How to Use Data and Analytics to Optimize Healthcare Supply Chains

Kara L. Nadeau, Healthcare Industry Contributor

Monday, March 18, 2024

Measuring performance in supply chains today is overwhelming given the vast quantities of supply data generated by (IT) information technology solutions - from raw materials to customer data. But effective data visualization and the application of advanced analytics is critical in the transition from a reactive supply chain to one that predicts potential changes and acts accordingly to negate negative impacts and drive positive outcomes.

While types of supply chain processes vary from one industry to the next, there are core elements of supply chain data analytics that can be leveraged across the board. Healthcare supply chain leaders are increasingly applying lessons learns and best practices from other industries that have achieved optimal supply chain performance through effective supply chain analytics. In doing so, they are transforming unstructured data into descriptive and predictive analytics to operate with greater efficiency, accuracy and cost savings.

Let's take a look at the state of supply chain analytics today, including types of analytics for supply chain, supply chain analytics tools, and how healthcare supply chain teams are leveraging analytics for supply chain optimization.

Table of contents

What are supply chain analytics?
Types of supply chain analytics
The value of analytics for healthcare
Areas that benefit from advanced analytics
Real-world examples of analytics in action
How to get started with supply chain analytics
Supply chain analytics tools and software

What are supply chain analytics?

Supply chain analytics involves the use of data analysis tools and methodologies to analyze, interpret, and optimize various processes within the supply chain, such as procurement, production, distribution, and inventory management.

Supply chain managers can leverage these insights to improve many aspects of their supply chains: production processes, demand forecasting, transportation management, warehouse management, distribution networks. etc.

By doing so, organizations can find opportunities to improve efficiency, reduce costs, enhance customer satisfaction, and mitigate risks using actionable insights and facilitating data-driven decision-making across the supply chain network.

In most health systems and hospitals, the data required for healthcare supply chain analytics and healthcare supply chain optimization is scattered. It can be locked up in different IT systems that don’t speak with one another (e.g., ERP, EHR, WMS). When supply chain teams rely on manual, paper-based processes, data capture and recording is largely reliant on manual intervention (e.g., supply chain personnel or clinical team member keying information into the ERP or EHR).

Sources of supply chain performance data include:

Enterprise resource planning (ERP) system: Item, supplier, pricing, procure-to-pay (P2P), contracting data.
Electronic health record (EHR) system: Clinical product utilization data.
Supply chain management (SCM) system: Automated inventory tracking data (from RFID tags, barcodes), item level data (e.g., expiry dates), warehouse and logistics data.

As more health systems and hospitals transition to cloud-based systems and automated, digital supply chain processes, they can integrate these data sources and apply analytics to enable a data-driven healthcare supply chain.

The role of data and analytics in supply chain optimization

With supply chain playing a more strategic role in healthcare organization operational, clinical and financial outcomes, data and analytics are critical.

In the past, supply chain teams were largely reactive, addressing issues as they occurred (e.g., backorders, stockouts) and correcting errors after the fact (e.g., PO errors, invoice discrepancies), because they lacked real-time visibility to data and control over processes.

Today, with process automation, system integration, digital data access and healthcare supply chain analytics tools, supply chain teams can continuously measure and track key performance indicators (KPIs) to proactively improve performance.

Types of supply chain analytics

"As healthcare increasingly embraces digital transformation and employs more modern data strategies, predictive and prescriptive analytics will play an essential role in building the supply chain of the future."

Jonathan Hodges, VP of AI and Enterprise Analytics, GHX

Advanced Analytics Chart the Future of the Healthcare Supply Chain (2022)

There are four main types of supply chain analytics that support informed decision-making and help organizations adjust to market changes.

Descriptive analytics

Descriptive analytics examine past events in the supply chain and help identify anomalies, patterns and trends. In the case of healthcare, this category of analytics can be used to determine what supplies are consumed in clinical care, in what quantities, at what frequencies, by which departments. From this statistical analysis, supply chain management can identify patterns in specific supply usage - such as seasonal surges - and use this information to inform strategies.

