Three Functional Dimensions Converge On Smart Manufacturing

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Three Functional Dimensions Converge On Smart Manufacturing

WHITE PAPER # 59 A MESA International white paper. 4/5/2018

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TABLE OF CONTENTS

THREE FUNCTIONAL DIMENSIONS CONVERGE ON SMART MANUFACTURING ..... 1

SMART FACTORY, DIGITAL THREAD AND VALUE CHAIN MANAGEMENT ............... 1

THE SMART FACTORY DIMENSION ...................................................................... 2

THE DIGITAL THREAD DIMENSION ...................................................................... 5

SPECIFICATIONS MANAGEMENT .................................................................... 5

OPERATIONS MANAGEMENT ........................................................................ 5

PRODUCT SERVICES MANAGEMENT. ............................................................. 5

Product and Process Specifications .............................................................. 6

Process Execution in Operations Management ............................................ 7

THE VALUE CHAIN MANAGEMENT DIMENSION ................................................... 8

A CONVERGENCE ON SMART MANUFACTURING ............................................... 10

The Legacy ................................................................................................ 11

Moving Forward ........................................................................................ 12

REFERENCES .................................................................................................... 13

AUTHOR .......................................................................................................... 13

CONTRIBUTING REVIEWERS ............................................................................. 13


Copyright © 2018 MESA and/or its licensors. All rights reserved. 1

THREE FUNCTIONAL DIMENSIONS CONVERGE ON SMART MANUFACTURING Smart Factory, Digital Thread and Value Chain Management

There are a myriad of new technologies coming into the manufacturing arena, each with

attractive 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 become a highly connected, or-

chestrated and optimized Smart Manufacturing enterprise, you are not alone.

Yet, consider the benefits of processes where:

Utilities auto-adjust based on environmental sensor data

Machines take corrective action and request maintenance to avoid costly dam-age

Part shelves report usage and are automatically replenished by suppliers

Recipes automatically adjust based on material received from suppliers with their actual composition details

Correction tasks for non-conformances are routed in parallel to multiple de-partments, including Engineering, Procurement, Inventory Control and into the supply chain

These are just some examples of the benefits within reach with new technology and

new business processes.

A clear set of goals and a roadmap to Smart Manufacturing is of the utmost importance

for each organization, but not easily realized because of the complexity of different or-

ganizational perspectives, data models and business processes that converge at manu-

facturing operations—processes that get products designed, outsourced, built, tested,

packaged and delivered to the customer in a consistent manner.

This paper discusses how to organize the convergence of processes supporting Smart

Manufacturing into three dimensions: smart factory, digital thread and 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 dif-

ferent stakeholders in the organization.


© Copyright MESA 2018. All rights reserved. 2

This three-dimensional model also explains the intersection of several manufacturing

improvement initiatives included under the scope of Smart Manufacturing: IIoT (Indus-

trial Internet of Things), Model-based Manufacturing and 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 physical fac-

tory processes, (b) optimize process control and (c) provide insights to where we can

further improve plant performance across safety, quality, cost and schedule metrics.

The smart factory is a connected cyber-physical system where the digital factory is

maintained in real-time synch with the physical activity at the factory floor.

For example, an assembly line with Smart Manufacturing automated and semi-

automated processes may do the following:

Monitor and visualize production flow in real-time to immediately address alerts, reroute around constraints, dispatch automated material handling and eliminate wasted idle and down time

Auto-identify parts going down the line to automatically load programs and ma-terials for each different product configuration

Automatically collect process and product data, and analyze and identify re-quired adjustments or improvements to avoid poor performance, rework and scrap

Automatically modify and divert the routing of a product through a flexible pro-duction line based on the product configuration or detected defects

Utilize sensors to manage equipment remotely 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 and information 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 a manufacturing service bus or edge connector 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 on the shop floor

Operations Management functions performed by systems including MES (Manufacturing Execution System) or MOM (Manufacturing Operations Management) optimize the flow of products through production processes and orchestrate the allocation of resources

Connected Enterprise Systems including PLM (Product Lifecycle Management), ERP (Enterprise Resource Planning) and SCM (Supply Chain Management) systems are maintained in real-time synchronization through A2A (application-to-application) data exchanges with production process status, resources used and products produced

Business Intelligence systems receive periodic updates of aggregated data for performance analysis and business metrics



Note that Figure 1 depicts a general flow between functional layers in the smart factory but does not imply that all data must follow a linear path through these functional blocks. These diagrams depict general functional layers that depend on each other for input, data transformation, orchestration and output. However, the few arrows depicted are a visual simplification of the many integration interfaces and data flows in the digitally connected smart factory.

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.

Technologies are the enabler behind the IIoT that are making it easier to connect and collect data directly from smarter machines. The IIoT can monitor, collect, exchange, analyze and deliver valuable new insights. Collected 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 adoption of the IIoT is interoperability between older devices and machines that use different protocols and have different architectures. Machine controls and cloud-based systems work on different time precision and latency demands. The connection bridges across layers of Smart Manufacturing need to coordinate between these different time domains and 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 easier and faster swapping and mixing of innovative multi-vendor equipment and software, which might be on-premise or in the cloud.

