Key Points to Consider When it Comes to Manufacturing Cloud Data

People have begun describing their cloud systems as "the fog." I get the feeling the joke is based on actual events. Are we" venting" gases into the atmosphere with our data during this digital era just like we did during the industrial age? Is it possible to simply throw our data in the cloud and expect a software application to analyze and organize it when we need it? But first, we must begin with an understanding of what the cloud can and cannot do, as well as knowing what is required to make the cloud an effective strategy for storing data that is easily accessed.

The Search Engine Experience

To date, we have been spoiled, and our expectations have been set high by search engines like google that gives us the ability to enter just a few words or a simple phrase and find specific details in an almost unlimited amount of information. It is amazing to think about the staggering amount of data that these search engines access, which presumably is in some sort of "cloud," that the search engine algorithm needs to sort through in order to return the result so quickly. The search engine is a terrific example of a cloud-based platform. The only thing that is on your device is a simple user interface and the search result, not the software or the data. Search engines are intended to exceptionally handle the "human" aspect of the data which did not take searching into consideration when designed. The human mind needs to look through the list to then choose the best most relevant result. Search Engine Optimization (SEO) methods have been designed to insert important items of data within web pages intended to assist search engines in understanding the content and meaning of the web data as the internet has evolved.

Considerations of Cloud Storage Systems

Web pages on the internet do not really reflect the true nature of cloud data storage. From a manufacturing perspective, storing data in the cloud is merely an additional way or location in which to put data. Many people consider it the same as an on-site server (now referred to as a "local cloud") or your computer's external hard drive (now referred as your "personal cloud") from a user's standpoint. It can be less expensive to store cloud data off-site, and it also offers more capacity, no maintenance, and aside from the service bills zero operating expenses. Nonetheless, there are a few key items to keep in mind when it comes to cloud storage. The first thing to consider is the level of confidence that the data is adequately secure and in some instances, such as with ITAR restricted data, ensure the physical storage is housed in "friendly places." Not surprisingly, cloud data is spread across data centers located all over the world and usually in areas that charge the least. Access to the data must be protected appropriately.

Secondly, you need to keep in mind the accessibility requirements. The internet connection determines the speed of links from a website to the cloud. Despite upload and download speeds seeming fast today, these same connections are supporting many different resources, such as all employees using an internet browser and even the coffee machine utilizes the same internet connection. If there are not dedicated lines set up for mission-critical systems, they are forced to split the bandwidth with an ever-growing amount of devices and services. System performance and capacity will not be restricted by limited connection issues if the cloud is hosting both your data and software, just like we discussed with the search engine instance. This may be acceptable in many use-case situations, such as analyzing long-term organizational statistical trends with Business Intelligence tools.

The Practical Bottleneck

We as humans have to try to decipher and choose the most logical result with a broad internet browser search like Google. Instead of simply returning a list of relevant web pages technologies like Siri, Cortana, Alexa, Echo, etc. are trying to become much smarter by providing answers to questions. Another level of data organization, beyond the typical SEO practices, is required to accomplish this objective. Transforming meaningful information out of random data is not an easy task, and the companies that have achieved this objective have invested a significant amount of time and money. Cloud-based or server-based, this has continuously been the same bottleneck for any computer system. Like the saying goes "Garbage In, Garbage Out," it is easier to add value and deliver meaningful information using more succinct stored data than it is with unclear, vague data.

Lightening the Cloud

The cloud is a perfect location for many solutions and applications that will possibly require the processing of large quantities of data. Reports for capacity planning, metrics of quality, and productivity over an extended period of time and analyzing traceability data are just a few examples. To be able to deliver these capabilities, the next decision to be made is what software should reside in the cloud? Simple methods for data search criteria configuration and reporting formats, such as Business Intelligence exist with the use of many enterprise-level packages. Unfortunately, it turns out that the practical bottleneck rules come into effect here. The actual manufacturing data needs to be very organized and meaningful in order for "off-the-shelf" analytics software to operate properly. But data that is directly obtained from the manufacturing process is definitely not organized or meaningful. For instance, without any other detail, incidents like "stop other than error" or "waiting for PCB" that are reported from machines are worthless. Substantial processing would need to occur to determine why the machine paused for a PCB, which could stem from many sources. To understand the real meaning for each phase of the analysis, a large, complicated assessment of an entire series of these "simple events," which possibly could be thousands hourly. A standard BI solution is not going to be able to provide the algorithm necessary to understand the nature of raw production data effectively. Developing a custom "raw production data processor" in the cloud would be costly but is another option to an "off-the-shelf" method.

For cloud-based data management solutions to work effectively, it is critical that intelligent processing of the production site data be conducted locally before that data goes into the cloud. Listening to and piecing together the many disparate data elements in precise time sequence, that originate from assorted machines, bar-code readers, sensors, material preparation transactions, etc. is the first step. The raw data can then be converted into meaningful, actionable pieces of information, thereby creating qualified events.

The Hybrid Theory

Advanced MES software systems already include direct connections and the means to process and locally manage data directly from both automated and manual production operations. Beginning with dashboards, which need meaningful events to be fed as they are happening, then generating alerts based on a live situation, the real-time data processing requirement is already well established. A new generation of smart systems like Industry 4.0 is already using this real-time data processing. These types of applications require faster and more accurate results than cloud-based data analysis can provide. Productivity is reduced if any delays longer than a sub-second occur, for instance, in the event of specific control of production machines such as responding to an incoming product for processing which needs to be qualified for routing conformance. Sending data from manufacturing into the cloud can become outdated since it can take minutes or even hours for the data to get there due to upload restrictions.

Inevitably, both cloud and site models will require software that is compatible. Smartly processing data received from the shop-floor and then turning it into information that is understandable would ideally be executed by leveraging a hybrid cloud model. The clear, meaningful record in the cloud is available for long-term, advanced analysis, local alert generation analysis, and functions of the smart Industry 4.0. Optimum value can be achieved with very little overhead when both cloud and on-site software are both compatible.

Manufacturing data can become useful, realizing expected values, when data in the cloud and cloud-based storage systems are used. The cloud is no longer considered the "fog" that some people have begun to experience. The standard "GIGO" principle still holds even though the manufacturing cloud data model and technology is relatively new.