- Smart Factory, Digital Thread, and Value Chain Management
There is a myriad of new technologies coming into the manufacturing arena, each with tempting value propositions. How does an organization know that they are investing in the right areas to stay competitive? If your organization is confused about where to start or where to focus investments on the journey to a future highly connected, orchestrated, and optimized Smart Manufacturing enterprise, you are not alone.
A clear roadmap to Smart Manufacturing is of the utmost importance for each organization, but not easily realized because of the complexity of different organizational perspectives, data models, and business processes that converge at the manufacturing shop floor—processes that get products designed, outsourced, built, tested, packaged, and delivered to the customer in a consistent manner.
This article discusses how to organize the convergence of processes supporting Smart Manufacturing into three dimensions or perspectives: (i) smart factory, (ii) digital thread, and (iii) value chain management. These different dimensions relate to different perspectives and systems coming from engineering, operations, and business management disciplines and help explain why the Smart Manufacturing endeavor requires collaboration among many different stakeholders in the organization.
This three-dimensional model also explains the intersection of several manufacturing improvement initiatives included under the scope of Smart Manufacturing: IIoT (Industrial Internet of Things), Model-Based Manufacturing, and Connected Enterprise. The Digital Thread dimension aligns with the goals of Model-Based Manufacturing, he Smart Factory dimension aligns with the goals of IIoT, and the Value Chain Management dimension aligns with the goals of orchestrating and optimizing the entire value chain in the Connected Enterprise.
The Smart Factory Dimension
From a Smart Factory perspective, we are interested in connecting equipment, resources and personnel in order to acquire real-time data through automated methods, analyze it, and leverage that information to (a) provide complete real-time visibility of factory processes, (b) optimize process control, and (c) provide insights to where we can further improve performance.
For example, an assembly line with Smart Manufacturing automated and semi-automated processes may do the following:
- Monitor production flow in real-time to eliminate constraints, dispatch automated material handling, and eliminate wasted idle time
- Auto-identify parts going down the line to automatically load programs and materials for each different product configuration
- Automatically aggregate product data, analyze and identify constraints and required adjustments or improvements
- Manage equipment remotely using sensors to conserve energy, reduce downtime and trigger preventive maintenance
To achieve these levels of automation, through which products, parts, and equipment interact among themselves with enhanced communication mechanisms, we will need resources and industrial automation equipment with communication standards to acquire and publish data to higher levels of processes in the Smart Factory stack including operations management and intelligence applications.
Figure 1: The Smart Factory Dimension
The Smart Factory dimension illustrated in Figure 1 includes the following connected processes and systems flowing from equipment and resources up to higher levels of process control, analytics, and intelligence.
- Smart Machines, Sensors, Tooling, and Workforce interact with each other via structured communications and integrated systems providing real-time data about their status and the processes they are executing
- Smart Apps, Controllers, OT-IT Bridges like the Manufacturing Service Bus provide the communication bridge between Operations Technology (OT) exchanging data directly with machines and tooling, and Information Technology (IT) systems and apps where personnel interface to execute supervision, production, inspection, and maintenance tasks at the shop floor
- Operations Management System optimizes the flow of products through production processes and orchestrates the allocation of resources
- Connected Enterprise Systems including PLM, ERP and SCM are maintained in real-time sync through A2A data exchanges with production process status, resources used, and products produced
- Business Intelligence System receives periodic updates of aggregated data for performance analysis and business metrics
The Smart Factory dimension is aligned with the goals of the IIoT (Industrial Internet of Things). The IIoT takes the concepts of ease of equipment connectivity, data acquisition and advanced analysis via cloud services from the Internet of Things (IoT) initiative in consumer markets and applies them to the next generation of automation for the factory floor.
An enabler behind the IIoT is that it is becoming easier to connect and mine data directly from smarter machines. The IIoT can monitor, collect, exchange, analyze, and deliver valuable new insights. Mined data is more accurate, consistent, near real-time, and enables organizations to sense inefficiencies and problems sooner, saving time, money, and driving smarter, faster business decisions for industrial companies.
A major issue slowing down the IIoT is interoperability between older devices and machines that use different protocols and have different architectures. In contrast to the automation achieved in the last few decades, the connectivity methods targeted under IIoT and Smart Manufacturing need to be open, standards based, and able to facilitate publish-subscribe methods over the internet.
