Manufacturing Operations Management Talk

Digital Lean with Smart Manufacturing

Introduction to Digital Lean Manufacturing

Lean has played a significant role for the past few decades in driving efficiency across manufacturing organizations by eliminating waste and focusing on customer value. Lean techniques have promoted simple intuitive visual and analytical approaches to decision making, problem-solving and continuous improvement.

However, today’s advanced manufacturing technology and complex product landscape is creating new challenges for manufacturers that are sometimes beyond the simple manual methods. Thankfully, digital technologies have also become more practical for manufacturers and can be used to implement Lean principles in this complex landscape.

Smart Manufacturing and Industry 4.0 technologies including IIoT, cloud computing, AI and robotics make it practical to implement and integrate digital solutions for new levels of insight, accuracy, speed, cohesion, automation, and flexibility.

Digital alone is not a magic bullet. Nor is it a replacement for traditional lean. The key to driving down production costs while increasing performance is to find the right combination of digital and traditional lean—one that fits the organization’s current situation and desired end state.

The term “Digital Lean” is often used to identify this new era of Lean practices that stays true to the Lean principles while embracing real-time data-centric techniques from Smart Manufacturing for enhanced decision-making, problem-solving and continuous improvement. Lean can become part of the culture and mindset for an organization that aims to create more value for customers while reducing effort and resource waste in production activities. A culture that remains open to changing processes and continuous improvement of productivity and quality.

 

 

Industry analysts Deloitte, Bain and BCG have studied manufacturers over recent years and documented that the cost reduction among manufacturers who implemented digital technologies along with Lean techniques was 30-40% higher than among those strictly using traditional Lean techniques. [1, 2, 3] Below is a list of examples of how digital Smart Manufacturing technologies are being used to enable those higher levels of productivity and cost reduction.  

 

 

Improved Decision-Making

Data Collection

Collecting more data in a timely manner is the backbone of Digital Lean techniques. The integration of OT (control systems, industrial networks, and systems) and IT (enterprise information systems) in Smart Manufacturing brings timely plant and production insights to the entire team managing and supporting production operations.   

Data is collected in real-time via sensors, gateways, and smart platforms that make data available to production systems, analytics and AI-based algorithms that turn data into insights for better decision-making and trigger automated alerts to the right personnel to handle non-routine situations. Data is more transparent and accessible, making it easier to display, share, and analyze among multiple stakeholders.

New sensors, data gateways and software not only make it practical to collect data directly from machines, but also make it possible for machines to automatically identify products as they show up at the workstation. For example, using auto-ID technologies like RFID, a machine can load the appropriate program and tools without manual intervention. This type of automated changeover makes it easier to increase the product mix without affecting efficiency and allows the operators to focus on more value-added activities.

Manufacturing systems of record are often maintained via manual methods yet many value stream analysis forget to include the time wasted on inefficient paperwork and wait time introduced by paper-based problem handling procedures. Much wasted time and effort can be eliminated by applying digital techniques to these record keeping processes. 

Metrics and Key Performance Indicators

An important step for the success of Digital Lean initiatives is to establish an agreement on Key Performance Indicators (KPIs) for the organization that encourage Lean behaviors. Standardizing metrics used in the organization helps to align the team on common goals and understanding of business priorities. Common KPIs for Lean include on-time delivery rate, cycle time, cycle time efficiency, asset downtime, cost of poor quality, and surplus inventory.

In traditional Lean, data for these KPIs would be collected over time, then later compiled, and manually analyzed in spreadsheets. With smart technology, this data can be collected automatically and continuously, allowing KPIs to be monitored in real-time or near real-time. This allows workplaces to quickly implement changes and adjustments to improve KPIs, as well as spot problems and waste early as it starts to happen.

For measurement of KPIs to be effective, the data must be correct, relevant, and consistent.  Ensuring this usually means standardizing processes, making sure that the information coming in via smart technology is the most relevant, and that employees are correctly using the smart technology. Metrics work best if employees buy-in, trust the data, and actively participate at all levels of an organization.

Visualization and Display Tools

Once an organization has defined its metrics and collected data to measure them, the next step is to make the information visible and accessible to the multiple stakeholders in the enterprise including management and the team at the shopfloor. It is important for all stakeholders to understand how production performance impacts the customer and the business goals for the entire enterprise.

Traditionally data is gathered manually and recorded in spreadsheets for after-the-fact analysis. With smart technologies, dashboards are configured to automatically use collected data to generate charts, diagrams, and other displays that support everyday decision-making processes.

