Experienced manufacturing teams already know what SPC is supposed to do. They use it to separate noise from signal, to avoid overreacting to normal variation, and to intervene only when a process is truly changing. As a working definition, Statistical Process Control (SPC) is a disciplined method for using statistical techniques to understand, monitor, and improve process behavior over time. Its purpose is not to eliminate variation, but to help practitioners distinguish between normal, expected variation and signals that indicate meaningful change. In theory, this is straightforward. In practice, many SPC programs struggle to earn trust. Charts exist, alarms fire, and reviews are held, yet the results often fail to align with what engineers and operators observe on the plant floor. When this happens, SPC is not abandoned, but quietly worked around.
SPC emerged in the early twentieth century as manufacturing shifted from craft production to mass production. As processes scaled and direct observation became impractical, engineers needed a way to understand process behavior using data rather than intuition alone. Walter A. Shewhart introduced control charts at Bell Laboratories to distinguish between common cause variation inherent to a process and special cause variation that indicated abnormal behavior. This distinction allowed engineers to focus their efforts where intervention would actually improve outcomes. The core insight was simple but powerful. Not every fluctuation is a problem, and reacting to noise creates instability. SPC provided, and still provides, a statistical framework to decide when actions are justified and when stability should be preserved. Shewhart’s work assumed stable processes, consistent measurement systems, and disciplined data handling. These assumptions were reasonable in tightly controlled environments, but they become fragile as manufacturing systems scale, automate, and integrate across many digital layers.
Traditional SPC theory assumes that data is clean, continuous, and trustworthy by default. In modern plants, measurements arrive from PLCs, DCS systems, lab systems, and manual sources, each with different timing, quality indicators, and failure modes. When gaps, bad quality flags, or late-arriving values are not explicitly managed, SPC results drift away from reality. Teams often compensate through manual filtering or ad hoc exclusions, which keeps charts alive but shifts the burden onto individual expertise rather than the system.
Even when data quality is addressed, SPC often fails because calculations are disconnected from how the process actually runs. Logic is scattered across dashboards, spreadsheets, MES reports, and scripts, each encoding slightly different assumptions. Statistics are frequently computed across calendar time instead of production time, blending running, idle, changeover, and fault conditions into a single population. Without explicit context such as machine state, product, batch, or recipe, SPC outputs describe activity, not process behavior.
SPC depends on history. Engineers must be able to retrieve consistent time-series data over hours, days, months, and years. That history must survive network interruptions, system upgrades, and scaling demands. A reliable historical layer ensures SPC analysis is based on what actually happened, not what was convenient to collect. Raw data storage, without interpolation is key.
Modern SPC does not operate in isolation from real-time operations. Engineers and operators rely on live signals to understand current behavior, respond to emerging special causes, and validate whether corrective actions are having the intended effect. Protocols such as MQTT, MQTT-based standards like Sparkplug B, and industrial interfaces such as OPC UA are now fundamental to digital manufacturing environments. These technologies provide low-latency, structured access to live process data and state information. However, real-time data does not replace SPC history. It complements it by allowing teams to observe variation as it develops, while SPC provides the statistical framework to interpret that variation correctly.
SPC metrics must be defined once and reused everywhere. This is key and where most manufacturing architectures have simply missed the mark. Calculations are often scattered across PLC code, SCADA scripts, and buried within the historian backend. Without a standardized methodology for cleansing data and then aggregating it, trusting the data becomes difficult, if not nearly impossible.
Control limits, sampling windows, exclusion rules, and statistical methods must be governed assets, not embedded assumptions.
When logic changes, engineers must be able to reprocess history to maintain consistency and auditability. Additionally, modern SPC requires explicit modeling of events and states. Running time, batch phases, recipe steps, and quality modes define when data is valid for statistical analysis. Therefore, context and governance are not an enhancement to SPC, they are prerequisites.
The Timebase Historian is a high-integrity time series historian designed for industrial workloads. Its role in SPC is to serve as the authoritative source of historical truth for raw measurements. Timebase focuses on reliable capture, durable storage, and predictable retrieval of time-stamped values. It does not attempt to interpret meaning or apply business logic.
With Timebase, engineers can trust that the raw data feeding SPC calculations reflects real plant behavior. To see how Timebase provides this foundation in practice, you can download Timebase for free and enjoy it without any licensing constraints.
Infohub by Flow Software transforms raw time-series data into usable and governed information. For SPC, this means defining when data should be analyzed, how statistics are computed, and how results are used and reused. Infohub is where the question “what does this data mean?” is answered once and applied consistently.
Since SPC depends on analyzing the right data at the right time, Infohub enables engineers to define events such as machine running and stopped periods, batch boundaries and recipe phases, changeovers and warm-up intervals, etc. and apply SPC calculations specifically for the boundaries of these events. Control charts and statistical measures computed against these event periods, not arbitrary clock time, provide the diagnostic insights that engineers require.
Measures within Infohub represent cleaned, aligned, and context-aware values that SPC calculations are built on. Rather than applying control logic directly to raw tags, Infohub computes measures that reflect how engineers actually reason about the process. Examples include averages, ranges, standard deviations, and counts calculated over defined time windows and/or event periods, such as during running time only or within a specific batch phase. Measures ensure SPC statistics are computed from consistent inputs, regardless of where or how the raw data originated. By standardizing measures, Infohub allows teams to reuse the same SPC foundations across charts, reports, and publish these stored results to other applications without re-implementing logic in each tool.
Infohub treats SPC calculations as governed assets. Control limits, rolling statistics, and capability indices are defined centrally and versioned. When definitions change, Infohub supports reprocessing historical results so SPC metrics remain consistent and auditable. To see how Infohub helps manufacturing teams operationalize SPC at scale, visit https://flow-software.com.
Timebase and Infohub deliberately separate responsibilities. Timebase stores history. Infohub interprets it. A typical SPC flow looks like this:
1. Raw measurements are collected and archived in Timebase
2. Infohub retrieves historical data
3. Infohub applies event context, calendars, and rules
4. SPC statistics and indicators are computed as governed measures
5. Results are published to dashboards, reports, APIs, or a Unified Namespace
With this solution stack, manufacturers gain trustworthy historical data for statistical analysis, consistent SPC definitions across teams and tools, and context-aware control charts aligned to real operations. Most importantly, SPC shifts from being a compliance exercise to a practical data-driven support system driven by expert teams.
Organizations struggling with unreliable history should begin with a reliable and cost effective historian to establish a clean data foundation. It is crucial not to be constrained by a licensing model that restricts the storage of process variables that operations would benefit from performing SPC on.
Those facing inconsistent KPIs and misleading SPC signals should start with Infohub, or a similar product, to centralize logic and context. For manufacturers seeking a complete SPC pathway at speed, deploying Timebase and Infohub together provides an end-to-end architecture from plant-floor signals to governed statistical insight. If you want to see how experienced manufacturing teams use Flow to scale SPC without rebuilding logic or losing trust, reach out to the team at Flow Software for an immediate demo.