Polish company Netwise S.A. joins the collana Group
Home / Polish company Netwise S.A. joins the collana Group The
Paul M.
13 August 2026
TL;DR:
AI in production boosts efficiency, quality and productivity on the shop floor
Key use cases include predictive maintenance, quality control and process optimisation
Microsoft Dynamics 365 and Azure could form the technological basis for industrial AI solutions
As a Microsoft partner, collana supports the end-to-end integration of AI into ERP and production processes
Particularly relevant areas of application include mechanical engineering, the pharmaceutical and life sciences sectors, and medical technology
Artificial intelligence (AI) in manufacturing refers to the use of data-driven algorithms to analyse, optimise and automate production processes. When used correctly, AI can help to make processes more efficient, improve quality, reduce costs and boost competitiveness.
AI in manufacturing encompasses technologies that use data to recognise patterns, support decision-making or automate processes, with the aim of making industrial operations more efficient and intelligent. It is a key component of digital transformation in the manufacturing industry.
It is important to make the following distinction:
Machine Learning: Algorithms identify patterns in historical and current data and use these to generate forecasts or classifications
Deep learning: Neural networks process particularly complex data, such as images, speech or sensor data
Generative AI: Systems generate and process content such as texts, summaries, documentation or answers to specialist questions
Computer Vision: Image-based systems detect objects, deviations and quality defects
In the context of Industry 4.0, AI complements traditional automation with data-driven pattern recognition, forecasting and decision support. Whilst automated systems often operate according to defined rules, AI models can identify correlations within data and adapt to changing patterns within a defined framework.
A reliable data source is essential, for example from ERP, MES, sensor and IoT systems. What matters is not just the volume of data, but above all its quality, timeliness, availability and meaningful integration.
The frequency with which data needs to be updated depends on the specific application. Whilst visual quality control or plant monitoring often requires real-time data, regular data updates may be sufficient for forecasting, document analysis or long-term optimisation.
AI can detect anomalies and unusual patterns that are difficult to replicate in rigid, rule-based systems. However, decisions and automated responses should take place within clearly defined limits and be monitored, particularly in the case of critical processes.
AI in manufacturing is no longer a topic for the future, but a direct response to the key economic and operational challenges facing the manufacturing industry.
Manufacturing companies are under considerable pressure: the shortage of skilled workers makes it difficult to retain expertise, energy and material costs are driving up costs, and customers expect short lead times and consistently high quality. In regulated sectors, there are also extensive requirements regarding documentation, traceability and process reliability.
At the same time, competitive pressure is mounting from international providers, digital pioneers and production sites with more favourable cost structures. Data-driven and AI-supported applications can help to make processes more transparent, allocate resources more effectively and enable more informed decision-making.
In suitable applications, artificial intelligence and predictive maintenance can have a significant impact. McKinsey cites potential reductions in machine downtime of between 30 and 50 per cent. AI-supported image processing can also automatically detect specific surface defects, deviations or unusual patterns. With a suitable database and appropriate technical design, quality checks can thus be carried out more quickly and consistently.
Depending on the specific use case, up-to-date analyses can speed up decision-making and relieve staff of repetitive or information-intensive tasks. Measurable improvements require clearly defined objectives, suitable data and consistent integration into existing work and production processes.
A representative Bitkom survey from 2025 illustrates just how relevant this topic has become: 42 per cent of the German industrial companies surveyed are already using AI in production, whilst a further 35 per cent are planning or discussing such applications. 82 per cent believe that AI will be crucial to the competitiveness of German industry in the future (Bitkom).
AI delivers measurable added value in production, particularly when it is used for clearly defined, data-driven use cases. Depending on the area of application, it helps to identify deviations at an earlier stage, improve decision-making, make knowledge available more quickly, or automate individual processes. The following applications are among the key areas of application in industrial practice:
By using AI in predictive maintenance, companies not only save costs but also significantly increase the efficiency of their production processes. Through the continuous analysis of sensor data, ML models detect anomalies at an early stage and predict the optimal time for maintenance. This significantly reduces unplanned downtime and enables the targeted optimisation of maintenance processes.
Computer vision enables precise, rapid and fully automated quality control. Surface defects, dimensional deviations or anomalies are detected in real time. These data-driven analyses lead to more stable processes and measurably better results, particularly in the pharmaceutical packaging and medical technology sectors.
AI models can analyse process and machine data, identify correlations between process parameters and derive recommendations for optimisation. Depending on the specific application, they can, for example, help to reduce energy consumption, lead times or material wastage. However, the automated adjustment of production parameters requires clearly defined limits, robust technical integration and – particularly in regulated sectors – appropriate control and approval mechanisms.
AI-based demand forecasting, inventory optimisation and dynamic scheduling can ensure more accurate planning throughout the entire supply chain. Integration with Microsoft Dynamics 365 Finance & Supply Chain Management ensures end-to-end transparency and enables data-driven analysis and well-informed decisions in real time.
With generative AI Using Microsoft Azure, custom knowledge agents can be developed for production and service environments. They can access shared sources such as technical documentation, maintenance manuals, process descriptions or machine information, and make the information contained therein available to staff via natural language.
