LC compatibility themes, which go beyond themes' intended characteristics in silos, increase industry practitioners' awareness of the underlying factors of BIM, IPD and LC integration.Originality/valueThis review article is the first of a kind to analyze the interaction of IPD, BIM and LC in the context of MMs in current AEC literature. This study concludes that BIM, IPD and LC share several joint cornerstones according to the existing MMs.

How Smart Technologies Affect the Decision-Making and Control System of Food and Beverage Companies—A Case Study

In order to cope with the current competitive scenario, more and more companies are increasingly challenged to redefine their current decision-making and control models in favour of lean, efficient and digitalised models. Recent advances in digital technologies offer new opportunities for management to digitise and automate decision-making and control processes, with numerous benefits, including in terms of sustainability. In many industries, especially in food and beverage sector, the implementation of smart technologies could play an important role and have a positive impact on business processes, making them sustainable. The literature on the use of smart technologies in food and beverage companies focuses on the impact that such technologies have on performance measurement and sustainability, but there is a lack of explanation of the impact of smart technologies on decision-making and control systems. This work aims to fill the gap in the literature by investigating, through a case study, the impact of smart technologies on the decision-making and control systems of food and beverage companies. The case study showed that the use of smart technologies in food and beverage companies has an impact both on control systems, which generate a range of information relevant to management control, and on decision-making systems, which support management in making optimal strategic decisions for the company.

Nonshivering thermogenesis revisited: sympathetic and non-sympathetic contributions

Factors of diet and environment contribute to complementary, additive mechanisms that influence the magnitude of diet induced thermogenesis generated in man and animals. Both short terms sympathetically mediated and longer acting thyroidal mediated pathways have been identified and appear to function in a metabolically coordinated fashion to facilitate biochemical and physiologic pathways of energy balance implicating multiple organs and peripheral tissues during periods of over- and under-nutrition. In a congenic lean rodent model known to express parameters of environmentally and diet induced mediated thermogenesis, thyroidal and sympathetic contributions each contributed to approximately 50% of the relative increase in VO2 in quietly resting animals after consuming a high energy palatable diet as determined in the presence vs. absence of α-methylparatyrosine, a sympathoplegic chemical agent. In contrast, in the obese phenotype of the corpulent and other rodent strains, both sympathetic and thyroidal contributions to adaptive thermogenic mechanisms are impaired in response to both nutritional and environmental challenge, contributing to a greater efficiency of energy metabolism and body fat accretion. Thus, the purpose of the present review is to approximate the qualitative distribution between the two primary factors linked to the physiologic process resulting in the expression of diet induced thermogenesis in a normally lean phenotype of rat, the LA/Ntul//-cp.

Lean Canvas and the Business Model Canvas Model in Startup Piecework

A newcomer in the social media industry, Piecework is dedicated to assisting small businesses in growing their operations in the age of globalization. Having the appropriate business model for a simple explanation of a company's business idea is essential when creating a startup. The Business Model Canvas was a business model that startups utilized frequently at first, but following the creation of the Lean Canvas, they were interested in using it for company development. The purpose of the comparison between the Business Model Canvas and Lean Canvas in the Piecework startup is to enhance the startup development strategy, gain a stronger market position, and gain a deeper understanding of the business framework based on both business models. To validate the concerns encountered, ten informants were interviewed using a qualitative methodology. Lean Canvas is thought to be more suitable for usage by Piecework because using Lean Canvas, Piecework may build services that are more suitable for customers, according to the comparison of the Business Model Canvas and Lean Canvas business models.

A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting

Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level prediction is a critical aspect of water resource management and requires accurate and efficient modelling techniques. This study reviews the most commonly used conventional numerical, machine learning, and deep learning models for predicting GWL. Significant advancements have been made in terms of prediction efficiency over the last two decades. However, while researchers have primarily focused on predicting monthly, weekly, daily, and hourly GWL, water managers and strategists require multi-year GWL simulations to take effective steps towards ensuring the sustainable supply of groundwater. In this paper, we consider a collection of state-of-the-art theories to develop and design a novel methodology and improve modelling efficiency in this field of evaluation. We examined 109 research articles published from 2008 to 2022 that investigated different modelling techniques. Finally, we concluded that machine learning and deep learning approaches are efficient for modelling GWL. Moreover, we provide possible future research directions and recommendations to enhance the accuracy of GWL prediction models and improve relevant understanding.

