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RSC Content Type: Explainer Brief

Short, high-clarity breakdown of a specific term or mechanism.

  • SCAR

    SCAR commonly refers to a Supplier Corrective Action Request, a formal request issued to a supplier to investigate, contain, and correct a nonconformance associated with the products or services they provide.

    What a SCAR is

    A SCAR is a documented request from a customer organization (such as an OEM or tiered manufacturer) to a supplier that typically requires the supplier to:

    • Describe the nonconformance and its scope
    • Implement immediate containment to protect ongoing production or fielded product
    • Perform root cause analysis of the issue
    • Define and implement corrective and, where applicable, preventive actions
    • Provide objective evidence of completion and effectiveness

    SCARs are often managed within quality management systems, supplier quality portals, or integrated MES/ERP and are common in regulated industries such as aerospace, pharmaceutical, and medical device manufacturing.

    Where SCARs are used in operations

    In industrial and manufacturing environments, SCARs appear in:

    • Supplier quality management: Linking incoming inspection results, nonconforming material reports, and supplier performance metrics.
    • Compliance workflows: Providing documented evidence of how supplier-related nonconformances are controlled and corrected.
    • MES/ERP integration: Tying nonconformance records and holds to specific lots, work orders, purchase orders, and supplier records.
    • Risk management: Feeding into supplier risk ratings, approved supplier lists, and escalation processes.

    Response expectations for a SCAR, such as timing for containment, preliminary analysis, and final corrective action, are usually defined by contracts, purchase order terms, or customer quality clauses, rather than by a single industry-wide rule.

    What a SCAR is not

    • It is not the same as a general internal corrective action request, although the processes are similar.
    • It is not a complete supplier quality program, but one tool within a broader supplier management framework.
    • It is not by itself evidence of regulatory compliance, although SCAR records may support audits and assessments.

    Common confusion

    The acronym SCAR can be used differently across organizations:

    • Supplier Corrective Action Request: The most common meaning in manufacturing and regulated supply chains.
    • Supplier Corrective Action Report: Sometimes used interchangeably, emphasizing the documented response from the supplier rather than the request itself.

    In practice, both usages refer to the same basic process: documenting, investigating, and correcting supplier-related nonconformances in a traceable way.

    Link to aerospace and other regulated sectors

    In aerospace and other highly regulated industries, SCARs are often tied to customer-specific quality clauses and sector standards. Organizations may define required response times for containment actions, preliminary root cause analysis, and final corrective actions, and may integrate SCAR handling with incident, concession, or deviation processes. Digital systems are frequently used to ensure that SCAR records are complete, time-stamped, and traceable to affected parts, lots, and configurations.

  • SAP ERP

    SAP ERP is SAP’s enterprise resource planning software suite used to manage and integrate core business processes across an organization, including finance, procurement, manufacturing, supply chain, and human resources. In industrial and regulated manufacturing environments, SAP ERP commonly serves as the system of record for planning, master data, and transactional business processes.

    Scope and core functions

    In a manufacturing context, SAP ERP typically includes:

    • Materials management (purchasing, inventory, material master data)
    • Production planning (MRP, capacity planning, production orders)
    • Sales and distribution (customer orders, delivery, billing)
    • Quality management at a business-process level (inspection lots, usage decisions, defect records)
    • Plant maintenance (maintenance orders, equipment records)
    • Finance and controlling (cost tracking, profitability analysis)

    SAP ERP is not primarily a shop-floor control system. It typically operates at higher planning and business-process levels, while integrating with manufacturing execution systems (MES), laboratory systems, and other OT/IT platforms.

    Relationship to SAP S/4HANA and MES

    The term “SAP ERP” most often refers to SAP’s classic ERP product line (such as SAP ERP Central Component, ECC). SAP S/4HANA is SAP’s newer ERP platform, but many users still refer to it generically as “SAP ERP” when describing its role in the architecture.

