With the factories becoming smart and Connected the process data is getting huge and curtail. One has to manage and monitor the data smartly and hence the factory modules. This story will discuss few advances and challenges in “Big Data Management in EMS Line”.
Different people have different understandings and expectations about what the Internet of Things (IoT) actually is, especially with respect to how it could work and what it could bring to the SMT assembly industry. There are a lot of expectations to fulfill as principles behind innovations such as Industry 4.0 take hold. A key concern, however, is whether any of these expectations are reasonable or whether there is a dependence on something that is fundamentally flawed, which unfortunately would seem to be the case of SMT and related production. The Industry EMS industry is currently in the midst of a data-driven revolution, which promises to transform traditional manufacturing facilities in to highly optimize smart manufacturing facilities. These smart facilities are focused on creating manufacturing intelligence from real-time data to support accurate and timely decision-making that can have a positive impact across the entire organization. To realize these efficiencies emerging technologies such as Internet of Things (IoT) will be embedded in SMT and EMS processes to measure and monitor real-time data from across the factory, which will ultimately give rise to unprecedented levels of data production. Therefore, EMS facilities must be able to manage the demands of exponential increase in data production, as well as possessing the analytical techniques needed to extract meaning from these large datasets. More specifically, organizations must be able to work with big data technologies to meet the demands of smart manufacturing. The ability to access, analyzes, and manages vast volumes of data while rapidly evolving the Information Architecture is increasingly critical to communications service providers. Industry 4.0 and Industrial Internet of Things (IIoT) discussions often focus on advanced connectivity. While connectivity is important for implementation, the entire enterprise infrastructure and system architecture also should be considered. For many years, businesses of all sizes have maintained on-site server hardware that hosts e-mail, databases, and other applications, such as enterprise resource planning (ERP) systems. In today’s hyper-connected world, even some of these basic electronic services are moving to the cloud, using Internet-based services rather than installing and maintaining servers locally. These offerings are known as software as a service (SaaS). Similarly, cloud-based services also are being adopted for IIoT and Industry 4.0 implementations.
Connected Machines & Big Data
With more connected machines and more data being collected, EMS companies will need advanced analytics to help convert the data from raw figures into actionable information as well as an operations plan. Basic machine analysis includes threshold monitoring of digital and analog values and operational timing analysis: total, minimum, maximum, and average, program state analysis, and energy usage calculations all fall into the set of analyzed data. This data can be stored in standardized formats with data compression, either locally in the controller, in a cloud-based solution, or on a server within the enterprise network or in a public cloud as business needs dictate. Further benefits arise from data analysis, and one example can be found with predictive maintenance insights. Logging data from operating hour counters, frequency analysis, or root-mean-square (RMS) calculations, for example, enables implementation of high-performance condition monitoring. The system facilitates limit value monitoring for different process data. Pattern recognition for detecting irregularities and repetitions in the recorded data further improves process-sequence reliability.
As per a research, 66 percent of manufacturers said that the use of the cloud has already improved business insights, while the same percentage said they are seeing improvement in plant productivity. Almost all of respondents – 90 percent – said that their data security improved after adopting professionally managed cloud services. In terms of mobile usage, 81 percent also indicated that having a scalable, secure cloud platform improved company-wide data accessibility.
Using data collected from the SMT shop floor to influence and control many aspects of the flow of production have been a part of manufacturing from the beginning. The introduction of more automated data collection systems with Microsoft Office applications to make charts and reports has progressively reduced the lead-time between data acquisition and utilization, bringing firstly daily reports, or shift reports, then hourly reports, and many systems more recently that are able to provide immediate reporting, delivering messages and reports on phones as well as live progress metrics on overhead progress displays. These mechanisms have actually been in place for some time, using complex software, though limited in scope. This limitation is not a result of the technology of data management or reporting, but mainly as a result of the poor condition of data availability from SMT machines. Unlike other industries, standards have never gained traction in SMT-based production.