In the past, organizations depended on custom integration, vendor-proprietary interfaces and separate network protocols for integration and automation at the factory. Moving forward with IIoT, organizations want to embrace open standards and Internet protocols to facilitate an easier swap and mix of multi-vendor equipment and software, which might be on-premise or in the cloud.
A big promoter of the IIoT is the Industrial Internet Consortium (IIC) which adopted the term, and promotes the move from older automation protocols to newer Internet-enabled IIoT protocols for industrial equipment.
The Operations Management system optimizes the flow of products through production processes and orchestrates the allocation of resources. It executes programs for processes like cutting, machining or 3D printing equipment, and collects data from operators or directly out of equipment for inspection, test, pick-and-place, or packaging processes. Data is collected in a structured form that allows distribution to multiple subscribing functions in the Smart Manufacturing system like quality verification and parts traceability.
Enterprise Systems manage all kinds of business processes in the organization from receiving orders from customers to scheduling production, planning deliveries, ordering materials, invoicing, receiving payment, and paying suppliers. The timely performance of these activities depends on real-time data from the connected Operations Management system through A2A (application-to-application) data exchanges
Business Intelligence systems aggregates and organize data into actionable metrics and Key Performance Indicators (KPIs) the represent the organization’s strategic goals. In the digitally connected Smart Manufacturing organization, management is automatically alerted of areas not performing to plans and expectations. Management must be able to drill down from metrics into causal analysis, and depending on analytical capabilities, systems might even be able to suggest areas for improvement.
The Digital Thread Dimension
The Digital Thread dimension of Smart Manufacturing starts with the engineering design definition of the product and follows the product lifecycle through its sourcing, production and service life ensuring that the digital definition of each product unit is aligned with the physical product. The digital data for each product includes every incorporated revision to the engineering definition and any deviations from the design specifications approved and executed on the product during its lifecycle. The flow of processes in the Digital Thread dimension is depicted in Figure 2 including:
- Specifications Management for design of product and processes including definition of 3D models and recipes, product variations and configurations, and engineering change management practices
- Operations Management which includes production and verification processes including programs and work instructions for automated 3D printing, machining, and verification against engineering specifications
- Product Services Management for maintenance of the product during its service life with data collected on product performance, modifications, and replacement of components.
Figure 2: The Digital Thread Dimension
In discrete manufacturing, the Digital Thread perspective is aligned with the goals of Model-Based Manufacturing and Model-Based Enterprise initiatives. The Digital Thread initiative aims for seamless threads of structured communications and data exchanges throughout the value chain that are accessible to all stakeholders across the extended ecosystem to ensure complete visibility and traceability of the digital and physical product from design through sourcing, production, and ultimately to the end user or customer.
The Digital Thread begins with a 3D model-based or recipe-based definition of the product from design teams flows into the Operations Management and the Supply Chain Management via standards of integration for pervasive distribution of the data throughout the connected Smart Manufacturing enterprise. Examples of communications in the Digital Thread include product and process specifications, test results, conformance issues, asset maintenance requirements, and details and approvals for deviations from standard specifications.
Digitally connected model-based design and manufacturing help connect previously disconnected functional departments through a logical thread of integrated data and processes which aids in (a) faster design revision distribution and new product introduction (NPI), (b) accurate translation of product to process specifications, (c) smarter business decisions with visibility of product performance during its lifecycle, and (d) quicker resolution of issues requiring engineering design changes.
The product design engineer states the materials, form, and fit requirements for the components in 3D models for discrete manufacturing, or the chemistry and physical transformations in a recipe for process industries. The manufacturability of a product is dependent on the particulars of design parameters and tolerances. The production and inspection process definition is a repeatable structured means of conveying the engineering intent to Operations Management. There should also be a design feedback loop between the design of the product and the design of the manufacturing processes, tooling design and inspection capabilities. There is a need for better ways to communicate specifications, capture the relation to actual measurements, and leverage that information for resolving issues on non-conformance to the requirements in a well-defined formal way.
