Standard SPC assumes normal data
Concentrations, viscosities and impurities rarely follow a normal distribution. Capability indices calculated as if they did give false confidence — or false alarms.
Zometric helps specialty and process chemical plants run reliable, in-spec operations across batch and continuous processes — condition-based maintenance with permit-to-work and turnaround support, non-normal SPC and capability, and real-time production and OEE on a single backbone.
Key standards
Specialty chemicals manufacturing blends continuous and batch processes, capital-intensive assets, and strict safety and environmental obligations under ISO 9001, ISO 14001 and OSHA PSM. Process data is often non-normal, reactors and utilities are reliability-critical, and an unplanned shutdown is expensive and hazardous. Zometric brings the process online: control charts with Box-Cox and Johnson transformations handle non-normal capability, a full CMMS schedules condition-based maintenance with permit-to-work and turnaround planning, and automated production logs turn reactor and line data into real-time OEE and yield. Batch and lot traceability ties material movements to quality and maintenance events across the plant.
The reality on the floor
Concentrations, viscosities and impurities rarely follow a normal distribution. Capability indices calculated as if they did give false confidence — or false alarms.
Reactors, compressors and utilities fail without warning when maintenance is reactive. Each unplanned trip risks safety, yield and environmental compliance.
Permit-to-work, turnaround scopes and lockout records sit in binders, slowing execution and weakening the audit trail OSHA PSM expects.
Over-concentration, over-fill and off-spec rework are tracked weeks later in spreadsheets, after the margin has already left the plant.
How Zometric helps
Each capability is delivered by a module you can adopt on its own and expand from.
Asset hierarchy to any depth, condition-based and time-based PM, work orders with permit-to-work and turnaround packs, MTBF/MTTR at every node, and recurring-failure detection with auto-escalation.
Delivered byMaintenanceControl charts with Box-Cox and Johnson transformations, distribution identification, and non-normal capability so Cp/Cpk/Pp/Ppk reflect reality for chemical process data.
Delivered byIn-Process Quality & SPCAutomated production logs, OEE, downtime detection with operator-logged causes, and schedule-vs-actual variance across batch and continuous lines.
Delivered byProductionZometric Edge bridges machines, PLCs, DCS, SCADA and historians into the core, so process data flows in automatically without rip-and-replace.
Delivered byZometric EdgeModule mix
One data backbone — begin with one module and add the rest as you scale.
Full-featured CMMS built for manufacturing, with predictive analytics.
Explore module →Catch process deviations before they become defects.
Explore module →Automated production logs, real-time OEE, downtime tracking, job scheduling.
Explore module →Standards & compliance
FAQ
Yes. The platform includes Box-Cox and Johnson transformations, distribution identification, and non-normal and nonparametric capability analysis, so capability indices are valid for data that isn't normally distributed — which is typical for concentrations, viscosities and impurity levels.
Yes. The CMMS includes a chemicals industry pack with permit-to-work and turnaround planning, alongside condition-based and time-based PM, spares management, and recurring-failure detection with auto-escalation.
Yes. The same data backbone handles batch and continuous operations — SPC and capability for in-process quality, automated production logs and OEE for throughput, and lot/batch traceability across both.
Through Zometric Edge for live machine, PLC, DCS, SCADA and historian connectivity, the Data Extractor for file-based sources, or manual entry on any device — often a mix, depending on the asset.
Start with your line
A 30-minute discovery call maps the right modules and standards to your factory — then a short pilot proves it on your real data.