Manufacturing Methodologies

Manufacturing methodologies include:
Lean manufacturing

A lean organization understands customer value and focuses its key processes to continuously increase it. The ultimate goal is to provide perfect value to the customer through a perfect value creation process that has zero waste.

To accomplish this, lean thinking changes the focus of management from optimizing separate technologies, assets, and vertical departments to optimizing the flow of products and services through entire value streams that flow horizontally across technologies, assets, and departments to customers.

Eliminating waste along entire value streams, instead of at isolated points, creates processes that need less human effort, less space, less capital, and less time to make products and services at far less costs and with much fewer defects, compared with traditional business systems. Companies are able to respond to changing customer desires with high variety, high quality, low cost, and with very fast throughput times. Also, information management becomes much simpler and more accurate.

Digital Twin

The Digital Twin concept in Industry 4.0 come into practice when we addressed the question like: “Do we need to adjust the product?” and then we think “Let’s look at the original specs, at the drawings, at snapshots, at the BOM, at simulation results, at VR iterations, at the real performance parameters” – We simply say then: “Let’s look at the digital twin!”
Digital Twin is a mirroring (or twinning) of what exists in the real world and what exists in the virtual world. It contains all the informational sets of the physical ‘thing’ meaning its cross-discipline – not just a mechanical / geometric representation, but also including the electronics, wiring, software, firmware, etc. not just the CAD model.
Digital Twins are currently used in the context of monitoring, simulation and predictive maintenance which are all incredibly valuable and potentially transformative in their own right, however, there would seem to be much more to it. As products of all types move to include connectivity, sensors and intelligence we can’t just think about the data streaming back from the field.
Without accurate “Context” – Digital Twin – time series data generated during production and ongoing operation is difficult or even impossible to understand and analyze. In addition, the ability to interpret and act upon these data often require traceability to prior information from related revisions – Digital Thread. To complicate matters further as artificial intelligence / cognitive computing is introduced the necessity for the Digital Twin becomes even greater.

Production Support with AI

In the industrial sector, AI application is supported by the increasing adoption of devices and sensors connected through the Internet of Things (IoT). Production machines, vehicles, or devices carried by human workers generate enormous amounts of data. AI enables the use of such data for highly value-adding tasks such as predictive maintenance or performance optimization at unprecedented levels of accuracy. Hence, the combination of IoT and AI is expected to kick off the next wave of performance improvements, especially in the industrial sector. AI-equipped controllers are meant to immediately detect signs of equipment irregularity by monitoring the status of equipment and processes and assure product quality.