In the ever-evolving landscape of the pharmaceutical industry, accurate forecasting of product returns has become a critical component of financial health. As blockbuster drugs face patent expirations and generic competitors enter the market, pharmaceutical companies must navigate complex challenges to maintain their financial fitness. This article delves into the intricacies of forecasting product returns, exploring its importance, methodologies, and impact on the pharmaceutical sector.
Table of Contents
The Significance of Product Returns in Pharmaceuticals
Product returns are an inevitable reality in the pharmaceutical industry. When generic alternatives hit the market, pharmacies and other points of care often find themselves with excess inventory of brand-name products. This surplus can lead to significant financial implications for pharmaceutical manufacturers if not properly managed.
Why Returns Happen
Returns typically occur when pharmacies have substantial inventory of a brand-name product at the time of generic launch. As the market share of brand-name prescriptions inevitably declines, pharmacies struggle to utilize their existing stock. Manufacturer return policies often protect pharmacies by allowing them to receive credit for at least 90% of the original purchase price[1].
The Extended Timeline of Returns
One crucial factor that complicates return forecasting is the extended timeline over which returns can occur. Brand-name drugs can often be returned for credit up to a year after their expiration date. Considering that drugs usually have 2.5 to 3 years of shelf life remaining when they reach pharmacies, returns can potentially happen up to four years after loss of exclusivity[1].
The Challenge of Forecasting Reserves
Accurately predicting reserve levels as a drug approaches loss of exclusivity is a complex task. However, new techniques are emerging to enhance forecast precision.
Data-Driven Forecasting Models
Modern forecasting models leverage data from multiple sources to improve accuracy. These include:
- Wholesale distribution center EDI 852 data
- Wholesaler EDI 867 data
- Targeted pharmacy surveys
- Historical returns data from analogous products
By combining these diverse data streams, manufacturers can create more robust and accurate forecasts[1].
Optimizing Forecasts and Return Estimates
Pharmaceutical manufacturers aim to strike a delicate balance between allowing for predicted returns and maintaining sufficient channel inventories to avoid lost sales. Several strategies can help optimize this process:
Early Identification of High-Inventory Chains
By pinpointing pharmacy chains with elevated inventory levels early on, manufacturers can work to ensure these inventories are appropriately reduced before loss of exclusivity occurs[1].
Facilitating Inventory Redistribution
Identifying opportunities for pharmacies to return inventory to wholesalers’ resalable stock after loss of exclusivity can help minimize direct returns to manufacturers[1].
Careful Order Management
Closely managing wholesaler orders in the weeks leading up to loss of exclusivity can help minimize both wholesaler inventory and downstream inventory speculation[1].
Refining Returns Policies
Tightening returns policies to discourage the crediting of “bundled” returns can further optimize the return process[1].
The Financial Impact of Accurate Forecasting
The importance of precise return forecasting cannot be overstated. Accurate predictions can save brand-name pharmaceutical companies tens of millions of dollars, significantly impacting investor returns and company valuations[1].
“Accurate forecasting of returns surrounding loss of exclusivity can save brand name pharmaceutical companies tens of millions of dollars, affecting how investments in those companies fare.”[1]
Beyond Traditional Forecasting: Patient-Level Approaches
While traditional top-down forecasting methods have their place, particularly in early-stage drug development, they often fall short as products move closer to launch. As brand teams begin developing strategic pricing, marketing, and sales plans, a more nuanced approach becomes necessary.
The Power of Patient-Level Forecasting
Patient-level forecasting models, such as those developed by IQVIA’s Market Access Strategy Consulting practice, offer a more dynamic and granular approach to market prediction. These models account for market dynamics as well as prescriber, patient, and payer behavior, providing the flexibility required to build complex scenarios and validate key brand assumptions[2].
Measuring Available Market Volume
One key advantage of patient-level forecasting is its ability to more accurately measure the available market volume. While traditional approaches might stop at identifying the total number of patients eligible for treatment, patient-level models go further. They evaluate the portion of the population that actually becomes available for treatment with the new product, accounting for factors such as patients already stable on existing therapies[2].
