Quality Of Data Products Research Articles

Method for enabling a root of trust in support of product-data certification and traceability.

Trust in product-data quality (PDQ) is critical to successful implementation of model-based enterprise (MBE). Such trust does not extend to the exchange and reuse of three-dimensional (3D)-product models across the product lifecycle because verifiable traceability in product data is lacking. This assurance is especially crucial when "siloed" manufacturing functions produce product data that is not fully interoperable and thus requires frequent reworking to enable reuse. Previous research showed how Public Key Infrastructure (X.509-PKI) from the X.509 standard could be used to embed digital signatures into product data for the purposes of certification and traceability. This paper first provides an overview and review of technologies that could be integrated to support trust throughout the product lifecycle. The paper then proposes a trust structure that supports several data-transaction types. Next, the paper presents a case study for common configuration management (CM) workflows that are typically found in regulated industries. Finally, the paper draws conclusions and provides recommendations for further research for enabling the product lifecycle of trust (PLOT).

Coastal Mooring Observing Networks and Their Data Products: Recommendations for the Next Decade

Instrumented moorings (hereafter referred to as moorings), which are anchored buoys or an anchored configuration of instruments suspended in the water column, are highly valued for their ability to host a variety of interchangeable oceanographic and meteorological sensors. This flexibility makes them a useful technology for meeting end user and science-driven requirements. Overall, societal needs related to human health, safety, national security, and economic prosperity in coastal areas are met through the availability of continuous data from coastal moorings and other complementary observing platforms within the Earth-observing system. These data streams strengthen the quality and accuracy of data products that inform the marine transportation industry, the tourism industry, fisheries, the military, public health officials, coastal and emergency managers, educators, and research scientists, among many others. Therefore, it is critical to sustain existing observing system networks, especially during this time of extreme environmental variability and change. Existing fiscal and operational challenges affecting the sustainability of observing networks will likely continue into the next decade, threatening the quality of downstream data and information products - especially those used for long-term monitoring, planning, and decision-making. This paper describes the utility of coastal moorings as part of an integrated coastal observing system, with an emphasis on stakeholder engagement to inform observing requirements and to ensure data products are tailored to user needs. We provide 10 recommendations for optimizing moorings networks, and thus downstream data products, to guide regional planners and network operators: 1. Develop strategies to increase investment in coastal mooring networks 2. Collect stakeholder priorities through targeted and continuous stakeholder engagements 3. Include complementary systems and emerging technologies in implementation planning activities 4. Expand and sustain water column ecosystem moorings in coastal locations 5. Coordinate with operators and data managers across geographic scales 6. Standardize and integrate data management best practices 7. Provide open access to data 8. Promote environmental health and operational safety stewardship and regulatory compliance 9. Develop coastal mooring observing network performance metrics 10. Routinely monitor and assess the design of coastal mooring networks

Energy and quality performance assessment of emerging and conventional food preservation technologies

The energy performance of emerging food pasteurisation technologies (high pressure processing, microwave volumetric heating, ohmic heating) are evaluated to establish whether they can offer significant reductions in energy consumption and overall carbon emissions, relative to conventional processes, while delivering equivalent microbiological lethality, nutritional and organoleptic quality under commercially-representative processing conditions. Product quality (vitamin C and flavour compounds) data have been collected using established analytical and instrumental methods to benchmark achievable product quality improvements. The results show that for maintaining the raw product quality, the emerging electro-technologies are more energy- and primary resource-efficient, subject to identified operating parameters.

Transparent Acquisition and Processing of Energy Data in the Field of Industrial Production - Requirements and Applications

Energy efficiency becomes a critical production factor due to legal regulations, sustainable customer-side production requirements and the increasingly volatile supply situation. Energy transparency is of crucial importance for the definition of strategic measures to increase energy efficiency. However, the transparent collection and processing of energy data in production not only forms the basis of industrial energy management, but also facilitates the additional optimization of subareas in the production environment. Especially in the context of production-related data, such as process and product quality data and production planning and control information, as well as combined with expert knowledge, energy data provides a wide range of application fields. This paper analyzes the requirements for a holistic, transparent collection and processing of energy data and identifies possible applications for the optimization of production and production-related areas.

