# How hyperautomation is reshaping industrial workflows beyond basic task automation
Manufacturing and industrial operations have reached an inflection point where traditional automation no longer suffices to meet the demands of modern production environments. The convergence of artificial intelligence, robotic process automation, and intelligent analytics has given rise to hyperautomation—a paradigm that extends far beyond simple task automation to orchestrate entire operational ecosystems. Unlike conventional automation that addresses isolated processes, hyperautomation creates interconnected, self-optimising workflows that respond dynamically to changing conditions across production lines, supply chains, and quality management systems.
Global manufacturers are witnessing transformation rates that seemed impossible just five years ago. The integration of machine learning models with enterprise resource planning systems now enables predictive capabilities that anticipate equipment failures before they occur, whilst intelligent document processing eliminates bottlenecks in logistics operations. This technological evolution isn’t merely about replacing human workers with digital counterparts; it’s about augmenting industrial capabilities to achieve levels of operational excellence previously unattainable through manual processes or basic automation alone.
The industrial sector faces unique challenges that make hyperautomation particularly compelling: complex regulatory requirements, stringent quality standards, real-time coordination across distributed facilities, and the need to integrate legacy systems with cutting-edge technologies. How do organisations bridge the gap between disparate systems whilst maintaining production continuity? The answer lies in sophisticated orchestration frameworks that harmonise multiple automation technologies into cohesive, intelligent workflows.
Hyperautomation architecture: orchestrating RPA, AI, and process mining in manufacturing ecosystems
The foundation of industrial hyperautomation rests upon a multi-layered architecture that integrates robotic process automation, artificial intelligence, and process mining technologies into a unified operational framework. This architecture functions as the central nervous system of modern manufacturing facilities, coordinating activities across production, logistics, quality control, and administrative functions. Unlike monolithic automation systems of the past, today’s hyperautomation platforms employ modular, API-driven designs that allow seamless integration with existing manufacturing execution systems, enterprise resource planning platforms, and specialised industrial applications.
At the core of this architecture lies an orchestration layer that manages workflow execution, exception handling, and decision routing across multiple automation technologies. This layer determines when to deploy RPA bots for structured, repetitive tasks, when to invoke machine learning models for predictive analytics, and when to escalate complex scenarios to human operators. The sophistication of this orchestration becomes apparent when you consider that a single production order might trigger dozens of automated processes across multiple systems, each requiring precise timing and data synchronisation to ensure manufacturing efficiency.
Integration of UiPath and blue prism with machine learning models for predictive maintenance
Leading RPA platforms such as UiPath and Blue Prism have evolved beyond simple screen scraping to become sophisticated automation orchestrators capable of integrating with advanced machine learning models. In predictive maintenance applications, these platforms connect to IoT sensor networks collecting vibration data, temperature readings, and acoustic signatures from industrial equipment. The RPA bots continuously feed this telemetry into trained ML models that identify anomalous patterns indicating potential equipment failures. When the predictive algorithms detect degradation signatures, the RPA system automatically generates work orders, schedules maintenance windows to minimise production disruption, and orders replacement parts from inventory management systems.
This integration demonstrates the synergistic potential of combining rule-based automation with cognitive capabilities. Whilst the RPA handles structured data extraction, system updates, and workflow coordination, the machine learning models provide the intelligence needed to interpret complex sensor data and make probabilistic assessments about equipment health. Manufacturing facilities implementing this integrated approach report reductions in unplanned downtime of up to 40%, alongside significant decreases in maintenance costs through optimised parts inventory and labour scheduling.
Process discovery through celonis and SAP signavio for workflow optimisation
Process mining technologies like Celonis and SAP Signavio have revolutionised how manufacturers identify automation opportunities and optimise existing workflows. These platforms analyse event logs from enterprise systems to create detailed process maps showing exactly how work flows through an organisation—including variations, bottlenecks, and inefficiencies that aren’t apparent in documented procedures. In industrial settings, process mining reveals hidden complexities such as manual workarounds employees use to compensate for system limitations, approval loops that unnecessarily delay production orders, and data quality issues that propagate errors across multiple departments.
