Connect981 – Content Dev

Glossary Tag: process monitoring

  • SL 1

    SL 1 is a cybersecurity security level commonly used in industrial control system standards to describe protection against casual or accidental misuse, rather than deliberate, well-resourced attacks.

    Core meaning

    In industrial and OT cybersecurity, Security Level 1 (SL 1) usually refers to the lowest defined level of protection in a multi-level scheme (often SL 1 through SL 4). It typically includes:

    • Basic user authentication (for example, unique logins, simple password policies)
    • Foundational network protections (for example, simple firewalls or access lists)
    • Basic hardening and configuration control (for example, disabling unused accounts or services)
    • Protections mainly aimed at preventing inadvertent changes and casual probing

    SL 1 generally assumes that an attacker has limited motivation, limited skills, and limited resources. It is not intended to address targeted, sophisticated, or persistent cyber attacks.

    Use in industrial and regulated environments

    In regulated manufacturing and critical infrastructure, SL 1 is often applied to:

    • Non-safety-critical support systems that still connect to OT networks
    • Legacy equipment that cannot be upgraded to higher security levels
    • Zones where risk assessments show low impact to safety, quality, or regulatory outcomes

    Risk-based architectures may mix different SLs across zones and conduits. Some systems or zones may appropriately target SL 1, while others require SL 2, SL 3, or higher, depending on criticality and risk.

    Relationship to standards

    Security levels, including SL 1, are commonly associated with industrial cybersecurity frameworks and standards. These schemes define capability requirements for each level in areas such as access control, data integrity, system availability, and change management. The detailed criteria vary by standard, but the intent of SL 1 remains a baseline of protection against non-targeted threats.

    Operational implications

    In practice, specifying SL 1 for a system or zone typically means:

    • Documenting the target security level in design and risk assessments
    • Implementing minimum controls aligned with that level
    • Recognizing that the system is not designed to withstand focused, skilled attackers

    For OT, MES, and integrated IT/OT systems, SL 1 serves as a reference point for scoping security controls and for explaining why some systems should not be expected to meet higher levels such as SL 3 or SL 4.

    Common confusion

    • Not the same as “no security”: SL 1 includes basic, documented protections and is more than an unmanaged or open system.
    • Not a maturity level: SL 1 describes targeted technical capability against defined threat types, not organizational process maturity.
    • Not universally required: Some assets may intentionally remain outside formal SL classification, depending on risk and architecture.

    Tie to the risk-based context

    When deciding whether a system should aim for SL 1, SL 2, or higher, organizations typically consider:

    • Impact on safety, product quality, and regulatory compliance if compromised
    • Feasibility of implementing stronger controls on existing OT and legacy equipment
    • Availability of compensating controls at the network or procedural level

    Within a risk-based security program, SL 1 is a deliberate design choice for low-risk environments, rather than a default for all systems.

  • 21 CFR Part 11

    21 CFR Part 11 is a U.S. Food and Drug Administration (FDA) regulation that defines the criteria under which electronic records and electronic signatures are considered trustworthy, reliable, and equivalent to paper records and handwritten signatures for FDA-regulated activities.

    Scope and purpose

    Part 11 applies to FDA-regulated organizations, such as pharmaceutical, biotechnology, medical device, certain food, and other life science manufacturers, when they use electronic systems to create, modify, maintain, archive, retrieve, or transmit records required by FDA regulations or submitted to the FDA.

    It covers both:

    • Electronic records: Data and documents stored and managed in electronic form (for example, batch records in MES, quality records in QMS, equipment logs in SCADA or data historians).
    • Electronic signatures: Computer-based methods of sign-off that are intended to be the legal equivalent of handwritten signatures.

    Key regulatory expectations

    Although specific implementations vary, 21 CFR Part 11 commonly refers to requirements in areas such as:

    • System validation: Demonstrating that software and computerized systems (for example, MES, LIMS, QMS, DMS, SCADA) perform as intended and consistently.
    • Record integrity and security: Protecting records from unauthorized access, alteration, or deletion, including role-based access control and secure storage.
    • Audit trails: Computer-generated, time-stamped audit trails that independently record the date, time, user, and nature of actions that create, modify, or delete electronic records.
    • User identification and authentication: Unique user IDs and robust authentication mechanisms to ensure that actions and signatures can be reliably attributed to a specific individual.
    • Electronic signature controls: Defined signature components (such as user ID plus password or biometric), linking of signatures to their records, and procedural controls over signature use.
    • Policies and procedures: Documented procedures, training, and administrative controls that govern how systems are used, maintained, and periodically reviewed.
    • Record retention and retrieval: Ensuring that records remain readable, accessible, and retrievable for the required retention period.

