Objective 1

  

Integrate interoperable information systems for lifecycle (PLM), enterprise (MES/ERP), and value chain domains (Supply chain and logistics) suited for smart manufacturing architectures

Product Lifecycle Management (PLM)

Challenges Investigation and adoption of PLM solutions suitable for different manufacturing sectors. Accounting for the unpredictability in the product life cycle owing to the rapid change in the associated factors. Effects of interventions on the overall adaptability, portability, and security of services provided to users.

Research Opportunities Integration and optimization of PLM stages and tools.   Development and testing solution models for their adaptability and robustness. Modeling of data management and exchange in concurrent engineering. Strategic evaluation of PLM tools for project structuring and coordination. Developing strategies and algorithms for computer-aided process planning

Objectives Research in the areas of enterprise management and product information management. Manageability, sustainability, availability, and security of information exchanges between any kinds of products, users, and systems. Reliably and dynamically manage context-aware product data and services, and efficiently support product context acquisition, discovery, and reasoning. Providing resources to finetune processes to development in the market. Generate scenarios and prototype demonstrators for evaluation and validation of the developed models.

Equipment / Software Smart Factory testbed  Workstations  PLM Software

 

Manufacturing Execution System (MES)

Challenges The ERP and MES do not work on the same time scale: an ERP rarely works on periods shorter than a half-day, while the MES works either minute by minute, or over ten or twenty-minute stretches Most ERPs are not designed to collect and process finely-meshed data in real-time which leads to issues in interfacing it with the MES or other systems with high updated frequency Lack of MES to adapt quickly to a strategic or management change which happens in ERP as this may mean a large volume of change to be implemented at the MES level

Research Opportunities Studying and testing real-time enabling interfaces across different modules between ERP and MES and their physical implications Using the sandbox environment to study how the same information may be reflected across different applications individually to ensure data stays intact and conveys the same information when different applications use different representation format Developing code-free integration platforms that automate the technical data transformations and provide a visual drag-and-drop interface to orchestrate the needed application programming interface (API) connectivity

Objectives Real-time production adjustments: Ensuring demand changes at ERP level to be translated and transferred accurately at the shop floor level via. MES Achieving more realistic and dynamic ERP schedules by incorporating the latest improvements at the shop floor reflected by MES Getting information related to any downtime due to equipment or facility failure to be reflected at the ERP level for pushing back delivery date commitments

Equipment / Software Computer  ERP System Fishbowl License

 

Supply Chain Management (SCM)

Challenges FRAGMENTED DATA: Information in most organizations exists in silos. The sales department has its projections and budget, production has its production schedules, buyers have supplier cost and delivery schedule data, etc. The focus of this fragmentation of data is designed to serve the purposes of the individual departments in the organization instead of that of the entire supply chain. MAPPING OF SUPPLIERS: To attain true visibility the entire supply chain has to be mapped. When mapping complex supply chains far upstream, this can become complicated with smallholder farmers, artisanal miners, or home workers and each of the suppliers and customers have their own silos of information, not commonly shared with other supply chain partners.

Research Opportunities Creating Smart Connectivity with UN/CEFACT. Investigation of the characteristics, importance, and limitations in supply chain process modeling by comparing frameworks and reference models like SCOR, GSCF, VRM, CPFR, ISA95, and SAP. Investigation on factors that prevent companies from seeking more supply chain visibility, what information should a company disclose about compliance in its supply chain and products to the public, what are the potential risks associated with disclosing supply chain information to the public, how should a company mitigate risks from such disclosure. Investigation on evaluating methods of costs and benefits from transparency and implementing disclosure and how should they do so to maximize shareholder value.

Objectives To create a Business Process Model and Notation (BPMN) of specific domains in the manufacturing industry. Extend the BPMN models to enable semantic information flow among different domains using standards like UN/CEFACT.

Equipment / Software Computer systems  Open source modeling software

Collaborators:  NIST, USA

 

Objective 2

Build sustainable models of design and manufacture by analyzing the cradle to grave impacts of new connected systems and products                   (Sustainable Product Design and Manufacturing) 

Challenges Lack of affordable measurement systems and analytics tools for assessing and managing sustainability across the production process. Ensuring sustainable manufacturing requires integrated systems approach using multi-disciplinary methodologies. Since these methodologies can be expensive to implement, many companies, particularly small and medium-sized companies do not use them. Most companies have no way to track and aggregate sustainability-related data of individual processes and factories.

Research Opportunities Designing and implementing environmentally conscious manufacturing processes through simulations and actual implementation Exploring modular design approach to facilitate assembly, maintenance, and disassembly/remanufacturing Energy management and efficiency improvement of manufacturing systems through the application of IoT and AI-based techniques Studying practical metrics for measuring the sustainability of manufacturing, repeatable life-cycle assessment (LCA), and optimization and improvement of the system to decrease environmental effects Developing an integrated analytics platform that is cost-effective as well as comprehensive in providing an overall assessment of the system’s sustainability using the metrics.

Objectives Pursual of sustainable manufacturing through ecologically aware designing  Incorporation of principles of sustainability into the field of Smart Manufacturing Development of sustainable manufacturing by remanufacturing end-of-use products Assessment of sustainable performance for a system by quantifying the potential environmental and economic impacts

Equipment / Software Computer systems  LCA software  Cloud Subscription  LCI Database Subscription  Measurement and data analysis