Pharma Analytics: Optimizing the Pharmaceutical Value Chain with Big Data Analytics
The Pharmaceutical industry, traditionally, has been slow to adopt cutting-edge technologies. A complicated supply chain, a plethora of government compliances and regulations, tight profit margins, and burgeoning competition have been significant reasons hindering the switch. But now, the pandemic has increased these obstacles and has more or less forced pharma companies to transform how they monitor, control, and optimize their supply chain.
It has given them a choice: Either capture the offerings of new-age technology or capsize into deep waters. Also, the Internet’sInternet’s ubiquitous nature and the extent of growth other industries have experienced after gaining decision support from data analytics have finally worked their magic on pharma executives around the world, who are now starting to show serious intent on setting up a sustainable and robust framework for pharma analytics and supply chain management.
Existing Challenges in the Pharmaceutical Supply Chain
The seamless operation of the pharma supply chain is highly critical to most other supply chains because the product it transports is a high priority necessity. Any disruption in the process could potentially cause irreversible damages. Let’s look at some of the challenges plaguing the pharma supply chain over the years.
- Drug Shortages and the inability to manage unexpected surges in demand. No mechanism in place to predict demands.
- Shrewd management of pharma inventory is not possible with traditional processes.
- There is no specific process to ensure the integrity and quality of the drug that reaches the patient’spatient’s hands.
- Supply chain managers do not have transparency over several parts of the supply chain because they aren’t digitized.
- There is no means to curb medical wastages or to study the environmental impact of the supply chain.
- No fall-back mechanism to mitigate the impact of natural or artificial disasters.
We will look into implementation ideas of Big Data analytics, Advanced Analytics, Machine Learning, IoT, and other subsets of AI in the pharma supply chain to alleviate the problems mentioned above.
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.
For example, a VAR time-series model that analyses the correlation between Google search trends, trending YouTube videos, online news articles, and the demand for medicines was effective. Artificial Neural Networks (ANNs) have been widely studied and applied, providing outstanding forecasting accuracy. Q-learning algorithms can be used with big data analytics to help patients find which drug is available in which retail pharmacy.
The consumption dynamic here is highly dependent on the type of medicine and disease it addresses. Hence climatic, environmental, demographic, and cultural influences need to be factored in. This problem statement is ideal for clustering techniques such as the K-means algorithm to categorize medicines and patients into different segments and train ML algorithms separately for each category.
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.
Random forest algorithms can optimize manufacturing processes based on online and offline manufacturing process data. Big Data and RFID-based tracking can make granular tracking of medicines at every point of the supply chain a reality.
Blockchain can also become an essential enabler in providing transparency in the pharma supply chain, preventing diversions and disruptions.
Also Read,
Blockchain in Healthcare: Opportunities, Use Cases and Life Science Solutions
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.
The heavy quality control essential to maintaining supply chain integrity can be eased in with the help of Data Analytics.
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.
Cold chain monitoring is one of the most critical use cases combining the abilities of IoT and Big Data Analytics in the pharma supply chain. IoT sensors can automate the monitoring and regulation of temperature and pressure during the transport and storage of medicines, thereby eliminating human errors, avoiding losses due to products not meeting standards, and increasing real-time transparency through the supply chain.
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
This section elaborates on the different possible data sources for the pharma supply chain. The efficiency of a complex process depends on multiple factors. When the levers are so many, it is only natural that Big Data Analytics should also get its data from various sources to be labeled as ‘’heterogeneous’’ and for the insights generated to be accurate.
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.
Payoda helps organizations use technology to organize, formulate, and derive in-depth knowledge from the available data and fine-tune their business strategies will indeed have a say in their growth in the future.
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Authored by: Mohan Bharathi
