Purpose - This paper aims to investigate the definition of Lean Production and the methods and goals associated with the concept as well as how it differs from other popular management concepts. Methodology/Approach - The paper is based on a review of the contemporary literature on Lean Production, both journal articles and books. Findings - It is shown in the paper that there is no consensus on a definition of Lean Production between the examined authors. The authors also seem to have different opinions on which characteristics that should be associated with the concept. Overall it can be concluded that Lean Production is not clearly defined in the reviewed literature. This divergence can cause some confusion on a theoretical level, but is probably more problematic on a practical level when organizations aim to implement the concept. This paper argues that it is important for an organization to acknowledge the different variations, and to raise the awareness of the input in the implementation process. It is further argued that the organization should not accept any random variant of Lean, but make active choices and adapt the concept to suit the organization-s needs. Through this process of adaptation, the organization will be able to increase the odds of performing a predictable and successful implementation. Originality/Value - This paper provides a critical perspective on the discourse surrounding Lean Production, and gives an input to the discussion of the implementation of management models. Keywords - Lean Production, Definition, Construct Validity, Total Quality Management Paper type - Conceptual paper

https://www.ep.liu.se/ecp/033/025/ecp0803325.pdf

Defining Lean Production : Some conceptual and practical issues

This study aims to analyze lean manufacturing effect on financial, non-financial and sustainability performances.Causal maps are used for defining the inter-relation and intra-relation of lean factors.Bayesian Belief Networks are used for linking the lean factors.Effects of various combinations of lean factors on business performance are analyzed.Developed scenarios can be a lead for defining the business strategies based on lean thinking in any industry. The challenge of agility for adopting new business norms creates the need for measuring performance under changing conditions. This study aims to demonstrate the financial and non-financial consequences of implementing different combinations of lean techniques on the business performance. Bayesian Belief Network is used in studying the effects of factors under changing conditions. There are seven lean factors and four achievements studied to analyze the impact on three performance indicators. Bayesian Belief Network is constructed on the lean aspects that stimuli flexibility, reliability, quality and time of operations, which will have positive impacts on the financial, non-financial and sustainability performances of suppliers. A case study is carried out for suppliers in the automotive industry and scenarios with different combinations of lean factors are studied. This study gives a new vision in applying Bayesian network for business performance measures considering both the tangible and intangible results under changing business conditions.

Assessment of lean manufacturing effect on business performance using Bayesian Belief Networks

Comprehensive review of load forecasting with emphasis on intelligent computing approaches

Due to the participation of large-scale electric vehicles (EVs) in Vehicle-to-Grid (V2G) services, V2G dispatch centers need to predict the charging and discharging (C&D) loads of electric vehicles in a short time period. This study proposes a novel machine learning based approach for EV load forecasting in power supply systems facing high resource uncertainty. This method takes advantage of both Gradient Boosting Decision Tree (GBDT) algorithm and Time Convolutional Network (TCN) model. This study considers the service decisions of EV users and uses the GBDT algorithm to classify the EV discharge load dataset, with 92% accuracy. Also, the TCN model is used to capture local temporal features and predict the future C&D loads. In comparison with other baseline models, e.g. CNN-BILSTM, LSTM, PSO-BP, the stability of the TCN model is superior in real data charging load forecasting, and it is shown that the TCN model has the smallest error. The feasibility of the proposed GBDT-TCN hybrid model is verified by numerical cases,and achieves the RMSE of discharging forecasting less than 6.23%. 

A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network

Reliability estimation for two-parameter Weibull distribution under block censoring

A transfer learning method for electric vehicles charging strategy based on deep reinforcement learning

The information obtained at high stress levels in accelerated life tests is generally extrapolated with an appropriate statistical model to estimate parameters of interest at normal stress levels. However, these statistical analyses do not take batch di..

Effect of Not Having Homogeneous Test Units in Accelerated Life Tests

In order to retain competitive advantages, many manufacturing organizations have applied Lean Six Sigma techniques to improve production processes. The general approach for implementing Lean Six Sigma is to perform various projects to tackle specific problems or areas. However, with the manufacturing system and its external environment becoming more and more complex, it is simply not possible to solve all the problems given the limited resources. The purpose of this study is to develop a model that provides a systematic evaluation for potential opportunities to enhance the effectiveness of Lean Six Sigma. Deriving from the Bayesian Network methodology, the proposed model combines a graphical approach to represent cause-and-effect relationships between events of interests and probabilistic inference to estimate their likelihoods in the area of process improvement. The developed model can be used for assessing the problems associated with Lean Six Sigma initiatives and prioritizing efforts to solve these problems.

Applying Bayesian Network Techniques to Prioritize Lean Six Sigma Efforts

A data-driven load forecasting method for incentive demand response

Nonintrusive load monitoring (NILM) methods considering multiple representative features can improve load identification. However, these existing methods assemble all features into one classifier, which easily causes overfitting and increases the computational complexity during the training process. To tackle these problems, this article proposes a novel stacking ensemble learning (SEL)-based load identification framework considering physical and cyber feature descriptors (CFDs), which can nicely fuse features by integrating different types of features into corresponding classifiers of the SEL model. The proposed framework includes two main parts: 1) comprehensive features are constructed based on a physical feature descriptor (PFD) and a CFD to fully explore the representative feature space and 2) the SEL model is developed to enhance the mutual complementary ability of different features and increase the variability of base classifiers. To evaluate the effectiveness of the proposed method, experiments are conducted on a public dataset. Numerical evaluations for residential building loads show that the proposed method significantly improves the identification performance and outperforms prior methods.

Stacking Ensemble Learning-Based Load Identification Considering Feature Fusion by Cyber-Physical Approach

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