Manufacturing Operations Management Talk

Good OT-IT Bones for Smart Manufacturing

Smart Manufacturing strives for higher levels of connectivity in the enterprise with information flowing in near real-time between production and business systems to achieve highly orchestrated physical and digital processes within plants, factories and across the entire value chain.

In this article we discuss a bit more about the infrastructure bones you want to futureproof your Smart Manufacturing strategy and provide security, interoperability, and scalability as you evolve your systems. This Smart Manufacturing infrastructure includes the integration of the operational technology (OT) running machines and automation in the plant with the information technology (IT) running the business and providing tools like simulation, advanced analytics, and machine learning platforms to enable real-time semi-autonomous control and optimization of production processes.

Smart manufacturing requires data from many different things in the plant including machines, tablets, wearables, and sensors. Different things and systems are designed to exchange data in different ways for different purposes.

The systems controlling machines need to act on data within fractions of a second, however people and systems running the business usually act on data between days or shifts. The different technologies and systems needed to run these very different processes lead to natural layers within the Smart Manufacturing system landscape dedicated to (i) connected things, (ii) connected systems, and (iii) connected teams.

Smart Manufacturing requires a flexible systems architecture to automate activities, pass data seamlessly, and enable new levels of optimization, predictive and prescriptive analysis in manufacturing operations management and enterprise processes—a systems architecture that is interoperable, scalable, and futureproofed for easy upgrades as technology continues to evolve.  

Connected Things

Smart Manufacturing must provide broad, secure connectivity among devices, processes, people, and businesses in the ecosystem, securing data integrity, protecting intellectual property, shielding against cyberattacks, and maintaining business continuity with minimal impact to performance of the overall network of networks. Security includes identity verification schemes for every “thing” in the ecosystem, implementing schemes for access control, data traffic monitoring, software patches, fault-tolerance, high availability, anomaly detection, issue containment, and data recovery.

Data integration for production automation traditionally involves proprietary application interfaces from specific technology vendors and leaves manufacturers with hundreds of point-to-point connections that only a specialized group of people understand. To avoid having data trapped in proprietary data silos, organizations can organize data in a distributed architecture leveraging technologies like data lakes, data platforms and open standards-based information models. 

Smart Manufacturing systems should implement interoperable solutions to empower a connected ecosystem of devices, systems, and people communicating in a natural yet structured manner. The manufacturer must be able to engage in B2B (business-to-business) data exchanges with the supply chain management systems of ecosystem partners with data preferably collected directly from production systems and transmitted via M2M (machine-to-machine) and A2A (application-to-application) integration APIs (application programming interfaces) that increasingly are more open and standards-based to enable application portability and multi-vendor hardware and software plug-and-play solutions.

Information models are used to contextualize and organize the data from production processes along with the relationship to ingredients, resources, and production orders. Information models enable data to be shared for multiple purposes among multiple stakeholders in the enterprise.

For example, captured data can represent a change in temperature, a completed batch, a drop in production line efficiency, or a customer complaint under a specific level of the information hierarchy. When the data captured from processes is contextualized and communicated using information models, the applications and users receiving the data can understand the context of the process it came from for a meaningful analysis.

OPC UA (open platform communications unified architecture) is a popular standard for information modeling with a growing library (OPC Cloud Library) of reusable information models for specific types of machines and industrial processes. For example, a process engineer can download an OPC UA information model for interoperability among food processing equipment instead of creating their model from scratch. Not only does the engineer benefit from the prior work of collaborators on the standard, but they also benefit from applications that have been designed to work with the standard.

When applications consume data via information models and open APIs from a shared data platform, data producers and consumers are not directly connected. Instead, they communicate through a mid-tier data platform that enables the organization to easily replace applications without breaking all the data connections. Organizations can continue to add more AI applications that monitor data, detect patterns, and trigger actions and alerts based on predicted outcomes.

Connected Systems

Smart Manufacturing systems must be scalable across all functions, facilities and the entire value chain with cost growing linearly—instead of exponentially—as load and complexities increase. Cloud computing, virtualization, and containerization techniques allow performance to be maintained by distributing the workload and scaling of computing power as the needs of the organization evolve. Edge computing solutions make it possible to place latency-sensitive real-time control applications on-premises close to the machines while other production applications, like metrics dashboards, take advantage of cloud services for scalability and ecosystem connectivity.   

Modular applications (a.k.a. apps) are easier to maintain and replace than larger systems with a broad functional footprint. The combination of interoperable and modular solutions makes the architecture more future proof allowing systems, components, and resources to be added, modified, replaced, or removed with ease to accommodate the changing demands.

For example, a modular app that tracks job starts and stops in production might send updates to both a manufacturing execution system (MES) for job status and to an enterprise resource planning (ERP) system for tracking employee labor time sheets. To increase interoperability, smart manufacturers will typically standardize on the use of APIs to transmit data among these internal systems. 

Low-code development platforms have evolved to make it easier for organizations to create the modular apps they need with their own OT-IT personnel. Low-code platforms can empower users to build custom solutions that improve their operations while the IT department maintains architectural consistence and governance. Operations and IT teams can develop a better balance and collaboration with these types of solutions. However, organizations must still evaluate build versus buy decisions, and consider the long-term maintenance of internally developed custom solutions.  

The integration of OT and IT is an essential step in the journey towards creating a fully connected, dynamic and flexible Smart Manufacturing systems architecture. It’s not a small step to take, but without it, the ability of a manufacturer to transact and participate effectively in a highly connected manufacturing ecosystem will be limited.

Integrated IT and OT systems enable more streamlined processes across production and business functions. For example, an IIoT sensor can collect operational data on a machine at the factory and send it over a wireless network to an IT application that performs predictive maintenance analysis. That application can trigger a maintenance order in the maintenance system to dispatch a mechanic and perform maintenance on the machine to avoid the potential of a longer downtime due to an unplanned machine malfunction. 

The reality for many industrial organizations is that they have implemented multiple generations of equipment and systems that should all be connected. The good news is that organizations can gradually transition in their journey to a fully integrated OT-IT systems landscape.  There are methodologies and technologies available that allow a smooth gradual transition. A rip-and-replace approach is usually not the recommended approach.  

Companies that master the OT-IT convergence and implement Smart Manufacturing techniques ahead of their peers will have a competitive advantage in highly digitally connected supply chain including quicker visibility into issues that would affect the delivery of materials, parts and products, and quicker resolutions of those issues.

Connected Teams

To reach the goal of Smart Manufacturing, the organization must not only implement technology to improve the business it must make sure people are ready for new technology-enabled processes and organizational change.

Even in the most automated plants humans must interact with machines to set up, monitor and control the manufacturing processes, ensuring that they are working properly and producing a quality product. People interact with equipment through a human-machine interface (HMI) and interact with applications through a user interface (UI).

An HMI delivers information from machines to operators, allowing them to control, monitor, record, and diagnose machines. HMIs can be integrated into a single machine or a collection of complex devices that must all work together under one control system architecture commonly referred to as supervisory control and data acquisition (SCADA). An HMI can monitor locally or remotely real-time data from a process like water level, the pH, the water pump, the level of dissolved solids or a certain toxic chemical. The water pump can be turned on or off based on tank levels using the HMI. In addition, the HMI can display alerts if the pH is below a certain level, and this can be adjusted using the touchscreen display.

A UI is the way we work with apps designed to perform tasks like managing or collecting data from assembly operations. Many modern-day industrial UIs for the smart factory are multimedia rich. They allow users to receive integrated SMS alerts about the status of machines, email alerts, and view work instructions with integrated videos of the production processes. UIs can be graphical dashboards at the desktop or phone with plant related metrics or be more specialized on a wearable device like a scanner mounted on a glove. 

Whether it is an HMI or UI, human and machine interaction is a key part of a Smart Manufacturing system. These interfaces empower end-users to make better decisions with access to accurate real-time data that with visualization optimized to highlight conditions important to running each specific production process. As technology continues to evolve to assist the manufacturing worker, we can expect more voice activated interfaces and AI driven virtual assistance features will be incorporated into the UI for the factory worker.

Many organizations are still working with manual workflows defined in standard operating procedures (SOPs) that document how employees fill out paper forms and route them to multiple departments for disposition and approvals. Some have started implementing semi-automated workflows that integrate applications and tasks across departments to automate repetitive tasks and routine decisions.

Smart Manufacturing aims to streamline business processes and workflows across departments that support production operations, so they are in synch and optimized. Workflow solutions can be used to integrate modular applications, facilitate process orchestration, and integrate the tasks needed to capture data, contextualize, analyze, and trigger needed actions. For example, a supply chain transaction may need all information related to a certain customer, which could be in many different segments of the enterprise. Workflow software can collect all the information and respond automatically, which saves employees from multiple tedious manual steps. Organizational change may also be required to achieve improved streamlined business processes and get products designed, outsourced, built, tested, packaged and delivered to the customer in a consistent manner.

This might all sound like a lot of change for your organization but remember that Smart Manufacturing can be done gradually in multiple stages. For example, a smart manufacturer might first install real-time capabilities for production monitoring. After this, they could integrate smart measurement devices for quality tracking, and later, systems to track raw materials as they are used in production to avoid material shortages. 

However, it is important to establish a shared data architecture early in the Smart Manufacturing journey along with guidelines for connectivity, information models, security, data exchange, and scalability. These shared practices will ensure that each technology adoption project is contributing to the overall Smart Manufacturing vision for the company.

 

A Glossary of Terms in Smart Manufacturing

Publishing this glossary to help reduce inconsistencies in terminology when teaching Smart Manufacturing techniques. I hope this serves as a good starting point for your own glossary needs.

Algorithms - A logic and mathematical program designed to systematically solve a problem. Algorithms are coded into software to form the rules by which artificial intelligence functions.

Application Programming Interface (API) - A set of programmed instructions, definitions, and standards that define how one piece of software interacts with another. APIs enable greater interoperability between devices in a smart manufacturing infrastructure.

Artificial Intelligence (AI) –A computer program with algorithms that enable a machine or computer to perform tasks that normally require human intelligence, such as visual perception, deduced correlation, speech recognition, and decision-making. Artificial intelligence allows machines to perform a process with autonomy. AI can learn from experience, sometimes with human assistance, in order to improve future decisions.

Augmented Reality (AR) - A technology that superimposes a computer-generated image overlaid on a view of the real world. AR may be used to train and guide employees through work processes.

Augmented Worker - An operator, technician, or other employee empowered by information technology (IT) and operational technology (OT). The augmented worker has improved effectiveness and enhanced capabilities enabled by smart manufacturing tools.

Autonomous - Self-governing. Autonomous systems can be configured to make routine rules-based decisions independent of human interaction.

Big Data - A large collection of structured and unstructured information from devices, assets, or processes during their operation. Big Data can be analyzed to make calculations and reveal patterns, trends, and associations between process inputs and outputs.

Cloud Computing – A combination of hardware and software computing technology typically provided by a third party that allows clients to access, store, and process data remotely through an internet connection. Cloud computing relies on sharing computing resources rather than having local servers or personal devices to handle applications. Servers used in cloud computing can provide multiple clients with access to unlimited storage and processing capabilities.

Cyber-Physical Systems - A system that combines physical equipment and devices with software to monitor and control industrial processes. In a cyber-physical system, physical objects and processes are synchronized in real time with their counterpart virtual objects.

Connectivity – The capability for devices and systems to exchange messages and share data across networks. Connectivity is a key aspect of smart manufacturing.

Controller - A hardware device that uses software and logic-based programming to provide electrical or API control signals to machines. A controller may be a central processing unit (CPU) or a programmable logic controller (PLC).

Data - A collection of numbers, facts, and measurements about a process or product. Data can be created, communicated, and recorded by sensors and communicated via data gateways.

Data Analytics (Analytics)- The techniques and tools used for analyzing process data to make decisions and conclusions about the process based on patterns and trends in the data. Data analytics are becoming more automated and advanced with the implementation of artificial intelligence.

Data Contextualization - The process of identifying data to the context around it (like the what, when, and who) to make it more useful. Data contextualization allows users to better interpret data and use it to make decisions.

Data Exchange Standards - facilitate the sharing of structured data across different information systems. A data exchange model is the intermediate representation used as a specification for data transfer. The source applications must translate their data into the data exchange format.

Data Historian (Historian) - is a type of database that’s designed to collect and store time-series data from various sources around a process plant. Historian software is often used with SCADA systems to collect data created by operational technology devices.

Data Lake - A computer application that manages massive amounts of raw data from a variety of sources including structured and unstructured data. Data lakes can be cloud-based or located on premises.

Data Models - An abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities.

Data Visualization - The organized graphical representation of information collected from a system or process. Data visualization tools, like graphs and interactive maps, help humans understand data collected in smart manufacturing and make better informed decisions.

Digital Thread - refers to the communication framework that allows a connected data flow and integrated view of the asset’s data throughout its lifecycle across traditionally siloed functional perspectives. The digital thread concept raises the bar for delivering “the right information to the right place at the right time.

Digital Twin – a digital representation of a product, asset or process that is synchronized to the current conditions of the physical product or process via real-time collected data. Digital twins that are enhanced with AI capabilities can use real-time and historical data to simulate and predict future conditions.

Digital Supply Chain – A network of companies that contribute to a product line with materials, components, and services, and are connected via internet data exchanges between their enterprise systems to enhance the products and services delivered to their customers. A digital supply chain connects suppliers and stakeholders throughout the entire product lifecycle.

Edge Computing – The practice of storing and processing data on local devices at or near the data source. Edge computing can distribute processing tasks across multiple edge devices to improve processing speeds and it is usually an intermediary step before sending data to a cloud computing system.

Efficient - Time and resource usage is optimized including labor, material, and energy.

Enterprise Quality Management System (EQMS) – A software application that integrates and manages all processes that relate to ensuring quality compliance. An EQMS can connect quality data from multiple sources across the product value chain including inspection systems, MES, and ERP systems.

Enterprise Resource Planning (ERP) – Manufacturing ERP systems manage and improve the use of company resources, from production scheduling to inventory control and production orders. ERP systems can be made up of dozens of integrated modules such as procurement, general ledger, material requirements planning, etc. ERP is often integrated into the product lifecycle management, manufacturing execution systems, and supply chain management systems.

