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RSC Topic: Statistical Process Control (SPC)

  • What is the Root Cause Analysis process in manufacturing?

    In manufacturing, Root Cause Analysis (RCA) is a structured approach for finding and eliminating the underlying causes of quality problems, equipment failures, and safety incidents. The goal is to prevent recurrence, not just to fix visible symptoms.

    Typical Root Cause Analysis process

    1. Define and contain the problem

      • Describe the problem clearly: what, where, when, how big, and how often.
      • Contain the issue to protect customers and operations (e.g., quarantine stock, stop the line, switch to backup equipment).
      • Agree on the problem statement before investigating causes.
    2. Collect data and evidence

      • Gather process data (parameters, settings, SPC charts, machine logs).
      • Inspect materials, parts, tooling, and fixtures involved.
      • Interview operators and maintenance staff who were present.
      • Capture time, shift, lot, and equipment identifiers to spot patterns.
    3. Map the process

      • Document the end-to-end process flow (process map or value stream map).
      • Identify where the defect or failure is first detectable and where it may actually originate.
      • Compare the documented process to how work is really done on the floor (actual vs. intended process).
    4. Identify possible causes

      • Use structured tools such as Fishbone (Ishikawa) diagrams, 5 Whys, or cause-and-effect matrices.
      • Consider multiple categories: Man (People), Machine, Method, Material, Measurement, and Environment.
      • List all plausible causes without judging them too early.
    5. Analyze and verify root causes

      • Narrow down from possible to probable causes using data, tests, and experiments.
      • Check whether each suspected cause can fully explain the observed problem pattern (where, when, frequency, and severity).
      • Use methods such as correlation analysis, design of experiments (DoE), or controlled trials when appropriate.
      • Confirm the root cause with objective evidence; avoid relying only on opinion or hierarchy.
    6. Develop and implement corrective actions

      • Define actions that address the verified root causes, not just the symptoms.
      • Use engineering changes, process adjustments, training updates, or supplier actions as needed.
      • Update procedures, work instructions, and checklists so the new way of working is clear.
      • Assign responsibilities, deadlines, and resources; track completion formally (e.g., 8D reports, CAPA system).
    7. Validate effectiveness

      • Monitor defect rates, downtime, scrap, rework, or incident metrics after changes.
      • Confirm that the specific problem does not reappear over an agreed observation period.
      • If the issue persists or shifts elsewhere in the process, revisit the analysis and assumptions.
    8. Standardize and prevent recurrence

      • Embed changes in standard work, control plans, and maintenance routines.
      • Improve mistake-proofing (poka-yoke), alarms, interlocks, or inspection points where appropriate.
      • Share lessons learned with other lines, plants, or products that use similar processes.
      • Maintain documentation so future teams understand the history and rationale for changes.

    Common tools used in manufacturing RCA

    • 5 Whys analysis
    • Fishbone (Ishikawa) diagrams
    • Process mapping and value stream mapping
    • Failure Mode and Effects Analysis (FMEA)
    • Statistical Process Control (SPC) and Pareto charts
    • Design of Experiments (DoE) for complex or interacting causes

    Risk and limitations to be aware of

    • RCA reduces risk but does not eliminate it. New failure modes and rare conditions can still occur.
    • Superficial analysis is a common failure mode. Stopping at the first obvious cause (e.g., operator error) often misses deeper systemic issues (design, training, workload, or management decisions).
    • Data quality matters. Incomplete or inaccurate data can lead to false conclusions and ineffective actions.
    • Bias and blame can distort results. RCA is most effective when it focuses on systems and processes rather than assigning individual fault.
    • Changes can introduce new risks. Engineering or process changes should go through proper risk assessment, validation, and change control before full rollout.

    In summary, RCA in manufacturing is a disciplined, evidence-based cycle of defining a problem, understanding the process, identifying and confirming root causes, and making controlled changes that are monitored over time. Its effectiveness depends on quality of data, cross-functional participation, and a focus on system improvements rather than quick fixes.

  • Process Window

    Core meaning

    A **process window** is the defined range of input and output conditions within which a manufacturing process is expected to operate in a stable, capable, and safe manner. It expresses the allowable variation for key parameters before the process is considered out of control, unsafe, or at risk of producing nonconforming product.