The insights generated by process mining directly inform hyperaut
omation initiatives by highlighting which process variants are most suitable for RPA, where AI-driven decisioning would add value, and which hand-offs between systems or teams should be redesigned altogether. Rather than guessing where to deploy bots, manufacturers can use Celonis and SAP Signavio to quantify cycle times, rework rates, and waiting periods, then prioritise initiatives with the highest potential impact on throughput and cost reduction. As hyperautomation programmes mature, continuous process mining acts like a “digital MRI” of the factory, showing how changes in one area ripple across the wider manufacturing ecosystem and enabling data-driven optimisation at scale.
Low-code platforms: mendix and OutSystems in industrial digital twin development
Low-code platforms such as Mendix and OutSystems are becoming critical enablers for industrial digital twin initiatives, especially where hyperautomation is the strategic objective. Instead of relying solely on traditional software development cycles, engineering and operations teams can rapidly build applications that mirror physical assets and production lines, connecting to real-time data streams from PLCs, SCADA systems, and IoT gateways. These digital twins simulate machine behaviour, production scenarios, and maintenance schedules, providing a virtual environment where you can test automation logic, RPA workflows, and AI models before deploying them on the shop floor.
Within a hyperautomation context, low-code applications act as the “glue” between disparate technologies. A Mendix app might orchestrate data from an MES, an ERP like SAP S/4HANA, and a predictive maintenance model, presenting operators with a unified cockpit for decision-making. OutSystems can be used to build dashboards that not only visualise OEE (Overall Equipment Effectiveness) but also trigger UiPath or Blue Prism bots to execute corrective actions—such as rescheduling orders, adjusting production parameters, or flagging anomalies for human review. This combination of low-code and digital twins accelerates innovation cycles and reduces the risk associated with large-scale industrial automation projects.
For manufacturers striving to move beyond basic task automation, low-code digital twin solutions also democratise hyperautomation. Process engineers, maintenance planners, and even line supervisors can participate in solution design without deep coding expertise. As a result, domain knowledge is captured directly in the applications that drive production optimisation, rather than being lost in translation between business and IT. This collaborative approach significantly shortens time-to-value for hyperautomation initiatives and supports continuous improvement based on real-world feedback.
Event-driven architecture and API management with MuleSoft for real-time decision making
Event-driven architecture (EDA) and robust API management are essential for hyperautomation in manufacturing environments where milliseconds matter. Platforms like MuleSoft provide the connectivity fabric that allows machines, MES, ERP, WMS, and quality systems to exchange data in real time through standardised APIs. Instead of relying on nightly batch jobs or manual file transfers, critical events—such as a machine fault, a quality deviation, or a rush customer order—immediately propagate through the ecosystem, triggering automated workflows and decision engines.
In practice, MuleSoft can expose APIs from legacy manufacturing systems and wrap them in modern, secure interfaces that RPA bots, AI services, and low-code apps can consume. When an event occurs on the production line, an EDA-based integration framework routes the message to the appropriate hyperautomation components: a predictive model that estimates impact on delivery dates, a scheduling engine that re-sequences work orders, and an RPA bot that updates customer-facing portals. This event-driven connectivity transforms the factory from a reactive environment into a proactive, self-adjusting system.
From an operational standpoint, you can think of EDA as the nervous system signals and MuleSoft as the set of arteries and veins that ensure data flows where it’s needed. By embracing API-led connectivity, manufacturers gain the agility to plug in new automation tools—such as computer vision services or new MES modules—without rewriting core integrations. This modularity is critical as hyperautomation programmes evolve, allowing you to scale from pilot projects to enterprise-wide deployments while maintaining governance, security, and performance.
Intelligent document processing transforming supply chain and logistics operations
While hyperautomation often evokes images of robots on the shop floor, some of the most dramatic gains in industrial workflows come from digitising and automating document-heavy supply chain processes. Purchase orders, invoices, bills of lading, customs declarations, and quality certificates still arrive in a variety of formats, often requiring manual data entry and validation. Intelligent document processing (IDP) applies AI, OCR, and machine learning to extract, classify, and validate this information automatically, integrating it directly into ERP, TMS, and WMS systems.
By rethinking how documents flow through the supply chain, manufacturers can remove bottlenecks that previously slowed down goods receipt, shipment processing, and financial reconciliation. Hyperautomation frameworks combine IDP with RPA and event-driven integrations so that once a document is ingested, downstream actions—such as updating inventory, confirming deliveries, or initiating payments—are executed with minimal human touch. The result is not only faster cycle times but also improved data quality, which is essential for accurate planning and analytics.