    SAP, as a vendor, also offers MES and manufacturing-focused products such as SAP Digital Manufacturing and legacy SAP ME/MII. These are separate from SAP ERP, even though they share data and may be deployed together. In regulated manufacturing, SAP ERP commonly coexists with:

    • A dedicated MES for detailed work instructions, real-time execution, and enforcement of shop-floor rules
    • Quality, LIMS, and other systems that handle detailed records and evidence

    Operational use in regulated environments

    Within regulated operations, SAP ERP commonly:

    • Stores master data such as materials, bills of material, routings, and resources
    • Generates planned and process orders that are dispatched to MES or shop-floor systems
    • Captures business-level confirmations, goods movements, and batch records at a summary level
    • Supports traceability by managing batch numbers, serial numbers, and inventory status

    Execution details, operator actions, and equipment-level data are often managed in MES or other specialized systems and then interfaced back to SAP ERP for posting and reporting.

    Common confusion

    • SAP vs. SAP ERP: “SAP” is the vendor. “SAP ERP” is the ERP product family. Not every SAP product is an ERP system.
    • SAP ERP vs. MES: SAP ERP handles planning and business transactions. MES manages real-time production execution, detailed work instructions, and in-process data collection on the shop floor.
    • SAP ERP vs. SAP S/4HANA: S/4HANA is SAP’s modern ERP platform. In many architectures it plays a similar role to legacy SAP ERP but on a different technology stack.

  • Configuration versioning

    Configuration versioning is the practice of assigning identifiable versions to a system, application, device, process, or equipment configuration as it changes over time. It commonly refers to keeping a controlled history of settings, parameters, logic, mappings, templates, and other non-code configuration items so current and prior states can be identified and compared.

    In industrial and regulated environments, configuration versioning is used to understand what configuration was active at a given time, who changed it, when it changed, and what changed between versions. This can apply to MES rules, ERP integration mappings, PLC or SCADA parameters, electronic forms, work instruction settings, quality workflows, user-role configurations, and similar controlled setup data.

    Configuration versioning does not mean any change is automatically approved or released. It is about maintaining version identity and history. Approval workflows, change control, and document control may be related, but they are separate controls.

    What it typically includes

    • Version numbers, revision IDs, or other unique identifiers for a configuration state

    • Records of additions, removals, or edits to configuration items

    • Date, time, and user attribution for changes

    • Comparison between versions

    • Rollback or restoration to a prior known configuration, where supported

    • Linkage to deployment, release, or change records in broader governance processes

    Common confusion

    Configuration versioning is often confused with document version control and source code version control. Document version control applies to files such as SOPs, specifications, or controlled forms. Source code version control applies to software code. Configuration versioning focuses on operational settings and structured system behavior that may exist outside code files or formal documents.

    It is also distinct from a full audit trail. An audit trail may record every event or field-level change. Configuration versioning usually emphasizes named, recoverable configuration states and their revision history.

    Manufacturing context

    In manufacturing systems, configuration versioning commonly appears where system behavior must remain stable and explainable across production runs, quality events, or integration updates. Examples include versioned routing parameters in MES, revised inspection plan settings, changes to label templates, or updates to ERP-to-MES field mappings. The goal is to preserve a reliable record of which configuration was in effect for a given operation or period.

  • low-code platform

    A low-code platform is a software platform used to build applications, workflows, forms, dashboards, and integrations mainly through visual configuration rather than traditional hand coding. It commonly provides drag-and-drop design tools, reusable components, workflow logic, data models, and connectors to other systems.

    In manufacturing and regulated operations, a low-code platform is often used to create internal business applications such as deviation workflows, digital forms, approvals, maintenance requests, issue tracking, supplier collaboration steps, or simple MES-adjacent and quality processes. It can sit above existing systems like ERP, MES, QMS, or document control tools, or connect to them through APIs and integration services.

