Manufacturing Analytics Software: Drive Value With Real-Time Data

Manufacturing analytics software is a business intelligence (BI) tool for capturing, aggregating, and analyzing manufacturing data analytics. Through the collection and manipulation of large quantities of data from sensors and other systems within a manufacturing business, manufacturing analytics reveal insights about processes and functions that can lead to improvements across all levels of an organization.

Using software to manage this process results in easy-to-understand insights that are actionable in real-time and centralized so that everyone can use them—from operators to managers to the CEO.

The results of using manufacturing analytics software are apparent in both the short and long term, with obvious boosts to customer satisfaction and reductions in errors and unplanned downtime. It provides immediate insight and control of the shop floor as well as long-game predictions to help manufacturers improve processes and stay agile even in a changing market.

Using everything from demand forecasting to machine utilization information, manufacturing analytics software serves to generate useful insights to guide business decisions in a data-driven, trustworthy, and impactful way.

The idea behind utilizing advanced manufacturing analytics software is to move from descriptive analytics into predictive analytics. This means that manufacturers can advance from using data to simply “keep track of things” and instead leveraging data for more granular insights that decision-makers can use to guide the future of the organization. For example, with predictive analytics, manufacturers are able to predict issues on the shop floor before they happen, resulting in improved output and cost-savings.

During this phase, manufacturers collect and aggregate data from a variety of sources throughout the organization. Manufacturers commonly collect data from:

• Machine Tools: A well of valuable data, machine tools provide information about statuses, modes, alarms, overrides, loads, speeds, and feeds. This data can be readily collected via ethernet using MachineMetrics solutions
• Connected Systems: Using APIs, connected systems such as ERP, CAM, and tooling and maintenance systems can be used to fill information gaps and provide context to machine data.
• Operators: Humans fill in remaining gaps that can’t be captured using the previous two data collection methods. This can include adding context to test runs that occur after maintenance (which may otherwise appear as short production runs which can skew insights) as well as information like why a machine is idle without an alarm.
• Sensors: Sensor data can be gathered from practically anywhere in a facility. When attached to machines, they may measure vibration, power draw, or temperature. In other locations in the shop, they could measure temperature, noise levels, light levels, air quality, humidity, and more. Sensors are considered a relatively cheap and easy method to gather a vast and varied array of data in manufacturing environments.

All data sources are not created equal, and the information collected using discrete methods is not a plug-and-play step toward generating insights. First, data has to be transformed into formats that can work together and standardized so that like is compared with like when the data is analyzed.

For example, you want to use and compare time and duration data with other time and duration data—if that data “column” also has data about the number of scrap parts generated in a production run or the humidity levels on the shop floor, then your “time” insights would be flawed from the start. Instead, standardizing data puts like data in the same “column” and in the same format, e.g. one hour recorded as 00:01:00 instead of 01:00:00 since mixing the two formats would result in wildly inaccurate predictions.

During this phase data becomes readable and accessible to workers throughout the manufacturing facility. It may be transformed into visual representations such as charts and graphs and become visible in a centralized way and/or pushed to relevant systems throughout the organization. In any case, the goal at this stage is to take raw insights and make them usable and actionable. Some ways manufacturers commonly make data visible and accessible for decision-making are:

• Shop floor dashboards that can guide floor workers on their progress and status in relation to production goals
• Tablets with part counts at all machines
• Remote visibility into operations via centralization that allows decision-makers to access insights from anywhere.
• Deeper analysis via reports