How to Make Your Factory Smarter

BY ZAC ELLIOTT, MENTOR GRAPHICS – A SIEMENS BUSINESS

■ Figure 1. Application layers in a smart factory.

For electronics manufacturers to turn their operations into smart factories capable of autonomous optimization of interconnected processes, the many disparate computer systems need to be integrated. Given the complexity and cost associated with this undertaking, the path to a smart factory may seem out of reach to all but the largest manufacturers. To enable the widespread adoption of smart factory functionality, barriers that are a result of complexity, effort, and cost need to be lowered. This article describes a generalized approach that small to large manufacturers can begin to use to build a smart factory infrastructure.

Challenges facing electronics assembly

Because of the existing level of technical sophistication and automation used to manage the complex manufacturing process, computer integrated manufacturing (CIM) systems are an essential part of any electronics manufacturing operation. Many different computer systems, automated robots, and technical experts work together to execute manufacturing and to streamline processes, optimize the supply chain, and manage product quality. Linking these existing pieces together can be an arduous task that requires a multidisciplinary team of technical process experts, product engineers, operational resources, and business process owners.
As the business requires greater flexibility, lower overhead, and stricter quality standards, the electronics assembly industry is rapidly adopting improvements in automation and analytics to meet these challenges. Smart factory and Industry 4.0 initiatives were developed to guide the next step to integrating manufacturing processes with business processes, enabling autonomously and continuously optimizing operations.

Although integration can be a complex and expensive proposition, closely coupling the systems used to manage the equipment, the factory, and the enterprise provides tangible benefits. The ability to share information and control not only between individual equipment but also between equipment and business systems opens the opportunity to further automate and optimize sophisticated manufacturing processes.

Immediate needs of electronics manufacturing

Manufacturers need solutions that enable them to make tangible progress toward the fundamentals of Industry 4.0 and building smart factories. SMT equipment vendors have responded by expanding the scope of their CIM systems, from beyond controlling an individual machine, to managing the entire production line or other ancillary processes such as material management. Through partnerships with other equipment vendors, they can now offer end-to-end solutions.
Systems created by an equipment vendor is the optimal solution for a particular equipment platform and begins to address the need for integrated, autonomous manufacturing, but they leave out the business processes needed for most smart factory functions. Manufacturers often have a mix of equipment vendors as well. A given manufacturing site has multiple SMT platforms and various third-party equipment platforms to support. Considering these circumstances, a generalized approach to creating a smart factory makes more sense for smaller manufacturers to be able to integrate and connect these heterogeneous environments with minimal complexity and cost.

A generalized smart-factory infrastructure

We can begin by defining a more general infrastructure. Existing CIM applications can be grouped into the three primary, functional layers: process, site, and enterprise applications (Figure 1).

DEFINING THE PROCESS-APPLICATIONS LEVEL
At the lowest layer, process applications control or manage a given manufacturing process. These are the machine vendor applications, Programmable Logic Controllers (PLC), sensors, or custom applications that run equipment, collect data, and guide a person or process. These applications may create event data that is valuable to other processes or to the higher-level infrastructure. They usually require information from the higher-level infrastructure, such as material information, work orders, and flow control.

DEFINING THE SITE-APPLICATIONS LEVEL
Sitting above the process-specific applications are the site applications that manage the overall manufacturing flow. The MES infrastructure, process engineering, quality management, material management, and finite production-planning applications are typicalsite-level functions. In many cases, these applications are consuming the event data created by specific processes to actively manage production operation-to-operation. The site-level applications provide the flow control, work-order details, and material information required by the process-specific operations.

DEFINING THE ENTERPRISE-APPLICATIONS LEVEL
At the top level are the enterprise applications that manage higher-level, cross-functional business processes. Some examples of enterprise-level applications are ERP, MES, Manufacturing Operations Management (MOM), Product Lifecycle Management (PLM), and business analytics. These applications may receive data that was aggregated from the process-specific applications and then were summarized by the site-level applications. The enterprise applications are responsible for providing the site-level applications with the overall resource, material, and production plans.

Once we have delineated between the layers of applications in the smart factory, we can then see a clear flow of data: business requirements flow down from the enterprise applications to the site applications. The site applications translate the requirements into concrete manufacturing plans which flow down to the process applications. The process applications gather event data to send back up to the site applications. The site applications aggregate and summarize the relevant event data to be sent finally up to the enterprise applications.

■ Figure 2. Application-layer responsibilities in a smart factory.

A common language can be used to build trust

Beyond the connection and exchange of data, normalized data is required to integrate disparate automated processes and computerize human decision-making. Normalized data is data that is expressed in a single language with a consistent meaning regardless of the source of the information.