The Role of Gross-to-Net Impact in Forecasting
Another critical aspect often overlooked in traditional forecasting methods is the gross-to-net impact on the brand. While some forecasts may estimate costs like COGS, payer rebates, and patient assistance programs, patient-level approaches use real-world payer and patient-level data to provide a more accurate picture of revenue potential[2].
The Importance of Agile Forecasting in Healthcare
In today’s rapidly changing healthcare landscape, the ability to produce quick and frequent financial forecasts using the most recent data is crucial. This agility allows healthcare organizations to adapt to hard-to-predict swings in demand and ongoing regulatory uncertainty.
The Role of Advanced ERP Systems
Cloud-based ERP systems, such as NetSuite’s solution for healthcare and life sciences, provide organizations with the data visibility, transparency, and tools needed to navigate this complexity. These systems enable forward-looking, actionable forecasting that helps organizations operate more effectively in an ever-changing environment[3].
Modern Forecasting Techniques in Healthcare
Healthcare financial professionals are increasingly moving away from fixed annual budgets based on historical data and towards more real-time forecasting and agile planning. Three key techniques are emerging as particularly valuable:
- Driver-based planning
- Rolling forecasts
- Scenario planning
These techniques work best in combination, with driver-based planning focusing on the root causes of financial results, rolling forecasts setting a more accelerated cadence, and scenario planning analyzing various future possibilities to manage their potential impact on performance[3].
The Multifaceted Nature of Healthcare Forecasting
Forecasting in healthcare extends beyond financial management. It plays crucial roles in clinical decision-making and informing government agencies. However, for the purposes of this article, we’ll focus on its financial applications.
Types of Data Used in Healthcare Financial Forecasting
Accurate, up-to-date financial forecasts in healthcare require data from various internal and external sources, including:
- Patient electronic health records
- Clinical data (e.g., readmission rates)
- Financial data (revenue, expenses, cash flow)
- Patient volume
- Insurance claim data
- Economic statistics
- Demographic information
- Regulatory change requirements[3]
The Frequency of Forecast Updates
The optimal frequency for updating healthcare financial forecasts depends on the volatility of the specific provider’s market. However, a typical cadence is monthly or quarterly, with a rolling 12-month outlook[3].
The Importance of Forecast Accuracy Metrics
While forecast accuracy is crucial, it’s important to remember that it’s a means to an end, not an end in itself. The ultimate goal is to achieve efficiency, profitability, and optimal business results.
Choosing the Right Metrics
When measuring forecast accuracy, it’s essential to use metrics that match your planning processes. This often involves using several metrics in combination and choosing the right aggregation level, weighting, and lag for each purpose[4].
Continuous Monitoring and Exception-Based Processes
Given the large number of forecasts typically involved in retail or supply chain planning, it’s crucial to implement an exception-based process for monitoring accuracy. This approach helps demand planners focus on the most relevant deviations without getting overwhelmed by the sheer volume of data[4].
The Role of Product Classification in Forecast Monitoring
Effective forecast monitoring often involves classifying products based on their importance and predictability. A simple starting point is to use ABC classification (based on sales value) and XYZ classification (based on sales frequency)[4].
Tailoring Exception Thresholds
For high-value, high-frequency products (e.g., AX products), high forecast accuracy is both realistic and crucial. Therefore, exception thresholds should be kept low, and reactions to forecast errors should be swift. Conversely, for products with low sales frequency, the process must be more tolerant of forecasting errors[4].
Special Considerations in Forecast Monitoring
Certain products or situations may require special attention in forecast monitoring. For example:
- Fresh or short-shelf-life products
- New types of promotions
- Product introductions
These scenarios often warrant careful monitoring as forecast errors can quickly translate into waste or lost sales[4].
The Limitations of Black Box Forecasting Systems
Some forecasting systems on the market operate like black boxes, where data goes in and forecasts come out without much visibility into the process. While this approach might work if forecasts were 100% accurate, the reality is that forecasts are never fully reliable.
The Importance of Actionable Insights
To derive value from monitoring forecast accuracy, you need to be able to react to exceptions. Simply manually correcting erroneous forecasts is not a long-term solution, as it does nothing to improve the forecasting process itself[4].
Best Practices for Effective Forecasting
To maximize the value of your forecasting efforts, consider the following best practices:
- Focus on what matters: Measure what you need to achieve, such as efficiency or profitability, and use this information to focus on situations where good forecasting makes a significant difference.
