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In the article How McKinsey’s 2016 Analytics Study Defines the Future of Machine Learning, the investigates a McKinsey study released in January of this year that shows how far big data and analytics have come in the previous 5 years as well as what to expect in the very near future.  The main insights gleaned show that the use of big data is growing in numerous industries, and that the key realizing the value of big data is integration and adopting from the top down.  For manufacturing specifically, machine learning leading to preventative maintenance and predictive analytics is one of the most powerful advantages that has emerged.  Unfortunately, just about every industry examined in the paper has still has not captured more than 50% of the potential value from data and analytics.

The original paper released by McKinsey released in 2011 speculated that manufacturing would find the greatest potential value in data and analytics from possible drops of 50% in product development cost, 25% lower operating cost, and 30% gross margin increases.  As of 2017, the new McKinsey paper shows that manufacturing has only captured 20-30% of this total value with the main issues being siloed data in legacy IT systems along with skeptical and sometimes unsupportive leadership.  With regard to one of big data’s most powerful uses, machine learning, manufacturing stands to realize significant value from machine learning.  In the areas of real-time optimization, predictive maintenance, and forecasting, there is definitely some value to be had in regard to machine learning.  In the areas of strategic optimization, predictive analytics, and understanding data trends, there is substantial value to be had.

Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing.  Unfortunately, manufacturing as a whole has lagged in adoption with a few advanced players capturing a significant amount of the value.  Digging deeper to see exactly where the money has been saved, analytics has benefited the supply chain and production aspects of manufacturers, but only the small groups that have adopted the technology.  McKinsey estimates that only 5-10% of manufacturers have used analytics for financial savings in the supply chain and production aspects.  In the supply chain, the cost savings has come in the form of 10-15% reduction in product cost, and in production side, the cost savings has come in the form of 10-15% reduction in operating costs.  Overall, the main hindrances to more big data implementation is senior management involvement and designing and implementing the appropriate organization structure to support data and analytics.

Do you believe big data analytics adoption accelerate over the coming years?

Do you believe the results of the study are completely accurate?

How far away are we from a time where if manufacturers are not using analytics, they will not be able to compete in the market?


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