Michael George, Jr.

President, Inc., Dallas, TX, USA

Mr. George is President of, Inc., providing support for its clients in the application of Digital Transformation to their process improvement initiatives. Mr. George is a Management Consultant and Co-author of Lean Six Sigma in the Age of Artificial Intelligence (McGraw-Hill, 2019). Mr. George is an accomplished Lean Six Sigma practitioner working to advance the application of AI solutions to drive improvements in warfighter readiness and organizational improvements in cycle time.

Mr. George is also President of Blackland Group, LLC. Mr. George is responsible for managing the investment portfolio companies of Blackland, including Kessington, LLC a holding of Blackland Aerospace, LP. Mr. George led the acquisition and financing of Kessington, LLC and Prikos & Becker, LLC, both portfolio companies of Blackland Aerospace, LP.

Prior to this, Mr. George was Vice President of Marketing & Business Development for George Group’s Federal Services division, and led George Group’s entry into the Federal Government marketplace. Mr. George was subsequently responsible for developing more than $160 Million worth of prime Government contracts for George Group. The contracts contributed significantly to George Group’s dramatic growth of 50% per year between 2004 and 2007.

Mr. George has an in depth understanding of planning and implementation of Lean Six Sigma, Artificial Intelligence, and other process improvement initiatives.

Mr. George holds a B.A. in Physics and a Master of Business Administration from Southern Methodist University.


Solving Mission Impossible: Using AI and IoT to Improve On-Time Delivery

Michael George Jr., co-author of Lean Six Sigma in the Age of Artificial Intelligence, will share how Artificial Intelligence (AI) and the Internet of Things (IoT) were combined with other improvement strategies to address a challenge and improve performance impossible to solve through traditional means and technologies. Specifically, this session will focus on the “mission impossible” challenges that need to be overcome to improve On-Time Delivery and OTIF (on-time in-full). Even with all the advances in ERP and other systems, companies (manufacturers, ecommerce, servicers and others) still struggle to achieve OTIF at levels much better than 50-60%.

Why is it so hard to be on-time consistently? What are the challenges with traditional methods? Current scheduling systems are “fire and forget.” Work, jobs or orders are launched into a system, and it is assumed based on standards, jobs will exit at such and such time.

Unfortunately, when variation and other unexpected issues occur, current scheduling, ERP and other systems can’t accurately predict delivery dates and are late recognizing when jobs will be delayed. System limitations, inability to account for unplanned issues, inaccurate delivery dates and no early warning signs all lead to missed deliveries and unhappy customers.

Unlike guidance systems for ballistic missiles that constantly adjust to hit a target, traditional scheduling systems provide no ability to do a “mid-course” correction to bring late jobs back to on-time status. Michael will briefly describe how AI solves the “Traveling Salesman Problem” and how it relates to on-time delivery and AI. More importantly, he will show how AI, IoT and other technology and approaches were and are used to do what traditional scheduling software and typical improvement methods cannot.

Michael will explore an example that resulted in a patented process that was used to give more accurate promise dates to customers and achieve a 98% on-time delivery rate. IoT and AI made the impossible possible, providing updated delivery times every 15 minutes while accounting for an almost infinite combination of changing factors from variation to absenteeism. Armed with enhanced real-time alerts to potential late deliveries, Michael will share the reprioritization and interventions management can now take to ensure on-time delivery. Even though this example is a manufacturing one, lessons learned are applicable to service organizations as well.


Government Organizations


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