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Identifying the root causes of part-to-part variation is a central problem in most six-sigma programs, especially of modern manufacturing processes. This is challenging as the sources and patterns of the variation are often unknown or previously unidentified. A small literature aims to address this problem by discovering unknown, previously unidentified variation sources, in a manner that helps understand their nature, from only a sample of measurement data. However, the common solution of this literature is unideal for this objective in terms of both methodology and metrology aspects. This paper proposes a convolutional generative modeling framework for optical scanning data to address this limitation. The proposed approach can correctly discover the true variation sources and visualize their individual patterns in two manufacturing examples, without any prior knowledge of the variation. The approach also outperforms state-of-the-art methods in these examples.

Dimension control and variation reduction are vital for composite parts' assembly processes. Due to the nonlinear properties of composites, physics-based models cannot accurately and efficiently approximate the assembly processes. In addition, conventional robust parameter design (RPD) and statistical process control (SPC) cannot actively compensate for dimensional errors or prevent defects. This article proposes a surrogate model-based optimal feed-forward control strategy for dimensional-variation reduction and defect prevention in the assembly of composite parts. The objective is accomplished by (i) developing a grouped Latin hypercube sampling approach tailored to the problem; (ii) adopting a universal Kriging model for dimensional prediction and then embedding the model into an optimal feed-forward control algorithm; and (iii) conducting a multiobjective optimization to determine the control actions. A case study reveals that the developed methodology can effectively reduce the mean and standard deviation of dimensional deviations for the assembly of composite parts.

The fundamental interest of investors in econometric modeling for excess stock returns usually focuses either on short- or long-term predictions to individually reduce the investment risk. In this paper, we present a new and simple model that contemporaneously accounts for short- and long-term predictions. By combining the different horizons, we exploit the lower long-term variance to further reduce the short-term variance, which is susceptible to speculative exuberance. As a consequence, the long-term pension-saver avoids an over-conservative portfolio with implied potential upside reductions given their optimal risk appetite. Different combinations of short and long horizons as well as definitions of excess returns, for example, concerning the traditional short-term interest rate but also the inflation, are easily accommodated in our model.

Cost-saving initiatives targeting surgical supplies management have become increasingly common to address rising healthcare costs. However, few studies provide details on hospital stakeholder experiences or learnings from implementing such initiatives. Thus, we sought to evaluate hospital stakeholder satisfaction with conversion to a sole supplier for hemostasis products, in addition to economic and clinical impacts, to help develop best practices for implementation. This cross-sectional study assessed satisfaction with converting to a sole supplier for hemostasis products at a large US academic medical center using qualitative interviews and quantitative surveys with clinical and non-clinical stakeholders, on the decision-making process, conversion, and vendor-supported hemostasis optimization program (HOP) post-implementation (February 2022-May 2022). Perioperative hemorrhage events, adjunctive hemostat utilization, and total annual hospital expenditure on hemostatic products were also evaluated pre- and post-conversion (2020-2022) to identify impacts on clinical and economic outcomes. Ten hospital stakeholders completed qualitative interviews (n = 7 surgeons, n = 2 surgical technicians, n = 1 administrator) and 22 completed quantitative surveys (n = 6 surgeons, n = 5 surgical technicians, n = 11 nurses). Survey respondents noted overall satisfaction with conversion, including the level of input they provided in the decision-making process (75% were somewhat to extremely satisfied), availability of hemostatic agents during the conversion process (87%), and the vendor-supported HOP (100%). The outcomes analyses revealed a nearly 25% decrease in supply expenditure without changes to the number of postoperative hemorrhage events following implementation. Converting to a sole vendor for hemostasis products was achieved with high satisfaction among hospital stakeholders and cost savings to the institution without impacting the quality of patient care. This study provides a roadmap of best practices for other institutions interested in implementing similar initiatives and presents an example of a successful transition to a sole vendor for hemostasis products driven by effective supplier, institution, and hospital stakeholder coordination.

