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Case study · Manufacturing

From manufacturing data to predictive demand intelligence.

Quorlytic engineered an AWS data platform that unified a manufacturer's ERP, sales, production and inventory data through automated ETL, put Power BI reporting on one trusted layer, and added a Python demand forecast built on the same history.

  • Manufacturing
  • Data Engineering
  • Analytics
  • Machine Learning
  • AWS

Deepesh PatelBy Deepesh Patel, Founder & Data EngineerUpdated

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  1. Manufacturing data
  2. Ingestion
  3. ETL
  4. AWS data platform
  5. Business intelligence
  6. Demand forecast
  7. Planning decisions
Illustration: operational sources flowing through ingestion and ETL into a curated data platform, feeding reports and a demand forecast.
The engagement as built: operational sources, ingestion and ETL, one curated AWS data platform, then reporting and a demand forecast. Original illustration, not a client screenshot.

Summary

The engagement at a glance.

Quorlytic built an end-to-end data platform on AWS for a manufacturing enterprise. Automated ingestion and ETL brought ERP, sales and order, production, inventory and spreadsheet data into a curated Amazon Redshift layer; Power BI reporting and a Python demand-forecasting capability were then built on that single, trusted foundation.

Published Sep 27, 2026 · Client anonymised with their approval

  1. The client's planning data sat in separate systems: ERP, sales and order history, production and inventory data, and hand-maintained spreadsheets.
  2. Reporting and forecasting both needed one reliable, centralised history that did not yet exist.
  3. Quorlytic engineered an AWS data platform with automated ingestion and ETL.
  4. The ETL standardised and integrated the sources into analysis-ready datasets in Amazon Redshift.
  5. Power BI reporting gave stakeholders one view across sales, production and inventory data.
  6. A Python demand forecast, built on the same history, added a forward-looking signal for planning.

The challenge

The data existed. A trusted history did not.

The organisation had the data it needed to plan. It did not have it in one place, in one shape, or in a form anyone could trust without rebuilding it by hand.

  • Planning data was spread across an ERP system, sales and order history, production and inventory data, and spreadsheets.
  • Each source had its own structure, so combining them for a report was manual and repeated every time.
  • Historical comparisons depended on whoever assembled the numbers, rather than on one agreed dataset.
  • Forecasting leaned on past figures and judgement, because there was no consistent history to model from.

Before

  1. Separate operational sources
  2. Manual, repeated consolidation
  3. Standalone reports
  4. Little forward-looking insight

After

  1. Operational sources
  2. Automated ingestion
  3. ETL and transformation
  4. Curated AWS data platform
  5. Power BI and demand forecast
  6. Planning decisions

The engineering approach

Building the data foundation first.

Reliable analytics and machine learning require reliable data. So the work started with the foundation, not the dashboard and not the model.

A dashboard built on manually consolidated data inherits every inconsistency in it, and a forecast trained on an unreliable history learns the inconsistency as if it were demand. Building ingestion, transformation and a curated layer first meant reporting and forecasting would share one definition of the history.

  1. Stage one

    Ingestion

    Data from each source was brought automatically into a landing area in Amazon S3, so the platform no longer depended on someone exporting and sending files.

    • ERP data
    • Sales and order history
    • Production and inventory data
    • Spreadsheets and files
    • AWS Lambda for lightweight serverless steps

  2. Stage two

    ETL and transformation

    AWS Glue ETL cleaned, standardised and integrated the sources into consistent datasets. This is where a transactional ERP and a hand-maintained spreadsheet were made to describe the same products, periods and quantities in the same way.

    • Cleaning and standardisation
    • Mapping differing structures to one model
    • Integrating sources into analysis-ready datasets
    • Automated, repeatable runs in place of manual preparation

  3. Stage three

    Curated analytical layer

    The integrated data was modelled into a curated layer in Amazon Redshift: the single, trusted history that both reporting and forecasting read from.

    • Analysis-ready datasets
    • One definition of the history
    • Shared by BI and the forecast

Architecture

A scalable data platform on AWS.

Each layer, the job it does, and the services that do it. Only services the engagement used are shown.

  1. SourcesERP, sales and order history, production and inventory data, spreadsheets and files
  2. IngestionAutomated collection into a landing area, with lightweight serverless stepsAmazon S3AWS Lambda
  3. ProcessingETL: cleaning, standardisation and integrationAWS Glue
  4. Curated layerAnalysis-ready datasets and the trusted historyAmazon Redshift
  5. IntelligenceBusiness reporting, and a demand forecast built on the same historyPower BIPython
  6. DecisionsPlanning informed by historical performance and projected demand

Data quality

Trusted data before advanced analytics.

