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

 

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.

Three Dimensions Converge on Smart Manufacturing

- Smart Factory, Digital Thread, and Value Chain Management

There is a myriad of new technologies coming into the manufacturing arena, each with tempting value propositions. How does an organization know that they are investing in the right areas to stay competitive? If your organization is confused about where to start or where to focus investments on the journey to a future highly connected, orchestrated, and optimized Smart Manufacturing enterprise, you are not alone.

A clear roadmap to Smart Manufacturing is of the utmost importance for each organization, but not easily realized because of the complexity of different organizational perspectives, data models, and business processes that converge at the manufacturing shop floor—processes that get products designed, outsourced, built, tested, packaged, and delivered to the customer in a consistent manner.

This article discusses how to organize the convergence of processes supporting Smart Manufacturing into three dimensions or perspectives: (i) smart factory, (ii) digital thread, and (iii) value chain management. These different dimensions relate to different perspectives and systems coming from engineering, operations, and business management disciplines and help explain why the Smart Manufacturing endeavor requires collaboration among many different stakeholders in the organization.

This three-dimensional model also explains the intersection of several manufacturing improvement initiatives included under the scope of Smart Manufacturing: IIoT (Industrial Internet of Things), Model-Based Manufacturing, and Connected Enterprise. The Digital Thread dimension aligns with the goals of Model-Based Manufacturing, he Smart Factory dimension aligns with the goals of IIoT, and the Value Chain Management dimension aligns with the goals of orchestrating and optimizing the entire value chain in the Connected Enterprise.

The Smart Factory Dimension

From a Smart Factory perspective, we are interested in connecting equipment, resources and personnel in order to acquire real-time data through automated methods, analyze it, and leverage that information to (a) provide complete real-time visibility of factory processes, (b) optimize process control, and (c) provide insights to where we can further improve performance.

For example, an assembly line with Smart Manufacturing automated and semi-automated processes may do the following:

• Monitor production flow in real-time to eliminate constraints, dispatch automated material handling, and eliminate wasted idle time
• Auto-identify parts going down the line to automatically load programs and materials for each different product configuration
• Automatically aggregate product data, analyze and identify constraints and required adjustments or improvements
• Manage equipment remotely using sensors to conserve energy, reduce downtime and trigger preventive maintenance

To achieve these levels of automation, through which products, parts, and equipment interact among themselves with enhanced communication mechanisms, we will need resources and industrial automation equipment with communication standards to acquire and publish data to higher levels of processes in the Smart Factory stack including operations management and intelligence applications.

 

Figure 1: The Smart Factory Dimension

The Smart Factory dimension illustrated in Figure 1 includes the following connected processes and systems flowing from equipment and resources up to higher levels of process control, analytics, and intelligence.

• Smart Machines, Sensors, Tooling, and Workforce interact with each other via structured communications and integrated systems providing real-time data about their status and the processes they are executing
• Smart Apps, Controllers, OT-IT Bridges like the Manufacturing Service Bus provide the communication bridge between Operations Technology (OT) exchanging data directly with machines and tooling, and Information Technology (IT) systems and apps where personnel interface to execute supervision, production, inspection, and maintenance tasks at the shop floor
• Operations Management System optimizes the flow of products through production processes and orchestrates the allocation of resources
• Connected Enterprise Systems including PLM, ERP and SCM are maintained in real-time sync through A2A data exchanges with production process status, resources used, and products produced
• Business Intelligence System receives periodic updates of aggregated data for performance analysis and business metrics

The Smart Factory dimension is aligned with the goals of the IIoT (Industrial Internet of Things). The IIoT takes the concepts of ease of equipment connectivity, data acquisition and advanced analysis via cloud services from the Internet of Things (IoT) initiative in consumer markets and applies them to the next generation of automation for the factory floor.

An enabler behind the IIoT is that it is becoming easier to connect and mine data directly from smarter machines. The IIoT can monitor, collect, exchange, analyze, and deliver valuable new insights. Mined data is more accurate, consistent, near real-time, and enables organizations to sense inefficiencies and problems sooner, saving time, money, and driving smarter, faster business decisions for industrial companies.

A major issue slowing down the IIoT is interoperability between older devices and machines that use different protocols and have different architectures. In contrast to the automation achieved in the last few decades, the connectivity methods targeted under IIoT and Smart Manufacturing need to be open, standards based, and able to facilitate publish-subscribe methods over the internet.

In the past, organizations depended on custom integration, vendor-proprietary interfaces and separate network protocols for integration and automation at the factory. Moving forward with IIoT, organizations want to embrace open standards and Internet protocols to facilitate an easier swap and mix of multi-vendor equipment and software, which might be on-premise or in the cloud.

A big promoter of the IIoT is the Industrial Internet Consortium (IIC) which adopted the term, and promotes the move from older automation protocols to newer Internet-enabled IIoT protocols for industrial equipment.

The Operations Management system optimizes the flow of products through production processes and orchestrates the allocation of resources. It executes programs for processes like cutting, machining or 3D printing equipment, and collects data from operators or directly out of equipment for inspection, test, pick-and-place, or packaging processes. Data is collected in a structured form that allows distribution to multiple subscribing functions in the Smart Manufacturing system like quality verification and parts traceability.

Enterprise Systems manage all kinds of business processes in the organization from receiving orders from customers to scheduling production, planning deliveries, ordering materials, invoicing, receiving payment, and paying suppliers. The timely performance of these activities depends on real-time data from the connected Operations Management system through A2A (application-to-application) data exchanges

Business Intelligence systems aggregates and organize data into actionable metrics and Key Performance Indicators (KPIs) the represent the organization’s strategic goals. In the digitally connected Smart Manufacturing organization, management is automatically alerted of areas not performing to plans and expectations. Management must be able to drill down from metrics into causal analysis, and depending on analytical capabilities, systems might even be able to suggest areas for improvement.

The Digital Thread Dimension 

The Digital Thread dimension of Smart Manufacturing starts with the engineering design definition of the product and follows the product lifecycle through its sourcing, production and service life ensuring that the digital definition of each product unit is aligned with the physical product. The digital data for each product includes every incorporated revision to the engineering definition and any deviations from the design specifications approved and executed on the product during its lifecycle. The flow of processes in the Digital Thread dimension is depicted in Figure 2 including:

• Specifications Management for design of product and processes including definition of 3D models and recipes, product variations and configurations, and engineering change management practices
• Operations Management which includes production and verification processes including programs and work instructions for automated 3D printing, machining, and verification against engineering specifications
• Product Services Management for maintenance of the product during its service life with data collected on product performance, modifications, and replacement of components.

Figure 2: The Digital Thread Dimension

In discrete manufacturing, the Digital Thread perspective is aligned with the goals of Model-Based Manufacturing and Model-Based Enterprise initiatives. The Digital Thread initiative aims for seamless threads of structured communications and data exchanges throughout the value chain that are accessible to all stakeholders across the extended ecosystem to ensure complete visibility and traceability of the digital and physical product from design through sourcing, production, and ultimately to the end user or customer.

The Digital Thread begins with a 3D model-based or recipe-based definition of the product from design teams flows into the Operations Management and the Supply Chain Management via standards of integration for pervasive distribution of the data throughout the connected Smart Manufacturing enterprise. Examples of communications in the Digital Thread include product and process specifications, test results, conformance issues, asset maintenance requirements, and details and approvals for deviations from standard specifications.

Digitally connected model-based design and manufacturing help connect previously disconnected functional departments through a logical thread of integrated data and processes which aids in (a) faster design revision distribution and new product introduction (NPI), (b) accurate translation of product to process specifications, (c) smarter business decisions with visibility of product performance during its lifecycle, and (d) quicker resolution of issues requiring engineering design changes.

The product design engineer states the materials, form, and fit requirements for the components in 3D models for discrete manufacturing, or the chemistry and physical transformations in a recipe for process industries. The manufacturability of a product is dependent on the particulars of design parameters and tolerances. The production and inspection process definition is a repeatable structured means of conveying the engineering intent to Operations Management. There should also be a design feedback loop between the design of the product and the design of the manufacturing processes, tooling design and inspection capabilities. There is a need for better ways to communicate specifications, capture the relation to actual measurements, and leverage that information for resolving issues on non-conformance to the requirements in a well-defined formal way.

As an example, in discrete manufacturing today there is a lot of manual interpretation, transformation, and translation of data between engineering and manufacturing systems. In addition to being inefficient, each time data is manually converted from one format to another, it introduces a chance for misinterpretation and error. For example, in current processes, CAD models need to be manually converted to (a) computer numerical control (CNC) programs for machining, (b) coordinate measurement machine (CMM) programs for inspection, and (c) manufacturing execution system (MES) illustrated work instructions for assembly. During these manual processes, the associativity to objects in the CAD model is usually lost. When a CAD model revision comes down the pipe, the engineers and programmers must do a thorough review of the entire model to avoid missing anything instead of concentrating with confidence on a few highlighted revised areas. In future processes, with structured digital handoffs, systems will be able to easily highlight revisions, do impact analysis on downstream programs and instructions, and facilitate the automated incorporation of changes.

The Digital Thread will provide a formal framework for the controlled and automated interplay of authoritative technical and as-built data with the ability to access, integrate, transform and analyze data among disparate systems throughout the product lifecycle. In the Digital Thread, the product’s data “travels” along with the physical product and evolves through data collected at each step of its manufacturing process. By “travel” we mean that the data needs to be easily accessible at any time during production and referenceable to each product’s lot or serial number. The scope of data includes as-designed requirements, validation and inspection records, as-built records with part genealogy traceability, and as-tested data. The Digital Thread needs to be able to deliver the digital product data along with the physical product to the end customer. For some products, the thread of the product data will continue into Product Service to maintain the product during its entire service life.

The Value Chain Management Dimension

An important dimension to achieving a fully connected extended enterprise in Smart Manufacturing is the Value Chain Management perspective. Value Chain Management focuses on minimizing resources and accessing value at each stakeholder function along the chain, resulting in optimal process integration, decreased inventories, better products, and enhanced customer satisfaction.

The scope of Value Chain Management spans from managing suppliers of materials and parts, to managing the handover of information through internal departments including the production shop floor, and all the way to managing the delivery of the product to the end customer. It encompasses the procedures, forms, and data handoffs that link these organizational entities into a value chain that delivers a final product and services for that product to the end customer.

The standardization of IT practices that ERP started decades ago for cash-to-order processes within the organization—covering activities like contracts, procurement, receiving, invoicing, purchase orders, delivery, and payment—must be extended now across the entire value chain with an emphasis on open data exchange standards that enable publish/subscribe connections across the internet and cloud services. Configurable repeatable patterns of orchestrated activities across the value chain will enable highly automated, efficient, and agile business processes.

