Smart Production Lab

Vertical Integration

From shop floor to ERP system

 
Vertikale Integration

Another key element of Industry 4.0 is vertical integration, which enables seamless communication between different systems at different levels of the production system along the automation pyramid: from the actuator and sensor level on the shop floor to the control level, process control and management level through to the enterprise level.

Vertical integration in the Smart Production Lab

Vertical integration in the Smart Production Lab also follows the automation pyramid, with the focus currently on smart production, production planning and ERP.

Production planning

Production planning is the connecting link between the management and automation levels and thus acts as a central communication hub in vertical integration.

  • Vertical integration
  • Production planning
  • Production optimisation

Applied research & development

  • Context-related machine control (DNC programs)
  • Lean setup management: KPI generator for throughput time and OEE analysis
  • Interactive detailed planning and simulation of planning scenarios
  • Advanced planning and scheduling through digital integration of stocks and ordering
  • Vertical integration: production cockpit for a real-time view of production
  • Smart energy management

Lean setup management

Manufacturing processes are linked to digital tools in production (OEE portals) in order to increase efficiency, reduce setup times in operative processes and ensure real-time data availability and lean process optimisation. This approach is combined with autonomous manufacturing robots through to industrial batch production with the aim to achieve the ideal of customised mass production with batch size one.

  • 3D printing, 3D scanning, technical TV
  • Assembly optimisation using GUNT assembly kits
  • SPL commissioning
  • Autonomous order processing
  • Digital value management
  • Simulation of optimal production control

Use case 'Advanced' – Digital setup (Lean 4.0)

Efficient setup under the perspective of digital plant integration leads to shorter throughput times. This requires a consistent approach using lean methods.

Research questions

  1. How can setup options be optimised in the context of Industry 4.0?
  2. What influence does digitalisation have on OEE (Overall Equipment Effectiveness) optimisation?
  3. What opportunities and advantages does real-time data availability offer in the context of I4.0 induced setup optimisation?

Aims

  1. Reduce variance of suppliers and products
  2. Reduce process times and costs
  3. Simplified replenishment without intermediate storage

Our contribution

The focus is on analysing the requirements for the introduction of Lean 4.0 concepts in order to create lean processes through digitalisation. New methods for the evaluation and selection of machine/material combinations for lean setup management are also explored.

Use case 'Research' – Digital optimisation 4.0

Real-time production control of autonomous manufacturing robots, from prototype through to industrial batch production, is aimed at achieving the ideal of customised mass production with batch size one.

Research questions

  1. How can the optimisation possibilities in the context of Industry 4.0 be maximised?
  2. What influence does digitalisation have on OEE optimisation?
  3. What opportunities and advantages does real-time data availability offer in the context of I4.0 induced process optimisation?

Aims

  1. Optimise variance through proactive needs analysis
  2. Reduce process times and costs through digitalisation
  3. Simplified digital processing of production orders

Our contribution

The research activities in this area focus on determining the organisational and structural prerequisites for introducing the Lean 4.0 concept. The focus group also supports the evaluation and selection of materials suitable for Lean 4.0.

Vertical Integration

Vertical integration is defined as interconnected IT systems which are equipped with industrial interfaces and cover all functions from order acceptance, rough and detailed planning through to machine control and from sensors and gateways to production analysis.

  • Design of classical vertical integration
  • Implementation of first systems and interfaces
  • Implementation of digital production
  • Real-time analysis of production data and integration of data bypasses
  • Development of new system architectures including IoT approaches

Use case 'Advanced' – Vertical integration

State-of-the-art vertical integration means paperless production through seamless bidirectional data integration along the automation pyramid (levels 1 to 4 and shop floor). This approach integrates all IT systems from ERP and MES through to control and production.

Research questions

  1. How does paperless production work?
  2. What functions are useful for modern production and which levels (1-4) and systems (SPS, SCADA, MES, ERP) cover these functions?
  3. What system interfaces are suitable and what communication standards emerge?

Aims

  1. Demonstrate a digital production process
  2. Modelling system architecture and function assignment
  3. Demonstrate seamless data integration

Our contribution

The focus is on achieving vertical integration using a SAP S4, a MES solution from Industrie Informatik and a B&R machine interface. This involves implementing all interfaces for demonstrating a paperless digital production and seamless data integration from ERP to shop floor and back for reporting.