Diagnostic analytics

Once an issue or trend is identified using descriptive analytics, diagnostic analytics help find the root causes of problems, enabling organizations to address issues at their source. This could involve more advanced data analysis techniques, such as drill-down, data discovery and data mining.

In healthcare, for example, descriptive analytics can uncover that something occurred (e.g., Perioperative services ran out of an item), diagnostics analytics can be used to determine what caused the event or issue (e.g., PAR levels were set too low).

Predictive analytics

Predictive analytics are used to forecast future outcomes based on historical data. Techniques such as machine learning, regression analysis, and other predictive modeling methods are employed to identify trends and make predictions about future events.

Predictive analytics are crucial for proactive supply chain management, helping organizations to anticipate issues, optimize inventory levels, and improve demand planning.

Prescriptive Analytics

Prescriptive analytics are the most advanced type of analytics, which not only predict future outcomes but also suggest the best course of action to take in response to those predictions. This type of analysis utilizes advanced tools such as optimization and simulation algorithms to provide recommendations on how to respond to future events.

In the realm of healthcare supply management, prescriptive analytics can be used to ensure the right supplies are reliably available for patient care. USF Health’s Morsani College of Medicine offered the following example:

"By implementing automated dispensing cabinets (ADCs) with prescriptive analytics capabilities into their infrastructure, inventory management systems can develop better plans for how they can keep their facilities stocked."

Supply chain analytics benefits for healthcare

Today, supply chains involve tremendous cost pressures, continued supply chain disruptions and a growing emphasis on linking medical/surgical supplies to patient care quality and outcomes. Therefore, supply chain leaders need visibility into their operations and the ability to control them.

Supply chain analytics can be applied both up and down the healthcare supply chain to analyze supplier resiliency, supply availability, logistics network effectiveness, costs, usage and other factors, even predict future demand and predict future outcomes (clinical, financial).

Reduce costs

Analysis of supply procurement and usage can guide supply chains in reducing costs. Examples include, right-sizing inventory levels to reduce waste, standardizing on items to reduce variation and maximize contract savings, even collaborating with clinicians to link clinical and supply chain data to determine if specific supplies lead to costly adverse events and readmissions.

For example, in comparing two clinically equivalent items - one that costs twice that of the other - they might find use of the less costly option leads to fewer surgical site infections - infections where the hospital would be responsible for covering the cost of treatment.

Improve patient service

The main goal of supply chains in any industry is to meet customer demand. In healthcare, that means ensuring supplies are available to clinicians and patients when they are needed. There are many data sources and analytics methods that can be applied to support supply availability - from analyzing supplier sources of raw materials and components, through to analyzing customer data (e.g., clinician usage) to identify patterns and trends.

Lack of critical supply availability can negatively impact patients, their care quality, as well as their experience and satisfaction in many different ways. For example, a patient's scheduled surgery is cancelled because the required knee implant didn't arrive on time, or maybe the wrong size implant arrived.

From a patient service perspective, that means not only having to reschedule the procedure, but also likely making arrangements to take more time off work, engage a family member or friend to drive them to/from the hospital, find child/pet care, etc., along with having to live longer with knee pain.

Improve operational efficiency

Statistical analysis of processes can help leaders uncover inefficiencies and address them. For example, an analysis of procurement and shipping data finds requisitioners in different departments are placing separate orders to the same supplier multiples times per month, leading to the procurement team having to process multiple purchase orders (PO) and the accounts payable team having to process multiple invoices.

The supplier is also shipping multiple deliveries each month that the warehouse team must receive and process. Working with stakeholders to consolidate orders to the supplier and shipments from them alleviates the procurement, AP and warehouse teams of unnecessary work.