    A process window typically includes:

    – **Input parameters**: e.g., temperature, pressure, speed, feed rate, dwell time, humidity, reagent concentration, line speed, machine set‑points.
    – **Output responses**: e.g., critical quality attributes (CQAs), dimensions, weight, torque, viscosity, particle size, or other measured product characteristics.

    It is usually defined using engineering studies, statistical analysis (such as Design of Experiments), and historical production data.

    Use in industrial and regulated environments

    In manufacturing and regulated operations, the process window commonly refers to:

    – The **approved ranges** for critical process parameters defined in process descriptions, recipes, work instructions, or master batch records.
    – The **limits configured** in MES, SCADA, DCS, or equipment control systems for alarms, interlocks, and parameter checks.
    – The **operational target and tolerance** used by operators, process engineers, and quality personnel to judge whether the process is running normally.

    Process windows may be linked to different types of limits, for example:

    – **Engineering/operating limits**: broader ranges where the equipment can technically run without damage.
    – **Control limits**: statistically derived limits used in control charts, often narrower than specification limits.
    – **Specification limits**: ranges related to product requirements or regulatory filings.

    Boundaries and exclusions

    The term **process window**:

    – **Includes**: quantitative ranges, combinations of parameters, or multidimensional spaces where acceptable operation has been characterized.
    – **Includes**: graphical representations (e.g., contour plots or 2D/3D diagrams) that show feasible regions of operation.
    – **Excludes**: informal rules of thumb or undocumented practices that have not been defined or justified as acceptable ranges.
    – **Excludes**: pure scheduling windows (time slots in planning systems) that do not describe process conditions.

    In many organizations, only ranges that are documented, reviewed, and controlled (for example via change control) are treated as formal process windows.

    Relationship to quality and process control

    From a quality and operations standpoint, a process window is used to:

    – **Monitor process behavior**: comparing real‑time data to the defined window to detect deviations or drifts.
    – **Support investigations**: checking whether deviations, nonconformances, or complaints coincide with operation outside the window.
    – **Design and improvement work**: using the window to understand robustness and sensitivity to changes in inputs or materials.

    Manufacturing systems (e.g., MES, LIMS, historian, SPC tools) often encode process windows as parameter limits, rules, or models. When live data approach or exceed window boundaries, the system may raise alerts, block processing steps, or trigger review workflows according to site rules.

    Common confusion and related terms

    The term **process window** is sometimes used interchangeably with related concepts, but there are distinctions:

    – **Process window vs. specification limits**: specification limits apply to product characteristics (what is acceptable in the final or in‑process product), while a process window often focuses on process settings and conditions (how the product is made). They are related but not identical.
    – **Process window vs. design space**: in some regulated domains, a design space represents the multidimensional combination of input variables demonstrated to provide quality. A process window may be a selected or narrower operating region within that broader design space, chosen for routine production.
    – **Process window vs. normal operating range (NOR)**: a normal operating range is often a narrower, more practical band within the process window where the process is usually run. The process window may be slightly wider, capturing acceptable but less typical operation.

    Clarifying which of these concepts is meant is important in documentation, systems configuration, and communication between engineering, operations, and quality teams.

    Application in site context

    In the context of industrial operations and manufacturing systems, **process window** commonly appears in:

    – **MES and batch records**: as parameter ranges for steps (e.g., mix speed 200–250 rpm; temperature 70–75 °C).
    – **Equipment and OT systems**: as configured alarm bands, interlock thresholds, and recipe limits.
    – **Quality and operations intelligence tools**: as visual overlays on trends or dashboards, highlighting when parameters operate inside or outside the defined window.

    These windows support traceability and review by associating production results with the exact process conditions under which they were achieved.

  • Cp

    Cp is a process capability index used in statistical process control and quality engineering. It commonly refers to the ratio between the allowed specification width and the natural spread of a process, typically estimated as six standard deviations.

    In practical terms, Cp indicates the potential capability of a process to fit within upper and lower specification limits if the process is stable and centered between those limits. A higher Cp value means the process variation is small relative to the tolerance range.

    Cp does not show whether the process average is actually centered on target. Because of that, it does not by itself describe the actual defect risk when the process mean is shifted. For that reason, Cp is often reviewed alongside Cpk, which accounts for centering.

