Quality Improvement In Manufacturing Research Articles

Research on Whether Quality Policies Can Promote the High-Quality Development of China’s Manufacturing Industry and Its Configuration Paths in the Context of Sustainable Development

High-quality development enhances economic quality and resource allocation efficiency through various effective and sustainable means, with particularly notable results in the manufacturing sector. Quality policies, through establishing standards, norms, and incentive measures, aim to foster technological innovation, efficient resource utilization, and quality improvement in manufacturing, thereby supporting its high-quality development. This study explores the impact of quality policies on high-quality development in manufacturing and their configurational pathways, providing theoretical foundations and practical guidance for optimizing policy combinations and promoting sustainable development. This analysis is based on quality policies released from 2013 to 2022 and sample data from 30 Chinese provinces. The findings reveal that quality policies significantly promote high-quality development in manufacturing, particularly through incentive, capacity building, and system changing instruments, while command instruments may exert certain pressures on high-quality development. Configuration analysis further indicates that a single instrument is insufficient to support high-quality development in manufacturing fully; the synergistic effect of multiple policy instruments is essential, and regions should optimize policy combinations according to their characteristics. This study provides valuable insights for sustainable development, offering a deeper understanding of the complex causal relationship between quality policies and high-quality development in manufacturing.

Reliability-oriented product manufacturing quality improvement approach based on R-KQC and DMAIC

ABSTRACT Reliability-oriented product quality improvement is of great significance to assurance the fitness of quality control and management activities in the manufacturing process. A novel reliability-oriented product manufacturing quality improvement approach based on DMAIC (i.e. define, measure, analysis, improvement, control) and driven by reliability-oriented key quality characteristic (R-KQC) is proposed to prevent reliability degradation and improve product fitness quality in the manufacturing process. First, with the manufacturing reliability based on RQR chain taken into consideration, the connotation of R-KQC is defined. Second, a reliability-oriented DMAIC process driven by R-KQC is proposed. The R-KQC identification method based on feature selection and Shaply additive explanations (SHAP) is proposed. In order to improve the quality of manufacturing process effectively, R-KQC improvement method of response surface and R-KQC control method based on time-between-events (TBE) control chart are proposed. Finally, an illustrated example of an insulating base is provided to verify the effectiveness of the proposed approach, the average failure rate of the latest batch is reduced by 37.8%.

Acquisition or investment: a manufacturer quality improvement strategic choice

PurposeThis paper explores how the manufacturer’s strategic choice (acquisition or investment) impacts product quality in a supply chain comprising two complementary suppliers and a common manufacturer.Design/methodology/approachThe manufacturer faces six strategic choices to improve product quality: acquiring or investing in the high-capable supplier, the low-capable supplier, or both. As the Stackelberg leader, the manufacturer determines which strategy is adopted, while suppliers are separately responsible for components’ quality and wholesale prices. The authors use game theory and calculate the model with Mathematica.FindingsThe authors develop analytical models to analyze how acquisition costs, investment proportions, component importance and quality improvement coefficients influence decision-makers. The results show that the highest quality may not benefit the manufacturer. Investing in or acquiring a low-capable supplier is better than a high-capable supplier under certain conditions. If the gaps between two suppliers’ quality improvement coefficients and the importance of two components are dramatic, the manufacturer should choose an investment strategy.Originality/valueThis study contributes to the complementary supply chain management by comparing two kinds of strategies-acquisition and investment, with a high-capable supplier and a low-capable supplier.

CDIO-TRIZ-Based Approach on Manufacturing Quality Improvement

CDIO-TRIZ-Based Approach on Manufacturing Quality Improvement

Supplier's investment in manufacturer's quality improvement with equity holding

<p style='text-indent:20px;'>This paper considers a supply chain in which an upstream supplier sells a component to a downstream manufacturer facing a price and quality sensitive demand. The supplier has a chance to make investment in the manufacturer, which can not only enable the supplier to hold equity shares in the manufacturer and thus achieve profit sharing with the manufacturer, but also provide resources for the manufacturer to improve its product quality. Under any given investment strategy of the supplier, the equilibrium decisions of the two chain members on wholesale price and profit margin are characterized. Then, the supplier's optimal investment strategy is derived. The paper considers three competition models: supplier Stackelberg (SS), manufacturer Stackelberg (MS), and vertical Nash (VN) models, which correspond to different market power structures. The paper shows that the investment can always increase the market demand. Moreover, in both the SS and VN models, the value of the supplier's investment for the entire supply chain, comes from not only the quality improvement but also the profit sharing caused by equity holding; while in the MS model, the investment value comes only from the quality improvement.