Old school Andon boards are replaced by digital Andon boards with dashboards updated in real-time at monitors mounted at hallways around the plant, as well as accessed through mobile devices and computers at any business office that needs access to the data.   

Improved Resource Productivity

Resource Optimization  

Simulation techniques can be used for what-if analysis to make decisions about what actions to take to optimize the use of resources and help achieve cost, quality, and schedule targets.

Centerlining is an approach to reduce process variability and increase machine efficiency in manufacturing. Manual centerlining processes include standardizing setup and quality practices, such as regular measurements by operators or inline measurements. With digital tools, operational data is collected directly from sensors or mobile devices and the corresponding machine or process adjustment actions can be performed quicker. AI-based techniques can automatically derive adjustments using historical data.

Optimization techniques that leverage real-time data include the use of artificial intelligence (AI) and machine learning (ML) algorithms to detect unusual patterns and trends that might be causing the use of more effort, resources and energy than usually required to perform the task.

Reduced Equipment Downtime

Unplanned downtime can cause big delays and time wasted waiting while equipment is repaired, and production is rescheduled to work around equipment issues.

Preventive maintenance is usually performed on machines based on a fixed frequency schedule based on maintenance manual recommendations. Advanced sensors for conditions such as vibration, force and temperature can be installed on machines, monitored in real-time, and analyzed by AI-based predictive maintenance algorithms to trigger maintenance only when needed based on machine usage and performance instead of a fixed schedule. The increased accuracy of methods using real-time machine data reduces the amount of downtime and maintenance cost.

Smart machines with embedded sensors can monitor their own internal conditions and are able to immediately alert workers of breakdowns, tool wear, or malfunction.

Improved Quality

Work Standardization and Worker Guidance

When workers understand the standard, they know the best way to do the specific job and avoid the waste that would result from deviations and errors.

Smart Manufacturing techniques strive to have the right data available to the worker at the right time (when it is needed) and in the right form to minimize manual search and reformatting processes.  Data that includes the work instructions for complex production tasks and any data collected from machines that helps the operator make the right decision about next steps or adjustments needed to the process.

Virtual Reality (VR) techniques are becoming more popular to train workers on complex tasks without risking the impact of learner mistakes on the actual product, especially when the potential mistakes would be on high-cost items.

Digital work instructions are easier to maintain up to date than paper instructions. They can be turned into interactive tools to guide the worker as they perform the production job. Employees equipped with mobile devices, like smart glasses, can receive step-by-step instructions with illustrations or Augmented Reality (AR) visuals superimposed over the physical view of the production process.

Head-up displays that provide work guidance can also provide visual warnings when AI-based assistance software detects any anomaly in the process.  These tools provide additional confidence for workers to take on more responsibility for self-inspection.

AI-based assistance can not only warn about quality concerns but can also warn the worker about safety concerns when handling hazardous materials or detecting hazardous conditions such as proximity to certain equipment like moving cranes or vehicles.

Reducing Errors and Process Variation

Digital Lean techniques can help with the quality challenge presented by the increased speed of change and complexity in today’s products. The cost of poor quality and reduction of defects are common types of waste tackled by lean techniques.

One of the techniques to increase quality is to add more in-process checks through either worker self-inspection or automated process monitoring with techniques like computer vision and AI that triggers warnings in real-time when anomalies are detected. This type of process monitoring, and warning system is equivalent to a digital poka-yoke technique.

Software solutions can track inspection and test samples to reduce the effort related to checks performed during the production process. For example, a smart screwdriver that captures torques values can be connected real-time to the production system, to not only let the operator know when to stop at the correct torque value, but also to automatically collect the data as part of the inspection records for the product.

When problems do happen, the data collected through smart systems allows for faster and better root-cause analysis. Mobile devices can be used by employees to submit error reports, observations, or suggestions for improvement from the shop floor.

The Lean technique of Jidoka refers to the ability to stop a process when an issue is found and immediately alert the supervisor and support personnel that can assist in solving the problem. Old school Andon lights used to indicate that the line was stopped are replaced by event triggered alerts that are displayed at dashboards and broadcasted via email to the respective support department to assist in clearing the issue as soon as possible.

Software solutions can help expedite the response to quality issues as soon as they are detected to quickly identify the root-cause and minimize the impact of the problem. The greater level of detail of data collected by smart systems makes it easier to find the root causes of problems and reduce waste by analyzing the relationships and patterns found in historical data. AI-based techniques can also be used to help identify potential root causes of failures.