As illustrated in Figure 3, the Value Chain Management dimension includes processes for:

• Customer Management with online interaction with customers for quicker custom product configurations, order in-process visibility, and approvals for changes, deviations or delays
• Compliance Management maintaining organizational guidelines, coordinates audits and monitors compliance performance with internal departments and external regulatory agencies
• Operations Management delivering real-time information from production processes to other business management functions and orchestrates activities into the supply chain to make sure that materials, parts, and subassemblies arrive at the right place at the right time
• Resource Management of personnel and equipment required to make the product, provide product services, and maintain the equipment up and running with the required capabilities and certifications
• Supplier Management with functions from identifying and establishing the supply chain with the right partners to monitoring, synchronizing, and maintaining the required quality levels.

Figure 3: The Value Chain Management Dimension

The new Smart Manufacturing ecosystem aims to create closer relations and interactions with customers in processes and services. Customer Management includes functions for customizing orders to customer preferences, providing more visibility to in-process order status, coordination of deliveries, download of data for each product shipment, known issue alerts for purchased products, warranty claims and issue resolution, approval for changes and deviations to contract specifications, and coordination of service subscriptions and service orders. Some organizations have a Program Management function that closely works to achieve program goals with the customer organization and the supply chain.

Since the Value Chain Management dimension encompasses procedures that link the enterprise departments into a connected value chain, it is necessary to have a Compliance Management function which maintains organizational guidelines, coordinates audits, monitors compliance among internal departments, and coordinates with external industry and government regulatory agencies. The Compliance Management function maintains the brand’s quality reputation.

Compliance Management functions include the business processes that (a) document and control standard practices, (b) record history for reporting and auditing purposes, (b) handle resolution of issues and tracking of corrective action and continuous improvement efforts. Compliance Management ensures that corporate procedures are aligned with industry regulations for safety, environmental protection, risk mitigation, and quality management as documented industry standards such as ISO9001, ISO14000, ISO45001, and ISO31000.

Operations Management touches every dimension in Smart Manufacturing performing a very critical coordination function. Operations Management orchestrates activities into the supply chain to make sure that materials, parts, and subassemblies arrive at the right place at the right time. It provides demand signals for resources and delivers real-time information from production processes that includes the context of orders, specifications, and resources. Good data from Operations Management enables confident decision-making in all parts of an organization including production, quality, maintenance, sales, and engineering. This information is essential to allow management to drill down from corporate Key Performance Indicators (KPIs) into causal analysis to uncover areas for improvement.

To ensure the operational outcomes desired we must properly train personnel on required skills and keep track of the required certifications for specialty jobs. Resource Management includes Workforce, Facilities, and Equipment Management. Equipment Management includes the care of the plant, environmental controls, machines, equipment and tools handling trouble-call tasks as well as scheduled preventive maintenance and calibration service tasks.

Workforce Management includes maintaining the right level of workforce with the right level of skills and certifications to perform the required production and inspection tasks. It includes tracking attendance and labor costs in concert with the Operations Management system. Human Resources has the challenge to attract the next generation of the Smart Manufacturing workforce with the required new skills for the job.

Supplier Management includes the activities for sourcing materials and components to suppliers, coordinating the proper production of those components at the supplier site including supplier qualifying and auditing, negotiating contracts, scheduling deliveries, managing warehouse and stockroom, receiving and inspecting incoming materials and parts, and handling of warranty issues, returns, and corrective actions with suppliers. Product design changes must be carefully coordinated with impacted suppliers. In the connected smart supply chain, digital data about materials and components must be delivered by suppliers along with the physical units in a way that allows easy roll up of the data to higher levels of assembly by the Operations Management system for full parts genealogy traceability. Communications into the supply chain is performed via the internet through B2B (business-to-business) data exechanges.

A Convergence on Smart Manufacturing

Smart Manufacturing strives for higher levels of connectivity, orchestration, and optimization of processes in the manufacturing value chain. To reach this goal, the organization must (i) align the intersection of the different organizational perspectives into a cohesive set of functional roles, data models, and business processes that converge to get products designed, outsourced, built, tested, packaged, and delivered to the customer in a consistent manner, and (ii) figure out how to leverage the many technology building blocks available into a flexible architecture of systems that helps automate the activities as much as possible, pass data seamlessly, and enable new levels of optimization, predictive, and prescriptive analysis in enterprise processes.

The organization can start trying out new technology but cannot complete the analysis of the required technology building blocks until the functional requirements to support the new connected enterprise are fully understood.  The three-dimensional model for Smart Manufacturing in Figure 4 provides a functional framework to start laying out business processes in the industrial ecosystem, the interactions required between activities, and the data exchanges required to support those interactions. Operations Management is a common central function in these three dimensions and has the critical role of coordinating the convergence of the digital, physical, and business process dimensions.

Figure 4: Three Dimensions Meet for Smart Manufacturing

The flow of information in the typical legacy manufacturing environment is, at best, full of manual information handoffs with a lot of human data interpretation and transformation along the way. Legacy processes were commonly designed around the sequential handover of paper documents between different departments. Within major enterprise systems such as ERP or PLM most processes are based or focused on departmental issues; the processes are rarely cross-functional. 

Modern processes can be reinvented around new cyber-physical paradigms that promote real-time response, collaborative teams, and more parallel tasks across production and supply chain. Consider the benefits of processes where utilities auto adjust based on environmental sensor data, where machines take corrective action and request maintenance to avoid costly damage, where part shelves report usage and are automatically replenished by suppliers, where correction tasks for non-conformances are routed in parallel to multiple departments including Engineering, Procurement, Inventory Control, and into the supply chain.

To minimize delays and communication errors among intra-departmental processes, process outputs need to be connected as inputs to successor processes. Communication and data processing among activities should avoid manual data input and translation errors whenever possible. Publish and subscribe data services must connect enterprise systems, web applications, mobile devices, and cloud services in a system of systems to ensure the pervasive distribution of the data.

There will be some technical challenges along the way to create the Smart Manufacturing connected enterprise. For example, data exchange standards will need to evolve and be adopted by hardware and software vendors, and security concerns will need to be addressed at all levels of enterprise communications. But the biggest challenges ahead are cultural. How do all involved embrace new business models, new processes, and new levels of transparency among departments and partners in the ecosystem?

Organizations will soon overcome these barriers and realize a network of connected partners, systems, and resources that will result in the transformation of conventional value chains and the emergence of new manufacturing practices and business models that leverage the higher levels of connectivity to achieve new levels of orchestration, optimization, and customer service.

Reducing Cost of Quality with Integrated MOM and EQMS

– Using Manufacturing Operations Management Software to Reduce Cost of Quality

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

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

Breaking Down the Cost of Quality

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

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

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

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

 

 

 

The Relationship between Prevention and Failure Costs

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

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

 

  

 

 

Six-Sigma Projects Reduce the Number of Defects

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

 

 

 

 

 

 The Effect of Improving the QMS Processes

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

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

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

Reducing the Cost of Quality Failure Prevention

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

Work Instructions and Process Change Management 

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

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

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

Verifying Personnel Training and Certification 

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

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

Verifying Equipment Calibration 

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

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

Reducing the Cost of Quality Appraisal

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

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

Is 100% Inspection Needed?

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

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

Automated Inspection and Statistical Process Control (SPC)

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

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

Automated Quality Metrics 

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

Process Audits

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

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

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

Reducing the Cost of Quality Failures

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

Discrepancy Documentation

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

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

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

Workflow Driven Routing of Tasks

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

Rework, Re-inspection, and Re-testing

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

Corrective and Preventive Actions (CAPA)

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

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

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

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

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

The Cost of External Quality Failures

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

Conclusion

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

References

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

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

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

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

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

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

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Demystifying the Digital Thread and Digital Twin Concepts

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

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

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

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

Expected Benefits

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

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

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

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

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

As-Is and To-Be Scenarios

The As-Is handover between departments:

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

The desired To-Be scenario with a digital thread:

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

Interesting Use Cases  

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

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

Potential Challenges

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

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

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

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

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

References

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

 

Eliminating the Hateful Eight Types of Disconnectivity in Smart Manufacturing

“For hundreds of years, humans and machines have worked together. But something peculiar has happened recently: the machines are now speaking to us—and to one another”
                                                              -- Booz Allen, Vice President at Sedar LaBarre [1]

That would be a great quote from Booz Allen, if it weren’t slightly ahead of its time. New machines want to speak to us and want to be part of orchestrated processes in the factory, but we are not there yet. The mission ahead for Smart Manufacturing initiatives is to make it happen and realize the enormous potential from new levels of synchronization spanning from product design to order, realization, and delivery.

How Smart Manufacturing complements Lean Manufacturing efforts

Smart Manufacturing can be viewed as a process improvement movement complementary to the Lean Manufacturing movement of the past two decades. Lean Manufacturing initiatives design out the overburden, inconsistencies and waste to achieve enhanced processes that deliver products smoothly and consistently. In contrast, Smart Manufacturing initiatives are bringing the physical and digital worlds together designing in connectivity and orchestration to achieve enhanced processes that deliver products and their data smoothly and consistently to customers. Going forward, both Lean and Smart Manufacturing will be interrelated improvement efforts in the manufacturing value chain.

Eliminating the Hateful Eight Types of Disconnectivity

There are eight types of disconnectivity in the manufacturing value chain that need to be eliminated as organizations embark on the Smart Manufacturing journey to synchronize, automate and optimize the physical and digital processes.
Each of the communication and process participants listed in this slideshow need to be systematically connected to achieve the desired Smart Manufacturing goals, which we discussed in the prior article titled: “On the Journey to a Smart Manufacturing Revolution.” [2]

#1 Equipment

Machines and equipment are getting smarter with more onboard computer processing power and more connectivity to the Internet. This advancement in connectivity is referred to as the Internet of Things (IoT). For example, at home, I can set up my printer to directly order ink when it is low and have it shipped to my house. At the factory, we refer to this type of internet connectivity direct from machines as the Industrial Internet of Things (IIoT).

Equipment, machines, robots and advanced sensors can be connected to systems that automatically adjust or switch operations based on sensing the product, diagnostic or environmental conditions. IIoT connectivity needs to be orchestrated in the overall Smart Manufacturing architecture to, for example, avoid excessive downtime at the factory, because each of our machines has auto-requested maintenance a few days apart instead of coordinating a single service day of downtime for the entire line.

According to a research report from the analyst firm Berg Insight, the installed base of wireless IIoT devices reached 10 million in 2014 and will grow at an annual growth rate of 27% to reach over 43 million by 2020. [3] Yet, many organizations are struggling to reap efficiencies and insights from the connectivity and data because they haven’t created the proper technical and decision making structures to organize connected processes and analyze data for real business value.

#2 Workforce

Yes, people are an integral part of Smart Manufacturing.

People are resources that need to be connected to the processes orchestrated and data produced by the Smart Manufacturing system. We might appreciate disconnectivty when we are on vacation, but definitely not at work. We want to know what is happening at our operations as soon as possible. We want to be able to see when customers raise issues, when the production line is not running smoothly, and when problems are not being resolved in a timely manner.

