Data and Technology – Critical Links to Ensuring Medications Get to Patients on Time

Looking forward, supply chain enhancements will come through global data collection around a variety of disruption patterns, from weather to social unrest to shipping.

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Supply chain leaders know there are various reasons for supply disruptions. It can be an upstream disruption, a global pandemic or even something as unusual as a ship blocking the Suez Canal. In the environment of rising drug prices and supply chain uncertainty, technology has become an enabler that can help hospitals and health systems access the right data to stay ahead of the curve and ensure uninterrupted flow of critical drugs and products to minimize revenue loss and impact on patient outcomes. 

Optimizing the supply chain

Advanced technology, such as analytics and machine learning, makes it easier than ever to find new synergies and avoid risks in the complex market and regulatory environment in which we operate. Customers sharing utilization data with their wholesaler can provide key order and supply insights, which enable a smoother flow of product through the supply chain.

This also allows the management of distribution operations in ways that previously wasn’t possible, including optimizing inventory, labor, logistics and helping to ensure drug availability for customers and patients at the right place, at the right time and at the right quantity. Analytics and machine learning help identify inefficiencies and support the development of predictive or prescriptive algorithms to improve them. A data platform that blends dozens of different datasets into algorithms can look for patterns of supply disruptions in a variety of ways.

The supply chain can also be improved by optimizing labor and transportation to reduce the overall distribution cost. With ability to better anticipate demand based on order and inventory data exchange or even demand coordination with health systems, a wholesaler can optimize labor scheduling, reduce overtime, delivery costs and ultimately squeeze costs out of its distribution operations.  

The supply disruption precognition model is generally very accurate in detecting normal disruptions before they happen, which enables wholesalers to purchase extra product ahead of the shortage and minimize or even prevent the impact on customers’ ability to treat their patients. In developing a longer-term predictive model driven by machine learning, supply chains could experience:

·         Improved accuracy in detecting supply disruptions before they happen

·          Visibility into product availability to minimize impact

·          More reliable demand forecasts to communicate upstream

Another effective way to improve product flow through the supply chain is to create an accurate long-term wholesale demand forecast at the item level and publish it to manufacturers. In turn, the manufacturers commit to supplying those quantities in order to better serve customers and patients.

Data delivers accuracy and efficiency

Utilization of nationwide visibility of de-identified dispensing data that carries within itself demand signal for drugs across different regions and markets better informs product purchasing and inventory placement across the distribution network to more nimbly respond to that demand. Monitoring such events as seasonal weather-induced allergy fluctuations or a cold and flu season requires a tremendous amount of data and a machine learning model to help serve customers better and get critical drugs to patients who need them.

The delivery of medications from manufacturers to pharmacies, providers and hospitals has been taking place since the early 1800s. Today, data and analytics enhance efficiency and accuracy. For example, companies can now predict the impact of potential changes in supply or demand and make the necessary changes to avoid added costs or delays. The goal is to reduce the cost of healthcare and drugs for patients and customers. One way this can be accomplished is by being responsive to customers’ demand fluctuations and ensuring that needed products are stocked by monitoring levels of dispense activity regionally. This provides two to three weeks lead time in the replenish cycle, which can benefit health system pharmacies by ensuring their preferred product is available and cost efficient. Ultimately, how we use supply chain data will have an impact on patient care when we use it to be more efficient for our customers.

Cloud computing accelerates progress

The amount of transactional data it takes to achieve a truly accurate picture of our demand forecast and supply risk makes it impossible to rely on any sort of an on-premise infrastructure to train and execute these models and analytics. As the industry has migrated to the cloud, we’ve been able to incorporate more data, more sophistication, and thus, more accurate models. We can now execute them faster in a matter of hours and minutes rather than weeks and days.

While operating in the cloud brings efficiency and automation, it is critical to remain intensely focused on customers and their ability to find indispensable value in the products, data and insights that help them get their work done and take care of their providers and patients. They must have confidence that their pharmaceutical wholesaler and its suppliers are able to translate big distribution and product management optimization problems into data and digital solutions that make a real tangible difference.

Supply chain leaders should be asking if their pharmaceutical distributor’s platform is set up and calibrated to scale on a moment’s notice? Can they deliver the right product better, faster, more affordably, in spite of weather events and supply disruptions?

Looking forward, supply chain enhancements will come through global data collection around a variety of disruption patterns, from weather to social unrest to shipping. These datasets can be analyzed to identify obvious supply patterns before they impact the supply chain at all.