Inventory Control Methods in a Long-Term Care Pharmacy

Abstract

ArticleThe basic goals of inventory management are to minimize inventory investment while maintaining a balance in supply and demand that meets customer expectations. 1 Exploring and understanding the optimal utilization of inventory is critical to pharmacy operations. Health care service organizations, such as pharmacies, have lagged behind industrial and retail sectors in developing and implementing effective process improvement strategies and techniques. 2 Although representatives of pharmacies and other health care service organizations have made significant efforts implementing strategies to improve the delivery and quality of health care products and services, less effort has been spent on actual process improvement. The improvement focus of these organizations has been the end products or services rather than the actual processes that yield the end products or services. 1 Out-of-stock (OOS) events are indicators of inventory process failures in need of improvement. 3 An OOS event refers to an event that occurs when an item is unintentionally not available for sale for a certain contiguous time period. OOS events represent both a failure to meet customer expectations and an inventory control process failure. We defined inventory control for this study as the process of minimizing the investment in inventory while balancing supply and demand such that OOS events are reduced to customer specifications.One protocol designed to improve operational performance and eliminate process defects is Six Sigma. Six Sigma is a rigorous and focused implementation of data and statistical analysis. Six Sigma involves a simple model known as Define-Measure-Analyze-Improve-Control, or DMAIC. 4 BackgroundOrthodox inventory theory is founded on classical economic order quantity (EOQ) methodology, developed in 1915 by a Westinghouse employee. 5 According to orthodox 534073P MTXXX10.1177/8755122514534073Journal of Pharmacy TechnologyWatson and Moliver research-article2014Abstract Background: Current inventory theory is based on simulated data and unrealistic formulae. Inventory replenishment processes are therefore commonly disrupted, and out-of-stock (OOS) events are unnecessarily frequent. Objective: OOS events at a large-volume, long-term care pharmacy in North Carolina were compared among 4 sequentially applied methods of inventory control: (a) a manual system without Six Sigma protocol, (b) a manual system with Six Sigma protocol, (c) a computer-assisted system with Six Sigma protocol, and (d) an automated system with Six Sigma protocol. Methods: Daily OOS rates were recorded for 11 weeks during the implementation of each method. Between-group comparisons were performed, and time-series analyses were conducted during each implementation to determine the significance of the change in OOS rates over the evaluation period. Results: In terms of the 2 manual systems, OOS rates were lower for the system to which a Six Sigma protocol was applied. In terms of the 3 Six Sigma systems, ranked differences were significant. The computer...

Coping with demand volatility in retail pharmacies with the aid of big data exploration

“…In a simulation-based approach, Kroger Company -which operates 1,950 in-store pharmacies around the United States -managed to reduce out-of-stocks but no consideration was taken in terms of demand seasonality, trends and "disturbances" in demand pattern (Zhang et al, 2014). Watson et al (2014) compared the efficacy of various six sigma analytical models in improving the inventory replenishment policies and minimising out of stock rates in a large volume pharmacy setting. While out of stock rates were reduced, the research was based on process improvement and undesired "shortage" events with a lack of consideration of historical demand data.…”

“…Despite their numerous benefits, VAR models have been applied only scantily for demand estimation in pharmaceutical industry. Watson et al (2014) favour the application of advanced VAR models in minimising out of stock rates in pharmacies by emphasising that the assumptions of conventional inventory theories (e.g., EOQ policies) were insufficient and unrealistic. Kim et al (2015) attempted to evaluate the impact of user generated data from social media blogs by employing the VARX model in considering the impact of exogenous variables on upstream demand forecasting on pharmaceutical supply chains.…”