Technicians, inspectors and material movers need function-specific applications that dispatch them to prioritized tasks and enable them to update status and document issues immediately. Some functions are performed at a desktop, other functions are performed on-the-go on a mobile device—function specific applications connect people at the place where they need to perform the function.
Some workforce resources might be external to the organization. As an example, smaller manufacturers can’t afford to keep every specialized skill as a full-time employee, and might need to route tasks to third-party contract technicians.

Managers need dashboards that display status, trends and alerts to focus their attention on where it is needed most. However, the old passive information model of aggregating data and providing metrics to managers to digest and take action is not enough to achieve new levels of efficiency.
New active manufacturing systems need to automate routine decisions and trigger action based exceptions and unpredictable patterns that require experts to manually analyze for root-cause. There are always plenty of exceptions to manage in daily operations, so don’t worry, people will always be needed.

Legacy paper-based procedures promoted departmental silos by creating paperwork between people in different disciplines, departments and companies in the manufacturing value chain. Social communication and collaborative work tools can connect teams and communities in new ways breaking down old silo boundaries and creating new levels of efficiency in processing and trouble shooting procedures.

As the manufacturing enterprise becomes more connected, it creates the need for a new breed of talent—one that embraces computer skills as much as mechanical skills. The next generation of manufacturing talent must be able to collaborate across multidisciplinary walls, connect digital factories, interpret results from analytics, and protect supply chains from cyber intrusions. Luckily, millennials are tech savvy, learn fast and embrace teamwork. Manufacturers will face strong competition from high-tech companies in recruiting this talent and will need to elevate the importance of strategies to recruit, develop and retain the workforce of tomorrow.

#3 Design

There are currently many manual steps and translations between the virtual product and process designs modeled in 3D and the programming and configuration of physical manufacturing and inspection processes. This type of connectivity is referred to as the “digital thread” spanning from design into supply chain, manufacturing, inspection, and product service.

One of the first applications of the digital thread is between product design and 3D printing processes. A few more of the enhanced processes targeted for the digital thread include: (a) simulation of the virtual production processes and validation of designs in collaboration with partners before physical processes are ever changed, (b) flow of revised specifications into the supply chain to ensure subassemblies are received to the correct design revision level, and (c) automation of translations between design specifications and instructions loaded into automated production and inspection machines.

#4 Parts

One of the goals of Smart Manufacturing is to create flexible manufacturing processes that can adapt to build different products and support mass customization business models. Connected auto-identified parts and materials can help achieve that type of flexibility.

Accuracy of product genealogy becomes more difficult to achieve as product variances and changes increase in a multi-tier supply chain. Accuracy can only be guaranteed if identifiers are read directly from components and materials with automatic identification (auto-ID) tags, like RFID, either embedded in the component or in the packaging. Common applications for RFID include tracking goods in the supply chain, reusable containers, high-value tools assets, parts on inventory shelves, and parts issued to manufacturing.

Flexible manufacturing processes need to (a) recognize the configuration of each product as it traverses the line, (b) load the right parts and programs to machines, and (c) compare component parts as they are loaded into machines and assembled into product in order to verify the correct bill of material configuration.

RFID tags can contain more than part and serial numbers, they can have information about hazardous warnings, expiration date or storage requirements that could trigger special handling processes at the shop floor. For example, automated processes could alert staff if material has expired due to the time it spent outside of the required refrigeration temperature.

#5 Processes

Business processes are chains of tasks that produce a specific service or product for specific customers. Tasks in a business process can span across organizational departments and system boundaries. In order to minimize delays throughout the business process, task outputs need to be connected as inputs to successor tasks. Communication and data processing among tasks should avoid manual data input and translation errors whenever possible.

Legacy business processes where designed around the sequential handover of paper documents between different departments. Modern business processes can be reinvented around new cyber-physical paradigms that promote real-time response, collaborative teams and parallel tasks across production and supply chain. Consider the benefits of processes where utilities auto-adjust based on environmental sensor data, where machines take corrective action to avoid damages, where part shelves report usage and are automatically replenished by suppliers, where correction tasks for non-conformances are routed in parallel to multiple departments including Engineering, Procurement and Inventory Control.

Automated processes are standardized processes, and standardization promotes best practices and reduces overhead tied to maintenance of multiple ways of doing similar functions in the organization.

#6 Suppliers

To optimize today’s highly outsourced manufacturing value chain, supply chain management has to switch off the traditional procurement function managed by paper documents, emails and phone calls. Suppliers need to connect digitally with the companies they supply via web portals, supply chain hubs, and business-to-business (B2B) communication exchanges.

A demand driven supply chain can reduce inventories and schedule performance. Automated demand triggers can be achieved to replenishment and move material based on sensing part installation or empty shelves.

The orchestration of multi-tier supply chains for complex products like aircraft or medical devices require additional attention to connected change management processes for product design information.

#7 Customers

Manufacturers have traditionally faced challenges in collecting reliable data to understand and adjust to changing customer needs. Data collected via surveys is unreliable, expensive and burdens the customer.

Today’s data analytics capabilities represent a new frontier for customer satisfaction. The ability to collect and analyze customer usage data in real time allows manufacturers to make quicker adjustments. For example, remotely collecting usage data on farms from deployed windmills can reveal shifting wind conditions and immediately allow a remote operator to optimize the degree of effort that specific windmills are exerting within those farms. This type of data visibility direct from customers allows manufacturers to not only fix current products, but also anticipate the need for new features before customers ask for them.

Customer involvement in the middle of some business processes can lead to better customer experiences. Customers want more visibility of progress on long projects, and online processes to coordinate orders, deliveries, audits, warranty claims and returns. Customers should be able to remotely approve changes like deviations from specifications, late deliveries, and corrective actions.

#8 Records

As more machines are connected, it would be easy to fall into data hoarding practices. Data should be stored for a specific period of time as part of a connected system strategy, and not just collected because it might be useful one day.

Data hoarding practices can be expensive and make it hard to find real productivity improvement areas among all the noise.
Different documentation and data records have varying degrees of precision, control and time persistence requirements in support of different business processes in the organization. Some data can be of a temporary nature and only needed during the execution of a specific business process, but some data has persistence requirements.

It is important that systems of record are well connected as sources and destinations for data in processes orchestrated by Smart Manufacturing systems. Regulated industries require detailed production history records. As an example, the FDA's quality system regulation (21 CFR Part 820) requires medical device manufacturers to establish and maintain device history records (DHRs) for each batch, lot and unit they produce. The DHR must include as-built history that can be validated against the respective product design (as-designed) and applicable processing and labeling standards.

Pulling it Together With Security and Communication Standards

Security and communication standards are two important considerations that apply to each of the eight areas of connectivity for smart manufacturing systems.

The connected nature of future Smart Manufacturing systems provide great opportunities for optimization but also increase the exposure to potential cyber threats. A strong cybersecurity program spanning people, processes and technologies is necessary to protect information assets and the trusted relationships with suppliers and customers.

A key missing capability to achieve Smart Manufacturing is the ability to practically and affordably knit together a threaded orchestrated business processes across varied equipment, systems, suppliers and customers.

Today, vendors provide custom one-off solutions that are complicated, expensive, require enormous amounts of time to implement, and are built on proprietary communications. To achieve affordable plug-and-play capabilities, next-generation hardware and software technologies need to work together through common standardized communications.

The list of disconnectivity areas above serves as a roadmap for identifying gaps and opportunities towards new levels of connectivity for future Smart Manufacturing systems. They are all of equal importance on the journey to future Smart Manufacturing excellence.

References
[1] “Capitalizing on Connectivity: The Top 5 Priorities for Industrial Manufacturers”, Booz Allen Hamilton Inc., January 2015

[2] “On the Journey to a Smart Manufacturing Revolution”, Conrad Leiva, IndustryWeek IdeaExchange, December 2015.

[3] “The installed base of wireless IoT devices in Industrial Automation reached 10.3 million in 2014”, Berg Insight blog post, May 2015

[4] “5 Signs you are a Big Data Hoarder”, Bernard Marr, Data-Informed.com, August 2015

[5] “MESA White Paper #52: Smart Manufacturing – The Landscape Explained”, Smart Manufacturing Working Group, MESA.org, January 2016

This article was originally published as "Eliminating the Eight Types of Disconnectivity in Smart Manufacturing" at IndustryWeek.com, Feb 2016

 

Vision 2020 for Quality in the Extended Value Chain

– 20/20 visual acuity into the supply chain by year 2020

Many manufacturing companies depend heavily on their supply chains to deliver a quality product on-time to the end customer. It is typical to have more than 50% of a product’s components provided by suppliers. Hence, visibility and quality management into the supply chain has become essential to the delivery of a quality product. As a data point, Gartner surveyed supply chain leaders in 2014 and found that improving quality of supplied components was a No. 1 priority [2].

20/20 is a measure of “perfect” visual acuity or clarity of vision. 20/20=1 represents 100% vision. If we were creating a scale of visual acuity into our supply chains what would our current vision be? 20/40=50%, 20/100=20%? Gartner’s survey results show that very few manufacturers would claim visual acuity near 20/20. In fact only 22% reported to have quality management functions integrated into their supply chain management and many of those only have visibility into their first tier of suppliers. It’s no wonder that over half of the high-profile quality failures of the past decade, ranging from flawed product launches, customer and patient safety incidents, and product recalls, are rooted in the extended value chain [2]—failures and recalls with a lingering negative effect on the product brand.

Many companies are focusing their quality efforts internally and are treating the supply chain separately. It is typical to see quality managed for internal operations with a combination of multiple Manufacturing Execution Systems (MES) or Manufacturing Operations Management (MOM) software and silos of quality managed for suppliers via multiple separate quality and ERP software.  However, quality management would be better done in unison with an integrated MOM, EQMS and supply chain management software solution, so accountability and points of verification are optimized throughout the value chain. For future competitiveness, it will not be enough to focus on schedule and cost reduction, it is necessary to increase visual acuity on the quality side. Gartner consultants are recommending that manufacturers set their goals to improve supply chain visual acuity with regards to quality by 2017 [2]. Manufacturing companies can follow Gartner’s advice and set their plans for 2017 or at least shoot for the year 2020. That should be easy to remember: 20/20 Quality by 2020 : )

The last big era of progress in quality management was in the 1980’s with the rise of Total Quality Management, the publishing of the ISO9001:1987 standard for quality management systems, the refinement of the Plan-Do-Check-Act (PDCA) cycle, and the development of the Six Sigma methodology. The current renewed interest in quality management into the supply chain and the publishing of the latest ISO9001:2015 revision could be milestones marking a new renaissance for quality management—an opportunity for big progress. ISO9001:2015 renews emphasis on risk and supply chain management.

What does 20/20 vision include for quality management? The following is a list of visibility requirements for quality in the value chain as it relates to the continuous PDCA cycle which is at the heart of the ISO9001 standard.

