The Covid -19 pandemic continues to disrupt the Pharma industry. As uncertainty around the pandemic lingers and refuses to go away, Pharma leaders are facing extraordinary challenges due to the following forces at work:

Rapid shift in demand of drugs due to the impact of Covid-19 – The pandemic has impacted the demand of drugs for various therapeutic areas differently. There is an unprecedented surge in demand for drugs which are being considered as treatment candidates for Covid-19 e.g  Remdesivir (Gilead), Actemra (GNE) and Kevzara (Regeneron). There has also been a significant upsurge in demand for symptomatic medicines like antivirals, pain medications and ICU medicines which are used for managing complications from Covid-19. On the other hand, delays in elective surgeries and non-essential treatments have led to huge drop in Rx for many categories, and a rise of product switching in favor of self administered drugs

Geographical risk due to Covid-19 changing very quickly – After weeks of shutdown, some countries and states are cautiously reopening their economy. As regions open up, there are new emerging hotspots which can modify the density of cases, and hence the downstream impact on key decisions for Pharma Cos like inventory allocation for treatment therapies, supplier management  and execution of clinical trials. Given the rapidly evolving dynamics with Covid-19, companies need to ensure that they are using the most updated data and case forecasts for decision making

Pharma forecasts are broken – For an Industry which relies very heavily on forecasting, the historic data on which all forecasting, planning and distribution systems are built on has changed. Many of the previous signals used for forecasting like seasonal patterns, events, channel characteristics and patient behavior might not hold true going forward. There are new behaviors like hoarding, preference for self administered drugs and movement to telehealth which challenge pre-existing assumptions. Forecasters need to factor in this “black swan” scenario into their assumptions, and the geographical risk of cases would be one of the key factors impacting Pharma KPIs

Given this scale of disruption, how can Pharma companies solve this?

Unprecedented problems can still be solved with conventional solutions. With the right tools, Data science can provide much needed clarity, direction and guidance on what is happening now , and what is expected to happen ahead. We propose a 5 step approach with a Covid-19 Control Room for Pharma companies which composes of the following components.

  1. Covid-19 geographic risk assessment – Assess how the Covid-19 epidemic would play out with estimation of Covid-19 cases at Country, State and County level. There are multiple sources of forecasts like IHME, Northeastern University, Columbia University or you can build custom SIER Models. Models are only as good as their assumptions, so it is advisable to look at forecasts from multiple models to capture the possible range of outcomes for assessment of the geographical risk. A dashboard view like the one below, with the ability to customize the forecast would be a foundation for the Covid-19 Control room. This base estimation of geographical risk can be used to model scenarios for a range of decisions e.g inventory allocation, supplier risk and clinical trial management being some of them

    Covid-19 geographic risk assessment

  2. Segmentation of drugs based on categories of consumption – If you observe the pattern of how demand is getting disrupted across therapeutic areas, there are 3 key demand archetypes that would emerge.
    • Direct impact – For drugs which are in late stage clinical trials for treatment of Covid-19 e.g Actemra (GNE), Kevzara (Regeneron), Lopinavir+Ritonavir (AbbVie) – effects of hoarding or upsurge are leading to more than 5X increase in  sales, with demand quickly outstripping supply. This has already led to shortages. Remdesivir which received emergency use authorization by FDA might be in shortage for a long time – Gilead reported there’s only enough of it for 200k patients around the world
    • Secondary impact – For drugs which help in symptom management e.g pain/anaesthetic drugs like Paracetamol & Ibuprofen, antivirals like Rapivab and respiratory drugs have seen a huge uptick in demand e.g 91% increase in Paracetamol, 27% in Beclometasone, 23% increase in Salbutamol in the first week of Mar’20 compared to Mar’19. There are also a class of drugs used for Covid-19 complication management e.g  ICU drugs like Epinephrine, Fentanyl and Oxycodone which have seen demand surges and reported shortages
    • Negative or no impact – For some categories, demand has fallen sharply. In office administration volumes show huge drop in demand including certain categories of prescription drugs e.g -45% Rx for Pediatric Antibiotics. There are shifts based on mode of administration e.g IV administered oncology therapies show decreased demand relative to oral therapies
  3. Calibrate demand sensing for each demand archetype – Understanding the demand archetype of drugs in the company’s portfolio would enable forecasters to calibrate the demand post disruption and improve the accuracy of their forecasts. One of the key signals of drug demand at the distributor level is the geographic risk. For some like Actemra and Kevzara, the increase in demand would be directly proportional to the number of cases with Covid-19 in any region. E.g we know that Kevzara is a treatment candidate for patients with severe pneumonia due to Covid-19. Signals like number of expected cases, admission rates, patient demographics & access to the drug can be used to derive an accurate estimate of the demand for the drug. Similar analysis is needed based on demand archetype to calibrate forecasting techniques for other therapeutic areas  given this ‘structural break’ in historic time series data due to Covid-19
  4. Develop strategies for short, medium and long term – Once you have a sense of impact on the therapeutic area, organize your efforts for  the Covid-19 Control room which would provide a perspective on  short term ‘Crisis management’, medium term ‘Risk management’ and long term ‘Restoration to normal’ initiatives. For Supply chains, the initiatives would be:
    • Short term Crisis Management – Inventory risk assessment & allocation
    • Medium term Risk management – exploring options for ramping up production, managing supplier risk and reducing lead time
    • Long term Restoration to Normal – capacity planning, planning for recurring cycles of pandemic