  • Sources with different structures were standardised before they reached reporting, so a figure meant the same thing whichever system it came from.
  • Integration happened once, in the pipeline, instead of again in every report.
  • Automated, repeatable pipeline runs replaced ad-hoc preparation, so the history behaved the same way each time it was refreshed.

Business intelligence

Turning operational data into business intelligence.

Once the data was curated, reporting stopped being a consolidation exercise. Power BI read from the Redshift layer, so every report started from the same history.

  • Reporting across sales and order, production and inventory data from one model
  • Historical trends and period comparisons on a consistent history
  • Forecast output presented alongside actual performance
Analytics layerIllustrative
Illustrative layout of the reporting layer: history and projected demand in one view. Not client data, and not a screenshot of the delivered dashboards.

Machine learning

Adding a forward-looking demand signal.

Reporting explains what has already happened. Planning needs a view of what may happen next. Quorlytic added a demand-forecasting capability in Python, trained on the curated history the platform now held.

  1. Curated historical data
  2. Feature preparation
  3. Model training
  4. Evaluation
  5. Demand forecast
  6. Reporting and planning

How to read the forecast. The forecast is a decision-support signal: a projection of likely demand to inform planning, not a certainty and not an automated decision.

Reporting and forecasting were not delivered as separate experiments. Both read the same curated history, so projected demand could sit next to actual performance in one view, and a planner compared like with like.

Decision intelligence

From reporting to decision intelligence.

  1. Descriptive

    What happened?

    Power BI reporting on a single, curated history.

  2. Diagnostic

    Why is it happening?

    Trends and comparisons across sales, production and inventory data.

  3. Predictive

    What may happen next?

    A demand forecast built on the same history.

Technology

The stack, by function.

Technologies used on this engagement. No partnership or certification is implied.

  • CloudAWS
  • Storage and ingestionAmazon S3AWS Lambda
  • Data engineeringAWS Glue
  • Data platformAmazon Redshift
  • Analytics and BIPower BI
  • Machine learningPython

Outcomes

What the platform enabled.

Outcomes are described qualitatively. No figures from the client's environment are published, so no performance, accuracy, time or cost result is claimed.

  • A unified data foundation

    Data that previously lived in separate systems and spreadsheets became available through one curated analytical layer.

  • Automated data preparation

    Repeatable pipelines replaced manual, repeated consolidation.

  • One trusted history

    Reporting and forecasting read the same curated data, so they describe the business the same way.

  • Clearer visibility

    Power BI gave stakeholders one view across sales, production and inventory data.

  • Forward-looking planning

    A demand forecast added a projected view alongside historical reporting.

  • A base for further AI

    The platform gives future analytics or machine-learning work a curated history to start from.

Engineering insight

The model was not the starting point. The data foundation was.

A useful forecast depends on:

  • Consistent history
  • Standardised definitions
  • Repeatable ingestion
  • Integrated, curated datasets
  • One layer shared by BI and ML

Questions

About this engagement.

What this study does not claim

  • No forecast accuracy is published. Accuracy depends on the client's data and horizon, and a figure without its evaluation method would mislead.
  • Forecasts are decision-support estimates, not certainties; planning decisions remain with the client's teams.
  • The client is not named, and no figures from their environment are published.

What did Quorlytic build for the manufacturing company?

An end-to-end data platform on AWS: automated ingestion and ETL into a curated Amazon Redshift layer, Power BI reporting on that layer, and a Python demand-forecasting capability built on the same history.

How was AWS used?

Amazon S3 held ingested data, AWS Lambda ran lightweight serverless steps, AWS Glue ran the ETL, and Amazon Redshift held the curated analytical layer.

What role did ETL play?

The ETL cleaned, standardised and integrated ERP, sales and order, production, inventory and spreadsheet data into consistent, analysis-ready datasets, so reporting and forecasting could rely on one history.

How was the business intelligence layer built?

Power BI reporting read from the curated Redshift layer, giving stakeholders one view across sales, production and inventory data, with forecast output alongside actual performance.

How was machine learning used?

A demand-forecasting capability was developed in Python on the curated history, to give planning teams a projected view of demand. Its output is a decision-support estimate, not a certainty.

Why was data engineering needed before machine learning?

A forecast is only as reliable as the history it learns from. Building ingestion, transformation and a curated layer first gave the model, and the reports, one consistent and trusted history.

Related capabilities

The services behind this work.

Related insights

The engineering behind this study.

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