Manufacturing Analytics – Why, What, How?

The buzzword going around nowadays is analytics. Simply put, it is all about making sense of data. And why do we want to do that? One of the goals would be to improve. And what do we want to improve… performance, output, etc.

In the context of manufacturing, we want to improve throughput and quality while keeping costs under control. The primary reason a manufacturing organization has to focus on equipment availability, performance, and quality is because these factors are within the control of the organization and will determine how it fares in the competitive environment. Unless the organization has a continuous improvement focus on these items, it will find itself lagging its peers in the marketplace.

Could we improve the number of parts produced by the machine per day? Could we improve machine utilization? Could we reduce unplanned downtimes? Could we get more value from our investments so that unit costs would go down? Answering these questions related to improvements in the manufacturing environment needs data and analysis.

The first step towards improvement is to measure. With measurement, we start getting data. Depending on the frequency of measurement, we could get lots and lots of data. The parameters to be measured depend on the goals to be achieved. For example, if we are looking at improving machine availability and reducing downtime, we may be interested in measuring loads, temperatures, vibrations, … typically indicative parameters of machine health.

The next step in this process is to analyze it. With the parameters mentioned above, we would like to understand the average, min, max parameter values in addition to trends, patterns, outliers, etc. In the good old days, all this process – data collection and analysis – was performed manually, if at all. The equipment operator’s job is to run the machine. Use of an operator’s time in collecting large amounts of data is not practical. Due to the efforts and complexities of manual data collection and analysis, the efforts towards improvement were limited.

The quantity of data could also overwhelm the speed of manual analysis. In addition, with the advent of tabulated sheets and statistical tools, organizations made some improvement in the analysis of data. However, even these improvements peaked soon.  What was needed was real-time data collection and analysis so that the required decision making and preventive or corrective actions could be real-time as well.

With the improvement in machine communication, machines could now communicate and output information real-time. With the use of databases, analysis and storage capabilities improved. However, infrastructure limitations soon posed hurdles. Real-time data collection and analysis across a variety of manufacturing assets in a uniform manner requires capabilities to collect, transform and analyze large datasets.

To understand data patterns, clusters and trends in order to diagnose issues correctly and make predictions from this data requires machine learning algorithms and large amount of variable computing power and infrastructure. Such algorithms and infrastructure are easily available in the cloud and hence “manufacturing analytics” – which is essentially the ability to make sense of your manufacturing data for the right decision making  – is easier in the cloud*.

[*For organizations with genuine security concerns, there are mechanisms to enable local deployments]

Manufacturing analytics helps organization take their manufacturing performance to the next level, sustain it and take steps to improve it. Getting the manufacturing information into a platform where we can see it on our laptop or pad allowing us to make smart real-time decisions. One can not only monitor manufacturing parameters for different equipment and devices across the factory floor but also receive event driven alerts and notifications based on predefined rules and conditions. Having access to large datasets across long time-spans also allows us to systematically observe trends, clusters and patterns; apply machine learning algorithms and perform predictive analytics.

This helps us in situations such as avoiding unplanned downtimes. Instead of performing reactive maintenance or having scheduled maintenance activities, we can proactively perform maintenance activities based on actual equipment data. Due to the near real-time nature of the streamed data and analytics, it is easier for manufacturing personnel and manager to react faster to situations needing quick decision making and actions. Dashboards can be configured for individuals based on their roles so they can readily have a view of the current status of the machines they are responsible for.

While we have seen, why we need a manufacturing analytics solution and what it provides us, let us also briefly look at what it is not. A manufacturing analytics solution is not focused on process control or manufacturing execution. Manufacturing analytics is all about collecting and analyzing manufacturing data for process improvement. A strategic focus on manufacturing analytics can also provide a key differentiator to an organization while competing in the market.

With MINTVIZOR, all it takes is a couple of hours and you can have your manufacturing data in the form of ready to use dashboards and reports in a web browser available on any device. In addition, you can get alerts and notifications generated through rules and machine learning algorithms. This helps make it . very easy for organizations to move quickly on the part of radical improvements on the factory floor.

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