A new model to monitor very small effects of a polynomial profile

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Karim Atashgar ◽  
Leila Abbassi
Keyword(s):  
Phase Ii ◽  
Average Run Length ◽  
New Method ◽  
Profile Monitoring ◽  
Statistical Process ◽  
Small Shift ◽  
Content Type ◽  
Proposed Model ◽  
Product Profile ◽  
Small Change

PurposeDifferent real cases indicate that the quality of a process is better monitored by a functional relationship rather than the traditional statistical process control (SPC) methods. This approach is referred to as profile monitoring. A serious objective in profile monitoring is the sensitivity of a model to very small changes of the process. The rapid progress of the precision manufacturing also indicates the importance of identifying very small shift types of a process/product profile curve. This sensitivity allows one to identify the fault of a process sooner compared to the case of lack of the capability.Design/methodology/approachThis paper proposed a new method to monitor very small shift types of a polynomial profile for phase II of the SPC. The proposed method was named as MGWMA-PF. The performance capability of the proposed approach was evaluated through several numerical examples. A real case study was also used to investigate the capability of the proposed model.FindingsThe results addressed that the proposed method was capable of detecting very small shift types effectively. The numerical report based on the average run length (ARL) term revealed the more sensitivity of the proposed model compared to other existing methods of the literature.Originality/valueThis paper proposes a new method to monitor very small shift types of a polynomial profile for phase II of the SPC. The proposed method provides detecting a very small change manifested itself to the process.

A novel fractional discrete nonlinear grey Bernoulli model for forecasting the wind turbine capacity of China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wen-ze Wu ◽  
Wanli Xie ◽  
Chong Liu ◽  
Tao Zhang
Keyword(s):  
Wind Turbine ◽  
Nonlinear Coefficient ◽  
Predictive Performance ◽  
Prediction Performance ◽  
New Method ◽  
Grey Model ◽  
Content Type ◽  
Proposed Model ◽  
Whale Optimization ◽  
Turbine Capacity

PurposeA new method for forecasting wind turbine capacity of China is proposed through grey modelling technique.Design/methodology/approachFirst of all, the concepts of discrete grey model are introduced into the NGBM(1,1) model to reduce the discretization error from the differential equation to its discrete forms. Then incorporating the conformable fractional accumulation into the discrete NGBM(1,1) model is carried out to further improve the predictive performance. Finally, in order to effectively seek the emerging coefficients, namely, fractional order and nonlinear coefficient, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients.FindingsThe empirical results show that the newly proposed model has a better prediction performance compared to benchmark models; the wind turbine capacity from 2019 to 2021 is expected to reach 275954.42 Megawatts in 2021. According to the forecasts, policy suggestions are provided for policy-makers.Originality/valueBy combing the fractional accumulation and the concepts of discrete grey model, a new method to improve the prediction performance of the NGBM(1,1) model is proposed. The newly proposed model is firstly applied to predict wind turbine capacity of China.

Bayesian networks for statistical process control with attribute data

2019 ◽  
Vol 36 (2) ◽  
pp. 232-256 ◽  
Author(s):  
Barry Cobb ◽  
Linda Li
Keyword(s):  
Process Control ◽  
Bayesian Networks ◽  
Statistical Process Control ◽  
Average Run Length ◽  
Large Change ◽  
Small Sample ◽  
Statistical Process ◽  
Content Type ◽  
Average Percentage ◽  
Attribute Data

PurposeBayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The paper aims to discuss this issue.Design/methodology/approachThe BN provides a graphical and numerical tool to help a manager understand the effect of sample observations on the probability that the process is out-of-control and requires investigation. The parameters for the BN SPC model are statistically designed to minimize the out-of-control average run length (ARL) of the process at a specified in-control ARL and sample size.FindingsThe BN model outperforms adaptivenpcontrol charts in all experiments, except for some cases where only a large change in the proportion of sample defects is relevant. The BN is particularly useful when small sample sizes are available and when managers need to detect small changes in the proportion of defects produced by the process.Research limitations/implicationsThe BN model is statistically designed and parameters are chosen to minimize out-of-control ARL. Future advancements will address the economic design of BNs for SPC with attribute data.Originality/valueThe BNs allow qualitative knowledge to be combined with sample data, and the average percentage of defects can be modeled as a continuous random variable. The framework of the BN easily permits classification of the system operation into two or more states, so diagnostic analysis can be performed simultaneously with statistical inference.