Process mapping as a framework for performance improvement in emergency general surgery

Emergency general surgery conditions are often thought of as being too acute for the development of standardized approaches to quality improvement. However, process mapping, a concept that has been applied extensively in manufacturing quality improvement, is now being used in health care. The objective of this study was to create process maps for small bowel obstruction in an effort to identify potential areas for quality improvement. We used the American College of Surgeons Emergency General Surgery Quality Improvement Program pilot database to identify patients who received nonoperative or operative management of small bowel obstruction between March 2015 and March 2016. This database, patient charts and electronic health records were used to create process maps from the time of presentation to discharge. Eighty-eight patients with small bowel obstruction (33 operative; 55 nonoperative) were identified. Patients who received surgery had a complication rate of 32%. The processes of care from the time of presentation to the time of follow-up were highly elaborate and variable in terms of duration; however, the sequences of care were found to be consistent. We used data visualization strategies to identify bottlenecks in care, and they showed substantial variability in terms of operating room access. Variability in the operative care of small bowel obstruction is high and represents an important improvement opportunity in general surgery. Process mapping can identify common themes, even in acute care, and suggest specific performance improvement measures.

Systematic Innovation for Manufacturing Quality Improvement

This paper provides a detailed analysis on the systematic innovation process in improving the quality control of latex gloves production. The systematic innovation tool such as TRIZ is applied in this case study. Function analysis, cause and effect chain analysis, physical contradiction, By-separation model and 40 Inventive Principles are applied in order to derive some feasible and low cost solutions to alleviate the problem. Findings revealed that the rejected (leaking) gloves on the production line will be manually monitored by a checker during the air blowing test and will be discarded by the same checker instantly. The main root cause is that the quality control worker is not able to concentrate all the times to detect the torn gloves, mainly is due to the fast speed production line and other distractions. The problem is solved by applying function analysis, physical contradiction, by-separation tool and Inventive Principles to generate low cost but elegant solutions within the defined scope of several constraints and without making the production line more complex. Therefore, it can be concluded that TRIZ is a systematic and innovative problem solving methodology.

Combining process and product information for quality improvement

This paper considers a two-step monitoring scheme that is used to jointly monitor process and product characteristics for manufacturing quality improvement. The two-step monitoring scheme uses an X¯ chart to continuously monitor the mean of the process characteristics. When the process sample mean falls outside of control limits, a warning signal triggers an np chart to check product quality. If the number of nonconforming products exceeds a pre-determined threshold, corrective actions are initiated to restore the manufacturing process back to normal. Otherwise, the process will continue with X¯ chart monitoring individually. We derive an economic model for the two-step monitoring scheme, which considers general cases where sampling costs for both process and product quality characteristics are involved. It is shown that the two-step monitoring scheme includes the traditional process and product control charts as special cases. We compare the two-step monitoring scheme with the individual process and product control charts, respectively, in terms of economic efficiency. Analytical conditions are derived under which the two-step monitoring scheme is preferred against the individual charts in practice. Numerical examples are provided to demonstrate the effectiveness and sensitivity of the comparison.

Manufacturing quality improvement and setup cost reduction in a vendor-buyer supply chain model

Quality improvement and setup cost reduction of any production system are endless procedure. Customer's demand is always intended to have the best quality product and the industries always try to improve the quality of products. This paper develops a two-echelon supply chain model with quality improvement of products and setup cost reduction under controllable lead time. The lead time demand follows a normal distribution and in the second case, it does not consider any specific distribution except a mean and standard deviation. Both models are solved analytically to obtain global solution. Two improved iterative algorithms are developed in order to obtain the optimal results of decision variables numerically to minimise the total system cost. The expected value of additional information is calculated to show the financial effect for collecting the information about lead time demand distribution. Some numerical examples and sensitivity analysis are given to illustrate the model. [Received 8 December 2016; Revised 27 March 2017; Accepted 21 April 2017]

Two-echelon supply chain model with manufacturing quality improvement and setup cost reduction

For quality improvement purposes often times, a manufacturing unit has to change certain parts of equipment. Any such changes in the assembly line manufacturing system or production process involves a cost known as the setup cost. Minimizing the setup cost and improving the product quality is of prime importance in today's competitive business arena. This paper develops the effects of setup cost reduction and quality improvement in a two-echelon supply chain model with deterioration. The objective is to minimize the total cost of the entire supply chain model (SCM) by simultaneously optimizing setup cost, process quality, number of deliveries, and lot size. Numerical examples are provided to illustrate the model.