Increase agility

Analytics for supply chain can increase agility, particularly when it comes to managing stock outs, backorders and other potential disruptions. Leveraging data to identify alternative, approved suppliers of clinically equivalent products in the event of an availability issue, procurement can quickly pivot to ensure clinicians have the item when they need it.

In terms of customer service, the ability to predict future demand by analyzing past supply consumption trends makes supply chain increasingly agile in supplying what clinicians need in the days, weeks, months ahead. Commonly used items are available to clinicians, without them even asking.

Better decision-making

Analytics in supply chain planning ultimately improve decision making, enabling leaders to make decisions based on data-driven insights as opposed to inaccurate, incomplete information and best guesses.

Leveraging a cloud-based ERP solution with analytics capabilities (machine learning and artificial intelligence) that is integrated with other systems critical to supply management and tracking (e.g., EHR), supply professionals have at their fingertips real-time insights to quickly make the right choices for their organizations, clinicians and patients.

How to implement analytics in healthcare supply chains

Historically, healthcare supply chain leaders, in particular, faced an uphill battle accessing the data they needed to perform analytics for performance. Establishing and measuring any level of supply chain analytics in the days of manual processes and legacy systems lacking integration typically required significant time and effort from the healthcare organization's IT or business intelligence (BI) teams.

In other cases, they had to rely on their IT system vendors' data scientists or data engineers to assimilate and normalize unstructured data, perform the data analytics, and return to them analytics that were understandable and usable.

In either scenario, by the time supply chain management had data analytics in hand, they were likely outdated and their value limited.

Modern supply chain analytics have come a long way as health systems and hospitals have evolved to cloud-based enterprise resource planning (ERP) systems and digital processes that can seamlessly integrate with external systems. The introduction and implementation of supply chain management (SCM) solutions featuring advanced analytics capabilities have put the power of data analysis into the hands of leaders.

But data and supply chain analytics software alone is not enough to make supply chain analytics actionable. Talent is critical. Supply chain managers today need to understand the value of supply chain data analytics, and how to apply them in supply chain planning, in business transactions with supply chain partners and in their efforts to meet consumer demand (e.g., clinicians and patients).

Here are 7 steps to get started with a healthcare supply chain analytics program.

1. Identify data sources

Data relevant to analytics in healthcare supply management can span a broad range of IT systems in a health system or hospital, including ERP, EHR and SCM solutions, standalone inventory management systems, financial systems, etc.

The scope of the healthcare supply chain analytics initiative will determine which systems must be tapped for data to inform the process. For example, if a supply chain team wants to determine its primary distributor’s backorder rate, they may be able to access this data through their ERP system.

On the other hand, if healthcare supply chain optimization efforts are broader, as in assessing how individual products impact total cost of care, the required data sources will be broader in scope as well, encompassing the ERP, EHR and financial systems (maybe additional systems/sources as well).

2. Establish a data foundation

Once the supply chain team has identified the systems that will serve as data sources for analytics in supply chain management, it needs to determine how it will assimilate and normalize the data from these systems in a single location.

Complete, accurate and up-to-date data is the lifeblood of any healthcare supply chain analytics program. Supply chain teams that attempt to manually piece together disparate sources (e.g., in Excel spreadsheets) run the risk of incomplete, inaccurate and erroneous data that will impact the validity of their analytics, and in turn, the effectiveness of their healthcare supply chain optimization initiatives.

“Modern supply chains generate data from a multitude of sources…Integrating data from these disparate sources into a cohesive and unified analytics platform can be a complex undertaking. Data may be stored in different formats, structures, and locations, requiring careful data integration strategies,” stated the Internal Association of Business Analytics Certification (IABAC) in a recent article.[2]

As more health systems and hospitals transition to cloud-based technologies, system and data integration become more streamlined and accurate.