A Framework for Multivariate Process Monitoring and Diagnosis

2013 ◽  
Vol 315 ◽  
pp. 374-379
Author(s):  
Ibrahim Masood ◽  
Adnan Hassan
Keyword(s):  
Performance Monitoring ◽  
Average Run Length ◽  
Mean Shift ◽  
Control Process ◽  
Accurate Diagnosis ◽  
Superior Performance ◽  
Process Mean ◽  
Statistical Process ◽  
Small Shift ◽  
Monitoring And Diagnosis

Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing statistical process control frameworks are only effective in shift detection but suffers high false alarm, that is, imbalanced performance monitoring. The problem becomes more complicated when dealing with small shift particularly in identifying the causable variables. In this research, a framework to address balanced monitoring and accurate diagnosis was investigated. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on synergistic model, and monitoring-diagnosis approach based on two stages technique. The study focuses on correlated process mean shifts for cross correlation function, ρ = 0.1 ~ 0.9 and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. The proposed design, that is, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network gave superior performance, namely, average run length, ARL1 = 3.18 ~ 16.75 (for out-of-control process), ARL0 = 452.13 (for in-control process) and recognition accuracy, RA = 89.5 ~ 98.5%. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of multivariate correlated process mean shifts.

Business management in the football industry from a supply chain management perspective

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhaleh Memari ◽  
Abbas Rezaei Pandari ◽  
Mohammad Ehsani ◽  
Shokufeh Mahmudi
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Business Management ◽  
Relationship Management ◽  
Flow Management ◽  
Manufacturing Firm ◽  
Chain Management ◽  
Content Type ◽  
Proposed Model ◽  
Football Industry

PurposeTo understand the football industry in its entirety, a supply chain management (SCM) approach is necessary. This includes the study of suppliers, consumers and their collaborations. The purpose of this study was to present a business management model based on supply chain management.Design/methodology/approachData were collected through in-depth interviews with 12 academic and executive football experts. After three steps of open, axial and selective coding based on grounded theory with a paradigmatic approach, the data were analysed, and a football supply chain management (FSCM) was developed. The proposed model includes three managerial components: upstream suppliers, the manufacturing firm, and downstream customers.FindingsThe football industry sector has three parts: upstream suppliers, manufacturing firm/football clubs and downstream customers. We proposed seven parts for the managerial processes of football supply chain management: event/match management, club management, resource and infrastructure management, customer relationship management, supplier relationship management, cash flow management and knowledge and information flow management. This model can be used for configuration, coordination and redesign of business operations as well as the development of models for evaluation of the football supply chain's performance.Originality/valueThe proposed model of a football supply chain management, with the existing literature and theoretical review, created a synergistic outcome. This synergy is presented in the linkage of the players in this chain and interactions between them. This view can improve the management of industry productivity and improve the products quality.

scholarly journals Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 154
Author(s):  
Anderson Fonseca ◽  
Paulo Henrique Ferreira ◽  
Diego Carvalho do Nascimento ◽  
Rosemeire Fiaccone ◽  
Christopher Ulloa-Correa ◽  
...  
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Atacama Desert ◽  
Real Data ◽  
Interval Data ◽  
Total Quality ◽  
Statistical Process ◽  
Monte Carlo Simulation Study ◽  
Run Length ◽  
Study Results

Statistical monitoring tools are well established in the literature, creating organizational cultures such as Six Sigma or Total Quality Management. Nevertheless, most of this literature is based on the normality assumption, e.g., based on the law of large numbers, and brings limitations towards truncated processes as open questions in this field. This work was motivated by the register of elements related to the water particles monitoring (relative humidity), an important source of moisture for the Copiapó watershed, and the Atacama region of Chile (the Atacama Desert), and presenting high asymmetry for rates and proportions data. This paper proposes a new control chart for interval data about rates and proportions (symbolic interval data) when they are not results of a Bernoulli process. The unit-Lindley distribution has many interesting properties, such as having only one parameter, from which we develop the unit-Lindley chart for both classical and symbolic data. The performance of the proposed control chart is analyzed using the average run length (ARL), median run length (MRL), and standard deviation of the run length (SDRL) metrics calculated through an extensive Monte Carlo simulation study. Results from the real data applications reveal the tool’s potential to be adopted to estimate the control limits in a Statistical Process Control (SPC) framework.