Deep learning and metaheuristics application in internet of things: A literature review

Nowadays, every kind of devices with different sizes and shapes, from lamp to kitchen appliances and industrial machines, are connected and shares information digitally in large scale. Despite this tendency to use Internet in such gadgets, vast amounts of data are generated creating new challenges for researchers to analyze and control them. On the other side, Deep Learning (DL) is an appropriate tool for dealing with Internet of Things (IoT) needs, such as analyzing data, making predictions, classifying data. Acquiring the most accurate neural network inside a sensible run-time is a challenge. However, metaheuristics are the key to the success of the application of DL on IoT big data due to non-deterministic polynomial time (NP hard) problems in these areas. Many papers were published about metaheuristic in optimizing deep leaning models, but the literature lacks a study that precisely investigate the relationship between IoT, deep learning and metaheuristic. In this paper, a review of the metaheuristic's usages in the realm of IoT are presented.

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards.

Deep Learning models have presented promising results when applied to Agriculture 4.0. Among other applications, these models can be used in disease detection and fruit counting. Deep Learning models usually have many layers in the architecture and millions of parameters. This aspect hinders the use of Deep Learning on mobile devices as they require a large amount of processing power for inference. In addition, the lack of high-quality Internet connectivity in the field impedes the usage of cloud computing, pushing the processing towards edge devices. This work describes the proposal of an edge AI application to detect and map diseases in citrus orchards. The proposed system has low computational demand, enabling the use of low-footprint models for both detection and classification tasks. We initially compared AI algorithms to detect fruits on trees. Specifically, we analyzed and compared YOLO and Faster R-CNN. Then, we studied lean AI models to perform the classification task. In this context, we tested and compared the performance of MobileNetV2, EfficientNetV2-B0, and NASNet-Mobile. In the detection task, YOLO and Faster R-CNN had similar AI performance metrics, but YOLO was significantly faster. In the image classification task, MobileNetMobileV2 and EfficientNetV2-B0 obtained an accuracy of 100%, while NASNet-Mobile had a 98% performance. As for the timing performance, MobileNetV2 and EfficientNetV2-B0 were the best candidates, while NASNet-Mobile was significantly worse. Furthermore, MobileNetV2 had a 10% better performance than EfficientNetV2-B0. Finally, we provide a method to evaluate the results from these algorithms towards describing the disease spread using statistical parametric models and a genetic algorithm to perform the parameters' regression. With these results, we validated the proposed pipeline, enabling the usage of adequate AI models to develop a mobile edge AI solution.

A hybrid deep leaning model for prediction and parametric sensitivity analysis of noise annoyance.

Noise annoyance is recognized as an expression of physiological and psychological strain in acoustical environment. The studies on prediction of noise annoyance and parametric sensitivity analysis of factors affecting it have been rarely reported in India. A hybrid ConvLSTM technique was developed in the study to predict traffic-induced noise annoyance in 484 people based on ambient noise levels, as well as survey information. Ambient noise levels were obtained at different locations of Dhanbad city using sound level meter at varying intervals, viz. 09AM-12PM, 03PM-06PM, and 08PM-11PM. The proposed method was compared with some well-known neural network techniques such as K-nearest neighbors (KNN), artificial neural network (ANN), recurrent neural network (RNN), and long-short-term memory (LSTM). The experimental results indicate that the proposed method outperforms other techniques and can be a reliable approach for prediction of noise annoyance with an accuracy of 93.8%. It can be concluded from noise maps that the noise levels in all locations of the Dhanbad city were higher than 70 dB(A) and noise sensitivity is the most important input variable of traffic-induced noise annoyance, followed by honking noise, education, exposure hours, LAeq, sleeping disorder, and chronic disease. The study shall facilitate in developing a decision support tool for prediction of noise annoyance and promoting implementation of suitable public policy in urban cities.

