Enhancing the pharmaceutical supply chain with big data
The global pharmaceutical industry is growing, and according to the IQVIA Institute for Human Data Science, spending for this sector will hit $1.5 trillion by 2023. With this rise, manufacturers face the challenge of expanding distribution networks in order to meet the needs of a wider audience. Additionally, manufacturers are increasingly shifting from blockbuster drugs to biological and genomic medicines that have shorter life cycles. With biological medicine expected to account for over a quarter of the entire pharmaceutical market by 2020, manufacturers must also be able to deliver sensitive medicines within tighter time frames.
With intense competition playing out on a global stage, pharmaceutical manufacturers may find outdated IT systems and infrastructure limiting growth. Old IT systems can leave manufacturers blind to their inventory and distribution processes, hindering manufacturers from actively keeping track of products that run in and out of the supply chain. This capability is essential amid the diversion and potential for theft along the supply chain that fuels the industry’s counterfeiting issue. Additionally, the lack of insight into the ins-and-outs of the supply chain makes it challenging to ensure faster time to market to meet the growing demand for customized medicines.
Big data: an inside look into the supply chain
Harnessing big data can help these manufacturers build visibility throughout the supply chain and enable proactive action to mitigate the threats of diversion and counterfeiting. The path of a pharmaceutical drug from manufacturer to patient is a very different one from other goods like foods and beverages. Typically, the product leaves the manufacturer’s direct control and passes through many touchpoints in an increasingly complex supply chain. Compounding this complexity is the fact that many drugs are temperature-sensitive and require special handling. Pharmaceutical products that slip out of a prescribed temperature range lose their effectiveness or, even worse, become a danger to patients. If that happens, it’s not only a danger to consumers but also a massive cost.
Big data analytics help to improve the measurement of these factors by communicating while monitoring such variables as pressure and temperature.
In drug production and packaging, big data analytics enable condition monitoring and predictive maintenance through the collection of sensor data such as vibration and temperature, which significantly reduces machine downtime. Armed with this information, companies can implement proactive (rather than reactive) measures to identify potential component faults and rectify them before a machine fails. Manufacturers can also maximize the efficiency of maintenance teams to focus their time on degrading assets. Moreover, big data analytics can better track pharmaceuticals via product sorting. The use of cameras, vision systems and other inspection equipment are all tools that can monitor critical medications to ensure standards.
Protecting the supply chain against counterfeiters
The growth of the pharmaceutical industry presents an opportunity for counterfeiters to falsify lifesaving drugs, putting the health of at-risk patients in jeopardy. As a result, manufacturers must act fast to secure their supply chains as a preventative measure while working to remove falsified medicines from circulation or risk their brand’s reputation — and even worse, their patients’ health.
With the use of big data analytics and the latest sensor technologies, companies can understand the nuances of entire platforms at once while relying on the data identified to point out falsified and counterfeit products in real-time. These technologies can identify irregularities with medications that have infiltrated the supply chain and can pose a serious threat to consumers.
The solution may be closer than you think: Manufacturers can find solutions for supply chain challenges at events such as PACK EXPO East (March 3-5, 2020, Philadelphia Convention Center).
