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Priority-based replenishment policy for robotic dispensing in central fill pharmacy systems: a simulation-based

Nieqing Cao1, Austin Marcus1, Lubna Altarawneh1

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A new priority-based replenishment policy for Robotic Dispensing Systems (RDS) in Central Fill Pharmacy Systems (CFPS) significantly reduces medication shortages and prescription fulfillment delays.

Keywords:
3D discrete-event simulationCentral fill pharmacy systemMedication replenishmentPrescription fulfillmentPriority-based policyRobotic dispensing system

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Pharmacy Automation

Background:

  • Central Fill Pharmacy Systems (CFPS) utilize Robotic Dispensing Systems (RDS) for efficient, high-volume prescription fulfillment.
  • Timely replenishment of medication pills in RDS is critical to prevent shortages and prescription delays.
  • Existing replenishment processes lack systematic approaches due to complex system dynamics.

Purpose of the Study:

  • To develop an improved, priority-based replenishment policy for RDS within CFPS.
  • To address the need for systematic control policies in automated pharmacy fulfillment.
  • To enhance the efficiency and reliability of medication replenishment in automated systems.

Main Methods:

  • Development of a novel criticality function to assess refilling urgency for RDS canisters.
  • Implementation of a priority-based replenishment policy using inventory levels and consumption rates.
  • Utilizing 3D discrete-event simulation to emulate RDS operations and evaluate the policy.

Main Results:

  • The proposed policy effectively generates real-time replenishment sequences for RDS.
  • Numerical experiments demonstrated prevention of over 90% of machine inventory shortages.
  • Significant savings of nearly 80% in product fulfillment delays were achieved.

Conclusions:

  • The priority-based replenishment policy offers a practical and effective solution for RDS.
  • The policy enhances RDS replenishment processes, improving overall CFPS efficiency.
  • This approach is easily implementable and significantly reduces operational disruptions.