Systematic evaluation for bottleneck detection methods

This paper studies manufacturing processes in a wooden furniture manufacturing company. The company suffers from long manufacturing lead times and an unbalanced production line. To identify sources of waste and delay value stream mapping (VSM) and a discrete event simulation model is implemented. VSM is used to visualize and analyze the major processes of the company and provide quantifiable KPIs; the manufacturing lead-time and then Overall Equipment Effectiveness (OEE) settings. A discrete event simulation model is then built to analyze the company on a wider scale and provide the data required to identify bottlenecks. Building on the data gathered from the production lines and the simulation model, two-bottleneck detection methods are used, the utilization method, and the waiting time method. Then based on the comparison of the two methods a third bottleneck detection is utilized; the scenario-based method, to identify the primary and secondary bottlenecks. After the bottlenecks are identified, changes are then evaluated using the simulation model and radar charts were built based on the improved simulation model, which evaluates the effect of changes in the utilization and OEE results. This work managed to neutralize the effect of one of the main bottlenecks and minimize the effect of the other. The manufacturing utilization was increased by 15.8% for the main bottleneck resources followed by 2.4% for the second one. However, it is hard to convince the traditional administration of this small size manufacturing plant to adopt a completely revolutionizing, costly, and risky (at such level) lean manufacturing approach. This paper studies and provides a much lower in cost and verified scheme of enhancement.

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Development of bottleneck detection methods allowing for an effective fault repair prioritization in machining lines of the automobile industry

Production Engineering ◽

10.1007/s11740-016-0672-9 ◽

2016 ◽

Vol 10 (3) ◽

pp. 329-336 ◽

Cited By ~ 10

Author(s):

Michael Wedel ◽

Philipp Noessler ◽

Joachim Metternich

Keyword(s):

Automobile Industry ◽

Detection Methods ◽

Bottleneck Detection ◽

Machining Lines

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Application of a Causal Discovery Algorithm to the Analysis of Arthroplasty Registry Data

Biomedical Engineering and Computational Biology ◽

10.1177/1179597218756896 ◽

2018 ◽

Vol 9 ◽

pp. 117959721875689

Author(s):

Camden Cheek ◽

Huiyong Zheng ◽

Brian R Hallstrom ◽

Richard E Hughes

Keyword(s):

Signal Detection ◽

Hip Arthroplasty ◽

Clinical Performance ◽

National Registry ◽

Detection Methods ◽

Causal Discovery ◽

Systematic Evaluation ◽

Causal Relationships ◽

Patient Registries ◽

Large Patient

Improving the quality of care for hip arthroplasty (replacement) patients requires the systematic evaluation of clinical performance of implants and the identification of “outlier” devices that have an especially high risk of reoperation (“revision”). Postmarket surveillance of arthroplasty implants, which rests on the analysis of large patient registries, has been effective in identifying outlier implants such as the ASR metal-on-metal hip resurfacing device that was recalled. Although identifying an implant as an outlier implies a causal relationship between the implant and revision risk, traditional signal detection methods use classical biostatistical methods. The field of probabilistic graphical modeling of causal relationships has developed tools for rigorous analysis of causal relationships in observational data. The purpose of this study was to evaluate one causal discovery algorithm (PC) to determine its suitability for hip arthroplasty implant signal detection. Simulated data were generated using distributions of patient and implant characteristics, and causal discovery was performed using the TETRAD software package. Two sizes of registries were simulated: (1) a statewide registry in Michigan and (2) a nationwide registry in the United Kingdom. The results showed that the algorithm performed better for the simulation of a large national registry. The conclusion is that the causal discovery algorithm used in this study may be a useful tool for implant signal detection for large arthroplasty registries; regional registries may only be able to only detect implants that perform especially poorly.

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Image Analysis of Intramembrane Particles in Freeze-Fractured Biomembranes Using Computer Simulation Techniques

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100114694 ◽

1975 ◽

Vol 33 ◽

pp. 214-215

Author(s):

D.J. Benefiel ◽

R.S. Weinstein

Keyword(s):

Membrane Proteins ◽

Freeze Fracture ◽

Integral Membrane Proteins ◽

Systematic Evaluation ◽

Intramembrane Particles ◽

Modeling Methods ◽

Simulation Techniques ◽

Freeze Fracture Electron Microscopy ◽

Fracture Electron ◽

Computer Simulation Techniques

Intramembrane particles (IMP or MAP) are components of most biomembranes. They are visualized by freeze-fracture electron microscopy, and they probably represent replicas of integral membrane proteins. The presence of MAP in biomembranes has been extensively investigated but their detailed ultrastructure has been largely ignored. In this study, we have attempted to lay groundwork for a systematic evaluation of MAP ultrastructure. Using mathematical modeling methods, we have simulated the electron optical appearances of idealized globular proteins as they might be expected to appear in replicas under defined conditions. By comparing these images with the apearances of MAPs in replicas, we have attempted to evaluate dimensional and shape distortions that may be introduced by the freeze-fracture technique and further to deduce the actual shapes of integral membrane proteins from their freezefracture images.

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DNA nick end labeling: a reliable marker for apoptosis?

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100141184 ◽

1995 ◽

Vol 53 ◽

pp. 962-963

Author(s):

Anne F. Bushnell ◽

Sarah Webster ◽

Lynn S. Perlmutter

Keyword(s):

Cell Death ◽

Cultured Cells ◽

Apoptotic Cell ◽

Morphological Characteristics ◽

Detection Methods ◽

Tissue Preparation ◽

Preparation Methods ◽

Dna Laddering ◽

Disease States ◽

Reliable Marker

Apoptosis, or programmed cell death, is an important mechanism in development and in diverse disease states. The morphological characteristics of apoptosis were first identified using the electron microscope. Since then, DNA laddering on agarose gels was found to correlate well with apoptotic cell death in cultured cells of dissimilar origins. Recently numerous DNA nick end labeling methods have been developed in an attempt to visualize, at the light microscopic level, the apoptotic cells responsible for DNA laddering.The present studies were designed to compare various tissue processing techniques and staining methods to assess the occurrence of apoptosis in post mortem tissue from Alzheimer's diseased (AD) and control human brains by DNA nick end labeling methods. Three tissue preparation methods and two commercial DNA nick end labeling kits were evaluated: the Apoptag kit from Oncor and the Biotin-21 dUTP 3' end labeling kit from Clontech. The detection methods of the two kits differed in that the Oncor kit used digoxigenin dUTP and anti-digoxigenin-peroxidase and the Clontech used biotinylated dUTP and avidinperoxidase. Both used 3-3' diaminobenzidine (DAB) for final color development.

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