Improved Flow

Production Flow, Line Balancing and Scheduling

The idea of maintaining a consistent flow in the plant and production process is one of the important principles in Lean manufacturing.  The concept behind the lean technique of Heijunka is to not only level production effort throughout the plant but also to set the production pace to match customer demand patterns and reduce manufacturing waste by leveling the type and quantity of production output.

When the company makes products in smaller batches and maintains smaller inventory buffers there is greater flexibility to meet changing customer demand patterns. Digital production systems make it easier to track smaller product batches, change production schedules to match changing customer demand, and make schedule adjustments to work around issues causing unplanned bottlenecks and affecting the rhythm of operations.  

Traditionally, the size of production runs for Heijunka are not recalculated often due to the complicated nature of calculating the ideal run size for each Heijunka wheel iteration. The use of data from integrated production systems makes it easier to recalculate the run size more often to match customer demand and account for current resource availability, production time, and quality expectations. This is especially true in a high mix production environment.  

Demand Pull and Just-In-Time Inventory

Lean manufacturing encourages switching from planning and receiving materials and parts based on schedules to just-in-time methods that establish smaller buffers and signal demand pull based on actual inventory consumption. The manufacturer is purchasing materials based on actual versus forecasted needs. However, manual ways to signal demand can cause delays and errors.

Digital techniques that monitor inventory levels and automatically signal demand for replenishment based on actual inventory levels and lead times can improve accuracy and avoid delays while also maintaining low inventory levels.

Kanban is a visual cue mechanism used to signal, as material is pulled from inventory, that the stock level has reached the replenishment level and an order needs to be triggered.  Digital Kanban can be implemented and automated or operator-driven triggers from the line can immediately send digital requests for replenishment of materials. Auto-ID technology such as RFID sensors can be used to track the consumption of parts in real-time and trigger replenishment automatically. Smart systems can also analyze usage trends to recommend changes to the replenishment levels based on actual parts usage and scheduled product orders. 

A “control tower” system can collect data and direct material movement inside and outside the factory and integrated value chain. The production plans for each day can be adjusted by an AI-based scheduling algorithm based on the latest information on current factory conditions, orders, customer priorities, capacity, inventories, and supplier lead times.  

Digital lean techniques can go further and enable efficient make-to-order processes to avoid the building of goods that sometimes end up on the shelf and not being required by any customer.

Kaizen or Hoshin Kanri?  

We have discussed examples of how Smart Manufacturing technologies are helping manufacturers take Lean to a new level—Digital Lean. The next natural question is: where do we start? There are some general guidelines like start small, focus on return on investment (ROI), and look for “low hanging fruit” to achieve early successes. We can apply Kaizen processes to start quick with an aggressive view of continuous process improvement.

However, there is something additional to consider with Digital Lean initiatives. It is also worthwhile to consider a more strategic Hoshin Kanri approach to deciding priorities. The organization will eventually want to scale implementation of digital process improvements and integrate the new data sources across the enterprise. This is where a Smart Manufacturing strategy for the organization comes into play. Kaizen thinking alone can drive a culture of solving problems in isolation and could end up creating new silos of information if there is no concerted effort to create a platform to share information in the enterprise.

Many companies have started digital initiatives and stalled because they skipped the important step of linking the Smart Manufacturing initiatives to the business strategy. Hoshin Kanri is a method for ensuring that a company's strategic goals drive progress and action at every level within that company. Business goals provide the framework that holds the initiatives together. Continuous-improvement tactics are the muscles to achieve the business goals. Both are important.

If you do value stream mapping as part of your Kaizen or Hoshin Kanri analysis, make sure to include all the steps related to managing the paperwork and reporting that goes along with production operations. You might be surprised at how much time is wasted in the value stream due to the inefficiency of paper-based procedures.

