The River Logic Blog

Eric Kelso

Eric Kelso is Vice President of Product Management for Enterprise Optimizer at River Logic. He has over two decades experience building and implementing prescriptive analytics optimization software, and has successfully completed more than 50 major projects in the discrete, process manufacturing and consumer packaged goods industries.
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Recent Posts

A Prescriptive-First Approach to Driving Business Impact with Analytics – Part I

Author’s comment: this is part 1 of 2 of a hypothetical case study. The story described below can be any company willing to consider using a Prescriptive Analytics approach to improve their decision-making abilities.

Prescriptive Analytics: It’s More Than Rules and Big Data

I recently read Prescriptive models take analytics one step beyond, written by Scott Robinson, Louisville Metro government, and published by SearchBusinessAnalytics.

­­­­Enterprise Optimizer Modeling Series: A Chemical Company Example

This is the first blog post in a series aimed at highlighting important Enterprise Optimizer® (EO) modeling features and uses. All information below is based on an EO model created for a real customer. High-level background description is provided, but no confidential information is disclosed.

Background

River Logic partnered with a Top 5 consulting company to design, build and implement a Sales & Operations Planning (S&OP) modeling platform for a large chemical company (“ChemCo”) with operations throughout the Americas and Europe. The impetus for this project was a definable gap in their S&OP planning process.

Microsoft Office Excel Scenario Manager and Prescriptive Analytics

River Logic’s Enterprise Optimizer® and Microsoft Office Excel® have a long and close working relationship. For two decades, EO’s prescriptive analytics-based models have read data from and written data to Excel workbooks. Consistently one of EO’s most popular data sources, Excel makes building prototype EO models or conducting quick, one-off consulting projects considerably easier. It has also been an excellent option to analyze EO model solution results, either with Excel’s built-in features or using add-on technologies like Tableau.

8 Data Requirements for Advanced Logistics Modeling

As I began to write about advanced logistics modeling, the first thing that popped into my head, literally, was “damn motorcycles”. I’m currently at 35,000 feet, flying home after a couple weeks with my wife visiting relatives and friends in Thailand. My mother-in-law’s home in central Bangkok used to be on a quiet, dead end street (“soi”, in Thai). At least, that was until about 10 years ago, when a major Asian parcel delivery company determined (somehow) that the best location for their Thai head office was further in our soi!

Monte Carlo Optimization for Beginners

The Monte Carlo method is a well-known simulation technique that uses statistical random sampling to solve mathematical problems. In use for about 85 years, many variants exist across a wide range of disciplines. If not familiar, I suggest reading this Wiki page.

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