In today's modern manufacturing environment, any process variability is an attack on quality and throughput. Variability reduction is paramount and necessary if higher levels of quality are to be obtained. Hence, this study evaluates the effectiveness of the Design of Experiments (DoE) as a strategic tool for reducing variation in the automotive component manufacturers in South Africa. As with the evolution of the manufacturing process, methods to identify and eliminate process variability are developed. Most of these methods are based on statistical DoE. A DoE is a test or series of tests that enables the experimenter optimise the yield of a process or minimise variability. Consequently, this study focuses on the effectiveness of DoE for variability reduction in the automotive sector in South Africa. Of the 193 individuals identified for participation, 164 completed the questionnaires. Middle-level Managers from four automotive componentmanufacturing companies in the eThekwini District Municipality participated in the study. The study investigated production and the related experiences of the automotive component manufacturing companies that have adopted a DoE strategy. Descriptive and correlation were used to analyse data. The results indicates that DoE reduces product variation in the automotive component manufacturers in South Africa. In order to maximise performance, a comprehensive variability reduction policy must be developed, which aligns DoE tools to business performance. DoE has the ability to screen a large number of variables to find important ones during product reformulation process in the Product and Development functional areas.

Applying a land-based designed pitch controller on a floating wind turbine may cause severe instability. A common strategy to overcome this problem is to reduce the closed-loop bandwidth of the pitch control system. In doing so, the generator speed variation increases possibly leading to shutdowns because of overspeed. This study uses a parallel path modification to avoid instability without increasing the generator speed variation. The results of comprehensive simulations and load calculations carried out on a benchmark wind turbine are presented. These demonstrate that by using the proposed method it is possible to apply the land-based designed pitch controller on its floater-based equivalent.

PurposeThis paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.Design/methodology/approachBased on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.FindingsThe combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.Originality/valueA combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

The online sensing system provides the possibility for quick variation source identification of the assembly process. However, owing to the cost and time limit of the process, the sensor locations and sensor number for variation source identification are limited. The causal network method considering multi-source information is developed to assist identifying root causes of the dimension variation. Based on the proposed method, the diagnosis ability is evaluated, and then the minimal feature number and optimal measurement features are selected based on a sensor optimization algorithm. However, the random sampling uncertainty caused by insufficient sample size may affect the estimation accuracy of observation nodes and thus the misidentification rate of variation sources. By using Monte Carlo simulation, this paper evaluates sampling uncertainty of different sample size. Furthermore, by using probabilistic reasoning method with uncertain evidence, i.e., virtual evidence, the effect of sample size on the correct identification rate is analyzed. A dash panel case study is provided to illustrate the optimal feature selection procedures and the robustness to the sample uncertainty.

Selective assembly is a cost-effective approach for reducing the overall variation and thus improving the quality of an assembled product. In this process, components of a mating pair are measured and grouped into several classes (bins) as they are manufactured. The final product is assembled by selecting the components of each pair from appropriate bins to meet the required specifications as closely as possible. This approach is often less costly than tolerance design using tighter specifications on individual components. It leads to high-quality assembly using relatively inexpensive components. In this article we describe the statistical formulation of the problem and develop optimal binning strategies under several loss functions and distributional assumptions. Optimal schemes under absolute and squared error loss are studied in detail. The results are compared with two commonly used heuristic schemes. We consider situations in which only one component of the mating pair is binned, as well as cases in which both components are binned.

A Design of Experiment (DoE) is a strategy for planning, conducting, analysing and interpreting the experiment so that valid conclusions can be drawn efficiently and economically. It has the ability to reduce product and process variability. Hence, this study examined if DoE is an appropriate tool to support Lean Six Sigma in selected automotive component manufacturing companies in South Africa. The automotive component manufacturing sector uses various tools aimed at reducing variability. This includes Lean Six Sigma. Consequently, companies that participated in the study have (over and above Lean Six Sigma) adopted a DoE strategy. Thus, the study was designed to establish if DoE is an appropriate tool to support Lean Six Sigma. Of the 123 participants identified, 107 completed the questionnaires. Middle managers of four automotive component manufacturing companies in the eThekwini District Municipality participated in the study. The study investigated production and the related experiences of the automotive component manufacturing companies. Descriptive and correlation were used to analyse data. Results indicated that the appropriateness of DoE to support Lean Six Sigma in various business activities (like finance, strategy and product development) has no relation to both product improvements through reformulation during product development and process optimisation using quality control tools. However, study participants provided a number of reasons for implementing DoE in the automotive component manufacturers. It is advised that the automotive component manufacturers develop a comprehensive variability reduction policy that aligns DoE tools to business performance.