A grounded theory of selecting lean and agile practices for software development

AbstractThe practices of lean and agile methods are important to manage and guide projects development in different situations. However, selecting the appropriate practices of lean and agile methods is a daunting task, owing to the availability of hundreds of practices. This wide variety of available practices bewilders potential users, in terms of the best practices that may yield satisfactory solution when applied to the project development. Lean and agile practices selection model is necessary to guide both academic and industrial experts to choose the appropriate practices for a specific project development. Thus, this study aimed to develop lean and agile practices selection (LAPS) model that presents a clear picture of how developers can use LAPS model to select and adopt different practices from various lean and agile methods. Qualitative research method (e.g., grounded theory) was used to interview 18 software developers across 13 countries. The validated qualitative analysis results showed that the proposed LAPS model consists of two main components, which are (1) lean and agile practices that include 41 best practices related to lean and agile methods and (2) factors that affect the process of lean and agile practices selection including five main factors and 17 subfactors associated with the five main factors. The two case studies conducted assured the validity and applicability of the lean and agile practices selection model.

Hepatocyte-derived DPP4 regulates portal GLP-1 bioactivity, glucose production, and its absence alters liver disease progression.

Elevated circulating dipeptidyl-peptidase 4 is a biomarker for liver disease, but its involvement in gluconeogenesis and in metabolic-associated fatty liver disease (MAFLD) progression remains unclear. Here we identified that DPP4 in hepatocytes but not Tie2+ endothelial cells regulates the local bioactivity of incretin hormones and gluconeogenesis. However, the complete absence of DPP4 (Dpp4-/-) in aged mice with metabolic syndrome accelerates liver fibrosis without altering dyslipidemia and steatosis. Analysis of transcripts from the livers of whole body Dpp4-/- displayed enrichment for inflammasome, p53, and senescence programs compared to littermate controls. High-fat high-cholesterol (HFHC)-feeding decreased Dpp4 expression in F4/80+ cells, with only minor changes in immune signaling. Moreover, in a lean mouse model of severe non-alcoholic fatty liver disease (NAFLD), phosphatidylethanolamine N-methyltransferase (Pemt -/-) mice fed with HFHC diet, we observed a 4-fold increase in circulating DPP4, disassociating its release from obesity. Lastly, we evaluated DPP4 levels in patients with hepatitis C infection with dysglycemia (HOMA-IR > 2) who underwent direct antiviral treatment (with or without ribavirin). DPP4 protein levels decreased with viral clearance, and DPP4 activity levels were reduced at longer-term follow-up in ribavirin-treated patients, although metabolic factors did not improve. These data suggest elevations in DPP4 during HCV infection are not primarily regulated by metabolic disturbances.

Modeling 4Ps of Toyota way principles in MSMEs supply Chain-an empirical approach

Micro, Small & Medium Enterprises (MSMEs) have witnessed many remarkable contributions to the country's economy in the recent years. But, till now it suffers from profit, quality, efficiency and others issues. The previous contributions by researchers have enlightened on the MSMEs in the context of applying lean principles. This paper aim to guide MSMEs in embarking on transformation exercise by means of applying principles and tools of lean to its supply chain. And, also to establish the relationship between application and understanding of relevance of lean principle model for MSMEs. It is observed that to be successful in the market, competition is not between one company to another company but, between one supply chains to the other supply chain. Company’s competitive strategy is best supported by balance between the supply chain responsiveness and efficiency. So, the scale of this paper is not just restricted to MSMEs but to its entire supply chain. In this epidemic situation, companies are facing a high risk of surviving in the market. The interlinked nature of the supply chain makes them vulnerable to wide range of risk, as the buying patterns of the consumers across the supply chain is changing rapidly due to unpredictable situation. So, to sustain in the market, competition is to see how efficiently the entire process is set by implementing lean principles. The exact need and area of implementation of 4Ps of Toyota way principles are done to entire supply chain of MSMEs Finally; gap that exists in applying and relevance of 14 Toyota way principles for the MSMEs in taking care and how, lean principles to become more effective during pandemic situation is reflected. A structured questionnaire was prepared on 4Ps of Toyota way, with its predefined attributes and data was collected from MSMEs through survey forms. The quantitative data collected was outlined to rank the 14 principles applied in the MSMEs supply chain, and also the extent of perceiving their relevance. The results reflected that all the attributes of each 4Ps model were appreciated by the respondents for their relevance, but still many of them lack in application.