A Workflow-Based Framework for Curating Product Analytical Data and Statistical Results for Lot Release

Purpose Demonstrate the use of a knowledge management and data curation (KProMS) system to support a collaborative research project involving the generation of extensive critical product quality data and the investigation of alternative statistical sampling/analysis strategies for product release.

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using sensors are often related to the costs associated with them and the inaccessibility of critical locations within machines or processes. Manufacturing processes, as a large group of applications in which data analytics can bring significant value to, are the focus of this study. As the cost of instrumenting the machines in a manufacturing process is significant, an alternative solution which relies solely on product quality measurements is greatly desirable in the manufacturing industry. In this paper, a minimal-sensing framework for machine anomaly detection in multistage manufacturing processes based on product quality measurements is introduced. This framework, which relies on product quality data along with products’ manufacturing routes, allows the detection of variations in the quality of the products and is able to pinpoint the machine which is the cause of anomaly. A moving window is applied to the data, and a statistical metric is extracted by comparing the performance of a machine to its peers. This approach is expanded to work for multistage processes. The proposed method is validated using a dataset from a real-world manufacturing process and additional simulated datasets. Moreover, an alternative approach based on Bayesian Networks is provided and the performance of the two proposed methods is evaluated from an industrial implementation perspective. The results showed that the proposed similarity-based approach was able to successfully identify the root cause of the quality variations and pinpoint the machine that adversely impacted the product quality.

The establishment of optimal ground‐based radar datasets by comparison and correlation analyses with space‐borne radar data

ABSTRACTCalibration error, due to ground‐based radar (GR) hardware parameters, introduces biases to GR reflectivity factor data. As a result of this, GR observations from the China new‐generation weather radar network have sometimes been found to be inconsistent, even when observing precipitation in the same area. In this paper a method, called the Available Best Comparable Data set (ABCD) method, is proposed. The method evaluates the radar reflectivity bias in GR datasets by using TRMM Standard Product (TSP) 2A25 data from Tropical Rainfall Measuring Mission (TRMM) satellite‐borne precipitation radar (PR). The analysis is supported by simultaneous measurement data from two precipitation events that occurred in the middle and lower reaches of the Yangtze River between June and July 2010; data were collected during these events by three radars in Nanjing, Changzhou and Nantong (Jiangsu province, China). The ABCD method could correct deviations in the GR reflectivity factors; the GR‐corrected values were consistent with those observed by automatic weather stations. The method can be applied to improve the quality of precipitation data products from the GR network and their quantitative application for the southern region in China.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.
 
 Index term: IP Approach, IP-Map, information quality, data quality.
 REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

Decreasing the diesel fuel consumption and CO2 emissions of industrial in-field chipping operations

Plantation forestry accounts for a rapidly increasing proportion of the global wood supply, which makes it especially urgent to reduce its environmental impact. In that regard, much benefit can be obtained by reducing fossil fuel consumption during tree harvesting operations, which represent a main contributor to CO2 emissions in the plantation wood supply chain. This test was conducted in Brazil on three industrial delimber-debarker-chippers (Peterson Pacific DDC 5000H), which are the largest fuel consumers among the machines used for harvesting plantation eucalypt. Production, fuel use and product quality data were collected for each machine on a daily basis under the standard 2100 RPM engine setting, and under a new reduced engine setting (1820 RPM). Fuel consumption and productivity data were collected continuously for 30 days (triple shift) for each machine and treatment. Under the new treatment, fuel use and emissions were reduced in all machines, without any negative effects on productivity and product quality. In fact, both debarking performance and chip size distribution were improved. The experiment showed how a simple adjustment of engine setting may cut fuel consumption and CO2 emissions by an average of 17% over the control values. Engine setting adjustment is a basic measure, easy to implement and suitable for application to a wide range of activities and environments.

