The pharmaceutical retail sector has undergone a remarkable evolution, expanding its range of products and services to meet the growing needs of customers. Traditional brick-and-mortar pharmacies have diversified their offerings, catering to a broader spectrum of healthcare requirements. With an increasing number of customers to be served, pharmacies face the challenge of guaranteeing a swift service while keeping it attentive and personalized.
The main challenge faced by our client was the increasing number of people in their pharmacies, which combined with a time-consuming and inefficient selling process led to increased waiting times and, consequently, a negative perception of service quality and the potential of losing customers. Due to the multi-factor nature of the problem, our goal was to perform a thorough diagnostic to identify the most promising impacting causes and develop a clear roadmap of initiatives to be implemented in the pharmacies and central operations, associated with a quantitative estimative of the expected benefits associated with its implementation.
The solution began with an in-depth observational study of several pharmacies. The team spent significant time on-site, studying daily routines, and pinpointing the bottlenecks that contributed to the long waiting times. To aid this, a toolset was developed to track each task with timestamps. This data-driven approach helped in comprehending workflow efficiency, which in turn, allowed us to identify tasks that were consuming too much time and could therefore be targeted for improvements.
Since the client’s queuing solution included different types of “tickets” – normal, special, and priority – we sought to deepen our understanding of the customer journey for each category. This enabled us to analyze specific tasks and bottlenecks associated with each ticket type and craft targeted solutions accordingly.
With a solid understanding of the daily operations, we embarked on mapping the processes for each ticket type and identified the key tasks that contributed to the waiting lines. We meticulously analyzed potential improvements to these tasks, with a broad scope that included changes to the information system, modifications to employee tasks, organizational shifts, and even physical changes to the pharmacy layout and the provision of consumables.
We also recognized the need to test certain ‘what-if’ scenarios that would be impossible to physically implement without disrupting the pharmacy operation. Queries like ‘Should we open an express counter for normal tickets?‘ or ‘What is the optimal distribution of the staff between shifts?‘ needed answering.
To resolve these, we developed an event-based simulation tool that could simulate the pharmacy environment, essentially creating a digital twin of the pharmacy queues.
This tool factored in customer arrival rates, the number of counters, the number of employees on duty during each shift, and the way counters were assigned to different ticket types, allowing us to predict the expected distribution of the waiting times for different configurations without having to implement it physically.
Equipped with a plethora of insights from the observational study, process mapping, potential improvements analysis, simulation modeling, and several discussions with the stakeholders, we were ready to present a comprehensive plan of action. We thoroughly detailed every possible improvement, deep diving into the pain points identified within the process.
One notable result from the project, highly supported in the simulation analyses, was the recommendation against implementing a new express counter for normal tickets. Contrary to the client’s initial assumptions, our simulations demonstrated that this would merely add complexity to the system, increasing the global waiting time without a significant reduction in the express line. This realization was pivotal, helping the company avoid unnecessary resource expenditure and potential operational disorder.
This process of data-driven analysis, open discussion with stakeholders, and rigorous modeling of potential solutions exemplified a structured analytical approach to problem-solving. It ensured that the proposed improvements were not just theoretically sound but also practically feasible and aligned with the business’s real-world dynamics.
All the recommendations were consolidated into a clear roadmap for the company, dividing the proposed enhancements into priority bins based on a conjunction of the ease of implementation and the operational impact. The implementation of the two main priority groups of enhancements is expected to decrease the average waiting time by 45%-65% and the probability of a customer waiting more than 10 minutes by 47%-70%.