Event-driven - Decisions are made when critical events occur or are predicted to happen instead of waiting until next report is published.

Human-Machine Interface (HMI) - is a user interface or dashboard that allows a user to interact with a device, controller, or machine. An HMI can be connected to a PLC that allows an operator to monitor a program and interact with a PLC. An HMI can range from a physical control panel with buttons and indicator lights to an industrial PC running dedicated HMI software with a graphical user interface.

Information - Information is when you take the raw data you have and analyze it or manipulate it by combining it with other contextual data, trending it over time, assessing patterns, and relating it to experiential knowledge to transform that data into insights you can use to make decisions.

Information Technology (IT) – IT has traditionally been associated with the office environment and includes the information systems and communication infrastructure used to run the business functions. IT resources include computers, data storage, networking devices, and processes to create, process, store, secure and exchange all forms of electronic data.

Industry 3.0 - The third industrial era of manufacturing development, which began in the late 1970s. Industry 3.0 revolutionized machine manufacturing by introducing microcomputers and developing advanced software applications for automation.

Industry 4.0 - The current industrial era of manufacturing development, starting in the early 2000s, which is characterized by devices and equipment that connect to the Industrial Internet of Things (IIoT). Industry 4.0 uses automation, digital communication, and data analytics to create a more connected manufacturing enterprise.

Industrial Control System - An automatic mechanism used to manage dynamic processes and maintain proper operating conditions by adjusting or maintaining physical control parameters. Industrial control systems allow for more precise and repeatable processes across networks of manufacturing processes and equipment.

Infrastructure - The network, data transfer and computing hardware and software used to run information systems in the enterprise and exchange digital communications with external partners through the internet.

Internet Of Things (IoT) - the connection via the internet of computing devices and sensors embedded in devices and equipment we use every day that enables them to send and receive data among each other via standard internet communication methods.

Industrial Internet of Things (IIoT) – A network of physical sensors, equipment, instruments, and other devices used in manufacturing connected via embedded computing systems or external gateways allowing them to send and receive data. The IIoT allows devices to exchange data and automate processes with minimal human intervention. This connectivity allows for data collection and exchange in support smart manufacturing techniques for improved decision making and performance improvement.  

Information Model - Represents concepts and the relationships, grouping, constraints, and rules to specify data semantics and organization for a specific application. Information models are sharable, stable, and organized structure of information requirements for the domain context. Information models improve data analysis, management, and security mechanisms.

Information Silo - Data collected and organized for a specific process control purpose which is closed off from the larger enterprise data landscape. Information siloes hinder collaborative and enterprise optimization efforts by preventing information to reach other departments and parts of the value chain.

Information Technology (IT) - includes the information systems and communication infrastructure used to collect and organize the information necessary to run the business functions of an organization. The IT department ensures an organization's systems, networks, data, and applications all connect, function properly, and are secure.

Interoperability - the ability of software and hardware from different machines, processes, and vendors to share and exchange data. Enabling interoperability often requires additional computing devices, like gateways, that can translate different types of data. Interoperable devices often communicate through an application programming interface (API). Interoperability is a key characteristic of Smart Manufacturing systems.

ISA-95 - is an international standard for the integration of enterprise and control systems. ISA-95 consists of models and terminology. ISA-95 defines a framework for organizing information factory automation and manufacturing operations.

IT-OT Convergence - is the end state sought by manufacturing organizations whereas instead of separation between IT and OT as different technical areas of authority and responsibility, there is an integrated approach to process optimization and information flow between production automation and enterprise information systems.  

Machine Learning (ML) - the use of computer systems that learn and adapt to make decisions without following explicit programmed decision rules. ML uses algorithms and statistical models to analyze and draw inferences from patterns in data. ML models may use supervised, unsupervised, or reinforcement learning methods. ML systems can analyze data to build predictive models and make decisions autonomously.

Manufacturing Execution Systems (MES) - a software application that monitors, tracks, and controls the performance of the processing of materials and production of finished products. MES applications collect performance data from multiple machines, monitor quality and manage the execution of automated and manual production tasks.

Manufacturing Operations Management (MOM) – is a term often used as an alternate to MES. It is sometimes used to refer to the functional components of manufacturing operations management (as in ISA-95 standard) in contrast to using the term as a category of software applications.

Operational Technology (OT) – a system comprised of hardware and software that controls industrial operations. The term OT has traditionally been associated with industrial environments and includes the hardware and software systems that control and execute processes on the shop floor including data acquisition, supervisory control systems (SCADA), programmable logic controllers (PLC), and computerized numerical control (CNC) machining systems.

Pattern - A pattern can be defined as anything that follows a repeating trend and is exhibited regularly in the data. The recognition of patterns can be done physically, mathematically or with algorithms. Pattern recognition in machine learning indicates the use of computer algorithms for identifying the regularities in the given data. Pattern recognition is widely used in the new age technical domains like computer vision, speech recognition, face recognition, and predictive maintenance.

Predictive Maintenance - A maintenance approach that involves collecting data related to machine operation to establish normal operating parameters, anticipate machine failures, and schedule service based on observed performance instead of based on a predefined preventive maintenance schedule. Predictive maintenance reduces maintenance downtime while preventing issues and unplanned downtime.  

Process Control System (PCS) – The combination of data from sensors and machines, and programmed logic in computer systems for real-time monitoring and regulation of manufacturing processes to improve performance and reduce errors. Process controls are a key part of production automation and smart manufacturing.

Process Orchestration – refers to the practice of managing tasks in end-to-end processes to minimize waits times and optimize the use of resources. In Smart Manufacturing, process orchestration is enhanced through the constant pulse reading on the plant and tight collaboration with support functions to resolve issues proactively.

Programmable Logic Controller (PLC) – A control device with an embedded computer processor that uses logic programmed software to provide electrical or digital control to machines and processes. A PLC can replace many physical relays and hard-wired connections in a process. PLCs are widely used in industrial automation.

Product Lifecycle Management (PLM) - A system that maintains data on every aspect of the product from design to prototype to retirement. PLM has traditionally been focused on engineering design functions, but some PLM solutions follow a product through its entire lifecycle including design, sales, production, and service.

Production Process Optimization (Optimization) - the practice of constantly measuring the effectiveness of production processes and striving to make continuous process improvements. Production process optimization in smart manufacturing is driven by the collection and analysis of data.

Production Management System - A software application or system that allows manufacturing organizations to coordinate all aspects of production from obtaining raw materials and components to managing production personnel and production output. Production management systems include tools to organize, collect, and analyze production data.

Productivity - is how well a business manages its resources and uses them to produce profits. It is a measure of performance that compares the amount of goods and services produced (output) with the amount of input resources used to produce those goods and services.

Protocols - standards and rules used by network devices to interact with each other. Essentially, protocols are the language that networked devices use to communicate. It was not uncommon to see different manufacturing devices designed to communicate using different protocols. IIoT and SM initiatives are promoting convergence to fewer standards to lower the cost of integrating systems.

Real-Time – transmitted at instantaneous interval of time that computers require to acquire, process, and transmit data. Real-time is virtually the same as actual time because computers process data nearly immediately. The term is often used to mean near real-time for delivery of data immediately after collection instead of accumulated and transmitted periodically with some delay.

Sensor - a device, often embedded within another device or equipment, that detects a physical stimulus and turns it into a signal that can be measured and recorded.

Smart Factory - A factory that implements Smart Manufacturing techniques to integrate automation, data, and analysis to run the entire production process. A smart factory is ready to exchange data with other nodes in digital supply chains and to respond to changing market demands with agility.  

Smart Manufacturing (SM) - is an advancement of traditional manufacturing automation. SM is the information-driven, event-driven, efficient, and collaborative orchestration of business, physical and digital processes within plants, factories, and across the entire value chain. SM leverages the IIoT to increases connectivity and data exchange between all areas of production operations and the enterprise transforming and improving ways in which people, process and technology operate to deliver the critical information needed to impact decision quality, efficiency, cost, and agility.

SM Applications (SM Apps) - Modular software applications that performs one or more manufacturing operations management functions and can be easily assembled, configured, and integrated through open methods. SM Apps perform functions that you might otherwise find in ERP, MES, or PCS software. SM Apps are connected to other systems and applications through interoperable smart manufacturing methods, APIs, workflow, and/or platforms.

Smart Manufacturing Platform (SM Platform) – a set of integrated software tools and applications that help manufacturers to collect, distribute, and analyze data automatically to make informed decisions and facilitate continuous orchestration and optimization of business processes in response to current conditions. SM platforms leverage information modeling, IIoT, and AI technologies, and both edge and cloud computing. SM Platforms make it possible to integrate existing and future plant data with analytics, modular apps, and information systems across the manufacturing enterprise and supply chain.

Supervisory Control and Data Acquisition (SCADA) – A control system architecture used to monitor and control industrial processes. SCADA can make control decisions locally or remotely for one or more facilities.  

Supply Chain - A supply chain consists of complex network of companies and suppliers that produce and distribute a product. A supply chain consists of a company, its suppliers, its distributors, and its customer service providers. These companies create a network of “links” in the supply chain that move the product along from the suppliers of raw materials to those organizations that deal directly with users. Supply chains have a physical flow that involves the transformation, movement, and storage of goods and materials. As important is the information flow that not only coordinates and tracks the day-to-day physical flow but also coordinates the customer service, financial and partnering activities in the supply chain. All these activities are highly integrated via business-to-business (B2B) data exchanges in a digital supply chain.

Supply Chain Management (SCM) – is the active management of supply chain activities to maximize customer value and achieve a sustainable competitive advantage. SCM includes coordinating demand planning, sourcing, production, inventory, transportation logistics, and warranty service.  SCM software provides functionality to plan, implement, and control the operations of a supply chain to maximize efficiency.

Supply Chain Resiliency - is the ability of a supply chain to both resist disruptions and recover operational capability after disruptions occur. A resilient node in the supply chain adapts to schedule and product changes with minimal intervention, easy reconfiguration, and optimized process and material flows. Smart manufacturing helps manufacturers become quick to react to changes in demand, resilient to disruption and capable of maintaining business continuity through adaptability, modularity, and minimal redundancy. Smart Manufacturing allows collaborative decision-making and orchestration to get the right product to the right place at the right time.

Sustainable manufacturing – is achieved when manufacturing operations are maintained with minimum negative impact on the environment. Sustainable manufacturing involves the use of green manufacturing processes that minimize environment pollution and smart manufacturing practices that optimize the use of resources. SM drives sustainable manufacturing of products through processes and systems that optimize use of resources and minimize negative environmental impacts. SM optimizes the use of energy as a direct ingredient, instead of treating it as overhead, and contributes to a circular product lifecycle by facilitating information for reuse, remanufacturing, and recycling scenarios.

Transparency – means establishing near real-time shared visibility of information across internal departments, divisions, and external partners to facilitate orchestration of supply chain process and quick resolution of issues. End-to-end transparency includes information from top-level KPIs, such as overall service level, to very granular process data, such as the progress of goods in each plant.  Transparency among suppliers can ensure a good foundation for making decisions around planning, execution, and exception management.

Warehouse Management System (WMS) – A computer software program that manages an organization’s inventory and monitors supply chain fulfillment operations. WMS software coordinates incoming products, movement, storage, tracking, and delivery of products.

Workflow – is a series of activities that are necessary to complete a task. Workflows include the sequence of industrial, administrative, or other processes through which a product or work task passes from initiation to completion. Workflow software can orchestrate discrete tasks needed to capture data, contextualize it, analyze it, put it into actionable form, and trigger multiple actions through integrated enterprise systems.

Value chain – aims to give a higher competitive advantage by going beyond the tactical processes in supply chain management and integrating companies in closer partnership for research, innovation, and after-sales services that maintain products, extend their lifetime, and enhance the consumer experience.

Most Popular Blog Posts on Manufacturing Operations Management

In this post, I am highlighting the most read blog posts on Manufacturing Operations Management, MES and Quality Management from the last few years in case you missed them. You will find they are still very relevant today.  

Over five years ago I wrote about The Collapse of The ISA95 Manufacturing Operations Management Model and years later we have seen that many have made similar observations including LNS Research.

A few years back, I wrote about the Terminology Confusion around MES versus MOM terms and how a panel at MESA International tackled the topic. This post has some great information about the history of MES and MOM models including the CIM, Purdue and ISA95 models.  

I still see today many using OEE as a main KPI but many have also joined in the skepticism over this metric. This blog post explains The Dangers Of OEE As KPI For Manufacturing Operations Management.

In some organizations quality initiatives like Six Sigma are viewed separate from Lean Manufacturing initiatives but many organizations have joined in viewing them together into a Lean Six Sigma philosophy. This article discusses The Role of Quality Management Within The Lean Manufacturing Philosophy.

Juran, The Father Of Quality, Pareto, And Perhaps Six Sigma, is still an inspirational figure to many today. Some of his impact is summarized in this post.

Thanks for reading and providing feedback that enhances the coverage of these topics for everyone.

IT-OT Convergence Is a Requirement for Smart Manufacturing

Operational Technology (OT) and Information Technology (IT) have had a long history of isolation from each other in many industrial organizations. However, they have been slowly converging on the scene as companies make advances towards Smart Manufacturing methods. Companies that master this convergence ahead of their peers will have an advantage towards realizing the Smart Manufacturing vision and optimizing their manufacturing operations management practices.

IT, OT, and IT-OT convergence

OT has traditionally been associated with industrial environments and includes the hardware and software systems that control and execute processes on the shop floor including data acquisition, supervisory control systems (SCADA), programmable logic controllers (PLC), and computerized numerical control (CNC) machining systems.

IT has traditionally been associated with the office environment and includes the information systems and communication infrastructure used to run the business functions. IT resources include computers, data storage, networking devices, and processes to create, process, store, secure and exchange all forms of electronic data.

Each one of these technology areas have been traditionally managed by separate departments in many manufacturing companies and staffed by personnel with a different set of skills, training, and career path.