PLAN

• Supplier Network Map. Mapping of critical suppliers in the multitier supplier network. Suppliers handling unique IP (intellectual property), trade secret, equipment or tooling critical to production.
• Verification Requirements. Definition of inspection and verification requirements that need to flow down to suppliers. Especially those critical-to-quality characteristics that are tied to design engineering specifications in revision controlled 3D models and PMI (product manufacturing information). Confirmation and verification of qualification of suppliers in lower tiers handling critical-to-quality components.
• Performance Goals. Clear expectation of quality and schedule measures and goals for each program and supplier tier.

DO

• Progress Reports. Visibility of manufacturing progress to scheduled dates on long cycle time made-to-order components. Perhaps even in-process verification of critical-to-quality characteristics as the components are manufactured at the supplier site.
• Performance Measures. Reporting of standard simple metrics that the entire supply chain can use to communicate performance to schedule, quality and collaboration goals.
• Change Control. Management of engineering change notice (ECN) processes to ensure that the respective supplier has received the specification changes and is incorporating the change into the desired effectivity including units in process.

CHECK

• Verification Results. The earlier there is visibility of any issue with components the better. It is desirable to move more inspection and verification to the supplier site whenever possible. Inspection results should be available electronically along with product delivery date updates. Some critical characteristics might even be verified in-process. In-process and delivered product inspection results can be provided by the supplier via supplier portals or transmitted electronically via B2B (business-to-business) interfaces up the supply chain. Inspection results reported by suppliers and gathered through source and receiving inspection allows calculation of the real capabilities of each supplier to hold the required tolerance levels in specifications.
• Nonconformance Documentation. Whether defects are found before the supplier ships a component, at receiving inspection, or at production, it is necessary to have visibility of defects, non-conformances, and the root-causes identified for those issues. Containment and corrective actions can be initiated with specific suppliers to avoid further shipment of units with critical problems until they are resolved.
• Supplier Audits. Suppliers must be reassessed periodically via supplier audits for compliance to regulatory and contractual requirements. Companies need to maintain visibility of supplier audit history and ongoing audit scheduling arrangements.

ACT  

• Corrective Actions. Whether tied to poor performance, specific product defects or audit findings, supplier management must maintain visibility of all corrective actions open with suppliers, their respective root-cause investigation, and resolution implementation plans.

Regulated industries including food, drugs, medical devices, and aerospace have additional accountability interest into their supply chains. These industries have been (a) adding these type of requirements to the industry specific ISO9001 extensions including ISO13485 and AS9100, and (b) participating in the development of standards for communicating quality requirements and results in the supply chain within standards groups like OAGi (Open Applications Group).

Manufacturers like Boeing and Land-O-Lakes, organizations like MESA and NIST, and software vendors like Oracle, e2open and iBASEt, are collaborating within OAGi to (a) develop B2B integration use cases to help manufacturers understand what message standards are available and how to use them to orchestrate a business process with B2B interactions, (b) add message standards to on-board suppliers including sync parties, and (c) add message standards for quality management including inspection requirements, inspection orders, certificate of analysis, and inspection results.

The above efforts by ISO and OAGi, and referenced publications from Gartner are some examples of the renewed interest in quality and the focus on moving quality management processes into the supply chain. There are more examples out there and it is important that organizations are engaged in these types of efforts to improve quality acuity into the supply chain by 2020. Companies should review the assignment of quality functions and governance in their organizational structures, and refresh and integrate the quality management systems across the enterprise to facilitate enhanced quality management processes into the multi-tier supply chain.

The next era of connected quality management into the extended enterprise is coming. Will your organization be ready?

The efforts to extend and adopt standards for the exchange of quality information in the supply chain are far from complete. It is a good time for industry leaders to engage in these activities to ensure their needs are represented and that standards continue to evolve. Contact OAGI.org for more information.

References

[1] “Improve Multitier Supplier Quality by Leveraging Visibility”, Ray Barger, Simon Jacobson, and Christian Titze, Garter, 2015

[2] “Supply Chain is Missing the Mark on Quality”, Jacobson, Suleski, Barger, Chadwick, Stevens and Dorman, Gartner, 2015

[3] “The Evolution of Six Sigma”, Jim Folaron, J.P. Morgan Chase & Co., www.asq.org, 2003

[4] OAGI - Quality Content, B2B Network Collaboration, and Smart Manufacturing Working Groups, http://www.oagi.org/dnn2/OurCommunity/WorkingGroups.aspx

 

Four Good Trends for Manufacturing Execution System Adoption

The Gartner research group and MESA (Manufacturing Enterprise Systems Association) recently completed their annual survey of manufacturers (over 100) on their usage of manufacturing systems. Thanks to several years of data, they are able to look at trends in the manufacturing industry and it was generally good news in 2014 for the Manufacturing Execution System (MES) market—for both users and vendors.

#1. MES is finally Accepted as part of Enterprise IT Landscape

MES projects have a proven track record of benefits and are now viewed at the same strategic and enterprise level of importance than PLM and ERP projects. Most companies are not putting MES projects in queue behind PLM and ERP projects anymore. Manufacturing competency is recognized as an important competitive differentiator.

#2. MES Benefits Expectations focused on Improving Product Quality

More companies view quality as an integral part of their manufacturing systems strategy and are seeing the benefits of integrating quality versus keeping quality as a separate process and system.

#3. MES Barriers Move from Credibility to Agreement

As MES is now viewed as critical part of the enterprise systems platform, the entire enterprise needs to participate in the requirements definition, selection process and rollout plan. The shift from local to corporate level project can slow down projects and the achievement of early benefits.

#4. MES Implementations Move Away from Heavy Customization

The increased availability of industry specific mature MES solutions means that companies are looking at more out-of-the-box implementations and should expect less need for expensive custom extensions to commercial products. To benefit from OOB functionality, it is important that companies do their homework and look for MES products that fit their needs instead of assuming that customization is a required part of a project with any MES product. In today’s market, heavy customization is usually a symptom of selecting a product that does not fit the industry or manufacturing type.

There are many more valuable insights in the recorded webcast than the four listed above, so if you are interested in these subjects, I recommend the whole webcast and presentation which is found for members at MESA.org (see References below). I also recommend joining MESA.org for full access to this and other valuable reference resources.

Reference:

“Has MES Come of Age?”, Recorded webcast by Julie Fraser of IYNO and Rick Franzosa of Gartner, MESA.org, 2014   https://services.mesa.org/ResourceLibrary/ShowResource/47eff093-b4f9-417e-9aa1-1ddbf3e82757

 

Eight Potential Barriers to Strategic Manufacturing Process Improvements

The tune at leading companies has changed from cost cutting to product and service innovation. The need to incorporate technology innovation faster into product lines is fueling an executive push for higher levels of process improvements. Can we scale our old practices to handle higher levels of product and process change? Probably not. We probably need to rethink processes at a strategic level to enable new business paradigms and increase speed for product updates and new product
introduction.

Engineers are using the latest CAD technology to make better 3D models faster with more reusable components, more product and manufacturing information (PMI). Model-Based Manufacturing practices are leveraging 3D models to simulate and accelerate production process innovation, develop shop floor work instructions, and create automated inspection and test procedures. So what is holding back our organizations from these types of process innovations? Why are some organizations still working with 2D drawings, spreadsheets, and paper work instructions at the shop floor?

Is IT investment cost holding us back?

U.S. aerospace and defense companies have been generating large amounts of cash reserves for quite some time. The largest aerospace and defense contractors have tens of billions of dollars in cash available to them. [2] There is still uncertainty that the U.S. will pass a defense budget in the
near future but companies have to move forward with process innovation or be left behind by the competitors that are gearing up with strategic investments. The cash reserves are there in many companies to make the required investments. However, the tight budgeting processes, that organizations put in place to control the wallet over the last few years, could be holding the organization back from large strategic investments.

Is commercial software maturity holding us back?

Based on Gartner’s 2014 Hype Cycle for Discrete Manufacturing and PLM information technology [3], commercial applications for Manufacturing Execution Systems (MES) and Enterprise Quality Management (EQMS) are in the “slope of enlightenment” moving to a stable maturity state. Applications for Model-Based Manufacturing (MBM), Supplier Quality Management, Manufacturing Integration Standards, and Operations Intelligence are beyond the hype peak for early adopters and are expected to fully mature within 5 to 10 years in the market.

Even companies that are industry followers versus leaders and very conservative in technology adoption, should probably be investing in these technologies which are ready for the pragmatic majority of the market. Some technologies like Model-based Manufacturing can be implemented in a pilot area to gear up organizations for the next program or product initiative.

Are we complacent with small continuous improvements?

Organizations with a proven record of continuous process improvement through localized short term Kaizen and Six Sigma efforts might have a hard time tackling the bigger strategic projects. “Good enough” can be the enemy of excellence.

Kaizen and Six Sigma efforts look to identify and eliminate areas of waste and variability. But
focusing solely on those efforts can lead to organizations overlooking initiatives like Model-Based
Manufacturing that can enable new business paradigms and revenue opportunities. The organization can fall behind in the changing competitive landscape.

It is not always possible to achieve higher levels of performance through small incremental
localized efforts—efforts that slightly improve on old business processes. That is why some
organizations follow the Lean practice of an annual Hoshin Kanri (business strategy improvement)
effort in which the organization makes an assessment of what resources and capabilities it needs to
stay competitive, win new business and grow.

What else could be holding us back?

Significant strategic process re-engineering can only happen when organizations face head on the
following potential barriers.

Dependence on Manual Processes

With the technology available today, the organization should not be relying on paper-based or spreadsheet-based processes at the shop floor to collect data, track progress, and prepare product or service documents for the customer. Yet, a recent survey [4] by IYNO Advisors found that over 50% of the companies surveyed were depending on homegrown or spreadsheet systems for their production systems. Many other companies are still depending today on paper-based processes at the shop floor.

Even traditional Lean Manufacturing practitioners like Toyota are now embracing information technology to raise the organization’s performance to a new level. This was the topic of a presentation by Toyota US CIO, Tim Platt, who spoke about "Lean's High Tech Makeover" [1] at the 2014 Industry Week conference. If an organization like Toyota can move beyond the manual Lean processes they promoted for years, your organization can change too and embrace automated Lean processes supported by information technology.

Poorly Defined Business Processes

It can be difficult to see the opportunities for improvement right in front of us if we don’t have a
good understanding of the current business processes. I have been lucky to participate in very eye
opening value stream mapping activities with experts from production, quality and engineering
departments. I have seen how different departments can often be so focused on their own daily duties that they do not give much thought to how their work is affecting downstream activities. We expect someone else in the organization is looking at the performance of the entire system. But the reality is that many organizations do not dedicate a team to enterprise level improvement and do not spend enough time cross training personnel on business processes that cross departmental walls. Many times enterprise level processes are not documented and often very few employees understand the overall value chain across the organization. These type of value mapping exercises across departments can uncover hidden gold.

