What It Is: Simply said, orchestration is the means by which a process is managed across a set of entities in a connected ecosystem

Why It Matters: From a digital manufacturing standpoint, thinking of a facility as a connected ecosystem of digital elements creates a framework by which we can manage, coordinate, analyze, and optimize processes for the purposes of improving operating performance

The above diagram highlights the elements of the blueprint from the previous article on Digital Manufacturing (link below) that will be discussed below

Key Concepts

• Fundamental to leveraging the blueprint is the concept of treating a facility as a collection of digital entities that are part of a dynamic, integrated ecosystem
• Because equipment can and likely will vary facility-to-facility (or even within a facility itself), there is a benefit to defining the architecture at a logical and abstract level, and then mapping to into individual physical elements present in the production environment.  This allows for significant flexibility in the execution layer, while providing a common, reusable design that can be leveraged across facilities for the purposes of enabling analytics and optimization that otherwise would be much more expensive and complex to deliver at scale
• For the purposes of illustrating the concept, I will describe the base assumptions related to different components at each layer of the blueprint, using material movement as an example
• The example is not meant to be exhaustive, but rather to highlight some key assumptions to help highlight how the layers of the blueprint are meant to interact and interoperate at the macro-level.  There are definitely more components involved at an implementation-level (e.g., choices of whether to use OPC UA or MQTT for IT/OT integration at a facility)
• The intention is not to suggest that the model in this example would be implemented in a big bang approach, but rather through a sequence of incremental steps as will be outlined below
• The basis for how the model is intended to operate is fundamentally rooted in Object-Oriented Analysis and Design (OOA/OOD), so some familiarity with those concepts may be helpful when reviewing the next set of assumptions

The diagram above represents the subset of components that are relevant for this more detailed illustration of how the blueprint is intended to work.  I will break this down into three parts: how the model fits together overall, providing some clarity on the role of each of the components that are called out (using the numbers above as a guide), and finally how this could be approached from an incremental standpoint over time.

Overall Concepts

• The blueprint assumes a base framework for organizing, tracking, and managing work across a set of digital components in a connected factory environment
• The means for coordinating work is largely about orchestration, which assumes business rules and workflow that govern how processes in the facility work, and for which performance data would be collected for the purposes of performing analytics
• The more than the infrastructure is extended to include additional processes, devices, and capabilities, the more optimization opportunities could be surfaced within and across facilities over time

Key Elements of the Model

1. MES and WMS – The base assumption is that most or all of the material movement requests will originate in one of these two applications
2. Movement Equipment – The assumption is the integration for the various types of material movement equipment will be done so that there is as much standard data exchanged as possible to facility cross-device analytics and process and performance optimization
3. End User Access – For the purposes of making a forklift “appear” relatively equivalent to its autonomous counterparts, the assumption is that the operator will both be provided work assignment through a mobile device and that the device itself will be digitally “visible” through a set of sensors that are added to provide location and other available telemetry data
4. Traffic Management and Location Services – It is assumed, given the near real-time nature of the devices, that location tracking and some level of traffic management will occur in the OT environment itself.  There is assumed to be a sufficient level of secure and reliable wireless connectivity available at a facility to enable this capability
5. Configuration Management – At an overall level, from a modeling standpoint, the goal is to enable the framework by designing each of the individual components as an “actor” (an entity capable of interacting with the rest of the digital ecosystem) with a set of associated capabilities and operating characteristics that ultimately help to track and evaluate its function and performance within a given facility.  This is the core area where the object-oriented analysis and design of various components of the digital facility environment would be defined and built out over time.  Managing by configuration allows for an abstraction of the operating model from the physical equipment and actors at a given location
6. Actor Management – As individual components are identified and integrated into the digital facility environment, there needs to be a mechanism to identify and assign the logical entities of the design to physical assets, along with tracking basic information about their state (for example a specific type of actor would exist for each derived type of autonomous vehicle, but that would need to be actively mapped to each physical device that is in place, so there is a way to translate the logical to the physical, and track things like battery status, whether the actor is presently addressing a task, awaiting work, completed a task successfully, etc.)
7. Work Management – With various types of connected components identified and being managed, there needs to be a capability to establish rules for what kind of work should be served by which kinds of devices.  Having a separate component that is aware of both the existing configuration and types of actors available creates a dynamic way to analyze, adjust, and optimize that distribution of work as needed without needing to change any of the integration in place
8. Orchestration – Having a configuration defined, a way to map logical entities to physical assets, and assign work to one or more components in a digitally connected ecosystem, the orchestration capability can provide a dynamic mechanism to manage material movement across connected actors, while tracking process and task performance
9. Process Optimization – This capability would specifically look at data collected via the above components and look for optimization opportunities that could be fed back into work management and orchestration
10. Facility Data & Analytics – This set of components is highlighted simply to note that data would be gathered at the edge in multiple formats to support local tracking and optimization, as well as model integration and execution
11. Simulation – With data being published to the enterprise from individual facilities, the simulation component would be tasked with modeling various scenarios to identify performance improvement or cost optimization opportunities that could be fed back to the facility level
12. Cross-Facility Optimization – Given the results of various simulation scenarios, a level of cross-facility optimization should be possible, whether that involves fleet composition, work assignment, or some other dimension surfaced through the simulation process
13. Enterprise Data & Analytics – Different than the facility data environment, the enterprise solutions would have a broader focus across facilities for model development and process improvement identification
14. Remote Monitoring – This capability is somewhat separate from those involving orchestration and optimization, but the point is that, once you have a standard for connecting and exposing telemetry and other data associated with digital components, a level of remote monitoring and support is possible that could provide efficiencies at scale

