# SPC Implementation Readiness Assessment > Source: https://netray.co/tools/spc-implementation-readiness-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Answer ten questions about your data, measurement systems, training, and culture to find out whether an SPC rollout will stick or stall. ## Category field-service ## Tool **Kind:** assessment **Questions:** #### Have you identified and documented the critical-to-quality characteristics that SPC would monitor? **Options:** ##### No, critical characteristics have not been formally identified **Score:** 0 ##### Partially identified for a few products, mostly informal **Score:** 1 ##### Identified for most products but not linked to control plans **Score:** 2 ##### Fully identified, documented in control plans, and reviewed with customers **Score:** 3 #### How is process measurement data collected on the shop floor today? **Options:** ##### Mostly not recorded, or on paper that is filed away **Score:** 0 ##### Paper or spreadsheets, entered manually and inconsistently **Score:** 1 ##### Digital entry at most operations, but in disconnected systems **Score:** 2 ##### Automated or systematic digital capture tied to work orders **Score:** 3 #### Have your measurement systems passed a gauge R&R or MSA study? **Help:** SPC on data from unverified gauges charts measurement noise, not process variation. **Options:** ##### No MSA or gauge R&R studies have been performed **Score:** 0 ##### Studies done years ago or only on a few gauges **Score:** 1 ##### Studies on most critical gauges, refresh schedule inconsistent **Score:** 2 ##### Current MSA on all critical gauges with a managed calibration program **Score:** 3 #### Are your key processes documented with standard work and known process parameters? **Options:** ##### Processes rely on operator experience, little documentation **Score:** 0 ##### Setup sheets exist but are often out of date **Score:** 1 ##### Documented for most processes, revision control is weak **Score:** 2 ##### Standard work with controlled parameters at every key process **Score:** 3 #### Do operators and supervisors understand basic variation concepts (common vs. special cause)? **Help:** SPC fails when every chart signal triggers machine adjustment, which adds variation instead of removing it. **Options:** ##### No exposure to variation concepts or SPC training **Score:** 0 ##### A few quality staff trained, operators not at all **Score:** 1 ##### Basic training delivered but not reinforced or applied **Score:** 2 ##### Operators trained, and over-adjustment is actively discouraged **Score:** 3 #### Is there a defined reaction plan for what happens when a process signals out of control? **Options:** ##### No, response would be improvised **Score:** 0 ##### Informal escalation to a supervisor or engineer **Score:** 1 ##### Written reaction plans for some processes, unevenly followed **Score:** 2 ##### Documented reaction plans with containment steps and ownership **Score:** 3 #### Have you run process capability studies (Cp/Cpk) on the processes you intend to control? **Options:** ##### Never, capability is unknown **Score:** 0 ##### One-off studies for specific customer demands only **Score:** 1 ##### Capability known for some key processes, not maintained **Score:** 2 ##### Capability baselined and tracked for all candidate processes **Score:** 3 #### How committed is plant leadership to acting on SPC signals, even when it means stopping production? **Help:** The hardest SPC test is the first time a chart says stop and the schedule says ship. **Options:** ##### Leadership views SPC as a customer checkbox **Score:** 0 ##### Supportive in principle, but schedule pressure always wins **Score:** 1 ##### Willing to act on signals for critical characteristics **Score:** 2 ##### Explicit policy: verified out-of-control conditions stop the process **Score:** 3 #### Can quality data be connected to ERP records (work orders, lots, machines, operators)? **Help:** Without ERP context, chart signals cannot be traced to root causes like material lots or machine assignments. **Options:** ##### No linkage; quality data has no traceability context **Score:** 0 ##### Manual cross-referencing is possible but slow **Score:** 1 ##### Partial integration for some lines or products **Score:** 2 ##### Quality data is captured against work orders and lots in or alongside the ERP **Score:** 3 #### Is there a named owner with time allocated to lead the SPC program beyond the initial rollout? **Options:** ##### No owner identified **Score:** 0 ##### Assigned to the quality manager on top of existing duties **Score:** 1 ##### A trained owner exists with partial time allocation **Score:** 2 ##### Dedicated owner with authority, time, and a sustainment plan **Score:** 3 **Bands:** #### Not Ready: Build Foundations First **Min:** 0 **Max:** 39 **Verdict:** An SPC rollout now would chart unreliable data and stall within months. Your gaps are foundational: measurement systems, data capture, and documented processes must come before control charts. This is common and fixable, but sequencing matters. **Recommendations:** - Identify and document critical-to-quality characteristics for your top two or three products before anything else. - Run gauge R&R studies on the measurement systems those characteristics depend on, and fix or replace failing gauges. - Replace paper data collection with digital capture at one pilot work center, tied to work order numbers. - Establish standard work and controlled process parameters for the pilot processes. - Defer control charts until capability baselines exist; premature charts destroy operator trust in the program. #### Partially Ready: Pilot with Guardrails **Min:** 40 **Max:** 69 **Verdict:** You have real foundations but meaningful gaps, most often in training, reaction plans, or ERP integration. A focused pilot on one or two critical processes can succeed now, but a plant-wide rollout would outrun your infrastructure and credibility. **Recommendations:** - Select one high-value process with verified gauges and known capability as the pilot line. - Write and rehearse a reaction plan before the first chart goes live, including who can stop the process. - Train the pilot area's operators on common versus special cause, emphasizing why over-adjustment makes things worse. - Connect pilot quality data to ERP work orders and lots so every signal is