Leveraging the data – Solutions to reduce waste in production (Part 1)

InfinityQS® is a leading data collection, inventory and analysis service provider for manufacturers around the world. InfinityQS® process improvement solutions often revolve around the application of automation and technical analysis to better control quality.

The role of technical guidelines for quality

Many years ago, InfinityQS worked with a beverage manufacturer with the desire to reduce costs but did not know how because they had digitized their entire process. By focusing on analyzing a line, the consultants realized the key point in their technique. The company often filled the liquid with each bottle, which led to the liquid being spilled out, and of course, the prices of those liquids were not cheap.

Upon closer inspection of the digitized data collected earlier, experts have identified what technical guidelines need to be changed, and what are the standard quality specifications that can help the process. Production efficiency is optimal. Taking that as a foundation, the company made a series of improvements (both large and small) to improve their manufacturing processes. As a result, they have saved over a million dollars per year on each line.

Extrapolating with more than 20 lines remaining in the factory, you can see that the cost savings are astronomical numbers.

No need for new technology, no need for modern equipment, instead, innovative solutions come from the data available at the organization. Technical guidelines for quality are extremely important in determining whether the current operating process is really effective. In addition to the technical instructions, data in the stream is also an element that InfinityQS goes into analysis in the improvement process.

Why evaluate off-line data?

On every production scale, when looking for a solution to improve quality, InfinityQS experts say they tend to be interested in off-the-first data. However, manufacturing enterprises usually focus on parameters of productivity, cost and profit. The reality is so, but why do manufacturers need to pay more attention to off-stream data? The reason is simple: They can extract a lot of information from this data, such as the objective factors affecting the productivity of the entire line or the errors that even experienced managers also cannot recognize.

In the following, we will continue to show what businesses can do to make the most of the data they have.

Productivity and Quality Office