How CIM and IoT Can Make Your Factory Smart
With so many extremely clever scientists and engineers in our industry, the whole complex technology of electronics assembly requires people with specialist skills. When it comes to linking all these processes together, there is more than just a simple data connection to think about. The content of data from the myriad of different processes and technologies firstly needs to be understood and then needs to be transformed into information which is then actionable, not just by a human, but by a computer.
There is no one person who can understand every aspect of every process, and every opportunity for smart factory functionality. Accessibility to new IoT manufacturing data connections is a multi-way technology. As well as higher level systems such as the manufacturing execution system (MES) or enterprise resource planning (ERP) systems making smarter decisions, the same IoT data is also available to every machine vendor, each of whom know their technology down to the finest detail. Here is where we will see the bulk of the smart solutions for assembly manufacturing coming from.
Driving this is the machine vendor agnostic data acquisition and utilization, with complementary functionality through machine, line, factory and enterprise levels. This promotes healthy differentiation between vendors in the same market space, not only in terms of machine performance, but also in terms of the smart solutions that they can provide based on the data collective.
This paper explores the changes in culture between the past, where data was simply sent point to point, and today’s multi-layered IoT technology-based solutions, as well as the effects and opportunities that are here now for the taking.
To create smart factories capable of autonomous optimization of interconnected processes, it is necessary to integrate the myriad of disparate computer systems used in electronics manufacturing. 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, it is therefore necessary to reduce the complexity, effort, and cost by defining a general approach to the smart factory infrastructure.
As business needs challenge the electronics assembly industry to support increased flexibility, lower overhead, and stricter quality standards, the industry is rapidly adopting improvements in automation and analytics to meet the challenge. The so-called smart factory and Industry 4.0 initiatives are aimed at further integrating manufacturing processes with business processes to autonomously and continuously optimize operations.
The unique challenge to the electronic assembly industry is the existing level of technical sophistication and automation presently used to manage the complex manufacturing process. Computer integrated manufacturing (CIM) systems are necessary to any electronics manufacturing operation. Many varied computer systems, automated robots, and technical experts work together not only to execute manufacturing but also to design streamlined processes, optimize the supply chain, and manage product quality. To begin linking these existing processes together can be an arduous task requiring a multidisciplinary team of technical process experts, product engineers, operational resources, and business process owners.
Although integration can be a complex and expensive proposition, there are tangible benefits to closely coupling the systems used to manage the equipment, the factory, and the enterprise. The ability to share information and control not only between individual equipment but also between equipment and business systems offers the ability to further automate and optimize sophisticated manufacturing processes.
Immediate Effects on Electronics Manufacturing
Considering the challenges facing the industry, electronic manufacturers will be seeking solutions that represent tangible progress toward the fundamentals of Industry 4.0 and the smart factory, namely autonomous, continuous optimization of operations. SMT equipment vendors have been the first to respond to the needs of the market by expanding the scope of their CIM systems beyond simply controlling the individual machine to managing the entire production line or other ancillary processes such as material management. Through partnerships with complimentary equipment vendors, entire end-to-end solutions can be offered to the market.
Although systems provided by the equipment vendor will be the optimal solution for their equipment platform and will begin to address the need for integrated, autonomous manufacturing, there is still significant complexity in connecting the web of business processes needed for most smart factory functions. Due to a variety of factors, many manufacturers have a mix of equipment vendors.
A given manufacturing site may have multiple SMT platforms and a broad range of third-party equipment platforms to support. To integrate and connect these heterogeneous environments with minimal complexity and cost, it is advantageous to define a generic approach to the smart factory.
Smart Factory Infrastructure
To define a more general infrastructure in which to work, the existing CIM applications can be grouped into the three primary layers that exist functionally: process applications, site applications, and enterprise applications. These groups of applications generally serve the same function in relation to each other, as shown in Figure 1.
Figure 1: Smart factory layers.
At the lowest layer are the process applications which 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, or guide a person or process. These applications may create event data that is valuable to other processes or to the higher-level infrastructure. To operate, these applications generally require information from the higher-level infrastructure, such as material information, work orders, and flow control.
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 typical site-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.
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 is aggregated from the process-specific applications and then 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.
With this delineation between the layers of applications in the smart factory, there is then 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.
Factory Intelligence Application
Depending on the point of view, there are many different, yet valid, perspectives on the performance of the factory. For instance, the fact the factory is on shutdown may be significant to a planner looking at overall factory capacity, but it is less significant to the production manager who simply wants to know if the machines are running efficiently when they are scheduled to run. With a mix of different customers, products, factories, lines, and machines, there may be hundreds of different KPIs to consider. Some of these measurements may be complex requiring data from multiple processes, for example overall equipment effectiveness (OEE) calculations, where we consider not only the performance of the factory resources but also the quality of the products being made.
With all this complexity, it is often the case that a bottleneck is caused by some external force 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 must be considered.
A site-level factory intelligence application would need to consider information coming from the both the enterprise applications and the process applications. To begin, the process-specific applications would provide performance data regarding the status of the equipment being managed. Next, the site-based constraints will 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 external the constraints influencing production, many optimization opportunities are possible. The process-specific layer can 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.
To read the full article, which appeared in the September 2018 issue of SMT007 Magazine, click here.