Manufacturing and retail

The manufacturing and retail industry has used data science to designing better products, optimize pricing, and design strategic marketing techniques. Some examples include the following:

• Price optimization: Generally related to the realm of linear programming, the challenge of price optimization, that is, pricing products, is now also being addressed with the help of machine learning. Dynamic pricing based upon market conditions, user preferences, and other factors are used as inputs to assess optimal pricing of products. (Source: https://www.datasciencecentral.com/profiles/blogs/price-optimisation-using-decision-tree-regression-tree).
• Retail sales: Retailers use algorithms to determine future sales forecasts, price discounts, and promotion sequences. (Source: http://www.oliverwyman.com/our-expertise/insights/2017/feb/machine-learning-for-retail.html).
• Production capacity and maintenance: In manufacturing, data science is being used to determine device maintenance requirements, equipment effectiveness, optimize production lines, and much more. The overall supply chain management is an area that has benefited and continues to earn profits from smart use of machine learning. (Source: https://www.forbes.com/sites/louiscolumbus/2016/06/26/10-ways-machine-learning-is-revolutionizing-manufacturing/#51d4927228c2).