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Price optimization
Developing an optimal pricing strategy is crucial for manufacturing organizations , as it directly affects their revenue . A good pricing strategy must consider various internal factors , such as material supply , manufacturing capacity , and costs , as well as external factors , like economic indicators , market sentiment , user preferences , and government regulations .
In order to achieve an optimal pricing strategy , manufacturing organizations need access to real-time information to help them determine how to achieve short-term revenue goals while also establishing fair and reasonable prices . Historical data can be valuable for building pricing models and predictive analytics , but advanced AI algorithms can use real-time data to help companies go deeper and build a comprehensive picture of the immediate and future impact of their pricing decisions .
Real-time information helps manufacturing companies create adaptable AI models and predictive analytics that capture patterns and nuances in the data . This allows companies to create dynamic pricing strategies that adjust to match competitors ’ prices , capture larger market share , or reduce losses .
Augmented analytics
Another significant challenge for manufacturing organizations is the need to reduce the complexity and manual intervention required to create useful analytics . These organizations need to gather data from multiple siloed sources , standardize the data , and then load it into a data platform with the proper data governance and quality controls . Companies can then use this intelligence to automate the data pipeline , train advanced AI models , and make informed decisions . But manufacturing staff don ’ t always have the technical expertise to operate these systems or to interpret the often voluminous output .
A new form of this process of data collection and analysis is called augmented analytics , which eliminates the need for extensive experience , knowledge , or technical
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