    Operational meaning in industrial and manufacturing systems

    In manufacturing and industrial operations, 21 CFR Part 11 typically affects how OT and IT systems are specified, deployed, and governed when they support regulated products or processes. Example impacts include:

    • Configuring MES, LIMS, or QMS workflows so that approvals, reviews, and sign-offs use controlled electronic signatures with appropriate authentication.
    • Ensuring automated data capture from equipment, PLCs, or data historians includes protected, reviewable audit trails for critical parameters.
    • Integrating ERP, DMS, and shop-floor systems so that regulated records remain complete and traceable across system boundaries.
    • Establishing validation and change control for software used in production, quality, and laboratory operations that generate or manage FDA-relevant records.

    What 21 CFR Part 11 does not cover

    • It does not define how to design processes or products; it focuses on controls for electronic records and signatures.
    • It is not a general IT security standard, although it intersects with cybersecurity and access control practices.
    • It does not apply to records that are not required by FDA regulations or filings, unless an organization chooses to apply similar controls broadly.

    Common confusion

    • Generic e-signature vs. Part 11 electronic signature: A basic electronic signature feature (for example, commercial e-sign tools) is not automatically considered Part 11 compliant. Additional technical controls, validation, audit trails, and procedural governance are typically needed.
    • System “being Part 11” vs. using it in a Part 11 context: Commercial software is often described as “Part 11 capable” or “supports Part 11 requirements,” but the regulation applies to how a system is configured, validated, and used within a specific quality system, not to the product alone.
    • 21 CFR Part 11 vs. EU Annex 11: Both address computerized systems and electronic records in regulated environments, but they are separate regulatory frameworks from different authorities and should not be treated as identical.

    Connection to electronic signature approval workflows

    When electronic signatures are used for approvals in regulated manufacturing environments (such as batch release, deviation approval, CAPA closure, or document change control), 21 CFR Part 11 is commonly referenced as the regulatory basis for ensuring those signatures are uniquely attributable, securely applied, and traceably linked to the associated electronic records.

  • What are the 5 main areas of digital transformation?

    In industrial and manufacturing contexts, “the 5 main areas of digital transformation” commonly refers to grouping digital change into five focus domains. One widely used, practical view for plants and regulated operations is:

    1. Customer and value delivery

    Digital technologies that change how value is delivered to internal or external customers. In manufacturing this can include:

    • Customer portals for order status, quality documentation, and certificates
    • Digital collaboration on specifications, drawings, and engineering changes
    • Integration of customer systems with ERP or MES for order, forecast, or quality data

    2. Connected operations and processes

    Digitization and integration of shop floor and support processes to improve safety, quality, cost, and delivery. Typical elements are:

    • Connected equipment (OT) and sensors feeding MES, historians, or data platforms
    • Digital workflows for production, maintenance, and material handling
    • Electronic batch records and digital traceability for regulated production
    • Real-time visibility of OEE, NPT, and other performance metrics

    3. Smart products and services

    Using digital capabilities in the products or services themselves, or in how they are supported. Examples include:

    • Products with embedded sensors, connectivity, or remote monitoring
    • Usage and performance data feeding back into design and process improvement
    • Digitally enabled service offerings such as predictive maintenance support

    4. Data, analytics, and integration

    Capabilities that turn operational and business data into reliable information for decisions and compliance. This area often covers:

    • Integration across MES, ERP, LIMS, QMS, PLM, and shop floor systems
    • Standardized data models for production, quality, and genealogy
    • Analytics and operations intelligence for yield, quality, and throughput
    • Controlled data access aligned with cybersecurity and regulatory needs

    5. Organization, people, and governance

    Changes to structure, skills, and ways of working that make digital solutions sustainable, especially in regulated environments. Typical components:

    • Digital skills and training for operators, engineers, and quality personnel
    • Governance for data ownership, system changes, and validation practices
    • Standardized digital work instructions and document control
    • Cross-functional alignment across IT, OT, quality, and operations

    Notes on variation

    Different frameworks label the areas of digital transformation in slightly different ways, and some emphasize four or six pillars instead of five. In manufacturing, any reasonable five-area model typically covers the same core ideas: how you serve customers, how you run operations, what you make and sell, how you use data, and how your organization supports digital ways of working.

  • CSV

    CSV (Comma-Separated Values) is a plain text file format used to represent tabular data, where each line is a record and fields within a record are separated by a delimiter, most commonly a comma. CSV files are widely used to move structured data between applications that do not share a direct integration.