    What Cp includes and excludes

    • Includes: comparison of process variation to specification limits.
    • Assumes: a reasonably stable process and a meaningful estimate of variation.
    • Excludes: process centering, special-cause instability, and broader system issues such as measurement error unless those are addressed separately.

    How it appears in manufacturing

    Cp is commonly used for critical dimensions, fill volumes, torque values, temperature-controlled steps, and other measurable characteristics in production and quality workflows. It may appear in SPC software, MES-connected quality records, capability studies, control plans, supplier quality reviews, and continuous improvement reporting.

    For example, a machining process may show a high Cp for a diameter tolerance, meaning the observed variation is narrow compared with the specification band. If the process mean drifts toward one limit, however, actual performance may still be unacceptable even though Cp remains high.

    Common confusion

    Cp vs. Cpk: Cp measures potential capability based on spread only. Cpk measures capability while also considering how centered the process is within the specification limits.

    Cp vs. Pp: Cp is commonly associated with short-term or within-process variation in a stable process. Pp commonly uses overall performance variation across a broader time window.

    Cp vs. control limits: Cp uses specification limits set by design or customer requirements, not control limits calculated from process behavior.

  • Process capability index (Cpk)

    Process capability index (Cpk) is a statistical measure used to indicate how well a stable process can produce output within defined specification limits. It compares the process average and variation to both the upper and lower specification limits, then reports the side where the process is performing worst.

    Cpk is commonly used in manufacturing and quality control to summarize whether a process is both centered and consistent enough to meet engineering requirements. A higher Cpk generally indicates that the process output is farther from the nearest specification limit relative to its variation.

    What it includes and what it does not

    Cpk includes two core ideas: process spread and process centering. It reflects not only how much the process varies, but also whether the process mean is shifted toward one specification limit.

    Cpk does not itself prove that a process is in statistical control, and it does not replace measurement system analysis, sampling plans, or product acceptance decisions. It is a capability indicator, not a direct statement that every part is conforming.

    How it is used in operations

    In production environments, Cpk is commonly calculated for critical dimensions, fill weights, temperatures, torque values, or other measurable characteristics with upper and lower specifications. Teams may review it during process qualification, ongoing process monitoring, supplier quality reviews, or continuous improvement work.

    In MES, QMS, SPC, or reporting systems, Cpk may appear as a quality metric tied to a part number, operation, machine, line, tool, or characteristic. It is often used alongside control charts and other process performance measures.

    Common confusion

    Cpk is often confused with Cp. Cp measures potential capability based on process variation alone and assumes the process is centered. Cpk adjusts for actual centering, so it is usually the more realistic index when the process mean is not exactly on target.

    Cpk is also sometimes confused with Ppk. While usage varies by organization, Ppk commonly refers to long-term or overall process performance using actual overall variation, whereas Cpk commonly refers to capability based on within-process variation under more controlled conditions.

    Practical interpretation notes

    • Cpk is most meaningful when the characteristic is measurable on a continuous scale and the process is reasonably stable.

    • The index depends on valid specification limits set by design or customer requirements.

    • Poor measurement quality can distort the result, so gage capability matters.

    • A single Cpk value does not explain the cause of variation or process shift.

    For example, a machining process for a bore diameter may show a lower Cpk if the average diameter drifts close to the upper tolerance, even if the overall spread has not changed.

  • Statistical process control (SPC)

    Statistical process control (SPC) commonly refers to the use of statistical methods to monitor, understand, and control variation in a process over time. In manufacturing, it is used to distinguish normal process variation from signals that may indicate a shift, drift, special cause, or loss of stability.

    SPC is primarily a process-monitoring discipline, not just a final inspection activity. It typically uses data collected during production, such as dimensions, weights, temperatures, torque values, fill volumes, or cycle times, and evaluates that data with tools such as control charts, run rules, and capability-related measures.

    SPC includes how data is sampled, plotted, and interpreted for ongoing control of a process. It does not, by itself, guarantee that product meets specification, and it is not the same thing as simple pass/fail inspection. A process can be statistically stable yet still produce output outside specification if it is centered or designed poorly.

    How SPC appears in operations

    In plant and quality workflows, SPC may be embedded in shop floor systems, quality software, MES, or connected measurement equipment. Operators, technicians, or quality personnel may record measurements at defined intervals, review control charts, and respond when the data shows an out-of-control condition or a non-random pattern.