In its March 2023 article, Rethinking healthcare ERPs using cloud-based platforms, consulting firm Grant Thornton stated: “For any healthcare organization to operate efficiently and effectively, its EHR and ERP systems must be interoperable and compatible. Interoperability enables sharing of data across patient and clinical systems, as well as administrative and financial systems. The cloud enables the use of the same technology across the organization, ensuring predictability and confidence in the data.”[3]

3. Implement a mechanism to maintain data integrity

Healthcare supply chain data is not static, rather, it can change millions of times per day. Take for instance item master data. Suppliers make 10 million item data changes each year and GPOs make 30,000 contract changes each month.[4] To maintain data integrity, leverage a cloud-based technology solution for item master maintenance, such as GHX Data Connect for Cloud, which continuously verifies and validates information, data sources (e.g., PO history, contract data) and connections.

In an industry white paper, Ryan M. Schaefer, MBA, Avera Health’s manager of MMIS, Supply Chain, described how adopting a cloud-based solution for item master data management has enabled the healthcare system to standardize data across its five item masters for greater accuracy and process efficiency. He stated:

“Through automation, we receive suggestions of product or catalog number changes relevant to our organization, review the changes and then quickly approve those items and have them scripted back into our system so that all five item masters are updated efficiently and effectively.”[5]

4. Target areas for improvement

Within the scope of the healthcare supply chain analytics initiative, target areas where improvement will have an actionable impact aligned with the supply chain team’s goals, and/or broader health system or hospital goals.

Wondering where to start? The Association for Health Care Resource & Materials Management (AHRMM) offers “key performance indicators (KPIs) that set the standard for supply chain management in the health care field.”[6] AHRMM Keys for Supply Chain Excellence include KPIs for supply chain performance such as:

Perfect order rate
Percentage of purchase order lines with expedited shipping costs
Supply accounts payable (AP) days

Health systems and hospitals that engage in digital, automated procure-to-pay (P2P) transactions through the GHX Enterprise Exchange for Cloud can measure and track KPIs such as these based on their transaction data through shared dashboards with their suppliers.[7]

5. Benchmark performance and set goals

Supply chain teams starting out on their analytics in healthcare supply management journey might find it helpful to understand how they compare to peer organizations (e.g., similar size, similar product mix/volumes) or industry leading practices in terms of performance metrics.

Various organizations, such as Gartner, offer general healthcare supply chain benchmarking figures that health system and hospital supply chain teams can use to guide their healthcare supply chain analytics programs and set goals.[8]

Healthcare organizations that transact business through the GHX Exchange have real-time access to their own performance metrics, and those of their peers and suppliers based on their digital transaction data. Through GHX Exchange dashboards, they can set goals and track performance for their chosen KPIs.

Each year GHX honors top performing supply chain teams based on these metrics through its GHXcellence, Supply Chains of Distinction and Millennium Club Awards. This year’s winners will be celebrated at the GHX Summit 2024 being held May 13-16 in Austin.[9]

6. Implement change

A supply chain team has established a credible data source for analytics, targeted areas for improvement and set goals – now it’s time to act on this information. Based on the insights surfaced through the healthcare supply chain analytics, the supply chain team can enact changes aimed at boosting performance in its chosen areas for improvement.

For example, to move the needle on a healthcare supply chain optimization initiative focused on increasing its rate of perfect orders, the supply chain team identifies suppliers with which it has the most exceptions and works with them to address the root causes of purchase order (PO) and invoice exceptions.

Or perhaps the team is leveraging its data-driven supply chain to address an internal performance metric, such as percentage of high dollar items that expire before being used. In this case, they can engage the clinical team involved in the management of this inventory in a collaborative effort to prioritize use of items nearing expiry.

7. Monitor and measure performance

The objective of a healthcare supply chain analytics initiative is to improve performance through targeted interventions. Therefore, having a mechanism for ongoing monitoring and measuring of metrics is a critical component of success.

