The Well Established Quality Improvement Method

Comment by Kaplan, Heather: I cut a bit which give us 1000 words to add in descriptions of our examples that we will highlight and then add some additional examples and flesh out some other areas (where I indicated)I really think the most important part of this will be weaving in the examples!

Statistical Process Control (SPC) is a method of controlling the quality of a manufacturing process and is most often affiliated with control charts. However, SPC in reality is a group of tools and includes additional statistical and evaluation/measurement methods. Smith, Megahed, Jones‐Farmer and Clark defined seven basic tools of SPC including; histograms, check sheets, Pareto charts, cause-and-effect diagrams, defect concentration diagrams, scatter diagrams and of course the aforementioned control charts (2014). These tools represent the scope and overall purpose/pursuit of SPC which is; “any statistical method designed to detect changes in a process over time” (Woodall & Montgomery, 1999, p. 377). Since control charts is often the most discussed of these seven tools; it is important to note there are many different types of control charts. The main control charts include; process charts which are R-charts and s-charts, non-conforming item charts also known as p-charts and np-charts and finally average numbers of non-conformity charts or c-charts and u-charts (Woodall & Montgomery, 1999).

Health care organizations are complex and require several interconnected parts to operate effectively. The employees, hospital staff, and administration work together to ensure that a health care organization runs properly. Sometimes health care organizations grow so large that the overall quality of the organization is diminished. Healthcare organizations are constantly changing and discovering ways to improve quality. Improving the quality of healthcare organizations is a challenging task. Individuals who are in charge of improving the quality of a health care organization must be highly trained and have clearly communicated goals. If the quality of a health care organization is improved, then its patient’s employees, hospital staff, administrators and surrounding community will benefit.

Traditional Design of Experiments focused on how different design factors affect the average result level. In Taguchi’s DOE (robust design), variation is more interesting to study than the average.

Quality assurance can be defined as a system to monitor to and evaluate a product or a service. It identifying and recommending measures to make improvements to set standards and performance and ensuring everything is working well aiming to avoid problems keeping everything running smooth and improve products and services. In relation to the teaching, training assessing it ensures the product/service which the learner is receiving being to correct standard and the internal quality assurance are the checks which are done to ensure this is occurring.

Quality is a cornerstone of successfully completing a project within the parameters of the project scope. The Six Sigma system improves products or services

The application of measuring and improving quality in the United States have been heavily influenced by four individuals. This paper will examine the contributions and theories of Walter Shewhart, W. Edwards Deming, Philip Crosby, and Joseph Juran. Finally, these theories will be used to develop a proposal for a quality monitoring system for the Pharmacy Department at Munson Medical Center.

Robust design (RD) based on the concept of building and improving quality into a design has been paid much attention by researchers and practitioners for years, and a number of approaches have been introduced in the literature. There exist many research attempts to integrate RD methodology with response surface methodology (RSM). The three-level full factorial design embedded higher-order polynomial models may be more useful in order to obtain better RD solutions than the second-order model. In this paper, we propose a dual response surface approach based on higher-order polynomial models in order to optimize the process bias and standard deviation responses at the same time while the process mean is restricted by the lower and upper specification limits. We also discuss why the three-level full factorial design is preferred over the other second-order designs, such as the traditional central composite design, with higher-polynomial models. In addition, the significance levels of the model fittings are sought out by the model selection procedure. A numerical example is given to illustrate the superiority of the proposed approach, as compared with the traditional methods.

The MODM methods intrinsically have with them the decision variable figures and values that are determinate on an immense and continuous integer domain with either an infinitive or even a larger array of choice that in most cases should be able to satisfy the decision makers’ constraints and the preference towards certain priorities (Chang, 2005). The MADM methods inversely are more generally discrete methods with a rather limited amount of predetermined alternatives (Kahraman, 2008). The

Single Variable Response (SVR) has been a methodology often used for qualitative analysis of the training results obtained from Evolutionary neural network (EvoNN) and Bi-Objective genetic programming (BioGP) [Pettersson et~al., 2007], [Jha et~al., 2015b]. In SVR, a style of variation is created by generating values between zero and one on time scale. The trend line is irregular, that is there are regions of constant values, sharp increases and sharp decreases in the line. This has been referred to as input signal in the following text. Here, an input signal is furnished for each variable (alloying element). The response of that signal (that corresponds to that particular variable) was checked with respect to the input signal for the objectives and constraints trained through the selected model. For SVR testing, the input signal (trend of variation) was used for one of the variables, while the other variables were kept constant at an average value. The model output response was plotted against the variable trend. The various responses were tabulated for each of the models.

