See How Analytics can help Demand Planning in Life Sciences during Covid-19
The COVID-19 pandemic has revealed the susceptibility of pharmaceutical supply chains. Pharma companies are fixating on risk management to ameliorate the resilience of their networks. Most of the quantifications they will take, including onshoring, over capacities, and redundancies will lead to higher costs. To decrement inventory levels across these incipient supply chains and control costs, pharma companies should withal fixate on amending their injunctive authorization orchestrating.
Demand Planning Challenges
The life sciences industry faces a variety of unique challenges in demand orchestrating during the COVID-19 pandemic. Life sciences organizations are struggling to meet supply, from raw materials through to the pharmacy, as a result of COVID-19 cognate disruptions. The crisis is transmuting the ordinate dictation for over the counter (OTC) medications and therapeutic contrivances and how people shop for them.
Meanwhile, demand for supplies for clinical research is a moving target. Some tribulations are delayed due to distancing measures. And incipient research is popping up to explore the efficacy of subsisting drugs on the treatment of COVID-19. For high-selling OTC medications and seasonal treatments like flu vaccines, plenarily automated traditional time series forecasting works well. However, for very specialized treatments and sudden occurrences like COVID-19, machine learning models have proven to be much more precise to forecast demand.
How Analytics can help Demand Planning in Life Sciences?
Demand sensing, forecasting, and orchestrating are critical to understanding the many variables at play, especially during a pandemic. These are intricate quandaries requiring advanced analytics to best understand how demand is transmuting on the ground – and to soothsay future changes as a result of the pandemic. For example, we can take data for demand spikes and lulls in regions first affected by the pandemic and utilize it to make prognostications in areas that have yet to experience their peak. Similarly, we can utilize data from regions that are reopening to forecast changes to injunctively authorize in other regions as they reopen. Organizations can then utilize this insight about the demand to apprise manufacturing and supply chain decisions as they work to mitigate disruptions.
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