Demand Forecasting Boosts Healthcare Logistics by 80% | Quantzig

What You’ll Find in this Case Study:

• Demand Forecasting Models
• The Need for Implementing Healthcare Forecasting Techniques
• Engagement Overview
• About the Client
• Healthcare Forecasting Challenge
• Healthcare Forecasting Business Impact
• Healthcare Forecasting Insights

Demand Forecasting Methods

Demand forecasting is a systematic measure of the future demand for a service or a product. These methods are basically divided into two, namely, survey-based methods and statistical methods.

• Survey-Based Methods: This is one of the most common and widely used demand forecasting methods. It encompasses the future plans and intentions of customers. Survey-based methods rely on customer surveys to identify the demand for an organizations offerings as well as to anticipate its future demand.

• Statistical Methods: Statistical forecasting methods are complex when compared with survey-based methods. Unlike other methods used to forecast demand, the statistical methods rely on the information obtained from industry forecasts along with cross-sectional and historical data obtained from several sources.

Best practices and tools for demand forecasting in healthcare

1. Comprehensive Data Integration and Analysis:

Accurate pharmaceutical demand forecasting necessitates a holistic understanding of various demand drivers. Leveraging comprehensive data sets that encompass historical utilization data, population projections, and epidemiological shifts provides a nuanced perspective. Utilizing advanced analytics software and tools, such as Clarify Health, allows healthcare organizations to integrate and analyze data effectively. By examining patient journey data, admission rates, and service line usage, healthcare leaders can identify patterns and variables that influence demand. This approach enables the development of informed predictions, considering factors like seasonal variations, demographic changes, and specific health conditions prevalent in the local population. A robust demand forecasting process relies on a complete picture of market dynamics, ensuring healthcare providers allocate resources efficiently and plan for future healthcare utilization needs.

2. Predictive Analytics for Future Utilization Trending:

To anticipate future healthcare utilization needs, healthcare organizations should adopt predictive analytics tools. These tools leverage historical utilization data and population projection data to forecast demand for specific services accurately. By analyzing prevalence of diseases and understanding consumer-centric care journey patterns, healthcare leaders can make high-confidence strategic plans for the next three to five years. Predictive analytics tools also consider external factors such as economic indicators and epidemiological data, enabling organizations to align growth opportunities with market demands. Clarify Health, as an example, facilitates effective growth strategy formulation by predicting future healthcare consumption behaviors and consumer preferences. This proactive approach ensures healthcare providers are well-prepared to meet the evolving demands of their patient populations.

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3. Real-time Data Visualization for Informed Decision-Making:

Implementing data visualization tools is crucial for healthcare organizations aiming to validate, monitor, and communicate demand forecasts effectively. By using analytics tools that provide a complete view of granular data, healthcare leaders can identify gaps in services, estimate patient numbers, and understand the effectiveness of outreach programs. Visualization tools enable the presentation of complex information in a clear and concise manner, facilitating data-driven decision-making. This transparency aids in communicating strategic plans, market positions, and growth potential to stakeholders. Access to real-time data visualization empowers healthcare leaders to adapt their strategies promptly based on emerging trends, improving the accuracy of demand forecasts and ensuring alignment with patient needs.

4. Continuous Benchmarking and Forecast Error Analysis:

To enhance the accuracy of healthcare demand forecasts, organizations should implement continuous benchmarking and forecast error analysis. Utilizing metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) helps assess the reliability of forecasting methods. This practice enables healthcare leaders to validate forecasts against actual outcomes and refine forecasting processes over time. By identifying and addressing forecast errors, organizations can improve the precision of their predictions and make necessary adjustments to strategic plans. This iterative approach ensures that healthcare leaders learn from historical data, refine forecasting techniques, and maintain a proactive stance in addressing healthcare demand and supply dynamics in competitive markets.

The need for Data-driven decision-making in healthcare:

1. Enhancing Strategic Planning through Data-Driven Insights:

In the rapidly evolving healthcare industry, strategic planning is essential for organizations to navigate competitive markets, address financial pressures, and ensure optimal patient care. The need for data-driven decision-making becomes evident as organizations strive to gain a comprehensive understanding of patient populations, market dynamics, and service utilization patterns. Leveraging tools and software for healthcare analytics allows leaders to extract valuable insights from historical data, benchmarks, and demand forecasts. These insights empower healthcare leaders to formulate robust strategic plans that align with the nuanced patterns of patient volume, loyalty, and competitive positioning. By embracing a consumer-centric approach rooted in real consumer-centric care insights, organizations can anticipate and proactively address patient leakage, thereby enhancing their market position and sustaining growth over time.

