Frequently Asked Questions

ONIQ combines artificial intelligence with proven Lean Manufacturing methodology in one platform. It gives production teams continuous transparency, faster decisions and measurable performance improvement across every value stream.

ONIQ is purpose built for discrete manufacturing, not generic office process mining. It combines data based value stream analysis, bottleneck detection and schedule deviation tracking with proven Lean tools such as Kaizen and value stream design. The AI-based software continuously analyzes production performance, uncovers inefficiencies, and recommends actions to drive measurable improvement. By combining Lean methodology with AI, ONIQ supports teams with clear, actionable guidance - making continuous improvement more efficient and scalable.

ONIQ is built primarily for environments where a countable unit such as an order, a piece or a batch serves as the basis for analysis. It also works well in hybrid environments like packaging or batch-based processes with clear order units. For purely continuous processes without piece or order logic, our team runs a short data workshop to check whether a meaningful reference unit can be established.

Yes. This is exactly where ONIQ delivers the strongest results. Products and orders can be grouped into categories and types, so statistically robust insights into lead times, bottlenecks and schedule deviation emerge even with high variance or lot size 1.

ONIQ is typically operational within a few days, connecting to your existing systems without complex IT projects. First meaningful analyses are usually available within a few weeks. Customers have used ONIQ to lift OEE by up to 20 percent during ramp up and cut throughput times by up to 50 percent.

Perfect data is not required. ONIQ connects directly to existing source systems, processes raw production data, and flags incomplete or inconsistent information. This creates a reliable foundation for analytics and AI while helping manufacturers improve data quality over time.

ONIQ is built for production teams, not data scientists. ONIQ uses the KPIs, structures and language manufacturing teams already work with, so no specialized AI or data analysis knowledge is required to use it productively from day one. ONIQ supports production managers, plant managers, process engineers, continuous improvement teams, operations excellence leaders, and shopfloor teams. It helps them identify performance losses, prioritize improvement opportunities, and make data-driven decisions across production.

A live, automatically generated digital twin of your actual production value streams, built directly from your existing production data. Unlike conventional process mining tools, it applies established value stream mapping notations and displays key KPIs at each workplace and process step. ONIQ combines operational data with the proven logic of value stream management.

ONIQ connects to standard data sources such as ERP, MES or shop floor data collection systems, regardless of vendor. Common approaches include direct connections (for example SAP RFC, OData or BW extracts) or file based transfers such as CSV or Parquet from existing ETL pipelines. The exact connection is defined together during a short data scoping phase.

No. ONIQ reconstructs value streams automatically from existing transactional data. There is no manual process mapping, configuration or data preparation required on your side. The ONIQ Connector automatically transfers and enriches this data in a comprehensive production data model, adding the context needed to reflect complex manufacturing processes.

Yes. Warehouse postings, goods receipts and issues are captured wherever they exist as distinct bookings or operations in your ERP or EDM system, and logistics data can be included where available. Cases with implicit pre picking steps are reviewed individually to find the best way to model them.

Yes. Where cost data is available in the bill of materials, ONIQ can display product mix and inventory as value or cost, not just quantity.

ONIQ automatically detects implausible or inconsistent data and can exclude it from analyses, so results stay reliable even with imperfect data. It also makes data quality issues visible and traceable. Inconsistencies such as planned versus actual times or missing routings frequently surface during analysis, which is often a valuable side benefit.

Yes. The Process Carbon Footprint capability uses the consumption and process data ONIQ already captures, such as energy, machine runtimes and material flows, combined with emission factors to reveal CO2 hotspots directly within the value stream.

Yes. New designs can be uploaded and compared directly with the current data-based state, including scenarios based entirely on development or greenfield data.

Lean Analytics helps manufacturers identify the main causes of cost, delays, and performance losses. It includes ready to use analyses for bottlenecks, downtime, WIP, cycle times, changeovers, supply issues, and schedule deviations, using the production data that is already available.

ONIQ calculates OEE based on ISO standards. Each component is shown transparently in the workstation comparison within the Analysis view, so users can clearly understand how the result is calculated. The calculation logic can also be adapted to specific customer requirements where needed.

ONIQ continuously determines where the actual bottleneck lies, whether within a single value stream, across value streams sharing the same workstations, or upstream through the bill of materials. It combines indicators such as utilization, cycle time, inventory coverage and availability according to established Lean principles, adapted automatically to the manufacturing type: line, assembly or job shop.

