ML is a complex technology that needs to be optimized strategically. Source: Shutterstock

ML is a complex technology that needs to be optimized strategically. Source: Shutterstock

ML can augment manufacturing but is a solid business case in place?

TECHNOLOGY has allowed manufacturers to boost efficiency and productivity beyond measure in recent times.

As the world is enabled by greater connectivity power and growing reliance on intelligent machines, manufacturers are feeling the pressure to use even more sophisticated solutions.

Machine learning (ML), in particular, is being extensively promoted as an indispensable tool in manufacturing.

It makes sense why the industry has been matched with the solution considering the fact that manufacturers harvest data just by operating the plants.

This is not only driven by the urgency to satisfy a growing demand for production but the realization that ML can significantly improve manufacturing operations.

Despite all the alluring prospects of deploying ML, manufacturers must first build a solid business case for its adoption. As groundbreaking as the technology can be, manufacturers must remain critical and practical about its applications.

Going over certain key considerations can help manufacturers assess the relevancy and necessity of ML which will then support the case and solidify deployment strategies.

# 1 | Is there a process that ML can specifically augment?

While it is natural for manufacturing operations to have bumps that slow them from achieving the desired goals, ML is not always the best fit.

Sometimes, these bumps are commonly inherent in processes that are not automated efficiently and in this case, what is actually needed is automation tools or robotics.

Look for complex processes with prominent issues like quality assurance cycle and logistics that would warrant the deployment of ML solutions instead, and build a case from there.

Processes that are demanding of employees’ retention and energy while at the same time costly for manufacturers to manually maintain can definitely benefit from ML.

# 2 | Are all current capabilities optimized efficiently?

A common scenario in the industry that undermines the true potential of ML is when the technology is adopted without any tactical direction for its application in a bid to achieve greater efficiency.

More often than not, existing technology tools are not only able to scale efficiency but can also perform functions that will add value to teams within the operation.

ML algorithms are meant to do more than just add efficiency. Manufacturers seeking to make operations smarter, harness predictive analytics capabilities, and develop better products supported by in-depth insights should explore ML.

What manufacturers need in this case is a systematic overview of available technologies and evaluate their impact on operational performance.

# 3 | Are there enough resources to develop and deploy ML?

Once a strong case is built, manufacturers must assess whether or not the necessary requirements and components needed to develop ML solutions are in hand.

This is obviously encompassing the financial and talent resources that an operation currently has. While ML has been proven to be cost-effective, there is still a price associated with its development and deployment.

Manufacturers are understandably racing to advance themselves with practical, revolutionary technology solutions like ML in order to stand out in the marketplace.

A solid business case should always, however, be a priority as it will help manufacturers leverage this new solution in ways that best augment their operations.