Reduce Process Variability Research Articles

The Challenge of Variability in Chimeric Antigen Receptor T cell Manufacturing.

Autologous Chimeric Antigen Receptor (CAR) T cell manufacturing involves the modification and expansion of T cells obtained by apheresis collection from a patient. The mechanism of apheresis collection and the specific clinical features seen in these patients combine to generate apheresis products with high variability of content. Manufacturers often attempt to minimize this variability such that processes can be standardize in accordance with Good Manufacturing Practices (GMP). Such standardization improves efficiency and helps to ensure robustness of the overall process. Apheresis product variability can negatively impact T cell manufacturing success. Patient and collection driven variability often leads to non-T cells entering the apheresis product. Many of these cells can directly or indirectly impair T cell activation and expansion, decreasing the manufacturing success rate. Therefore, patient driven variability observed in apheresis products, must be mitigated through downstream processing. T cell enrichment is one step in the manufacturing cycle that can reduce process variability by generating more uniform downstream material. However, current T cell enrichment methods have limitations. Much of this type of variability can be avoided by collecting patients earlier in their disease or treatment course, this is not current, widespread or standard practice. While variability poses challenges to successful CAR T cell manufacturing and mitigation strategies can be successful, more work is needed in this area.

Isolating and expanding endothelial progenitor cells from peripheral blood on peptide-functionalized polystyrene surfaces.

The expansion of human peripheral blood endothelial progenitor cells to obtain therapeutically relevant endothelial colony-forming cells (ECFCs) has been commonly performed on xeno-derived extracellular matrix proteins. For cellular therapy applications, xeno-free culture conditions are desirable to improve product safety and reduce process variability. We have previously described a novel fluorophore-tagged RGD peptide (RGD-TAMRA) that enhanced the adhesion of mature endothelial cells in vitro. To investigate whether this peptide can replace animal-derived extracellular matrix proteins in the isolation and expansion of ECFCs, peripheral blood mononuclear cells from 22 healthy adult donors were seeded on RGD-TAMRA-modified polystyrene culture surfaces. Endothelial colony formation was significantly enhanced on RGD-TAMRA-modified surfaces compared to the unmodified control. No phenotypic differences were detected between ECFCs obtained on RGD-TAMRA compared to ECFCs obtained on rat-tail collagen-coated surfaces. Compared with collagen-coated surfaces and unmodified surfaces, RGD-TAMRA surfaces promoted ECFC adhesion, cell spreading, and clonal expansion. This study presents a platform that allows for a comprehensive in vitro evaluation of peptide-based biofunctionalization as a promising avenue for ex vivo ECFC expansion.

Implementation of a Perinatal Depression Care Bundle in a Nurse-Managed Midwifery Practice

ObjectiveTo implement a perinatal depression care bundle at a midwifery practice to help certified nurse-midwives (CNMs) educate women about perinatal depression and direct those affected to mental health services. DesignQuality improvement project to implement a perinatal depression care bundle for care of pregnant women between 24 and 29 weeks gestation. Setting/Local ProblemCNMs practicing in a nurse-managed midwifery practice systematically screen all women for perinatal depression during pregnancy and the postpartum period but do not have a consistent method of providing anticipatory guidance about perinatal depression. ParticipantsAll CNMs in the midwifery practice providing prenatal care (n = 16) participated in implementation. Intervention/MeasurementsThe perinatal depression care bundle included three elements: (a) an educational handout; (b) a brief, provider-initiated discussion about perinatal depression; and (c) lists of local and online mental health resources. Four weeks after the care bundle was implemented, we conducted a retrospective chart review to assess CNMs’ adherence to the new bundle. ResultsOver 4 weeks, 51 prenatal visits met eligibility criteria for participation. CNMs implemented the perinatal depression care bundle for 22 (43.1%) eligible visits. CNM feedback indicated that the care bundle was brief, easy to incorporate into routine care, and well received by women. ConclusionThis project incorporated the use of a perinatal depression care bundle for women seen during routine prenatal care. Using a systematic approach to deliver perinatal depression education and resources reduces process variability and may destigmatize the illness, allowing women to feel empowered to seek help before depression symptoms become severe.