When measuring impact on healthcare supply chain optimization, supply chain teams can attempt to manually crunch before and after numbers. What’s more efficient and accurate – and far less time and labor intensive - is to leverage automated analytics tools that measure impact for you – and monitor it over time.

How healthcare businesses are using supply chain data

Ready for real-world impacts of analytics in healthcare supply management in optimizing supply chain in healthcare? Here are some examples:

Using contract compliance to improve margins

Learn steps taken by The Mount Sinai Health System supply chain team to reach their margin improvement goals and examples of strategies that worked to improve order accuracy, contract data quality for contract negotiations, and consistency in contract management in this GHX Summit 2024 breakout session recording.[10] Watch it now.

A proactive and resilient process for managing backorders

Empowered with unbiased, vendor-agnostic data and embracing a collaborative culture and with access to the right tools, Corewell Health’s supply chain team has tackled some of the most difficult procurement challenges. In this GHX Summit 2023 breakout session, team members describe innovative measures implemented to build a more proactive and resilient process for managing backorders.[11] Access the recording.

The Perfect Order metric

During a GHX Summit 2023 Summit20 Talk, GHX’s Dan Scelza defines the perfect order metric, the KPIs that comprise the metric, and how healthcare providers and suppliers can track it through tools, including the GHX Perfect Order Dashboard and trading partner scorecards.[12] Watch it here.

The Ottawa Hospital drives supply chain automation

Want a quick take on healthcare supply chain analytics success? Check out this 2-minute interview with Tracey Dennis of The Ottawa Hospital where she discusses how she and her team focused on processes and tools to drive automation within supply chain, earning them a GHXcellence Award as Provider of the Year, Canada.[13] Watch the 2-minute video

TUKHS Clinical Supply Optimization delivers $8M+ in savings

During the 2023 Supply Chain Summit, GHX honored The University of Kansas Health System (TUKHS) with the Elevating the Clinically Integrated Supply Chain Award as part of its 2022 GHX Excellence Awards program. The TUKHS Clinical Supply Optimization (CSO) team uses evidence-based analytics to optimize clinical, operational and economical stewardship. They are dedicated to ensuring the right product is available at the right price and the right time for the right clinical team using the right supplier (“5 Rights”). By doing so, the CSO team has delivered over $8M in savings for TUKHS.

How to get started with healthcare analytics

A solid foundation for a successful supply chain analytics program includes trustworthy data; defined goals and ways to measure them; supply chain analytics technology that presents data analysis in an understandable and actionable manner; and supply chain talent knowledgeable about how to use supply chain analytics to drive better performance.

Data analysis

Supply chain analytics can involve data from many different data sources depending upon the organization and it goals: customer demand data, sales data, business process data, inventory management data, data from supply chain partners, etc.

Regardless of the data type and source, supply chain analytics based on junk data are going to generate junk results. Using unreliable analytics to guide supply chain strategy will likely result in negative business implications. Therefore, analytics techniques must be built on a foundation of accurate, complete, timely, comprehensive data.

When pulling disparate data from different sources, it is important to transform raw data into structured data that the supply chain analytics platform can understand and process. Today's analytics platforms can employ business intelligence and artificial intelligence tools, such as machine learning to normalize data and make it useful.

Supply chain analytics goals

Then there is the decision of what the supply chain team wants to measure - what are the supply chain analytics most important to the organization? What are the supply chain team's performance goals and the key performance indicators (KPIs) they should leverage to measure their process toward achieving these goals?

While leaders of supply chains must determine what KPIs matter most to their organizations, there are industry resources available to help guide their decision making:

Gartner's Checklist of Supply Chain Metrics, which is organized by business processes, and includes metric-focus descriptions and sample metrics.
AHRMM Keys for Supply Chain Excellence is a resource specifically for healthcare supply chain key performance indicators (KPIs).