However, Smart Manufacturing has a highly connected vision for the factory of the future with information flowing in near real-time between production and enterprise systems to achieve highly orchestrated business, physical and digital processes within plants, factories and across the entire value chain. [1] For this future to become a reality, the convergence of IT and OT systems is a must-have requirement, even with the technical, cultural and security challenges that comes with it.

IT-OT convergence is the end state sought by manufacturing organizations whereas instead of separation between IT and OT as different technical areas of authority and responsibility, there is an integrated approach to process optimization and information flow between production automation and enterprise information systems.         

The evolution of IT-OT convergence

In the last two decades, the distance between OT and IT has become thinner. In part, this can be attributed to the ubiquitous use of internet and wireless connectivity for PCs and IIoT devices at the production floor.

While IT inherently covers communications as a part of its scope, OT has traditionally not been a networked technology, and especially not to the internet. Many industrial devices for monitoring or control didn’t have embedded computing capabilities. Those few devices with computing resources generally used closed proprietary protocols and PLCs rather than technologies that provide control through IT software on IT servers. The production control systems often relied on “air gapping” for security.

However, advances such as machine-to-machine communication, as well as the introduction of IIoT devices fitted to legacy industrial equipment are requiring that OT and IT departments start working closer together.

IIoT devices include a wide assortment of sensors for gathering conditions such as temperature, pressure, vibration, and chemical compositions. IIoT devices also include actuators that translate digital commands and instructions into physical actions, such as controlling valves and moving mechanisms.

IIoT devices can employ wireless communication over standardized networking protocols to communicate the relevant data from each physical system back to IT systems for monitoring and analysis purposes–IT systems that include applications, servers and storage which might be running on-premises or on the cloud. The results of that analysis can then be passed back to the physical system to allow more autonomous operation, enhance accuracy, benefit maintenance, and improve uptime.

Figure 1. IT-OT convergence has been evolving over the last few decades

The diagram in Figure 1 shows a timeline of how OT and IT systems have been evolving independently over the last few decades with a converging trajectory. OT systems like SCADA and PLCs have been adopting support for IT networks, data exchange standards and web interfaces. IT systems have been adopting a more modular approach to apps and support for APIs that help connect IIoT devices, machines and production systems.

Back in the 1970’s, the ISA95 standard (as depicted on the left side of Figure 1) prescribed a four-layer approach to the design of connected production and business systems with PLC, SCADA, MES, MOM, and ERP at different levels. In the last few decades, we have seen that the advent of Smart Manufacturing/IIoT platforms, IIoT devices, APIs, big data analytics and data lakes has challenged the layered approach in favor of a flatter approach with more connectivity options (as depicted on the right side of Figure 1). [2]

Integrated IT-OT systems enable new levels of process integration across production and business functions. For example, an IIoT sensor can collect operational data on a machine at the factory and send it over a wireless network to an IT application that performs predictive maintenance analysis. That application can trigger a maintenance order in the maintenance system to dispatch a mechanic and perform maintenance on the machine to avoid the potential of downtime due to a machine malfunction. 

Consider an IIoT-enabled wind turbine as another example of IT-OT systems working together. By itself, a wind turbine would be classified as industrial equipment along with all the equipment and electronics necessary to generate power and connect that power to the grid. However, IIoT sensors can be added to detect wind direction and intensity while communicating its output, condition (temperature, vibration, pressure), and status to a centralized controlling location. The central system analyzes the data, provides commands needed to autonomously configure the wind turbine for optimum performance under current weather, and can trigger workers to take action when unusual conditions are sensed.

Leveraging cloud and edge computing

The addition of edge computing capabilities to IIoT devices enables real-time data processing closer to the source. Instead of sending the data over a network to a centralized location for processing, the IIoT devices can analyze time-sensitive production process data and return insights quickly for direct monitoring of industrial conditions before the data becomes obsolete.

Edge capabilities are important because IIoT and OT devices are often part of a distributed network architecture, making transmission to a central processing location difficult or impossible in some cases. Edge devices can also maintain critical industrial systems running when a connection is down or interrupted, which would otherwise incur costly consequences.

Figure 2. An example leveraging cloud and edge computing for data analytics and storage

Figure 2 depicts an example of how IT and OT systems could be integrated leveraging cloud services and edge computing for enhanced analytical and data storage capabilities. Concerns about connection latency and bandwidth are some of the reasons for deploying workloads to the edge. However, moving processing and storage closer to users and machines can also address concerns about data protection while enabling decentralized autonomous decisions for machine and process adjustments on routine situations and expected changes of conditions. Gartner discusses more edge computing use cases in their “2021 Strategic Roadmap for Edge Computing”. [3]

Figure 3. An example of gradually transitioning in IIoT and big data platforms into the IT-OT landscape

The reality for many industrial organizations is that they have implemented multiple generations of equipment and systems that need to somehow be part of the convergence story. Figure 3 shows an example of how a company can transition gradually to a new architecture by implementing cloud services and an IIoT backbone. In this example, the company first moved their ERP system to the cloud. They also implemented an IIoT backbone in parallel to their SCADA and MES system and started connecting new equipment to it. The IIoT backbone is publishing data to a data lake in the cloud where predictive analytical models are analyzing the data.  Similar transition scenarios have been documented by LNS Research in the article “The Holy Grail and the Puzzle in Discrete and Batch Manufacturing Applications”. [4]

Culture and security challenges

IT-OT convergence is not just about technology, it is also an organizational convergence dealing with the structure of the internal business. IT and OT departments must reform their processes to accommodate each other, changes must be well communicated throughout the process, and employees need to be cross-trained. For example, a business might follow specific processes for storing and protecting IT data, but this process might have to be adapted or extended for converging OT systems.

With this new level of integrated systems, workers can be empowered with more insights to help them in their daily jobs. However, workers will need to be educated on new technology, methods, and insights for the organization to fully leverage the benefits of the new capabilities. 

As IT reaches more OT systems, air gaps can't provide adequate security for network communication and OT data. Organizations driving IT-OT convergence must educate and train staff to understand and implement adequate security. When implemented properly, IT-OT convergence can merge business processes, insights and controls through secure systems and a uniform governance model.

As an example, Georgia-Pacific implemented several procedural and organizational enhancements when implementing centralized operations data and integrating IT and OT departments including a central collaboration support center to share data and best practices. [5]

Overcoming culture and governance issues with a wider set of stakeholders across the business is not a trivial task and it could make the difference between a smooth or rough ride along the convergence journey. A recommended strategy is to use multi-disciplinary teams to help guide the effort and establish common terminology, drivers and goals among IT and OT teams. Cross-discipline collaboration is key to a successful initiative. [6] Early convergence on security practices will also be essential.

Benefits of IT-OT convergence

The following are some of the benefits of IT-OT convergence over separate IT and OT:

• Improved automation and visibility because integrated OT systems can transmit real-time production data to enterprise systems
• Faster time to implement Smart Manufacturing solutions into an IT-OT integrated environment
• More decentralized autonomous decisions at the edge near the work cell for routine situations and semi-autonomous triggering of alerts for non-routine situations
• Improved IT and OT governance, compliance, and security methods with shared auditing staff
• Effective device management because all IT and OT systems are seen and managed through a common methodology
• Efficient energy and resource usage, as OT systems can be integrated to IT analytics and AI for better data for performance optimization
• Readiness to integrate production data directly into the supply chain, both up and downstream from the manufacturing plant. Intelligent, automated processes based on supply chain and logistics integration can help minimize inventory and fine tune time-to-market delivery

Successful IT-OT integration is an essential step in the journey towards creating a fully connected, dynamic and flexible Smart Manufacturing enterprise. It’s not a small step to take, but without it, the ability of a manufacturer to compete and participate in a highly connected manufacturing ecosystem will be limited. The manufacturers that make early progress on IT-OT convergence will gain significant operational and market advantage.

References

[1] A Refined Smart Manufacturing Definition for 2021, C. Leiva, AutomationWorld, 2021

https://www.automationworld.com/process/iiot/article/21232436/a-refined-smart-manufacturing-definition-for-2021

[2] The Impact of the Internet of Things on MOM Solutions, A. Hughes, LNS Research, 2016

https://www.lnsresearch.com/research-library/research-articles/-ebook-the-impact-of-the-internet-of-things-on-mom-solutions

[3] 2021 Strategic Roadmap for Edge Computing, B. Gill, Gartner, 2020

https://www.equinix.com/resources/analyst-reports/edge-computing-strategies-gartner-2021

[4] The “Holy Grail” and the “Puzzle” in Discrete and Batch Manufacturing Applications, T. Comstock, LNS Research, 2021

https://blog.lnsresearch.com/the-holy-grail-and-the-puzzle-in-discrete-and-batch-manufacturing-applications

[5] Georgia-Pacific’s Approach to IT/OT Convergence, L. Rodriguez, AutomationWorld, 2020

https://www.automationworld.com/process/workforce/article/21137682/georgiapacifics-approach-to-itot-convergence

[6] Improving Production – How IT, OT and Quality Can Collaborate, W. Goetz/R. Rossbach, Pharmaceutical Technology Europe, 2018

https://www.pharmtech.com/view/improving-production-how-it-ot-and-quality-can-collaborate

 

A Brief History of Smart Manufacturing

Around 2005, connectivity, data, and computing power were advancing at Moore’s Law pace along with the Internet, eCommerce, social media, and smartphone platforms. The concept of cyberinfrastructure entered the vocabulary around that time.

The term Smart Manufacturing was coined in 2006 at a National Science Foundation workshop on Cyberinfrastructure [1]. It was called Smart Process Manufacturing at that time but was quickly shorten to Smart Manufacturing as the work evolved around the initial concepts.

At that time, the term cyberinfrastructure was being used in the context of implementing new applications that combined the power of data exchanges through networks that aggregate information about different facilities and locations with advances in data modeling and computational power. The NSF workshop outlined strategies for multi-scale dynamic modeling and simulation, large-scale optimization, sensor networks, data interoperability, requirements-driven security, and coined the term “Smart Plant”.

“The ‘Smart Plant’ is composed of ‘smart assets’ that not only provide their basic process function but provide proactive feedback on the economic, environment, health and safety performance of that asset in aggregation with the other assets and in the moment. Smart plants operate to tighter specifications and involve a much greater understanding of the processes, greater automation and decision support, expanded use of automation, data and data interpretation, and a new-generation workforce that is trained and oriented toward a knowledge and information mindshare.” [1]

In parallel, Germany was working on a similar initiative completely independently called Smart Factory, and a couple years after that, they renamed it Industrie 4.0. Both Smart Manufacturing and Industrie 4.0 have evolved in parallel. Industrie 4.0 had a focus on cyber-physical systems while Smart Manufacturing has focused on highly connected information-driven manufacturing. There is a big overlap on both agendas, and we will continue to see parallel and joint efforts going forward.

In 2010, the Smart Manufacturing Leadership Coalition (SMLC) gathered a group of over 50 industry leaders in a workshop to advance the development of the infrastructure and capabilities needed to deliver the full potential of Smart Manufacturing. The group documented goals for Smart Manufacturing in the report “Implementing 21^st Century Smart Manufacturing” [2] along with challenges like affordability, usability, interoperability, customer integration, protection of proprietary data, and cyber security.

In 2014, the DKE/DIN Industrie 4.0 German Standardization Roadmap Version 1.0 [3] was published. The Germans stressed standardization as key to the success of the Industrie 4.0 initiative. The roadmap noted the importance of:

• Integration of technical processes and business processes
• Digital mapping and virtualization of the real world
• The integration of data-enabled “smart” products with production systems
• Extensive use of the internet

The roadmap defined cyber-physical systems in the plant as seamlessly integrating digital data from the physical production process and “smart” products into synchronized information systems that optimize the production workflow through simulation and analytical tools.  

The German initiative is soon followed by similar industrial initiatives in other countries that took notice of the importance of advancing manufacturing in a global economy competition.

Between 2010 and 2016, early adopting manufacturers in the U.S. continued to advance the implementation of Smart Manufacturing techniques. Organizations including the Manufacturing Enterprise Systems Association (MESA), the Industrial Internet Consortium (IIC), and the Smart Manufacturing Leadership Coalition (SMLC), brought together manufacturers, consultants, technology vendors, and academia to accelerate the implementation and document the practices and progress in Smart Manufacturing. [4]

Manufacturers implementing Smart Manufacturing are not just reducing cost, they are implementing technology-enabled business models and turning traditional factories from cost centers into profitable innovation centers through the integration of technologies including:

• Industrial Internet of Things (IIoT) 
• Smart machines and collaborative robotics 
• Cloud and edge computing  
• Enterprise integration and API management platforms
• A2A and B2B standards for multi-vendor interoperability
• Big data processing and predictive analytics capabilities 

In 2016, MESA International published the report “Smart Manufacturing Landscape Explained” [5] and NIST published the paper “Standards Landscape for Smart Manufacturing” [6].

In 2016, CESMII—the U.S. Smart Manufacturing Institute—was formed as one of multiple Manufacturing USA institutes focused on bringing together industry, academia, and federal partners to increase U.S. manufacturing competitiveness and promote a robust and sustainable national manufacturing R&D infrastructure. CESMII was established with a mission to radically accelerate Smart Manufacturing technologies adoption including advanced sensors, controls, platforms, and optimization models. The CESMII Roadmap for Smart Manufacturing was published in 2017 [7].

By 2017, Smart Manufacturing has gained wider adoption. Trade organizations and consulting firms were documenting success stories and practices as in the report by Deloitte titled “The Smart Factory” [8]. Consulting organizations also started publishing guidance like the Singapore Smart Industry Readiness Index [9] to help manufacturers assess their business practices and establish roadmaps towards higher levels of Smart Manufacturing adoption.

Smart Manufacturing was recognized as including vertical and horizontal integration of connectivity, intelligence, workforce, and automation across multiple dimensions of business processes including product lifecycle, operations, and supply chain.

Today, Smart Manufacturing technologies and practices have matured but the adoption has not crossed the chasm and moved beyond the early adopters into the early majority for wide adoption in the ecosystem. It is necessary to move to the next stage of adoption—the democratization of Smart Manufacturing.