    What it includes and what it does not

    A low-code platform usually includes:

    • Visual application and workflow design
    • Prebuilt UI components and templates
    • Rules, logic, and approval routing
    • Data capture forms and basic reporting
    • Connectors or APIs for system integration
    • Security, roles, and deployment controls defined by the platform

    It does not automatically replace core transactional systems such as ERP, MES, PLM, or QMS. It is also not the same as custom software development, even though many low-code platforms allow some scripting or code extensions when needed.

    Operational meaning

    Operationally, a low-code platform is often the layer used to digitize manual or spreadsheet-based processes without building a full custom application from scratch. For example, a manufacturer might use one to create an operator escalation form, an NCR intake workflow, a training acknowledgment app, or a process exception review flow that exchanges data with ERP or MES.

    Where controls matter, organizations typically evaluate how the platform handles access, versioning, change management, audit trails, integrations, and data ownership. Those capabilities vary by product and configuration.

    Common confusion

    Low-code platform is commonly confused with no-code platform. No-code tools are generally aimed at users with little or no programming and rely more fully on configuration only. Low-code platforms usually still support technical users and may allow code for advanced logic, integrations, or user interface behavior.

    It is also commonly confused with an application platform or BPM/workflow tool. A low-code platform may include workflow and application functions, but the term refers more broadly to the development approach and tooling model, not only process automation.

  • industrial automation and control systems

    Industrial automation and control systems (IACS) are the combined hardware, software, networks, and related infrastructure used to monitor, control, and automate industrial processes and equipment. They are central to operational technology (OT) environments in manufacturing, energy, utilities, and other industrial sectors.

    IACS typically include sensors, actuators, controllers, human-machine interfaces, engineering workstations, and the communication networks that connect them. They may operate as stand-alone systems or be integrated with higher-level systems such as MES, SCADA, ERP, and quality management systems.

    Typical components of IACS

    While specific architectures vary, industrial automation and control systems commonly include:

    • Field devices such as sensors, transmitters, drives, and actuators that measure process conditions and execute control actions.
    • Controllers such as PLCs, DCS controllers, and RTUs that execute control logic and sequencing.
    • Supervisory systems such as SCADA, HMI, and historian servers that visualize, log, and coordinate process data and alarms.
    • Engineering and maintenance workstations used to configure control logic, update firmware, and manage system configuration.
    • Industrial networks and communication protocols that connect devices, controllers, and supervisory systems.
    • Supporting infrastructure including time synchronization, authentication services, and sometimes interfaces to IT systems.

    Use in manufacturing and regulated environments

    In manufacturing plants and other regulated operations, IACS are used to control production lines, utilities, and supporting processes. They influence product quality, equipment performance, data integrity, and worker safety. Example uses include:

    • Automated control of mixing, filling, packaging, and assembly operations.
    • Monitoring of critical parameters such as temperature, pressure, and flow.
    • Interlocks, safety functions, and shutdown logic at the control level.
    • Data collection for batch records, traceability, and audit trails when integrated with MES or quality systems.

    Because IACS directly affect physical processes, changes to configuration, software, or topology are often subject to formal change control, validation, and documented testing, especially in regulated industries.

    Relationship to cybersecurity and IEC 62443

    Industrial automation and control systems are a primary focus of industrial cybersecurity standards such as IEC 62443. In that context, IACS are treated as cyber-physical systems that require:

    • Identification and segmentation of control system assets and zones.
    • Defined roles and responsibilities for asset owners, integrators, and product suppliers.
    • Controls for secure development, configuration, operation, and maintenance over the system lifecycle.

    Cybersecurity for IACS considers not only confidentiality and integrity of data, but also availability and correct, timely control of the physical process.

    Scope and boundaries

    The term industrial automation and control systems commonly includes:

    • Process control systems (PCS) and distributed control systems (DCS).
    • Programmable logic controllers (PLCs) and their associated networks.
    • Supervisory control and data acquisition (SCADA) systems.
    • Building automation or utility control systems when they are part of industrial operations.