Leader–follower narcissism and subgroup formation in teams: a conceptual model

2019 ◽  
Vol 34 (6) ◽  
pp. 429-442 ◽  
Author(s):  
Manuel London
Keyword(s):  
Team Performance ◽  
Team Identification ◽  
Goal Congruence ◽  
Content Type ◽  
Team Outcomes ◽  
Performance Pressure ◽  
Proposed Model ◽  
Leaders And Followers ◽  
Trait Activation ◽  
And Performance

Purpose Drawing on existing theory, a model is developed to illustrate how the interaction between leaders and followers similarity in narcissism and goal congruence may influence subgroup formation in teams, and how this interaction influences team identification and team performance. Design/methodology/approach The proposed model draws on dominance complementary, similarity attraction, faultline formation and trait activation theories. Findings Leader–follower similarity in narcissism and goal congruence may stimulate subgroup formation, possibly resulting in conformers, conspirators, outsiders and victims, especially when performance pressure on a team is high. Followers who are low in narcissism and share goals with a leader who is narcissistic are likely to become conformers. Followers who are high in narcissism and share goals with a narcissistic leader are likely to become confederates. Followers who do not share goals with a narcissistic leader will be treated by the leader and other members as outsiders if they are high in narcissism, and victimized if they are low in narcissism. In addition, the emergence of these subgroups leads to reduced team identification and lower team performance. Practical implications Higher level managers, coaches and human resource professions can assess and, if necessary, counteract low team identification and performance resulting from the narcissistic personality characteristics of leaders and followers. Originality/value The model addresses how and under what conditions narcissistic leaders and followers may influence subgroup formation and team outcomes.

Lubricant compatibility of FKM seals in synthetic oils

2019 ◽  
Vol 72 (5) ◽  
pp. 557-565
Author(s):  
Dilek Bulut ◽  
Tatjana Krups ◽  
Gerhard Poll ◽  
Ulrich Giese
Keyword(s):  
Polyethylene Glycol ◽  
Design Methodology ◽  
Elevated Temperatures ◽  
Failure Mechanisms ◽  
Standard Test ◽  
New Method ◽  
Static Tests ◽  
Content Type ◽  
Lip Seals ◽  
First Results

Purpose Elastomer seals are used in many applications. They are exposed to lubricants and additives at elevated temperatures, as well as mechanical stresses. They can only provide good sealing function when they have resistance to those factors. There are many elastomer-lubricant compatibility tests based on DIN ISO 1817 in industry. However, they are insufficient and costly. Correlations between the tests and the applications are inadequate. The purpose of this study is investigating lubricant compatibility of fluoroelastomers (FKM) seals in polyethylene-glycol (PG)- and polyalphaolefin (PAO)- based synthetic oils and developing a methodology to predict seal service life. Design/methodology/approach A new compatibility test which is more sufficient in terms of time and cost was developed and compared with a standard test, currently used in industry. Compatibility of FKM radial lip seals with PG- and PAO-based synthetic oils with different additives was investigated chemically and dynamically. Failure mechanisms were examined. Findings The new method and the Freudenberg Flender Test FB 73 11 008 showed similar results concerning damages and similar tendencies regarding wear. The additive imidazole derivative was the most critical. Static tests give indications of possible chemically active additives, but alone they are insufficient to simulate the dynamic applications. Originality/value The paper describes a new method to investigate elastomer-lubricant compatibility and gives first results with a variety of lubricants.