    • At a machining center, diameter measurements may be charted every hour to detect tool wear before parts drift out of control.
    • In packaging, fill-weight data may be monitored to identify a process shift rather than relying only on end-of-line rejects.
    • In regulated production, SPC records may be retained as part of broader quality evidence, depending on the process and system design.

    What SPC includes

    • Collection of process data over time
    • Use of control charts or similar statistical monitoring tools
    • Evaluation of common-cause versus special-cause variation
    • Defined reactions when statistical signals appear
    • Support for process understanding and ongoing control

    What SPC does not include by itself

    • Final product release decisions on its own
    • Calibration or validation of measurement systems
    • Root cause analysis, although SPC may trigger it
    • A guarantee of process capability or conformance to specifications

    Common confusion

    SPC is often confused with acceptance inspection, process capability, and measurement system analysis.

    • SPC vs. inspection: Inspection checks whether units meet requirements. SPC monitors whether the process behavior remains statistically controlled over time.

    • SPC vs. process capability: Capability metrics such as Cp or Cpk compare process performance to specification limits. SPC focuses first on whether the process is stable enough for those metrics to be meaningful.

    • SPC vs. MSA or Gage R&R: MSA evaluates whether the measurement system is reliable enough to trust the data. SPC uses that data to monitor the process.

    Related standards and systems context

    SPC is widely used within quality management and continuous improvement programs and may be connected to MES, QMS, ERP-integrated quality records, or manufacturing analytics. It is also commonly associated with broader manufacturing and quality frameworks, but the term itself refers specifically to statistical monitoring and control of process variation.

  • Cpk

    Cpk is a process capability index that quantifies how well a stable process can produce output within specified tolerance limits, taking into account both the spread of the data and how centered the process mean is between the specification limits.

    What Cpk represents

    Cpk compares the natural variation of a process (usually estimated using the process standard deviation) against the distance from the process mean to the nearest specification limit. It is typically used when both an upper specification limit (USL) and a lower specification limit (LSL) are defined.

    In common form:

    • Cpk = minimum of (CPU, CPL)
    • CPU = (USL − mean) / (3 × standard deviation)
    • CPL = (mean − LSL) / (3 × standard deviation)

    A higher Cpk value indicates that, assuming a stable and approximately normal distribution, more of the process output is expected to fall within the specification limits. A Cpk close to zero suggests the process mean is near or outside at least one specification limit.

    Use in manufacturing and regulated environments

    In industrial and regulated manufacturing, Cpk is commonly used to:

    • Assess if a process is capable of meeting customer or internal specifications before full-scale production.
    • Monitor ongoing process performance as a quality and risk indicator, often alongside leading indicators like equipment condition or setup accuracy.
    • Support decisions about process adjustments, equipment maintenance, or improvement projects.
    • Provide quantitative evidence in PPAP, validation, or qualification activities, subject to applicable procedures.

    Cpk values are often calculated from measurements captured in MES, SPC, LIMS, or quality systems and may be surfaced in dashboards as part of process performance or leading indicator panels.

    Assumptions and limitations

    Interpreting Cpk correctly depends on several conditions:

    • Stable process: The process should be statistically stable over the period of data collection. Large shifts, trends, or seasonal effects reduce the usefulness of Cpk.
    • Representative data: The sample should represent normal operating conditions, not just best-case or heavily screened data.
    • Distribution shape: Cpk is most straightforward to interpret when the process data are approximately normally distributed.
    • Specification clarity: Cpk is defined with respect to stated LSL and USL. It does not apply if only a target without limits is defined.

    Cpk itself does not guarantee compliance and does not describe the underlying causes of variation. It is one metric in a broader control and quality management framework.

    Common confusion

    • Cpk vs. Cp: Cp considers only the process spread relative to the specification width and assumes the process is perfectly centered. Cpk also accounts for how far the mean is from the center, so Cpk is always less than or equal to Cp.
    • Cpk vs. Ppk: Ppk is a performance index using overall (often long-term) variation, including between-lot or between-shift effects. Cpk uses within-process variation under stable conditions. Both can be reported, but they answer slightly different questions.
    • Cpk vs. control limits: Cpk is based on specification limits (requirements). Control limits in SPC charts are based on process behavior. A process can be in statistical control with a low Cpk if it is stable but not capable of meeting specifications.

