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(Biospectrumindia) Need For Pharma Value Chain To Be More Adaptive And Robust :

    Need For Pharma Value Chain To Be More Adaptive And Robust :

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Need For Pharma Value Chain To Be More Adaptive And Robust :

Originally Posted: Biospectrumindia

There is a need for collaboration between logistics and distribution to mitigate the supply chain crisis, which has been largely affected due to COVID-19

The COVID-19 pandemic has impacted the pharmaceutical supply chain and has brought to the forefront existing glaring challenges such as lack of transparency in the supply chain, need for minimising supply chain stress, logistical issues due to improper distribution, along with poor emphasis on regulations. The pharma supply chain has become even more complex to navigate due to COVID-19; it has revealed substantial vulnerabilities in the value chain, shortages need to be mitigated and a much broader scale of collaboration for logistics and distribution needs to be put in place.

Pharmaceutical supply chain as it stands today

With critical implications across manufacturing, diversification and R&D strategies, business leaders are being pushed towards rethinking production, focusing on cost-cutting and designing effective crisis management strategies for ensuring long-term stability.

Many executives are grappling with questions around fulfilling demand subject to current constraints and bottlenecks, while many others are coming to realise that by designing an agile and adaptive supply chain they can react proactively to changing demand and supply scenarios. In the long term, this will help companies maintain high customer service levels without incurring significant costs.

Over the past couple of decades, pharma supply chains have become increasingly globalised. How can the pharmaceutical value chain move towards becoming more adaptive, resilient, agile and robust given the ongoing crisis?

1. Prioritising analytics: Pharma companies can utilise advanced analytics on key data points such as clinical trial data, operations, patient behaviour data and social listening - which will provide a competitive edge. By collating data from multiple sources, ensuring data consistency and accuracy, pharma companies can identify patterns to make useful predictions. By deploying effective analytics solutions, companies can optimise costs, deliver quick value, process health data, and gain valuable insights available in real-time. Using consistent criteria and lenses to prioritise market-relevant analytic investments will allow companies to move the needle on sales.

2. Risk assessment and scenario planning: The need of the hour is to have clear visibility of global supply chain strategies, assess risks involved and proactively share risks with suppliers. It is important to run scenario planning to be better prepared to solve problems as soon as they arise.

3. Collaborative planning and forecasting: A collaborative approach can help pharma companies minimise supply chain disruptions by identifying weaknesses and opting for corrective actions powered by trustworthy insights. It is important to put together collective intelligence with suppliers to forecast requirements, improve forecast accuracy and identify opportunities that were not possible to see before. In terms of purchase options, it is now possible to automate demand forecasting across various product categories and eventually plan delivery schedules and optimise routes.

Digital technologies such as automation, data analytics, cloud, and cognitive computing will result in real-time tracking of shipments, better prescription adherence, on-time deliveries and accurate dosing. The use of technologies such as AI/ ML, business intelligence and visualisation, etc. will enable pharma companies to remove redundancies and eliminate repetitive tasks.

Pharma companies are today sitting on heaps of data and by using supply chain analytics focus can be drawn on granular metrics, predictive analytics, forecasting, inventory optimisation, supply distribution and cost information as well as cost differences/ gaps. For instance, manufacturing analytics can help gather data from multiple data sources and use powerful visualisation tools and ML models to gain insights when it comes to:

  • gaining visibility across the supply chain - right from demand and inventory visibility to delivery of goods
  • in-depth cost analysis to track materials and production costs through multiple layers of information
  • real-time measurements of parameters to track correct process conditions thereby reducing failures, waste and improving on-time delivery 

Increasing reliance on technology

There are significant challenges for pharma companies before they begin to realise the advantages of big data analytics. Right from setting up the right infrastructure to defining data privacy rules on customers’ data and integrating siloed data to service cross-functional insights etc. - the challenges are widespread.

Such times make us realise the need for greater collaboration between stakeholders (both internal and external), a need for better supply and distribution networks, better logistics, and reliance on technology as opposed to manual processes.

It is our responsibility as contributors to the world economy to continue to improve the maturity of our processes and adopt technologies based on the maturity.

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