Six Sigma in Project Management for Software Companies

Six Sigma approach is considered as a very successful tool in manufacturing quality improvement. From the last two decades, Six Sigma has helped many companies e.g., Honda, American Express, Ford, Honda, General Electric, Sony, Hitachi, Motorola, Texas Instruments etc. to be successful. The goal of any “Six Sigma” improvement effort made by the company is to obtain a long-lasting defect rate of only 3.4 defective parts-per-million (PPM) manufactured. Recently, the Six Sigma approach was introduced in the software development industry. Some software companies are making an attempt to adapt Six Sigma for their business and development processes so that they can improve their process by lowering the amount of defects and all. But there are some misconceptions about the applicability of Six Sigma in the software industry. Furthermore there is no generic software quality amendment or improvement solution predicated on Six Sigma approach. So there is a demand to invalidate or negate the misconceptions related to the applicability of Six Sigma and to develop a generic software company quality amendment or improvement solution predicated on Six Sigma approach. In this paper we take a primary step to search out such a solution for the software companies so that they can manage their projects successfully with fewer defects, better quality and high customer satisfaction. The paper starts from Six Sigma concept identification and manufacturing investigation. After conducting some interviews, a case study and several other case studies reviews from the industries, we elaborate our method so that we can find out the way to improve the process and manage the projects effectively. We expect paper to be useful enough for the software or IT companies when applying Six Sigma in their company for Process Amendment or improvement & Project Management. This research paper includes the DMAIC methodology in order to manage the projects in the software companies so as have less defects, more client satisfaction, more repeat clients, better quality and good return on investment.

Manufacturing Quality Improvement with Data Mining Outlier Approach against Conventional Quality Measurements

Background/Objectives : This research study has been conducted in a scientific way to help manufacturing engineers and the management team to find out the hidden information from the data which are generated during the everyday manufacturing process. Methods/Statistical analysis: The methodology adopted in this activity is applying outlier analysis which is a data mining technique. The inter quartile range findings and analysis has been used here to find the hidden useful information from the process data with which a better insight could be established towards the improvement of quality of the product. The data used here have been collected from the automotive engine assembly and testing process. The study compares the results between the conventional and outlier analysis. Findings: The conventional style of checking and approving the engines based on the value pattern of Specific Fuel Consumption (SFC) which uses the design specification comparison with the actual data generally yields very minimal scope for the improvement of product quality in the perspective of design, Safety and reliability of the product because of the adherence of the same design specifications of the part drawings supplied by various suppliers. The competitive automotive manufacturing domain demands a different approach with which a better scope could be identified towards the improvement of product quality which is undoubtedly data mining. The outlier analysis using inter quartile range on the sample data of 500 engines revealed many important aspects where the improvement scope for quality has been identified as 15,000 Parts Per Million (PPM) against400 PPM of conventional quality analysis for the same data. Improvement/Application: This research is to offer an inclusive model hypothetically with actual data both for engineering and management people of manufacturing domain about the insights and benefits of employing data mining techniques towards the improvement of product quality with proven results.

Batch-to-Batch Variation: A Key Component for Modeling Chemical Manufacturing Processes

For chemical manufacturing processes, the chemical kinetics literature contains virtually no mention of quantitative models that involve batch-to-batch variation. Models for chemical process manufacturing quality improvement are being more carefully considered, particularly by the pharmaceutical industry and its regulators. This is evidenced in part by the recent ICH Q11 regulatory guidance on drug substance manufacture and quality. Quality improvement has been defined as a reduction in variation about a target. Hence the modeling of process variation plays an important role in quantifying quality improvement. Given that batch-to-batch variation is often a dominant source of process variation (usually exceeding measurement error variation), it is important for process models to incorporate such variance components. In this paper, we show how chemical kinetic models can incorporate batch-to-batch variation as well as measurement error. In addition, we show that these models can be used to quantify the reli...

Contract efficiency for a decentralized supply chain in the presence of quality improvement

AbstractIn this paper, we study a joint pricing and product quality decision problem in a decentralized supply chain consisting of one manufacturer and one retailer. Although the manufacturer decides the product quality with an associated cost, the retailer decides the retail price. We aim to study and compare different contract formats for this decentralized supply chain. There is a trade‐off in the choice of contracts: simpler format contract (with a few parameters) is less complicated, but the contract efficiency is low. We start with the simplest one‐parameter contract: a wholesale price contract that serves as the benchmark. We then study how contract efficiency can be improved by adding one more parameter. Specifically, we consider three two‐parameter contracts that are commonly used in reality: two‐part tariff contract, revenue‐sharing contract, and effort cost sharing contract. We find that the contract efficiency is improved under all the three contracts, but in different ways: the improvement in contract efficiency under each of them dominates the other two when manufacturer's quality improvement effectiveness is relatively low, moderate, and high, respectively. Furthermore, through numerical examples, we find that under some cases, a choice from these three two‐parameter contracts can achieve a close‐to‐perfect efficiency (&gt;85%). Finally, we investigate whether a combination of the three two‐parameter contracts can achieve coordination. Interestingly, we find that only the combination of effort cost sharing contract and revenue‐sharing contract can achieve coordination, whereas combinations of either of them and two‐part tariff contract cannot.