As an example, in discrete manufacturing today there is a lot of manual interpretation, transformation, and translation of data between engineering and manufacturing systems. In addition to being inefficient, each time data is manually converted from one format to another, it introduces a chance for misinterpretation and error. For example, in current processes, CAD models need to be manually converted to (a) computer numerical control (CNC) programs for machining, (b) coordinate measurement machine (CMM) programs for inspection, and (c) manufacturing execution system (MES) illustrated work instructions for assembly. During these manual processes, the associativity to objects in the CAD model is usually lost. When a CAD model revision comes down the pipe, the engineers and programmers must do a thorough review of the entire model to avoid missing anything instead of concentrating with confidence on a few highlighted revised areas. In future processes, with structured digital handoffs, systems will be able to easily highlight revisions, do impact analysis on downstream programs and instructions, and facilitate the automated incorporation of changes.
The Digital Thread will provide a formal framework for the controlled and automated interplay of authoritative technical and as-built data with the ability to access, integrate, transform and analyze data among disparate systems throughout the product lifecycle. In the Digital Thread, the product’s data “travels” along with the physical product and evolves through data collected at each step of its manufacturing process. By “travel” we mean that the data needs to be easily accessible at any time during production and referenceable to each product’s lot or serial number. The scope of data includes as-designed requirements, validation and inspection records, as-built records with part genealogy traceability, and as-tested data. The Digital Thread needs to be able to deliver the digital product data along with the physical product to the end customer. For some products, the thread of the product data will continue into Product Service to maintain the product during its entire service life.
The Value Chain Management Dimension
An important dimension to achieving a fully connected extended enterprise in Smart Manufacturing is the Value Chain Management perspective. Value Chain Management focuses on minimizing resources and accessing value at each stakeholder function along the chain, resulting in optimal process integration, decreased inventories, better products, and enhanced customer satisfaction.
The scope of Value Chain Management spans from managing suppliers of materials and parts, to managing the handover of information through internal departments including the production shop floor, and all the way to managing the delivery of the product to the end customer. It encompasses the procedures, forms, and data handoffs that link these organizational entities into a value chain that delivers a final product and services for that product to the end customer.
The standardization of IT practices that ERP started decades ago for cash-to-order processes within the organization—covering activities like contracts, procurement, receiving, invoicing, purchase orders, delivery, and payment—must be extended now across the entire value chain with an emphasis on open data exchange standards that enable publish/subscribe connections across the internet and cloud services. Configurable repeatable patterns of orchestrated activities across the value chain will enable highly automated, efficient, and agile business processes.
As illustrated in Figure 3, the Value Chain Management dimension includes processes for:
- Customer Management with online interaction with customers for quicker custom product configurations, order in-process visibility, and approvals for changes, deviations or delays
- Compliance Management maintaining organizational guidelines, coordinates audits and monitors compliance performance with internal departments and external regulatory agencies
- Operations Management delivering real-time information from production processes to other business management functions and orchestrates activities into the supply chain to make sure that materials, parts, and subassemblies arrive at the right place at the right time
- Resource Management of personnel and equipment required to make the product, provide product services, and maintain the equipment up and running with the required capabilities and certifications
- Supplier Management with functions from identifying and establishing the supply chain with the right partners to monitoring, synchronizing, and maintaining the required quality levels.
Figure 3: The Value Chain Management Dimension
The new Smart Manufacturing ecosystem aims to create closer relations and interactions with customers in processes and services. Customer Management includes functions for customizing orders to customer preferences, providing more visibility to in-process order status, coordination of deliveries, download of data for each product shipment, known issue alerts for purchased products, warranty claims and issue resolution, approval for changes and deviations to contract specifications, and coordination of service subscriptions and service orders. Some organizations have a Program Management function that closely works to achieve program goals with the customer organization and the supply chain.
Since the Value Chain Management dimension encompasses procedures that link the enterprise departments into a connected value chain, it is necessary to have a Compliance Management function which maintains organizational guidelines, coordinates audits, monitors compliance among internal departments, and coordinates with external industry and government regulatory agencies. The Compliance Management function maintains the brand’s quality reputation.
Compliance Management functions include the business processes that (a) document and control standard practices, (b) record history for reporting and auditing purposes, (b) handle resolution of issues and tracking of corrective action and continuous improvement efforts. Compliance Management ensures that corporate procedures are aligned with industry regulations for safety, environmental protection, risk mitigation, and quality management as documented industry standards such as ISO9001, ISO14000, ISO45001, and ISO31000.