    It typically excludes general office IT systems (email, office productivity tools), business-only applications (CRM, HR), and consumer automation devices, even when they share similar technologies.

    Common confusion

    • IACS vs IT systems: IACS are focused on controlling physical processes in real time; IT systems are focused on information processing, storage, and business workflows. In many plants, the two are interconnected but governed differently.
    • IACS vs SCADA/PLC: SCADA or PLCs are specific system types or components. IACS is a broader term that covers the full set of industrial control equipment, software, and networks.
    • IACS vs OT: OT (operational technology) is a broad category of technologies that monitor or control physical devices and processes. IACS are a major subset of OT, focused specifically on automation and control.

  • How do we handle nonconformities that affect both quality and security?

    Handle nonconformities that affect both quality and security as a single, linked issue that is processed through both your quality management and cybersecurity processes. Splitting them into separate tracks without coordination usually creates gaps in risk assessment, corrective actions, and evidence for audits.

    1. Establish clear criteria for “quality + security” nonconformities

    First define what qualifies as a joint issue in your context. Typical triggers include:

    • Product or process nonconformance that is suspected to be caused by a cyber incident (e.g., tampered parameters, manipulated test results, unauthorized MES changes).
    • Security incidents that may have altered manufacturing records, recipes, configurations, or evidence needed for product release or traceability.
    • Compromise of systems that are part of validated or qualified processes (e.g., MES, historians, QMS, equipment controllers) where integrity of quality data is uncertain.

    The exact criteria depend on your risk assessment, system landscape, and regulatory obligations. Document them in procedures so operations, quality, and IT/OT security interpret events consistently.

    2. Use a single master record with cross-links to QMS and security

    In brownfield environments, you typically will not have a single system that can cleanly own both quality and security workflows. Instead:

    • Create one master record in the system that has the strongest regulatory traceability requirements, usually the QMS (e.g., nonconformity or CAPA record).
    • Open a corresponding security/incident ticket in the corporate incident response tool or OT security system.
    • Link the records explicitly using IDs in both directions and reference them in deviation reports, risk assessments, and change control.

    Do not rely only on email or informal notes for these links. The master record should clearly state that security and data integrity are in scope so reviewers can see the full context.

    3. Run a joint impact and risk assessment

    Assess impact across both quality and security dimensions before deciding on containment or release decisions.

    At minimum, consider:

    • Product and patient/customer impact: Could altered data or process steps affect safety, performance, regulatory compliance, or field reliability?
    • Data integrity: Are historical records, batch data, test results, or genealogy still trustworthy? How far back could compromise extend?
    • System scope: Which systems (MES, PLCs, DCS, LIMS, QMS, ERP) may have been affected? Are any validated or qualified?
    • Regulatory and contract requirements: Are there obligations to notify regulators, certification bodies, or customers for security-related quality risks?
    • Containment risk: Could security containment actions (e.g., isolating a line, blocking accounts, patching) create new process or quality risks?

    This assessment should be performed collaboratively by quality, operations/engineering, and IT/OT security, not by one function alone.

    4. Coordinate containment across production, quality, and IT/OT

    Containment must limit both quality and security impact, while recognizing constraints on downtime and validation:

    • Stabilize the process: Pause affected lots/batches, label and segregate suspect material, and freeze product release decisions until minimum integrity is established.
    • Secure the environment: IT/OT may isolate systems, disable accounts, or roll back configurations, but should coordinate with operations to avoid unsafe or uncontrolled shutdowns.
    • Preserve evidence: Ensure logs, configuration snapshots, and relevant batch records are preserved before systems are rebuilt or restored.

    Document containment decisions and tradeoffs, especially if security best practices must be staged or adapted to avoid unplanned outages of validated systems.