If we don’t have a good understanding of key business processes across the organization documented, it is difficult to perform a proper evaluation and optimization of the entire value stream. Tools like value stream maps, business process activity diagrams, BPMN diagrams or SIPOC diagrams for each department are very useful tools in identifying processes that might not scale to handle a lot of innovation volume and speed in our product lines.

Insufficient Accurate Information 

To justify bigger investments in process, product or equipment changes we need good data backing up the justification. We need better data to justify a new MES system which we need in order to collect accurate data to make better informed decisions in the future. Are we are caught in a Catch-22 situation?

It is important to know that we are delivering on time and paying the bills, but we also need to know more details about engineering and production processes if we want to improve those processes. It is important to know our delivered product quality, inspection results and defect counts but also more details about defect and cause classification to help us tackle the root cause of the problems.

Without the detail data, it is easy to always end up finding someone to blame for the problem
instead of taking a more methodical view to problem solving and finding the true system weaknesses. If we find that we keep blaming all problems on (a) lack of training, (b) personnel carelessness, and (c) human error, we should pause and figure out if we are jumping to conclusions because we do not have good data to identify other sources of potential issues. We want data that will point to any of the potential resources causing the problem which could be personnel related but could also be related to equipment, processes or product design.

Information maintained manually in different departmental databases or spreadsheets (information silos) is likely not consistent and could possibly lead to different conclusions and priorities in different departments. This information inconsistency can be tackled by implementing MES/EQMS information systems and integration standards that keep information synchronized between these production systems and other enterprise systems including PLM and ERP.

Lack of Flexibility to Change Processes

Are we evaluating software solutions based on our organization’s needs for growth and improvement or based on “fit” to our old legacy processes? It would be natural for the IT department to have a bias towards maintaining and improving the old custom systems that they developed versus looking to adopt new commercial software solutions. Commercial software benefits from the input and investment of many customers into one solution. If instead of looking at commercial solutions, the team keeps developing custom IT solutions and justifying the ongoing investment because the company is “different” than the competition, it might be good to get a second opinion from a neutral third party consultant.

It is also natural for engineering, manufacturing engineering, production control and quality
departments to be comfortable with the processes they have used for decades. It is good to expose
the team periodically to new ideas through trade conferences where they can see how other companies are innovating their business processes.

The cost of rebuilding custom integrations for new systems can also be a constraint. Integration
interfaces between enterprise applications based on integration standards like OAGIS make it easier
to replace functional modules in the enterprise system and stay up-to-date with best-in-class
functionality for different departments.

Fear of Too Much Change At One Time

Organizations might have a concern with making big changes all at once that will end up adversely
affecting production cost and schedule for even a few months. The risk associated with this learning
curve on new technology and business processes can be mitigated by implementing first in a pilot
area or “teaching plant” that can serve as an education platform for the rest of the organization
and proof point on the benefits and gains of implementing the new business processes.

Summary

The reality for most organizations include a mix of several of the barriers listed above. It is
important not to get put off by these challenges. Focus on how investments in new information
technology and new business processes can contribute to more contract wins, margin gains, and the
development of new agile, flexible and lean production operations. Expect production operations
remodeled based on the insights gained in your pilot implementation programs.

References:

[1] “Toyota Talks about Lean Manufacturing’s High-Tech Makeover”, manufacturing-operations-
management.com, 2014
http://www.manufacturing-operations-management.com/manufacturing/2014/05/toyota-talks-about-lean-manufacturings-high-tech-makeover.html

[2] “Aerospace and Defense Industry: Navigating a Global Future”, Trade & Industry Development, Jim Kemp, 2014
http://www.tradeandindustrydev.com/industry/aerospace-defense/aerospace-and-defense-industry-navigating-global-f-9187

[3] “Hype Cycle for Discrete Manufacturing and PLM, 2014”, Gartner, Halpern/Jacobson/Suleski, 2014
https://www.gartner.com/doc/2808322/hype-cycle-discrete-manufacturing-plm

[4] “Profitable Projects Webinar: Transforming Project-based Operations”, IYNO and Deltek,
Fraser/Kestel/Leiva/White, 2014
http://more.deltek.com/LP=3958?sourceid=49&utm_source=deltek-com-generic&utm_medium=deltek- com&utm_campaign=generic-Deltek-com&cmp=deltek-com_deltek-com-generic_generic-Deltek-com

 

Managing Risk and Improvement in the Extended Enterprise via Corrective Action

Both continuous improvement and risk management are considered minimum compliance process requirements in the latest versions of Quality Management System (QMS) regulatory standards including ISO9001, AS9100, and ISO13485. Corrective and Preventive Action (CAPA) projects can be used to manage both risk mitigation and process improvement types of projects. 

When we look at processes in the entire product lifecycle starting at the supplier, continuing through manufacturing into aftermarket maintenance and repair services on the product, we find the following sources for potential CAPA projects:
• Audit Findings
• Inspection Findings
• Discrepancy Reports
• Equipment Maintenance Trouble Calls
• Product Warranty Claims
• Customer Complaints
• Performance Metrics Analysis

Whether a project is needed to mitigate risk or improve performance, many of the steps we follow are common and merit a similar discipline. Lean Manufacturing and Six Sigma practitioners use slightly different terminology and methodologies for process improvement, but they are really more similar than different.

Six Sigma practitioners use the DMAIC process to systematically reduce variability in a manufacturing process. Lean practitioners refer to the Toyota problem solving methodology and to kaizen events.

For diving down into specific urgent issues once they are uncovered, the Toyota Problem Solving process and the 8-Discipline (8D) process are very similar. The 8-Disciplines (8D) process is often used because it adds steps to contain the problem.

The Toyota Problem-Solving process and the 8D process have a lot of similarities but in many ways do not exactly align. The 8D process has an emphasis on corrective and preventive actions which are more appropriate to obvious quality issues, and the Toyota Problem-Solving process has more emphasis on defining the problem which might not be obvious on productivity issues.

Form the Team – The 8D process recognizes the importance of assembling the right team and places it as the first step. A small team is needed with the right mix of skills, experience and authority to resolve the problem and implement solutions. Ensure that everyone on the team has the time and inclination to work on the problem together. Formal meetings should initiate the effort and monitor progress. The company’s Corrective Action Database should be used to document findings, decisions and progress.

Describe the Problem – It is critical to define a clear problem. The problem statement provides the starting point for investigation. Terms used in the description must be understood by the entire team. Problem description should include the following:
(a) Who – Who is affected by the problem? Who first observed the problem? To whom was the problem reported to?
(b) What – What type of problem? What has the problem? What is or isn’t happening? What is the physical evidence?
(c) When – When did it first happen? When was it first observed? Is it a recurring problem? How often? What is the trend? (random, cyclical, continuous) Has it been observed before?
(d) How Much – What is the magnitude of the problem? How many units or components are affected? How much cost (labor and material) is associated to this problem?
(e) Why – Why is it a problem? What is the impact? What processes are affected? Is production stopped completely or partially? Is there a work-around? Are customer delivery dates impacted?

Some of the questions above might also be viewed as assessing the risk and impact of a potential problem recurrence. The risk can be given a risk score by multiplying a Likelihood Rating, Severity Rating and a Detectability Difficulty Rating.

Contain the Problem – Identify interim containment actions to prevent further cost impact from the problem. Is the problem tied to a machine? Is it tied to a supplier component? Are more lots affected by this problem? Suppliers must be informed as soon as possible about issues to prevent further shipments with problems. Inventory parts which might be affected must be identified and inspected. If problem has a life- threatening consequence, products in the field have to be identified for potential recall.

Identify the Root cause – An investigation and failure analysis is performed to determine the root cause of the problem. It is not enough to identify the first-level or intermediate-level causes of the problem. The true root cause has to be uncovered and corrected or a similar problem will eventually manifest itself.

Root cause analysis may start with brainstorming sessions among team members and move on to data analysis that further explores multiple potential causes. Different kinds of charts and diagrams are used in data analysis including histograms, Pareto charts, scatter charts, and concentration and affinity diagrams.

It is important to document the analysis discussion, findings and conclusions using cause-and-effect charts and fault tree diagrams. A five-whys type of analysis is also useful to understand all the intermediate-level causes between the root cause and the problem manifestation.

Corrective Actions – This discipline entails identifying all possible corrective actions to address the root cause of the problem. The owners of the corrective actions, the target dates for completion and the rationale behind each should be documented. It is sometimes a good idea to have a preliminary evaluation of the corrective action to test its effectiveness before wasting time and money on fully implementing an incorrect solution.

Corrective action includes further containment of potentially affected similar production or inventory based on the root cause identified. If problem has a life-threatening consequence, products in the field have to be identified and notifications issued for recall or repair.

Permanent Correction – Once the corrective actions have been defined and tested, the next step it to implement them and verify their effectiveness. If the corrective actions are found to have deficient effectiveness it might be necessary to return to the prior step and create new corrective actions.

Preventive Actions – Lessons learned from the above investigation and corrective actions might be able to benefit other processes or areas with similar vulnerabilities even if not affected under the current situation. Preventive measures should be outlined if it is possible to prevent similar problems in the future. Preventive actions, like corrective actions, require owners and target dates.

Acknowledge Success – The last step of the 8D process is management’s formal recognition of the accomplishments of the team. This demonstrates the commitment of the management team to the continuous improvement process and is good internal publicity for the effectiveness of these initiatives.

 

Unique Requirements for Operations Management in Complex Discrete Manufacturing

What is Complex Discrete Manufacturing?

Complex discrete manufacturing industries manufacture complex highly engineered products with longer product cycle times and multiple levels of subassemblies in their bills of material. Many of these companies make and engineer products to order. They make “discrete” product units, as in individually separate or distinct, and they need to track manufacturing history down to each serialized product unit. In contrast, batch production manufacturers track production per work center, day, batch or lot.

Complex discrete manufacturers include companies that manufacture products for aircraft, space, military weapons, complex medical devices, robots, and specialized industrial equipment. For many of these products it is necessary to manage complex diverse product configuration, long product life cycle, along with increased market pressure for shorter time to market for new products and stricter regulatory compliance oversight.

Some of the characteristics that define this type of manufacturing environment include the following:

• Long cycle times, low volume, make-to-order or engineer-to-order
• Complex product with deep bills of material (BOM)
• Highly skilled labor performing manual assembly and fabrication work including complex NC machines and special materials like composites
• Complex process routing sequences with decision points and loops
• High flow of engineering changes affecting work-in-process
• Production is not repetitive and mechanics must be alerted to changes
• Data collection during production includes manual data entry, verifications and signatures
• Personnel have qualification requirements and equipment have calibration and certification requirements
• Documentation requirements include complete history for each product unit and traceability of components installed and material used

Unique Requirements for Manufacturing Operations Management

On the surface, many manufacturing systems seem to have a similar functional footprint, but some cater to specific industries with very specialized functionality. To make an informed assessment of a manufacturing operations management (MOM) system it is necessary to drill down to more specific requirements and narrow down to the solutions that can truly handle your industry and type of manufacturing. Finding the right solution fit can hugely affect the effort, time frame, cost (total cost of ownership) and results of a MOM system implementation initiative.