ONTOLOGIES OF PRODUCT DATA QUALITY MANAGEMENT

While expanding of computer usage for Product Life-cycle information support, several challenges, caused by dependence of computer documents upon interpretation means, are arising. Product data quality (PDQ) challenge is discussed in this article. Absence of data quality defects is assumed as product data quality. Data quality defect, in turn, is existence of some computer product model features, impeding downstream handling of product model. Product data quality management is assumed as interleaved transactions for detection and healing of data quality defects. The principal tasks that facilitate improvement of product design documents' quality were outlined. Computer product data quality is considered as one of aspects of quality of product design documents represented in computer form. The following tasks have to be solved for data quality improvement: detect influence of data quality defects on downstream handling of computer documents, detect interrelationship of data quality defects, set sequence of product data quality transactions, detect product shape model deviation arisen due to model healing. The new integrated approach to product data quality issues is implemented. Free accessible materials and also results of R&D [1] and experience of multy-year exploitation and permanent improvement of APM Studio modules for quality check, healing and import of CAD models [2] were considered while outlining of principal tasks mentioned in this article.

Importance of Continued Data Quality Assessment of Syndromic Production Data

ObjectiveTo explore the quality of data submitted once a facility is movedinto an ongoing submission status and address the importance ofcontinuing data quality assessments.IntroductionOnce a facility meets data quality standards and is approved forproduction, an assumption is made that the quality of data receivedremains at the same level. When looking at production data qualityreports from various states generated using a SAS data qualityprogram, a need for production data quality assessment was identified.By implementing a periodic data quality update on all productionfacilities, data quality has improved for production data as a whole andfor individual facility data. Through this activity several root causesof data quality degradation have been identified, allowing processesto be implemented in order to mitigate impact on data quality.MethodsMany jurisdictions work with facilities during the onboardingprocess to improve data quality. Once a certain level of data qualityis achieved, the facility is moved into production. At this point thejurisdiction generally assumes that the quality of the data beingsubmitted will remain fairly constant. To check this assumption inKansas, a SAS Production Report program was developed specificallyto look at production data quality.A legacy data set is downloaded from BioSense production serversby Earliest Date in order to capture all records for visits which occurredwithin a specified time frame. This data set is then run through a SASdata quality program which checks specific fields for completenessand validity and prints a report on counts and percentages of null andinvalid values, outdated records, and timeliness of record submission,as well as examples of records from visits containing these errors.A report is created for the state as a whole, each facility, EHR vendor,and HIE sending data to the production servers, with examplesprovided only by facility. The facility, vendor, and HIE reportsinclude state percentages of errors for comparison.The Production Report was initially run on Kansas data for thefirst quarter of 2016 followed by consultations with facilities on thefindings. Monthly checks were made of data quality before and afterfacilities implemented changes. An examination of Kansas’ resultsshowed a marked decrease in data quality for many facilities. Everyfacility had at least one area in need of improvement.The data quality reports and examples were sent to every facilitysending production data during the first quarter attached to an emailrequesting a 30-60 minute call with each to go over the report. Thiscall was deemed crucial to the process since it had been over a year,and in a few cases over two years, since some of the facilities hadlooked at data quality and would need a review of the findings andall requirements, new and old. Ultimately, over half of all productionfacilities scheduled a follow-up call.While some facilities expressed some degree of trepidation, mostfacilities were open to revisiting data quality and to making requestedimprovements. Reasons for data quality degradation included updatesto EHR products, change of EHR product, work flow issues, engineupdates, new requirements, and personnel turnover.A request was made of other jurisdictions (including Arizona,Nevada, and Illinois) to look at their production data using the sameprogram and compare quality. Data was pulled for at least one weekof July 2016 by Earliest Date.ResultsMonthly reports have been run on Kansas Production data bothbefore and after the consultation meetings which indicate a markedimprovement in both completeness of required fields and validityof values in those fields. Data for these monthly reports was againselected by Earliest Date.ConclusionsIn order to ensure production data continues to be of value forsyndromic surveillance purposes, periodic data quality assessmentsshould continue after a facility reaches ongoing submission status.Alterations in process include a review of production data at leasttwice per year with a follow up data review one month later to confirmadjustments have been correctly implemented.