Use case 'Research' – New system architectures

The dissolution of four-stage vertical integration is investigated taking into account IoT approaches. New system architectures will be developed to use and analyse production planning and control functions in distributed systems.

Research questions

  1. Which functions will in future be covered by systems of the automation pyramid and which by cloud or SaaS products?
  2. Which production data will in future be managed centrally or locally or both?
  3. What new system architectures emerge from the combination of IoT and vertical integration?

Aims

  1. Establish data bypasses to systems in the cloud
  2. Real-time analysis of production data from distributed systems
  3. Develop new system architectures

Our contribution

Research focuses on investigating which functions of classical vertical integration will in future be performed at which levels and by which services. This involves analysing potentially redundant data streams, the role of central and distributed systems and the development of new system architectures.

Smart production

Smart production includes the manufacture of products using new technologies and the use of digitally connected machines. The integration of augmented reality technologies and modern setup scenarios provides the basis for process optimisation and digital learning.

  • Additive manufacturing
  • Process optimisation – lean production
  • Digital learning

Applied research & development

  • Variance optimisation through proactive needs analysis taking into account customer-specific quality requirements
  • Augmented reality: reduction in process times & costs
  • Smart additive manufacturing
  • Learning 4.0 using augmented reality: individual (hands-free) on-demand learning
  • IoT capable machines: process optimisation through real-time machine models
  • Digital twin: virtual asset management

Augmented Reality (AR)

Augmented reality can be used to create quick wins for manufacturing companies: shorter travel times, no wait times, assistance in monotonous work, on-demand training and simulation etc.

  • Evaluation of current AR technologies for industrial applications with a focus on data glasses
  • Lowering entry barriers in the field of AR through deployment of familiar applications
  • Collecting good practice examples of augmented reality applications along the industrial value chain
  • Creation of a test environment for AR use cases
  • Empirical usability test – voice recognition
  • Analysis of potential applications of augmented reality in the field of Learning 4.0

Use case 'Advanced' – Process optimisation using AR

AR technologies such as smartwatches or data glasses provide tailored information for specific industrial processes. Voice controlled, power autonomous data glasses enable location-independent access to relevant process data.

Research questions

  1. Which AR technologies are suitable for which industrial applications?
  2. How does the use of AR data glass lead to improvements in the industrial value chain?
  3. Which applications can reasonably be used on industry-scale AR glasses?

Aims

  1. Survey of AR technologies suitable for industrial application
  2. Rapid deployment of AR data glasses (quick win for industry)
  3. Development of specific application scenarios for industry

Our contribution

Research activities focus on the analysis and development of AR application scenarios for industry with major emphasis being placed on process optimisation. Interfaces and voice control in the industrial environment are also analysed.

Use case 'Research' – Personnel development 4.0

AR makes it possible to access individual information directly at the workplace or to perform (decision-making) simulations. The use of virtual 3D models also allows staff to work on or with virtual machines in order to acquire the necessary skills.

Research questions

  1. What opportunities do AR technologies offer for staff training?
  2. How can data be processed and transferred to the wearable via interfaces?
  3. How must learning scenarios be designed to ensure AR-based skills acquisition?

Aims

  1. Develop AR-based learning methods
  2. Create interfaces for data transfer to the wearable
  3. Develop use cases for AR learning along the value chain

Our contribution

Research activities focus on the analysis and development of AR-based learning scenarios along the industrial value chain. Special emphasis is placed on on-demand learning at the workplace and training using 3D models. The research results provide the basis for a learning management system for (industrial) enterprises.

Internet of Things (IoT)

The Internet of Things relies on the IP capability of machines and things in the manufacturing environment so that their data can be made available for the production process. Integrated and interlinked IT systems are able to communicate with each other across companies and locations, thus paving the way to more flexible production.

