How predictive analytics will impact the supply chain in the 2020s
The healthcare supply chain continues to be at a pivotal juncture, taking increased advantage of innovative technology to deliver affordable value-based care by accelerating efficiencies and improving access to quality data. This includes the adoption of predictive analytics that give healthcare professionals forward-looking insights that are essential for planning and strategic decision-making.
In a recent survey conducted by GHX, leading supply chain professionals from around the U.S. cited using predictive analytics as an operational priority. This is because of the significant benefits supply chain professionals anticipate from adding the power of real-time data and advanced analytics software to their toolkits. Armed with this information, leaders will not only gain insight about what is happening today, but also anticipate what to expect tomorrow and plan accordingly.
This new level of visibility and insight is expected to transform the healthcare supply chain, further improving efficiency, reducing costs, and staying true to the industry’s mission to deliver the highest quality care to patients.
Here are three areas where predictive analytics is expected to have a significant impact in healthcare.
Properly managing inventory levels is a challenge. In fact, many hospitals still cling to the "more is better" mindset when it comes to supplies. More inventory on hand reduces the risk of running out, ensuring the highest possible care can be delivered to patients at all times. However, excess supply drives up costs and increases waste for those products that expire before use.
Just-in-time (JIT) delivery is one way hospitals combat overstock and waste. Although it requires quite a bit of manpower to execute, JIT delivery is a highly efficient system, well suited for the regular course of business. However, if there is a disruption, such as a natural disaster or flu outbreak, JIT is less efficient and costs can soar.
As more data becomes available through increased levels of automation, supply chain teams can use predictive analytics to better forecast demand. Synthesizing historical and real-time data from multiple systems will enable teams to gain insight into case mixes, anticipate the types of cases that are likely to come through the door, and the supplies required to support those cases.
Moreover, predictive analytics will enable more intelligent, data-informed decisions throughout the entire delivery process. With better planning, a distributor can understand what a provider needs to stock. Manufacturers can use that data to see what needs to be produced, including the raw materials. This will let the industry reduce excess capacity from delivery systems, and ultimately reduce risk.
Now, let’s look at this in the context of trigger events, such as a natural disaster or severe flu outbreak. During these times, the supply chain is strained as demand rises for common supplies, such as IV fluids. Predictive analytics can plan for alternative supply paths in advance of the trigger events, which will allow providers and distributors to adapt quickly when these events occur.
Predictive analytics can also change the way in which the industry manages contracting. Imagine an environment where a system understands an organization’s historical contracting approach, particularly the frequency with which contracts are renewed and how pricing has been determined. Analytics based on aggregated data allow supply chains teams to identify upticks or downticks in product use to improve the contract negotiation process.
For example, let’s say a provider receives an annual price increase from a supplier. Yet, every year the provider sees a 5 percent volume increase for that particular product. Predictive analytics tools synthesize historical utilization and patient volume to predict demand for the year ahead. The anticipated increase in patient and, subsequently, product volume would potentially enable a provider organization to increase its contract tier. The price per unit would decrease and margins would improve.
Backorders present a familiar headache for supply chain teams, regardless of industry. The challenge is that backorders aren’t identified until the order is placed. This initiates an entire work stream to determine where that product or a comparable alternative can be found.
In the future, predictive analytics will be used to anticipate product shortages. Supply chain teams will be aware of backorders before orders are placed, giving them time to identify which replacement products are best based on their requirements.
The healthcare industry has spent much of the last decade modernizing its processes and technology. As a result, data that was previously unavailable or siloed now sits at our fingertips. Predictive analytics tools are poised to help the industry further mine supply chain data to gain the necessary insight to reduce costs and improve efficiency across the continuum of care.