A procedural approach for realizing prescriptive maintenance planning in manufacturing industries

Prescriptive maintenance planning is an essential enabler of smart and highly flexible production processes. Due to increasing complexity, traditional maintenance strategies lack in fulfilling present-day production requirements. This paper proposes a novel procedural approach for prescriptive maintenance planning in manufacturing companies. Multivariate data analysis and simulation tools are utilized to analyse historical data (product quality data, machine failure data and production program data). Based on identified data correlations and incoming real-time machine data, system failures are predicted and prescriptive maintenance measures are proposed. Results from real implementations in the automotive manufacturing industry are presented to demonstrate the effectiveness of the proposed approach.

A Quality Model for the Evaluation AAL Systems

The Ambient Assisted Living (AAL) domain aims to support the daily life activities of elders, patients with chronic conditions or disabled people. The need to support these elderly, in difficulty has prompted researchers to focus on the field of home care. Several platforms have been developed over the last two decades, based on several functionalities to the improvement of their quality. Hence, Quality Criteria (QCs) must be identified and well defined to successfully achieve the system purposes. The goal is to obtain a set of data quality characteristics that would be applicable to the context of AAL systems and evaluate them using data coming from multiple sensors. To do this, we based our work on ISO/IEC 25012 and ISO/IEC 25010 standards that establish QCs respectively on system and software product quality and data quality, to extract the most relevant QCs that could be more fitting for AAL systems. We have applied an approach to extract the most important criteria than can be associated with AAL system based on the acquisition, the transmission, and the treatment processes. To convince all stakeholders including both technologies and end users of AAL systems, high quality must be guaranteed, but measuring quality in AAL system is difficult because of the variety of stakeholders. To evaluate the entire data quality model for AAL system, we will apply an evaluation procedure on this model, which consists first make our model hierarchically, then evaluate it using the metrics based on the sensor data and the concept of fuzzy logic.

Gaps Analysis of Integrating Product Design, Manufacturing, and Quality Data in The Supply Chain Using Model-Based Definition.

Advances in information technology triggered a digital revolution that holds promise of reduced costs, improved productivity, and higher quality. To ride this wave of innovation, manufacturing enterprises are changing how product definitions are communicated - from paper to models. To achieve industry's vision of the Model-Based Enterprise (MBE), the MBE strategy must include model-based data interoperability from design to manufacturing and quality in the supply chain. The Model-Based Definition (MBD) is created by the original equipment manufacturer (OEM) using Computer-Aided Design (CAD) tools. This information is then shared with the supplier so that they can manufacture and inspect the physical parts. Today, suppliers predominantly use Computer-Aided Manufacturing (CAM) and Coordinate Measuring Machine (CMM) models for these tasks. Traditionally, the OEM has provided design data to the supplier in the form of two-dimensional (2D) drawings, but may also include a three-dimensional (3D)-shape-geometry model, often in a standards-based format such as ISO 10303-203:2011 (STEP AP203). The supplier then creates the respective CAM and CMM models and machine programs to produce and inspect the parts. In the MBE vision for model-based data exchange, the CAD model must include product-and-manufacturing information (PMI) in addition to the shape geometry. Today's CAD tools can generate models with embedded PMI. And, with the emergence of STEP AP242, a standards-based model with embedded PMI can now be shared downstream. The on-going research detailed in this paper seeks to investigate three concepts. First, that the ability to utilize a STEP AP242 model with embedded PMI for CAD-to-CAM and CAD-to-CMM data exchange is possible and valuable to the overall goal of a more efficient process. Second, the research identifies gaps in tools, standards, and processes that inhibit industry's ability to cost-effectively achieve model-based-data interoperability in the pursuit of the MBE vision. Finally, it also seeks to explore the interaction between CAD and CMM processes and determine if the concept of feedback from CAM and CMM back to CAD is feasible. The main goal of our study is to test the hypothesis that model-based-data interoperability from CAD-to-CAM and CAD-to-CMM is feasible through standards-based integration. This paper presents several barriers to model-based-data interoperability. Overall, the project team demonstrated the exchange of product definition data between CAD, CAM, and CMM systems using standards-based methods. While gaps in standards coverage were identified, the gaps should not stop industry's progress toward MBE. The results of our study provide evidence in support of an open-standards method to model-based-data interoperability, which would provide maximum value and impact to industry.

