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Step 1: Define Operational Goals & KPIs

We define the operational outcomes: reduce downtime, improve yield, shorten lead times or cut scrap. We identify affected lines, products and SLAs, and the actions that follow each signal.

We set clear KPIs, calculation rules and alert thresholds so results are measured consistently across teams.

• What we do: pick target lines, set outcomes, write KPI formulas and set action thresholds.
• How we align: meet operations, maintenance, quality and supply teams early to agree goals and reporting cadence.

02

Step 2: Collect & Integrate Plant & Supply Data

We gather data from MES, PLCs, SCADA, ERP, WMS and supplier feeds into a secure staging area. We validate sensor streams, reconcile inventory records and capture production events for analysis.

We keep a data inventory listing each source, owner, refresh cadence and access rules so teams know where to look and who to contact.

• What we do: connect sensors and systems, validate streams, fix issues and move clean data to production.
• Tools we use: MQTT/OPC-UA connectors, Time-series DBs, ETL pipelines (SQL, Python) and preview dashboards (Grafana, Power BI).

03

Step 3: Clean, Standardize & Mask

We clean and standardize timestamps, sensor units, SKUs and log formats so data is ready for modelling. We mask or limit access to sensitive fields while keeping transformations auditable for audits.

Standardized data reduces errors in OEE, scheduling and supplier reports.

• What we do: normalise sensor units/times, map SKUs, handle missing data and mask sensitive fields.
• Tools we use: Python (Pandas), Time-series tools, SQL and scripted exports for review.

04

Step 4: Explore & Visual Diagnostics

We explore sensor trends, downtime patterns, capacity constraints and quality issues. We build cohort splits and visuals to surface root causes and validate business hypotheses.

We share dashboards with operations and quality teams for early feedback before building models or controls.

• What we do: run summaries, stratify events, check time trends and outliers, and produce review visuals.
• Tools we use: exploratory notebooks (Python + Plotly) and dashboards (Grafana, Power BI).

05

Step 5: Feature Engineering & Operational Transformations

We convert raw signals into operational features: rolling vibration averages, temperature trends, throughput rates, and supplier lead-time distributions. We design features with operations and quality teams so they are meaningful and actionable.

We version and store feature tables so experiments can be reproduced and results validated later.

• What we do: compute time windows, normalise features, build downtime/quality features and version outputs.
• Tools we use: NumPy, Pandas, Spark; store tables as Parquet/CSV and surface to Grafana/Power BI.

06

Step 6: Modeling & Explainability

We build models starting with simple, interpretable baselines and only move to more complex methods when they add clear value. Use cases include failure prediction, yield forecasting and supplier risk scoring. We validate over time and check performance across lines.

We provide explainability artifacts and model cards so operations, maintenance and compliance teams can understand outputs and decisions.

• What we do: train baselines, evaluate advanced models when needed, validate over time and produce explanations.
• Tools we use: scikit-learn, XGBoost, Prophet/ARIMA, SHAP/LIME and MLflow for tracking.

07

Step 7: Deploy, Automate & Integrate

We deploy models and analytics through secure APIs, edge services or embedded dashboards so they fit into MES, CMMS or ERP workflows. We containerize services and document interfaces to reduce disruption.

We automate alerts, maintenance workflows and supplier escalations so teams get timely, actionable notifications while preserving a full audit trail.

• What we do: package models, expose APIs or embed dashboards, set up alerts and automation for escalations.
• Tools we use: REST APIs, edge deployments, Docker/Kubernetes, orchestration (Airflow) and automation (n8n/Make), dashboards in Grafana/Power BI.

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Step 8: Monitor, Validate & Iterate

We continuously monitor data quality, model calibration, and real-world impact to spot drift or degradation. We run silent-mode checks and chart reviews to confirm performance in practice.

We treat deployments as live systems: collect feedback, retrain when needed, and keep model cards and change logs up to date to preserve governance and trust.

• What we do: monitor for drift, run silent evaluations, measure impact and update models as needed.
• Tools we use: scheduled ETL + monitoring scripts (Python), dashboards (Grafana/Power BI), automation (n8n/Make) and retraining pipelines (MLflow/Spark).