Cloud Shift Focus from Mass Production to Mass Customization
Having complete control and real-time visibility across their supply chain is vital for manufacturers today. We live in a customer-centric world where customer expectations for product design, quality and availability are set very high. It’s also a more disposable and faddish world where customer appetite is very strong for the latest and greatest products, meaning the life cycle for many products, particularly consumer electronics, is continually shortening. Customers are also very interested in more personalized products, which mean many manufacturers are leaning more toward mass customization the mass production of individually customized products than the traditional manufacturing focus of mass production. As an aside, there will also be more interest from certain groups of consumers in traditional, non-digital manufacturing techniques and a desire to purchase truly hand-crafted products leading to the establishment of more boutique manufacturing plants. To meet the demand for mass products which are then tailored to the needs of specific individuals, EMS manufacturers need to determine where that personalization of products will occur whether on the shop floor or, given rapid changes in buying patterns, as close to point of ultimate purchase as possible in the warehouse or perhaps even within the store. The manufacturing process will continue to become more and more digitized. EMS companies will have to make use of new and emerging materials and technologies. They will have to put into place business processes which will enable them to collaborate more closely on innovation and design with a broader set of communities including partners, customers and potentially anyone around the world with some useful input and expertise. Some manufacturers will find their primary purpose shifts so while they still make and produce products; their core business is more about the paid services attached to those products. For example, today’s provider of a smart watch is tomorrow’s web services expert provider. So, the watch becomes less of the business focus and more of a free item complementing the subscription services that provide that watch with all manner of easily consumable and useful data including real-time news, traffic alerts, weather forecasts, sports results, etc.
Upgrading to IoT
The SMT shop-floor information environment resembles the Internet more in terms of massive amounts of unmanaged data than the structured approach of data belonging to Internet services such as on-line banking or shopping. This is a serious problem for those who would like to extend the industrial IoT to SMT. Significant change needs to happen for it to be viable. The first port of call would naturally be to ask the SMT machine vendors to consider the adoption of some standard through which all critical and useful information can be efficiently extended. However, the practice of using
proprietary communication software as a barrier to competition should be relaxed, as well as accepting that more detailed information about the machine and its performance will be exposed, which can in theory be used to compare the performance between vendors. The place to start for IoT projects is to identify the business challenges and real-world use cases. The needs are typically rooted in quality, production throughput intelligence, predictive maintenance, and overall equipment health. For example, a manufacturer may:
- Seek to increase machine uptime, enable production managers to get real-time notifications on mobile phones when production is hit with expected complications or when product quality from a line decreases beyond a given threshold. Defining these parameters up front helps give the project definition and scope.
- Enable the collection and streaming of the data needed to help make analytics and notifications possible. This requires identifying what database type and software analytics tools should be deployed for the task and choosing a communications protocol compatible with those tools, as well as with the machine controllers in the facility. Many emerging and powerful analytics tools for manufacturing support IoT protocols, though many existing industrial controllers still do not support them.
- Take advantage of convergence of automation technology (AT) and information technology (IT) happening now. This is exactly where PC-based controllers help by capitalizing on this convergence and by allowing fast and efficient implementation of new programming standards and protocols, regardless of whether they originated in the automation and controls realm or in the IT world.
- Use software and hardware that facilitate integration. Some industrial suppliers are already implementing protocols, such as MQTT and AMQP, into the control platform with access directly from programmable automation controller (PLC) languages. PC-based controllers also can be used as small, flexible gateways that can connect to traditional PLCs using the legacy field-bus protocol and can translate that data into IoT protocols, then send that data to local or cloud-based servers. This eliminates “islands of automation” in the emerging smart factory, even for plants that can’t yet completely do away with the more limited, traditional hardware PLCs.
Big Data Management = Better Asset Utilization
Additionally, having access to data, such as running product size, enables analysis of how the machines are mostly being used, and it can aid in optimizing machine operation and next-generation machine design. For example, take a machine that will run products from 5 mm to 500 mm in size. If 98% of the time the majority of the installed base only runs products from 120 mm to 220 mm sizes, that insight helps the OEM to offer a new machine model optimized for size and cost that focuses on a maximum product size of 220 mm. SMT Machine and equipment suppliers are bringing additional benefits to end users through predictive maintenance and condition-monitoring features as well. Collecting and managing data from machines on a subscription-based model ensures that the end user’s machines run as optimally as possible and that the appropriate people are notified when concerning anomalies are seen during operation. Equipment suppliers are ideally suited for this brand of analytics, as they are experts in their particular equipment. This also boosts intellectual property and value proposition to remain ahead of competition.
Conclusion
At last we can say yes, it’s really happening. IIoT and cloud computing strategies are taking a more physical presence as companies begin to capitalize on tangible benefits. Machine optimization and continuous improvement efforts remain key elements in growing a global company, and PC-based control and next-generation analytics are helping the most competitive manufacturers harness big data to reach the ultimate destination of the smart enterprise