References

[1] Workshop on Cyberinfrastructure in Chemical and Biological Process Systems: Impact and Directions, National Science Foundation, Davis, 2006

[2] Implementing 21^st Century Smart Manufacturing, SM Leadership Coalition, 2011

https://www.controlglobal.com/assets/11WPpdf/110621_SMLC-smart-manufacturing.pdf

[3] The German Standardization Roadmap for Industrie 4,0 Version 1.0, DKE German Commission for Electrical, Electronic & Information Technologies of DIN and VDE, 2014

https://www.din.de/resource/blob/65354/1bed7e8d800cd4712d7d1786584a7a3a/roadmap-i4-0-e-data.pdf

[4] On the Journey to a Smart Manufacturing Revolution, IndustryWeek, Leiva, 2015

https://www.industryweek.com/technology-and-iiot/systems-integration/article/21967056/on-the-journey-to-a-smart-manufacturing-revolution

[5] Smart Manufacturing Landscape Explained, MESA International, 2016

https://www.pathlms.com/mesa/courses/14866

[6] Standards Landscape for Smart Manufacturing, NIST, 2016

https://nvlpubs.nist.gov/nistpubs/ir/2016/NIST.IR.8107.pdf

[7] Smart Manufacturing-Leveraging the Democratization of Innovation, CESMII, 2017

https://www.compete.org/storage/EMCP_SmartManu_Program_FINAL.pdf

[8] The Smart Factory, Deloitte, 2017

https://www2.deloitte.com/content/dam/insights/us/articles/4051_The-smart-factory/DUP_The-smart-factory.pdf

[9] The Singapore Smart Industry Readiness Index, Singapore Economic Development Board, 2017

https://www.edb.gov.sg/en/about-edb/media-releases-publications/advanced-manufacturing-release.html

 

Smart Manufacturing Champions Must Address Organizational Culture Upheaval

Photo by JESHOOTS.COM on Unsplash

There is very good technology available today that helps manufacturers solve real problems, but that is not what Smart Manufacturing is about. Smart Manufacturing is a transformational opportunity that requires comprehensive cultural change to truly leverage technology in a future state where teams are working together to achieve an optimized value creation process for internal stakeholders, through to customers and shareholders.

New technologies should be explored with a clear understanding of how they support the desired future mix of products, services and business models for the enterprise.

A transformed digital manufacturing enterprise is enabled by technology and, more importantly, a shared mindset of enterprise-level transparency, optimization and enhanced decision-making.

There are several natural forces challenging transformation in mature organizations.

The first challenge is the existing ingrained organizational culture—the legacy patterns and shared assumptions that have worked for years and are passed down to new team members as best practices.

Legacy thinking can make it difficult to institutionalize new processes encouraging transparency, collaboration and viewing external resources as partners instead of suppliers.

The second challenge might be counterintuitive, but many successful organizations are moved by the relentless pursuit of incremental improvements. They have ingrained a culture of cost reduction over the last two decades. Organizations will need to focus on rewarding progress toward the future vision versus rewarding solely based on performance improvement.

A third challenge is the emotional side of business transformation—emotions fueled by a lack of understanding of differing perspectives, motivations and concerns among subcultures within the organization—is often underestimated.

The transformation champion acts as group therapist at times, facilitating convergence and recognizing that each subculture sees the initiative through different lenses.

Some managers might be wary of the initiative if they have had a bad prior experience with automation or IT projects. Other managers might try to push for results too quickly, underestimating the effort in scaling solutions to the entire enterprise. It is important to pace the transformation and make time to evaluate the needs for different types of products and production processes along the way.

The engineering and IT team can help with that evaluation. Engineers consider themselves craftsmen and experts in building elegant solutions. They want an efficient overall system and will resist initiatives that feel half-baked in a rush to rewire everything. The initiative will be better accepted by this subculture if they feel recognition and ownership in new processes.

Production technicians might be wary for different reasons, such as a lack of new skills or a reduction of the workforce. It is not enough for the initiative champion or coach to spend all of his or her time on training technical skills; it is just as important to facilitate open forums to allow employees to express their concerns.

The Smart Manufacturing transformation is as much of a cultural and emotional upheaval as it is a reengineering of processes and systems. Concerns about organizational changes and upskilling must be managed throughout. The new organizational culture must embrace continuous change of the business and ecosystem considering the new reality of a continuously changing marketplace.

 

Article was published as: Digital champions must address cultural upheaval, Conrad Leiva, SME Smart Manufacturing, May 2020

Top Seven Manufacturing Challenges in 2020

We have been hearing about the promised value of Industry 4.0 for several years. 85% of businesses agree on this potential. However, manufacturing productivity has been flat for the last decade and many industry digital initiatives are stalled. Many manufacturers have not started initiatives and many others are in assessment mode with a few pilot projects.

Some of the challenges for manufacturers are new:

#1. Uncertain demand rebound for some goods

Although manufacturing is not on the top five on the list of impacted industry jobs during this pandemic, it is still showing a big impact at number six on the list. However, it is not a question of whether it will bounce back or not, it is more a question of when and how fast. We hope manufacturing will be one of the sectors to bounce back quickly from the impact of the pandemic.

#2. Higher productivity is needed to reshore

One of the encouraging trends is reshoring. There are several reasons for the shift from offshoring to reshoring. Economies in many go-to offshoring countries have grown stronger, resulting in increased wages for their residents. In the locations where labor is still inexpensive, the infrastructures often cannot handle complex manufacturing operations. Industry is also looking to reshore suppliers in critical medical and food supply chains where reliance on imports surfaced big issues during this pandemic.

To sustain the reshoring trend, manufacturers must continue achieving increases in productivity using new technologies like smart manufacturing and robotics to automate many of the processes that used to require intense human labor. All in all, this resurgence in reshoring will lead to an increase in made in the U.S.A products in the future.

But prior challenges for manufacturers are still there:

 #3. Global competitive pressures

The U.S. is no longer on the top of the list of manufacturing most competitive countries when you consider innovation capability, health, skills and business environment. What will it take for U.S. companies to reclaim and hold the top spot? Manufacturers will have to shift their focus to advanced technologies and smarter talent for greater competitiveness.

Shifts in sourcing and shipping logistics are constantly evolving throughout the manufacturing industry, especially as a result of the COVID-19 pandemic. The ongoing trade war between China and the U.S. rages on, causing the short-term outlook for domestic companies to be uncertain. Manufacturing supply chains will need to be more resilient in adapting to potential future disruptions.

Manufacturers should focus on delivering the best digital customer service experience possible and increase their efforts toward digital projects that build agility and scalability to help to manage risk in supply chains during disrupted and uncertain times.

#4. Exponential pace of technology innovation   

From sensors, to automation, artificial intelligence, robotics and cloud computing, the challenge for many manufacturing companies is keeping up with the rapid pace of technology innovation. Not only to use it in their products but also to leverage it in their processes and customer services.

Newer industrial automation equipment incorporates sensors, controllers, computers, and network connectors. The use of robotics including robots working alongside humans is expected to triple or quadruple the next five years. Yet, integration of equipment to manufacturing systems is still too expensive for many small and medium manufacturers.

Many manufacturers understand the important role technology plays, yet many also feel they are not providing their team the best tools for bottom-line success. Thus, there is often a disconnect between understanding the need for new technology and actual implementation and adoption.

#5. Increasing complexity in products and supply chain

Over the last years most manufacturers have seen their products become more complex. Not only are products increasing in complexity, but many organizations are not equipped with the right tools to manage the intricacies of complex product development and manufacturing.

Product designs are getting more intricate with new materials, more parts and an increasing number of electronic components, microprocessors and embedded software. Not only are the number of components increasing, parts are sourced from multiple vendors and are now much smaller and more technologically advanced, adding another layer of complexity to the product and its manufacturing.

Organizations need information systems to help with these complexities in the product lifecycle starting with design, through supply chain and manufacturing operations. Modern supply chains are dynamic with a lot of change taking place, a lot of stakeholders involved, and increasing requirements for data exchange for higher collaboration, transparency and risk management.  

#6. Talent pool shrinking for manufacturers

During the past several years, the skills gap has been top of mind for U.S. manufacturers. We can forecast that the demand for talent in manufacturing will come back in the next few years, due to retiring baby boomers and reshoring trends. Many manufacturing jobs were going unfilled over the past few years and that trend will probably pick up again once manufacturing starts hiring again.

Even in an age of automation and robotics, skilled workers are still needed for their problem-solving capabilities, to fill managerial positions and to perform analysis. Baby Boomers are aging and retiring and there are simply not enough skilled workers to fill the positions they’re leaving. To fill the widening skills gap, manufacturers will need to train existing workers to perform skilled tasks and get creative in their efforts to attract workers.

#7. Bombarded by vendor’s varied propositions

In an ever-accelerating digital era, businesses feel pressured to meet rapidly changing customer demands, reinvent or evolve themselves, and beat competitors to the punch by being the first to provide faster and better solutions. To thrive in this new data-rich era, manufacturers want to adopt new technology at the plant floor and mine their data into smart software systems to continuously monitor, analyze and optimize the use resources in their complex operations.

However, there are many technology options to evaluate and many vendors putting forward great sounding value propositions for big data, artificial intelligence, IoT platforms, workflow tools, PLM, ERP, MES, QMS, EAM, and supply chain management systems. In the quest to reap the benefits of these technologies, organizations may focus too heavily on the hype surrounding specific technologies, as opposed to whether the innovation can help solve their problem or business strategy.

It is essential that organizations put a good plan together that focuses on achieving business goals, adapting to marketplace changes, and taking significant steps towards achieving their long-term Smart Manufacturing vision.

Conquering the barriers holding back adoption of Smart Manufacturing technology could create a competitive advantage, while simultaneously helping to battle competition from overseas and the manufacturing skills gap. In future articles we will discuss strategies for conquering these challenges.

Six Trends Accelerating the Smart Manufacturing Future

The Smart Manufacturing future is upon us with intelligent, real-time orchestration and optimization of business, physical and digital processes within factories and across the entire value chain that delivers the end-product. A value chain wherein resources and processes are automated, integrated, monitored, and continuously evaluated based on high levels of transparency and data exchange as close to real-time as possible.

A vision for Smart Manufacturing within reach in the next two decades thanks to advances in platforms that are making integrated systems and technologies accessible to more manufacturers, and further empowered by consortia focused on making available the knowledge required to run Smart Manufacturing systems and ecosystems.   

Several trends are fueling optimism on achieving the Smart Manufacturing vision:

#1. Product manufacturing and service are converging based on new market demands

For many products, the market is switching from buying a mass-produced product off-the-shelf to buying a custom configured product as-a-service. These new blended manufacturing-service business models require more customer interaction and elevate the value of the digital data that goes along with the product during its service life. These ecosystems are forced to evolve to deliver the required data services.

#2. Manufacturers are redefining their processes and services around new enabling technologies

New technologies are not just enabling optimization of existing processes, companies are rethinking their processes, value chains, and business models. The proliferation of smart phones, Internet of Things (IoT), cloud services, low-code platforms, and DIY machine connectivity are causing manufacturers to empower the customer and internal citizen technologist (engineer, developer and data scientist) with more data and tools in order to accelerate the pace of innovation in the whole ecosystem.

#3. Hybrid human-machine processes are on the rise

About three decades ago, when innovation led to the high use of robotics in factories, many people predicted that within 10 years all factories would be filled with robots, and there would be no human operators. Decades later, more than half of the manufacturing tasks are performed by human operators.

Rather than replace humans, robots will work collaboratively with a balanced distribution of responsibility. Technologies like cobots, exoskeletons, artificial intelligence (AI), and augmented reality (AR) will augment, assist and empower the future worker.

#4. Manufacturing ecosystems are achieving higher levels of connectivity and transparency

There has been much progress in engineering, production automation, and enterprise business systems in the last few decades within their respective islands. Manufacturing operations Management (MOM) systems and paperless operations are the new norm in the factory. Machines are getting smarter with embedded computers, AI, and application program interfaces (APIs) ready to exchange data with MES and cloud platforms. Smart Manufacturing eliminates manual steps and inconsistencies bringing silos of information together and finally links them in a full digital thread of automated data exchanges.  

The smart factory becomes a node in a connected smart ecosystem with API requirements for partner and customer interaction. The required data exchanges go beyond purchase order, shipment and warranty data if the ecosystem is delivering new data services with the product. For example, if a company is including a product digital twin as an additional service, it must be ready to make accessible, to the ecosystem and customer, 3D and simulation models of the product along with each unit’s unique as-built and operational data.  

#5. Manufacturing systems are incorporating more automated intelligence 

The availability of low-cost sensing, pervasive connectivity and cloud computing services has made it practical to access and holistically analyze data across integrated systems. Unstructured datasets such as images, natural language, and even messages in social media have become part of the data available for analysis.  More integrated data and AI capabilities are bringing us closer to systems with automated routine decisions where humans intervene only when necessary.

#6. Manufacturing culture is embracing digital

Manufacturing companies are investing more into their organizational culture. There is a recognition that technology plays a key role in the company image and in attracting talent.  National governments have incentives for companies to adopt Smart Manufacturing and are supporting efforts to educate the workforce with the required new skills.  Staffing services are evolving to fill the demand for highly specialized skills.  

 

 

This Industry 4.0 Revolution is Very Different – It’s About Ecosystem Transformation

I experienced the end of the Third Industrial Revolution as I began my career in manufacturing. Closed government and private networks gave way to an open network called the Internet. Numerical Controlled (NC) machines, Programmable Logic Controllers (PLC), and Personal Computers (PCs) were adopted at the factory floor. They were integrated to automation and tracking systems based on spreadsheets and the first wave of commercial manufacturing software, Computer-Integrated Manufacturing (CIM), which became the Manufacturing Execution System (MES).

We are now amid the Fourth Industrial Revolution. It is fueled by the convergence and proliferation of multiple game-changing technologies. including smart phones, cloud computing, 3D simulation, 3D printing, robotics, augmented reality and AI—coupled with advances in speed, capacity and affordability of computers, networks, sensors and integration platforms that enable enhanced platforms for commerce and industrial systems.