Phasing in Capabilities Over Time

• As was stated in the overview above, the blueprint is meant to serve as a reference architecture to guide implementation efforts over time and promote long-term, sustainable value creation.  As such, it can be implemented in an incremental fashion, as long as certain steps are taken along the way to promote interoperability and extensibility of the design
• This may not be how the implementation flow works in practice, but provides a way to conceptualize one way to build out the model in parts that create incremental value over time
• Step 1: Model the Framework and Endpoints: This is arguably one of the most difficult steps, because it requires a level of understanding in terms of the longer-term workings of the model overall.  In this case, that means designing to expose and capture telemetry, route, and process performance data across a set of material movement devices
• Step 2: Standardize Integration: Once the model is developed, integration should be standardized to allow abstraction of the various material movement devices so that, from a process standpoint, the means by which material movement is decoupled from the activity itself.  This provides longer-term flexibility in changing the makeup of the fleet without having to redesign the infrastructure for how material movement is accomplished in a given facility (or for a specific product setup)
• Step 3: Incrementally Implement and Gather Data: The assumption is that one type of device could be brought online at a time to test and prove out the infrastructure (including route, task completion, and process performance data), then incrementally add more devices types until all are digitally integrated and collected
• Step 4: Expose to the Enterprise: Once the digital integration is accomplished within a facility (either at an individual or collective level depending on the business need), it can then be exposed to the enterprise to provide visibility on the behavior of the fleet at each location
• Step 5: Add Remote Monitoring: Depending on the operating model, once the devices are digitally integrated, it should be possible to add a layer of remote monitoring to support ongoing maintenance and reliability activities across facilities
• Step 6: Add Orchestration: With multiple types of connected devices, orchestration can be added to provide more of a dynamic capability for assigning directed work, whether that is to forklift operators or autonomous equipment
• Step 7: Analyze and Optimize at a Facility-Level: Having gathered performance data and established a more dynamic means to assign and manage work, facility-level optimization can be done to improve material handling across all connected devices (individually and collectively)
• Step 8: Integrate at the Enterprise-Level: With data gathered and analyzed across device types, it can be published to the enterprise data solutions to provide visibility into operating characteristics across individual facilities
• Step 9: Analyze and Simulate at the Enterprise-Level: Given data gathered across multiple device types and locations, it becomes possible to run simulations to model different scenarios for fleet composition and the relative impact of changing the makeup of devices and assignments by facility
• Step 10: Optimize Across Facilities: With the output of various simulation scenarios having been generated, a level of cross-facility optimization could be performed to further optimize enterprise-level operating performance

Wrapping Up

Hopefully, providing a more concrete example sheds more light on the power of managing a digital facility as a digitally connected ecosystem.  The manufacturing environment itself is fundamentally layered and complex, so the elegance and layering of the solution, along with how complexity is insulated and abstracted is important in building out a resilient infrastructure that can operate and optimize across a variety of production settings that leverage highly varied pieces of equipment.

