Craftsman Smart Factory study plan

Define the ISA-95 / Purdue reference architecture levels and map at least six common smart-factory components (PLC, SCADA, MES, ERP, edge gateway, sensor) to their correct levels

Differentiate IIoT from consumer IoT across latency, determinism, device lifecycle, and physical-consequence failure modes

Explain the roles of OPC UA, MQTT, and REST in an IIoT data path and identify which layer each typically serves

Diagram a reference smart-factory stack from sensor to cloud, labeling where OT and IT boundaries fall

Classify control system types (PLC, DCS, PAC, industrial PC) by application, scan-time needs, and redundancy model

Trace a closed-loop control sequence from sensor input through PID logic to actuator output and identify where scan time and jitter degrade performance

Compare the six major industrial robot configurations (articulated, SCARA, delta, Cartesian, cylindrical, collaborative) by workspace, payload, and typical task

Explain safety-rated functions (safe torque off, safe speed, light curtains) and how they differ from standard control logic

Calculate a simple PID output for a given error, integral, and derivative term to verify you understand tuning parameters

Distinguish fieldbus, industrial Ethernet, and wireless protocols (PROFINET, EtherNet/IP, Modbus TCP, EtherCAT, OPC UA, 5G, WirelessHART) by determinism, bandwidth, and topology

Select the correct protocol for three stated scenarios and justify each choice against latency, node count, and legacy constraints

Describe a data acquisition pipeline from sensor signal conditioning through ADC, edge buffering, and historian ingestion

Explain time synchronization (NTP, PTP/IEEE 1588) and why timestamp accuracy matters for event correlation

Identify common data-quality problems at the edge (missing values, clock drift, unit mismatch) and propose one mitigation for each

Integrate automation concepts with IIoT data flows by explaining how PLC and robot data reach an edge platform without disrupting deterministic control

Evaluate edge versus cloud placement for three analytics use cases (anomaly detection, OEE calculation, predictive maintenance) and defend each choice

Describe how a digital twin of a work cell would consume live controller and robot telemetry

Review your Week 1 architecture diagram and revise it to show control-loop and robotics data sources explicitly

Define OEE and calculate availability, performance, and quality from a provided shift dataset, then interpret which loss category dominates

Distinguish descriptive, diagnostic, predictive, and prescriptive analytics with one manufacturing example each

Explain digital twin maturity levels (asset, system, process) and the data fidelity each requires

Describe common predictive-maintenance feature types (vibration RMS, temperature trend, cycle-count) and the modeling approach each supports

Identify data-governance requirements for manufacturing analytics: data lineage, retention, and label quality

Apply the IEC 62443 zones-and-conduits model to segment a plant network and justify each zone boundary

Explain defense-in-depth controls across network, endpoint, identity, and physical layers, and identify the weakest link in a flat OT network

Differentiate IT security priorities (confidentiality) from OT priorities (availability, integrity, safety) and adjust a remediation plan accordingly

Describe safe remote-access patterns (jump host, MFA, session recording) and why direct vendor VPN into a control network is risky

Map functional safety requirements (risk assessment, safety integrity level, safety PLC) to the automation layer from Domain 3

Sequence a smart-factory rollout through assessment, pilot, scale, and sustain phases, assigning owners and exit criteria to each

Build a business case for one pilot using payback period, OEE uplift, and implementation cost estimates

Identify integration risks (legacy protocol gaps, MES-ERP mismatch, change management) and assign a mitigation to each

Define operational readiness criteria: training, runbooks, support model, and KPI baselines

Explain how to measure pilot success with pre-defined KPIs rather than after-the-fact metrics

Synthesize all six domains into a single integrated case study of a plant modernization

Complete a timed 60-question practice set weighted by domain percentage and score at least 75%

Review every missed question, tagging the underlying concept and the domain it belongs to

Re-derive the three most error-prone calculations (OEE, PID output, payback period) without notes

Produce a one-page formula and concept sheet for final review