ABBYY FlexiCapture and rossum AI for invoice and bill of lading automation
Solutions like ABBYY FlexiCapture and Rossum AI are at the forefront of intelligent document processing for industrial supply chains. ABBYY uses advanced OCR and layout recognition to capture data from semi-structured documents such as invoices and bills of lading, even when suppliers use different templates or languages. Rossum AI goes a step further by leveraging deep learning models that “understand” the document context, enabling high accuracy out of the box and continuous learning from corrections made by users.
In a hyperautomation scenario, invoices and transport documents received via email or EDI are automatically routed to ABBYY or Rossum for extraction. Validated data is then passed to RPA bots or directly via APIs into ERP systems like SAP S/4HANA or Microsoft Dynamics 365, updating vendor accounts, matching against purchase orders, and initiating three-way matching workflows. If discrepancies are detected—such as quantity mismatches or missing signatures—exceptions are flagged and routed to the right stakeholders through workflow tools, ensuring timely resolution.
Manufacturers that have implemented invoice and bill of lading automation report reductions of up to 80% in manual data entry effort and substantial decreases in processing time, often moving from days to hours. For you, this means finance and logistics teams can focus on managing exceptions, optimising freight contracts, and negotiating better terms instead of keying in line items. Over time, the clean, structured data generated by ABBYY FlexiCapture and Rossum AI feeds into analytics and process mining, revealing further opportunities for hyperautomation across the supply chain.
Natural language processing in contract analysis for vendor management systems
Contracts, framework agreements, and service-level agreements (SLAs) represent another rich area for hyperautomation in industrial operations. Natural language processing (NLP) techniques can be applied to unstructured contract text to extract clauses related to pricing, delivery terms, penalties, and compliance obligations. When integrated with vendor management systems and ERP platforms, these insights help ensure that day-to-day purchasing and logistics decisions align with negotiated terms.
Imagine being able to automatically flag every time a supplier’s delivery performance deviates from contracted SLAs, or when price increases exceed agreed thresholds. NLP models can parse thousands of pages of contracts, identify key entities and obligations, and map them to structured data fields within your procurement and vendor management workflows. Hyperautomation then links this intelligence to RPA bots that monitor incoming invoices, delivery notes, and performance reports, triggering alerts or corrective actions when variances occur.
This approach not only reduces legal and compliance risk but also strengthens supplier relationships by making expectations transparent and measurable. Instead of reactive fire-fighting when disputes arise, you gain continuous oversight of contract adherence. As industrial companies increasingly rely on complex, global supplier networks, NLP-based contract analysis becomes a powerful tool for ensuring that operational reality matches strategic sourcing decisions.
Computer vision applications in quality control and defect detection workflows
Computer vision is rapidly transforming quality control in manufacturing by enabling automated inspection of products, components, and packaging at scale. High-resolution cameras mounted on production lines, combined with deep learning models, can detect defects such as scratches, misalignments, missing parts, or incorrect labels far more consistently than manual visual inspection. When integrated into a hyperautomation framework, these vision systems do more than just flag defects—they trigger downstream workflows to isolate affected batches, adjust process parameters, and update quality records.
Think of computer vision as giving your production line a set of highly trained “digital eyes” that never tire and can detect patterns invisible to the human eye. Once a defect is identified, RPA bots can automatically create non-conformance records in QMS systems, initiate rework or scrap processes, and update customers or regulators when required. Over time, defect data feeds machine learning models that correlate quality issues with machine settings, material lots, or operator shifts, enabling proactive adjustments to prevent recurrence.
By combining computer vision with intelligent document processing—for example, linking scanned certificates of analysis with real-time inspection results—manufacturers can build a closed-loop quality assurance system. This not only improves product reliability and reduces warranty claims but also supports traceability requirements in regulated industries such as automotive, aerospace, and food and beverage. Ultimately, computer vision-based hyperautomation helps you shift from reactive quality control to predictive, data-driven quality management.
End-to-end production line orchestration through hyperautomation frameworks
Hyperautomation reaches its full potential when it orchestrates the entire production value chain, from order intake and planning to manufacturing execution, quality assurance, and shipment. Rather than optimising isolated tasks, end-to-end orchestration focuses on synchronising all moving parts of the production line so that materials, machines, and people work in harmony. This is where integration between MES, ERP, IoT platforms, and decision engines becomes the backbone of a truly smart factory.