Such assistance systems can, for example, answer questions about plant facilities and work processes, prepare information for shift handover, or support the onboarding of new staff. This requires clearly defined data sources, access rights and processes for maintaining and ensuring the quality of the content provided.
The AI-based Service Agent ILAI It can automatically capture and categorise customer enquiries and route them to connected service, ticketing or ERP processes. Potential areas of application range from general service and status enquiries to technical issues or spare parts processes. ILAI can be used in multiple languages and tailored to company- or sector-specific content. This helps to reduce the workload on service teams when dealing with recurring enquiries and speeds up processing times.
For companies with particularly high standards regarding data sovereignty, AI models and associated applications can be developed entirely or largely (as Offline AI) can be operated within the organisation’s own IT infrastructure. This allows sensitive documents and business data to be processed without transferring them to external AI services. Possible applications include, for example, local document analysis or the automated anonymisation of personal data. Whether a solution meets regulatory and data protection requirements must be assessed on the basis of the specific use case, the architecture and the organisational measures in place.
|
Use Case |
Benefits |
Example |
|---|---|---|
|
Predictive Maintenance |
Reduces unplanned downtime and maintenance costs |
Monitoring of CNC spindles in mechanical engineering |
|
Predictive Quality |
Reduces scrap and increases first-pass yield |
Computer Vision in MedTech and Pharmaceutical Manufacturing |
|
Process optimisation |
Reduces energy and material consumption |
Self-optimising parameters in process manufacturing |
|
Supply Chain Intelligence |
Improves planning and stock management |
Integration with Microsoft Dynamics 365 F&SCM |
|
Generative AI in practice |
Speeds up access to knowledge and documentation |
Maintenance manuals and shift handover via chat |
|
AI in Customer Service (ILAI) |
Automates service processes and reduces the workload on teams |
Customer enquiries regarding service, status or technical issues |
|
Offline AI |
Ensures data sovereignty in regulated sectors |
Local analysis and anonymisation of confidential documents |
AI applications can be particularly effective when they are not operated as isolated, stand-alone solutions, but are meaningfully integrated into existing ERP, data, production and service processes. This requires not only technological expertise but also a deep understanding of business processes, the existing system landscape and the desired business objectives.
Many companies operate with fragmented data landscapes, heterogeneous legacy systems and inconsistent data quality. In existing brownfield environments in particular, relevant information is often scattered across different ERP, MES, machine and documentation systems. Furthermore, many companies lack the necessary combination of business process knowledge, data literacy and technical AI expertise. This makes it difficult to prioritise suitable use cases and define robust requirements for implementation.
Added to this are requirements relating to data protection, data sovereignty, information security and regulatory governance. Depending on the use case, requirements from the GDPR and the EU AI Act be taken into account.
Last but not least, the human factor plays a crucial role: without active change management and clear communication, there is often a lack of acceptance amongst the workforce. A successful AI roll-out therefore requires a clear data and integration strategy, measurable objectives and KPIs, as well as close coordination between business units, IT, the organisation and implementation.
A high-performance ERP environment can provide a vital data and process foundation for AI applications in production. Dynamics 365 Business Central and Finance and Supply Chain Management provide the structured data required by AI models. Integrated Copilot and AI features in Dynamics 365 can be supplemented by bespoke applications built on Azure AI Services. These applications can be seamlessly integrated into existing ERP and business processes via Microsoft platform services, APIs and interfaces.
On Microsoft Azure, scalable chatbots and knowledge agents can be implemented with enterprise-grade security, access and governance mechanisms. The specific solution can be designed to support compliance with relevant data protection requirements and the EU AI Act (Microsoft). Depending on the system landscape, the solutions can be integrated with platforms such as SharePoint, Teams, Dynamics 365 or document management systems. This makes shared corporate knowledge accessible via a central, natural-language user interface.
For companies with particularly stringent data control requirements, collana implements local AI solutions that, depending on the architecture, can be operated on-premises, in a private cloud or in isolated environments. Possible applications include local document analysis and the automated anonymisation of sensitive content.
Microsoft Fabric and Power BI can be used to consolidate data from ERP, production and other source systems and analyse it in dashboards. Key performance indicators range from OEE and production output to quality, stock and supply chain metrics. Integration with Dynamics 365 makes it easier to incorporate structured ERP data into the analysis. The timeliness and reliability of the analyses depend on the quality of the source data and the chosen update and integration mechanisms.
A successful introduction of AI systems into production follows a clearly structured approach, ranging from the initial analysis of potential through to stable operation.
Structured rather than open-ended: collana does not start with a technological decision, but with a clearly defined business problem. Use cases are assessed on the basis of their expected benefits, their feasibility and the available data. This enables an early decision to be made as to which ideas should be pursued further.
The first step towards AI-supported production involves an AI potential analysis and a use-case workshop, in which the use cases offering the greatest added value are identified in collaboration with the specialist departments. The focus here is on close collaboration between business, IT and production.
A data maturity assessment is then carried out to evaluate the existing infrastructure in terms of data quality, interfaces and the system landscape.
