With billions of dollars of drugs and medical devices moving through the global supply chain each year, manufacturers and their logistics vendors are eager to integrate new technologies from diverse fields into their operations with the goal of improving performance and lowering costs. While the integration of artificial intelligence is still nascent in the field, the vast amounts of data captured by a growing number of sensor-enabled devices moving through supply chains have put big data and analytics in the healthcare logistics mainstream.
Big data tools are capable of processing large amounts of data in different formats and from varying sources to enable scientists to identify patterns and gaps, which could suggest efficiencies, revenue opportunities, potential problems or competitive advantages that otherwise may not be evident. While most of the data generated are stored and used for routine documentation, planning and forecasting, companies are growing in their ability to use data to support strategic initiatives and create predictive models under different scenarios. A more data-driven supply chain also allows for easier benchmarking within the company and compared to competitors.
For example, real-time data can be processed by manufacturers and their supply chain vendors through descriptive analytics to reveal operations patterns. The same companies can then use predictive analytics to accurately forecast how their supply chain might evolve, while prescriptive analytics can be used to develop risk-mitigation strategies to fix identified weaknesses.
Logistics services — which used to be considered a more tactical aspect of supply chain operations — are now rapidly evolving due to data analytics integration. These operations produce large amounts of data including shipment and vehicle locations, time stamps, shipment status alerts, customer-specific data and more. Additional data points common to many healthcare products include the temperature of the shipment cargo, batch codes, and the time and location of pickup and delivery.
Developing Insights Into Temperature Management
Every year the global pharmaceuticals industry suffers a loss of over $15 billion worth of product due to temperature variations during transit. More than 60 percent of this loss comes during transportation of the products. This is especially true for the healthcare supply chain, where even minor variations in temperature can affect the integrity of the product. To alleviate this issue, supply chain providers and their manufacturer customers are using big data and analytics to identify where and when deviations in temperature control are most likely to occur. Historical data can allow them to deploy optimal packaging designs and to use cold chain facilities and transportation, while real-time data analysis can spot specific shipments where intervention is required immediately to save the product.
While data about the shipments themselves is an expected first place to look for supply chain optimization, service providers are also closely evaluating data about the warehousing and transportation assets. Many cargo and storage spaces must maintain constant levels of temperature, pressure, humidity and light exposure. Healthcare products often can’t be mixed with inventories of other products, so data-driven processes that can track how shipments are batched and stored are valuable. When equipped with smart sensors and diagnostic systems, a supply chain provider’s delivery vehicles can produce data on their performance, location and availability. Finally, analytics tools applied to these data sets can support decision-making for preemptive vehicle maintenance, spoilage and damage mitigation, and optimizing delivery routes in real time.
End-to-End Supply Chain Visibility
Complete, real-time visibility of the healthcare supply chain has potential to alleviate the losses that manufacturers suffer due to counterfeit drugs, theft, damage and spoilage. Big data solutions of the future will integrate data from multiple channels, such as manufacturers, carriers, suppliers, 3PL partners and provider facilities, in order to describe the journey of the product through its complete lifecycle, enabling the stakeholders to identify and address pain points along the way.
In many developed countries’ healthcare systems, about 15 percent of drug products delivered to healthcare facilities are wasted due to expiration. To a large extent, this is attributed to a shortage of skilled labor to conduct drug inventory management in these facilities. Thankfully, predictive analytics are proving extremely useful at improving inventory stock-keeping, storage planning and overall inventory management. For example, automated tracking systems can reduce the need for manual inventory management and considerably reduce drug wastage due to expiration and spoilage, since facility personnel can be proactively alerted to the status of products in storage. With inventory data now digitized, healthcare facilities can produce a variety of reports faster and more accurately, helping them meet accreditation and government regulation requirements.
Historically, the data generated from the supply chain for drugs and medical devices has been viewed as primarily unidirectional mapping from the point of origin for the product all the way to patient consumption or healthcare facility location. However, as personalized medicine begins to find its way into the mainstream, the composition, packaging and delivery of many healthcare products will be heavily influenced by data coming up from the customers themselves. This data could be related to clinical requirements, customer preferences or preferred location of use. By combining this data with the other information related to supply chain operations, manufacturers and their logistics providers will be able to create more customized solutions that hold the potential to bring new advances to patients.