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Supply Chain Thought Leadership Video Series:
Applying Machine Learning to DC Productivity



Feb. 27, 2018

Artificial intelligence generally and "machine learning" specifically are all the rage these days.

But in supply chain, machine learning specifically seems to be mostly discussed in terms of higher level planning applications, such as learning over time how to improve a forecast.


However, there are also many opportunities for using machine learning in distribution to drive productivity gains, says Krishna Venkatasamy, chief technology officer at Lucas Systems, a provider of mobile work execution systems in logistics.


In this excellent video discussion, Venkatasamy details opportunities in DC labor planning, dynamic slotting and more with SCDigest editor Dan Gilmore.


The data has been available for years, Venkatasamy says - we just haven't had the tools to leverage it until fairly recently.


This video is part of an on-going Thought Leadership series with Lucas experts and SCDigest on topics associated with mobility and productivity in the DC.


Not to be missed for managers interested in significantly increasing the productivity of mobile DC workers.


Please view this short discussion now.


Also, see the Part 1 video in this series: Augmented Reality in the DC - What's Real, What's Not and What Do Users Need to Know? or Part 2: Addressing the Growing Labor Crisis in US DCs

  Have a question for Lucas Systems on machine learning in distribution? Send it Here and we will forward on for a personal response.  
  Download the new white paper "Three Paths To Picking Productivity With Voice and Mobile Work Execution" which looks at why results from Voice picking differ across implementations - and how your Voice system can be at the top of the pack.


Related Content


New White Paper:

Three Paths To Picking Productivity With Voice and Mobile Work Execution



Many DCs realize 40% productivity gains using Voice in their picking processes, while others see improvements of 10% or less.

This paper looks at how different approaches to implementing Voice affect the magnitude of productivity gains.

Use the guidelines in this paper to estimate the potential results in your DC.



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