    Characteristics in manufacturing and regulated environments

    In industrial operations and manufacturing systems, CSV commonly refers to files used to:

    • Import or export master data (materials, equipment lists, recipes, part numbers) between ERP, MES, LIMS, and other systems
    • Transfer production records, test results, and quality metrics from shop-floor or OT systems into reporting or analytics tools
    • Stage data for one-time migrations or cutovers between legacy and new systems
    • Share data with suppliers, contract manufacturers, or partners where system-to-system integration is limited

    Although the name suggests commas, other delimiters such as semicolons or tabs are sometimes used. CSV itself does not define data types, validation rules, or metadata. These must be agreed separately or enforced by the importing system.

    What CSV includes and excludes

    CSV files include:

    • A text-based representation of rows and columns
    • Header rows that may define column names
    • Simple escaping rules for delimiters and line breaks within fields, such as quoting

    CSV files do not include:

    • Built-in schemas, field types, or constraints
    • Formatting, formulas, or macros found in spreadsheet files
    • Versioning, audit trails, or access controls

    Operational considerations

    When CSV is used for production, quality, or compliance-relevant data, organizations typically pay attention to:

    • Consistent column ordering, naming, and delimiters across files and systems
    • Character encoding (for example UTF-8) to avoid data corruption
    • Time zone and format conventions for timestamps and numerical values
    • Procedures for generation, review, transfer, and storage in line with internal quality or data integrity requirements

    Common confusion

    CSV is often confused with:

    • Spreadsheet files (such as XLSX): These can contain multiple sheets, formatting, and formulas. CSV is plain text and contains only raw values laid out in a single logical table.
    • Database exports or backups: Databases may be exported as CSV, but a CSV file is not a database. It has no indexes, constraints, or query engine.

    Despite its simplicity, CSV remains a common interchange format in manufacturing and quality workflows, particularly where lightweight, system-neutral data exchange is needed.

  • Connect 981

    Connect 981 commonly refers to a software platform name used in manufacturing and regulated operations. In this context, it is best understood as a product or system identifier rather than a general industry standard, method, or compliance term.

    As a platform term, Connect 981 may describe software that supports connected operational workflows such as manufacturing execution, quality events, traceability, work instructions, data capture, or integration between shop floor and business systems. The exact feature set depends on how the platform is implemented, so the term itself does not inherently mean MES, ERP, QMS, or PLM, even if it may interact with those systems.

    What it includes and excludes

    Connect 981 includes the idea of a specific named digital system or application environment used to coordinate manufacturing-related information and process execution.

    • It includes product-level references such as platform, application, module set, or connected workflow environment.
    • It may include integration with OT and IT systems, depending on deployment.
    • It does not by itself name a standard, regulation, protocol, or KPI.
    • It does not automatically imply certification, validation status, or compliance outcome.

    Operational meaning

    In day-to-day use, Connect 981 would typically appear in discussions about how operators, engineers, quality teams, and planners access and exchange production data. Examples may include recording execution data, managing quality records, routing work, linking evidence to orders or parts, or passing information between enterprise systems and shop floor processes.

    Common confusion

    Connect 981 is commonly confused with a category of software rather than a named platform. A product name is not the same as the function it may serve.

    • MES: a manufacturing execution system category focused on production control and execution.

    • QMS: a quality management system category focused on quality processes and records.

    • ERP: an enterprise resource planning system category focused on business planning and transactions.

    Connect 981 may overlap with one or more of these categories, but the term itself most directly refers to the platform name.

  • digital manufacturing

    Digital manufacturing commonly refers to the use of connected digital systems, data, and models across the manufacturing lifecycle to plan, execute, monitor, and improve production. It links design, engineering, production, quality, and supply chain through software, integrated data flows, and feedback from the physical shop floor.

    What digital manufacturing includes

    In industrial and regulated environments, digital manufacturing typically involves:

    • Digital design and engineering data, such as CAD and PLM-managed product definitions and bills of materials (BOMs)
    • Manufacturing execution and control systems, including MES, SCADA, and other OT/IT integrations that drive work orders, routings, and sequencing
    • Digital work instructions and travelers that replace or augment paper with controlled, versioned electronic content
    • Integrated quality and compliance workflows, such as electronic records, traceability, nonconformance management, and audit trails
    • Data collection and analytics from machines, test equipment, and operators for visibility into OEE, yield, scrap, and other KPIs
    • Connected supply chain processes, including ERP integration, materials planning, and supplier collaboration supported by shared digital data

    Digital manufacturing is not a single product. It is a way of operating where processes, equipment, and people are coordinated through interoperable digital systems rather than isolated paper processes or stand-alone tools.