For Additional Information: In Practice: Digital Manufacturing – The Blueprint

Excellence doesn’t happen by accident.  Courageous leadership is essential.

Put value creation first, be disciplined, but nimble.

Want to discuss more?  Please send me a message.  I’m happy to explore with you.

-CJG 03/24/2026

What It Is: This manufacturing blueprint is intended to serve as the overarching conceptual design for a future state digital factory environment.  It is meant to help inform and guide design decisions, adjusting as new capabilities are available, and allow for incremental evolution over time that conforms to an overall, integrated design.

Why It Matters: Designing a future state manufacturing environment is a complex activity for multiple reasons, the layering of technical and non-technical components, the interaction of human and machine elements, layers of processes (some or all of which may not be standardized), safety and failure points in the production process, operating conditions and constraints, limitations of technology infrastructure at physical locations, and so on.  Having a blueprint creates a “north star” concept that can inform individual implementation efforts in the interest of guiding investments, capturing the cumulative value of those efforts, and ultimately optimizing worker productivity, improving safety, reducing unplanned events, limiting waste, improving quality, and optimizing production over time in an intentional and disciplined way, driven by a combination of architecture and engineering.

Summary View – Concepts

• Designing a connected ecosystem requires a broader view of the model itself, with a mindset geared towards reality, not an ivory tower that doesn’t and may never exist
• That being said, the “building blocks” need to be identified so the future state model is architected and elements of the design can be leveraged in a way that components will “fit” and interoperate seamlessly, even if they are built through incremental efforts over time (if at all)
• The above diagram is an evolution of the Purdue (ISA-95 Standard) model, meant to show the various connected groupings of elements that will comprise the future manufacturing environment, from the IT/OT environments within a facility to the enterprise elements and those that serve digital workers, customer, suppliers, and partners
• The facility OT environment mimics Levels 0-2 of the Purdue model, with a separation of concerns between equipment (digital and non-digital), industrial control systems, the applications and data solutions, and ultimately the visibility layer that interact directly and provides visibility into that equipment. It is a foregone conclusion that for many manufacturers where more than one facility is in operation, the machinery and composition of this environment will vary, especially as the footprint increases
• The facility IT environment represents the applications and data solutions, some connected, some standalone, that operate at a facility at a logical level (i.e., there may be cloud-based applications utilized, but that are facility-focused), running on edge computing resources.  The separation of concerns between the application and data layers reflects the conceptual architecture I discuss further in my Intelligent Enterprise 2.0 article (linked below)
• The enterprise level supports the applications and data solutions that provide capabilities that span facilities, serve broader needs (e.g., ERP, Finance, Procurement), and enabling computing capabilities that would not make economic sense at each facility (e.g., model development)
• All of these environments are supported by a common set of infrastructure elements that are secured and enabled to provide integrated monitoring and auditing, along with standard integration methods, between the IT/OT environments, the facility and cloud, and these internal capabilities and end users who need to access and consume the services
• The end user layer is meant to represent and include all the stakeholders, internal and external to the organization (including contractors at a facility, as an example) who need secure, role-based access to the technologies across all three layers of the environment

Blueprint View – Concepts

The next level of elaboration of the model above includes a representative set of solution components in each category of the larger model.  Given the differences across types of manufacturing (discrete, process, etc.) and wide variety of equipment that can come into play, the first step of applying this concept in a “real world” situation would be to tailor and adjust it to the right components that can and would apply (present or future state) to a facility of set of facilities.

Facility – OT Environment

• As was mentioned in the introduction above, the variability in equipment itself can be very significant, so there is a representative set included for the purpose of illustration, but it would need to be modified as appropriate to a specific organization
• The primary concepts are the sub-groupings within the equipment category, from entire standalone, non-digital core assets to digitally enabled equipment, sensors, and robotics that can work in concert with those items
• The supporting layers, from industrial controls, applications, and interfaces are largely consistent with the Purdue model, with additional elements for some more advanced capabilities that come into play as we evolve the broader model
• The overall assumption with the OT environment remains that it is focused on execution of the manufacturing process, real-time data and decision-making that enables workers on the shop floor