Complex discrete manufacturing organizations have unique requirements in their manufacturing and quality management business processes including: (a) carefule resources certification management, (b) complex product/process configuration and change management, (c) detailed integrated quality control processes, (d) detailed product unit history and records archival.

Watchful Resources Certification Management

Personnel must be certified competent on the basis of education, training, skills and experience. Personnel qualification processes must be standardized and documented. In addition, equipment resources must also be maintained to assure their capabilities, especially measurement equipment used to verify the product. The equipment and tools maintenance and calibration processes must be standardized and documented. An MOM system can verify calibration status for equipment and also verify that personnel signing a job has the required active certifications.

Complex Product/Process Configuration and Change Management

The manufacturing of a complex product like an aircraft or satellite involves the management of a continuous stream of engineering changes directed at work in process. The integration of engineering systems and MES can create a seamless link between product development, manufacturing planning, and manufacturing execution functions that closes the loop on engineering changes and assures that as-built configuration matches as-designed.

Detailed Quality Control Processes

Beyond providing visibility into areas for improvements, the manufacturing information system should provide process control procedures to implement and sustain quality improvements including in-process inspection and verification steps, statistical process control (SPC), alerts to out-of-control conditions, and integrated handling for discrepancies found during production including defect containment and corrective actions to eliminate recurrence.

Because of the high investment in parts and labor that goes into these types of products, they are rarely scrapped. Instead, these industries require rework, repair and deviation handling procedures to ensure that deviations are documented, reviewed and approved by the appropriate personnel. The integration of production and quality systems can ensure that deviation instructions cannot be skipped by the mechanic performing the work. Deviation history is also considered part of each product unit history.

Detailed Product Unit History and Records Archival

The manufacturing information system needs to maintain production history documentation down to the details in each product unit versus tracking to the batch level. The system documents exactly who, what, when, how and why―who completed the job, what equipment was used, which parts were replaced, and who approved the changes.

 

The Dangers of OEE as KPI for Manufacturing Operations Management

There is much hype in some circles about OEE (Overall Equipment Effectiveness) as a KPI (Key Performance Indicator)  for manufacturing companies, and as we know from experience,  we must always approach hype with some level of skepticism. This article discusses three reasons why OEE is a potentially dangerous KPI for a manufacturing company to rely on for business decisions and operations management.

What is OEE?

OEE is a measure of how well equipment in a plant are utilized in relation to their full potential. It was conceived to quickly identify areas contributing to big productivity losses including breakdowns, long setups, frequent stops, and defective output.

OEE = Availability x Performance x Quality. Availability, performance, and quality are good solid metrics, and the OEE formula is simple, so why not use OEE as a KPI for the organization? What dangers could be lurking under such simplicity?

Danger #1: OEE does not relate to the company’s true business goals

If the company gets paid to run machines at full capacity all day, OEE might be a decent metric for the business. For example, if you run an electric power company or a chemical processing plant, you might find that OEE relates to your bottom line. However, that is not the reality for many manufacturers. Companies that manufacture discrete products to fulfill customer orders have business goals related to factors that influence customer buying decisions: Schedule, Price/Cost, and Quality. Their KPIs should align with their business goals and OEE does not.

Danger #2: OEE does not address the real constraints to production 

From studying Eliyahu Goldratt’s “The Goal” and his Theory of Constraints (TOC) principles, we understand that the most important considerations in manufacturing operations are to keep the plant running to a “drum beat” and to mitigate the risks of any constraints that can affect the plant rhythm and choke the production rate. The Theory of Constraints is a holistic view that takes the entire plant into account. OEE is focused on local optimization of each work center, but the goal is optimization of the entire production system. OEE assumes that the goal is to keep each work center busy and producing at 100% capacity all the time. However, in the context of the entire production system, it might be acceptable to have areas of low utilization.

The goal is not to keep every work center and piece of equipment busy all the time; the real goal is to get product out on time to match demand, at a low cost, and with high quality. The organization’s metrics should be directly related to the real business goals that lead to the ultimate goal of most manufacturers: higher profits.

The Theory of Constraints is used by manufacturers to identify their production bottlenecks and then work to improve and eventually eliminate them. Not all resources in a plant are potential bottlenecks, so only resources that are (or have potential to become) constraints to production should be closely monitored and optimized for the company to achieve its real manufacturing goals. If the organization focuses on “fixing” work centers with the lowest OEE numbers, it might be under optimizing the overall macro manufacturing process. 

Danger #3: OEE is an aggregate metric that can obfuscate instead of clarify areas for improvement

Aggregate measures like OEE have the risk of hiding underlying issues. Each component of OEE in and of itself (availability, performance and quality) provides better visibility into the organization’s performance. When the sub-metrics are multiplied by each other, as is done with OEE, the resulting number can end up hiding the areas that have the most problems. For example, an area might have high availability and utilization numbers, but a low quality number, but because all of the numbers are multiplied together, the low quality number is hidden and therefore, not addressed.

Not only does OEE hide underlying issues, but it also muddies the waters when it comes to determining areas for improvement. OEE assumes that each of the sub-metrics have equal importance, but for many organizations, a 1% labor performance loss is not as important as a 1% quality loss. For example, it is easy to increase quality by increasing cost. The trick is to increase quality while reducing cost. An area with 90% quality and 70% performance has a different problem than an area with 70% quality and 90% performance, but they can both have the same OEE rate. 

In Summary

Considering the three dangers stated above, do OEE numbers really tell us anything important or useful about our business? Will they lead to more sales and profit? Or do they potentially misguide improvement prioritization efforts?

In addition to the dangers listed above, using OEE as a way to benchmark the business against others is not wise unless we are comparing across very similar types of businesses. The idea that you can have a benchmark goal for OEE of 85% across the industry is unsound. Perhaps it should be 95% for one type of process, and perhaps 70% is okay for another type of process.

If OEE is not the silver bullet metric for manufacturers, which one is? We shall explore that topic in a future blog post. What metrics should an organization look at to monitor profit, schedule and quality goals? What metrics help identify areas for improvement and areas of efficiency loss due to poor quality, low speed, equipment downtime or supplier issues? We should explore some Lean Six Sigma metrics like Cycle Time Efficiency and methods of identifying the underlying causes behind the loss of efficiency. Stay tuned. 

More information on OEE definition in this older post: 

“Overall Equipment Effectiveness (OEE) Or Overall Resource Effectiveness?”
http://www.manufacturing-operations-management.com/manufacturing/2009/11/overall-equipment- effectiveness-oee-or-overall-resource-effectiveness.html

 

Toyota talks about Lean Manufacturing and Digital Technology

I was at Industry Week’s annual conference last week and had the pleasure to listen to one of Toyota’s VP of Information Systems, Tim Platt, talk about "Lean's High Tech Makeover".  Mr. Platt shared how Toyota is leveraging information technology in their current Lean Manufacturing efforts to gain more efficiencies. He showed examples of how information technology is supporting the Lean principles of Jidoka, just-in-time and standardization.

Three case studies were presented for US plants totaling around $3.5M per year in hard savings from implementing information systems to achieve the Lean principles.

The following are some examples of how information technology is helping achieve new levels of
performance with these Lean principles:

Jidoka Lean principles include 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 boards are replaced by visual dashboards that provide the same visibility within the work center but also provide visibility from any computer in the plant to any work center Andon board. Old school Andon lights used to indicate that the line was stopped are replaced by event triggered alerts that are broadcasted via email to the respective support department to assist in clearing the issue as soon as possible.

Standardization principles are enforced via a manufacturing execution system (MES) that provides
work instructions to encourage process repeatability and reduction of variance.

Pokayoke tooling and mechanical methods are complemented with sequence and data collection
enforcement by an MES to make sure the correct process is followed every time.

Just-in-time principles can be applied with system generated Kanban triggers. Heijunka type work
leveling to achieve smooth flow can be assisted by the use of special scheduling software that
incorporates Lean principles and similar principles like drum-buffer-rope.

It was very refreshing to hear a Toyota executive talking about leveraging information systems
to implement Lean principles after years of hearing old school Lean practitioners taking the
hard line against information technology.

Some of us have talked for years about the information flow being a critical part of the value
chain that requires waste reduction to achieve a Lean Information Value Chain.

The examples from Toyota, one of the companies that taught us Lean Thinking, continue to
reinforce that information technology and Lean Manufacturing are not mutually exclusive. Another source for examples leveraging information technology to achieve Lean is MESA’s “Lean
Manufacturing Strategic Guidebook”; a resource available to MESA premium members at mesa.org.

References:
“Toyota’s Lean High-Tech Makeover”, a presentation by Tim Platt, VP Information Systems, Toyota US, May 2014
“Lean Manufacturing Strategic Guidebook”, MESA International, 2010

MES versus MOM Terminology Confusion – Manufacturing Execution System or Manufacturing Operations Management

It is 2014 and the MESA (www.mesa.org) organization is wrestling with an important decision. Does it keep using the term MES (Manufacturing Execution System) or move to the term MOM (Manufacturing Operations Management) to describe manufacturing systems? The debate was very active in LinkedIn’s MESA International group forum and also at a recent MESA webcast panel discussion.

Is it time to change the term “MES” to another term just when it is starting to gain recognition and acceptance? Is there any good reason for the change?

First some historical perspective

The terminology timeline shows the introduction of acronyms for manufacturing systems over the last few decades in three major areas: engineering, inventory planning, and production shop floor. We can see in the diagram that just about every 10-12 years, the software industry gets an itch to introduce a new term. These new terms usually signal an evolution in function or footprint for a software category. It has been one of the ways the software industry defines “progress”.

For example, PLM (Product Lifecycle Management), was introduced in 1995 as an evolutionary vision above the definition of PDM (Product Data Management). Software vendors working on solutions for the engineering departments soon jumped to use the term and fulfill that vision. The bar was set high for the PLM vision and vendors and manufacturing companies are still working on fulfilling that vision.

The term ERP (Enterprise Resource Planning) stopped evolving in the mid 90’s. Did that signal a maturity for that category of solutions? There were minor attempts to introduce an ERP 2.0 term around 2007 and 2010 but they didn’t succeed. The term MES (Manufacturing Execution System) has been around since the early 90’s when it was introduced by AMR Research to be the successor of the term CIM (Computer Integrated Manufacturing).

The CIM term was introduced in 1973 by Joseph Harrington and published in his book “Computer Integrated Manufacturing” in 1979. The term gained popularity around 1984. In the CIM wheel published by SME (Society of Manufacturing Engineering) we start seeing functional blocks for CAD Design, Scheduling, Tooling, Production Control, Quality Control, Maintenance, Material, Fault Diagnosis, Material, and Document management. Building blocks that we will later see in the MES definition.