2. Improving Resource Allocation and Operational Efficiency:

Data-driven decision-making is imperative for healthcare organizations seeking to optimize resource allocation and enhance operational efficiency. Through accurate pharmaceutical demand forecasting analytics, organizations can analyze granular data, local population needs, common health conditions, and socioeconomic factors influencing healthcare demand. This nuanced understanding enables precise allocation of resources, ensuring that healthcare services are tailored to meet specific locality and service category demands. Additionally, by incorporating tools for healthcare supply analytics, organizations can align supply and demand dynamics effectively, preventing overutilization or underutilization of resources. This strategic alignment fosters a balanced healthcare delivery process, minimizes waste, and contributes to cost-effectiveness, all critical factors in the face of financial pressures and competitive markets.

3. Adapting to Changing Healthcare Utilization Trends:

The healthcare industry experiences continuous shifts in utilization patterns influenced by factors such as demographic changes, epidemiological shifts, and evolving consumer preferences. Data-driven decision-making is essential for anticipating and adapting to these changes, ensuring healthcare organizations remain agile and responsive. With the aid of predictive analytics tools, organizations can forecast future healthcare utilization needs and consumption behaviors. By considering external factors, economic indicators, and market dynamics, healthcare leaders can develop high-confidence strategic plans for a three to five-year horizon. This proactive approach allows organizations to align growth opportunities with changing demands, fostering superior care, patient loyalty, and a competitive edge in the dynamic healthcare environment.

4. Enhancing Patient-Centric Care through Informed Decision-Making:

To provide patient-centric care, healthcare organizations must prioritize data-driven decision-making to understand and meet patients’ needs effectively. A consumer-centric approach involves analyzing complete data sets that encompass patient journey data, service offerings, and unique demands within a locality. By implementing effective data visualization tools, healthcare leaders can communicate and collaborate on care delivery processes, ensuring a comprehensive view of patient populations and their healthcare journeys. Real-time insights enable healthcare providers to identify gaps in services, estimate patient numbers accurately, and tailor outreach programs. This personalized approach not only enhances patient satisfaction but also contributes to the effectiveness of healthcare delivery. Data-driven decision-making thus becomes the cornerstone for healthcare organizations striving to advance planning, address patient needs, and deliver high-quality, patient-centric care in an ever-evolving industry.

The Need for Implementing Healthcare Forecasting Techniques

Virtually every industry today is challenged by the dynamic nature of customer expectations, regulatory laws, market dynamics, and the ongoing advancements in technology – all of which are compelling factors for players across industries. Also, due to such factors companies are being forced to re-examine their demand management and pharmaceutical demand forecasting techniques.

However, a major challenge in demand forecasting revolves around the identification of the right demand forecasting techniques to suit their needs. There are several techniques and there is no standard technique that empowers an organization to identify future risks. Hence, it is essential to keep abreast of the different techniques within a particular industry.

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Engagement Overview

The client operating in the healthcare logistics sector wanted to leverage our expertise to identify the accurate pharmaceutical demand forecasting for enhancing business efficiency. They were looking at devising effective analytics-based strategies to enhance efficiency and calculate optimum inventory levels. This engagement not only helped them achieve their set goals but also played a key role in identifying critical parameters for future success.

About the Client

A global healthcare logistics service provider. The client is a market leader in the logistics industry, specializing in the development, packaging, and distribution of unique healthcare products to meet the needs of healthcare companies.

Healthcare Forecasting Challenge

The healthcare logistics service provider was facing several challenges due to logistics disruptions at multiple inflection points. To tackle this challenge, the client wanted to evaluate the forecasting methods adopted by their peers and implement the best forecasting techniques to avoid adverse impacts on their bottom line.

Healthcare Forecasting Business Impact

The solutions offered by Quantzig enabled the healthcare logistics service provider to achieve a remarkable 80% improvement in their ability. The pharmaceutical demand forecasting was devised using qualitative and quantitative information. Also, the adopted methods generated forecasts translated into greater visibility across business functions, improvements in profit margins, and enhanced efficiency and helped them identify balance points and inventory optimization techniques.

Healthcare Forecasting Insights

The complexities associated with demand forecasting methods have bedeviled businesses for decades and the healthcare logistics sector is no exception. Today, businesses have access to vast amounts of data making the entire process of implementing demand forecasting methods much easier and accurate. Though such techniques have enabled businesses to improve profitability, it still remains a high-stakes guessing game based on software applications and analytics, both of which depend on simplistic business rules.