Analyse production data around the questions that matter on the shop floor: where time is lost, which assets or products vary, how throughput and cycle time change, where scrap or rework concentrates, and whether KPI definitions are trustworthy enough to support action.
Custom scope. Delivery timing is confirmed after the relevant data, definitions and access method are reviewed.
Manufacturing data rarely arrives in a standard shape. The right engagement depends on source readiness, the number of lines or sites, the KPI definitions you use and whether you need a one-time diagnostic or a repeatable reporting layer.
For a defined line, process or operational question using a manageable source set and agreed KPI definitions.
For manufacturers who need production, quality and downtime context connected across more than one operational source.
For teams that want the agreed production metrics and views to be refreshed repeatedly rather than delivered only once.
Why Custom Quote? Public manufacturing-analytics prices vary widely because a one-source diagnostic is materially different from multi-system data engineering, live dashboards or advanced modelling. Scope is confirmed before a commercial estimate is issued.
Share the operational question, the source systems or exports you have, and the level at which you need the answer — machine, line, shift, product, work order or plant.
Production Data Analysis is most useful when the business knows what it needs to understand but the existing reports, spreadsheets or machine data do not give a consistent answer.
Production is lower than expected, but current reports do not separate downtime, rate loss, quality loss and mix effects.
Reason codes, event logs or operator entries are available, yet the dominant loss patterns are difficult to see.
OEE, yield, throughput or cycle-time results differ because definitions, planned time or source logic are inconsistent.
A line balancing, maintenance, quality or process initiative needs a defensible before-state and repeatable measures.
A production event means little without the time, asset, product, order, shift and reason context around it. The analysis workflow therefore starts by mapping operational records into a common analytical grain.
States, counts, cycle events, alarms, tags or historian values.
Orders, operations, quantities, line context, reasons and execution history.
Inspection results, defects, rework, work orders and maintenance events.
Products, work orders, standards, routings and production-plan context.
Common timestamps, IDs, units, KPI rules and drill-down dimensions.
Production performance becomes easier to act on when the data distinguishes why planned time did not become good output. The exact loss tree is adapted to your process and definitions.
Production data often contains operational exceptions that generic BI cleanup misses. The analysis records what can be trusted, what was adjusted and what still limits interpretation.
| Check | Why it matters in production analysis | Typical treatment |
|---|---|---|
| Timestamp alignment | Machine, MES and quality records may not line up if clocks, time zones or event granularity differ. | Normalize time logic and document unresolved offsets. |
| Reason-code stability | A downtime category can change meaning after a system or process update. | Map versions or separate periods rather than merging blindly. |
| Asset / product IDs | Alias codes can split one machine or product into multiple analytical entities. | Create controlled mapping tables for the agreed scope. |
| Units and standards | Cycle targets, quantities and measurements may use different units or product-specific standards. | Convert only with confirmed rules and preserve source units. |
| Missing / duplicate events | Event loss or replay can materially distort downtime and count-based KPIs. | Flag gaps, de-duplicate with agreed keys and quantify limitations. |
The exact activities are confirmed in the statement of work. A typical engagement follows a practical sequence that keeps KPI definitions, data transformations and production interpretation visible.
Clarify the decision, production boundary, time period, assets, products and metrics that matter.
Inspect coverage, types, timestamps, identifiers, reason codes, missingness, duplicates and obvious inconsistencies.
Build the agreed measures and drill into losses, variation, segments, trends and operational comparisons.
Review calculations and exceptions, document assumptions, incorporate factual corrections and prepare the agreed outputs.
Only share data relevant to the agreed scope. The most useful starting package usually combines operational extracts with the definitions needed to interpret them.
Deliverables depend on the selected engagement. The package is designed to make both the result and the calculation logic understandable to operations and analytics stakeholders.
Agreed transformations, mappings and calculated fields for the scoped analysis.
Charts or dashboards for the agreed measures, dimensions and time periods.
Pareto, trend, segment, shift, product or asset views relevant to the question.
Data-quality findings, exclusions, assumptions and known interpretation limits.
Clear descriptions of the agreed KPI logic so reviewers can trace the result.
Observed patterns, priority areas for validation and next analytical questions.
The sequence keeps the customer involved where production definitions and operational interpretation matter most.
Define the production question, boundary, sources and desired outputs.
Receive the agreed extracts, access method and supporting definitions.
Assess coverage, fields, quality, identifiers, timing and usable history.
Build the agreed metrics, comparisons, loss views and drilldowns.
Review exceptions and surprising patterns with relevant stakeholders.
Deliver outputs, logic, assumptions and any agreed corrections.
