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7 Supply Chain Management Areas Where AI and ML Can Realize the Greatest Cost Savings

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Supply-Chain-Management-AI

Supply Chain Management AI

Supply Chain Management AI- According to a recent report from Syft, health care leaders fail to leverage data analytics in their supply chains. This shortcoming could result in them missing out on millions of dollars. There’s an urgent need to optimize supply usage throughout the enterprise and patient care continuum while cutting waste.

In addition to the increasing downward pressure hampering revenues, statistics anticipate that supply chain costs will exceed labor-related expenses by 2022.

These factors cause an increased need for health systems to optimize supply chain management (SCM). Syft’s research mentioned a McKinsey study that predicted a reduction of 20-50% in forecasting errors, too. The increase in available, reliable data helps hospitals take decisive action.

More than half of executives polled believed SCM controls expenses while reducing waste. Moreover, they thought it could grow margins by at least 1-3%.

A 2018 Navigant survey cited by Syft in its research found that hospitals spend over $25 billion more than necessary in their supply chains. However, individual hospitals have opportunities to save an average of 17.7% in their total supply expenses. Often, they can do that through artificial intelligence (AI) and machine learning (ML).

The 3 Major Parts of the Supply Chain Where AI and ML Could Help

Analysts see AL and ML being particularly helpful in three main supply chain parts. The first is commodity supplies such as surgical drapes, needles, and labels. Syft’s data indicates those things account for about 18% of what a typical hospital spends.

Then, there are the medical and surgical supplies used in moderately invasive procedures. They include things like bone nails and grafts, aortal stents and tracheal tubes. Hospitals spend an average of $13,286 on these items, with the amount accounting for more than a quarter of total spending.

Finally, AI and ML could promote cost savings for so-called provider preference items. They’re the supplies that doctors choose to treat individual patients, such as spinal rod implants and tibial knee prosthetics. Medical facilities tend to spend more than half of their supply budgets on these things.

Now, let’s look more specifically at seven SCM areas that AI and MC could cause the biggest cost savings for the health systems utilizing them.

1. Supply Standardization

Some provider-preferred items are extremely costly, but standardizing them is a challenging task. Physicians lack the time and resources required to compare the associated costs and patient outcomes when assessing options in the marketplace.

However, AI could provide doctors with tools that give them near-real-time statistics about how certain supplies perform. Such information makes a supply standardization goal more straightforward.

2. Inventory Level Optimization

Health systems must speedily and accurately assess the demand for inventory. Otherwise, providers may hoard supplies to avoid running out, or delays could occur while waiting for replenishments. Hospitals often use the time-series method of forecasting demand. It uses past usage trends to predict future needs.

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Article Credit: HIT 

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