- Understand the role of forecasts: Remember that forecasting is a means to an end, not an end in itself. It’s a tool to help you achieve better business results.
- Use multiple metrics: Ensure your forecast accuracy metrics match your planning processes and use several metrics in combination for the best insights.
- Automate monitoring: Implement automated systems to highlight only relevant exceptions, allowing your team to focus on what matters most.
- Compare like with like: When benchmarking your forecast accuracy against other companies, ensure you’re comparing similar businesses and understand how the metrics are calculated[4].
The Broader Context of Forecasting in Business Performance
While accurate forecasting is crucial, it’s important to view it as part of a broader performance management cycle. Leading organizations recognize that reliable forecasting is an essential component of their efforts to create and sustain business value.
Forecasting as a Core Business Capability
Rather than treating forecasting as merely a finance function responsibility, successful companies view it as a core business capability. They use forecasting to drive performance, identify opportunities and risks, and as the foundation for communicating with investors[5].
The Cost of Neglecting Forecasting
Organizations that neglect the forecasting process often incur costs in two significant ways:
- Unreliable business information leads to poor decision-making, despite the resources invested in developing forecasts.
- Inability to provide investors with the transparency and insight they demand, potentially affecting investor confidence and company valuation[5].
Integrating Forecasting into Performance Management
While some organizations attempt to improve forecasting by tweaking their budgeting or other traditional decision support processes, the most successful approach is to tackle these improvements in the context of an integrated performance management cycle[5].
“Without reliable forecasting at the heart of their performance management process, key opportunities and risks are likely to be missed.”[5]
Key Takeaways
- Accurate forecasting of product returns is crucial for pharmaceutical companies’ financial health, especially as drugs approach loss of exclusivity.
- Modern forecasting models leverage diverse data sources, including wholesale distribution data, pharmacy surveys, and historical returns data.
- Patient-level forecasting approaches offer more granular and dynamic predictions compared to traditional top-down methods.
- Agile forecasting, supported by advanced ERP systems, is essential in today’s rapidly changing healthcare landscape.
- Effective forecast monitoring involves using multiple metrics, implementing exception-based processes, and tailoring approaches based on product classification.
- Forecasting should be viewed as a core business capability integrated into the broader performance management cycle.
- Neglecting forecasting can lead to poor decision-making and reduced investor confidence.
- Continuous improvement and adaptation of forecasting processes are necessary to maintain financial fitness in the pharmaceutical industry.
FAQs
- Q: How long can pharmaceutical product returns typically occur after loss of exclusivity?
A: Product returns can potentially happen up to four years after loss of exclusivity, considering the extended shelf life of drugs and the typical return policies in place. - Q: What are some key data sources used in modern pharmaceutical forecasting models?
A: Modern forecasting models often use wholesale distribution center EDI 852 data, wholesaler EDI 867 data, targeted pharmacy surveys, and historical returns data from analogous products. - Q: How does patient-level forecasting differ from traditional top-down approaches?
A: Patient-level forecasting offers a more granular and dynamic approach, accounting for market dynamics, prescriber behavior, patient behavior, and payer behavior, allowing for more complex scenario building and assumption validation. - Q: Why is it important to use multiple metrics when measuring forecast accuracy?
A: Using multiple metrics provides a more comprehensive view of forecast performance, as different metrics can capture various aspects of accuracy and help identify areas for improvement. - Q: How can pharmaceutical companies optimize their forecasts and return estimates?
A: Companies can optimize forecasts by early identification of high-inventory chains, facilitating inventory redistribution, careful order management leading up to loss of exclusivity, and refining returns policies.
Sources cited:
[1] https://www.drugpatentwatch.com/blog/accurate-forecasting-product-returns/
[2] https://www.iqvia.com/locations/united-states/blogs/2020/12/patient-level-forecasting-and-asset-evaluation
[3] https://www.netsuite.com/portal/resource/articles/financial-management/healthcare-forecasting-tips.shtml
[4] https://www.relexsolutions.com/resources/measuring-forecast-accuracy/
[5] https://assets.kpmg.com/content/dam/kpmg/pdf/2016/07/forecasting-with-confidence.pdf
