November 23, 2020

AI-enable cost reduction in Manufacturing

A Challenging Environment

Similarly to most industries, manufacturing has to deal with fierce global rivalry, productivity, lower-priced imports, domestic cots, and productivity. Given the fact that the global economic environment remains significantly unstable, the business situation in most sectors is quite challenging.

The majority of business players within the manufacturing sector handle unpredictable situations such as unstable prices for their resources, selling price reductions & profit losses to remain competitive and in business. These are only some of the huge challenges that manufacturers have to overcome and stay viable.

Organizations and individuals now adopt a more socially responsible perspective. Business decisions affect the environment and businesses in the manufacturing sector have to reconsider their practices. Manufacturing is closely related to the use of energy and control of emissions. If manufacturers don’t follow sustainable engineering approaches may lack a competitive advantage in the best-case scenario. The worse-case is to harm their brand reputation and stay out of business eventually.

A leading player wished to improve the manufacturing process and save some resources at the same time. Long delays in their processes, increased scrapping rates, and shipping of faulty parts were some of the areas that needed improvement.

All these were affecting production cost and the scope was to reduce costs significantly.


The solution
The solution

The Suggested Solution

The solution combines superior technologies of machine learning to reveal automatically the best functional regime for complicated, multistage, manufacturing processes.

At the time the project started, there was a huge data variance. Even with siloing the provided information, it was very challenging to utilize any AI machine learning applications.

A sophisticated data gathering pattern from different levels and in different formatting has been developed. The machine learning system combined excel sheets, CSVs, access, and more.

The outcome was a single view data that combined data of more than 170,000 records, 120,000 peculiarities, and over a year of data production time. This would be impossible to happen by a group of human experts.

This provided an automated report to the end-user to utilize and make in-time business decisions. Decisions that reduced the cost of raw materials, minimized waste and the negative impact on the environment and increased productivity.


The Consequences

At first, the company performed its manufacturing processes with zero external scrap rates. They did not send any faulty part which means zero cost of returning. This also increased the customers’ satisfaction.

More automated procedures lead to increased availability for human labor. These resources allocated to other tasks that require increased manual involvement to be completed.

After applying AI empowered technologies, the company reduced its costs and saved significantly every month. This increased their profitability and made the company more competitive and viable in the long-run.

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