Filling Data Gaps in Your Production Line

Filling Data Gaps in Your Production Line

Digital transformation of the production line is necessary to fill gaps created by low quality or missing data. Manufacturers can deploy smart products across various production machines, turning them into cyber physical systems to collect quality real-time data. 

When it comes to manufacturing, manual data entry is among the biggest contributors to low quality and missing data. In fact, around 48% of manufacturing companies still utilize manual data entry and spreadsheets to document activity on their production line. What may have been an effective means to analyze processes in the past is no longer a viable solution. Manual data collection is inefficient, error prone, and limits the possibilities for data capture. 

There is a swift influx of smart products entering the market right now to address the issue of manual collection. The smart products are designed to capture data before, during, and after the production process, turning machines into cyber physical systems and thus narrowing the data gap. 

Predictive maintenance is a powerful technology that comes into play before the production line begins a new cycle. Take for example, Toyota’s digital transformation of its maintenance routines. Toyota deployed a collection of sensors on its manufacturing machines to capture data on their condition, while running artificial intelligence to predict when maintenance will be required. By deploying sensors on each machine, they become cyber physical systems that collect millions of real-time data points, filling the gaps in maintenance data.

Quality control is an incredibly valuable use of smart products along the production line. BMW, a leader in digital transformation when it comes to automotive manufacturing, utilizes cameras infused with artificial intelligence in order to automatically inspect quality of components. All of the image data is logged into a large database where the system continually works to compare historical component quality to new components while identifying discrepancies. This builds a substantial database of new quality inspection data. 

Overall equipment effectiveness is perhaps one of the most important metrics to measure at the end of a production cycle. Part of Volkswagen’s digital transformation included sensors and software to more effectively measure its equipment effectiveness. The company has various sensors to monitor the availability, performance, and quality of machines in real-time so that the data can be rapidly analyzed and optimizations can be made. The quantity and quality of this data is far beyond anything a human could collect, helping to fill that gap in valuable knowledge for the organization. 

Data is one of the most valuable resources for a manufacturer in the modern age. Digital transformation through smart products and cyber physical systems is essential to fill the gaps in data across the production line.