Pharma Analytics: Optimizing the Pharmaceutical Value Chain with Big Data Analytics

Existing Challenges in the Pharmaceutical Supply Chain

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  1. Drug Shortages and the inability to manage unexpected surges in demand. No mechanism in place to predict demands.
  2. Shrewd management of pharma inventory is not possible with traditional processes.
  3. There is no specific process to ensure the integrity and quality of the drug that reaches the patient’spatient’s hands.
  4. Supply chain managers do not have transparency over several parts of the supply chain because they aren’t digitized.
  5. There is no means to curb medical wastages or to study the environmental impact of the supply chain.
  6. No fall-back mechanism to mitigate the impact of natural or artificial disasters.

1. Avoiding Drug Shortages

In 2019, The European Association of Hospital Pharmacists estimated that around 95% of hospitals faced medicine shortages, at one point or another, over the last couple of years. Medicine shortages directly endanger patients. Overproduction is never a solution because most medicines are perishable and lose efficacy beyond their expiry. And, since manufacturing costs are high and profit margins are tight, wastage is never an option. The solution lies in inaccurate demand forecasts provided by Big Data Analytics tools combined with Machine Learning algorithms that analyze heterogeneous data available within and outside the pharma supply chain.

2. Improve Visibility and Coordination

The pharma supply chain is both lengthy and complex. Lack of transparency in the supply chain has been the bane of its efficiency. Data Analytics has the potential to smoothen workflows and promote seamless coordination between the multiple processes, parties, and personnel involved in the supply chain. Adaptive Neuro-Fuzzy Inference (ANFIS) models can measure and predict supplier performance based on purchasing data and orders, which will ultimately improve the coordination between pharmaceutical distributors and hospitals.

3. Combat Counterfeit products

The WHO 2017 reported that 10% of the globally distributed medicines are counterfeit and urged pharma companies to ensure the integrity of their supply chains. With Big Data Analytics and the latest sensor technologies, companies can identify counterfeit products in real-time. These technologies can detect irregularities with the medications that have infiltrated the supply chain. Data Analytics can analyze the drugs’ physicochemical data to ensure quality or detect spurious medicines at any layer of the supply chain in a non-invasive manner. Another method to prevent product falsification is a system that automatically counts the blister cards within drug packages on production lines relying on computer vision, feature extraction, and classification algorithms.

4. Prevent Cold Chain Failures

Several medicines can lose efficacy and even become unsafe to be administered to patients if they are not transported under controlled environmental conditions such as specific pressure and temperature levels. Pharma companies reported losses of over $35 billion last year due to failed cold chains. Traditional methods to monitor environmental conditions during transport have included siloed data sets, proprietary devices, sensors, and manual processes prone to errors.

5. Minimizing Pharma Supply Chain Footprint

Data Analytics techniques have the potential to mitigate the environmental impact of the Pharma supply chain. Text analytics techniques can extract useful information and help pharmaceutical companies evaluate and rethink their green supply chain practices. An ANN model can be designed based on public data to calculate drug wastage with each supplied batch.

6. Minimizing Disruptions in Supply Chain

Big data analytics enables monitoring of the condition and prescribes pre-emptive maintenance measures to the machinery involved in producing and packaging drugs. This is done with IoT sensors that continuously collect data such as vibration and temperature and pass it to Data Analytics tools. Instead of reactive measures, proactive measures are taken to identify the component where a fault is imminent, help rectify it promptly and significantly reduce machine downtime and thereby supply chain disruptions.

7. Disaster planning and crisis management

Supply chain disruptions are usually a natural extension of unexpected events such as natural disasters. Compound that with a sudden surge in demand for health products and services, it’s destined to be the ultimate nightmare for pharma companies and hospitals. Researchers have used simulations to understand the supply chain dynamics under disruption. These models mimic real-time supply chain scenarios by taking disease forecasts and transportation disruption into account and providing insights into the upcoming surge in need for medicines or anticipating probable delays. This and Bayesian networks have been used in studying the pharma supply chain’s vulnerability to weather risk and disruptions in transportation.

Data Sources for Pharma Supply Chain Big Data Implementation

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1. Product Data

This comprises both static and dynamic information such as the physicochemical composition of the drug, expiry/perishable nature, price, conditions for which it is prescribed, and other specifications. Big Data applications have been practical when factoring in both product and demand data.

2. Demand Data

This includes sales history and trends such as periods of spikes/ downfall in sales, demographic-based sales numbers, etc. Demand data is crucial for pharma manufacturers to plan, procure, manufacture, and supply the adequate amount of drugs to each retail pharmacist and healthcare center they cater to.

3. Planning Data

This includes information about the company’s internal operational performance metrics, marketing information, and information shared with business partners, such as demand forecasts and production plans. These data are mainly used to gain perspective into process performance or to be fed as inputs to simulation and forecasting models.

4. Manufacturing Data

Production capacity and constraints, or data generated by connected devices such as IoT sensors, come under the manufacturing data umbrella used to gain insights into manufacturing process performance or to feed simulation models.

5. Inventory Data

Inventory data such as available stock, stock required to be replenished, stock in transit at different supply chain layers, inventory policies, and costs are generally available in every pharma company’s internal information system, such as their ERP software. Usage of this data is still in its infancy and has a lot of scope for development. Inventory data may be put to good use at point-of-sale or point-of-care to improve the quality of ML models.

6. Logistics Data

This includes all the warehousing information, transportation details, and product return data. Track-and-trace data collected in the context of regulations can act as significant indicators for enhancing the pharma supply chain. This data can improve the monitoring of forwarding and reverse workflows in the supply chain and help reduce wastage and mitigate the influx of counterfeit products. Shipment information is a crucial enabler for quality assurance and feed for simulation, optimization, and forecasting models.

7. Supplier Data

This is essentially information about the suppliers and essential data points in the contracts that have been established. This information has been rarely used in applications, and studies suggest they can estimate supply risk.

8. Customer Data

Customer-generated data is mostly unstructured. Data Analytics processes them to extract useful information. Unstructured data can include patient medical history, written prescriptions, customers’ phone conversations with support executives, patient medical bills, and patient’spatient’s medication preferences and allergies. Consumers’ public data, such as Google searches, have been leveraged in demand forecasting models. However, in the healthcare industry, the usage of PHI is bound to raise important ethical issues related to medical data privacy, theft, and compliance issues.

9. Publicly Available Data

Government websites, online portals of health organizations, online news articles, and social networks are valuable data sources for the pharma industry. With the efficiency of Data Analytics techniques crossed with web mining or computer vision facilities, it is possible to analyze these unstructured data automatically and use them to improve supply chain processes further. Several case studies have used weather forecasts, disease outbreak data, and information available online to improve the precision of prediction models.

Analytics Services for Healthcare and Life Sciences

Surveys show that there are still several gaps in implementing Data Analytics in the pharma supply chain. Forecasting models built on top of Data Analytics have been proven to perform better when compared to classical statistical prediction models. In the modern technological world, data is available everywhere and for every industry in abundance.



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Payoda Technology Inc

Payoda Technology Inc


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