Where the last revolution felt focused on productivity gains through mass production and optimization of the factory, the current revolution feels focused on a new era of manufacturing supporting mass customization and products sold as services. It is finally possible to move forward with initiatives that started decades ago, about automation, data acquisition and bridging the IT (information technology like MES) and the OT (operational technology like PLCs) across many vendors and standards. However, many management teams are looking beyond the optimization and continuous-improvement mode of the last few decades.

The story behind this revolution is much bigger, and it is focused on transformation to new business models, orchestration of the value chain and evolving manufacturing ecosystems. Ecosystems with a mix of firms making parts and machines, developing software and providing services along with products. Separately, each ecosystem participant can focus and become very efficient in specialized competencies. Together, ecosystem partners can deliver complex solutions to the customer with a high level of customized service.

It is generally accepted that a great many business executives believe ecosystems are important—and that many are still unable to balance between investing in optimization of the current business and exploring the opportunities of new ecosystems and business models.

To get ready to participate in evolving ecosystems, start by making sure a digital backbone is established within each factory. A manufacturer still running the plant on paper will not be ready for a digital ecosystem. Every machine might not need to be connected to the factory system, but data-intensive stations placing a lot of components, running complex programs and inspecting parts should be providing data directly into a manufacturing system like MES. Every physical job needs a digital footprint in the MES. The MES is managing production and quality-control processes while updating in real time the production and customer orders in integrated enterprise systems like ERP with information that must eventually flow directly into the ecosystem network.

Technologies like data-integration hubs and IIoT platforms are facilitating the infrastructure. But establishing an efficient digital value chain and ecosystem is a complex issue for individual firms to tackle. Organizations must start thinking about the data and APIs needed for bi-directional communications among ecosystem participants and create a roadmap for how and when internal systems will support those types of data exchanges.

The journey to a connected ecosystem will take a few years with work to be done outside the four walls of the company. Industry groups, consortia, and institutes like MESA International, IIC, CESMII, and NIST are helping facilitate the convergence to digital infrastructure and data-exchange standards required to democratize the participation in ecosystems.

But many firms are sitting back and waiting. A far better move is to engage with these organizations now.

 

This article was published as: Ways to get ready to take part in evolving ecosystems, SME.org, C. Leiva, 2019

https://www.sme.org/technologies/articles/2019/october/ways-to-get-ready-to-take-part-in-evolving-ecosystems/

 

Taking Evolutionary Steps Towards Smart Manufacturing

In a recent article we discussed the hidden treasures within manufacturers’ existing systems. If the organization has already taken steps to go paperless and get the most out of existing systems, what do they work on next?

We tend to use the term digital transformation when we talk about Smart Manufacturing, but the term digital evolution might be more appropriate to describe the journey. We do not want to imply that this is a rip-and-replace all systems proposal. Many companies have already begun the transformation and have systems in place like a Manufacturing Operations Management (MOM), Manufacturing Execution System (MES), PLM and ERP that are important foundational linchpins. For these companies an integrate-and-extend strategy is more appropriate.

Leverage existing systems

Organizations can leverage the advances they have made in recent years. Extending and integrating existing systems can help mitigate risk and save money.

A recent survey from Gartner [1] and MESA International reveals that 98% of manufacturers believe there is more value to capture from their current manufacturing system and have identified next steps to extend and further integrate systems in their digital evolution journey.

Manufacturers have identified the following areas as small evolutionary steps towards the Smart Manufacturing goals:

Mine and join data across silos

Manufacturers have more data on-hand than they realize. Existing data might be trapped in silos or not organized to enable joining data across systems. For example, MES data is usually leveraged for operational metrics but could also be made available to join with data in other enterprise systems like ERP and PLM for higher levels of business analysis and optimization.

Industrial IoT (IIoT) platforms are capturing data straight out of sensors and smart machines for predictive analysis tied to equipment maintenance. This data could also be joined to MES for more automated and accurate real-time data collection.

Integrate via APIs

Application Programming Interfaces (APIs) can not only facilitate integration into a system of systems, but also expose data for mining across the enterprise.

API strategies that extend into the supply chain uncover a substantial amount of valuable data. This is because most supply chain visibility applications concentrate on connecting plan, source, make and deliver domains, and rely on ex-post-facto production data. In turn, most manufacturing enterprise applications provide real-time data and analytical tools focused solely on internal plant optimization and asset performance. Integration of these systems would provide better orchestration and optimization opportunities for the plan-to-produce and order-to-cash processes.

Analyze with AI

Artificial intelligence (AI) and robotic processes are helping manufacturers sense, extract, synthesize and analyze data across traditional silos. Not just across internal systems, but also across partners, suppliers and customers. AI can make it practical to organize these varied data sources into meaningful insights to fulfill the higher analytical and optimization goals of the Smart Manufacturing integrated enterprise.

References

[1] Survey Analysis: More MES Value to Be Captured from Supply Chain Collaboration and IIoT, Franzosa and Jacobson, Gartner, 2019

Manufacturing Operations Management Takes Center Stage for Industry 4

Many new technologies are being introduced these days into manufacturing, yet the Manufacturing Operations Management (MOM) or Manufacturing Execution Systems (MES)—which has been around for decades—is viewed as a fundamental enabler for the Smart Manufacturing and Industry 4.0 digital transformation. Technologies like augmented reality, artificial intelligence, cloud and edge computing, mobile, and auto-ID are being integrated into MOM solutions. Likewise, smart machines, sensors, and IIoT platforms are expanding capabilities to integrate to enterprise systems like MOM.

Regardless of rumors about the potential demise of MOM solutions, the facts are that many of the functions and features provided by MOM cannot be replaced by new IIoT platforms, even with analytics and apps. Alone, IIoT devices and platforms do not offer many new possibilities to manufacturers because they cannot provide operational context for data, cannot trigger actions in response to data, and are not designed to orchestrate processes across the factory and value chain. Many production processes need the human in the loop and MOM provides that platform.

MOM does not deliver Smart Manufacturing by itself. It needs to be integrated into the organization’s digital platform and “system of systems” including engineering, business, and automation systems. MOM does provide these important features required to integrate Operations into the Industry 4.0 digital transformation strategy:

• Intelligent Insights.Raw data captured from machines and workers during production must be turned into information by adding context and organization. Context includes data about the product specifications, process step, people, and equipment involved in the activity when the datum—like a dimensional measurement or environmental condition—is captured. Organization of stored data is also important to enable views that joins data from multiple activities for correlation and root-cause analysis. The organization of data makes it easier to aggregate data into significant metrics and key performance indicators (KPIs). It helps put the human in the loop to make better decisions quickly. 
• Data Stewardship and Governance.A proliferation of custom apps in production can lead to a multitude of data silos that are not coordinated to facilitate integration and aggregation. The MOM database provides de-facto governance, through its production-centric data model and integration interfaces, maintaining data clean, normalized, and organized to facilitate joins and searches across data in the enterprise.
• Standardization and Control. The MOM manages and enforces every aspect of the work steps including equipment, materials, and data collection for automated and manual processes. MOM enforces the policies and rules to make sure people and equipment are operating as they’re supposed to. The standardization makes production processes repeatable with consistent performance and quality.
• Response Action. MOM functions include methods to handle exception-driven events during the production process. Events or incidents like a suspect defect or process anomaly. MOM is the natural system to receive these types of notifications and alerts from an IIoT analytical platform. In MOM, these triggers can initiate procedures to record a discrepancy, change the equipment settings or operator instructions based on an automatically detected condition.
• Connection and Digital Thread. MOM provides an integration bridge between the plant and key enterprise systems including engineering’s PLM system, and procurement, inventory and cost management’s ERP system. This integration can be orchestrated to provide a digital thread linking the product’s engineering design specification to the product’s realization process, and even the product’s sustainment services in a way that facilitates the maintenance of a digital twin for each physical product unit.  The digital thread and digital twin can provide many benefits including better data exchange across the enterprise, and capability to analyze the product design against actual performance for continuous improvement. The digital thread accelerates the introduction of new products and variations by making it easier to implement design changes into downstream production definition elements like inspection requirements, machining programs, and work instructions.

The need for more real-time factory data and enterprise-wide connectivity is refueling the interest in MOM and MES. IIoT technologies are a great addition to the manufacturing IT landscape however, for the reasons listed above, MOM is needed more than ever as a foundational piece to the Smart Manufacturing and Industry 4.0 strategy.

 

Manufacturing Technology and IT Trends Update - Spring 2018

I was fortunate to participate at several industry conferences this spring listening and speaking with many leaders and practitioners working on innovation initiatives for their manufacturing companies under the general theme of Smart and Digital Manufacturing transformation. A few noteworthy observations are summarized below.

The digital transformation trend that started a few years ago continues stronger than ever. The digital business strategy for these companies has two main goals: (i) digital business optimization with goals of improved customer experience and improved productivity, and (ii) digital business transformation with goals of new business models and increased revenue through product and service offerings that leverage new levels of digital product data in this era of IoT.

Gartner states that around 65% of manufacturers surveyed are working on their digital business strategy and roadmap. These manufacturers expect that 50% of their production data and information will be automated by 2020.

MESA International’s survey, performed with Industry Week, shows that among U.S. manufacturers, the preferred term for this digital transformation is Smart Manufacturing (around 50%) followed by Connected Enterprise, Digital Manufacturing, and Industry 4.0 (each around 10-15%).

MESA also reports that 62% of manufacturers have already started with projects towards Smart Manufacturing. [1] Their survey is very consistent with the Gartner survey. Both reports are good benchmarks on industry-wide progress towards Smart Manufacturing goals.

 

There are a lot of new technologies we can apply to help us realize the Smart Manufacturing vision and manufacturers are currently trying many of them. Connected supply chain, Manufacturing Execution Systems (MES), and Robotics were on the top of manufacturers list of current projects related to Smart Manufacturing.

Gartner shows us the latest on their hype-cycle for these technologies. It is worthwhile noting that IIoT, cloud manufacturing systems and predictive analytics are heading down into the “trough of disillusionment” on the cycle which means that manufacturers have experimented with these technologies and are now resetting their expectations of value and practical use cases.

The Gartner survey also points out that manufacturers are currently focused on getting the foundational pieces in place including master data management, supply chain collaboration, and manufacturing execution system (MES) before they start implementing the next wave of innovation with IIoT, big data, and advanced analytics.

Edge computing devices are helping connect manufacturing equipment to the cloud. They can handle high frequency data acquisition on one side with analog, digital or serial input, they can perform computations and translations using controllers like Arduino, Beaglebone, or Raspberry Pi, and communicate on the other side with enterprise systems using standards understood by enterprise systems like MTConnect or OPC UA on XML or JSON.

In a recent report [2], Gartner warns that manufacturers are not paying enough attention to the intersection of plant operations management systems and supply chain management systems. 71% of manufacturers are working these initiatives independently in parallel and 85% report that integration of plant and supply chain is seen as a current challenge. Gartner predicts that manufacturers tackling this connection will likely see big benefits as new Smart Manufacturing ecosystems evolve in the next few years.

We are finally talking about robots and artificial intelligence (AI) beyond the simple use cases for replacing humans on brute strength, repetitive, or unsafe tasks. We are starting to see more use cases where robots and AI are working side by side collaborating and assisting the workforce with functions like feature detection, image analysis, natural language interfaces, and smart advisors.

One of the areas of AI assistance is computer vision that will monitor the work area and automatically guide or warn the technician by performing (a) visual inspection on things like cracks or scratches, (b) parts tracking that detects correct or incorrect parts as they are installed, (c) process monitoring that can detect out of sequence steps or detect safe practices.  

There is progress with machine learning (ML) tools where humans assist in training the machine and can adjust for bias and unusual scenarios as they come up in the collected data. We are still in experimentation stages with deep learning where machines can train their own neural networks based on image recognition and historical data.

Yet, much of the AI talk is still aspirational. Examples of what visionaries are looking for include the following types of tasks:

• Scheduling production based on maximum machine performance and least impactful changeovers;
• Assigning available staff with the greatest efficacy for a process and product;
• Managing maintenance schedules for least downtime impact;
• Forecasting and optimizing material inventory based on the latest actual completion dates;
• Coordinating delivery routings with awareness of truck locations, schedule, and traffic conditions;
• Identifying sales trends that haven’t yet been identified by humans and recommend production volume changes

Indeed, we are seeing a lot of exciting technology advances but how do we put it all together and thread it into a new Smart Manufacturing enterprise? MESA International reports that many have started pilots and initiatives tied to trying out these technologies but Gartner reports that 2/3 of manufacturers are working out their strategies for Smart Manufacturing. There is still much to define to get from vision to reality. 

Manufacturers need tools to help them navigate the ocean of technologies, systems, platforms and connection alternatives. One recent assessment tool is the Singapore Smart Industry Readiness Index [6] which helps companies assess at a high level their readiness in dimensions of process, technology and organizational structure. MESA International has several tools for assessment and is working on more of these types of tools to help manufacturing IT staff. Their current tools include: MESA Manufacturing Operations Management (MOM) Capability Maturity Model [7], Metrics Maturity Framework [8], and the MESA Smart Manufacturing Page [10] which is periodically updated with new resources and many are open to non-members. I encourage you to check out the resource references listed below for more information on advancing your Smart Manufacturing initiatives.