In practice, end-to-end orchestration means that a customer order can automatically trigger a cascade of automated actions: availability checks, material reservations, machine scheduling, quality plan assignment, and logistics booking. RPA bots, AI services, and digital workers collaborate within a common framework, guided by business rules and real-time data. The outcome is a production environment that resembles an air traffic control system—continuously monitoring, predicting, and adjusting to keep everything running smoothly and safely.
MES integration with ERP systems: SAP S/4HANA and microsoft dynamics 365
The integration of Manufacturing Execution Systems (MES) with ERP platforms such as SAP S/4HANA and Microsoft Dynamics 365 is central to end-to-end hyperautomation. MES manages real-time production activities—work order execution, machine states, operator actions—while ERP oversees planning, procurement, finance, and order management. When these systems are tightly coupled via APIs and event-driven workflows, data flows seamlessly from the shop floor to the top floor, enabling accurate, timely decision-making.
For example, when an MES records that a batch has completed, an integration layer can instantly update SAP S/4HANA with confirmed quantities and consumption data. RPA bots may then trigger automatic goods movements, update production costs, and generate quality certificates for customer portals. In Dynamics 365 environments, similar integrations ensure that production progress directly informs available-to-promise calculations and revenue forecasts. This eliminates the traditional lag between physical production and administrative updates, which often leads to data discrepancies and planning errors.
From a hyperautomation perspective, MES–ERP integration also provides the feedback loop needed for continuous optimisation. Process mining tools can analyse end-to-end execution logs, while AI models use historical and real-time data to propose schedule changes, maintenance windows, or parameter optimisations. You move from a static “plan–execute–report” cycle to a dynamic, adaptive loop where execution informs planning in near real time.
Real-time production scheduling using autonomous decision engines
Traditional production scheduling often relies on static rules, spreadsheets, or legacy APS tools that struggle with the complexity and volatility of modern manufacturing. Autonomous decision engines, powered by optimisation algorithms and machine learning, bring hyperautomation to scheduling by continuously recalculating the best possible plan as conditions change. These engines take into account constraints such as machine capacity, tooling availability, setup times, labour shifts, and due dates, generating schedules that balance throughput, cost, and service levels.
When integrated with MES and ERP, an autonomous decision engine can automatically re-sequence work orders after a machine breakdown, a rush order, or a material shortage. The updated plan is pushed to operator terminals, and RPA bots adjust related transactions in the ERP—such as rescheduled delivery dates or revised purchase orders. This is akin to having a GPS for your factory: instead of following a fixed route, the schedule continuously adapts to traffic conditions to keep you on time.
Manufacturers that adopt real-time, AI-driven scheduling report measurable improvements in on-time delivery performance and asset utilisation. For you, this means being able to promise shorter lead times with greater confidence, while reducing overtime and firefighting. As hyperautomation matures, these decision engines can incorporate additional factors, such as energy prices or carbon footprint targets, aligning operational decisions with sustainability and cost objectives.
Iot sensor data aggregation and automated response mechanisms in smart factories
IoT sensors are the sensory network of smart factories, capturing data on temperature, vibration, pressure, energy consumption, and more. However, the real value for hyperautomation emerges when this sensor data is aggregated, contextualised, and linked to automated response mechanisms. Edge gateways, time-series databases, and streaming analytics platforms collect and process data from thousands of devices, feeding it into predictive models and rule engines that drive real-time actions.
Consider a scenario where a temperature sensor on a critical machine exceeds a threshold. Instead of waiting for an operator to notice an alarm, the hyperautomation framework can immediately trigger a series of steps: slow down the line to reduce load, schedule a maintenance ticket in the CMMS, notify the shift supervisor via mobile app, and update the production schedule to account for reduced capacity. These automated responses help prevent minor deviations from escalating into costly downtime or quality issues.
Aggregated IoT data also fuels long-term optimisation. Machine learning models trained on historical sensor data can identify subtle patterns that precede failures or quality drifts, enabling predictive maintenance and adaptive control. By combining sensor analytics with RPA, MES, and ERP, manufacturers create a closed-loop system where data from the physical world directly informs digital actions—turning the factory into a living, self-regulating organism rather than a static collection of machines.
