Deloitte Industry Print Process Modeler 4.2 Download
Berry Spitsberg
The Business Process Framework (also known as eTOM) is a comprehensive, industry-agreed, multi-layered view of the key business processes required to run an efficient and agile digital enterprise. It provides a common language for use across departments, systems, partners and suppliers, reducing cost and risk of system implementation, integration and procurement.
eTOM helps organizations understand, design, develop and manage IT and network applications in terms of business process requirements, so applications will better meet business needs. It sets a vision for business process-driven approaches to managing the enterprise, ensuring integration among all vital enterprise systems concerned with service delivery and support.
eTOM's business-oriented view of the enterprise is useful for planners, managers, strategists and others who need to view the enterprise in business terms, without immediate concern for the nature of the way that these business needs are organized or automated within the business. Therefore, eTOM emphasizes issues such as process structure, process components, process interactivity and the business roles and responsibilities to which these relate.
eTOM is a reference framework for categorizing all the business activities that a service provider will use in a structured manner that allows these to be addressed at various levels of detail. The processes are grouped by domains and vertical category context and are decomposed starting from high level core processes that depict key activities succeeded by lower level unique task activities.
eTOM serves as the blueprint for process direction and provides a reference point for internal process reengineering needs, partnerships, alliances, and general working agreements with other enterprises.
Get involved with the project that covers the information systems architecture part of ODA, including the ODA functional architecture, Information Framework (SID), Functional Framework (FF) and Business Process Framework (eTOM).
National Academies of Sciences, Engineering, and Medicine; Division on Earth and Life Studies; Board on Chemical Sciences and Technology; Committee to Identify Innovative Technologies to Advance Pharmaceutical Manufacturing. Innovations in Pharmaceutical Manufacturing on the Horizon: Technical Challenges, Regulatory Issues, and Recommendations. Washington (DC): National Academies Press (US); 2021 Feb 24.
In simple systems, the final outputs of the process depend solely on measurable inputs. An example of a simple system is the process of compressing granules into tablets in which the granules have been preprocessed to provide the desired composition and structure for tablet formation. During the compression process, tablet weight and hardness depend on tablet-press inputs and granule attributes, but the process has no dynamic inputs or dynamic outputs beyond the control of humidity, which can affect plasticity. Thus, outputs, such as tablet weight and hardness, can be predicted and controlled more easily than, for example, glycosylation with mannose-type N-glycans in the production of monoclonal antibodies.
As pharmaceutical manufacturing processes become more integrated, their complexity as systems will increase; this is the case for advanced manufacturing applications, such as continuous manufacturing and intensified operations (Huang et al. 2020). The complexity of pharmaceutical processes has implications for the measurement, modeling, and control technologies used in their design and operation. The measurement of critical quality attributes and process parameters might require a broader and more sophisticated portfolio of sensor technologies. The models, although based on equations rooted in fundamental knowledge, will typically need to be supplemented with data-derived relationships, perhaps involving ML, that span the knowledge gap. The control systems might require a portfolio of hierarchical, model-based and adaptive control technologies. AI and specifically ML methods might need to play substantial roles in predicting and controlling the performance of complex pharmaceutical-manufacturing systems.
Sensors or analyzers are devices used to detect or measure a system characteristic or property. From a process perspective, sensing can be accomplished in three main configurations: in-line, at-line, and off-line. In-line measurements are taken directly from the process (for example, a pH measurement inside a reactor). At-line measurements are taken next to the process (for example, a tablet-weighing station near a tableting machine), typically with automatic sampling. Off-line measurements are taken outside the manufacturing suite (for example, an impurity measurement in a quality-assurance laboratory). Although all the sensors provide useful information about the manufacturing process, only in-line and some at-line sensors can be considered process analyzers because only they can provide timely information on the health of the process to support process-control decisions. Offline sensors, typically laboratory analytic instruments, are commonly used to measure the final quality of a product, to ensure thorough product characterization during development, or to develop calibrations for in-line and at-line sensors.
During the pharmaceutical-development phase, information is obtained through process studies that establish scientific understanding of the product and processes. Off-line sensors tend to provide the more detailed information about the chemical and physical characteristics of materials that helps to build that understanding. However, these analytic tools do not provide real-time results and so are deployed in off-line configurations to obtain data that require high resolution, such as data on molecular structure, glycosylation, impurities, and crystal structure. Several innovations in such analytic methods have advanced to the stage where they will support filings within the next 5 or more years.
Another tool that should see increasing use in the future for the evaluation of therapeutic proteins is two-dimensional nuclear magnetic resonance spectroscopy, which has the potential to be used to compare structural attributes of proteins (Schiel 2020). That potential capability is important because structural similarity is hypothesized to be indicative of functional similarity and thus could inform decisions about safety and efficacy. Additional tools noted by Schiel (2020) that could soon find their way into biopharmaceutical development and quality-assurance laboratories include
When designing strategies for pharmaceutical-process monitoring and control, engineers have gravitated toward simple, robust, low-maintenance sensors. The output of such sensors typically is only one process measurement per device (univariate sensors). Examples are pH meters, mass flow meters, thermocouples, scales, and humidity sensors. Although such sensors do support quality assurance, their primary role is as part of the rudimentary automation system. Specifically, the process variable measurement that the sensor provides is typically used as part of a low-level feedback control strategy centered on a single unit operation. Because they typically do not measure quality attributes, such sensors alone cannot enable active process control of product quality and cannot provide enough observability to support more advanced control strategies.
In response to the process analytic technology (PAT) initiative, the industry has taken steps to adopt sensors that monitor multiple process variables and, most important, quality attributes (outcomes). Some of the most promising process sensors are based on vibrational spectroscopy (Romero-Torres et al. 2009). They offer multiple benefits, such as in situ measurements, no need for sample preparation, and rapid scanning. However, they do require tailored calibrations, which are normally constructed by using multivariate statistical approaches.2 Thus, companies need to expand (and even redefine) their analytic-chemistry (and supporting) competences with chemometric skills that are not part of traditional academic curricula. Such novel and sophisticated sensors are also more expensive and less rugged than the classic sensors. Thus, the adoption of these spectroscopy-based sensors for process monitoring has been slower than might be expected. Nevertheless, the major companies have invested in the development of measurement and control strategies that use spectroscopic sensing devices and have actively shared their experiences throughout the industry (Futran 2020). In the next 5 years, the Food and Drug Administration (FDA) will need to continue developing workforce competences in spectroscopic methods and their deployment constraints. Although the technologies are not new to the pharmaceutical industry, they are not yet standard (Futran 2020).
Other novel process sensing approaches that are receiving attention are based on electric capacitance volume-tomography measurement of mass flow of particulate streams (Li et al. 2015) and dielectric spectroscopy for viable-cell density, cell size, intracellular conductivity, and membrane capacitance (Opel et al. 2010). The in-line measurement of mass flow in continuous solid oral-dosage lines offers the benefits of enabling direct monitoring of intermediate process streams to establish the state of control and of enabling decoupling of control structures.
The challenges in adopting novel sensing approaches are closely tied to the maturity of the sensing technology and the level of customization and rigor needed for its intended use. As discussed above, advanced and multipurpose sensing technologies typically require tailored multivariate chemometric models for monitoring or quantifying chemicals or properties in complex mixtures. The custom models need to be developed, validated (including design of new validation protocols), maintained, and updated by experts who understand the science behind the sensing mechanism, the complex-mixture properties (and dynamics), and the fundamentals behind the multivariate algorithm used. Given that the competences needed are not part of any academic curricula but rather a specialization, it is challenging to recruit a critical mass of talent to develop and support these applications.
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