SACA Smart Factory Data Analytics 2

Question 1

What is 'OEE' (Overall Equipment Effectiveness) and how is it calculated?

Accepted Answer

OEE = Availability × Performance × Quality, expressing the percentage of scheduled production time that is truly productive — a perfect score of 100% means running at maximum speed, producing only good parts, with no downtime

Answer

OEE = Availability × Performance × Quality. Availability = (Operating Time / Planned Production Time). Performance = (Actual Output / (Operating Time × Ideal Rate)). Quality = (Good Parts / Total Parts). A world-class OEE for discrete manufacturing is 85%; typical industry average is 60%.

Question 2

What is 'predictive maintenance' and how does it differ from preventive maintenance?

Accepted Answer

Predictive maintenance uses real-time condition monitoring data (vibration, temperature, oil analysis) to predict when a specific component will fail — scheduling maintenance just before failure. Preventive maintenance replaces components on fixed time/cycle intervals regardless of actual condition

Answer

Predictive maintenance (PdM) monitors actual equipment condition to predict remaining useful life — performing maintenance only when indicated, reducing unnecessary replacements. Preventive maintenance (PM) uses fixed intervals based on average failure rates — replacing healthy components and missing failures occurring between PM intervals.

Question 3

What is a 'digital manufacturing execution system' (MES) and what data does it connect between ERP and factory floor systems?

Accepted Answer

A system bridging ERP business systems (orders, materials, quality specs) with factory control systems (PLCs, robots, test equipment) — managing work orders, tracking WIP, collecting production actuals, and enforcing quality procedures in real-time

Answer

MES (ISA-95 Level 3) sits between ERP (Level 4, business planning) and automation (Level 1-2, control). It receives production orders from ERP, dispatches work to machines, tracks WIP and traceability, collects actual production data (cycle times, quality results, downtime), and reports actuals back to ERP for cost accounting.

Question 4

What is 'Statistical Process Control' (SPC) and what does a 'control chart' reveal?

Accepted Answer

A methodology using statistical limits (control limits) on time-series charts of process measurements to distinguish between normal random variation (common cause) and significant process shifts (special cause) requiring investigation

Answer

SPC control charts (Shewhart charts: X-bar/R, CUSUM, EWMA) plot process measurements over time with statistically derived Upper and Lower Control Limits (UCL/LCL = mean ± 3σ). Points outside these limits or non-random patterns signal that the process has changed — distinguishing meaningful signals from random noise.

Question 5

What is 'machine learning anomaly detection' in industrial equipment monitoring and how is it trained?

Accepted Answer

An ML model trained on normal operating data that learns the expected sensor signature patterns — flagging deviations that exceed learned normal variation as anomalies potentially indicating developing faults

Answer

Anomaly detection models (autoencoders, isolation forests, LSTM-based) are trained exclusively on 'normal' equipment operation data. The model learns the expected relationships between hundreds of sensor variables; during operation, any pattern that deviates significantly from learned normality is flagged as an anomaly — potentially indicating a developing fault.

SACA - Smart Automation Certification Alliance Practice Test

SACA - Smart Automation Certification Alliance Practice Test

SACA Smart Factory Data Analytics 2