How Machine Learning is impacting Manufacturing
As a subset of AI, Machine Learning allows transformative outcomes in an increasingly connected world. The number of use cases where ML is used for intelligent operations is rising exponentially.
In fact, the results of this technology are visible in our daily lives such as the fraud alert of credit cards, Facebook Newsfeed and much more. From searching something on Google to going through your social media profile, machine learning is working everywhere.
But, why this technology has become so popular in the modern world?
The answer to this question is – data. The availability of a mind-boggling huge amount of interconnected data on a global scale has made machine learning more important than ever. The same reason has made it essential for businesses to leverage machine learning in the form of Manufacturing Analytics.
Slowly but steadily, manufacturers are incorporating the technology in their regular functions. There are several steps of manufacturing that become much more efficient and valuable with the availability of machine learning.
Here are three of the most valuable steps of manufacturing that gain the maximum positive impact of machine learning.
Demand forecasting for service parts
The success of the service provided after sales depends on the knowledge of potential demands. A clear picture of the future demands, makes it easier for manufacturers keep the cost of the initial service low after the sales.
The forecasting of the demand for parts and products depends on the upcoming trends and past events. Hence, manufacturers need to gain an accurate picture in both departments. However, most planners fail to see the future demands due to the lack of data. They find themselves struggling with stocking locations, inventory management, cost increase, and many other factors. This mostly happens due to the unavailable technologies.
Resolving the problem for good, machine learning technologies provide the much-needed accuracy in the demand forecasting. The machines monitor and track the associated factors and predict the demand in an efficient manner.
New product launch
Understanding the demand for service parts becomes much more difficult when a new product is launched. The manufacturers struggle to find out when their product will need repair or replacement. This lack of knowledge sometimes turns into expensive customer service.
With a new product launch, manufacturers can leverage machine learning to accurately judge the analytics and algorithms related to product’s success. Using the data from different sources such as social media, sales, web traffic and others, manufacturers can find out all about the product’s success in the market. This knowledge then leads to the prediction of the after-sales service demand.
Price optimization for service parts
The traditional pricing methods create confusion with diversity in the pricing of a product at different locations. The customers don’t feel satisfied, which is never good for a manufacturer. The same goes for the cost of the service parts provided after the sales.
With machine learning, manufacturers have no need to worry about the weather, demand, seasonality, location and other factors. The machines do the complete job and help in creating a full-proof pricing model.
Machine learning is ready to make manufacturing more efficient and cost-effective. Are you ready to take the next step to boost your manufacturing business? Do write to us and let us know.
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