The personnel responsible for process selection must be cognizant of the effect of quality characteristic variances on the quality of the product.

Multi-criteria (or) attribute decision making (MCDM/MADM) methods have very high applications in industries for solving real world engineering problems. In the present work, MCDM methods of weighted sum method (WSM), Weighted Products Method (WPM) and TOPSIS, have been employed for the computational analysis of multi-responses. AA7075 has been taken as work piece for the experimentation and the experiments were done on CNC lathe as per the Taguchi’s standard L9 Orthogonal Array. The cutting parameters of speed, feed and depth of cut were taken as experimental inputs and Material Removal Rate (MRR) and Surface Roughness (Ra) were considered as responses. From the Weighted sum method (WSM) and Weighted Products Method (WPM) the optimal combination for multi-responses were found at ninth alternative, i.e. speed: 2000 rpm, feed: 0.4 mm/rev and depth of cut: 0.75 mm. Similarly, from the TOPSIS results, the optimal combination for multi-responses were found at seventh alternative, i.e. speed: 2000 rpm, feed: 0.2 mm/rev and depth of cut: 1 mm. Analysis of variance (ANOVA) has been done by using MINITAB-16 statistical software to find the influence of cutting parameters on the multi-responses. From the ANOVA results of WSM, WPM and relative closeness coefficient (C+) of TOPSIS, it is found that feed rate has high influence (For WSM, WPM and C+ the F values are 60.50, 60.30 and 91.42 respectively) in affecting the multi-responses.

The decision maker can evaluate system performance and robustness by analyzing the Pareto frontier variations due to uncertainties. As well as, the designer can distinguish the favorable or high-risk regions of design space. The robust design of structures is formulated as a multi-criteria optimization problem wherein both mean value and the standard deviation of the objective function are minimized [4]. In this reference, the two-criterion optimization problem is converted into single optimization problem and is solved by a gradient-based optimization algorithm. Jaeger et al. [8] proposed a procedure for reliable and robust optimization of an aircraft at the conceptual design phase. The main advantage of this approach is that it permits designers to update uncertainties from the historical database at each step of optimization. To reduce the computational cost, response surface approximations are constructed by Monte Carlo Simulation (MCS). Jafarsalehi et al. [9] has implemented deterministic and nondeterministic approaches in the design of Remote Sensing Satellites (RSS) under uncertainty. In this reference, a composition of multidisciplinary design optimization and reliability-based design optimization methods was introduced. MCS used for uncertainty modeling. Multidisciplinary Design Feasible (MDF), Collaborative Optimization (CO), Reliability Based Multidisciplinary Design Feasible (RB-MDF) and

The change in society’s attitude from “let the buyer beware” to “let the producer beware” was fuelled by all the following factors EXCEPT: a. government safety regulations. b. product recalls. c. popularity of TQM as a quality tool. d. the rapid increase in product-liability judgments. Answer: c AACSB: Reflective Thinking Skills Which of the following is most appropriate in describing the quality efforts undertaken with the scientific management philosophy? a. Defect prevention was emphasized. b. Quality circles were extensively used. c. Use of inspection was wide-spread. d. Quality was every worker’s responsibility. Answer: c AACSB: Analytic Skills The creation of separate quality departments in the early 1900s caused: a. indifference to quality among workers and their managers. b. upper management to be more knowledgeable about quality. c. production quality to

Statistical methods such as Loss Function; eliminating variations of design characteristics and "noise" through robust design and processes