References:

[1] Research Report: Seeking Common Ground for Smart Manufacturing, MESA International, 2018

https://services.mesa.org/ResourceLibrary/ShowResource/7b029261-c659-4d14-9799-9918cf1f3711

[2] Harvest the Value of Smart Manufacturing in the Supply Chain - Not the Factory, S. Jacobson, Gartner, 2018

https://www.gartner.com/document/3874477?ref=solrAll&refval=205017407&qid=f538c5f5cc9d6de268c42dd554d1bd1a

[3] Four Best Practices to Manage the Strategic Vision for the Internet of Things in Manufacturing, S. Jacobson, Gartner, 2016

https://www.gartner.com/document/code/307794?ref=grbody&refval=3843663

[4] Webcast: Smart Manufacturing: Continuous Improvement or Strategic Transformation, MESA International, 2018

https://services.mesa.org/ResourceLibrary/ShowResource/16a26f39-7e0a-4cdb-b2f5-71af93941b1c

[5] Managing Disruption Requires Supply Chains to Foster Innovation and Scale the Digital Supply Chain: A Gartner Trend Insight Report, M. Griswold, Gartner, 2018

https://www.gartner.com/document/3870491?ref=solrAll&refval=205017548&qid=f538c5f5cc9d6de268c42dd554d1bd1a

[6] Singapore Smart Industry Readiness Index, Singapore Economic Development Board, 2018

https://www.edb.gov.sg/en/news-and-resources/news/advanced-manufacturing-release.html

[7] MESA Manufacturing Operations Management (MOM) Capability Maturity Model, MESA International, 2016 (available to MESA members only)

https://services.mesa.org/ResourceLibrary/ShowResource/a4fcb3cc-bc28-4f87-84cb-3da7432cc3b2

[8] Metrics Maturity Framework - A Guide to Assessment and a Roadmap to Increased Performance, MESA International, 2016  (available to MESA members only)

https://services.mesa.org/ResourceLibrary/ShowResource/4c5956c7-c7c3-4ed7-9689-bbfadafba3be

[9] Seeking Common Ground for Smart Manufacturing – Research Report, MESA International, 2018

https://services.mesa.org/ResourceLibrary/ShowResource/7b029261-c659-4d14-9799-9918cf1f3711

[10] MESA’s Smart Manufacturing Page (periodically updated with new resources and many for non-members)

http://www.mesa.org/en/the-road-to-smart-manufacturing.asp

[11] Smart Manufacturing: Continuous Improvement or Strategic Transformation? – Recorded Webcast, MESA International, 2018

https://services.mesa.org/ResourceLibrary/ShowResource/16a26f39-7e0a-4cdb-b2f5-71af93941b1c

 

The Third Dimension of Smart Manufacturing—Value Chain Management

As companies make progress on their smart manufacturing initiatives, the competitive intensity of new products, services and ecosystems raises the stakes for all manufacturers. There is a need to think differently about business models, ecosystems and ways to outflank the competition. The smart manufacturing strategy isn’t just about incremental change or cost savings; it’s about innovating products and services and incorporating innovation at a much higher pace than ever before.

Smart manufacturing is an endeavor to achieve higher levels of intelligence, orchestration and optimization within our factories and across the manufacturing value chain. This endeavor requires synchronization along three key dimensions: smart factory or Industrial Internet of Things (IIoT), product lifecycle and value chain management.

Many companies seem to be investing more on the product lifecycle and smart factory dimensions than on the value chain dimension. However, manufacturers focused on optimizing ecosystems offering new value and service levels to customers might very well end up being the true winners of the smart manufacturing race.

It is important to note that there are exceptions to the pattern of internal vs. ecosystem focus on smart manufacturing. For example, the food manufacturing industry is prioritizing more on the supply chain side. The industry is motivated to achieve higher levels of traceability in the food supply chain to meet customer demand for more information and guarantees about what they are consuming.

It is easy to be enticed by cool technology like highly automated machines and robots, and the local optimization of specific processes in the factory. The benefits can be realized quicker and the process change landscape is completely within the company’s authority to make it happen. However, the goals of the smart manufacturing vision will not be achieved through strict accumulation of gains from continuous improvement projects within each plant. The value chain dimension will need to be tackled with a combination of strategic alliances and new business processes that link the value chain with new levels of digital data traveling along with physical materials, components and products. This type of initiative will require executive-level strategic vision. These projects might not show short-term payback and will require work outside the company walls to achieve new levels of collaboration with partners and suppliers.

The goal of the value chain management dimension is to minimize resources and access value at each stakeholder function along the chain, resulting in optimal process integration, decreased inventories, better products and enhanced customer satisfaction. The scope 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 enterprise resource planning (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 across each partner’s manufacturing operations management system.

The value chain dimension in smart manufacturing requires as much or more attention than the smart factory dimension. Working on value chain innovation will yield much higher payback by positioning the company as a valuable partner in future manufacturing ecosystems.

There will be some technical challenges along the way to create the smart manufacturing connected enterprise, but the biggest challenges ahead are probably cultural. Companies that figure out how to establish and embrace new business models, new connected processes and new levels of transparency among partners in the ecosystem will be the leaders of future smart manufacturing ecosystems.

 

This article appeared at Automation World blog April 2018: The Third Dimension of Smart Manufacturing—Value Chain Management

 

Smart Manufacturing - Six Reasons to Not Delay the Digital Transformation

In the past, it was perfectly rational to observe technology innovation as it went through the adoption cycle and wait for early adopters to incur the cost of experimentation of the first wave of adoption. In this article, we discuss why wait-and-see is not a wise approach towards today’s Smart Manufacturing transformation.

Industry analysts including McKinsey & Company [1] and the World Economic Forum [3] have documented the current undisputable momentum building up in industries including manufacturing towards a new digital enterprise reality. Has your organization embraced this enterprise digital transformation? Figure 1 shows the results from a recent survey by Industry Week [4] and it reveals that indeed most surveyed manufacturers have intent to move forward with a Smart/Digital Manufacturing initiative. However, when asked about the roadmap and timing of these efforts, manufacturers have significantly less enthusiastic answers.

Manufacturers are often held back by the lack of a concrete roadmap, the perception of security risks, and the overwhelming number of approaches and technologies being talked about under the heading of Smart Manufacturing. Given the confusing landscape, is it better to wait a little and wait for early adopters to incur the cost of experimentation with the first wave of adoption of these new processes and technologies?

This is a strategic question that each organization should ask itself—very soon; because the clock is ticking, and surveys indicate that competitors are moving forward. Each organization should evaluate the disruptive potential of Smart Manufacturing in their corner of the market. The executive team should strategically discuss questions like these:

• How will Smart Manufacturing disrupt our industry in the next five to ten years, and what new ecosystems and competitors will emerge?
• How would a digital enterprise and real-time data help our business achieve strategic goals? Where is the value for our company, and how can we maximize it?
• What new business value can we bring to our customers based on real-time data, higher levels of interaction, and the ability to deliver digital data with the product?
• What new capabilities, skills, and mind-sets will we need in our organization? How will we identify, recruit, and retain the right new talent?
• What should we pilot now to start capturing value and accelerate our journey? Do we understand how to establish an IT infrastructure that enables Smart Manufacturing?

As the intensity of global competitors developing new products, services and ecosystems raises the stakes, the pressure is on manufacturers to think differently about business models; cultivating additional revenue streams and finding novel ways to outflank the competition. The Smart Manufacturing strategy isn't just about incremental change or cost of savings, it's about innovating and incorporating innovation at a much higher pace than ever before.

Reasons to not delay the Smart Manufacturing initiative

Continuous improvement strategies that depend on Kaizen events and small incremental changes might have worked for organizations in the past few decades but may not work now to achieve the transformation required for Smart Manufacturing. At least not without a planned roadmap leading to a future state that establishes the required systems infrastructure to connect the enterprise and enable the business to participate in new manufacturing ecosystems with OEMs and peer suppliers.

#1. New entrants will disrupt markets

The Smart Manufacturing revolution is expected to disrupt markets and change the competitive landscape. Methods of measuring market share might require update and the organization might not detect market changes until it is too late. It is natural to maintain defensive focus against the same usual competitors while new ones might go unnoticed. New market entrants with innovative products and service offerings can start to take revenue away from the traditional market before the organization and industry analysts notice the changes. By then, the organization could have lost key contracts and lost market position.

#2. Innovation leaders will dominate redefined markets

History shows that companies leading disruption of markets enjoy an ongoing advantage in those markets for many years. Especially when the resulting market lowers costs and pricing for end customers. Competitors that implement Smart Manufacturing sooner can drive prices down due to their increased levels of efficiency and productivity. Your organization might be left with lower profit margins and less money to invest on catching up and implementing the required technology to quickly join the redefined market ecosystem. Will your old customer see you as a legacy supplier or see you as a supplier that is innovative and evolving with the market?

 #3. New ecosystems will evolve quickly

A big part of the intent fueling the Smart Manufacturing revolution is the desire to create ecosystems tying multi-tier suppliers into new value chains that efficiently deliver products with high customer configurability, products with high traceability of components, and products sold as services to end customers—products that go beyond the physical unit with a digital footprint that travels along with the product during its lifecycle. If your organization is not ready to join these ecosystems as they are forming, it could be harder to join them later. Especially if competitors that join early establish a good reputation. New ecosystems will not want to introduce risk into processes and supply chains that are working. Opportunities might be limited to filling in gaps and replacing unreliable suppliers. Quality, reliability, speed, and the ability to participate in the chain of required data exchanges will be more critical than pricing to join new ecosystems focused on offering premium customer service. Your organization could soon be facing new ways of conducting business with old customers.

#4. Talented resources could become scarce

It is already difficult to acquire the required new talent into manufacturing companies. The manufacturing skills gap is well documented. [5] [6] A latecomer to Smart Manufacturing might find it even harder to find talented resources if the best resources were hired into the early adopters as job seekers perceived them to be the innovative leaders and better workplace choices.

#5. Evolution is a valid path

Creating a new division or acquiring a newer company are valid paths into Smart Manufacturing, but they are not the only paths. Organizations can evolve their practices with different approaches including piloting new techniques at new green-field programs and implementing technology that makes it practical to bridge older equipment at brown-field sites into newer Information Technology (IT) infrastructures and processes. When organizations implement digital transformation, they are not just enabling new business models, they are also making the old models work more efficiently by improving visibility, control, velocity, and customer service. These type of systems and process enhancements  strengthen relations with existing customers and puts the organization in a better position to tackle new initiatives knowing there is a reliable revenue stream from an existing customer base.

#6. Internal shadow IT is probably not waiting

Departmental managers around your organization will continue to do process improvement projects and some will entail the development of apps to tackle specific problems within their area of responsibility. After all, it is easy these days for non-IT personnel to develop apps and deploy them on their own smart phones. Shadow IT is a symptom of an organization that is not updating IT systems and infrastructure fast enough to keep up with the internal needs for process improvement.

However, shadow IT efforts can create more problems than they solve and will not get the organization to the required Digital Manufacturing platform. There needs to be a plan in place orchestrated by the IT department providing guidance and governance on security, data models, standardized enterprise systems, and data exchange practices. Otherwise, we can end up with a mishmash of tools and apps that don’t play well together, need to be updated separately, and cannot be efficiently sustained in the long term. Shadow IT is not a game you want to play in the high stakes arena of Smart Manufacturing.  Even if the use of apps continues to be prevalent in the Digital Manufacturing landscape, the IT department should be empowered to architect them as part of an integrated enterprise systems landscape. The longer the organization waits to institutionalize Smart Manufacturing, the bigger the effort will be to move departments off their custom independent solutions and into enterprise integrated processes.

In Summary - Do not wait

Organizations should move forward without delay, define their end-state strategic goals, and their roadmap with progressive milestones toward their Smart Manufacturing vision. To advance the initiative effectively, it is important to get specific about the organization’s digital transformation roadmap.  

The industry is going through an exciting revolution and these are great times to be working in manufacturing. Companies with highly connected and adaptable systems will have a big advantage in future markets.

References

[1] “Digital manufacturing: The Revolution will be Virtualized”, Hartmann, King, Narayanan ; McKinsey & Company; 2018

[2] “Why Digital Strategies Fail”, Bughin, Catlin, Hirt, Willmott; McKinsey & Company; 2018

[3] “Digital Transformation of Industries”, World Economic Forum and Accenture; 2016

[4] “Manufacturing’s Digital Transformation: Is Your Company Leading the Way or Falling Behind?”, IndustryWeek, 2018

[5] “The Skills Gap in US Manufacturing 2015 and Beyond”, Deloitte, Manufacturing Institute, 2015

[6] “Out of Inventory: Skills shortage threatens growth for U.S. Manufacturing”, Accenture, The Manufacturing Institute, 2014

 

This article appeared in IndustryToday, March 2018, "Digital Manufacturing: Reasons to Not Dawdle" 

 

Related article:

Eight Potential Barriers To Strategic Manufacturing Process Improvements

 

 

 

 

Three Dimensions Converge on Smart Manufacturing

- 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.

Manufacturing Operations Management Software ROI Fuels New Initiatives

Manufacturing Operations Management (MOM) software remains on the top of the list of prioritized manufacturing investments in surveys by both Gartner [4] and LNS Research [2]. Not only is it on the top of the list, LNS found that 68% of manufacturers were viewing the MOM as part of their enterprise systems strategy.

Why are MOM investments critical to manufacturers?

“MES (aka. MOM) applications boost manufacturing reliability by digitally enforcing processes, methods and procedures. Because MES applications also provide genealogy and traceability information required for compliance, they serve as a strategic nucleus for broader manufacturing architectures. MES data is also an important resource for determining discrete product quality. When scoped, implemented, and integrated properly with other processes for product supply, MES applications can support growth goals of the business while removing costs and complexity, driving process optimization, improving product quality, and directly impacting the bottom-line.”

Quote from Hype Cycle for Discrete Manufacturing and PLM – 2016, Gartner [4]

 

Leading companies are sharing their MOM success stories in surveys, at conferences and press articles, and the word is getting out. An LNS report [5] shows that implementers of MOM systems have improved Total Cost Per unit by 22.5%, Net Profit Margins by 19.4%, and On-Time Delivery by 22%. Improvements for MOM users were generally double the average among all respondents.

Specific improvements listed by manufacturers include:

• Common user interface across the enterprise
• Elimination of duplication of input
• Integrated interdepartmental information
• Platform of interdepartmental collaboration and workflow
• Closed-loop quality management

 

A similar survey performed by Gartner and MESA International [1] showed that most manufacturers implementing MOM/MES had achieved significant improvements within 12 months of implementation. Over a third of the surveyed manufacturers saw results within three months.

Because the MOM serves as a critical link in the information value chain 

MOM offers immediate benefits that are easy to quantify for the investment payback. However, manufacturers that stop their ROI analysis here risk missing the larger savings and productivity gains that impact strategic business outcomes. For many companies, MOM has served as a critical technology linking manufacturing operations with customer and supply chain processes.