Digital worker deployment for materials requirement planning and inventory reconciliation
Digital workers—software agents that combine RPA, AI, and workflow capabilities—are increasingly being deployed to handle complex planning and reconciliation tasks that sit at the intersection of production, procurement, and logistics. Materials Requirement Planning (MRP) is a prime example. Instead of running MRP as a periodic batch job and manually reviewing exceptions, digital workers can continuously monitor demand signals, stock levels, supplier lead times, and production schedules, proposing or executing adjustments in near real time.
In practice, a digital worker might analyse planned orders in SAP S/4HANA or Dynamics 365, compare them against actual consumption data from MES, and identify components at risk of shortage. It can then trigger purchase requisitions, propose alternative materials, or re-sequence production orders to minimise disruption. Similarly, for inventory reconciliation, digital workers can cross-check physical counts from handheld scanners or RFID systems against book inventory, automatically posting adjustments, investigating discrepancies, and updating warehouse slotting strategies.
By offloading these data-intensive, rules-driven tasks to digital workers, you free planners and inventory managers to focus on scenario analysis, supplier collaboration, and strategic stock decisions. The result is a more resilient supply chain and production environment where stockouts, excess inventory, and last-minute expediting become exceptions rather than the norm. Over time, the continuous learning built into these agents improves forecast accuracy and planning quality, amplifying the benefits of hyperautomation.
Cognitive automation in quality assurance and compliance management
Quality assurance and regulatory compliance are traditionally seen as necessary overheads in industrial operations, often associated with extensive documentation, manual checks, and periodic audits. Cognitive automation turns this paradigm on its head by embedding intelligence into QA and compliance workflows, enabling continuous monitoring, rapid root cause analysis, and automated evidence gathering. This is particularly vital in regulated sectors such as pharmaceuticals, medical devices, and food processing, where failure to comply can lead to product recalls, fines, and reputational damage.
By combining machine learning, NLP, and advanced analytics with RPA and workflow orchestration, manufacturers can move towards a model of “compliance by design.” Data from production systems, lab information management systems (LIMS), and document repositories is automatically analysed for deviations and non-conformances, with corrective and preventive actions (CAPA) triggered and tracked through digital workflows. The outcome is a more robust, transparent quality system that supports both operational efficiency and regulatory assurance.
Automated root cause analysis using machine learning algorithms in deviation management
Deviation and non-conformance investigations are often time-consuming and heavily reliant on expert knowledge. Machine learning algorithms can significantly accelerate this process by identifying patterns and correlations across large volumes of production, quality, and maintenance data. For instance, unsupervised learning techniques can cluster similar deviation cases, revealing recurring issues related to specific machines, materials, or process parameters.
Within a hyperautomation framework, when a deviation is logged—manually by an operator or automatically by a monitoring system—an AI engine can immediately analyse relevant contextual data: recent process changes, environmental conditions, sensor readings, and maintenance activities. It then proposes likely root causes and recommended actions to quality engineers. RPA bots can assist by assembling all necessary documentation, such as batch records, calibration certificates, and training records, into a single digital dossier for review.
This approach turns what used to be a forensic, backward-looking exercise into a proactive, data-driven practice. Instead of spending days gathering and cleaning data, your quality teams can focus on validating insights and implementing effective corrective actions. Over time, the models improve as more deviation cases are resolved, creating a virtuous cycle where each incident strengthens the organisation’s collective intelligence.
Regulatory compliance monitoring through continuous control automation in pharmaceuticals
Pharmaceutical manufacturing is governed by stringent regulations such as EU GMP, FDA 21 CFR Part 11, and ICH guidelines, which require rigorous control over processes, equipment, and data integrity. Continuous control automation uses hyperautomation technologies to embed compliance checks directly into day-to-day operations, rather than relying solely on periodic audits or manual oversight. This includes automated verification of electronic signatures, audit trails, access rights, and environmental monitoring conditions.
For example, RPA bots can continuously verify that critical process parameters remain within validated ranges and that any deviations are properly documented and escalated. NLP algorithms can scan batch records and change control documents for missing approvals or inconsistent information, flagging issues before they reach regulators. Integration with LIMS and EMS (Environmental Monitoring Systems) allows continuous tracking of microbiological and environmental data, ensuring that cleanroom conditions meet regulatory thresholds.