Production analytics often touches systems and improvement programmes that extend beyond analysis. These boundaries are made explicit before work starts.
These are realistic use situations, not client case studies. The exact approach depends on the data your operation actually records.
Compare rate by product, line, shift or time period and locate where observed cycle performance diverges from the agreed standard.
PerformanceSeparate dominant stop categories, durations, frequencies and event clusters before deeper engineering investigation.
AvailabilityIdentify where quality losses cluster by product, machine, operation, batch, shift or time window when the source data supports those dimensions.
QualityReconcile how availability, performance and quality are being calculated and document where source limitations affect comparability.
KPI governanceReview duration, sequence and variation around product changes, setup windows or campaign transitions where those events are recorded.
FlowNormalize relevant context and compare performance without assuming that different assets, products or calendars are directly equivalent.
BenchmarkingScope, systems, OEE, data readiness, deliverables, timing and boundaries — answered before you commit to an engagement.
It is the structured analysis of production records and operational signals to understand how a line, machine, product, shift or process is performing. Depending on the agreed scope, the analysis can cover availability, throughput, cycle time, downtime, quality losses, scrap, rework, yield, changeovers, bottlenecks and related production patterns.
The exact source set depends on your environment. Common inputs include MES exports, SCADA or historian data, machine or PLC event logs, ERP production orders, QMS inspection data, CMMS maintenance records, shift logs and structured spreadsheets. Access or exports are agreed before work begins.
No. An MES can provide useful production context, but it is not a prerequisite for every analysis. A meaningful diagnostic may be possible from existing exports, spreadsheets or machine-event data if timestamps, production quantities, product or order context and reason codes are sufficiently complete.
OEE can be included when the required definitions and source data are available. The calculation depends on agreed availability, performance and quality logic, including planned production time, ideal cycle or rate assumptions, good quantity and loss treatment. KPI definitions are confirmed before final results are treated as decision-ready.
The analysis can identify patterns associated with lost production time or rate, such as downtime categories, slow cycles, micro-stops, changeovers, product mix, quality loss or shift variation. It should not claim root cause from correlation alone; operational validation is needed before conclusions are acted on.
Useful inputs normally include the relevant data extracts, data dictionaries or field descriptions, KPI definitions, machine or line identifiers, product or SKU context, shift calendars, planned downtime rules, reason-code definitions and a contact who can explain process exceptions. Only the inputs needed for the agreed scope should be shared.
Deliverables are confirmed in the scope and can include a cleaned analysis dataset, KPI definitions, diagnostic findings, charts or dashboards, loss Pareto views, production-segment comparisons, data-quality observations, assumptions and a handoff note describing how results were calculated.
Not as part of a standard analysis engagement. Control-system programming, hardware changes, sensor installation, MES configuration and production-control changes require separate technical scope and appropriate site ownership.
Dashboarding or repeatable reporting can be scoped when the source data and refresh method are suitable. A one-time diagnostic does not automatically include live integrations, cloud infrastructure, data warehousing or continuous data pipelines.
Manufacturing analytics varies materially with the number of sources, data quality, history, plant or line count, refresh requirements, KPI complexity and integration needs. For that reason this page uses Custom Quote rather than an unsupported fixed price.
A delivery date is confirmed after source availability and scope are reviewed. Timing is affected by data extraction, data-quality issues, mapping across systems, stakeholder access, the amount of historical data, the number of production assets and the level of validation required.
Data-quality checks are part of the analysis workflow. Missing timestamps, duplicate events, inconsistent units, changing reason codes, unmatched product IDs, incomplete downtime records and clock or time-zone issues are documented and either corrected with agreed rules or flagged as limitations.
Yes, that can be scoped, but cross-site analysis usually needs additional normalization because sites may use different machine identifiers, calendars, reason codes, measurement units and KPI definitions. Multi-site harmonisation is therefore treated as a custom complexity factor.
Predictive models can require a separate advanced-analytics scope with sufficient history, labelled events, engineering context and validation. A production diagnostic should not be presented as a predictive-maintenance solution unless that work is explicitly included.
If a calculation or interpretation changes because an agreed rule was applied incorrectly, it is corrected within the confirmed analysis scope. New data sources, new KPIs, new lines, additional sites or materially different questions are treated as scope changes and may require a revised estimate.
Rudrriv reviews the production context and the question you want the data to answer. Clarification may be requested, then the proposed scope, inputs, delivery expectations and commercial terms are confirmed before the engagement proceeds.
Use Requirement Details to describe the production question, the data sources or exports you have, the line or plant scope and any deadline context. You do not need to complete a long qualification form.
Email ID, Phone and Requirement Details are required. Name is optional.