Application of lean practices in small and medium-sized food enterprises

2013 ◽  
Vol 116 (1) ◽  
pp. 125-141 ◽  
Author(s):  
Manoj Dora ◽  
Dirk Van Goubergen ◽  
Maneesh Kumar ◽  
Adrienn Molnar ◽  
Xavier Gellynck
Keyword(s):  
Food Processing ◽  
Lean Manufacturing ◽  
Raw Materials ◽  
Current Status ◽  
Food Sector ◽  
Statistical Process ◽  
Content Type ◽  
Lean Practices ◽  
The Status ◽  
Manufacturing Techniques

Purpose – Recent literature emphasizes the application of lean manufacturing practices to food processing industries in order to improve operational efficiency and productivity. Only a very limited number of studies have focused on the implementation of lean manufacturing practices within small and medium-sized enterprises (SMEs) operating in the food sector. The majority of these studies used the case study method and concentrated on individual lean manufacturing techniques geared towards resolving efficiency issues. This paper aims to analyze the status of the lean manufacturing practices and their benefits and barriers among European food processing SMEs. Design/methodology/approach – A structured questionnaire was developed to collect data. A total of 35 SMEs' representatives, mostly CEOs and operations managers, participated in the survey. The study investigated the role of two control variables in lean implementation: size of the company and country of origin. Findings – The findings show that lean manufacturing practice deployment in food processing SMEs is generally low and still evolving. However, some lean manufacturing practices are more prevalent than others; e.g. flow, pull and statistical process control are not widely used by the food processing SMEs, whereas total productive maintenance, employee involvement, and customer association are more widespread. The key barriers encountered by food SMEs in the implementation of lean manufacturing practices result from the special characteristics of the food sector, such as highly perishable products, complicated processing, extremely variable raw materials, recipes and unpredictable demand. In addition, lack of knowledge and resources makes it difficult for food processing SMEs to embark on the lean journey. Originality/value – The gap in the literature regarding the application of lean manufacturing in the food sector is identified and addressed in this study. The originality of this paper lies in analyzing the current status of the use of lean manufacturing practices among food SMEs in Europe and identifying potential barriers.

Loyalty program in Africa: risk-seeking and risk-averse adventurers

2014 ◽  
Vol 69 (2) ◽  
pp. 137-157 ◽  
Author(s):  
Shogo Mlozi
Keyword(s):  
Consumer Behavior ◽  
Consumer Products ◽  
Loyalty Programs ◽  
Risk Averse ◽  
Loyalty Program ◽  
Content Type ◽  
Risk Seeking ◽  
Proposed Model ◽  
First Time ◽  
The Relationship

Purpose – This article aims to test the relationship between expected attractiveness-satisfaction-loyalty for international adventure tourists visiting Tanzania. The proposed model is based on travel consumer behavior theoretical constructs extracted from the literature. Design/methodology/approach – This article aims to test the relationship between expected attractiveness-satisfaction-loyalty for international adventure tourists visiting Tanzania. The proposed model is based on travel consumer behavior theoretical constructs extracted from the literature. Findings – The findings for overall model differed from the moderating factors of high risk, low risk, first-time visit and repeat visit. Also, the results are interesting when satisfaction is tested as a mediator. Practical implications – Practitioners could consider the fact that repeat visits may change tourists’ perceptions toward destination and may even increase their inclination to take on risks. This may impact innovation of consumer products in tourism. Also, policy makers could benefit on how loyalty programs can be developed to increase performance. Originality/value – The study offers specific strategic recommendations toward different groups of tourists (i.e. first-time, repeat visitors, risk averse, risk seeking) and proposes logic for setting up a loyalty program as a long-term strategy for success.

scholarly journals How to evaluate sustainability of supply chains? A dynamic network DEA approach

2017 ◽  
Vol 117 (9) ◽  
pp. 1866-1889 ◽  
Author(s):  
Vahid Shokri Kahi ◽  
Saeed Yousefi ◽  
Hadi Shabanpour ◽  
Reza Farzipoor Saen
Keyword(s):  
Big Data ◽  
Supply Chains ◽  
Real World ◽  
Performance Optimization ◽  
Network Dynamic ◽  
Network Dea ◽  
Content Type ◽  
Dea Model ◽  
Proposed Model ◽  
First Time

Purpose The purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score. Design/methodology/approach A dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach. Findings This paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately. Research limitations/implications In real world, managers face with big data. Therefore, we need to develop an approach to deal with big data. Practical implications The proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc. Originality/value For the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.

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