Optimization vs AI to make things Better

I was reminded that my early experiences with government and enterprise systems dealt with the optimization of systems.  That is, the mathematical means of linking a specific mathematical statement of a problem, with value goals and constraints, to a specific best possible solution.   We used the predecessors of ILOG, and CPlex directly for these problems.    We saved millions using these methods.  Of course optimization does not have the current hype.

Now how is AI, as it currently defined,  dissimilar from Optimization?    Usually because the Optimization approach is more specifically and numerically defined.     If AI uses human-like intelligence, it is usually not precisely mathematical.   And unfortunately not as closely tied to specific business process.   Not saying that AI cannot use optimization methods, it just usually does not.   So there should be a strong consideration towards using more precise and direct and process oriented methods.

Was pointed to this company that works the space, have never worked with them:

Optimization Direct Inc., co-founded by Dr. Robert Ashford, a pioneer in the field of optimization, and Dr. Alkis Vazacopoulos, a leader in the industry, markets IBM® ILOG® CPLEX Optimization Studio®, the world's leading software product for modeling and optimization.

CPLEX Optimization Studio* solves large-scale optimization problems and enables better business decisions and resulting financial benefits in areas such as supply chain management, operations, healthcare, retail, transportation, logistics and asset management. It has been applied in sectors as diverse as manufacturing, processing, distribution, retailing, transport, finance and investment.

CPLEX Optimization Studio is an analytical decision support toolkit for rapid development and deployment of optimization models using mathematical and constraint programming. It combines an integrated development environment (IDE) with the powerful Optimization Programming Language (OPL) and high-performance ILOG CPLEX optimizer solvers. CPLEX Optimization Studio enables clients to:

Optimize business decisions with high-performance optimization engines.

Develop and deploy optimization models quickly by using flexible interfaces and prebuilt deployment scenarios.

Create real-world applications that can significantly improve business outcomes. ...... "

Technical Experience Details

 Franz A. Dill

Substantial Consulting Considered in 2015

Data Decision Scientist.  In particular in application areas that use or combine data analytics, decision processes and cognitive interaction. Experienced in designing,evaluating and delivering systems in the enterprise to executives and decision makers in a Fortune100 company known for Analytics excellence. Experience with R, SAS, SPSS, Splunk, System Modeler, BPM systems, Tableau, Spotfire, Optimization Mathematics (CPlex). Lisp, Expert Systems ...   Managed the design, construction and delivery of dozens of systems that led to hundreds of millions of dollars in confirmed value. A sought out resource for the solution of the most wicked problems.  Consulting with IBM on the application of Watson based Advisory Systems.  A Writer, Explainer, Solver, Innovator.   Multiple University board and advisory connections. More in links below.

Franz A. Dill,   Retired Procter & Gamble

PKL Knowledge Partners LLC -  Data Decision Scientist

Cognitive Systems Institute

Advanced Analytics Consulting

Blog: http://eponymouspickle.blogspot.com

Short Bio: http://tinyurl.com/pabggc2

Phone:  (513) 405 7387

Twitter: @FranzD

Franzdill@gmail.com

Cincinnati, Ohio Area 

New Supply Chain Book

I was introduced to a new and visually instructive book on optimizing the supply chain:   Supply Chain Network Design: Applying Optimization and Analytics to the Global Supply Chain (FT Press Operations Management) by Michael Watson, Sara Lewis, Peter Cacioppi and Jay Jayaraman.  This book includes many of the methods which we used for years in the enterprise.  Now they are integrated in packages like iLog/Cplex.   As I peruse it, this book seems to include many of the approach details you need to solve these kinds of problems effectively. The methods are often too deeply hidden in books and manuals.   More information to follow as I seek use it to apply to a specific client interaction.

The book also has a site and blog.     #IBMSCGS

Business Analytics, Past and Future

I have been a long time practitioner of business analytics, the use of quantitative methods to improve business processes. Starting at the Defense Department, where we built military simulation models, and ending up at P&G.  My first project there, when I arrived in 1977, was to improve warehouse efficiency using IBM's mathematical programming system: MPSX.  That followed with using the same mathematical optimization approaches for scheduling, supply chain siting and executive decision making.

So I was happy to see in the July/August Issue of Analytics Magazine an article by Arnold Greenland on the history and current state of business analytics at IBM.    This allows me to reflect on the growth and impact of analytical methods at IBM and elsewhere.  Since then the original MPSX package has disappeared, and has been replaced by the acquired Ilog/Cplex, which goes far beyond the original package, adding nonlinear methods as well.

Shortly after being introduced to MPSX, in 1980, we addressed the analysis of unstructured data, typically consumer comments in unstructured text, using recently developed methods called  'Content Analysis'.  These permitted the semantic analysis of multiple human languages.  An early attempt to look at and understand unstructured 'Big Data'.

During all of this time we also used a number of statistical methods throughout the enterprise to explore and improve systems.  SPSS and SAS were in frequent use.  A package called Clementine allowed us to use advanced logical methods, like artificial neural nets, to implement what were essentially statistical methods, to store and implement specific decision rules.   These methods could then be inserted in both software and hardware processes.  Clementine also permitted the structural exploration of the decision process.  Ultimately Clementine was acquired by SPSS, and were eventually absorbed into Modeler.

During the 1990's, a heady time for artificial intelligence, we implemented expert systems using a now defunct language called M1.  Which were successful for a number of complex industrial management processes.  Some of the same capabilities can be seen in JRules.  That work has been extended into recent AI explorations like that of IBM's Watson.

What is further interesting now is that many of these methods are now available to the small and medium sized business.  We live in a time where the tools are available,  just go out and use them.