Additional Resources

CESMII, the US Smart Manufacturing Institute, has established a set of tools to help manufacturers jump start their Digital Lean and Smart Manufacturing journey through alignment of business strategy and technology strategy for increased operational performance, customer service, and competitive edge. For more information on CESMII and the strategic roadmap building tools, you can visit: https://www.cesmii.org/education/roadmap-tools/

References:

[1] When Lean Meets Industry 4.0 -  The Next Level of Operational Excellence, D. Küpper, A. Heidemann, J. Ströhle, C. Knizek, D. Spindelndreier, BCG, 2017

https://www.bcg.com/publications/2017/lean-meets-industry-4.0

[2] Digital Lean – A Guide to Manufacturing Excellence, P. Serlenga, I. Leppavuori, I. Moraes, M. Forlini, Bain & Company, 2019 

https://www.bain.com/contentassets/47b06ba77050462caa1aa70050b37c5a/digital-lean-playbook_v5_final.pdf

[3] Digital lean manufacturing - Industry 4.0 technologies transform lean processes to advance the enterprise, S. Laaper, B. Kiefe, Deloitte, 2020

https://www2.deloitte.com/us/en/insights/focus/industry-4-0/digital-lean-manufacturing.html

 

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.

Challenges To Overcome for Manufacturing Breakthrough Performance

The Opportunity

The manufacturing sector has an opportunity to grow significantly in the years ahead. Manufacturing companies that create products from raw materials or components, either by machine or by hand, in plants, factories, and mills are a significant driver of the U.S. economy.

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.
• During the pandemic, many companies realized the fragility of their supply chains and they are now looking to reshore suppliers in critical medical and food supply chains.

As an industry, manufacturing is undergoing a digital transformation. The recent proliferation of technologies like IIoT, artificial intelligence, 3D printing, robotics and Smart Manufacturing are disrupting the sector so much that many manufacturers are upgrading the way they perform and manage production and are even rethinking their business models and supply chains—all of which are elevating the bar for manufacturing operation management and excellence.

The Challenges to Overcome

However, the sector could miss out on much of this opportunity if several key challenges are not addressed for manufacturers.  

• Need for Higher Productivity
• Complexity of Products and Technology
• Systems and Data Integration  
• Supply Chain Resiliency
• Lack of Skilled Talent

Need for Higher Productivity

For U.S. manufacturers to be competitive in the global marketplace they must achieve higher levels of productivity through adoption of advanced technologies, smart manufacturing techniques, and a higher skilled workforce.

The resurgence in reshoring will lead to an increase in U.S. made products in the future. However, to sustain the reshoring trend, manufacturers must achieve higher performance using new technologies like smart manufacturing and robotics to automate many of the processes that used to require intense human labor.

Labor is not the only expensive resource for manufacturers, the smart manufacturing innovation needed includes the technologies that contribute to reduce the industry’s energy usage, carbon footprint, and use of nonrenewable resources (like oil, natural gas, and coal).

Complexity of Products and Technology

One thing is certain: change and progress are inevitable. The pace of technology innovation continues to increase and product, production processes and supply chains continue to get more complex. Manufacturers have a long history of embracing technology to drive efficiency. Now they must also use technology to manage the complexity of new products and processes.  

From sensors to automation, artificial intelligence, 3D printing, robotics and cloud computing, a key challenge for many manufacturers 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.

We have been hearing about the promised value of Industry 4.0 technologies for several years and many manufacturers agree on this potential. However, the industry productivity has been flat for the last decade. Many manufacturers have started their digital transformation initiatives but a big percentage of them have stalled.

Systems and Data Integration  

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. There is often a disconnect between understanding the need for new technology and actual implementation and adoption.

Manufacturers have been enhancing their production systems and implementing newer industrial automation equipment that incorporates sensors, controllers, computers, and network connectors. The use of robotics including robots working alongside humans is expected to triple or quadruple over the next five years. Yet, the cost and complexity of systems and data integration remains a barrier to wider adoption of Smart Manufacturing technologies and techniques that allow enhanced data-driven operations in a secure, open, and scalable way.

The good news is that new Smart Manufacturing solutions are entering the market for low-cost, scalable infrastructure that enables a highly connected and data rich operating environment— solutions that not only improve productivity but also augment the workforce and supply chain.

 Supply Chain Resiliency

Shifts in sourcing and shipping logistics are constantly evolving throughout the manufacturing industry, but it was especially true as a result of the COVID pandemic. Other global factors like trade wars, military conflicts, and climate change are causing uncertainties and risk concerns for manufacturers. Manufacturing supply chains will need to be more resilient in adapting to potential future disruptions.

Product designs are also getting more intricate with an increasing number of electronic components, microprocessors, and embedded software. Not only are the number of components increasing, but parts are much smaller, more technologically advanced, and sourced from multiple vendors adding more complexity to the product and its supply chain.

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. Organizations need information systems to help with these complexities in the product lifecycle starting with design, through supply chain and manufacturing operations.