    For example, here is an illustration of Inventory Risk assessment with key insights on Inventory Allocation at county level for a demand archetype with direct Covid impact.  An inventory risk assessment and allocation solution would compute the mismatch between demand and supply – measured by the ‘shortfall from required DoH’ to surface alerts. This would ensure that inventory allocation is optimal – precious drugs are sent to the critical locations and hospitals who need it most.

    inventory-risk-assessment-and-alert

  5. Build early warning indicators – Covid-19 infections, as some expect, might stay and relapse long into the future – even after the first wave. Machine learning can be used to constantly analyze and correlate parameters like case rate, death rate and growths with anomaly detection systems which detect shifts in cases and identify emerging hotspots of infection. This will help companies quickly recalibrate decisions e.g  here is a view of how an autonomous Anomaly detection system for the Covid Control room is built at country, state and county levels enabling decision makers to zoom in and out to identify hotspots quickly.

    build-early-warning-indicators

As we have all experienced, every day is a new unprecedented chapter in this outbreak of Covid-19. Strategies leveraging data and tools at our disposal can help Pharma companies win the battle against this pandemic. Companies that execute on these strategies will have a clearer view of what is expected to happen, and hence better prepared to face the challenges which lie ahead.

This Article is part 2  in the series – ‘Managing Pharma Supply Chains in times of Covid-19

For more  insights on how Pharma companies can Optimize their Supply chains, please click here.

Even in stable times, Pharma supply chains are fragile and as complex as they can get. As the Covid-19 pandemic continues to wreak havoc on countries around the world, Pharmaceutical supply chains have come under immense pressure. In this article, we would cover some of the key challenges which Pharma supply chain Executives face on the frontlines, and how Analytics and Data Science can be leveraged to overcome these challenges.

Covid-19 disruptions are going to test the strength of Pharma Supply Chains:

  • Stockouts are a real risk: According to a study by University of Minnesota, 80% of the drugs marketed in the United States, including 19 of the 20 top-selling brand names, are made overseas. The global nature of supply chains, regulatory challenges and high uncertainty makes stockouts a real risk – jeopardizing the health of millions of patients who depend on life saving drugs
  • Operational metrics for Pharma Cos would need drastic improvement: With an average inventory of 258 DoH , the Pharma industry has one of the largest inventory stockpiles – 2-4X larger than FMCG at 72 DoH. However, given the staggering scale of this pandemic and the real risk of stockouts, Pharmas would need to rethink and optimize their inventory allocation strategies to ensure that the current inventory of drugs is allocated to the channels and regions with the most urgent need
  • Shift in Consumption Patterns: As drugs are identified as potential treatments for Covid-19, the demand may quickly surpass the supply.  These drastic shifts in consumption patterns have already been observed with Covid-19 treatment candidates e.g Genentech’s  Actemra, Sanofi & Regeneron‘s Kevzara and Gilead’s Remdesivir, an experimental drug for Covid-19. In early April, the FDA reported shortages of hydroxychloroquine and chloroquine, antimalarial drugs that were speculated to be front-runners for a possible Covid-19 therapeutic. This shortage has impacted patients with Lupus, where chloroquine is a life saving drug
  • Long-term Cyclicity of a Recurring Pandemic: Taking a page from history, the Spanish Flu epidemic hit in waves, the second wave more lethal than the first.  If the Covid-19 virus proves to be seasonal, the impact of the pandemic might happen in waves over a 1-3 year period before stabilizing. Pharma Cos should be prepared for detecting and responding to new drivers of demand with very high momentum. Some new drivers of demand could be – preference for self administered drugs due to a drop in hospital visits, higher propensity for hoarding and increased demand for normal uses of certain drugs e.g  acetaminophen to treat fever & flu symptoms

Given the scale of disruption, how can Pharma Supply Chain Executives approach these challenges ?