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

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 Industrie 4.0 and Manufacturing USA initiatives recommend an industry/academia approach through organizations like IIC for evolving standards outside of government influence. For more information on data exchange standards for Smart Manufacturing and the many organizations working on them, refer to the MESA Whitepaper #58: The Importance of Standards in Smart Manufacturing.



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 functions.

Enterprise systems manage all kinds of business processes in the organization, from receiving customer orders 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 data exchanges.

The business intelligence systems aggregate and organize data into actionable metrics and key performance indicators (KPIs) used to monitor and execute the organization’s strategic goals. In the digitally connected Smart Manufacturing organization, management has dashboards to visualize metrics and alert them in real-time to areas not performing to plans and expectations. Management must be able to drill down from dashboards, to metrics, to task and resource details for causal analysis, and depending on analytical capabilities, systems might even suggest areas for improvement. The ultimate goal is for the system to answer questions we did not even know to ask.

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, produc-

tion and service life, ensuring that the digital definition of each product unit aligns with

the physical product. The digital data for each product includes every incorporated revi-

sion to the engineering definition and any deviations from the design specifications ap-

proved and executed on each product unit or batch during its lifecycle. The flow of pro-

cesses in the digital thread dimension shown in Figure 2 includes:

Specifications Management for design of product and processes, including def-

inition of 3D models and recipes, product variations and configurations, and en-

gineering change management practices

Operations Management, which includes production and verification processes,

such as programs and work instructions for automated 3D printing, machining

and verification against engineering specifications

Product Services Management for maintenance of the product during its ser-

vice life with data collected on product performance, modifications and re-

placement of components or software.



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. This ensures complete visibility and traceability of the digital and physical product from design through sourcing, production and ultimately to the end user or customer.

Product and Process Specifications

The digital thread begins with a 3D model-based or recipe-based definition of the product from design teams and flows into operations management and supply chain management via integration standards for widespread distribution of the data throughout the connected Smart Manufacturing enterprise.

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 design feedback loops between the design of the product and the design of the manufacturing processes, tooling design and inspection capabilities.

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.



Process Execution in Operations Management

Better ways are needed 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.

Examples of communications in the digital thread include product and process specifications, equipment set points, quality parameters, test results, conformance issues, asset maintenance requirements, and details and approvals for deviations from standard specifications. In Smart Manufacturing, the feedback loops are much faster and improve the pace of change without compromising integrity, and, in fact, enhance product integrity.

For example, in discrete manufacturing, current practices have a lot of manual interpretation, transformation and translation of data between engineering and manufacturing systems. Not only is this inefficient, but each time data manually coverts 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 easily understand different CAD data, highlight revisions, do affect analysis on downstream programs and instructions and facilitate the automated incorporation of changes.

Similarly, in process industries, recipe data such as material quantities, process parameters and quality parameters today flow manually from product development to pilot plants to full-scale production across plants based on unique production line capabilities. Each step requires “translation” and introduces opportunities for error and interruption in the information chain from product development to customer. In future processes, with structured digital handoffs, systems will automatically determine capabilities of cells and plants to produce within new specifications, highlight changes required to downstream programs and instructions, and adjust process and quality parameters to ensure the final product meets new customer requirements.

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. Additionally, for some products, the scope will continue into product service to maintain the thread during the entire product service life. The digital thread needs to deliver the digital product data along with the



physical product to the end customer. In pharma and food, product data will cover full traceability of all components from “farm to fork.”

THE VALUE CHAIN MANAGEMENT DIMENSION

An important dimension to achieving a fully connected extended enterprise in Smart Manufacturing is the value chain management functions. Value chain management focuses on minimizing resources and increasing value-add at each stakeholder function along the chain, resulting in optimal process integration, decreased inventories, better products and enhanced customer satisfaction. These concepts, originally spearheaded by Lean Manufacturing, now reach a new level of transparency, automation and optimization.

The scope of value chain management spans managing suppliers of materials and parts, to managing internal departments, including the production plant floor, all the way to managing the delivery of the product to the end customer. It encompasses the procedures, forms and data exchanges that link these organizational entities into a value chain that delivers a final product to the end customer along with its digital traceable historical records.

The standardization of IT practices that Enterprise Resource Planning (ERP) started decades ago for cash-to-order processes—covering activities like contracts, procurement, receiving, invoicing, purchase orders, delivery and payment—must now be extended across the entire value chain with higher requirements for process and product data, and 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:

Supplier Management with functions from identifying and establishing the supply chain with the right partners to monitoring, synchronizing and maintaining the required quality levels

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, calibrations and certifications

Operations Management delivers real-time information from production processes to other business management functions, and orchestrates activities into the supply chain to make sure that materials, ingredients and components arrive at the right place at the right time

Compliance Management maintains organizational guidelines, coordinates audits and monitors compliance performance with internal departments and external regulatory agencies

Customer Management with online portals give customers quicker custom product configurations, order in-process visibility and approvals for changes, deviations or delays



Figure 3: The value chain management dimension

The new Smart Manufacturing ecosystem aims to create closer relations and interactions with customers in design, production and services processes. 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 include program management in the general scope of customer management, working closely across departments, with customers and suppliers to achieve customer program goals.