Supply chain analytics technology

The human brain cannot process the vast amounts of data required for advanced supply chain analytics. Therefore, supply chain teams require advanced supply chain analytics technology, such as cognitive analytics, to perform the analysis for them. In a recent Gartner survey, chief supply chain officers (CSCOs) identified advanced analytics among the top two emerging technology investments.

The approach to supply chain software development and implementation depends upon maturity of the organization's supply chain networks (and what resources they have in-house), what they are trying to achieve in terms of supply chain performance, and certainly budget and resources for implementation and usage of the supply chain analytics tools.

A strong healthcare supply chain technology and solutions provider with experience in cloud transformation and advanced supply chain analytics can help healthcare organizations bridge capabilities gaps.

Examples of supply chain analytics tools

Global Healthcare Exchange provides a range of supply data and analytics tools for healthcare providers and suppliers. For example:

Category Optimization by GHX Lumere uses data pulled directly from a health system to provide evidence-based savings strategies, spend and physician utilization insights, and tracking capabilities to ensure accuracy and compliance.
GHX Provider Intelligence helps improve operational and financial performance with comprehensive information to identify supplier trends and contract utilization metrics.
GHX Data Connect for Cloud delivers expanded item data that includes clinical attributes, purchase order history and contract data—all in your ERP.
Syft Synergy hospital inventory software enables hospitals to gain much greater traceability, visibility, and accuracy of supplies and resources from dock-to-doc™ with real-time integration to key components of the ERP, EHR, MMIS and ORIS.
Consignment implant order automation helps improve consistency, increase visibility, manage pricing more effectively and better control the invoice-to-payment process for implantable devices.

Common FAQs about healthcare supply chain analytics

Q. What are the challenges of implementing supply chain analytics software?

A. Lack of system integration and poor data quality and completeness are major challenges to supply chain analytics software deployment.

Q. What are the best practices for using supply chain analytics?

Setting a successful foundation for supply chain analytics includes: establishing goals for your analytics program, integrating systems that contain the data you require, establishing an accurate and complete data source, implementing a cloud-based system with advanced analytics capabilities, and engaging supply chain team members who are willing and able to take charge of your analytics initiatives.

Q. Can small healthcare providers also benefit from supply chain analytics?

A. The development of intuitive, easy to implement, technology solutions for process automation, digital data capture and analytics has made it possible for virtually all healthcare provider organizations to apply analytics in healthcare supply chain management, regardless of their size and resources.

Q. What are the key metrics to track in healthcare supply chain analytics?

A. KPIs for healthcare supply chain optimization span the supply chain spectrum, from metrics on procure-to-pay (P2P) performance, through to product utilization at the point of use (POU) and subsequent patient outcomes. When optimizing supply chain in healthcare, stakeholders should select metrics that align with their department and organization’s goals.

Q. How can I measure the success of my supply chain analytics initiatives?

Establish supply chain key performance indicators (KPIs) aligned with your organizational goals. Then align your supply chain analytics initiative with these KPIs. That will allow you to baseline, measure and track performance and progress toward your goals over time.

Q. How does emerging technology integrate with healthcare supply chain management?

A. Cloud-based solutions, including enterprise resource planning (ERP) systems, and automated and digital processes are streamlining system and data integration for healthcare supply chain analytics.

Q. What are the challenges in implementing data analytics in healthcare supply chains?

A. One of the greatest challenges is establishing a complete, accurate and up-to-date data source for data-driven healthcare supply chain initiatives – and continuously maintaining its integrity. Healthcare organizations that still rely on manual processes and disjointed systems typically have the toughest time in taking this foundational step for analytics in healthcare supply management.

Disclaimer: The third-party contributor of this piece is solely responsible for its content and accuracy, and the views expressed do not necessarily reflect the opinion of GHX.

Kara L. Nadeau

Healthcare Industry Contributor

Kara L. Nadeau has more than 20 years of experience as a writer for the healthcare industry, working for clients in fields including medical device/supply manufacturers and distributors; software, solution and service providers; hospitals and health systems; and industry associations.