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