Currently, in the electronics assembly industry, no standard defines the complex data content required to model manufacturing processes in the smart-factory flow. Although some vendors support proprietary interfaces to their own technology, technical expertise is required to map the data content from one technology to another. Existing legacy standards focus on connecting and moving data, but accept no responsibility for the complex data content to the degree that is necessary for trusted decision-making. As a result, existing integrations and solutions tend to limit the focus to a narrow slice of a larger business process, or they only function point-to-point between partner-vendor solutions or custom integrations.

Applying IoT with a normalized language

The emergence of the Internet of Things (IoT) in the manufacturing industry has provided increased capability to connect processes and acquire data, but still an infrastructure must be defined to manage the capabilities and distribute the information between multitudes of possible data streams. Considering the three levels of applications discussed previously, and if we assume a single language is used for normalization, a factory can then be connected by defining the responsibilities of each layer of applications (Figure 2).
Redefining application responsibilities in the smart factory

In the connected factory, process applications are responsible for exchanging raw data with equipment and operators, and they normalize the events and information into a single language for consumption externally. Internally, each application can function optimally for the given equipment or process, but each application uses the same generic interface to describe the manufacturing operations.

Site applications are responsible for adding perspective from the complete line and qualifying the data collected to identify root cause and bottlenecks. Because the process applications all produce the same type of normalized events in a single language, minimal effort is required to connect processes for the highest level of detail and perspective.

Enterprise applications are responsible for distributing the information from the underlying infrastructure for external use. In addition to the existing enterprise-level resources such as ERP and MES, a gateway to the IoT manufacturing infrastructure enables discovery of resources in the factory and the events supported by the resources.

With this infrastructure, individual manufacturing processes easily share information and control, and the entire manufacturing flow is exposed to an external interface. Smart applications connect the individual manufacturing operations with business processes to optimize the production flow.

Intelligence makes for a smarter factory

People have many different, and valid, perspectives on the performance of a factory. For instance, the factory being shutdown is significant to a planner who is concerned with overall factory capacity, but the shutdown is less significant to the production manager who simply wants to know if the machines will be running efficiently when they are scheduled to run. With a mix of different customers, products, factories, lines, and machines, hundreds of different KPIs need to be considered. Some of these measurements are complex, requiring data from multiple processes; for example, Overall Equipment Effectiveness (OEE) calculations in which we consider not only the performance of the factory resources but also the quality of the products being made.

With this complexity, a bottleneck is often a result of something that is not being measured. A machine may not be operating because of an actual malfunction in the equipment, or it may be waiting for some upstream or downstream process. Perhaps the operator is on break, or there is a shortage of materials causing the downtime. To identify the root cause of a problem and provide for an actionable response, these external forces need to be known.

Valor IoT Manufacturing Intelligence, a site-level factory intelligence application, fills in this gap by considering information from enterprise applications as well as process applications. Process-specific applications provide performance data about the status of equipment. Site-based constraints can be used to qualify any process status based on constraints such as the overall factory schedule, material availability, or the upstream/downstream bottleneck.

With information about process performance and the external constraints influencing production, many optimization opportunities are possible using an intelligence application. The process-specific layer is able to optimize based on external knowledge from other processes and higher-level applications, while the site-application layer benefits from detailed process information from each individual equipment.

A closed-loop feedback scenario

A prime example of applying these layers in a smart factory is in closed-loop feedback. In this scenario, measurements taken at one process are used to automatically adjust the operation of another process to maintain a consistent result. For example, the SMT machine adjusts placements based on drift data measured at the AOI.

A site-level analysis application collects the placement and material information from the SMT machine through the process-specific application managing that equipment. Next, the real-time measurement results coming from the AOI are collected and analyzed to identify a process-control problem. The results of this statistical analysis are fed back to the SMT machine so that adjustments and compensations are made that are appropriate for the equipment.

Because a normalized interface is used at the AOI and the SMT machine, this application functions across different platforms while allowing each individual equipment to autonomously take the optimal action for its technology.

For many manufacturers of electronic assemblies, maintaining an efficient supply chain is key to success.

Finite-planning scenario

Finite planning of the SMT schedule is significantly improved and optimized through automation and computerization. In the typical situation, the ERP system manages the customer demand and material requirements in coarse granularity with little detail of the resources used in manufacturing. Once the work order demand is generated in the ERP system, diligent work is put into developing a production plan to satisfy the orders. Complicated spreadsheets and workbooks are used to model the manufacturing flow and to manage constraints that are external to the ERP system. Unexpected changes in the customer demand or the manufacturing constraints are difficult to integrate into the existing plan. Optimization of product groupings happens infrequently outside of the day-to-day planning activity.