A combination of strategic and operational moves leveraging Analytics and Data Science capabilities will help you get to the critical insights necessary for getting started.

  • Gain a realistic view of your current state: Creating a transparent view of your supply chain and assessing the current state is your first step. Quick dashboards and ad-hoc analysis will give you a perspective of what is happening on the ground, and in the moment. The views should be built to assess 3 key stages in your Supply chain:
    • Multi-tier Supply Assessment– What are the most critical components of Supply? What is the risk of interruption? What is the next best action for high risk Suppliers?
    • Inventory Audit– Where does your allocated Inventory lie both in-house and with distributors? What amount of this Inventory is finished goods vs blocked  for quality control and testing? What is the volume of Inventory in transit?
    • Demand – What is the most realistic estimate of customer demand?  Are there any specific NDCs with disproportionate impact on demand? How is the demand distributed at the Distributor, Geo and NDC level? Are the underlying assumptions of demand signals still robust? What are the emerging drivers of demand which might be getting missed?
  • Break down your perspective – Short term and Medium term: Organize your efforts with a Covid-19 Command center which would provide a perspective on  ‘Short term Crisis management’ and ‘Medium term Planning Ahead’ initiatives. This would ensure that teams on the ground continue to have bias for action, without  getting blindsided by what’s coming ahead.
    • Crisis Management teams– Focus on the most immediate tasks where speed is of essence. This team would focus on the most high impact disruptions and build quick dashboards/reports to get a transparent view of the current situation and generate critical insights for operational teams on the field
    • Planning Ahead teams– Look ahead to answer questions on mid-term and long term impact – like testing underlying assumptions, bringing in new intelligence from external analysis, identifying and integrating new signals and data sources into the analysis and developing scenarios for the future
  • Develop Scenarios for multiple versions of the future: The nature of the current Covid-19 pandemic is such that the arc of impact would be varied and staggered across the world. Take the US for example, every state and county is experiencing the pandemic differently. Hence, supply chain teams need to develop a scenario based decision making frame to assess how the pandemic would pan out, and what are their best moves at the moment . Here , the scenarios need to be built at 2 levels :
    • External Scenarios– Evaluate impact of the pandemic and effectiveness of the response at macro and micro levels across countries, states and down to county levels on key metrics like demand and supply
    • Internal Scenarios – Simulate impact of moves based on a Pharma’s ability to respond to the crisis. This would involve tweaking Supply chain parameters like manufacturing and shipping lead times, safety stock assumptions to identify what is the next best action that should be taken by Channel Inventory, Demand Planning and Manufacturing teams in the medium and long term
  • Factor for Uncertainty and Anomalies:  During times of uncertainty , one of the most powerful tools in the arsenal of Data Science is Anomaly detection. Over here, the unknowns are shifts in consumption patterns and cyclicity of recurring pandemic which would be hard to detect with human judgement. Once fed with the historic data, powerful ML algorithms can help you quickly spot unknown unknowns in your data, down to the most granular levels of detail – helping you set up algorithmic trigger points to flag alerts. This should be one of the key pillars of response for the  ‘Medium term Planning Ahead’ workstream. Some examples of metrics to be looked at:
    • Analyzing past demand patterns with anomaly detection models would help quickly spot which NDCs are impacted by the shifts in consumption patterns to predict stock outs at zip code level
    • Anomaly detection algorithms to flag emerging new hotspots of emerging Covid-19 cases at a country, state and county level impacting distribution and logistics
  • Be prepared for a fundamental change in the nature of your Data: With more than 3 billion people in lockdown, this epidemic will bring dramatic changes in patient, distributor and regulatory behaviors around the world.  Covid-19 is a perfect example of a ‘structural break’ in your time series data – with implications both in the short and the long term. Pharma companies might need to look for unconventional sources of data for getting insights during times of uncertainty. For example  – Google Search trends have been found to be good predictors of demand for certain types of drugs, and can also help us find emerging Covid-19 outbreaks. They can also reveal symptoms like ‘loss of smell’ that at first went undetected.

The current crisis has plunged entire countries and the Pharma industry into times of uncertainty. Building a transparent view of the current state, scenario based planning and proactive detection of anomalies are key tools on the frontlines for defense.

By acting intentionally today and using the tools at our disposal, Pharma companies can weather this crisis, emerging stronger and building resilience for the future. And in the process, enhancing and saving many lives around the world.

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