By implementing continuous control automation, pharmaceutical manufacturers not only reduce the risk of compliance breaches but also streamline regulatory inspections. When auditors request evidence, hyperautomation frameworks can instantly compile complete, tamper-evident data packages, including logs, reports, and approvals. This shifts the organisation from a reactive stance—scrambling to prove compliance—to a proactive, always-audit-ready posture.
Statistical process control enhanced by predictive analytics and alert systems
Statistical Process Control (SPC) has long been a cornerstone of quality management in manufacturing, using control charts and capability indices to monitor process stability. Hyperautomation enhances SPC by integrating predictive analytics and automated alert systems that detect issues earlier and respond faster. Instead of relying solely on periodic sampling and manual chart reviews, real-time data streams feed into analytics engines that continuously assess process health.
Advanced models can identify subtle shifts and trends that may not yet breach control limits but indicate emerging instability. When such patterns are detected, alert systems notify operators and quality teams, while RPA bots can automatically initiate investigations or adjust process settings within defined boundaries. For example, if a filling line shows a gradual drift in volume, the system can recommend or apply minor parameter tweaks before products fall out of specification.
This predictive SPC approach reduces scrap, rework, and customer complaints by catching issues at their inception rather than after defects have accumulated. For you, it means moving from a reactive “inspect-and-correct” mindset to a preventative, real-time control strategy. The combination of traditional SPC techniques with modern predictive analytics represents a powerful fusion of established quality science and cutting-edge hyperautomation technology.
Workforce augmentation: human-bot collaboration in complex industrial scenarios
Hyperautomation in industrial environments is not about replacing human expertise but about augmenting it. Complex scenarios—such as commissioning new lines, handling product introductions, or managing multi-site production transfers—still require human judgment, creativity, and problem-solving. What changes is how these experts interact with data and systems. Software bots, AI co-pilots, and digital assistants take on the heavy lifting of data collection, analysis, and routine decision execution, allowing people to focus on higher-value work.
Consider a maintenance engineer equipped with an augmented reality (AR) headset and an AI assistant. As the engineer inspects equipment, IoT sensor data, historical failure modes, and manufacturer documentation are overlaid in real time. A digital worker in the background updates CMMS records, orders spare parts, and schedules follow-up tasks based on the engineer’s findings. This seamless collaboration between human and machine reduces errors, shortens repair times, and accelerates knowledge transfer to less experienced staff.
Similarly, in production planning meetings, AI-powered dashboards can simulate multiple scenarios—changes in demand, supplier delays, or capacity constraints—while digital workers implement approved plans across MES and ERP systems. Your teams spend less time debating whose spreadsheet is right and more time aligning on the best course of action. As hyperautomation matures, organisations that actively design human-bot collaboration patterns, invest in upskilling, and involve frontline staff in automation design will gain a significant competitive advantage in productivity and employee engagement.
Measuring hyperautomation ROI: KPIs for process efficiency and operational excellence
To sustain investment in hyperautomation, industrial leaders must demonstrate clear business value. This requires a disciplined approach to measuring return on investment (ROI) using KPIs that reflect both efficiency gains and strategic outcomes. Classic metrics such as cycle time reduction, throughput increase, and labour hours saved remain important, but they are only part of the picture. Hyperautomation also impacts quality, safety, compliance, and customer satisfaction, all of which contribute to long-term operational excellence.
Before launching major initiatives, it’s essential to baseline current performance for key processes—such as order-to-cash, procure-to-pay, production scheduling, or deviation management—and define target improvements. You might track automation coverage (percentage of process steps executed by digital workers), straight-through processing rates, exception rates, and mean time to resolution for incidents. On the financial side, cost per transaction, inventory turns, and maintenance cost as a percentage of asset value provide concrete indicators of impact.
Beyond hard metrics, leading manufacturers also measure softer, yet critical, dimensions such as employee satisfaction with new tools, reduction in manual data handling, and improved decision-making speed. Combining quantitative KPIs with qualitative feedback helps you refine hyperautomation roadmaps and prioritise initiatives that deliver both economic and human-centred value. Over time, organisations that treat hyperautomation as a continuous improvement journey—regularly reviewing results, expanding scope, and updating technologies—will see compounding benefits in agility, resilience, and competitiveness.