Supervised subgraph augmented non-negative matrix factorization for interpretable manufacturing time series data analytics

Data analytics has been extensively used for manufacturing time series to reduce process variation and mitigate product defects. However, the majority of data analytics approaches are hard to understand for humans who do not have a data analysis background. Many manufacturing conditions, such as trouble shooting, need situation-dependent responses and are mainly performed by humans. Therefore, it is critical to discover insights from the time series and present those to a human operator in an interpretable format. We propose a novel Supervised Subgraph Augmented Non-negative Matrix Factorization (Super-SANMF) approach to represent and model manufacturing time series. We use a graph representation to approximate a human’s description of time series changing patterns and identify frequent subgraphs as common patterns. The appearances of the subgraphs in the time series are organized in a count matrix, in which each row corresponds to a time series and each column corresponds to a frequent subgraph. Super-SANMF then identifies groups of subgraphs as features that minimize the Kullback–Leibler divergence between measured and approximated matrices. The learned features can yield comparable prediction accuracy (normal or defective) in case studies, compared with the widely used basis expansion approaches (such as spline and wavelet), and are easy for humans to memorize and understand.

A framework for the impact of lean six sigma on supply chain performance in manufacturing companies

The use of lean six sigma in manufacturing industry has been more intense in the past decade. This method comprises two concepts: lean and six sigma, each with its own objectives. Lean is a concept that aims to optimize the value stream process by eliminating all kind of waste. Meanwhile, the goal of six sigma is to increase process capability in the value stream by aiming for zero defects and reduced process variation. The supply chain performance of manufacturing companies is determined by the ongoing internal activities reflected in the value stream, therefore both lean and six sigma applications are hypothesized to have an impact on the performance. Based on the reviewed literature, this study aims to produce a framework that can be used to explain the impact of lean and six sigma on supply chain performance. In the proposed framework, lean indicators are reflected in just-in-time, quality management and employee involvement, whereas six sigma indicators are characterized by the six sigma variables. At the other end of the equation, supply chain performance is defined by four constructs which are supplier selection, production, delivery and logistic performance, and warehouse.

A new right-skewed loss function in process risk assessment

Due to globalisation, competitive companies realise that providing a more reliable, predictable, and robust product/process is a prerequisite for satisfying their customers and running a successful operation. Many quality improvement techniques focus on reducing process variation in line with the 'loss to society' concept. The widespread use of loss functions in industrial applications has increased their popularity with different loss-handling features. Developments relating to the inverted probability density functions (PDFs) have allowed the application of particular loss functions in a wide range. This paper presents the inverted Wald loss function as a new member of the inverted probability loss family. The important features of the proposed right-skewed loss function are discussed, and the risk functions associated with some process distributions of interest are obtained. Moreover, the proposed loss function and its performance are illustrated on the basis of a comparative study and an industrial example, including the monitoring of loss. [Received: 22 May 2018; Revised: 11 August 2018; Revised: 29 October 2018; Revised: 14 January 2019; Accepted: 23 January 2019]

Putting Green Lean Six Sigma Framework into Practice in a Jute Industry of Bangladesh: A Case Study

Lean Six Sigma is a fact driven tool that joins two powerful business strategies known as Lean Manufacturing and Six Sigma. Lean Six Sigma is adopted in an industry for eliminating waste, reducing process variations, and preventing defective product manufacturing which in turn reduces energy consumption and overall negative environment impacts. In that context, this case study uses LSS framework with environmental considerations to eliminate waste and improve industry’s performance without causing many negative impacts on environment. Steps by step improvement programs, analysis of raw materials and energy usages are performed to reduce process variation, energy usage and carbon-dioxide emission. The framework is used to define, measure, analyze, improve and control (DMAIC) method in a popular jute industry named “Company X”. Traditional impact assessment tools were also integrated.

