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An Insight Into Big Data-Driven Package System

By ESOutlook | Friday, May 24, 2019

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The world is bound by tech-driven innovations with the increased dependence on phones, watches and other such devices. Data is the fundamental force, backing the evolution of technology. However, people do not utilize all the information available; either it is overwhelming, or there is no time to review data. This is a case in our personal lives as well as the professional environment. A large amount of information can be dazzling if looked at too many metrics at once. Here's how a robust and systematic approach can be reached to achieve data-driven goals. Read On!

• Define clear goals

Goals of an organization should be clear. Organizations should have a clear-cut idea if trying to commercialize a new lightweight package design that meets performance specs, disrupts the market or enhance the bottle blowing capabilities to generate more ROI.

• Zero on key data

The organization should prioritize valuable and pertinent information. Focusing attention will remove data perplexities and help in providing a better chance in accomplishing the goal.

• Make it a team effort

Companies often make the mistake of involving only manufacturing for concerns relating to production. Including marketing, procurement and other teams assist in achieving better output.

• Analyze data regularly

Analyzing the data systematically becomes essential. Historical data should also be compared, in order to solve the problems, which an organization faces.

• Monitor production windows

Various bottle combinations, coupled with the use of post-consumer recycled resin, have different production windows. As some are wide and some are restrictive, the operators must make sure that critical decisions can be made on the fly so that downtime is reduced.

• Modify older equipment

Not having the latest production equipment doesn't mean that critical data can't be accessed. Most material can be retrofit with the software needed to produce meaningful data for the operation.

The main aim is to find the area where scrap is at an agreeable level and that the bottle meets aspired performance attributes. Harnessing the data correctly in real time can drastically impact profitability. For packaging operations, the purpose is to minimize the scrap levels while producing efficiently-made packaged goods that satisfy quality assurance requirements.

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