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Order Management Optimization

Automated Inventory Management, order routing, demand forecasting and Supply Chain Optimization with AI

Problem

Order management in the pharmacy context refers to receiving, processing, and fulfilling medication orders. It involves various complex tasks, including inventory management, order tracking, and logistical coordination. Despite advancements in technology, many pharmaceutical companies still rely on manual and outdated systems to handle these processes. This manual approach often leads to inefficiencies, delays, and errors in the order management workflow.

Size of the Problem

  • The global order management market in the pharmaceutical sector is valued at $10 billion and is expected to grow at a compound annual growth rate of 11.5% between 2022 and 2028. (1)
  • 70% of pharmaceutical companies claim that order management is a critical challenge for their business. (2)
  • Pharmaceutical companies, on average, lose 2% of their revenue due to errors in order management. (3)
  • 85% of pharmaceutical companies state that order management automation is a priority for their business. (4)

Why it matters

Efficient order management in pharmacies is crucial for several reasons:

■ Patient Safety: Timely medication delivery is essential for patients, especially those with chronic illnesses who rely on a consistent supply of medications. Delays and errors in order fulfillment can have severe consequences for patient health and well-being.

■ Cost Reduction: Inefficient order management practices can lead to increased operational costs, such as excess inventory, stockouts, and manual labor. Optimizing order management processes can help reduce costs and improve the profitability of pharmaceutical companies.

■ Regulatory Compliance: The pharmaceutical industry is heavily regulated, with strict inventory tracking and traceability requirements. Non-compliance with these regulations can result in penalties, recalls, and damage to the company's reputation.

Solution

The application of generative and traditional artificial intelligence offers highly effective and specific solutions to address challenges in pharmacy order management. Here are concrete examples of how AI can make a difference:

■ Predictive Demand Planning: AI algorithms have the ability to analyze historical data, market patterns, and other relevant factors to make accurate predictions about the future demand for medications. This allows pharmaceutical companies to optimally adjust their inventory levels, minimizing the risk of situations such as stockouts or overstocking.

■ Intelligent Order Routing: AI-driven systems can automatically direct orders to the most appropriate distribution centers, considering factors such as inventory availability, proximity to the customer, and delivery time. This approach significantly optimizes the order fulfillment process, reducing delivery times and enhancing the customer experience.

■ Automated Inventory Management: AI can continuously and in real-time monitor inventory levels, automatically generating purchase orders when levels reach a predefined reorder point. This eliminates manual inventory checks and significantly reduces the risk of experiencing stockouts or unwanted overstock.

■ Supply Chain Optimization: Through AI algorithms, it is possible to comprehensively optimize the entire supply chain. This involves considering multiple factors such as demand, delivery timelines, transportation costs, and inventory levels. AI enables pharmaceutical companies to minimize operating costs, improve delivery times, and enhance the overall efficiency of the supply chain, thereby contributing to more efficient and profitable order management.

Datasources

  • Internal Order Histories: Records of past orders from the pharmacy provide valuable information about historical medication demand, delivery times, and other purchasing patterns.
  • Supplier Data: Supplier information regarding product availability, delivery lead times, and pricing is crucial for inventory management and replenishment decision-making.
  • Market Data: Market data sources, such as pharmaceutical industry reports, sales trend analyses, and competitor data, can help better understand the business environment and forecast future demand.
  • Patient Data: To ensure patient safety, accurate records of patients, their medical prescriptions, and specific medication needs are essential.
  • Real-Time Inventory Data: Real-time inventory management systems provide up-to-date information on stock levels, which is critical for avoiding stockouts or overstock.
  • Logistical Information: Data on logistics, such as transit times, shipping costs, and distribution routes, are essential for supply chain optimization and efficient order routing.

Citations

  1. Grand View Research. (April 14, 2023). Global Order Management in the Pharmaceutical Industry Market Size, Share & Trends Analysis Report by Component (Software, Services), by Deployment (On-Premise, Cloud-Based), by Organization Size (Large Enterprises, Small & Medium-Sized Enterprises (SMEs)), by Region, and Segment Forecasts, 2022-2028.
  2. Gartner identifies the top strategic technology trends for 2022. (October 18, 2021). Gartner.
  3. Deloitte. (March 28, 2022). The State of the Pharmaceutical Supply Chain in 2022.
  4. Future of pharma operations. (October 27, 2022). McKinsey & Company.

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