Manufacturers must increase their efforts toward digital supply chain projects that build agility and scalability to help to manage risk during disrupted and uncertain times. Manufacturers must focus not only on improving cost and quality, but also on delivering the best customer experience and competing in a future market landscape of highly integrated manufacturing and service ecosystems.

Lack of Skilled Talent   

Today’s manufacturers need workers with more advanced technical skills than ever before. Unfortunately, there aren’t enough workers with these skills to fill the many roles available today, creating what is known throughout industry as the “skills gap.”

During the past several years, the skills gap has been top of mind for U.S. manufacturers. The data shows that the demand for jobs in the manufacturing sector has been and continues to grow. These are good paying jobs with a median salary of $46,000. Jobs that have a significant economic impact on the economy for the country.

As reshoring trends continue and more baby boomers retire, many manufacturing jobs will continue to go unfilled over the next decade. In this age of automation and robotics, skilled workers are still needed to operate and maintain complex machinery, integrate technology and processes, fill managerial positions, and perform decision-making and problem-solving tasks. To fill the widening skills gap, manufacturers will need to not only attract new workers, but also train existing workers on new skills and technologies.

Many of the software technology required for Smart Manufacturing solutions are becoming more affordable, yet the time, complexity, cost and knowledge required for implementation and integration continue to be a barriers to adoption for the average manufacturer.

Smart Manufacturing solutions for interoperability and enhanced insights are valuable tools, however to truly mine that potential value, manufacturers also need (i) a talent pool ready to implement and sustain the solutions, and (ii) a workforce ready to leverage the insights and embrace a culture that values transparency and collaboration.

Smart Manufacturing education is needed to teach the new workforce how to implement and leverage the many practical technical options available. Solutions that make it easy to integrate data collection from production assets and processes without constraining a manufacturer to a single vendor.

Solutions are needed for both (i) education at universities and colleges, and (ii) training at trade schools, community colleges and high schools. Manufacturers are asking universities and colleges to help get the Smart Manufacturing education down to high school level, so it does not require a four-year degree to get the skills.

Manufacturers also need training to upskill the workforce. There is a need for continual learning because the skills for new methods and technologies need to be refreshed every four or five years.

One way to expand the talent pool is by attracting more underrepresented groups such as women and ethnic minorities to manufacturing careers and education. To help attract this talent pool, education options need to be more affordable and modular to make it easier to acquire the skills while working. Micro-credentials can be a solution if that can be standardized for the industry so they are credible to manufacturers and valuable on the resume of job seekers. 

In addition, manufacturing education has to be cool with engaging hands-on exercises with technologies like robotics and 3D printers. Education organizations can help attract more students to manufacturing and win over parents, so they encourage their kids into manufacturing careers. Today’s Smart Manufacturing solutions are not only increasing productivity for manufacturers but are also making an impact on climate change. Students should be able to simply scan a QR, download an app to their phone and realize that today’s manufacturing is not their grandfather’s manufacturing.

It is encouraging that the U.S. government is investing to help manufacturers tackle these challenges. To learn more about how CESMII, the Smart Manufacturing Institute, is helping tackle these challenges through ecosystem, technology and education solutions visit www.cesmii.org.

The Evolving Smart Manufacturing Supply Chain

As more manufacturers adopt Smart Manufacturing and manufacturing operations management methods increasing connectivity and data availability within their organizations, they are also realizing that a connected supply chain becomes a strategic competitive advantage in the marketplace. A report from Gartner predicts that in five years, 50% of large organizations will compete as collaborative digital ecosystems rather than discrete firms, sharing inputs, assets, and innovations. [1] A new degree of collaboration and integration in new manufacturing ecosystems enables not only enhanced visibility, but also increased speed and resiliency.

The rapid adoption of digital technologies keeps fueling disruptive change in the marketplace. Computer technology, so small it fits comfortably into our mobile phones, has become nearly completely embedded in consumers’ lives and is making its way into industrial equipment, enabling information sharing, communication, and operational analysis in real time.  Internet access and Wi-Fi has become widely available at an affordable cost. Consumers use mobile devices to shop and order goods using real time information about inventory availability and receive same day delivery of their orders. 

Speed and data are the currency of today’s supply chain. Companies that learn to coordinate a supply chain in real time are becoming better options for their customers, quicker to see new opportunities in the market, and quicker to respond to disruptive changes in their markets.

