Regulations in the life sciences industry in general have increasingly tightened with each passing day. While at the same time, medical services have extended reach to far-flung corners triggering the need for enhanced business operations at every stage of the product life cycle. Over the next few years, volumes for products and services are anticipated to grow multifold pushing the overall pressure on operations in the industry including the medical device manufacturers.
This exponential rise in the workload will in turn increase the demand for trained resources going forward. In light of these developments, we feel it will be hard for the human resources alone to manage any segment of the operation in its entirety. Additionally, even a single operational error not governed by the regulatory framework will significantly affect business performance metrics. All these factors build the case for introducing Artificial Intelligence (AI)-based solutions to manage certain segments of business operations for the medical device makers.
Let us look at the scenario using the example of medical device supply-chain operations. In the case of medical devices, outstanding inventory, wastage (expiry of unused goods or in-transit damage), theft and counterfeit products are amongst the top supply chain challenges. Despite the unusually high cost of peripheral inventory, companies need to maintain it at point-of-care to ensure uninterrupted supply. Therefore, the inventory cost can be deduced only after balancing risk between the maintenance of business on one end and efficient product circulation among the medical service providers on the other. Since the risk of losing business is higher than the cost of inventory, most companies maintain a large, usually disproportionate to consumption, inventory.
Given the circumstances, the question that needs to be asked is, “Do we have technology that can reduce the inventory cost and pilferage without putting business supply at risk?” AI can be the answer to this question combined with new age technologies like the Internet of Things (IoT) to optimize business spend and can help meet primary business goals – speed, accuracy, and quality.Current trends
The existing systems have shown revenue loss for the manufacturers during the product lifecycle. Losses due to theft of valuable products by patients and staff amounts to USD 52 million a year in the US alone. In the European Union, thefts during cargo shipments cost Euro 8.2 billion annually. Additionally, the replacement inventory for healthcare devices has very low turnover with devices typically sold and replaced less than twice a year. This implies the peripheral inventory amounts to over 20 times of the products sold at any point in time. Considering the current value of the medical device market at USD 786 billion, it can be concluded that material worth USD 1.5 trillion is dead investment. As a result of the low inventory turnover, about 7-10% of products expire on the hospital shelves without ever being used. Moreover, counterfeit products also contribute to loss of revenue for medical device companies.How can AI help address the existing issues?
How can AI help address the existing issues?
Currently, many AI solutions are complementing human capabilities and driving efficient business solutions in multiple areas. AI interacts with Enterprise Resource Planning (ERP) systems and collects information on stock availability, centrally and on the periphery. It also locates products nearing expiry and sends an automated mail to the inventory in-charge. It understands the consumption pattern based on previous trends and reminds the supply in-charge to check the status of the stock at Medical Centers.
When combined with IoT technologies such as RFID, AI tracks the accurate consumption, replenishment, and removal of the stock and provides inputs for efficient global management of the product inventory. In the production supply chain, it learns from ordering patterns and suggests production line up based upon the availability of finished goods. Additionally, it checks the availability and requirement of raw materials for predicted production and alerts respective stakeholders.
AI is also equipped to handle another important aspect in the supply chain process i.e. counterfeit products and thefts. As the machine learns about the batch release and UDI pattern over time, it is able to identify suspicious activity at the time of billing. The system can immediately trigger panic alert to designated stakeholders. Additionally, it can take similar actions for illegitimately marketed product when it identifies the already known batch number.To conclude
Maintenance of smaller inventory, better material management and low pilferage can potentially be amongst the most important contributions of AI to supply chain management. AI can also contribute to improved clinical quality and performance, and help meet regulatory compliance. And this is just the start. We can expect to see many more applications of AI in the medical device supply chain segment sooner rather than later.