A high‐performance computing framework for Monte Carlo ocean color simulations

SummaryThis paper presents a high‐performance computing (HPC) framework for Monte Carlo (MC) simulations in the ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer code named MOX, developed by the authors to create a virtual marine environment for investigating the quality of OC data products derived from in situ measurements of in‐water radiometric quantities. A consolidated set of solutions for performance modeling, prediction, and optimization is implemented to enhance the efficiency of MC OC simulations on HPC run‐time infrastructures. HPC, machine learning, and adaptive computing techniques are applied taking into account a clear separation and systematic treatment of accuracy and precision requirements for large‐scale MC OC simulations. The added value of the work is the integration of computational methods and tools for MC OC simulations in the form of an HPC‐oriented problem‐solving environment specifically tailored to investigate data acquisition and reduction methods for OC field measurements. Study results highlight the benefit of close collaboration between HPC and application domain researchers to improve the efficiency and flexibility of computer simulations in the marine optics application domain. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons, Ltd.

Effects of IFNγ administration on allograft rejection in ginbuna crucian carp

DORIS, as other techniques for space geodesy (SLR, VLBI, GPS) has regularly progressed to meet the ever increasing needs of the scientific community in oceanography, geodesy or geophysics.Over the past 10 years, a particular emphasis has been placed on integrity monitoring of the system, which has contributed to the enhancement of the overall availability and quality of DORIS data products. A high level of monitoring is now provided by a centralized control of the whole system, including the global network of beacons and the onboard instruments, which perform a constant end-to-end survey. At first signs of any unusual behavior, a dedicated team is activated with well-established tools to investigate, to anticipate and to contain the impact of any potential failures. The procedure has increased the availability of DORIS beacons to 90%.The core topic of this article is to demonstrate that DORIS has implemented a high-level integrity control of its data.Embedded in the DORIS receiver, DIODE (DORIS Immediate Orbit Determination) is a Real-Time On-Board Orbit Determination software. Its accuracy has also been dramatically improved when compared to Precise Orbit Ephemeris (P.O.E.), down to 2.7 cm RMS on Jason-2, 3.0 cm on Saral and 3.3 cm on CryoSat-2. Specific quality indices were derived from the DIODE-based Kalman filters and are used to monitor network and system performance. This paper covers the definition of these indices and how the reliability and the reactiveness to incidents or anomalies of the system are improved.From these indices, we have provided detailed diagnostic information about the DORIS system, which is available in real-time, on-board each DORIS satellite. Using these capabilities, we have developed real-time functions that give an immediate diagnosis of the status of key components in the DORIS system. The Near-Real Time navigation system was improved and can distinguish and handle both satellite events and beacon anomalies.The next missions to use DORIS will be Jason-3 and Sentinel-3, and then Jason-CS and SWOT (Surface Water and Ocean Topography). The real-time information on satellite positions should be better than 2.5 cm RMS on the radial component. Science products will benefit from this improvement in DORIS’s performance and data integrity.

DORIS system and integrity survey

DORIS, as other techniques for space geodesy (SLR, VLBI, GPS) has regularly progressed to meet the ever increasing needs of the scientific community in oceanography, geodesy or geophysics.Over the past 10years, a particular emphasis has been placed on integrity monitoring of the system, which has contributed to the enhancement of the overall availability and quality of DORIS data products. A high level of monitoring is now provided by a centralized control of the whole system, including the global network of beacons and the onboard instruments, which perform a constant end-to-end survey. At first signs of any unusual behavior, a dedicated team is activated with well-established tools to investigate, to anticipate and to contain the impact of any potential failures. The procedure has increased the availability of DORIS beacons to 90%.The core topic of this article is to demonstrate that DORIS has implemented a high-level integrity control of its data.Embedded in the DORIS receiver, DIODE (DORIS Immediate Orbit Determination) is a Real-Time On-Board Orbit Determination software. Its accuracy has also been dramatically improved when compared to Precise Orbit Ephemeris (P.O.E.), down to 2.7cm RMS on Jason-2, 3.0cm on Saral and 3.3cm on CryoSat-2. Specific quality indices were derived from the DIODE-based Kalman filters and are used to monitor network and system performance. This paper covers the definition of these indices and how the reliability and the reactiveness to incidents or anomalies of the system are improved.From these indices, we have provided detailed diagnostic information about the DORIS system, which is available in real-time, on-board each DORIS satellite. Using these capabilities, we have developed real-time functions that give an immediate diagnosis of the status of key components in the DORIS system. The Near-Real Time navigation system was improved and can distinguish and handle both satellite events and beacon anomalies.The next missions to use DORIS will be Jason-3 and Sentinel-3, and then Jason-CS and SWOT (Surface Water and Ocean Topography). The real-time information on satellite positions should be better than 2.5cm RMS on the radial component. Science products will benefit from this improvement in DORIS’s performance and data integrity.