The supply chain woes that resulted in weeks of empty shelves and missed deliveries during the COVID-19 pandemic highlighted the need to enhance collaboration features in the supply chain to become more resilient and improve the ability to absorb, adapt and recover from a disaster or disruptive event. In fact, according to Accenture [2], companies are increasingly prioritizing restructuring their supply chains and approaches to production to counteract disruptions.  

Manufacturers are not only enhancing traditional supplier management features like procurement and issue management, but they are also implementing enhanced features like demand-capability matching, dynamic fulfillment, and product data services. The new ecosystems favor suppliers with specialized modular capabilities and services that can be recombined and scaled as required to accommodate market and supply chain changes.

In this journey to a highly connected ecosystem, small and medium manufacturers (SMMs) can take a low-risk incremental approach as long as they establish and follow a strategic Smart Manufacturing roadmap. Practical tools to establish such a roadmap are accessible to manufacturers through CESMII – the U.S. Smart Manufacturing Institute [3].

Manufacturers can first establish the internal technology-enabled, insight-driven infrastructure and culture required for transparency and collaboration. Cloud services and B2B integration can help them take that collaboration to a new level in the supply chain. Examples of the incremental approach are found in a guidebook from MESA International [4].  One example describes how a manufacturer rolled out modules in multiple stages:

• Production Monitoring – real-time visibility into the performance of production activities
• Quality Tracking – incorporating smart digital attachments and measurement devices (e.g., calipers, gauges) to wirelessly transfer the measured values to a tablet SM app for the inspector
• Material Requirement Tracking − real-time material availability status updates to the shop and ERP to proactively avoid machine starving time due to material non-availability

The solutions were implemented gradually in one plant with an initial investment of around $20K before they were rolled out to four more plants. The benefits realized included improvement of data accuracy, product quality, reduction of material loss and rejected parts yielding savings of $32K within 18 months. Another example describes how a manufacturer extended these smart methods into their supplier chain to improve the visibility of component inventory commitments and remove the uncertainties that were causing delays in their production and client deliveries.

Check out the CESMII resources and the MESA guidebook for more examples on how your company can get started on the Smart Manufacturing journey.

References

[1] Gartner Predicts 2022 Supply Chain Strategy, S. Bailey, N. Sandrome, Gartner, 2021

https://www.shippeo.com/en/resources/gartner-predict-2022-supply-chain-strategy

[2] Why supply chain innovation paves to road to resilience, M. Reiss, Accenture, 2021

https://www.supplychain247.com/article/accenture_on_operations_why_supply_chain_innovation_paves_the_road_to_resil

[3] Smart Manufacturing Business Transformation Tools, CESMII – The Smart Manufacturing Institute, 2022

https://www.cesmii.org/sm-acceleration-toolkit/

[4] A Low-Risk, Incremental Approach to Smart Manufacturing for Small & Medium Manufacturers, A. Seshan, C. Leiva, S. Zippel, R. Spurr, M. Ford, J. Zhu, J. Winter, MESA International, 2022

https://www.pathlms.com/mesa/courses/39290

 

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 Benefits Beyond Performance Improvement

Smart Manufacturing is transformational, radically impacting the performance of the manufacturing ecosystem through measurable improvements in areas such as: speed, agility, innovation, quality, costs/profitability, safety, asset reliability and energy productivity. ​

The decision on how to embark on the smart manufacturing journey should align around a strategic vision for the future of the organization. The steps on the journey vary for different organizations with different types of processes and different market drivers so they cannot be easily generalized. But regardless of the strategic business drivers, demonstrable value along the journey is essential to successful adoption and incremental investment required to sustain the smart manufacturing journey.

Undertaking a smart manufacturing initiative and upgrading the manufacturing operations management strategy generally addresses the following broad categories of benefits with the ultimate result of improving profitability which in turn accelerates investments in more innovation.

Productivity and Cost Reduction  

The increased automation tied to many Smart Manufacturing projects can achieve great productivity improvements. Improvements that might give your company a competitive edge by raising the bar for price and quality in your market. Automation can reduce cycle time, labor time, and quality errors.

Data coming from monitored machines and processes feed into AI-driven insights to alert of pattern changes and suggest improvement for more efficient use of resources. For example, more predictable inventory requirements can lead to reduced safety buffers, and correlation of process and environmental data can lead to energy cost optimization.

Utilization and Reliability

Information coming out of connected high value machines can reveal asset performance issues and lead to higher levels of utilization and lower levels of production downtime. Predictive maintenance analysis helps prevent unplanned downtime by flagging equipment for proactive maintenance based on usage and performance data. These improvements not only lead to higher business continuity, but they can also increase machine availability for additional production output.

