Modeling package size–conscious automated dispensing cabinet replenishment to improve efficiency

Abstract

Abstract Purpose Optimization of automated dispensing cabinets (ADCs) has traditionally focused on modifying the inventory within these devices and ignored the replenishment process itself. Rounding replenishment quantities to the nearest package size, termed package size–conscious replenishment (PSCR), was investigated as a way to optimize labor needs for ADC replenishment. Methods A simulation of PSCR for a subset of medications stocked in ADCs at the University of North Carolina Medical Center was conducted. The simulation utilized real-world vend data and rounding factors to model the impact of PSCR on key ADC metrics. The final simulation utilized 2 months of ADC transactions across 410 medications in 149 ADCs. Four replenishment methodologies were simulated: standard replenishment and 3 PSCR strategies, including rounding down, rounding any direction, and rounding up. Results All 3 PSCR methodologies had significantly lower stockout frequencies than standard replenishment at 0.722% (P = 0.026) for rounding down, 0.698% (P = 0.024) for rounding any direction, and 0.680% (P = 0.024) for rounding up vs 0.773% for standard replenishment. PSCR methods were associated with significant time savings for both technician and pharmacist activities (P < 0.001 for all 3 strategies), with a savings of up to 0.27 technician and 0.52 pharmacist full-time equivalents estimated for the rounding-up methodology. Maximum carrying cost was higher for all 3 PSCR methodologies. Conclusion PSCR was modeled to significantly decrease both pharmacist and technician time needed to replenish ADCs while also decreasing stockout frequency. Modest increases in maximum carrying cost were also shown. The simulation created for this evaluation could also be utilized to model other components of the ADC replenishment process.