After the MES term was introduced by AMR Research, the MESA organization embraced it and started writing on the subject and educating manufacturing companies on the importance of creating a platform for managing operations at the shop floor. Michael McClellan also published a book in 1997, “Applying Manufacturing Execution Systems” which further defined the term and the functionality scope.

From page 2 of McClellan’s MES book: “MES is the step of integrating all of the activities between the planning layer and the automation layer as components of a proactive integrated on-line system providing a synergistic process that is greater than the sum of the parts.”

With the definition of MES by MESA we moved from “wheels” to “gears”. Note that there are gears in MES for everything you will later find in the MOM definition and more. Also note that McClellan had introduced a layer of Production Planning on top of MES and a layer of Automation Control under MES. The MESA papers also explained that MES would integrate with ERP, Supply Chain Management and Engineering Systems.

Areas of MES include from the following from its inception:

• Resource Allocation and Status
• Operations/Detail Scheduling
• Dispatching Production Units
• Document Control
• Data Collection/Acquisition
• Labor Management
• Quality Management (real-time analysis of measurements, problem identification)
• Process Management (monitoring)
• Maintenance Management
• Product Tracking and Genealogy
• Performance Analysis

It is also important to note that McClellan’s book also referenced several industry leading companies that were doing MES. This was not just an academic wish list. Discrete manufacturing companies were developing these MES applications.

In 2005, companies in process and batch manufacturing working within ISA95 decided to make their own diagrams and moved from “gears” to “oval eggs”.

The ANSI/ISA9-5 Standard for Enterprise-Control System Integration, made some nice clarifications and numbered the layers between machines and enterprise systems. For example, Layer 1 collects data in seconds, Layer 3 in hours, and Layer 4 in days.

However, they decided to rename the MES layer, Layer 3, to Manufacturing Operations Management (MOM) because they liked the word “Management” better than the word “Execution”. It was a naming preference, not a marker for an evolutionary step in manufacturing systems. It did not raise the bar for manufacturing systems; it was thought to be a better name.

You might actually see a few things in MES definitions which you don’t see in the MOM definition. The ISA95 standard was created by people working in the process and batch manufacturing industry where the manufacturing process is very equipment centric. In those industries, the process design is often the entire manufacturing plant or a fixed manufacturing cell with a flow designed to produce a recipe for a batch of product. In contrast, in discrete manufacturing the process design is more work order centric. The process is often designed for flexible manufacturing cells that can manufacture different types of configurations of product units for each work order. This is why there has always been a difference in how to model the different types of manufacturing. A major problem I have with the ISA95 standard is treating Quality as a separate silo. Quality is really part of every process. But I digress… back to discussions of the terms MES and MOM.

On the lighter side

I do think that the ISA95 team members underestimated how much we love acronyms! Because they might have thought twice about introducing the acronym “MOM”.

In this illustration you see an email I received about a week after the LinkedIn discussion had started with the title “MOMs email list”.

At first I thought, wow… I usually get a lot of unsolicited email offering PLM and ERP users email lists but this time it seemed like someone had an email list of MOM users. Sounded interesting so I opened the email to find out they were actually talking about email lists of Moms, Dads, Parents, Gamblers, Golfers, etc. I chuckled and pressed the delete button. This email illustrates a problem with the acronym MOM. Are we really going to ask the next generation to replace their old MOM and look for a new MOM? I find the MOM term a little hard to write about and use seriously in conversation about manufacturing systems.

A reality check

Is it worth it to transition to the term MOM to help align the discrete and process/batch camps? Even when the discrete manufacturing industries have done so much progress on the adoption of MES and establishment of COTS (commercial off the shelf) solutions for MES? Should the process/batch industries embrace MES even when they don’t feel they are “Executing” something? Google searches for “Manufacturing Execution System” were around 160 per month in 2008 and are around 480 per month in 2014. “Manufacturing Operations Management” searches are only at around 110 per month in 2014.

The move to a new term might be a setback for solutions focused at improving manufacturing operations. Two terms do not make it easier for companies to find the solutions they need. Confusion in the marketplace will only benefit the PLM and ERP software vendors that are trying to penetrate the manufacturing systems arena with broad and thin functionality. Perhaps these software giants are behind the move to change terms. Machiavellian tactics? Conspiracy paranoia?

Is there a third alternative?

Some are suggesting that we limit the use of the MOM term to discussions around the ISA95 model, discussions on “business processes” for Operations, and keep the MES term in relation to discussions about IT “systems” that help us support and execute those processes. Of course, that might start debates on how good or bad the ISA95 models are to represent discrete manufacturing processes versus process/batch manufacturing industries. That might be the topic of another article ; )

When MESA changed the “E” in MESA from “Execution” to “Enterprise” they were on to something. In discrete manufacturing, we are now developing manufacturing software for the extended enterprise. Driven by the reality that over half of our products’ subassemblies and components are manufactured by partners and suppliers.

Perhaps instead of focusing on the MES-MOM debate, we should focus on new terms for the future. Maybe change the “E” in MES and define the next generation of “Manufacturing Enterprise Systems” that will support better supplier collaboration and orchestration. Is it time for a four letter acronym? “eMES” for “Extended Manufacturing Enterprise Systems”? Some software vendors are already working on these solutions. It would be nice to have a market definition that supports a broader solution footprint for the broader problems manufacturers are trying to solve today.

Perhaps the term PPM (Production Process Management) or the term Collaborative Production Management (CPM used by ARC Group) will stick. I recommend everyone read the the paper by Michael McClellan titled “Improving Manufacturing Excellence by Managing Production Process across the Value Chain”. It is an example of how we are now looking for tools to manage across the entire value chain.

My vote is to embrace a new term when we are ready to raise the bar for the software category. There is a need to define a new footprint that extends into the supply chain, not at a separate Layer 4, but as an integrated part of the manufacturing process. We should focus as architects of manufacturing systems on this next generation and what we are going to call it. We should look ahead, not back.

References

• “Computer Integrated Manufacturing”, Krieger Pub, Joseph Harrington, 1979.
• “Computer Integrated Manufacturing” by Cheng WU, Yushun Fan, Deyun Xiao, 1999
• “MES Explained – A High Level Vision”, MESA International, 1997
• “Applying Manufacturing Execution Systems” Michael McClellan, CRC Press/APICS, 1997
• ANSI/ISA9-5 “Standard for Enterprise-Control System Integration”, ISA, 2005
• “Improving Manufacturing Excellence by Managing Production Process across the Value Chain”, Michael McClellan, 2012

 

Did You Know? People in Manufacturing Love their Jobs!

In a recent IndustryWeek Salary Survey, 85% of subscribers reported they were satisfied to very satisfied with manufacturing as a career. The high level of career satisfaction was not a product of a good economy or pay raises.

As it turns out, IW subscribers have remained steadily positive about manufacturing as a career choice even during difficult economic times and no pay raises. People involved in manufacturing love the sense of accomplishment that comes with the job. There is always something new going on at the factory. Manufacturing plants have to keep up with the latest technologies to stay competitive.

So why aren’t more people entering manufacturing careers? It seems we are not doing a good job of spreading the word. There is a need for more skilled labor in US manufacturing and people in manufacturing like their jobs.

Spread the word!

References:

2013 IW Salary Survey: Salaries Remain Upward Bound
http://www.industryweek.com/salary-survey/2013-iw-salary-survey-salaries-remain-upward-bound?page=3

There Really is A Talent Gap in Manufacturing
http://www.manufacturing-operations-management.com/manufacturing/2012/07/there-really-is-a-talent-gap-in-the-workforce.html

Motivating the Next Generation of Engineers and Manufacturers
http://www.manufacturing-operations-management.com/manufacturing/2010/07/motivating-the-next-generation-of-engineers-and-manufacturers-.html

Are We Managing Quality or Managing Poor Quality?

– The Need to Integrate Quality and Execution Management

Over the years quality control has evolved from practices focused on final product inspection by a quality inspector to practices that rely more on in-process self-inspection by the production technician with a much smaller auditing role for the inspector. Most quality professionals agree that it is much cheaper and more effective to design quality into the process rather than test and fix products at the end of the process.

Inspection of the product after it is made results in costly rework, scrap, and delays. Quality professionals must consider how to best control potentially problematic processes during manufacturing to assure acceptable quality at the end.

Today’s manufacturing technicians manage their own quality as part of their work processes, inspection requirements are built right into the manufacturing work instructions, and verification mechanisms are built right into tooling fixtures and work procedures. These additional inspection requirements for the technician can be simplified and automated by a Manufacturing Execution System (MES). For example, an MES can reduce the need for 100 percent verification on some processes and instead enforce skipping or sampling rules that will automatically prompt the technician as needed. The MES can also keep track of when auditing is required by an inspector. These rules could even vary according to each technician’s experience level on different types of jobs. This level of efficiency in dispatching verifications cannot be achieved with confidence using manual methods; it requires an MES tracking and enforcing the rules automatically.

Reactive Quality Management

Trying to manage quality practices using a separate Quality Assurance (QA) application rather than the MES used by the shop floor technician is as inefficient as managing qu ality through final product inspection instead of using in-process techniques.

Silo QA applications, like FRACAS (Failure Reporting, Analysis and Corrective Action System), are focused on reactive practices including:

• Final Product Inspection Plans 
• Documenting Problems and Failures 
• Corrective Actions 
• Failure and Correction related Metrics

These traditional stand-alone quality applications are reacting to errors and managing the results of poor quality. Newer MES systems need to integrate quality control throughout the manufacturing process and focus on preventive practices.

Proactive Quality Management

Although it is important to track defects, find the root cause and follow up on corrective actions, it is also important to manage quality proactively to reduce the opportunity for issues in the first place. If your organization does not include MES in the evaluation of a quality management solution, you can be missing out on the following preventive and proactive quality management functions which should be provided by the MES:

• Process Standardization and Visual Aids 
• Process Enforcement 
• Configuration Verification 
• In-Process Inspection 
• Statistical Process Control (SPC) 
• Production Process Verification 
• Personnel Qualification 
• Tool Control 
• Device History Records

Process Standardization and Visual Aids

Standard procedures should be documented for commonly used manufacturing processes and personnel should be trained on these procedures. Clear work instructions should be provided for each manufacturing step to further ensure process repeatability.

Pictures, 2D and 3D illustrations in work instructions are very useful to clarify tricky work sequences and prevent incorrect operation. The integration of CAD/PLM systems can be critical in some industries to ensure that 2D and 3D visualizations of the product and process reflect the correct engineering revision level and configuration.

Process Enforcement

The MES can enforce many aspects of the manufacturing process to help ensure repeatability and consistency. The MES can enforce a prescribed sequence for operations and make sure that operations cannot be signed complete until certain data is collected. Along with enforced controls comes the need to handle exceptions, therefore the MES must also provide easy methods for handling deviations that have been approved for specific units or lots by the appropriate personnel.

