When it comes to cash logistics for large cash point networks, it’s vital that you produce daily replenishment plans. In other words, the right cash, at the right time, in the right cashpoints.  

Arute’s Cash+ -Cash Optimization- software utilizes Machine Learning combined with Mathematical Optimization powered by Gurobi for constructing daily optimal replenishment plans. 

Moreover, using the same engine but by changing parameters and the constraints of the optimization model, more “Simulation Runs” can quickly be created to answer the following type of questions for Cash Logistics Planning:

• Cash-in-Transit (CIT) Amounts – Can the operation support more CIT? If so, how much CIT do we need for better availability?  Or should we outsource some CIT for optimal planning and how should we negotiate the prices? 
   
• Balance Reduction – If the cost of cash increases due to interest rates, how much balance should we decrease for minimizing cost? And could our CIT operation handle this if CIT numbers increase?  

• ATM Configurations – What is the ideal cassette configuration for the ATM network to achieve the lowest cost or to recycle more or to replenish less? 

Using the simulation methodology, cash planners, can try out various scenarios quickly to identify the impact of important operation-changing decisions before committing to them.  

In this talk, Mehmet Arıkkan, CEO of Arute Solutions, will detail the simulation method and provide solid examples of how simulation can be used to plan overall cash operations more efficiently. 

Even if you’re not in the business of cash logistics, this webinar will demonstrate how you can apply simulations of an optimization model to explore the impact of business decisions, before converting those decisions into action.