When a key industrial machine goes down, it is usually a whole line that goes down. On top of the repair cost, employees might end up idle, and production schedule might be affected, costing loads of money and unhappy customers. The brand reputation can be damaged, and orders can end up being cancelled.

Quality

It might have been okay to have a few defects in years past, but today’s customer has more access to information, more vendor choices, and is really looking for zero defects.

Smart Manufacturing technologies can be used to monitor quality aspects of the product and process in real-time to reduce process variability, eliminate undetected errors and catch issues as early as possible in the process to minimize scrap and rework costs.

Data with the right context and relation to process and resources allows analysis to dig past the symptoms to understand what is really happening and why it is happening. The additional metrics and insights can help identify human, machine, or environmental causes of poor quality for quicker resolution.

The ultimate benefits of higher quality go directly to improved customer relations with higher brand equity, lower warranty cost, and reduced risk of product recalls.

Beyond Continuous Improvement

The above areas of cost reduction, quality, and utilization improvement through automation and integration might be enough to justify many Smart Manufacturing technology investments, but it would be short sighted for the organization to miss the opportunity for a more strategic look into Smart Manufacturing for digital transformation of the business and ecosystem that yields the following additional strategic benefits.

Transparency, Speed and Collaboration

In Smart Manufacturing, data moves from machine-to-system and system-to-system without human intervention and is available for AI-driven insights and human analysis in near real-time across multiple production lines located anywhere in the world. However, Smart Manufacturing is not just about the data. In fact, many companies are inundated with data from new sensors and enhanced machine monitoring yet not realizing the full benefit from that data.

Transparency, speed, and collaboration are all linked together in the Smart Manufacturing vision.  Information flow must be designed for raw data to get contextualized into information and analyzed for insights that are provided back into multiple systems in the manufacturing ecosystem. Insights that drive event-driven autonomous actions for routine situations and enhanced human decisions for non-routine situations.

If the processes in the plant are still bound to paper-based forms, we cannot achieve the desired level of speed and semi-automated processes for Smart Manufacturing. If information sits around on desktops and takes hours or days to get in front of the right person, we are not achieving the desired benefits. The insights need to get to the right people at the right time to make the right decisions. Decisions that will prevent errors, prevent delayed actions, optimize outcomes, and get disseminated quicker into the whole value chain.

Traditional manufacturing plants operate in silos with minimal collaboration or knowledge sharing. Smart Manufacturing gives production lines, business processes and departments improved capabilities to communicate, share data, collaborate, and make improvements regardless of their systems, location, or time zone. These enhancements make it realistic to manage manufacturing operations with more precision and better collaboration among employees, suppliers and partners.

Smart Manufacturing creates an open atmosphere of information-based decisions where decision makers will have the trusted data when it is needed, where it is needed and in the most useful form. Problem solving will not be limited to localized decisions. Instead, problems can be prioritized and tackled based on a total enterprise and ecosystem picture.

Innovation, Agility and Resiliency  

Smart Manufacturing systems are integrated with open interoperable APIs allowing manufacturers to quickly change equipment, process flow, product configuration, labels, and packaging. A smart factory is equipped with modular solutions and systems that can easily be reconfigured to scale up or down production, introduce new products, create one off production runs, or create high-mix manufacturing opportunities. This agility makes the organization adaptable to changing demand and more resilient to handle market disruptions.

However, the biggest reward in a Smart Manufacturing strategy comes from leveraging the higher levels of connectivity and information to (a) enhance operating models, (b) provide more personalized product and service offerings, and (c) innovate partner ecosystems to drive higher revenue and customer value.

The increased speed of digital communications and the ability to quickly change product configuration means that we can have increased speed to market for new products and higher ability to capture market share.

Consumers increasingly want direct interaction with a brand and its manufacturing capability. The desire to co-create and customize products applies not only to consumers but also to B2B customers even if it is just a custom label, added feature, or additional product data. For example, Smart Manufacturing solutions can automate track and trace functionality and provide the customers with higher levels of digital data to go along with the product.

Consumers also expect higher levels of customer service and faster response to request for customizations or service issues. A highly connected ecosystem can disrupt traditional supply chains with highly orchestrated processes. Smart Manufacturing can put companies in a better position for partnering and exchanging data in such ecosystems to meet these market demands.

Transformation at a National Level

Smart Manufacturing opens new areas of innovation that will optimize the entire manufacturing industry to create higher quality products, improve productivity, increase energy efficiency, and sustain safer plant floors. 