Optimisation algorithms for improvement of a multihead weighing process

Mathematical optimisation is widely used to find the optimal value for an objective function, subject to constraints that try to simulate reality, and is fundamental to improving industrial processes. In this paper, we compare different optimisation approaches to solve the packaging problem in multihead weighing machines. In this problem, each package is made up from the loads in a subset of the multihead weigher's hoppers. The total weight of the packed product must be as close to a specified target weight as possible. We designed and evaluated a set of algorithms for this problem, considering both single-objective and bi-objective optimisation criteria. A new criterion for creating the packages is considered, and a different way of filling of the hoppers is studied with the aim of reducing process variability. Numerical experiments considering both a set of real data and the most important process performance parameters show the usefulness of our study.

38. Application of lean-six-sigma methodology in radiotherapy: Breast daily repositioning case

Purpose The concept of quality assurance in radiotherapy encompasses a comprehensive approach to all activities in the department. When hypo-fractionation regiment is adopted, the need of a continuous quality procedures updating is self-evident. At this aim, the Lean Six Sigma Methodology (LSSM) has been applied. LSSM was introduced in industry for providing near-perfect services to large processes, by reducing improbable occurrence. It consists of the synergetic adoption of two methods: (i) create a continuous process flow eliminating waste (Lean); (ii) reduce process variation (Six sigma). LSSM has been prospectively applied to breast patient repositioning. This is the first time the LSSM has been rigorously applied to a RT process. Methods Breast patients treated by hypo-fractionated RT in SIB delivered through VMAT technology with daily 2D-2D matching were considered. The five DMAIC (Define, Measure, Analyze, Improve, Control) LSSM steps were applied by an interdisciplinary team. The process was retrospective measured over 30 months (Jul2014–Dec2016) by querying the RT RecordV distribution symmetry improvement (Skewness moved from 1.4 to 1.1) and outlier eduction, verified by Kurtosis diminution, demonstrated better “normalization” of the procedure after the LSSM application. Conclusions LSSM was successfully applied in a RT department, allowing the breast repositioning matching procedure to be redesigned.

Chemically controlled aggregation of pluripotent stem cells.

Heterogeneity in pluripotent stem cell (PSC) aggregation leads to variability in mass transfer and signaling gradients between aggregates, which results in heterogeneous differentiation and therefore variability in product quality and yield. We have characterized a chemical‐based method to control aggregate size within a specific, tunable range with low heterogeneity, thereby reducing process variability in PSC expansion. This method enables controlled, scalable, stirred suspension‐based manufacturing of PSC cultures that are critical for the translation of regenerative medicine strategies to clinical products.

Order in the courts: propagating a culture of efficiency within the Irish Court Services

This paper exposes two 5S style experiments that introduced Lean Six Sigma (LSS) techniques to the Irish Court Services Dublin Circuit Court. Manufacturing concepts were adapted to the service industry, including a new definition of ‘informational workpiece’, to minimize waste and process variation reduction. Senior management subsequently embraced the authors’ recommendations of change management using a bottom-up approach. Other post-project outcomes include improved staff morale, decreased risk, and the simplification of complex processes.

Pulsed Electrodeposition of Highly Porous Pt Alloys for use in Methanol, Formic Acid, and Glucose Fuel Cells

AbstractWe demonstrate an electrodeposition process for the fabrication of highly porous PtCu alloy anodes. In the fabrication process, Pt and different amounts of a second noble metal (Pd, Ru, Au) are repeatedly co‐deposited with Cu from an aqueous electrolyte, followed by selective dealloying of Cu. In this way, highly porous PtCu alloys with roughness factors ranging from 400 to 4000 can be obtained. In all cases, both noble‐metal partners are present on the electrode surface, whereas the majority of copper is likely buried underneath. In addition, we can show that H desorption and CO stripping yield substantially different roughness factors, even when applied to PtCu anodes. Hence, when using or comparing results from different stripping methods, a calibration is required. Compared to PtCu anodes, small additions of Ru (ca. 3 at% Ru) lead to significantly enhanced catalytic activity for the electro‐oxidation of formic acid and methanol, whereas Au‐rich PtCu−Au alloys (ca. 75 at% Au) exhibit significantly improved electrocatalytic activity for glucose oxidation. In some cases, large variations impede the identification of significant differences in electrocatalytic activity. To reduce process variability and to increase the specific surface area, further optimization of the fabrication process is required. Similarly, the deposition of defined alloy compositions will require further investigation, as the composition of electrolyte and deposited alloy do not directly correspond.