traceable to context. - Set explicit success criteria for the pilot and review at 90 days before scaling. #### Ready: Scale and Automate **Min:** 70 **Max:** 100 **Verdict:** Your foundations support a serious SPC program. The remaining risk is sustainment: manual charting and disconnected data quietly erode programs that start well. Your priority is automating data flow and embedding SPC into daily management rather than proving the concept. **Recommendations:** - Automate data capture from gauges and machines wherever feasible to eliminate transcription and hindsight charting. - Integrate SPC results with your ERP so out-of-control signals link to lots, suppliers, and machines for fast root cause. - Roll out by process family using your pilot's reaction-plan template, not one-size-fits-all charts. - Report capability trends (Cpk) to leadership monthly alongside cost of quality to keep sponsorship strong. - Add AI-based pattern detection on top of classic rules to catch drift and tool wear earlier than Western Electric rules alone. ## Content **Intro:** This free assessment tells you whether your plant is ready to implement statistical process control, built for quality managers and engineers in discrete manufacturing, aerospace, and electronics. Ten questions cover the factors that decide SPC success: critical characteristic identification, measurement system integrity, data capture, training, reaction plans, leadership commitment, and ERP traceability. You get a readiness score with a specific verdict and prioritized recommendations. Most failed SPC programs did not fail at statistics; they failed at prerequisites, and this assessment surfaces yours before you invest. **Sections:** #### Why SPC programs fail and what this assessment checks **Body:** Industry experience is consistent: the statistics are the easy part. Programs collapse because gauges were never validated, so charts plotted measurement noise; because operators adjusted machines at every wiggle, adding variation; because the first out-of-control signal met schedule pressure and lost; or because charting stayed manual and quietly stopped after the champion moved on. Each assessment question maps to one of these documented failure modes. The scoring weights foundational items, measurement integrity and data capture, equally with cultural items like leadership commitment, because a deficit in either dimension is sufficient to sink the program regardless of strength in the other. #### What good looks like before the first chart **Body:** A launch-ready SPC environment has four properties in place before any control chart exists: **Bullets:** - Critical characteristics documented in control plans, with customer agreement where contractually required (AS9100, PPAP). - Measurement systems passing current gauge R&R with total variation under 10-20% of tolerance depending on criticality. - Digital data capture at the point of measurement, tagged with work order, lot, machine, and operator. - A rehearsed reaction plan naming who verifies a signal, who can stop the process, and how containment works. #### Interpreting your score **Body:** Scores below 40% mean foundations first: charting now would produce unreliable signals and burn credibility you will need later. Scores of 40-69% support a guarded pilot on one process where your prerequisites are locally strong, which builds the evidence and templates for wider rollout. Above 70%, the risk inverts: your danger is not starting badly but sustaining, since manual data entry and disconnected systems erode even well-launched programs within two years. Whatever your band, resist the temptation to launch plant-wide simultaneously; every durable SPC program we have seen scaled from a pilot that proved the reaction loop actually worked under schedule pressure. #### How Netray accelerates SPC readiness **Body:** Netray closes the two gaps that hold back most plants: data capture and ERP integration. We build digital measurement collection tied to Infor SyteLine, LN, or Baan work orders, so every data point carries lot, machine, and operator context automatically, and we integrate SPC signals back into ERP quality workflows so an out-of-control condition can hold a lot or trigger an NCR without manual steps. For plants further along, we add AI-based drift detection on top of classic control rules. Complete the assessment, then share your score and a Netray specialist will map your fastest path from your current band to a running pilot. **Cta:** Share your readiness score with Netray and get a tailored SPC implementation roadmap for your plant. **Faq:** #### How long does SPC implementation take? **Answer:** A single-process pilot with solid prerequisites typically runs 8-12 weeks from characteristic selection to a working chart with a rehearsed reaction plan. Plant-wide deployment across process families usually takes 9-18 months, driven mostly by measurement system remediation and training rather than software. Plants scoring below 40% on this assessment should budget an additional 3-6 months of foundation work before the pilot begins. #### Do I need MSA or gauge R&R before starting SPC? **Answer:** Yes, and it is the least skippable prerequisite. A control chart displays total observed variation, which includes measurement variation. If your gauge consumes a large share of the tolerance, the chart will signal randomly and operators will learn to ignore it, which kills the program culturally. Standard practice is requiring gauge R&R under 10% of tolerance for critical characteristics and under 20-30% for less critical ones before charting begins. #### Should SPC data live in our ERP? **Answer:** The measurements can live in a dedicated quality system, but they must be linked to ERP context: work order, lot, machine, and supplier. Without that linkage, an out-of-control signal is a dead end because you cannot trace it to a material lot or machine assignment. The strongest pattern is capture at the point of measurement, storage with full traceability keys, and integration so signals can hold lots or raise NCRs inside SyteLine or LN automatically. ## Search terms SPC readiness assessment, statistical process control implementation, SPC implementation checklist, SPC prerequisites manufacturing, control chart readiness, measurement system analysis MSA, SPC ERP integration --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json