In operational research, a Markov process is like predictingwhat's next in a game where each move depends only on where you arenow, not on how you got there. It's a way to understand and plan forfuture by looking at the present, without worrying about the past stepsyou took. This helps in making smart decisions in uncertain situations,like guessing what's likely to happen next in a system where thingschange randomly based on where they are now.EXAMPLE:let's say we're looking at a coffee shop's morning rush. Thenumber of customers arriving every 15 minutes can be seen as aMarkov process.Imagine the shop has three states based on the number of customers:Low (L), Moderate (M), and High (H). The transitions between thesestates could be something like:• If it's a Low period, there's a 60% chance it remains Low and a 40%chance it goes to Moderate in the next 15 minutes due to increasedfoot traffic.• If it's Moderate, there's a 50% chance it stays Moderate, a 30% chanceit goes to High if more customers start arriving, and a 20% chance itdrops to Low if people start leaving.• If it's High, there's a 70% chance it remains High, and a 30% chance itgoes to Moderate as the rush calms down.Using this setup, the coffee shop can predict the number of customersin the next time slot based only on the current number of customers,without considering how many came or left before.