Optimal process mean and quality improvement in a supply chain model with two-part trade credit based on Taguchi loss function

The two-part trade credit policy is developed to accelerate cash inflow that can avoid bad debt risk in the earlier economic order quantity (EOQ) models allowing only one period of time for delay in payment. Taguchi loss function has proved to be a more realistic function for fitting the actual quality loss cost in economic product quantity (EPQ) model. To minimise quality loss, optimal process mean setting shifts process mean to balance the cost outside the specification limits, quality improvement applies investment to reduce process variation. Supply chain integration has been proved that it can be used to minimise the entire cost more effectively than independent EOQ or EPQ models. This paper improves the earlier studies by incorporating the above research topics that have not been simultaneously discussed before, develops a supply chain model based on the Taguchi loss function, which combines the trade strategy from the retailer’s perspective and the quality adjustments from the supplier’s perspective to maximise total supply chain profit. We find that the trade credit terms definitely affect suppliers and retailers’ optimal decisions, and numerical examples can provide decision references for supply chain managers to set a trade credit policy and control quality.

Six-sigma application in tire-manufacturing company: a case study

Globalization, advancement of technologies, and increment in the demand of the customer change the way of doing business in the companies. To overcome these barriers, the six-sigma define–measure–analyze–improve–control (DMAIC) method is most popular and useful. This method helps to trim down the wastes and generating the potential ways of improvement in the process as well as service industries. In the current research, the DMAIC method was used for decreasing the process variations of bead splice causing wastage of material. This six-sigma DMAIC research was initiated by problem identification through voice of customer in the define step. The subsequent step constitutes of gathering the specification data of existing tire bead. This step was followed by the analysis and improvement steps, where the six-sigma quality tools such as cause–effect diagram, statistical process control, and substantial analysis of existing system were implemented for root cause identification and reduction in process variation. The process control charts were used for systematic observation and control the process. Utilizing DMAIC methodology, the standard deviation was decreased from 2.17 to 1.69. The process capability index (Cp) value was enhanced from 1.65 to 2.95 and the process performance capability index (Cpk) value was enhanced from 0.94 to 2.66. A DMAIC methodology was established that can play a key role for reducing defects in the tire-manufacturing process in India.

Cost-effective service excellence

This article integrates relevant literature to develop a conceptual model on the potential avenues to achieve service excellence at low unit costs, which we term cost-effective service excellence (CESE). To gain a deeper understanding of these strategies, their applicability and interrelatedness, we analyze how 10 organizations have achieved CESE. Our findings show that CESE can be achieved through three core strategies. First, a dual culture strategy provides a comprehensive set of high-quality services at low cost, largely driven by leadership ambidexterity and contextual ambidexterity. Second, an operations management approach reduces process variability and thereby allows the increased use of systems and technology to achieve CESE. Third, a focused service factory strategy can enable CESE through a highly specialized operation, typically delivering a single type of service to a highly focused customer segment. The use of the three approaches ranges from “pure” (e.g., mostly pursuing a dual culture strategy) to combinations of the latter two approaches with the dual culture strategy (e.g., a focused service factory strategy combined with dual culture). Our conceptual model provides an integrated view of the strategic options available to organizations that aim to pursue a strategy of CESE.