On this basis, a clearly defined proof of concept can be implemented. It verifies whether the selected use case works technically with the existing data and whether the previously defined quality and success criteria are, in principle, achievable.
If the assessment is positive, the solution is first piloted under realistic conditions and then gradually rolled out to the production environment. Depending on the use case, this involves integration with Dynamics 365, Microsoft 365, data platforms or other production and enterprise systems.
During operation, usage, output quality, technical availability and costs are monitored. Depending on the type of solution, models, prompts, data sources, search indexes or process rules can be adjusted. Retraining is only required if it is appropriate for the model in use and the specific use case.
The real transformation does not begin with a single AI tool, but with processes and data that are digitally accessible, consistent and machine-readable.
Whilst regulatory requirements, continuous processes and traceability are the main priorities in process manufacturing, the focus in discrete manufacturing is more on product variety, product configuration and service-oriented business models. However, both sectors benefit equally from data-driven optimisation and intelligent automation throughout the value chain.
In process manufacturing, for example, AI can help to analyse batch and process data, detect quality deviations at an early stage, or identify opportunities for optimising material and energy usage. In regulated sectors, it can also assist with the research, organisation and preparation of documentation.
In discrete manufacturing, potential areas of application for AI include variant management, quotation and product configuration, quality inspection and technical support. Particularly when dealing with complex machines, plant and bills of materials, data-driven assistance systems can help staff to search for, evaluate and select relevant information.
Companies in the DACH region must take into account various requirements relating to data protection, information security, transparency and governance when using AI. In Switzerland, the Data Protection Act also applies to AI-supported data processing. For services and applications within the EU, the provisions of the EU AI Act may also be relevant. The specific obligations that apply depend on the use case and the company’s role.
As a Microsoft Solutions Partner for Microsoft Cloud, the collana Group combines AI and data expertise with experience in ERP, manufacturing and service processes. This means that AI applications are not viewed in isolation, but are tailored to existing data, systems and operational processes right from the start.
AI in manufacturing refers to the use of data-driven systems that recognise patterns, make predictions, support decision-making or automate individual processes. Depending on the specific application, data from ERP, production, machine, quality control, sensor or documentation systems is used for this purpose.
Predictive Maintenance: Identifying abnormal machine conditions and better assessing maintenance requirements
Predictive Quality: Using image, sensor or process data to detect quality deviations
Process optimisation: Analysing relationships between parameters and identifying opportunities for optimisation
Supply Chain Intelligence: Data-driven planning of demand, stock levels and scheduling
Knowledge Agents: Making technical documentation and process knowledge accessible via natural language
AI in Customer Service: Automating the handling of recurring customer and service enquiries
The economic benefits of an AI project depend heavily on the use case, the initial situation, the quality of the data and the level of integration required. It therefore makes sense to define specific KPIs before implementation – for example, reduced processing times, lower downtime, less waste or a higher level of automation. A proof of concept can then demonstrate whether the expected outcomes are, in principle, achievable.
This requires a robust data foundation drawn from ERP, MES and IoT systems, as well as a stable IT and ERP infrastructure. Equally important are clear use cases, high data quality and structured change management to ensure the organisation is fully engaged.
The duration depends on the use case, the data available, the scope of integration and regulatory requirements. A clearly defined proof of concept can often be implemented within a few weeks. The transition to stable live operation usually requires additional time for integration, security, testing, governance and organisational roll-out.
Yes. Even medium-sized companies can make effective use of AI, provided the use case is clearly defined and addresses a specific operational benefit. A step-by-step approach involving a workshop, a feasibility study and a proof of concept helps to limit investment and gain reliable insights at an early stage. Depending on data and security requirements, cloud, hybrid or on-premises architectures may be suitable.
Microsoft Dynamics 365 can serve as a key data and process foundation for AI applications, for example in planning, supply chain, service or financial processes. For production-related applications, additional data is often required from MES, IoT, machine, quality or documentation systems. These sources can be consolidated via interfaces and Microsoft platform services.
The costs depend on the use case, the data available, the scope of integration, and the security and operational requirements. A clearly defined proof of concept is usually significantly cheaper than a production-ready, deeply integrated solution. Following an initial analysis of the use case, collana can provide a more precise assessment of the effort involved, the approach to be taken and the possible project phases.
In discrete manufacturing, typical areas of application include variant management, product configuration, assembly, quality inspection and technical service. In process manufacturing, the focus is often on recipes, batches, continuous processes, process parameters and traceability. In regulated sectors such as pharmaceuticals or medical technology, there are additional requirements regarding validation and documentation.
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Home / Polish company Netwise S.A. joins the collana Group The
The collana Group has launched the video podcast ‘CoreShift by collana’. The topics covered are: AI, digitalisation and transformation.
The collana Group has achieved a milestone that few Microsoft partners can boast: as a group of companies, it now holds all six available Microsoft Solutions Partner designations and is therefore automatically awarded the coveted „Microsoft Solutions Partner for Microsoft Cloud“ distinction.
This status is not down to any single area of expertise, but rather to the combined expertise of the Group’s ten specialist subsidiaries: from ERP implementations in manufacturing, retail and the healthcare sector, through bespoke software development, to business intelligence and AI solutions.