If we use the smart factory topology defined here, automation of the finite planning process functions at each layer of the factory. At the enterprise layer, the ERP system manages the customer requirements and the high-level site calendar.

At the site layer, a digital model of the production process is generated based on constraints in the factory. All lines, machines, processes, materials, transactions, and resources are taken into account in the model to create a simulation of the manufacturing process.
The process-specific layer performs two important functions. First, it supplies real-time–performance information from the manufacturing equipment through the IoT infrastructure. Second, it supplies the means to simulate production for the given process.

When all layers are working together, a fully optimized production plan can be developed. Demand from the ERP system is deconstructed into the individual manufacturing processes. Iterative simulations find the ideal manufacturing sequence using the static site constraints and live performance data from the factory. A feedback mechanism between the planning application and the equipment processes provides optimized programs and product groups based on the discrete demand. Changes in the demand or the constraints is continuously accounted for in the production schedule.

Lean material-management scenario

For many manufacturers of electronic assemblies, maintaining an efficient supply chain is key to success. Significant investments in ERP systems and automation ensure that materials are in the warehouse to satisfy customer demand; however, the act of moving material from the warehouse to the machine often involves many manual processes. Large line-side buffer stocks and lack of visibility into individual packages (reels, sticks, trays) of components contribute to a discrepancy between the real-world stock and the system inventory. With rich, detailed information available in the smart factory, a lean material engine can bridge the gap between the ERP inventory and the shop floor to provide

Just-In-Time (JIT) material logistics to the machines.

The first step to developing a lean material management engine in the smart factory is accessing information held in various systems. The ERP system provides the work-order demand that defines the sequence and schedule of products to run. The warehouse management system provides the detail of individual components that are available for production. At the process specific layer, the equipment system provides the machine program information, performance information, and material consumption details.

Next, using the production schedule, the current machine setup, and the live IoT data stream from the equipment, the lean material engine can work out when individual components need to be replenished: either on the current order as reels are exhausted or on an upcoming order during a changeover. With the connection to warehouse management, the lean material engine determines the ideal location from which to move components and automatically initiates the movement transactions. The consumption data reported by the individual machines is aggregated, and accurate reports are made to the ERP system.

Finally, the large line-side buffer stocks are unnecessary. Material is ordered from the warehouse or from Kanban storage only when it is needed on the machine. With the automated reporting of consumption and wasted materials from the machine, the ERP inventory is as accurate as possible.

Traceability scenario

Collecting traceability data has traditionally been a difficult requirement for manufacturers. The complexity and cost associated with collecting detailed, accurate data leads to inconsistent results because individual traceability requirements are negotiated between the customer and supplier on a product-by-product basis.

The IPC-1782 standard was developed to define a clear, industry-wide specification for traceability to improve the effectiveness and consistency of traceability data collection. This standard features several levels of traceability based on the risk involved in the product or process. The individual traceability levels vary in the detail and accuracy of the data collected. At the lowest level, summarized data is collected manually by operators; and at the highest level, comprehensive data is collected primarily from automated equipment.
Most of the data required for traceability is likely to exist within the operation, making it a matter of establishing a communication method for the data to be brought together in the form that the standard requires. Avoiding manual data collection decreases the cost of traceability implementation and increases the accuracy and timeliness of the data collected.

Avoiding manual data collection decreases the cost of traceability implementation and increases the accuracy of the data collected.

Most automated machines that have been in the market for many years have some sort of data availability, which requires machine-vendor software support. Data is also collected from transactional systems such as planning, material control, and verification operations, etc. All of these sources of data can be combined more easily through the adoption of a single data-exchange and normalization format, such as the Open Manufacturing Language (www.oml.com), available today and supporting IPC-1782 requirements.

The requirements for traceability data collection can be fulfilled by the existing process applications in the smart factory infrastructure. Because the process applications support a neutralized language and a normalized set of events, consistent information is aggregated by the site layer applications regardless of the particular machine platform. Detailed and consistent data is gathered regardless of the equipment platform.

Conclusion
Many opportunities exist today to implement improvements based on automation of manufacturing and business processes using this kind of generalized smart factory infrastructure. This approach enhances machine-vendor solutions through information exchange with other machines and site applications. By collecting data from all manufacturing processes in the flow defined here, with the process, site, and enterprise applications feeding each other in an intelligently managed infrastructure, historically troublesome requirements, such as traceability, are much easier to fulfil. Solutions that remove these barriers will help small and medium-sized manufacturers create smarter factories.

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