    Operational meaning

    Operationally, digital manufacturing shows up as:

    • Electronic release of work orders, routings, and revisions from ERP/PLM into MES and the shop floor
    • Operators using digital work instructions, checklists, and data entry forms at the point of use
    • Automated capture of as-built, as-inspected, and test data to build a digital record of each unit or lot
    • Centralized traceability, genealogy, and document control to support internal reviews and external audits
    • Dashboards and reports that provide real-time or near-real-time visibility into performance, bottlenecks, and quality issues

    Relationship to other concepts

    Digital manufacturing is closely related to, but distinct from, several adjacent terms:

    • Industry 4.0: A broader concept that includes cyber-physical systems, IIoT, and advanced automation. Digital manufacturing is one practical way organizations implement Industry 4.0 ideas.
    • Digital thread: The end-to-end data continuity across the product lifecycle. Digital manufacturing contributes to the digital thread by generating and consuming detailed production and quality data.
    • Smart factory: Often used to describe a highly automated, sensor-rich facility. Digital manufacturing can exist in both highly automated and largely manual environments, as long as the processes are digitally defined and managed.

    Common confusion

    • Not just 3D printing: In some contexts, digital manufacturing is equated with additive manufacturing. In regulated industrial operations, the term is broader and includes all digitalized processes, whether they use additive, subtractive, or assembly operations.
    • Not only design-side tools: CAD, CAM, and simulation tools are part of digital manufacturing, but the term also covers production execution, quality, and supply chain interactions on the factory floor.

    Use in regulated industries

    In regulated manufacturing sectors, digital manufacturing commonly refers to the coordinated use of systems such as PLM, ERP, MES, QMS, and plant-floor data sources to maintain traceable, controlled, and auditable records of how products are built, inspected, and released. This includes maintaining version control on specifications and instructions, capturing electronic production history, and linking nonconformance and CAPA processes to the underlying production data.

  • Event modeling

    Event modeling is a way of describing information systems, business processes, and user interactions in terms of time-ordered events and the resulting changes in system state. It focuses on what happens in a process, when it happens, and how data and systems respond to each occurrence.

    Core idea

    In event modeling, an event is something that has happened and is important to the system or process. Each event is typically tied to a specific time and to data that describes what changed. By mapping these events and the states they produce, teams can understand, design, and align systems and workflows.

    For industrial and manufacturing operations, events can include:

    • Operator actions, such as starting or completing a work step
    • Machine states, such as a line going into fault, idle, or run mode
    • Quality outcomes, such as an inspection pass, fail, or NCR raised
    • Logistics changes, such as material received, kitted, or issued to a work order
    • System integrations, such as an MES posting a production confirmation to ERP

    How event modeling is used

    Event modeling commonly refers to a structured practice of laying out:

    • Inputs: triggers, commands, or upstream events
    • Events: what is recorded as having happened
    • State: how key records or objects look after each event
    • Views and outputs: reports, dashboards, or documents that are derived from events

    In manufacturing and regulated environments, event modeling may be used to:

    • Design MES, historian, or IoT data models centered on production and quality events
    • Clarify how shop-floor events feed ERP, QMS, PLM, and traceability records
    • Support auditability by showing which events generate permanent records and evidence
    • Align OT and IT teams on how equipment signals, operator entries, and system messages are captured

    What event modeling includes and excludes

    Event modeling includes:

    • Defining the set of business-relevant events and their data payloads
    • Describing how events transition systems from one state to another
    • Visualizing the flow of events across time and across systems

    It does not by itself include:

    • Choosing specific technologies or message buses
    • Writing detailed control logic or PLC programs
    • Defining every user interface detail or screen layout

    Common confusion

    Event modeling vs. process mapping: Process maps (such as swimlanes or value stream maps) usually show activities, roles, and flows at a higher level. Event modeling focuses on discrete, time-stamped events and the data/state changes they cause, which is often more precise for system design and integration.

    Event modeling vs. event sourcing: Event sourcing is a software architecture pattern where system state is reconstructed from an append-only log of events. Event modeling is a broader analysis and design technique that can be used with or without event sourcing.

    Operational relevance in manufacturing

    In industrial operations, event modeling can help teams:

    • Identify which shop-floor events must be recorded for traceability and genealogy
    • Design MES and integration logic in line with standards such as ISA-95 without depending on any single implementation
    • Clarify how deviations, CAPA actions, and inspection results are triggered by specific events
    • Support operations intelligence by ensuring events carry the data needed for OEE, NPT, and quality metrics

    Used in this way, event modeling acts as a cross-functional language between engineering, IT, OT, quality, and operations teams when defining or improving manufacturing information flows.