 

 

As Gartner found out in their analysis [3], the largest benefits of MOM come from capitalizing on the visibility it provides into manufacturing performance and capabilities across the organization and supplier network. Participants in a 2015 Gartner study on the value of MOM reported that they were investing in MOM to drive continuous improvement, eradicate variability (that is, improve quality and compliance) and reduce costs. However, they also realized many more tangible and intangible benefits from the MOM implementation the following years.

Although these companies pointed out that the MOM couldn't be solely credited for all the performance improvements, they highlighted its pivotal role in creating an information environment that accomplished the following:

• Supported integrative and sustainable improvement initiatives by providing continuous performance feedback through real-time information visibility
• Provided the stakeholder visibility needed to manage and streamline the end-to-end supply chain
• Created a sense of ownership and performance consciousness among operations and supporting functional personnel
• Standardized processes across multiple production sites, allowing for resource sharing and flexibility

References

[1] 2016 MESA/Gartner Business Value of MES Survey, Meyer/ Jacobson/Franzosa, Gartner, 2017

 [2] The Global State of Manufacturing Operations Management Software - Weaving the Digital Thread Across Industrial Value Chains, Goodwin, LNS Research, 2015

 [3] Get the Most Out of Digital Manufacturing by Extending the Value of Your Manufacturing Execution System, Franzosa, Gartner, 2016

[4] Hype Cycle for Discrete Manufacturing and PLM - 2016, Halpern/Jacobson/Suleski/Franzosa, Gartner, 2016

[5] Metrics that Matter and the ROI of MES/MOM, Schwarz, MESA.org, 2016

[6] Has MES Come of Age? (recorded webcast), Fraser/Franzosa, MESA.org with IYNO and Gartner, 2014

 

 

Notes from the Smart Manufacturing and IIoT Round Table Discussion

I am sharing below some of my favorite highlights from the round table discussions on Smart Manufacturing at the 2017 Manufacturing & Technology Conference at Cleveland. MESA International organized this forum and there were many common discussion threads and questions among manufacturers moving forward with Smart Manufacturing and IIoT initiatives in their respective companies.

After going around the table with introductions we decided to frame the discussion around a repeating story line:  A manufacturer has multiple plants. They have a mix of plants running different Manufacturing Execution Systems (MES) plus some plants running on paper and spreadsheets. Some plants had over the years moved from spreadsheets to custom apps built on top of desktop database software and ended up with a few custom built MES solutions.

The manufacturer has just finished implementing ERP. The focus of the ERP project had been to standardize accounting practices and lower inventory costs. JIT initiatives drove the implementation of ERP a few years ago and they finally finished.

The manufacturer is now looking to connect manufacturing processes using a more standard solution across plants. 

Why? They have a new manager and he wants to be able to compare plants and prioritize the issues he is going to tackle. Where does he start? He needs visibility of metrics across the plants and needs numbers that he can use to compare. For real transparency, standard ways are needed to collect data and turn that data into metrics that are consistent across the plants.

There is also a push to lower the maintenance cost of the various custom MES solutions. The various MES solutions are slow to be enhanced and are not keeping up with OS and platform changes.

We agreed that this was a typical scenario that everyone could relate to. We continued with more in depth questions and examples to figure out approaches to better tackle next steps by learning from each other’s experience.

Q: The ERP vendor says they can handle the shop floor also. Should we go with the ERP vendor or go with an MES vendor? Should we get guidance from vendors or from a system integrator? Consultants are expensive resources.

A: The group had seen all the above approaches. The recommended practice is to select an advisor and vendor with experience in the specific industry. Interview and survey a few and select one you are comfortable with. This approach seems to yield the best guidance and results based on project experiences presented at the conference and documented by industry analysts.

Q: What are typical first steps that companies are taking on the journey to a future smart factory? What are companies doing now to get ready for the future?

A: One company started grabbing numbers from machines and putting them into a database. Managers liked it and wanted it in more areas. They said “Just Do It” .

Q: Did you do ROI analysis?

A: Justified first project as productivity improvement. Second project justified to better fulfill traceability requirements.

Q: Is it an all or nothing strategy? Is your plan to connect all machines or do you prioritize among machines and work centers?

A: One company decided to start with new work centers and new equipment first because the equipment had better support for connectivity and APIs. However, they are hoping to connect legacy equipment in the future.

Another company started by blueprinting their as-is processes.

Q: Did you apply Value Stream Mapping (VSM) to your as-is analysis? Some of the things we are working on within MESA is to create examples of applying VSM to manufacturing automation and IT projects. We would like to create templates and examples on how to justify and prioritize these types of projects using VSM type of methods.

A: We did not use something formal like VSM, but our management likes Lean so it would probably be a good idea.

Q: Many vendors pitch: give me your data and we can analyze it. But “getting the data” is the hard part. We are being asked to be “world class”. We are getting world class machines, but we are still chasing the process with clipboards and paper. A lot of manufacturing data on paper. Any ideas on best practices?

A: First, get your network in place. The right grade of network, cooling closets, etc. Then you need actionable data—not just any data. Spend time defining the problems you want to solve and the data needed to do the respective analysis. Be ready to shift gears as you do your analysis and find that your initial hypothesis was wrong. You don’t see correlation where you expected. You might need to start collecting different data. With the right infrastructure, it is easier to shift and add new types of sensors in different places.

Q: Management has seen the price tag to do the rest of plant and they asked us to take a step back and make sure we are doing the right thing. For example, should we keep working with this system integrator, with this vendor, or should we build it ourselves?

A: We use the OT/Automation group to do these projects. We do not wait around for IT. If we could only marry the IT team’s smarts of architecture and coding practices with the OT team’s smarts on the automation needs.

Q: Have you had to deal a loss of the initial drive to connect things?

A: No. We have had a steady increase of motivation. We found that having a “poster child” deployment helped a lot. We put a focused effort into making that project very successful. Now we have developed faith with management. They believe that if we connect the machines and get data, it will lead to more savings. They have seen the results of our champion deployments. Now everyone wants to replicate that success. They want it everywhere!

Q: How can MESA help you with your initiatives?

A: We find these roundtable discussions useful. We need more forums like this. People are more willing to share in these smaller groups among peers.

I did not capture the entire discussion in these notes—just my personal favorite highlights. Let us know if you feel any of these topics deserve further elaboration in future articles. If you find these types of discussions interesting, your team should consider joining MESA International. For more information visit www.mesa.org.

Karen Field from IndustryWeek magazine captured other perspectives on these discussions in her article: “MESA Turns to Crowd Sourcing to Tackle Manufacturers' Biggest Challenges”.

 

Reducing Cost of Quality with Integrated MOM and EQMS

– Using Manufacturing Operations Management Software to Reduce Cost of Quality

Tackling the price of quality goes beyond reducing the number of defects. It involves evaluating the entire quality management system. Following are some ideas to help manufacturing organizations reduce cost while improving quality levels through smarter use of integrated information systems.

Quality guru Philip Crosby wanted us to see quality management as an embedded part of everyday practice; instead of seeing quality as a separate complex process of verification, layered on top of production as overhead. Crosby described quality costs as the price of making things right the first time plus the price of making things right when they are not. Doing things right the first time, every time; is not as easy as it sounds, because many production processes have sources of variance and margins of error. To minimize the issues and defects that arise from those margins of error, we need a quality management system (QMS). Guidelines for a full QMS have been developed and documented over the last few decades by industry standards like ISO9001, AS9100 or ISO13485. 

Breaking Down the Cost of Quality

The most widely accepted method for measuring and classifying quality costs is the prevention, appraisal, and failure (PAF) model which divides quality costs into the four categories in Figure 1.

The PAF model is a combination of ideas developed in the 1950’s by multiple quality gurus including Juran [1] and Feigenbaum. [2] Their concepts emphasized that it is better to have more upfront preventive investment in quality management to minimize the costs of quality failures later in the product lifecycle. 

Research shows that the cost of poor quality (including rework, returns, and reduced repeat sales) can range from 15%-40% of business costs. [3] Feigenbaum referred to these costs as the “hidden factory”—the part of the factory dedicated to fixing bad work. [2][4]

See Figure 2 for examples of the breakdown of cost of quality.  

 

 

 

The Relationship between Prevention and Failure Costs

The graph in Figure 3 shows the relationship between prevention costs and failure costs. There is a natural cost tradeoff between how much an organization spends on prevention versus how much it spends on fixing failures.

If factors affecting cost of failures and cost of prevention are static, then the only way to reduce cost it to lower the number of defects. To radically change that equation, an organization must tackle reducing the costs of the quality management system itself at the same time they are trying to reduce the number of defects.

 

  

 

 

Six-Sigma Projects Reduce the Number of Defects

A traditional way to tackle reducing the cost of quality is to reduce the number of defects. This is the focus of most Six-Sigma projects. Figure 4 shows how improving the sigma level would reduce defects and reduce the costs of the related failures up to a point. At some point the cost of quality will go up to achieve higher quality levels unless the cost of quality is tackled directly.

 

 

 

 

 

 The Effect of Improving the QMS Processes

A different way to reduce the cost of quality is to make the processes for handling prevention and failures more efficient. Figure 5 shows how the cost curves would change when the focus is on improving the quality management system itself.

Additionally, when improving the efficiency of the quality management processes and reducing the cost of failures (cost of processing a non-conformance, cost of inspection through automation), the savings can be reinvested into better prevention methods, such as more accurate machines, better tooling, and more training, which would lead to even higher levels of quality.

The use of a Manufacturing Operations Management (MOM) or Manufacturing Execution System (MES) can make a big difference to the efficiency of quality management processes. The following are a few specific examples of how to reduce the cost of quality with improved integrated quality management processes.

Reducing the Cost of Quality Failure Prevention

Prevention costs are incurred to prevent or avoid quality problems. These costs are associated with the design, implementation, and maintenance of the quality management system. They are planned and incurred before actual assembly of the product at the shop floor. The prevention costs include specifications and inspection requirements for incoming materials, processes, and finished products, worker training and gauge calibration. 

Work Instructions and Process Change Management 

The manufacturing of a complex product (such as an aircraft or satellite) involves the management of a continuous stream of engineering changes directed at work in process.

On a shop floor with a paper system, changes to work instructions are written into the margins with red ink and stamped by a liaison planner. A copy of the redlined document is routed back to a process planner to get the changes incorporated into future releases of the work instructions.  

An MES system can combine (1) the redlining of the work instructions on the shop floor, and (2) the update of the template work instructions maintained by MES. 

Verifying Personnel Training and Certification 

Personnel must be certified as competent based on education, training, skills, and experience. Personnel qualification processes must be standardized and documented. 

On paper, certification of employee for the task is left for the supervisor to verify. An MES can validate each employee’s skills and certification against the latest training records before they sign on to a job.

Verifying Equipment Calibration 

Equipment resources must be maintained to assure their optimal performance to capabilities, especially measurement equipment used to verify the product. The maintenance and calibration processes for equipment and tools must be standardized and documented.

An MES can verify calibration status for equipment and gauges specified to be used for data collection. Tools can be calibrated more efficiently based on actual usage instead of using the traditional date driven expiration scheme which ends up in tools being calibrated more often than is truly needed.

Reducing the Cost of Quality Appraisal

Quality appraisal activities are the most conventional quality practices and the cost of these activities are a very visible expenses because it is easy to see the cost of inspectors, testers and their equipment in the balance sheet.

Appraisal is an expensive and unreliable way of achieving quality. Appraisal in its best form is verification that the production processes and preventive measures are working. Appraisal in its least productive form, is separating the good from the bad product, counting defects, scrapping and calculating yield. While these activities are a necessary part of the quality program, it is important to move towards more preventive methods to reduce the cost of failures—the cost of poor quality.

Is 100% Inspection Needed?

Ideally the preference is to move away from 100% inspection and towards more inspection by production personnel; leaving only a small percentage of random over inspection for quality management personnel. An MES can monitor the results from inspections and the history of defects. Based on that history, process improvement efforts can focus on areas where problems are found instead of blanket improvement campaigns spread among all areas of production.

Another way to reduce inspection levels below the 100% inspection, 100% of the time, is to only trigger comprehensive inspection based on production process validation (PPV) or first article inspection (FAI) rules. In other words, when there is (a) a change in design, affecting fit, form or function of the product, or (b) a change of manufacturing sources, processes, inspection methods, location, tooling or materials, that can affect fit, form or function.

Automated Inspection and Statistical Process Control (SPC)

Where inspection is needed, and a lot of data needs to be collected, there might be possibility of reducing the clerical cost of data collection using automated inspection methods such as CMM (Coordinate Measurement Machines) or visual inspection machines.

Inspection and test results coming out of these machines can be imported directly into the MES. Critical measures and results can be tied to data collection points and to SPC run charts to monitor control levels. The X-bar and R run charts produced could also trigger alerts automatically for out of control conditions. Automation and integration may yield new levels of efficiency over the traditional processes of inspectors running spreadsheets and separate SPC software processes on the side.

Automated Quality Metrics 

How many people are dedicated to putting spreadsheets and charts together for weekly meetings. This is clerical non-value added time that can be eliminated. In addition to automating SPC, systems can automate the calculation of all quality metrics off the information collected by MES software.

Process Audits

Process audits are used to confirm that the quality management system is functioning correctly. The organization can perform internal audits and external audits to suppliers as part of their periodic assessment.

Through an audit, an organization can identify a system’s ineffectiveness, take corrective action, and ultimately support continuous improvement. Process audits provide a form of assurance to management as well as regulators that your entity is following contractual and industry regulations.

Audit scheduling and check lists are an important part of the audit planning system. Audit findings should be documented and prioritized per risk and management objectives.

Reducing the Cost of Quality Failures

During inspections, it is possible to find some aspects of products, materials and components to be nonconforming to specifications. These nonconformances could lead to rework, scraping, returns and recalls all of which should be documented in nonconformance or discrepancy reports and classified in a database in such a way that the organization can use the data to determine costs and areas for improvement.