The required data volume depends on the product mix and the level of variation. In many cases, three to six months of production data provides a solid basis for identifying reliable patterns. During the initial data scoping, we assess the available order volume and define the level of detail at which meaningful conclusions can be drawn.

Yes. Analysis often surfaces master data inconsistencies, for example differences between planned and actual times, missing routings or incorrect workstation assignments. This is a frequently underestimated benefit of working with ONIQ.

Indirectly, yes. By reducing lead times, identifying bottlenecks and improving schedule adherence, ONIQ helps reduce work in process, which directly lowers the capital tied up in production.

No. Every finding can be explored through guided deep dives that trace the root cause step by step in a few clicks, so production managers and Lean specialists can follow the logic without a dedicated data expert.

Production AI Agents continuously monitor value streams, machines, and order flows to identify where action is needed. Built on the multi-stage digital value stream twin and Lean methodology, they prioritize high impact issues, reveal underlying drivers and root causes, and provide clear explanations and visual context for each finding.

Yes. The agents operate exclusively on the validated data of the Digital Value Stream Twin, not on unstructured data sources. Every flagged issue is explained directly in the value stream context and traces back to the underlying value stream maps or analysis, Gantt charts or loss breakdowns with one click.

Everyone. A chat based interface lets anyone, from the shop floor to top management, ask questions about production performance in plain language, with no data expertise or analytical training required.

ONIQ supports the integration of specific Large Language Models to meet your organization's IT governance and data privacy requirements, so you keep control over which models process your data.

The Production AI Agents continuously monitor incoming production data for consistency and plausibility, flag anomalies automatically, and can exclude them from analyses where needed, keeping results reliable even when source data is incomplete.

ONIQ is built to meet high security standards. ONIQ is certified according to ISO/IEC 27001, ISO/IEC 27017, and ISO/IEC 27018 and has also completed a TISAX assessment. Deployment, hosting, access, and LLM options can be aligned with your internal security and compliance requirements. For project specific security questions, our team is happy to walk through the details with you.

ONIQ's analyses are grounded in proven Lean methodology and a validated, production specific data model rather than a single AI model. That keeps the underlying logic reliable and explainable even as we continuously integrate newer, more capable AI techniques on top of that foundation.

No. The Digital Value Stream Twin already reflects the real structure of your production, so it doubles as the simulation model. Scenarios can be defined and evaluated immediately, with no modeling expertise, separate tool or setup effort required.

You can evaluate the impact of process performance changes such as cycle times, setup times and machine availability, demand and product mix shifts, and capacity or headcount scenarios, seeing the effect on throughput, WIP and bottlenecks before implementing anything.

Yes. In Value Stream Design, you can build your own target state value streams, adjust workstations, sequences and information flows, and compare them against the current data based state. This is useful for evaluating layout changes or process redesigns in advance.

Because simulations run directly on real production data within the existing value stream, results are typically available within a single working session rather than the days it can take with traditional simulation approaches.

Yes. For any scenario, ONIQ shows how bottlenecks evolve: which constraints are relieved, where new ones emerge, and how overall flow changes, all before a change is implemented.

Articles, partnership announcements and podcast appearances covering Lean Manufacturing, value stream management, AI in production, and sustainability topics such as carbon footprint reduction.

New articles and updates are added regularly as ONIQ develops new features, partnerships and case studies. Check back periodically or follow ONIQ on LinkedIn for the latest.

Yes. Content is available in both English and German. Use the language switch to view the version available for a given piece of content.

Yes. All articles are freely accessible and can be shared or used to support internal discussions about digital Lean transformation.

Reach out through the Contact page or the Get in Touch form and the relevant team will follow up with more detail.

A live walkthrough of ONIQ covering the Digital Value Stream, Lean Analytics, Production AI Agents and Value Stream Simulation, tailored to how these capabilities would apply to your own production environment.

Anyone involved in production improvement is welcome. This typically includes production or plant managers, Lean and continuous improvement specialists, and IT or digitalization stakeholders.

The demo itself is a no obligation introduction to the platform. There is no commitment involved in simply scheduling and attending.

A typical next step is a short, structured Proof-of-Value project: defining focus use cases, scoping the required data, a kickoff and training session, working through the use cases together, reviewing joint findings, and a value workshop that quantifies the business case before any decision on a full rollout.

Once a Proof-of-Value Project is agreed, kickoff can typically happen within a couple of weeks, using that lead time to prepare the data connection.