    Relation to leading indicators

    In many manufacturing systems, Cpk is treated as a lagging indicator because it is calculated from produced parts or batches. However, trending Cpk over shorter time windows or at critical process steps can be used in a more leading way, signaling emerging capability loss before quality issues appear in final inspection or customer returns.

  • control charts

    Control charts are graphical tools used in statistical process control (SPC) to monitor how a process behaves over time. They plot a sequence of measured values against time, along with a calculated process average (center line) and statistically derived control limits. The primary purpose is to distinguish normal, inherent variation (common-cause) from unusual variation (special-cause) that may indicate a process change, error, or emerging issue.

    Key elements of a control chart

    Most control charts contain:

    • Time-ordered data points representing a quality characteristic (for example, part dimension, fill weight, temperature, defect count).
    • Center line, usually the process mean, median, or target value.
    • Upper and lower control limits (UCL/LCL), typically calculated as the expected range of common-cause variation based on historical data and statistical assumptions.
    • Rules for interpretation, such as points outside control limits or non-random patterns that signal special-cause variation.

    Use in industrial and regulated environments

    In manufacturing and other industrial operations, control charts commonly appear as part of shop-floor quality checks, MES or SPC modules, and problem-solving methods such as Six Sigma. They are often used to:

    • Monitor critical process parameters and product characteristics in real time.
    • Detect special-cause variation early so operators can investigate and document potential causes.
    • Support capability analysis and continuous improvement projects by providing an objective history of process stability.
    • Provide documented evidence of ongoing process control for audits and quality system requirements.

    Control charts may be maintained manually on paper or generated automatically from OT/IT data, such as historian tags, MES records, or inspection systems.

    Types of control charts

    Control charts are selected based on the type of data and sampling approach. Common examples include:

    • X-bar and R / X-bar and S charts for continuous data collected in subgroups (for example, 5 parts per hour).
    • Individuals (X-mR) charts for single observations taken at a time, often used when subgrouping is not practical.
    • p, np, c, and u charts for attribute data, such as proportion defective, number of defects, or count of events per unit.

    In regulated environments, selection and setup of the chart type are typically documented within quality procedures or control plans.

    What control charts are and are not

    Control charts:

    • Show whether a process is statistically stable over time.
    • Help separate routine variation from signals that warrant investigation.
    • Provide input to root cause analysis and corrective or preventive actions.

    Control charts are not:

    • Guarantees that a process meets specifications or regulatory requirements.
    • Equivalents of specification limits; control limits are based on process behavior, not customer or regulatory specs.
    • Full quality systems; they are one tool within broader quality and operations management frameworks.

    Common confusion

    • Control limits vs specification limits: Control limits reflect actual process variation, while specifications are externally defined acceptance criteria. A process can be in statistical control and still produce nonconforming product if it is centered incorrectly or has too much variation.
    • Run charts vs control charts: Run charts plot data over time without statistically derived control limits. Control charts add those limits and structured rules for detecting special-cause variation.

    Relationship to ISO 9001 and Six Sigma

    Within ISO 9001-based quality management systems, control charts are commonly used as documented methods to monitor and control key processes, especially where consistent quality and traceable evidence are required. In Six Sigma and related methodologies, they are core tools for characterizing baseline performance, verifying process stability before capability analysis, and sustaining improvements in the Control phase.

  • measurement system analysis

    Measurement system analysis (MSA) is a structured evaluation of how accurate, precise, and consistent a measurement process is. It focuses on the measurement system itself, not the product or process being measured, and is commonly used in manufacturing and other regulated industrial environments.

    What a measurement system includes

    In this context, a measurement system typically includes:

    • Measuring devices and sensors (for example, gauges, scales, test equipment, inline sensors)
    • Measurement methods and procedures (work instructions, test methods, sampling plans)
    • People who perform the measurements (operators, technicians, inspectors)
    • Data collection and recording mechanisms (manual forms, MES, LIMS, SCADA, or other IT/OT systems)

    MSA examines how these elements behave together when measurements are taken in real operating conditions.

    What measurement system analysis does

    Measurement system analysis commonly addresses questions such as:

    • Repeatability: Does the same person get similar results with the same device under the same conditions?
    • Reproducibility: Do different people, shifts, or locations get similar results using the same method?
    • Bias: Is there a consistent difference between the measured value and a reference or known value?
    • Linearity: Does the bias change across the measurement range?
    • Stability: Does the measurement system drift over time?