Configuration Verification

Organizations assembling products with complex configurations and variations need to ensure that all component parts are coming from the correct bill of material (BOM) at the correct engineering revision level. An MES integrated with the Engineering system can ensure that the list of parts reflects the proper component parts and cross references to any incorporated Engineering Change Notices (ECNs), approved deviations and part alternates.

In-Process Inspection

In-process inspection verifies critical product attributes and characteristics during the manufacturing process and are usually performed by the technician instead of the inspector. These measures can relate directly to the product specifications or to intermediary dimensions specific to a production process, tooling fixture, or machine setup.

In-process inspection points should be inserted prudently as early in the process as it is reasonable. Decisions for inspection points and frequency are based on historical data collected by the MES and should be adjusted over time to address any problems areas.

Some MES systems allow specification of random over- inspection and audit rules for a quality inspector to verify that the technician is performing good measurements and that the prescribed inspection points are sufficient to identify issues as early as possible.

Statistical Process Control

Statistical Process Control (SPC) is a technique that utilizes statistical methods and control charts on collected data to detect and control variation, change, inefficiencies and deficiencies. SPC serves to:
Produce early warnings on out-of-control processes, before out-of-spec products are produced
Detect out-of-spec product immediately at the data collection point
Detect process inefficiencies that are otherwise hard to find with manual systems

Control charts are used to detect unexpected variations in processes. When a process shows variation with an unexpected, non-random pattern such as a shift, trend or cycle, the process is unstable, unpredictable, and “out of control”.

Specification limits (spec limits) define the range of deviations that is considered by the design engineer (or the customer) as acceptable. In contrast, control limits used in SPC are calculated based on the actual measurements taken at the process or machine. Control limits are adjusted by the system automatically based on the trend of actual measurements to (1) determine the real capability of the process compared with the expected capability and (2) to predict out-of-control trends before they lead to out-of- spec conditions.

Locations of the observations relative to the control chart control limits (typically at ±3 standard deviations) and centerline indicate whether the process in question should be investigated for assignable causes. These Western Electric rules are examples on how control limits are used to trigger proactive “out of control” alerts:

• Any single data point falls outside the 3σ limit from the centerline
• Two out of three consecutive points fall beyond the 2σ limit, on the same side of the centerline
• Four out of five consecutive points fall beyond the 1σ limit, on the same side of the centerline
• Eight consecutive points fall on the same side of the centerline

Production Process Verification

Production Process Verification (a.k.a. first-article inspection) is the inspection of key and critical attributes and characteristics on the first item(s) produced from a new product design or a significant process change in order to verify the capability of the process to produce the desired results.

These process verification requirements are usually not implemented as in-process inspections; they are usually performed on the completed product unit. However, there are advantages to triggering these additional inspection requirements directly from an MES system since the MES system can ensure that every significant change to the process plan or work instructions will trigger the respective additional inspection requirements.

Personnel Qualification

In addition to enforcing process sequence and data collection, the MES can also enforce that personnel is qualified to perform the job. Verification requirements like this are considered part of quality management system requirements in industry standards including IS09001, AS9100 and ISO13485.

The MES can verify that personnel skill certifications are up-to-date before performing any work to ensure that employees are qualified to run specific manufacturing equipment or handle certain materials or resources.

Tool Control

An MES with integrated tool calibration management can automatically verify that the tool or gauge used is still under calibration and is appropriate for the tolerance required. An advantage provided by an MES is that recalibration can be triggered based on tool usage and not strictly on dates.

Device History Records

A side effect of enforcing the product configuration and collecting all the manufacturing data during the work process is the automatic creation of the device history record or as-built documentation for each product unit. Some QA organizations have people dedicated to collecting log books form the shop floor and manually verifying that all the appropriate forms have been filled out. This function can be fully automated with an MES system for organizations that have to assemble this type of paperwork as part of the documentation package that is delivered with the product to the customer.

In Summary

Organizations evaluating QA and MES applications as separate applications for separate department silos are likely to miss out on a great opportunity to elevate quality management to a proactive function. QA applications evaluated separately tend to focus on reactive practices like FRACAS. MES evaluations performed without considering the needs of quality management are usually focused on visibility of schedule, status and performance metrics. So it is easy to miss out on the preventive and proactive quality management functions listed above.

In today’s fast pace manufacturing world we cannot try to inspect quality into a product; we must manage quality throughout the entire manufacturing process with an MES that integrates inspection practices and verification of certification of personnel, tools, and machines. It is not enough to simply track defect and failures on the side; we must integrate quality management practices that prevent errors. Technicians must be responsible for their own quality and an MES helps them do it.

A Step Back in Concurrent Engineering

The debate over the merits and risks of outsourcing are ongoing, but outsourcing remains a widely used business strategy. We have seen a lot of healthy discussion on how we should/could protect Intellectual Property (IP) in the context of outsourcing, but we have not seen much discussion on the effects of outsourcing on Concurrent Engineering. Industry was making great progress on Concurrent Engineering and Design-for-Manufacturing 10-15 years ago, but we might have taken some steps backwards in recent years as a natural result of the increase in outsourced manufacturing processes.

Concurrent Engineering efforts were bringing together product designers, manufacturing and quality engineers into a collaborative process. Instead, we are back to throwing the design over the wall, except the wall has moved further away. The “wall” used to be between our engineering and manufacturing departments; it is now between our product design organization and our contract manufacturers.

Are we trading off efficiencies in design and manufacturing for the benefits of cheaper labor? We might be getting better overall results anyway, but we could also be missing opportunities for more radical breakthrough improvements—improvements that could only be achieved when product designers and manufacturing engineers work closely together.

We could increase involvement from suppliers in the design process, but we are also asking suppliers to compete and bid for the manufacturing work. How much are suppliers willing to share when they are bidding on a project? Perhaps they don’t want to give up their “secret sauce”. Yes, IP is not limited to the product design! The manufacturing processes can have IP themselves. In fact, to further complicate the matter, the International Traffic in Arms Regulations (ITAR) requires companies working on DoD projects to not only protect the IP of certain product designs related to U.S. Defense, but also certain manufacturing processes that are deemed to be technologies the government does not want in the hands of certain countries.

Was Concurrent Engineering overrated anyway? Are new manufacturing technologies like 3D printing making the topic obsolete? Or should we be trying to develop more of that “secret sauce” manufacturing for our companies? Please join in and share your thoughts on this topic.

This Year’s Resolution: Continual Education

First, I must review my progress on last year’s resolution…

Last year I set a goal of “exercising each Monday, Wednesday and Friday at 6 AM for 30 minutes.” I knew I had to make some changes in my routine to improve my health and I did it! In my physical examination at the end of the year, I showed huge improvements and I managed to reduce the rates I was paying for life insurance. That was real return on investment (ROI) from my efforts!

How did you do on your goals? If you did well, congratulations! If you didn’t, don’t beat yourself up. Just look forward and set more realistic goals for this year.

Moving forward this year…

Our physical health and financial health are both very important and always need monitoring, but this year I will focus on another important area: continual education. A line from the ASQ A&D Division Chair, Ryan Nowosielski, in a recent newsletter resonated with me. He mentioned an interesting statistic: the average shelf life of technical knowledge in the A&D industry is 10 years. Only 3 percent of what you know today will be relevant in 10 years.

I never thought of it that way. It puts a different spin on the need for continual education. Continual education has become a must in today’s work environment and I believe that this fact will also change how we will view 4-6 year college degrees in the future. It will become more important in your résumé to show that you are a continual learner. It will not be enough to show that you achieved a degree years ago, if you can’t show that you have also been renewing your skills; either through on-the-job training on new skills and technologies, or through complementary education programs. Perhaps trade schools, colleges and universities will look to offer “education maintenance programs” as a subscription service in the future. We will also see corporations elevating the importance of their internal training programs to keep their employees’ skills up-to-date.

Further evidence of the knowledge shelf-life is the skills gap found in the unemployed workforce. Even though we have a huge pool of unemployed people we still have problems finding specific skills required for current jobs in job candidates. The capability to learn and keep up-to-date will become a more important job skill for candidates than any specific single degree.

Join me this year and let us make a resolution to learn one or two new skills. Perhaps there are a few areas of improvement in the company that we can lobby for, and in the process of helping the company, we can also pick up some new skills. We can spend some extra time this year to research a topic, read a book, learn a new software feature, take a class or get a new certification. My goal this year is to continue to expand my knowledge areas and refresh some areas that might need to be updated. Wish me luck and I wish you luck with your endeavors.

Happy New Year!

An interesting related article with good thoughts for employers on hiring talent:

http://www.tlnt.com/2012/04/03/5-ways-the-new-war-for-talent-will-change-the-workplace-forever/

 

Who is a Better Agent of Change? The Younger or Older Leaders in the Workforce?

Who is a better agent of change? The ambitious young managers trying to prove themselves or the experienced managers trying to improve the company and leave their mark behind?

The young leaders might be less biased by legacy practices, better trained on the latest tools, and more willing to accept the risk that comes with change. The older leaders might avoid repeating old mistakes and provide better judgment about the level of risk the organization can take. However, older leaders can also be overly cautious and more subject to accept the status quo as the best possible path. After all, they were part of the team that developed the current practices, so they are apt to believe that these practices are as good as they could possibly be. Do your leaders feel your business practices are already optimized? Do they feel that problems are due to people just not following the prescribed practices? Then perhaps they suffer of “old thinking” but this can happen to both younger and older managers.

I believe that Maslow’s pyramid of motivation is applicable in this discussion. It is true that different people can be in different places at different stages at their lives, but in general I believe that as leaders move up the corporate ladder they are also moving up in Maslow’s pyramid and are motivated to action in different ways. Some older leaders might be frustrated by the past, ready to “throw the towel”, and not willing to make waves as retirement nears. But other older leaders might be looking for self-fulfillment and ready to take the organization to a new level. Younger leaders might in general be more socially motivated looking for recognition and still building their self-confidence.

The best management team for change would be a perfectly balanced group of leaders with diverse backgrounds and motivations sharing a common vision to lead the organization to new levels of productivity and quality. But younger or older we must salute all leaders that embrace change as a necessary part of today’s business environment. Much is changing at an unprecedented pace and the tools available today are so much better than the tools we had ten or even five years ago.

Paraphrasing what I heard from Alan Mullaly a couple of years ago, “if the organization is not improving it must be falling behind.” I think it is true that leaders of different ages are motivated in different ways, but I also believe that leaders of any age are motivated in general, recognizing that change is necessary and that risk can be managed to guarantee successful process improvement.

Am I avoiding answering the original question? Perhaps. I have seen both older and younger managers as good agents for change. Some balance of maturity and urgency is needed to be a good agent of successful change. How do you feel about it?