Facility – IT Environment

• The facility IT environment brings in the data solutions and applications that provide a means to analyze, manage, and orchestrate the underlying shop floor activities
• Data solutions need to be able to manage multiple types of data, from time series data coming via sensors and equipment to graph-oriented representation of equipment hierarchies and video files from computer vision systems
• The application layer has traditional applications for manufacturing execution (MES), warehouse management (WMS), and so on, but with some additional capabilities that I will highlight in the next section of the article
• Again, not all applications identified in the “facility” layer may necessarily be running in the facility environment on edge computing resources, but logically, the assumption is that some or all of those identified are likely being utilized

Enterprise Environment

• The enterprise environment can and will include more applications than those listed, but the ones listed as for the purposes of reflecting ones that have a connection into the manufacturing ecosystem in one way or another
• The data solutions would process larger volumes of data, for purposes like model development (via a data lake), broader cross-facility analytics, simulation and so on
• Part of the role of the enterprise applications would also provide a means to connect the activity and execution from any individual facility to its associated supply chain, other facilities, and so on

Supporting Layers

• The Infrastructure layer, as represented, would provide connectivity and capabilities, from the individual facility to the enterprise, that provides a secure, reliable, and performant environment for other capabilities to be provided
• The Security layer would provide capabilities to enable end user access control and identity management, vulnerability management, and zero trust to the appropriate level from the OT layer to the enterprise
• The Intelligent Monitoring capability is aligned to my recent article on this topic (link below)

End User Access

• The end user layer is elaborated to include a representative set of end consumers of technology capabilities, from the shop floor worker and supervisors to customers, partners, and suppliers
• The assumption is that technology capabilities would be delivered through a defined set of mechanisms and devices for which standards can be developed to promote a consistent experience, regardless of delivery channel

From here, I’ll highlight some key components that are part of the evolution of the model itself.

Looking Forward – Concepts

The above diagram highlights three dimensions that are part of the evolutionary story of the digital facility, moving from the physical to the digital, from an “engineered” towards a more “architected” environment.  I will write additional articles to elaborate the concept of the execution model and each of these areas in more detail, so this is meant to provide a summary of the core concepts only

Digital Equipment

• The general premise is to evolve from a set of largely disconnected and non-instrumented core assets in a facility to a digital world of connected, intelligent assets that support and enable optimization and reliability
• A large number of organizations have digital equipment today, and sensors of various types.  The key aspect in the blueprint is how they are defined, modeled, and integrated into the larger ecosystem to enable other capabilities described below
• Given reality is and always will be a blend of the new and the existing, I am planning to write a specific article on how I think about the blending of the two from an architecture standpoint in the interest of maximizing the value of assets across facilities in a heterogenous environment

Artificial Intelligence

• While the term AI is thrown around more now than ever, understanding and identifying the touchpoints where it creates value in the manufacturing environment requires far more discipline and thought to obtain the most value for the investment
• The core concept in relation to AI (beyond basic AI/ML capabilities) is that it plays five fundamental roles in the future manufacturing environment: optimizing performance of equipment (via asset health), optimizing performance of a facility (via process optimization), optimizing performance across a set of facilities and a broader application ecosystem involving manufacturing processes, optimizing product and facility investments (via digital twin and simulation capabilities), and optimizing digital worker safety, efficiency, and efficacy (via agents and orchestration capabilities)
• Individual components related to the delivery of these capabilities are highlighted in the model above that will be explored in a future article on the future of distributed intelligence in manufacturing

Orchestration

• The final component in a digital manufacturing future is the most critical, which is creating the capability to digitally orchestrate activity between human and machine elements, within and across facilities, in a way that is monitored, analyzed and optimized over time
• There are various components identified on the diagram that will be part of a third future article on the overall model for execution and orchestration, but the key element is that there will be facility and enterprise elements to how the value is created over time, with the ability to scale these capabilities as new technologies and components are available within the broader, connected digital ecosystem

Wrapping Up

In drawing out the concepts associated with this blueprint, the overarching concept is that the future world of manufacturing needs to be more connected, insight-driven, and orchestrated in the interest of optimizing performance, safety, quality, and efficiency.  I hope the concepts were thought provoking.  Feedback is always welcome.

For Additional Information: InBrief: Defining Manufacturing Maturity, InBrief: Digital Manufacturing, InBrief: The Intelligent Enterprise 2.0, InBrief: Intelligent Monitoring

Excellence doesn’t happen by accident.  Courageous leadership is essential.

Put value creation first, be disciplined, but nimble.

Want to discuss more?  Please send me a message.  I’m happy to explore with you.

-CJG 03/23/2026