Pharmacy Capacity Scheduling for Reliable Prescription Delivery Dates

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional fulfillment systems fail to accurately predict site capacities of pharmacies, leading to missed commitments and significant resource expenditure when fulfillment dates cannot be met, causing delays and potential spoilage of medications.

Innovation Solution

An integrated system that employs real-time scheduling and machine learning to analyze various factors influencing pharmacy capacities, such as employee availability, historical data, weather, local and national events, and inventory, to determine accurate fulfillment dates and guide users in selecting viable delivery options.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional fulfillment systems provide fixed fulfillment dates without capacity analysis, then users can select any date freely, but the system cannot meet commitments due to overcapacity leading to missed deadlines and resource waste

Engineering Contradiction:

Improvecommitment fulfillment reliabilityVSAvoidmedication delivery delay

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary capacity analysis and site capacity predictions before users select fulfillment dates. By analyzing historical data, employee availability, weather conditions, and local events in advance, the system identifies feasible fulfillment dates and presents them to users beforehand, preventing overcapacity situations and ensuring commitments can be met.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors actual fulfillment performance and compares it with predictions, using this feedback to refine capacity models. The system also provides real-time updates to users about fulfillment status and adjusts future capacity predictions based on actual outcomes, creating a closed-loop system that improves reliability over time.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If the system analyzes multiple factors to predict site capacities accurately, then fulfillment date accuracy improves, but computational resources and system complexity increase

Engineering Contradiction:

Improvesite capacity prediction accuracyVSAvoidscheduling system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments capacity prediction into multiple independent models, each handling specific factors such as employee availability, weather conditions, local events, and historical performance. This modular approach allows the system to maintain high prediction accuracy while managing complexity through separate, specialized components that can be developed and maintained independently.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts the weight and importance of different prediction parameters based on current conditions and historical performance. Rather than using a fixed complex model, the system changes parameters adaptively, focusing computational resources on the most relevant factors for each specific prediction scenario, thereby maintaining accuracy while reducing unnecessary complexity.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If the system processes capacity data for multiple dates and pharmacies, then user delivery options increase, but computational resources and processing time increase

Engineering Contradiction:

Improvedelivery date optionsVSAvoidcomputational resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system performs capacity analysis for a focused subset of dates and pharmacies that are most relevant to each user request, rather than analyzing all possible combinations. By applying partial action principles, the system provides sufficient delivery options for users while avoiding the exponential computational burden of evaluating every possible fulfillment scenario across all pharmacies and dates.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system pre-calculates and caches capacity data for multiple dates and pharmacies in advance, storing results that can be quickly retrieved during user interactions. This preliminary processing reduces real-time computational requirements while still providing comprehensive delivery options, as the heavy lifting is performed beforehand when computational resources are more readily available.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12561644B2Smart order scheduling leveraging real-time fulfillment site capacity data

Publication Date: 2026.02.24 EXPRESS SCRIPTS STRATEGIC DEVELOPMENT INC

AI summary

Apparatuses, systems, and methods relate to technology to receive an electronic request from a user device of a user to fill a prescription, identify that the prescription is associated with a first medicine, identify a first pharmacy that is capable of providing the first medicine, and predict site capacities for the first pharmacy for a plurality of dates. The technology further determines a subset of dates from the plurality of dates based on the site capacities, determines delivery dates for the first medicine based on the subset of dates, and provides the delivery dates to the user device.