looks at how design models can inform the system and how to use models in production.

Dr. Davis said that smart manufacturing is based on a testbed approach. He explained that the SMLC’s portfolio of problems includes the following: smart machine operations, high-fidelity modeling, dynamic decisions, enterprise and supply chain decisions, and design and planning. All of these elements are data-intensive in some way, and an infrastructure is needed to manage these data appropriately.

Dr. Davis described smart manufacturing as a multilayered system, and he postulated that improvements can typically be made at points of handoff, or seams. Seams can be between different departments or vendors, between designers and manufacturers, or between business systems and control/automation. At the lowest layer, the microlayer, the focus is on insertion, rapid qualification, ICME, and informing control systems. There is a short time constant associated with these functions. The next layer, the mesolayer, is a much larger space and consists of the operational decisions. The focus is on operational performance and goals, maintenance, dynamic trade-offs, and people. The upper layer, the macrolayer, focuses on supply chain information and transitions to outside the company. Dr. Davis pointed out that there are seams within each layer as well as seams across layers. The time constants are different across the different layers, which creates seams as well. It is important to orchestrate applications—that is, manage the work flow. This construct allows one to manage time (a “window of action”). The work flow can be analyzed and then used to generate projections about the output. It also allows for tracking and traceability.

Dr. Davis then defined smart manufacturing intelligence and work flow. Smart manufacturing intelligence is characterized by

  • Applications that can share data, data that can share applications, and applications that can connect to applications to achieve horizontal enterprise views and actions.
  • Orchestration of standardized decision work flows based on structured adaptation and autonomy.
  • Actionable data, trust, and visibility across the supply chain.
  • In-time, in-production qualification of materials, products, and actions.
  • In-time, in-production, multidimensional (business, operations, supply chain, customer, maintenance, energy) performance and adaptation.
  • Cross-company operational data to improve performance.
  • Evolvable design models in manufacturing.

The SMLC also defined work flow, stating that the smart manufacturing work flow enables a dynamic orchestration of manufacturing steps across different time

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