Maximising value by using good data to drive good decisions

The industry-wide move towards big data and the digital oilfield is underpinned by good data. This paper outlines a suite of data management standards, systems and processes, and provides examples of how these have led to improved decision making. The approach involved the development of standards and streamlined business processes followed by the implementation of systems focusing on production data accessibility, quality and integration. Accessibility was addressed by making real-time data readily available from multiple devices so users spend more time using data instead of locating it. Quality was improved through the implementation of processes such as operational data validation (ODV) and production allocation (PA). Integration was facilitated so that users could view data from various systems in a single location. The implementation of data management standards, systems and processes led to improved decision making in the areas of external reporting, operating cost, safety, environment, commercial, reservoir management, well surveillance, and situational awareness. In particular, implementation of the ODV process ensured the completeness, accuracy and timeliness of data from reservoir to sales. Furthermore, improved accessibility and integration increased situational awareness, reduced troubleshooting time, and improved problem analysis. While the concept of data management and quality control is not new, the novelty is in the approach of developing robust standards, implementation of systems based on these standards, and creating the supporting business process and culture aligned to what drives value in the organisation. This is easily transferable and adaptable across all facets of the petroleum industry.

Impact of production requirements on high-quality ship product data models

Rules, standards and regulations have to be observed in every step of the ship design process. Ignoring these constraints in any case leads to higher costs going along with potentially lower product quality. Numerous investigations have shown that product data model correction activities require considerable resources (man-hours and although money = cost) to obtain an acceptable quality level, and in the worst case, a redesign of potentially larger parts of the ship structure has to be performed. Computer-aided design systems offer a large set of functions to define a product data model. However, in the current situation, these tools are not capable to fully ensure the correctness of the product data model while observing the relevant quality criteria which are generally shipyard specific as they are mainly imposed by the production facilities. A quality management system is introduced which is based on the ISO 10303-59 focusing on quality criteria to be applied for an efficient ship structure production process. The functions of the system will be demonstrated on weldability criteria. These criteria demand notches at distinctive constellations of components depending on their function and, for example, minimum distances and angles between structural parts due to welding processes and material requirements. As an additional criterion, sufficient excess material has to be assigned to plate edges functioning as block boundaries to allow for the correction of inaccuracies in the production process at even late stages. The integration of the product data quality management algorithms with the computer-aided design system, in this case AVEVA marine, shows an efficient correction of inapt data as the automatically detected errors can easily be fixed by means of a guided process including the visualization of the structural parts with the help of the computer-aided design system being applied for data modelling.

Consistency of quality attributes for the glycosylated monoclonal antibody Humira® (adalimumab)

Humira® (adalimumab) is a recombinant human IgG1 monoclonal antibody (mAb) glycoprotein consisting of 1330 amino acids that is specific for human tumor necrosis factor (TNF). The biological activity and clinical profile of mAb therapeutics, including adalimumab, is influenced by their protein structure and glycosylation patterns, which can be affected by the expression system, cell culture conditions and purification process methodology. While clinical outcome cannot yet be attributed to many of the individual structural features that constitute a mAb, it is evident that detailed structural attribute analysis is necessary if structural contributions to function are to be comprehensively defined. Adalimumab product quality data generated from over a decade of manufacturing across multiple production sites and through a series of manufacturing scale changes are presented here. These data reveal a consistent and tightly controlled profile for the product.