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Machine Learning Models in Product Development and Its Statistical Evaluation
Published in P. Kaliraj, T. Devi, Artificial Intelligence Theory, Models, and Applications, 2021
Quality control involves a collection of procedures that are implemented to ensure the quality of the manufactured product or performed service adheres to a defined set of quality criteria to meet the customer’s requirements. This method is based on the statistical techniques to determine and control the quality through sampling. Random sampling, probability, and statistical inferences are used to develop the process, using this method to control the product’s quality. There are two types of quality control tools, which include process control techniques and product control techniques. Process control techniques control the product development process through every stage of production. In contrast, the product control technique involves checking their units and determining their lot if they are within their specifications towards the final product before going to market. Here both the producer and consumer can determine their quality checking.
Control Charts
Published in Lawrence S. Aft, Fundamentals of Industrial Quality Control, 2018
The control charts examined up to this point have been control charts for variables, or characteristics that could be measured. When the use of linear measurements in not practical, then attributes are often counted and appropriate charts constructed. Some of the more common attribute control charts are number of defects per sample (c charts), number of defects per unit (u charts), and percentage defective charts (p charts.) A defect can be defined as “a departure of a quality characteristic from its intended level or state that occurs with a severity sufficient to cause an associated product or service not to satisfy intended normal, or reasonably foreseeable, usage requirements” (ASQC, 1983, p.13).A defective, on the other hand, is “a unit of product or service containing at least one defect, or having several imperfections that in combination cause the unit not to satisfy intended normal, or reasonably foreseeable, usage requirements” (ASQC, 1983, p. 15).
A Lean History of Lean
Published in Terra Vanzant Stern, Lean and Agile Project Management, 2017
The term Six Sigma is a statistical measurement based on defects per million opportunities (DPMO). A defect is defined as any nonconformance of quality. At Six Sigma, only 3.4 DMPO may occur. In order to use sigma as a measurement, there must be something to count and everyone must agree on what constitutes a defect. Normal distribution models look at three sigma, which is essentially 6,210 DPMO. Some processes are acceptable at lower sigma levels, and in many cases, six sigma is considered an ideal. Sigma (σ) is a symbol from the Greek alphabet that is used in statistics when measuring variability. In the Six Sigma methodology, a company’s performance is measured by the sigma level. Sigma levels are a measurement of error rates. It costs money to fix errors, so saving this expense can be directly transferred to the bottom line.
The application of Fuzzy Delphi Method for evaluating biopsychosocial factors for prioritization of patients
Published in IISE Transactions on Healthcare Systems Engineering, 2023
Hassan Rana, Muhammad Umer, Uzma Hassan, Umer Asgher, Faheem Jamal, Afshan Naseem, Nadeem Ehsan
According to Collins Dictionary, factor is “an element or cause that effect or contributes to a result.” The relationship of cause and effect is further clarified in the definitions of cause as “a thing, event, state, or action that produces an effect” and effect as “something that is produced by a cause” (Breslin, 2011). A root cause is considered a primary concern as it initiates complete cause-and-effect cycle or reaction which eventually leads to problem(s). American Society for Quality (Andersen & Fagerhaug, 2013) defines root cause as “a factor that caused a nonconformance and should be permanently eliminated through process improvement” and root cause analysis (RCA) as “a collective term that describes a wide range of approaches, tools, and techniques used to uncover causes of problems.” There is universal RCA methodology that can handle all the situations; nevertheless, RCA must include empirical methods together with appropriately selected tool for the problem being investigated (Barsalou, 2015).
Modified quality loss for the analysis of product quality characteristics considering maintenance cost
Published in Quality Technology & Quantitative Management, 2022
Kai Jin, Xintian Liu, Miao Liu, Kangfeng Qian
In general, quality is the degree to which a product meets customer needs. The quality loss function (QLF) could express the relationship between the economic loss and the degree of deviation from the target value of quality characteristics (Taguchi, 1986). Indeed, many improved QLFs have been developed, such as the Nominal-The-Best loss function (Li et al., 2018), the cubic QLF (Li et al., 2019), the present value model of quality loss (Teran et al., 1996), and the product quality loss model based on the life distribution (Zhao & Liu, 2012). For the relationship between product quality loss and service life distribution (Liu et al., 2021), the wear regularity has been modelled as the service QLF (Liu et al., 2020). To estimate the cost of quality of the product more accurately and reduce the random error, a quadratic exponential QLF model (Mao et al., 2020; Liu et al., 2020) and a dynamic quality characteristics model was used (Qian et al., 2021;). By considering the quality loss of the work-in-process obtained from the manufacturing system, an integrated maintenance optimization method for a running process before the failure of the multi-state production system was proposed (Zhao et al., 2021).
Developing resilience in disaster relief operations management through lean transformation
Published in Production Planning & Control, 2022
Amjad Hussain, Tariq Masood, Haris Munir, Muhammad Salman Habib, Muhammad Umar Farooq
Analyzing the situation at different stages of the DROs is important. As this work intended to use lean tools for the said purpose, it was important to investigate the scope of different available lean tools. Many researchers conclude the most frequently used lean tools and techniques sometimes also called quality management tools (Curry and Kadasah 2002; Antony, Kumar, and Madu 2005; Negrão, Godinho Filho, and Marodin 2017; Ivanov 2021), include Pareto Histograms, Fishbone diagrams, Process flow charts, SIPOC analysis, Plan-Do-Check-Act (PDCA), VSM, failure mode and effect analysis (FMEA), Gantt Charts, quality function deployment (QFD), statistical process control, trend analysis, and brainstorming. Keeping in view the objectives of this study in the light of the suitability of tools, the following lean tools were selected for this study: SIPOC Analysis, Fishbone Diagram, PDCA, KPIs, and VSM. Table 2 provides characteristics of these lean tools and their relevance to developing resilience in DROs. Table 2 also explains in detail how selected lean tools could help in investigating causes of delays, identification of important stakeholders, the relationship among the causes and effects, and how to design, implement and evaluate the effectiveness of interventions, regarding the promotion of DROs’ resilience. Implementation of lean philosophy has been concluded as a useful technique for performance improvement (Negrão, Godinho Filho, and Marodin 2017). Figure 1 (research methodology) further explains the relationship of the selected lean tools with the expected outcomes of this research.