    5. Perform integrated root cause analysis

    Root cause analysis should explicitly consider both process/quality and cybersecurity contributing factors. Common patterns include:

    • Process & equipment: Poor parameter control, inadequate verification of setpoints, missing independent checks.
    • Human factors: Shared credentials, bypassed controls, unvetted changes to master data or recipes.
    • Technical controls: Inadequate network segmentation, weak access control, insufficient integrity monitoring, missing change logs.
    • Management systems: Gaps in training, procedures, and change control covering both quality and security for OT systems.

    If you use formal methods (e.g., 5-Whys or fishbone diagrams), show explicitly where security-related causes and controls come into play. This is important for auditability and for preventing recurrences that cross domains.

    6. Design CAPAs that address both domains

    Corrective and preventive actions must be coherent across quality and security. Typical actions might include:

    • Process corrections: Rework, additional inspection, or product recall decisions based on validated data integrity and risk criteria.
    • Security hardening: Strengthening authentication, tightening change control on recipes/configurations, improving logging, or implementing OT security monitoring.
    • Data integrity measures: Rebuilding trust in data sets (e.g., re-running critical tests, cross-checking manual records, reconciling batch histories).
    • Governance changes: Updating procedures to require security review for changes to validated systems, training operators and engineers on cyber-impacted nonconformities.

    Be explicit about which system owns each action (QMS, ITSM, OT maintenance system, MES change workflow) and ensure due dates and effectiveness checks are aligned. Avoid duplicating the same action in multiple systems without synchronization.

    7. Respect validation, change control, and legacy system constraints

    In regulated, long-lifecycle plants, many quality-critical and OT systems are validated or qualified, and cannot be rapidly replaced or heavily modified without significant cost and downtime. When handling joint quality/security nonconformities:

    • Expect that “ideal” security fixes (e.g., rapid patching, architecture overhauls, replacing legacy controllers) may not be immediately feasible for validated equipment and software.
    • Use layered mitigations where needed, such as procedural controls, monitoring, or network-level protections, while planning longer-term validated changes.
    • Ensure any configuration change, patch, or system restoration follows documented change control and, where required, revalidation or verification.

    Full system replacement solely to resolve a combined quality/security issue is rarely practical in aerospace-grade or similarly regulated environments, due to qualification burden, revalidation cost, integration complexity, and extended downtime risks. Plan phased, risk-based improvements instead.

    8. Maintain traceability and audit-ready documentation

    Because these events cut across domains, documentation and traceability are critical:

    • Retain a complete chain of records: initial detection, risk assessment, decision logs, containment, root cause analysis, CAPAs, and effectiveness checks.
    • Ensure traceability from affected lots/batches and equipment to the nonconformity, and from the nonconformity to the associated security incident records.
    • Capture rationale for decisions, especially where business continuity or validation constraints limited the speed or scope of security changes.

    This documentation supports regulatory inspections, customer audits, and internal reviews, but it does not guarantee any specific compliance or certification outcome.

    9. Define ownership and communication paths

    Clarity on roles and escalation is essential before an event occurs:

    • Assign a lead function for joint issues, often quality for product-impacting events, with IT/OT security as co-leads for cyber incidents.
    • Predefine when to involve site management, corporate security, legal, and regulatory affairs.
    • Ensure operators and engineers know how to recognize and report issues that may have a security origin (e.g., unexplained parameter changes, inconsistent MES data).

    Periodic drills or tabletop exercises that include both nonconforming product scenarios and security incidents can expose gaps in your current approach.

    10. Fit the approach to your existing systems and maturity

    The specifics of handling joint quality/security nonconformities depend heavily on:

    • Which systems you have in place (QMS, MES, ERP, ITSM, OT monitoring) and how well they are integrated.
    • Your current validation state, documented procedures, and data integrity controls.
    • The regulatory frameworks and customer expectations that apply to your products and markets.

    Where tooling integration is weak, focus on clearly defined procedures, roles, and record-linking conventions. Over time, you can incrementally improve system integrations to reduce manual effort and the risk of things “falling between” quality and security workflows.

