Cost-savings with AI

3 ways of cost-savings in the manufacturing sector using AI

‘Beware of little expenses. A small leak will sink a great ship.’

Benjamin Franklin

The global manufacturing industry is growing and accounts for 16% of the global GDP. As the manufacturing industry grows, business leaders are eager to find new ways to achieve cost-saving. Traditional cost-saving techniques that require heavy input from humans are not able to keep up with modern technologies. Thanks to artificial intelligence (AI) technologies such as machine learning (ML) that enable cost-savings in the manufacturing sector in today’s times.


AI and Industrial Internet of Things


Artificial Intelligence (AI) algorithms are useful in analyzing large datasets generated daily by the manufacturing units. Furthermore, AI technologies have played a crucial role in developing Industrial Internet of Things (IIoT). IIoT is an advanced form of system where manufacturing mechanical or digital equipment is able to share data without requiring human interaction. These technologies have resulted in achieving a better control over manufacturing processes and thereby resulted in small or large costs-savings.


3 Ways Leading to Cost Savings using AI


AI technology helps in cost-savings in the manufacturing sector by three main ways.


  • Increased automation

When machines require human input, human errors due to fatigue, negligence or a lack of adequate training, result in machine failures. This reduces production and thereby results in losses. The use of AI results in increased operational efficiency, productivity, and safety. This invariably leads to cost reduction and an increase in profit margins. Although full automation of manufacturing units is a distant dream, in the near future efficiency based automation will become a norm.


  • Predictive maintenance

The aim of a predictive maintenance system is to raise early warnings of critical failures to avoid downtime. When a machine in a manufacturing process breaks down, it results in downtime during which no production is possible. The company may not meet its manufacturing goals and may incur heavy losses. To avoid this situation, maintenance is done at regular intervals. However, a machine may not need maintenance at a set interval. An ideal situation would be to conduct maintenance before a machine performance goes down or it breaks down. AI technologies have the ability to precisely predict when the maintenance activities are needed. Thus, by continued production and cutting costs on maintenance cost-savings are achieved.


  • Remote monitoring

Instead of installing physical sensors, the use of remote sensors in manufacturing units is the new norm. These remote sensors are algorithms that constantly monitor operating conditions such as temperature, pressure, or type of raw materials. These sensors identify operational inefficiencies that would otherwise go undetected or result in damage. The insights gathered from remote monitoring allows humans to take remedial actions in a timely manner. This ultimately results in the cost-savings due to the optimum use of available resources.

Due to these advantages, the manufacturers are interested in implementing AI tools but encounter major barriers such as a lack of enough data, long production cycles. Often these barriers are intertwined i.e., a long production cycle results in fewer data for the analyst. Business leaders with a long-term vision are therefore focusing on data collection and gradual investments to conduct a sophisticated analysis. In many cases even though a vast amount of data is collected, its use is limited to track day-to-day operations only. A competent data science team develops algorithms that spot patterns and reveal the areas of improvement. To seek a competitive edge, several companies are collaborating with an external data science company.

Process Optimization with Experts on Board

An external data science consulting agency helps their client to adopt specific strategies such as developing smart manufacturing systems, best practices in data sharing. To develop smart manufacturing systems the existing equipment needs to be monitored with the help of remote (or physical) sensors. When data are collected regularly, up-coming or inexperienced companies struggle to understand legal and organizational requirements of data governance. An expert data science agency helps the company to develop protocols needed for data privacy, security, sharing, and analysis.

The implementation of AI in manufacturing is in the early stages of development. AI and IIoT applications have potential to reduce costs in a variety of areas such as energy conservation, cybersecurity, operation technology (OT), and asset performance management. It is estimated that IIoT will be a $225 billion market by 2020. But the time to include AI technology in manufacturing is now.


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