  • Initial integration and visualisation of machine data
  • Selection and implementation of other IoT integration options
  • Retrofitting of all machines including gateways and digital interfaces
  • Data modelling of machines based on sensor data
  • Creation of digital twins and asset management in the cloud

Use case 'Advanced' – IoT capability of machines

Digital communication using integrated IT systems and the internet protocol (IP) forms the basis for the Internet of Things. Machines of different ages and based on different technologies will be equipped with IP capability and interconnected via distributed IT systems (retrofitting).

Research questions

  1. How can non-IP capable machines be made suitable for IoT applications?
  2. Which IoT integration options are practical and which communication standards will emerge?
  3. What sensor information must be processed for creating a virtual model (digital twin) of the asset?

Aims

  1. Explore practical concepts for retrofitting approaches
  2. Select appropriate retrofitting approaches
  3. Create a machine data model

Our contribution

Research activities include analysing and comparing possible settings and standards for the retrofitting of machines depending on the situation at hand. Possible connections to gateways of integrated on-premise or cloud solutions will be identified to provide the basis for machine data modelling.

Use case 'Research' – Digital twin

A digital twin, i.e. a virtual model of a machine or asset, is a valuable tool for simulating and optimising production processes. The underlying models are based on real-world sensor data of physical objects, material or energy flows.

Research questions

  1. Where can production data be processed effectively and efficiently? What are the advantages of changing to cloud systems?
  2. Which models and systems are required for creating a digital representation of assets and machines?
  3. What benefits does a digital twin offer in terms of flexibility, development cycles and production optimisation?

Aims

  1. Analysis of on-premise and cloud computing approaches
  2. Create a digital twin, asset management
  3. Benefit analysis for the digital twin in the production environment

Our contribution

Research activities focus on analysing a combination of on-premise and cloud systems. The results will be used for developing an asset management solution and a digital twin. This provides the basis for further research on potential benefits and optimisation in production.

Smart additive manufacturing

Digitally linking manufacturing processes to generative production processes will lead to a substantial increase in efficiency in the future. This integration will allow fast and cost-efficient production of models, samples, prototypes, tools and final products. The use of artificial intelligence (AI) makes it possible to optimise the production process and introduce autonomous process steps.

  • 3D printing – fused deposition modelling
  • 3D printing – stereolithography, 3D scan
  • Integrated cutting processes – Smart Production Lab
  • 3D printing – selective laser sintering (SLS)
  • Artificial intelligence (AI)
  • Autonomous order processing
  • Simulation of optimal manufacturing control

Use case 'Advanced' – Digital additive manufacturing

Additive manufacturing is based on digital data models to form components from shapeless materials (liquids, gels, pastes, powders) or shape-neutral (band-shaped, wire-shaped or leaf-shaped) materials using chemical and/or physical processes.

Research questions

  1. How can models, samples, prototypes, tools and final products be optimised for the specific application?
  2. How does additive manufacturing influence variance configuration for batch size one?
  3. What advantages do generative production processes bring to sustainable business success?

Aims

  1. Optimise variance through proactive needs analysis
  2. Reduce process times and costs for optimal flexibility
  3. Simplified processing of production orders

Our contribution

Applied research activities focus on identifying the technological requirements for introducing generative production processes and integrating them with digital business processes.

Use case 'Research' – Intelligent additive manufacturing

The digitalisation of manufacturing processes through generative methods provides flexibility and cost benefits. The focus is on intelligent assistance systems and autonomous control with major emphasis being placed on functional safety and the integrated application of digital test models.

Research questions

  1. What possibilities does artificial intelligence (AI) offer for optimal production control?
  2. What advantages do generative production processes bring to sustainable business success?

Aims

  1. Reduce process times and costs
  2. Optimised processing of production orders
  3. Dynamic synchronisation of customer requirements and production resources

Our contribution

The applied research activities in this field focus on identifying the technological requirements for introducing generative production processes using artificial intelligence (AI).

ERP & reporting

Modern ERP systems based on in-memory technologies represent one of the key elements of digital enterprises. In addition to mapping conventional business processes they also enable flexible planning, control and monitoring of the entire corporate value chain in real time.