Implementation of Lean Six Sigma framework with environmental considerations in an Indian automotive component manufacturing firm: a case study

Lean Six Sigma is a systematic data driven methodology that integrates two powerful business improvement strategies Lean Manufacturing and Six Sigma with the goal of removing wastes and reducing process variation. Lean Six Sigma has a positive effect on environmental performance as defect reduction and reducing process variation leads to reduction in raw material consumption, energy consumption and reduced scrap which in turn reduces the overall environmental impacts. In this context, this study uses a Lean Six Sigma framework with environmental considerations to reduce overall defects and environmental impacts concurrently to improve the firm’s operational and environmental performance. The framework is based on Define Measure Analyze Improve Control methodology where traditional Lean Six Sigma and environmental impact assessment tools are integrated to systematically deploy LSS strategies with environmental considerations. The framework is validated with an industrial case study conducted in an Indian automotive component manufacturing organisation and the inferences are derived. On successful deployment of the framework, the internal defects was brought down to 6000 ppm from 16,000 ppm and environmental impacts was reduced to 33 Pt from 42 Pt. Deployment of the developed framework helped in improving the firm’s sigma level and also reduced the overall environmental impacts.

Implementation of surgeon-initiated Oncotype DX ordering among patients with breast cancer to reduce chemotherapy wait times.

166 Background: Oncotype DX testing in breast cancer is associated with delayed chemotherapy initiation, which may adversely impact clinical outcomes and patient satisfaction. We sought to reduce delays between surgery and adjuvant chemotherapy initiation among patients who undergo Oncotype DX testing by developing standard ordering criteria and modifying surgical oncology, medical oncology, and pathology workflows. Methods: We implemented ‘reflex’ Oncotype DX testing for any patient ≤ 65 with the following tumor characteristics: T1cN0 (grade 2-3), T2N0 (grade 1-2), or T1/T2N1 (grade 1-2). A new process was developed whereby once pathology review is complete surgeons communicate the results to medical oncologists in real-time and initiate requests for Oncotype DX when appropriate. Medical oncologists can order Oncotype for cases outside the criteria. A streamlined pathology system for receiving and processing Oncotype DX requests was also developed. We then examined 649 consecutive breast cancer patients who received postoperative Oncotype DX testing and measured time intervals between surgery date, Oncotype DX order date, Oncotype DX result received date, and chemotherapy initiation date (if applicable) before and after intervention implementation. Results: The interventions reduced the turnaround time (TAT) from surgery to Oncotype DX order and time to when the results were available by 7.7 days and 6.3 days, respectively. A 6.5 day decrease in the mean time from surgery to chemotherapy initiation was observed. The decrease in the standard deviation also suggests a reduction in process variation. Conclusions: Developing Oncotype DX testing criteria and implementing streamlined workflows among providers has led to a significant reduction in Oncotype DX ordering TAT and wait times to chemotherapy initiation. [Table: see text]

Reinvent your supply chain through a pre-eligibility screening approach

In this era of ongoing financial pressures, traditional healthcare supply chain management methods cannot adequately address the required level of cost containment reductions. A new approach that uses pre-eligibility screening techniques and applies formulary management concepts can significantly help improve end-user knowledge and compliance at the point of requisition, leading to reduced process variability and labour, increased product standardisation and optimised supply expense management.

In-process sensing in selective laser melting (SLM) additive manufacturing

Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.

Six Sigma implementation in a manufacturing unit - a case study

The Six Sigma approach has been increasingly adopted in the manufacturing sector in order to make the process robust to quality variations, thereby, improving the quality and performance. In any manufacturing industry, Shainin Six Sigma methodology is highly adopted because of lesser data collection required for the study. Six Sigma improves the process performance of the critical operational process, leading to better utilisation of resources, decrease in variations and maintaining consistent quality of the process output. The DMAIC approach has been followed here to solve an underlying problem of reducing process variation and the associated high defect rate. This paper describes an application of Six Sigma define, measure, analyse, improve and control (DMAIC) methodology in a manufacturing unit, which provides a framework to identify, quantify and eliminate sources of variation in an operational process to improve the quality. The current investigation focuses on using DMAIC methodology to reduce the rejections in the pasting process during product X manufacturing, resulting in a saving of Rs. 3.96 lakhs per annum