Caplinger's Seafood has turned into a local place in Indiana, famous for offering seafood that boasts the freshness you'd find in a coastal area along the Gulf in Florida or Louisiana or somewhere in Alaska, Washington (state), or Idaho.

When the owners shifted their focus from the main dishes to the sides on the menu, they discovered exciting prospects, thanks to a clever application of linear and non-linear modeling.

In this episode, I look closely at Seidelson's (2020) case study first, to find out how Caplinger's in Indianapolis employed linear and non-linear programming to evaluate labor costs, forecast demand, and determine profitability for delicious items like chipotle slaw, fried okra, and the classic macaroni and cheese.

I investigate how decisions were made, and why they did not simply set out after the maximum output. As a result, a decision-making system was developed that they will continue implementing over time.

Additionally, this episode showcases information that inland seafood markets, cozy small-town diners, or local favorites benefit from by applying research to discover opportunities and apply directed improvements. This helps restaurants and similar enterprises discover there can be a level-headed agreement between statistical analysis and practical effectiveness in a vibrant environment like a kitchen.