  • Digitalisation of business processes
  • Real-time reporting
  • Flexible production planning and control

Applied research & development

  • Zero-latency vertical integration with manufacturing execution systems and control equipment at shop floor level
  • Hybrid advanced analytics: implementation of a central data lake with internal and external data for advanced big data analytics
  • Event-controlled provision of decision-relevant information in the form of smart reports via the internet
  • Track & Trace: location-based information

Track & Trace

This approach is used to locate and trace production factors using IT-based technologies.

  • Design of a suitable network for splitting asset and visitor WiFi
  • Installation of specific WiFi components and hyperlocation hardware and software
  • Development of a heatmap evaluation for visualising datastreams
  • Generating information and/or services based on the location data of assets or visitors
  • Big data evaluations for combination with other production data

Use case 'Advanced' – Hyperlocation

Hyperlocation uses different wireless technologies to collect location data. These location data are then visualised on a heatmap, which is similar to a geographic map with movement data.

Research questions

  1. How can visitors and assets be located wirelessly?
  2. Which technologies are suitable for which range and accuracy?
  3. What conclusions can be drawn from the data generated as to the use of mobile assets in production?

Aims

  1. Generate a heatmap of visitor flows and assets
  2. Assess the performance of location technologies
  3. Filter data according to dwell time as a basis for optimisation

Our contribution

Research activities are designed to examine wirelessly obtained location data for visualising movements on heat maps. This includes both the basic technological configuration and a technology comparison in terms of accuracy and range as well as the configuration of filter functions and data monitoring.

Use case 'Research' – Location-based information

Location-based information involves collecting location data in order to provide the end user with selective information about his/her current position or make the data available for other potential services.

Research questions

  1. What improvements can be derived from location-based information of mobile assets?
  2. What services can be offered to staff based on their position in the production process?
  3. How can Track & Trace data be combined with other information using big data analytics in order to identify correlations?

Aims

  1. Target/actual comparison of the paths of mobile assets for logistics and production
  2. Service engineering for Track & Trace applications
  3. Implement big data evaluations

Our contribution

Research activities focus on the benefits to be gained from information provided by the tracking and tracing of people, machines and other assets in production, such as safety instructions for production staff. Combining this information with other production-related data allows optimisation potentials to be identified in logistics and production.

Advanced analytics

Advanced analytics involves analysing data using mathematical and statistical methods and algorithms in order to generate new information, recognise patterns and calculate predictions including the associated probabilities.

  • Acquisition of sensor data in real time and transfer to SAP HANA
  • Presentation of machine data in real time
  • Developing SAP HANA into a central data lake using the Smart Production Lab as an example
  • Implementation of predictive analytics
  • Establishment of an adaptive rule engine for complex event processing
  • Prototyping of machine learning approaches in the production environment

Use case 'Advanced' – Real-time reporting

Digitalisation generates enormous data volumes which can be quickly selected, processed and evaluated using in-memory technologies. Machine and process data enable real-time monitoring of digital production systems as well as flexible and prompt control and optimisation.

Research questions

  1. How can unstructured data be converted into structured data for advanced analytics?
  2. How can event-oriented production monitoring be implemented?
  3. How can modern in-memory systems be used for real-time monitoring and analysis of digital production systems?

Aims

  1. Implement a data lake for production data
  2. Data integration based on SAP HANA
  3. Implement real-time production reporting

Our contribution

Research activities focus on the possibilities and limits of using in-memory systems in a digital factory. This includes the development and implementation of scenarios for processing and evaluating sensor data in real time as a basis for decision-making.

Use case 'Research' – Hybrid advanced analytics

This use case involves integrating established data warehouse structures with modern big data analytics. These scenarios enable event-oriented analysis or response in real time, identifying connections and deriving forecasts.

Research questions

  1. At which system level can predictive and prescriptive analyses be carried out?
  2. How can classical business intelligence approaches be integrated with modern advanced analytics concepts?
  3. At which levels should rules of a rule engine for complex event processing be implemented?

Aims

  1. Develop advanced analytics scenarios
  2. Design a hybrid big data analytics approach
  3. Establish an adaptive rule engine for complex event processing

Our contribution

The research activities in this field concentrate on hybrid advanced analytics approaches, integrating big data technologies with real-time ERP systems. Another aim is to develop use cases for the (semi-)autonomous monitoring and control of machines and production processes in real time.