Operations Management touches every dimension in Smart Manufacturing performing a very critical coordination function. Operations Management orchestrates activities into the supply chain to make sure that materials, parts, and subassemblies arrive at the right place at the right time. It provides demand signals for resources and delivers real-time information from production processes that includes the context of orders, specifications, and resources. Good data from Operations Management enables confident decision-making in all parts of an organization including production, quality, maintenance, sales, and engineering. This information is essential to allow management to drill down from corporate Key Performance Indicators (KPIs) into causal analysis to uncover areas for improvement.
To ensure the operational outcomes desired we must properly train personnel on required skills and keep track of the required certifications for specialty jobs. Resource Management includes Workforce, Facilities, and Equipment Management. Equipment Management includes the care of the plant, environmental controls, machines, equipment and tools handling trouble-call tasks as well as scheduled preventive maintenance and calibration service tasks.
Workforce Management includes maintaining the right level of workforce with the right level of skills and certifications to perform the required production and inspection tasks. It includes tracking attendance and labor costs in concert with the Operations Management system. Human Resources has the challenge to attract the next generation of the Smart Manufacturing workforce with the required new skills for the job.
Supplier Management includes the activities for sourcing materials and components to suppliers, coordinating the proper production of those components at the supplier site including supplier qualifying and auditing, negotiating contracts, scheduling deliveries, managing warehouse and stockroom, receiving and inspecting incoming materials and parts, and handling of warranty issues, returns, and corrective actions with suppliers. Product design changes must be carefully coordinated with impacted suppliers. In the connected smart supply chain, digital data about materials and components must be delivered by suppliers along with the physical units in a way that allows easy roll up of the data to higher levels of assembly by the Operations Management system for full parts genealogy traceability. Communications into the supply chain is performed via the internet through B2B (business-to-business) data exechanges.
A Convergence on Smart Manufacturing
Smart Manufacturing strives for higher levels of connectivity, orchestration, and optimization of processes in the manufacturing value chain. To reach this goal, the organization must (i) align the intersection of the different organizational perspectives into a cohesive set of functional roles, data models, and business processes that converge to get products designed, outsourced, built, tested, packaged, and delivered to the customer in a consistent manner, and (ii) figure out how to leverage the many technology building blocks available into a flexible architecture of systems that helps automate the activities as much as possible, pass data seamlessly, and enable new levels of optimization, predictive, and prescriptive analysis in enterprise processes.
The organization can start trying out new technology but cannot complete the analysis of the required technology building blocks until the functional requirements to support the new connected enterprise are fully understood. The three-dimensional model for Smart Manufacturing in Figure 4 provides a functional framework to start laying out business processes in the industrial ecosystem, the interactions required between activities, and the data exchanges required to support those interactions. Operations Management is a common central function in these three dimensions and has the critical role of coordinating the convergence of the digital, physical, and business process dimensions.
Figure 4: Three Dimensions Meet for Smart Manufacturing
The flow of information in the typical legacy manufacturing environment is, at best, full of manual information handoffs with a lot of human data interpretation and transformation along the way. Legacy processes were commonly designed around the sequential handover of paper documents between different departments. Within major enterprise systems such as ERP or PLM most processes are based or focused on departmental issues; the processes are rarely cross-functional.
Modern processes can be reinvented around new cyber-physical paradigms that promote real-time response, collaborative teams, and more parallel tasks across production and supply chain. Consider the benefits of processes where utilities auto adjust based on environmental sensor data, where machines take corrective action and request maintenance to avoid costly damage, where part shelves report usage and are automatically replenished by suppliers, where correction tasks for non-conformances are routed in parallel to multiple departments including Engineering, Procurement, Inventory Control, and into the supply chain.
To minimize delays and communication errors among intra-departmental processes, process outputs need to be connected as inputs to successor processes. Communication and data processing among activities should avoid manual data input and translation errors whenever possible. Publish and subscribe data services must connect enterprise systems, web applications, mobile devices, and cloud services in a system of systems to ensure the pervasive distribution of the data.
There will be some technical challenges along the way to create the Smart Manufacturing connected enterprise. For example, data exchange standards will need to evolve and be adopted by hardware and software vendors, and security concerns will need to be addressed at all levels of enterprise communications. But the biggest challenges ahead are cultural. How do all involved embrace new business models, new processes, and new levels of transparency among departments and partners in the ecosystem?
Organizations will soon overcome these barriers and realize a network of connected partners, systems, and resources that will result in the transformation of conventional value chains and the emergence of new manufacturing practices and business models that leverage the higher levels of connectivity to achieve new levels of orchestration, optimization, and customer service.