Smart Manufacturing can make US manufacturers more competitive in the global landscape, help towards onshoring more production, and offer the opportunity to boost employment in the US. As Smart Manufacturing is adopted, new technology-based high skill manufacturing jobs will become available in addition to related non-manufacturing positions.

In addition to raising US productivity, Smart Manufacturing is good for the environment by reducing waste of resources and energy consumption. Energy is directly saved as processes are optimized based on energy usage insights, and indirectly saved as waste of resources is reduced by reducing defects, scrap, and overproduction of inventory in a more efficient supply chain.

Even though some energy is created from renewable sources, much is still created from fossil fuels (coal, natural gas, and petroleum). Reducing energy consumption reduces carbon emissions for a healthier planet and improved quality of life.

Smart Manufacturing Champions Must Address Organizational Culture Upheaval

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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

Digital Supply Chain Resilience Tactics

The implementation of modern manufacturing operations management practices including manufacturing systems, lean manufacturing and six-sigma have improved effectiveness and operating costs within many manufacturing companies. However, it takes an entire supply chain to bring manufactured products to the marketplace and supply chains are more vulnerable than ever due to a range of risks including small inventory buffers, reduced slack in lead times, higher product complexity, and global interdependence.

We have learned that events like the COVID-19 pandemic, 9/11 and Hurricane Katrina can cause big and sudden disruptions in supply chains. Disruptions so big that regular inventory buffers of materials and parts cannot diminish the impact. Customers are left waiting for product deliveries and market shelves cannot be replenished on time.

During these times, it behooves companies in the same supply chain to innovate as a collaborative team to enable new levels of efficiency and resiliency in producing manufactured goods through higher levels of transparency, agility and orchestration. A general supply chain resiliency strategy strives to maintain performance measures during potential disruptions including fulfillment of orders, delivery reliability, and customer satisfaction.

Supply Chain Resiliency Definition

Supply chain resiliency is the ability of a supply chain to both resist disruptions and recover operational capability after disruptions occur. Where operational capability is the continuity of operations at the desired level of connectedness, control, and delivery of products and services to end customers.

Resistance capacity is the ability of a system to minimize the impact of a disruption through tactics including inventory buffers and supplier redundancy. Recovery capacity is the ability of a system to minimize the time between disruption onset and the time when the supply chain returns to regular performance levels of delivery to end customers. The recovery time includes (a) the time to detect the problem and (b) the time to orchestrate the response and recovery. However, it is important to note that, after big disruptions, the demands from the market might have changed and might not support or require the prior levels of performance.

Supply Chain Resiliency Tactics

• Minimize the time it takes to detect a disruption in the supply chain. Review practices and systems for supply chain monitoring, communication and orchestration. Establish a digital supply chain. Implement an early warning system that quickly bubbles up concerns from lower tiers in the supply chain.
• Increase the capability to switch to a different product mix to adapt to shifts in demands due to a market disruption. For example, in response to a weather or health related crisis, the demand for essential products increases as the demand for nonessential products decreases.
• Increase supplier loyalty through partnership programs and incentives for key suppliers to deliver during tough times. 
• Assess sensitivity to failure in supplier nodes in the supply chain for each product line. For each node, determine the possible impact of deliveries stopped for a few weeks. The impact to end-customer deliveries based on the expected time it takes to recover from the failure of the node.
• Review inventory buffers. It is tempting to increase buffers and hoard critical materials and parts to avoid any disruption but there are inventory costs associated with this tactic. 
• Design products for configuration flexibility to allow for the use of alternate materials and parts. If possible, design to use off-the-shelf parts  
• Establish supplier redundancy. Wherever possible, establish more than one supplier for critical material and parts. For off-the-shelf parts, buy from distributors that pool supplies from several suppliers to minimize the impact of losing one supplier during a disruption period.
• Work with suppliers ready to engage in a digital supply chain and flexible to converting production lines, switching processes, increasing capacity, and supplying alternate parts when needed. 

Major disruptions will always have an impact, but we can be better prepared for major and minor supply disruptions through risk mitigation strategies. 

 

Additional articles on the topic:

Supply Chain Resilience for an Era of Turbulence, The Economist, 2020

Building Supply-Chain Resilience for Turbulent Times, Supply Chain Brain, 2020

The Resilient Supply Chain Benchmark: Ready for Anything? Turbulence and the Resilience Imperative, The Economist, 2020