According to McKinsey & Company, digital transformation in manufacturing has the value potential of a 10-30% throughput increase.
The process of leveraging digital technologies to develop new customer-driven business practices and processes in response to changing business and market landscapes is known as “digital transformation”.
It can range from the adoption of cloud computing or artificial intelligence to the application of machine learning.
It is becoming an increasingly prominent consideration amongst manufacturers as they tackle labour shortages, supply chain uncertainties, and socioeconomic crises.
More and more businesses are turning to advanced technology to help build resiliency in the wake of such issues.
A recent Accenture survey revealed that more than half of Irish companies plan on investing in new technology to help quell operating expenses.
Here’s a look at some of the advantages of digital transformation in manufacturing.
Advantages of Digital Transformation
Improves Workflow
Digital transformation in manufacturing can drastically increase the efficiency of processes.
For instance, robotics can handle activities that are difficult for staff to complete and automated technology can increase operational speed and limit human error.
The flip side of this is that your employees can turn their attention to more skilled work.
Additionally, Internet of Things (IoT) integrations and machine learning can support predictive maintenance.
This helps manufacturers to limit expensive occurrences of machine downtime by closely monitoring machine performance and alerting maintenance teams to potential issues.
While the installation and implementation of automated systems and IoT technologies often calls for substantial initial investment, the dramatic increases in productivity mean you will quickly see a positive return in the form of long-term cost savings.
Circling back to the aforementioned McKinsey & Company report which states that advanced production methods can result in a 15-30% labour productivity increase.
Higher Quality Output
Automation and machine learning are also helping to improve quality control.
By using more sensors, automated testing, and quality control throughout the manufacturing process, firms can eradicate discrepancies in quality and ensure that each product is produced to the highest standard.
The data collected by AI and machine learning can also highlight where improvements can be made in the manufacturing process. This real-time data can be essential to maintaining a competitive edge.
Greater Transparency in the Supply Chain
Global supply chains have had to face unprecedented disruptions in recent years.
Covid-19, the cost of living and energy crises, materials and labour shortages, and the war in Ukraine have all made for a trumulteous few years.
For many manufacturers, technology and the digital transformation is playing a key role in their response to these challenges. Examples of this include:
- Powerful, demand-driven insights from machine-learning that tracks real-time data on buying patterns
- Increased efficiency via warehouse automation
- AI software that automatically tracks performance, highlights issues and their causes, forecasts declines, and suggests solutions
- Greater integration and communication of data throughout the entire supply network
Ultimately, manufacturers want more visibility over their supply chain management, and digital transformation is providing exactly that.
According to a recent Deloitte report, 80% of companies believe that increased employment of digital tools will lead to greater transparency throughout the supply chain.
Potential to Lower Environmental Impact
As a society, we are becoming increasingly conscious of our environmental impact and are factoring said consciousness into our purchasing behaviour. Consequently, there is now a far brighter spotlight on manufacturing practices.
Fortunately, technological advancements are facilitating a shift towards more eco-friendly manufacturing.
We previously mentioned how machine learning and AI technologies are improving operational efficiency. A positive knock-on effect of this is that more efficient processes use less energy to produce the same output.
Additionally, a refined supply chain management system can lead to fewer materials being required and less being wasted which has obvious positive connotations for the environment.
Lastly, smart IoT devices and energy management systems are providing manufacturers with greater control over their energy consumption. The data collected by these smart devices can also highlight opportunities for energy upgrades.
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