    Standard tools used in MSA include gauge repeatability and reproducibility (gauge R&R) studies, bias and linearity studies, and stability studies. Results are typically expressed as components of variation and compared to product tolerances or process variation.

    Role in industrial and regulated environments

    In industrial operations and manufacturing, MSA supports:

    • Quality control decisions, such as pass/fail inspection and release of lots or batches
    • Statistical process control (SPC), where unreliable measurements can hide or create false signals
    • Capability analyses and performance metrics, where measurement error affects calculated indices
    • Root cause analysis and problem solving, where poor data from measurement systems can misdirect investigations

    In regulated environments, MSA is often referenced in procedures for test method validation, equipment qualification, and data integrity, and is linked to standards or guidance for metrology and quality systems. The analysis and its results are typically documented, version controlled, and maintained as part of a broader quality management system.

    Relation to the 5 M’s of manufacturing

    Within the 5 M’s framework (Man, Machine, Material, Method, Measurement), measurement system analysis relates to the “Measurement” category. It provides a way to test whether observed variation in product or process data is due to the measurement system itself or to other causes. This helps ensure that conclusions drawn from cause-and-effect analysis are based on trustworthy measurements.

    What measurement system analysis is not

    • It is not general process capability analysis; it focuses only on the measurement system.
    • It is not a full calibration program, although it uses and depends on proper calibration.
    • It is not limited to a single device; it considers how people, methods, devices, and data systems interact.

    Common confusion

    • MSA vs. calibration: Calibration compares a device to a standard and adjusts it if needed. MSA evaluates the entire measurement system performance under use conditions, even when devices are calibrated.
    • MSA vs. inspection: Routine inspection uses measurement results to accept or reject product. MSA evaluates whether those results are reliable enough to support such decisions.

  • control chart

    A control chart is a graphical tool used in statistical process control (SPC) to monitor how a process metric behaves over time and to distinguish normal variation from signs of potential problems. It plots measured values in time sequence along with a calculated center line and statistically derived upper and lower control limits.

    What a control chart includes

    A typical control chart for manufacturing or other industrial operations contains:

    • Data points collected over time, such as part dimensions, weight, temperature, cycle time, or defect counts.
    • Center line, usually the process mean or target value for the metric.
    • Upper and Lower Control Limits (UCL/LCL), calculated from process variation (for example, using standard deviations) to define the expected range of common-cause variation.
    • Optional specification limits, which show customer or design requirements and are separate from control limits.

    In regulated or highly controlled environments, control charts are often generated and maintained by MES, quality management systems (QMS), or specialized SPC software, and may be referenced in work instructions, batch records, or validation documentation.

    How control charts are used operationally

    In manufacturing operations, control charts commonly support:

    • Real-time monitoring of critical quality attributes (CQA) or critical process parameters (CPP) to detect trends before they lead to nonconformance.
    • Distinguishing common vs. special causes of variation, helping teams decide when to investigate and adjust a process.
    • Continuous improvement and capability analysis, by providing a historical record of process stability and changes.
    • Leading indicators of potential quality issues, for example when points trend toward a control limit even though specifications are still met.

    Common control chart types in industrial settings include X-bar and R charts, X-bar and S charts, individual (I) and moving range (MR) charts, p-charts and np-charts (for proportion or count of defectives), and c or u charts (for defect counts per unit).

    What a control chart is not

    • It is not only a historical report; it is intended for ongoing monitoring and timely response.
    • It is not a simple run chart; control limits on a control chart are statistically calculated, not just visual guides.
    • It is not a guarantee of compliance; it is a tool that supports process understanding and decision making.

    Common confusion

    • Control limits vs. specification limits: Control limits reflect current process behavior and are calculated from data; specification limits come from requirements (design, customer, or regulatory). A process can be in control (within control limits) and still produce out-of-spec product if the process is centered incorrectly or has too much variation.
    • Control chart vs. run chart: A run chart shows data over time with a simple reference line or average. A control chart adds statistically based control limits and specific rules for interpreting special-cause signals.

    Link to leading indicators in manufacturing

    In the context of leading indicators, control charts are often used to monitor upstream variables that predict future quality or performance issues. For example, a control chart on a critical temperature, torque, or pressure parameter may signal emerging instability before scrap rates or customer complaints increase.