Multivariate Control Charts for Simultaneous Quality Monitoring of Isoniazid and Rifampicin in a Pharmaceutical Formulation Using a Portable Near Infrared Spectrometer

One of the keys to maintain high manufacturing quality improvement is the use of control charts. In this work, multivariate control charts based on significant principal component (PC) scores and net analytical signal (NAS) were developed to simultaneously monitor the quality of two active pharmaceutical ingredients (API) (isoniazid and rifampicin) in pharmaceutical formulation (laboratory samples and production samples) using a portable near infrared spectrometer. The limits for both multivariate charts were estimated using the quality specifications from the pharmaceutical formulation (± 5% of the nominal content of each API). The use of these multivariate control charts has provided a simple and powerful tool to evaluate the content of pharmaceutical formulations based on isoniazid and rifampicin capsules combined with near infrared spectroscopy. The procedure is rapid and adjustable for monitoring the production of the pharmaceutical preparations produced at UFRN/Brazil toward the process analytical technology (PAT) for the treatment of pulmonary tuberculosis.

Contract design for two-stage supply chain coordination: Integrating manufacturer-quality and retailer-marketing efforts

This paper investigates the issue of channel coordination for a two-stage supply chain with one retailer and one manufacturer. The demand is influenced by the retailer's sales effort and manufacturer's quality improvement efforts. We found that using the traditional two-part tariff contract alone cannot coordinate the supply chain well. Joining the two-part tariff contract with the quality effort cost sharing model remains ineffective in managing the two-stage supply chain. To effectively coordinate the channel members, we propose an innovative supply chain contract that integrates the endeavors of the manufacturer and the retailer. We identify the optimal level of retail sales effort, optimal level of quality-improvement effort and optimal supply chain profit. Sensitivity analyses are conducted to examine the impacts of changes in the costs of sales effort and quality effort on the performance of the supply chain.

Case study in Six Sigma methodology: manufacturing quality improvement and guidance for managers

This article discusses the successful implementation of Six Sigma methodology in a high precision and critical process in the manufacture of automotive products. The Six Sigma define–measure–analyse–improve–control approach resulted in a reduction of tolerance-related problems and improved the first pass yield from 85% to 99.4%. Data were collected on all possible causes and regression analysis, hypothesis testing, Taguchi methods, classification and regression tree, etc. were used to analyse the data and draw conclusions. Implementation of Six Sigma methodology had a significant financial impact on the profitability of the company. An approximate saving of US$70,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. The project also had the benefit of allowing the company to learn useful messages that will guide future Six Sigma activities.

Application of Matroid Theory for Diagnosability Study of Coordinate Sensing Systems in Discrete-Part Manufacturing Processes

Coordinate sensing systems have been widely used in various manufacturing processes to monitor product quality and automatically identify process faults. They play a crucial role in manufacturing quality improvement. For complex manufacturing processes, especially processes with multiple stages, typically not all process faults can be uniquely identified by a feasible sensing system. For these complex processes, a diagnosability study is an important issue for the evaluation and usage of coordinate sensing systems. This article conducts a diagnosability study of coordinate sensing systems based on matroid theory. The use of matroid theory improves understanding of existing results on diagnosability. In addition, new results and more efficient algorithms related to diagnosability study are investigated based on matroid theory. The developed theory and algorithms, which can be applied in a broad class of diagnosis problems, are demonstrated using a panel assembly process.

Allocation of quality improvement targets based on investments in learning

AbstractPurchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across‐the‐board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an “across‐the‐board” allocation of 20% quality improvement targets. © John Wiley &amp; Sons, Inc. Naval Research Logistics 48: 684–709, 2001

Allocation of variance reduction targets under the influence of supplier interaction

A common quality improvement strategy used by manufacturers is to periodically allocate quality improvement targets among their suppliers. We propose a formal modelling and optimization approach for assessing quality improvement targets for suppliers. In this approach it is understood that a manufacturer's quality improvement results from reductions in supplier process variances, which occurs only through investments in learning. A constrained nonlinear optimization model is developed for determining an optimal allocation of variance reduction target that minimizes expected total cost, where the relationship between performance measures and the set of design parameters is generally represented by second-order polynomial functions. An example in the fabrication of a tyre tread compound is used both to demonstrate the implementation of our proposed models as well as to provide an empirical comparison of optimal learning rates for different functional relationships between the performance measures and the set of design parameters.