THANK YOU FOR SUBSCRIBING
Pharma Tech Outlook | Friday, June 10, 2022
The migration to pharma analytics has been hampered by a complicated supply chain, a myriad of government compliances and laws, limited profit margins, and rising competition.
FREMONT, CA: Historically, the pharmaceutical business has been sluggish in adopting cutting-edge technologies. However, the COVID-19 Pandemic has exacerbated these challenges, forcing pharmaceutical businesses to rethink how they monitor, control, and optimize their supply chains. It has forced them to make a decision, either seize the benefits of cutting-edge technology or capsize into deep waters. The nature of the Internet and the magnitude of growth other industries have experienced due to data analytics-assisted decision-making. It has finally worked its magic to establish a sustainable and robust framework for pharma analytics and supply chain management.
Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.
Pharma supply chain footprint reduction
Data analytics approaches can reduce the pharmaceutical supply chain's environmental impact. Text analytics approaches can assist pharmaceutical businesses in extracting vital information and analyzing and rethinking their green supply chain operations. An ANN model based on public data is created to calculate drug wastage for each provided batch.
Supply chain disruption minimization
Big data analytics allows for the observation of the condition of the machinery used in the production and packaging of medications and the prescription of preventative maintenance actions. Instead of reacting to a problem, proactive measures are taken to identify the component that is about to fail, assist in its repair, and significantly reduce machine downtime and supply chain disruptions.
Combating counterfeit products
Companies may detect counterfeit products using Big Data Analytics and cutting-edge sensor technologies in real-time. These devices can spot problems with pharmaceuticals that have made their way into the supply chain. A system that uses computer vision, feature extraction, and classification algorithms to count the blister cards within medicine packages on production lines is another way to prevent product falsification.
Enhance visibility and collaboration
The pharmaceutical supply chain is long and complicated. The supply chain's inefficiency has been attributed to a lack of openness. Data analytics can improve workflows and encourage seamless coordination throughout the supply chain's many processes, partners, and individuals.
Drug shortages are avoided
Most drugs are perishable and lose potency after their expiration date. Overproduction is never a viable option. Wastage is never an option because manufacturing expenses are high, and there are tight profit margins. Patients can utilize Q-learning algorithms with big data analytics to determine which drugs are available in which retail pharmacy. The type of medicine and ailment it treats significantly impact the consumption dynamically.
More in News