  • AI-assisted root cause analysis

    AI-assisted root cause analysis commonly refers to the use of artificial intelligence techniques to help investigate why a problem occurred in a process, system, or operation. In manufacturing and regulated environments, it is typically used to support analysis of deviations, nonconformances, downtime events, yield loss, alarm patterns, or recurring quality issues by finding patterns, correlations, sequences, or anomalies across available data.

    It is a support method, not the root cause itself and not a guarantee that the suggested cause is correct. The output is usually a ranked set of likely contributing factors, evidence links, or investigation leads that people review alongside process knowledge, documented procedures, and formal quality methods.

    What it usually includes

    • Analysis of data from MES, ERP, QMS, historians, CMMS, SCADA, LIMS, or maintenance logs

    • Pattern detection across events such as scrap spikes, machine faults, parameter drift, changeovers, or supplier-related defects

    • Use of machine learning, statistical models, rules, knowledge graphs, or generative AI to summarize evidence and propose likely causes

    • Tracing possible relationships among materials, equipment states, operators, work orders, batches, or process settings

    What it does not mean

    • It does not replace structured problem-solving methods such as 5 Whys, fishbone analysis, fault tree analysis, or formal RCCA workflows.

    • It does not prove causation simply because it finds correlation.

    • It is not the same as corrective action, CAPA execution, or verification of effectiveness.

    • It is not limited to generative AI chat tools. Many implementations use analytics models or rules engines without a conversational interface.

    How it appears in operations

    Operationally, AI-assisted root cause analysis often appears as a feature in quality, manufacturing, reliability, or analytics software. A system may ingest event records, process parameters, genealogy, maintenance history, and inspection results, then highlight common precursors or probable drivers of an issue. For example, it may associate recurring surface defects with a specific material lot, machine setting range, tool wear pattern, or sequence of alarms before failure.

    In regulated manufacturing, the value of the term usually depends on traceable inputs, retained evidence, and clear distinction between system-generated suggestions and human conclusions.

    Common confusion

    AI-assisted root cause analysis is often confused with predictive maintenance, anomaly detection, or incident summarization. Predictive maintenance focuses on forecasting failures before they occur. Anomaly detection flags unusual behavior. Incident summarization condenses records or logs. AI-assisted root cause analysis is narrower: it focuses on explaining why a specific problem likely happened.

  • Drill-down

    Drill-down commonly refers to navigating from a high-level summary into progressively more detailed information. In manufacturing and industrial software, it is used to investigate a metric, event, record, or exception by moving from dashboards or aggregated reports into the underlying data.

    The term usually applies to reporting, analytics, MES, ERP, quality, and traceability systems. For example, a user might drill down from plant-level throughput to a production line, then to a work order, and then to a specific machine event or operator entry. The core idea is structured detail navigation, not just opening another screen.

    Drill-down includes hierarchical or linked exploration of related data, such as moving from:

    • a KPI to the transactions or events behind it
    • a nonconformance count to individual NCR records
    • a batch summary to lot, material, or genealogy details
    • an equipment alarm total to time-stamped alarm history

    It does not usually mean root cause analysis by itself, although drill-down is often a step used during investigation. It also does not necessarily imply write access or workflow action; many drill-down paths are read-only views for analysis and verification.

    Common confusion

    Drill-down is often confused with filtering, sorting, and search. Filtering narrows a data set by conditions. Sorting changes display order. Search locates matching records. Drill-down specifically means moving from summary information to the lower-level details behind that information.

    It can also be confused with traceability. Traceability focuses on lineage and relationships across materials, processes, and records. Drill-down is a navigation method that may be used to access traceability data, but it is not the same concept.

    How it appears in operations systems

    In regulated and quality-sensitive environments, drill-down is commonly used to review exceptions, reconcile data between systems, and inspect evidence behind reported metrics. Typical examples include moving from an ERP production summary into MES execution records, or from a quality dashboard into inspection results, deviations, or CAPA-linked records.