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PIESTACK

Manufacturing data analytics

Better Visibility Across Your Manufacturing Operations.

Production, sales, procurement, and inventory each keep their own records. PieStack connects that operational data and organizes it around agreed metrics, so plant, finance, and management teams work from the same numbers.

Book a Discovery CallSee the challenges we solve
Operational performance Live
Illustrative view: production, sales, and inventory in one consistent report.

Common challenges

Where manufacturing data gets stuck

The problem

Why management reporting breaks down

  • Every department keeps its own version of the truth

    Sales, production, procurement, and finance each report from different systems and spreadsheets, so figures rarely agree.

  • Reports take days to assemble

    Analysts spend their time exporting, copying, and reconciling data instead of explaining what it means.

  • Decisions are made on last month's numbers

    By the time consolidated figures reach management, the moment to act has often passed.

How PieStack fixes it: one governed pipeline from source to decision

Data SourcesLandingBronze · RawSilver · CleanedGold · Business-readyERPSQL ServerExcel / CSVPoint of saleInventoryProductionSchema checksDuplicatesValidationAnomaly flagsClean & standardizeReconciliationReports · Dashboards · AIContinuous pipeline monitoring

What we do

How PieStack helps manufacturers

01

Production and operational analytics

Production records, job logs, and operational data live in different places and are summarised differently by each team.

How we help

  • Agree KPI definitions with operations and finance before building reports
  • Bring production and operational records into one consistent model
  • Compare lines, shifts, or plants where the data allows
  • Production and output reporting
  • Agreed operational KPIs
  • Scheduled management views
Operational performance
ERP & production logsJob logsShift reportsExcel
KPIs agreedDuplicatesReconcile
  1. Data AggregationCombine records by line, shift, and plant
  2. Data ModelingApply agreed KPI definitions
  3. Data ValidationCheck totals against source reports
Operational reportsOutputYieldOn-time delivery
02

Inventory and procurement visibility

Purchasing and stock data are disconnected, so teams can't easily see what is on hand, on order, or not moving.

How we help

  • Connect purchasing, supplier, and inventory records
  • Flag ageing, slow-moving, and excess stock across items and locations
  • Show open purchase orders against current stock
  • Inventory position and ageing reports
  • Slow-moving stock views
  • Supplier and PO visibility
Inventory ageing
  1. Procurement
    Duplicate purchasingBuying items already in stock at another location
  2. Warehouse
    Ageing stockItems not moving for 90+ days
  3. Production
    Inaccurate inventoryRecorded and actual stock levels differ
  4. Sales
03

Sales and product profitability

Revenue is visible, but costs sit in separate systems, so margin by product, customer, or region is guesswork.

How we help

  • Combine sales with material and production cost data where it exists
  • Confirm what the source data can support before reporting margin
  • Track price, volume, and margin trends over time
  • Sales performance reporting
  • Product and customer margin analysis
  • Trend views
Revenue vs margin
SalesMapped
Material costsPartial
Production costsMissing link
  1. Data MatchingLink costs to products and orders
  2. Margin ModelCalculate margin where costs exist
  3. Data ValidationFlag products with incomplete costs

Report generation

By productBy customerBy region
04

Data integration and reporting automation

Recurring reports depend on someone exporting ERP data and rebuilding spreadsheets every week or month.

How we help

  • Load data automatically from ERP systems, databases, and files
  • Add validation and reconciliation checks so numbers can be trusted
  • Schedule management reports instead of rebuilding them
  • Automated data pipelines
  • Data-quality checks
  • Scheduled reports
Automated pipeline
ERP exportManual export
SQL databaseConnected
Excel filesFormat errors
  1. Scheduled IngestionLoad ERP, database, and file data automatically
  2. Quality ChecksCatch missing, duplicate, and malformed rows
  3. Data ModelingApply agreed metric definitions

Scheduled reports

ManagementInventorySales
05

Data foundations for AI

Forecasting and AI initiatives fail when historical data is scattered, inconsistent, or incomplete.

How we help

  • Structure and clean historical data first
  • Start from a specific decision and check the data can support it
  • Add conversational analytics or forecasting only where it adds practical value
  • Data-readiness assessment
  • Clean historical datasets
  • Conversational analytics or forecasting pilots
Conversational analytics
Historical dataOrdersProductionInventory
CompletenessConsistencyOutliers
  1. Data PreparationClean and structure history
  2. Readiness ReviewConfirm data can support the decision
  3. Model or AssistantForecasting or conversational analytics
Validated againstAgreed business questions

Business questions

Questions your data should be able to answer.

  • Which products are actually profitable once material and production costs are included?
  • How much of our inventory has not moved in the last 90 days, and where is it?
  • Are we buying materials we already have in stock at another location?
  • Why do production and sales report different numbers for the same month?
  • Which customers and product lines are driving growth, and which are declining?
  • How long does it take to close the monthly management report, and can it be automated?

Typical data sources

  • ERP systems and ERP report exports
  • SAP
  • Oracle
  • Microsoft SQL Server
  • Business databases
  • Excel and CSV files
  • Production and job logs
  • Other operational applications

Every manufacturer runs a different mix of systems. We start from the sources you actually use, including spreadsheets.

Technical walkthrough

Building a governed lakehouse from raw, messy files

See how layered architecture, Delta Lake, and Unity Catalog turn inconsistent source files into analytics-ready tables on Databricks.

Read the walkthrough

How we work

Our approach: start focused, then expand.

  1. 01

    Understand the business challenge

    We start with the decisions you need to make and the questions your team cannot answer easily today.

  2. 02

    Connect and prepare the data

    We access the relevant sources, then clean, standardize, and model the data around agreed metrics.

  3. 03

    Deliver useful analytics and insights

    Your team gets reports, dashboards, or conversational analytics, validated against your own numbers.

  4. 04

    Expand into production and more use cases

    Once the first use case proves its value, we harden it for daily use and extend it to new areas.

FAQ

Manufacturing analytics FAQs

Do we need to replace our ERP system?

No. We connect to the systems you already have, including ERP exports and spreadsheets, and build reporting on top of them.

Our data is messy. Can we still start?

Yes. Data preparation is usually the largest part of the work. We assess data quality early and agree what is realistic before building reports on it.

Which technology do you use?

We choose technology based on your needs. Databricks is an important platform behind our current solutions, particularly where several data sources need to be combined and governed.

How do we get started?

Book a discovery call. We will discuss your reporting challenges and suggest a practical first use case, which can be a focused pilot such as the Lakehouse Experience.

Start with a focused pilot

One business question, one or two data sources, and a working demonstration.

Explore the Lakehouse Experience

Talk to our team

Tell us about your manufacturing data challenges and we'll suggest a practical starting point.

Book a Discovery Call