Discrepancy Documentation

The cost of filling out a nonconformance report on manufactured or purchased parts includes the cost of:

• Problem Identification
• Containment Actions - activities to ensure nonconforming material is controlled and prevented from improper usage
• Correction and Disposition Documentation – instructions to correct and bring the product back to conformance, and instructions to handle component scrap or repair if needed

The use standard codes and descriptions for symptom, defect and cause types, can simplify and speed up the documentation process and provide more consistent data for analysis.

Workflow Driven Routing of Tasks

The costs of routing the discrepancy for disposition through a workflow process across multiple departments can be controlled by limiting the participants in each discrepancy to those who are needed instead of including the entire multi-discipline Material Review Board (MRB) in every review.

Rework, Re-inspection, and Re-testing

One of the advantages of using an MES is the handling of rework instructions to correct an issue. The same process planning tools used for authoring regular work instructions are used for authoring rework instructions and either append work to the original work order or edit the instructions for the affected units only. The resulting rework jobs should show on the same job list along with the planned work so the mechanics do not have to learn a different process for rework, re-inspection and re-testing. However, these activities do need to get classified, and segregated in reporting to the financial system as cost of poor quality.

Corrective and Preventive Actions (CAPA)

When failures do happen, the team should assess if adjustments are needed to the preventive measures to avoid recurrence for the same type of problem. This is done through the Corrective and Preventive Action (CAPA) process.

The cost of processing a corrective action includes the following activities:

• Problem Identification including recurrence data, cost impact and risk classification
• Analysis for root cause by a team of subject matter experts
• Implementation of an action plan to correct the root causes identified
• Verification tasks to make sure that the plan was implemented and effective

Do not flag every little problem for a full CAPA process. Select only problems that seems to be recurring and have a significant impact. This can be figured out by ranking issues based on a standardized risk assessment methodology.

It is also important to check history for similar problems and see if there have been solutions that have worked in other areas.

The Cost of External Quality Failures

The intangible costs of external quality failures, including customer dissatisfaction, loss of reputation and loss of future sales, might be hard to calculate but are not hard to picture as having a huge negative impact on the future of the business. The best way to reduce external failure cost is to not have it at all! The best way to avoid external quality failure costs is to focus on improving the other three costs of quality.

Conclusion

There is a natural cost tradeoff between how much an organization spends on prevention versus how much it spends on fixing failures. In addition to the traditional way of reducing the cost of quality by reducing the number of defects, it is possible to tackle the efficiency of the quality management system itself to further reduce cost of quality. The use of MES software leads to more embedded quality control throughout the entire manufacturing process, more ownership of quality by production personnel, and more smiles and fewer tears for everyone in the organization.

References

[1] “Quality Control Handbook – 1^st Edition”, Book by J. M. Juran, McGraw Hill, 1951

[2] “Total Quality Control”, Book by A.V. Feigenbaum, Harvard Business Review, 1956

[3] “A Review of Research on Cost of Quality Models and Best Practices”, Whitepaper by A. Schiffauerova and V. Thomson, International Journal of Quality and Reliability Management, Vol.23 No.4, 2006

[4] “Dr. Armand Feigenbaum on the Cost of Quality and the Hidden Factory”, Article by Tim Stevens, Industry Week, 1994

[5] “The Six Sigma Revolution”, Book by George Eckes, John Wiley & Sons, 2001

[6] “Strategic Initiative Guidebook on Quality and Regulatory Compliance”, Guidebook by MESA.org, 2013

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Your Next Enterprise App? Survey Says: Manufacturing Operations Management

An Industrial IoT Architecture Needs Data Distribution, Consolidation and Stream Processing

It is easy to come out of a presentation on “big data” these days thinking that the future of manufacturing operations management and manufacturing IT is a combination of three things: connected machines, data stored in a “data lake”, and an analytical engine. However, those closer to manufacturing systems management know that the reality of data storage and processing needs are vastly oversimplified by drawing a big data lake circle in the center of a diagram. In a prior article, “Unraveling The Hype On Data Lakes for Industrial IoT” [1], we discussed some of the myths around data lakes. In this article, we dig deeper into data processing and examine new and older technologies under the lens of the requirements to: distribute data from its source to departmental functions, consolidate views of data for multiple organizational levels, along with new processing opportunities presented by data streaming technology.

 The traditional data landscape across an enterprise is a myriad of islands of information with varied data structures and indexing schemes controlled by each department’s applications for functions including Procurement, Inventory, Production, Quality, Human Resources, Sales, Marketing, and Finances. Rather than trying to create a single mega system to manage the enterprise–one designed from the ground up with a consistent and integrated data model–organizations need ways to integrate, exchange and manage data across the departmental functions.  There are different methods and technologies available to perform data distribution, processing and consolidation tasks including:

• Enterprise Application Integration (EAI)
• Extract, Transform and Load (ETL)
• Enterprise Information Integration (EII)
• Data Stream Processing and Streaming Analytics

These techniques should be viewed as complementary with different functions in an overall IT technology stack. In fact, many vendors are combining multiple of these technologies into unified platforms in recognition of how they complement each other in different applications.

Enterprise Application Integration (EAI) - Application-to-application integration via messaged-based, transaction oriented, brokering and data transformation. Focuses on integrating business-level processes and reacting to events that occur during those business processes with transaction data messages for handoff to other business processes or systems. EAI integration mapping needs to be updated when applications change their integration interfaces. It can have simpler results than other methods for the end user because data is accessed via their main departmental application and integration is handled in the background by the IT department. Hub and spoke methods using middleware like Enterprise Service Bus (ESB) products facilitate message brokering between multiple applications.

Extract, Transform and Load (ETL) – Set-oriented point-in-time transformation of data for migration, consolidation and data warehousing. It can process very large amounts of data during non-peak use times and deliver data optimized for reporting, while freeing up transactional databases from the computational burden of complex queries. ETL scripts need to be updated when applications change their query web services or transactional data structures.

Enterprise Information Integration (EII) – Transparent data access and transformation layer providing a virtual consolidated view of data across multiple enterprise data sources. In other words, EII provides federated data queries across multiple data sources via one meta model. EII models can be placed on top of data warehouse models and join structured and unstructured data. It can provide real-time read and write access and manage data placement for performance, accuracy and availability. EII can be hard to implement when key fields and data types do not match across source data systems. EII can impact network bandwidth and have high demand on source systems during peak hours. Caching schemes improve performance by extracting data out of data source systems for reuse by multiple EII queries. The Hadoop toolkit is an example of a technology stack that enables EII techniques.

EII techniques are usually leveraged by data scientists for ad-hoc analysis while ETL techniques turn data into simpler structures for every day reporting needs of users that don’t need to understand the complicated data structures of source systems.

Data Stream Processing and Streaming Analytics

In contrast to the traditional database model where data is stored, indexed and subsequently queried, stream processing takes the inbound data while it is in-flight real-time through an EAI process and is able to look for alert conditions and patterns in the data stream or in-memory data aggregation—the “real-time data mart”.

There are some evolving combinations of the above techniques and technology stacks which use data stream processing techniques as a pre-processor during the extract phase of an ETL process before data ends up transformed and stored at the data warehouse.

Data in the Smart Manufacturing and IIoT IT Framework

Evolving Smart Manufacturing and IIoT systems and applications leverage these different data handling techniques to improve real-time analysis where needed, as well as provide improved availability of structured and unstructured data for different types of reporting and analysis where real-time data is not needed or even desired. For example, the weekly review by the management team of Key Performance Indicators (KPIs) for the organization should be based on data mined at a fixed time—not data mined real-time at different random times yielding different results for every query. We don’t want each manager showing up to the weekly meeting with a different set of data in their hands. In contrast, we want to know of a production machine about to go down as soon as possible especially if it will stop the production line.

Figure 2  shows an example of how we could put all these techniques to work together in an IT architecture for the enterprise connecting different types of data for different types of data processing applications. The reality for this picture at any one organization might have more additional parallel paths but this simplified view illustrates the general idea of how it can all work together.

 

 

 

 

 

  

The picture gets even more interesting in Figure 3 when we add the fact that we want to connect the entire value chain for a product including enterprise data from multiple manufacturing sites and operational data from multiple tiers of suppliers across the globe.

As we look forward to cloud integration we expect the data services to support integration to multi-tenant cloud applications like Salesforce and support for REST and JSON APIs.

 

 

 

 References:

“Unraveling The Hype On Data Lakes For Industrial IoT”, manufacturing-operations-management.com, 2016

 

This article was first published at IndustryWeek magazine September 27, 2016:

“A Smart Manufacturing IT Architecture Needs Data Distribution, Consolidation and Stream Processing”, Industry Week, 2016

 

Demystifying the Digital Thread and Digital Twin Concepts

The concepts of the digital thread and digital twin have been spearheaded by the military aircraft industry and their desire to improve the performance of future programs by applying lessons learned through these digital technologies in current and upcoming programs. However, these concepts and technologies are gaining interest beyond the aerospace and defense industry and are converging with the digital manufacturing and cyber-physical system goals of Industrie 4.0 and Smart Manufacturing.

In the Industrie 4.0 roadmap, a cyber-physical system refers to the digital representation of a physical system used to communicate properties and live status to other cyber-physical systems and applications in future smart factories. Smart Manufacturing strives to orchestrate and optimize business, digital and physical processes across smart factories and the entire product value chain. As system architects evaluate these initiatives, it makes sense that these similar digital connectivity goals start converging into a single framework strategy for manufacturing systems.

The digital twin refers to a digital model of a particular asset that includes design specifications and engineering models describing its geometry, materials, components and behavior, but more importantly it also includes the as-built and operational data unique to the specific physical asset which it represents. For example, for an aircraft, the digital twin would be identified to the physical product unit identifier which is referred to as the tail number.  The data in the digital twin of an aircraft includes things like tail number specific geometry extracted from aircraft 3D models, aerodynamic models, engineering changes cut in during the production cycle, material properties, inspection, operation and maintenance data, aerodynamic models, and any deviations from the original design specifications approved due to issues and work arounds on the specific product unit.

The digital thread refers to the communication framework that allows a connected data flow and integrated view of the asset’s data throughout its lifecycle across traditionally siloed functional perspectives. The digital thread concept raises the bar for delivering “the right information to the right place at the right time”.

Expected Benefits

The benefits expected from having a digital twin for each product unit include:

• Assessment of a system’s current and future capabilities during its lifecycle
• Early discovery of system performance deficiencies by simulating results way before physical processes and product are developed
• Optimization of operability, manufacturability, inspectability, and sustainability leveraging models and simulations applied during the entire lifecycle of each tail number
• Continuous refinement of designs and models through data captured and easily crossed referenced to design details

A Digital Thread is expected have the following benefits for manufacturers:

• Improve product quality by avoiding mistakes in manual translations of engineering specifications along the product value chain
• Improve the velocity of new product introductions (NPI) and the communication of engineering changes along the product value chain
• Increase the efficiency of digitally capturing and analyze data related to product manufacturing
• Allow manufacturers to deliver new services to customers along with physical product leveraging the digital data now available on the product

The digital thread should provide a formal framework for controlled interplay of authoritative technical and as-built data with the ability to access, integrate, transform and analyze data from disparate systems throughout the product lifecycle into actionable information. The product lifecycle includes: Design, Procurement, Test & Evaluation, Production, Field Operation, and Sustainment Services. The digital thread and digital twin include as-designed requirements, validation and inspection records, as-built data, as-flown data, and as-maintained data.

As-Is and To-Be Scenarios

The As-Is handover between departments:

• Design 3D model thrown over the wall from Product Engineering to Manufacturing Engineering
• Manufacturing Engineering creates 2D drawings from 3D models and hands over to Quality and Suppliers
• Conformance requirements are thrown over the wall from Quality to Production
• The product unit is thrown over the wall from Production to customer and sustainment cycle without any data on the specific unit beyond the original design
• Production archives as-built to records for auditing purposes

The desired To-Be scenario with a digital thread:

• 3D model linked to visuals for production process instructions are created by Product Engineers working together with Manufacturing Engineers
• Product characteristics are linked to 3D models and extracted directly out of designs into conformance requirements
• Conformance requirements are linked to manufacturing process and inspection instructions
• As-built data is delivered by Production along with product unit to customer and available for sustainment services to continue evolving the unit’s data during operation and maintenance services
• Product design changes follow the same data flow and automatically update downstream models, references and instructions.

Interesting Use Cases  

Today we must take the experts to the physical product unit to help troubleshoot issues where they are found. Tomorrow, with a digital twin, we could have experts in different locations looking at the same digital representation of the actual product unit. The Material Review Board (MRB) processes for nonconformance and deviations could be improved by involving more experts remotely and leveraging simulations to figure out the impact to the overall product performance before rework deviations are approved.

During sustainment services, maintenance technicians can receive direct support with the latest and most accurate information on the state and condition of the equipment. Data from smart connected equipment would be aggregated, analyzed and transformed into actionable information for the maintenance staff. Technicians can focus on trouble-shooting the issues at hand rather than performing lengthy diagnostics processes at every service visit.

Potential Challenges

Can people responsible for upstream lifecycle functions understand and accommodate the needs of people in downstream functions for the overall better outcome to the entire program?  Will feedback mechanisms be put in place to ensure we continue to improve the upstream processes that serve the downstream processes?

We have already seen concepts like design-for-manufacturing well adopted by industry. The concepts of the digital twin can be viewed as evolutionary and a natural progression on that type of thinking beyond the manufacturing process.

Can vendors see beyond their desire to hold a segment of the market hostage to their formats and products? Could they endorse open data exchange standards where needed to facilitate multi-vendor participation in the digital thread?

Can manufacturers, hardware, and software vendors move forward when many are overwhelmed trying to meet short term improvement goals promised to customers and shareholders?

The digital thread and digital twin concepts have been proposed as incremental steps and leading companies are moving forward with these initiatives. Only time will tell but the potential benefits of the digital thread and digital twin seem obvious. The vendors that endorse and provide these types of connectivity techniques could end up being favored by the market in the long term over vendors with proprietary closed solutions.

References

[1] Global Horizons Final Report: United States Air Force Global Science and Technology Vision - AF/ST TR 13-01, United States Air Force, 2013