VALUE STREAM MAP OPTIMIZATION MODEL IN THE FIELD OF EDUCATIONAL SERVICES

The relevance of the implementation the Lean Production quality management model in higher education institutions (HEIs) in Ukraine has been proven to optimize the stream of HEIs business processes in order to improve the quality of functioning of HEI as an economic activity subject and increase the competitiveness of educational services, scientific and educational products. The modern approaches of scientists to the Lean Higher Education and the construction of a process model of activity and the identification of key business processes of HEIs are analyzed. The purpose of the article is to develop the optimization model for the stream of business processes based on Lean technologies and to apply the model to visualize and optimize the key process of business process at the Kharkiv National University of Radio Electronics (NURE). A DFD model of the optimization process of the Value Stream Map (VMS) - a quality management tool created within the framework of the Lean methodology for process stream analysis - was developed. The model optimization criteria are proposed. During the development of the map optimization model, the peculiarities of the result of the HEI activity, which is a combination of educational service, educational and scientific product, were taken into account. The practical significance of the article lies in the study of the HEI key process "Development of educational and methodological support of the educational process". Decomposition of the main process was carried out. The second and third levels processes for the value stream mapping was distinguished. The current state VSM of the process "Development of educational and methodological support of the educational process" was developed, the indicators of the effectiveness of the value stream at the department of information and measurement technologies of the NURE were calculated, the "bottlenecks" of the stream were analyzed. Measures to optimize "bottlenecks" based on the criterion of minimizing time are proposed. Prospects for further research in the chosen direction are determined as the implementation of Lean technologies in the activities of HEIs are the development and optimization of VSM for all key processes of HEIs.

Study on The Cost Attribution Method of Power Grid Equipment Maintenance under Multi-dimensional Lean Management Model

The current conventional cost manage- ment model of power grid operation has the problem of single cost apportionment standard, which leads to the poor attribution accuracy. In this regard, the cost attribution method of grid equipment maintenance cost under multi-dimensional lean management mode is proposed. The whole life cycle cost of grid equipment operation is analyzed, and the total cost of grid equipment asset operation and maintenance is calculated, and finally, the operation cost method is used to construct the cost attribution model of maintenance cost. The analysis of the experimental results shows that when the proposed method is used to attribute the maintenance cost cost, the attribution error value of the method is low and has a more desirable cost attribution effect.

ФОРМУВАННЯ ЛОГІСТИЧНОГО МЕНЕДЖМЕНТУ НА ПРИНЦИПАХ LEAN-ЛОГІСТИКИ

The article is devoted to the study of the issue of formation of effective logistics management on the principles of lean logistics. The article analyses the scientific and methodological basis for implementing the principles of logistics management by Ukrainian enterprises. The study defines the essence of the definition of "Lean logistics". It has been established that the principles of Lean logistics are based on cost control, waste reduction, speed increase and maximum use of customer values, which allows maximising the benefits of logistics activities. It is specified that Lean logistics is a sign of the maturity of management of a logistics enterprise and a new management model created to adapt to market development in an economically unstable situation. It is emphasized that adherence to the principles of Lean logistics will allow the enterprise to increase the level of competitiveness on the market. The companies that are currently actively implementing Lean logistics technologies in their operations have been identified. On the basis of theoretical material and research of practices of applying Lean-logistics in the process of entrepreneurial activity, the main goals, principles and key principles of implementation of Lean-logistics are identified. The tools and methods for implementing the Lean logistics technology are defined. The main stages of Lean logistics implementation are highlighted. The main technological aspects of lean logistics implementation are identified. An analysis of the implementation of the lean logistics concept by Ukrainian enterprises has made it possible to explore the key conditions for development and obstacles to the introduction of Lean innovations. It is noted that the main driver of the implementation of Lean logistics technology is personnel. It is stated that in order to implement Lean logistics, the most important stage is planning, since it significantly determines whether an efficient logistics process will be formed and whether the implementation of this innovation will bring additional benefits for the enterprise. In conclusion, it is noted that the implementation of the Lean logistics model is a requirement of the time and the key to increasing the development and competitiveness of an enterprise.