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 that maintains organizational guidelines, coordinates audits, monitors compliance among internal departments and coordinates with external industry and government regulatory agencies. The compliance management function aims to protect the brand’s quality reputation.

Compliance management includes business processes that (a) document and control standard practices, (b) record history for reporting and auditing purposes and (c) handle the 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 by industry standards such as ISO9001, ISO14000, ISO45001 and ISO31000.

Operations management touches every dimension in Smart Manufacturing, performing a 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 include the context of orders, specifications and resources. Useful data from operations management enables confident decision-making in all parts of an organization, including production, quality, maintenance, inventory, sales and engineering. This information is essential to allow management to drill down from corporate key performance indicators (KPIs) into historical process details for causal analysis to uncover areas for improvement.

To achieve the operational outcomes desired, personnel must be trained on required skills and keep track of the required certifications for specialty jobs and equipment. 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 the workforce with the right level of skills and certifications to perform the required production and inspection tasks. This includes tracking attendance and labor costs in concert with the operations management system. Human Resources has the challenge of attracting 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 warranty issues, returns and corrective actions with suppliers. Product design changes must be carefully coordinated with affected suppliers. In the connected smart supply chain, suppliers must now deliver digital data about materials and components 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 are performed via the internet through B2B (business-to-business) data exchanges.

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) apply the many technology building blocks available into a flexible systems architecture that helps automate the activities, pass data seamlessly, and enable new levels of optimization, predictive and prescriptive



analysis in enterprise processes. MESA International is developing guidance and education to help organizations achieve these goals.

The organization can start exploring new technology but cannot complete the analysis of the required technology building blocks until the functional requirements to support the new connected enterprise are 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 Legacy

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 departments, legacy enterprise systems, including ERP (Enterprise Resource Planning) and PLM (Product Lifecycle Management), usually covered processes focused on compartmentalized departmental functions—the processes were rarely cross-functional. For Smart Manufacturing, processes need to focus on orchestration and optimization across departmental boundaries.



Moving Forward

Reinventing modern processes around new cyber-physical paradigms can promote real-time transparency, fast response, collaborative teams and orchestrated workflow with more parallel tasks across engineering, production and 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-subscribe data services must connect throughout enterprise systems, web applications, mobile devices and cloud services in a system of systems to ensure the pervasive distribution of the data. Quality of data is essential with fully connected systems despite advances in machine learning, algorithms and artificial intelligence. Organizations must be wary of machine decision-making when the data is not clean and accurate.

There will be some technical challenges along the way to create the Smart Manufacturing connected enterprise. For example, hardware and software vendors will need to evolve and adopt data exchange standards, and address security concerns at all levels of enterprise communications. However, the most significant 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? How do we motivate hardware and software vendors to support higher levels of connectivity via open standards?

Organizations will soon overcome these barriers and realize a network of connected partners, systems and resources. This network 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. The success resulting from early adopters of these philosophies is already stimulating a snowball effect across multiple industries.

Organizations must develop a clear set of goals and a roadmap to Smart Manufacturing. The roadmap needs to take into account the potential for dynamic change as technologies mature and develop. The MESA International organization will continue providing materials like this paper to help guide these transformational efforts along with the community of peers and experts to support manufacturers along the journey to a Smart Manufacturing future.


© Copyright MESA 2018. All rights reserved. 13

REFERENCES

MESA Whitepaper #58: The Importance of Standards in Smart Manufacturing, Hannah/Leiva/Noller, MESA International, 2018

AUTHOR

Conrad Leiva, Chair of the Smart Manufacturing Working Group at MESA International (www.mesa.org), and VP Product Strategy and Alliances at iBASEt (www.ibaset.com)

CONTRIBUTING REVIEWERS

Jimmy Asher, Vice President of Product Strategy at Savigent Software, Inc. (www.savigent.com)

Mike James, Chair Board of Directors at ATS Global (www.ats-global.com)

Stu Johnson, Enterprise Software Product Marketing at Plex Systems (www.plex.com)

Erik Nistad, Chairman of the International Board of Directors at MESA International (www.mesa.org)

Ian Tooke, Chief Innovation Officer at Grantek (www.grantek.com)

https://services.mesa.org/ResourceLibrary/ShowResource/96cc3f1a-e706-47f1-878f-274840602f41


http://www.ibaset.com/

http://www.savigent.com/

http://www.ats-global.com/

http://www.plex.com/


http://www.grantek.com/



About MESA: MESA promotes the exchange of best practices, strategies and innovation in managing manufacturing operations and in achieving plant-floor execution excellence. MESA’s industry events, symposiums, and publications help manufacturers, systems integrators and vendors achieve manufacturing leadership by deploying practical solutions that combine information, business, manufacturing and supply chain processes and technologies. Visit us online at http://www.mesa.org.


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Three Functional Dimensions Converge On Smart Manufacturing