Supply chain efficiency can make or break any corporation, especially those in the pharmaceutical industry. Supply and demand can fluctuate wildly in medicine, as we all saw during the COVID-19 pandemic. Predicting when demand will increase or supply will be disrupted is a challenge that can benefit immensely if done well. The best way to do this is to analyze historical supply chain data to determine patterns and produce forecasts.
In this blog, we’ll explain the best ways to utilize pharmaceutical supply chain data, what insights can be derived from it, and why Snowflake AI Data Cloud is the best cloud-based data warehouse to land all your data.
Pharmaceutical Supply Chain Pain Points
The COVID-19 pandemic exposed vulnerabilities in the worldwide supply chain, with one of the most notable areas being the pharmaceutical industry. Sudden increased demand and interruptions in every facet of the supply chain led to extreme shortages of medications and medical supplies.
These challenges persist even today, and even without the pandemic, any number of events could occur to disrupt the supply chain, such as natural disasters, international trade disputes, and cyberattacks. Facing these challenges requires a vast knowledge of every aspect of the supply chain to be ready if something happens. The best way to achieve this is by analyzing the data.
Using Pharmaceutical Supplier Data
Transparency
The first step in using supply chain data to your advantage is to have complete transparency over every aspect of your supply chain. Knowing every supplier and manufacturer and how they’re intertwined is essential to spotting bottlenecks and planning for potential issues.
To spot risks due to severe weather, it comes down to knowing the smallest details of suppliers, such as their financial viability, how well they adhere to quality standards, and even their geographical location.
Diversification
Knowing all about your suppliers can then help you diversify them. Ensuring they are spread out over multiple regions will greatly reduce the overall risk of regional disruptions like weather or trade issues. Some advanced analytics on this data can then be used to see disruption patterns before they happen so you can quickly make a change at your supplier before any break in the supply chain occurs.
AI Modeling
The data can also create AI models to stress test the system. Models can provide insights such as an overreliance on a single region for suppliers, a change in supplier performance, and quality control metrics. These can be used to create alert systems to inform decision-makers of potential issues or create contingency plans.
Delivering Pharmaceutical Supply Chain Insights
Having your supply chain data together can deliver insights into every aspect of the process. These insights can help planners identify and address potential vulnerabilities in the chain, acting on them before they become an issue.
Demand
Historical data can predict demand based on seasonality, market trends, and cyclical variations. Sales trends can also be combined with external factors such as unemployment rates, GDP growth, and consumer spending to generate more in-depth analysis. This is another good use case for AI models, as they can create predictive models of how demand may change, helping prevent oversupply and shortages.
Inventory
Combining supplier performance analysis with demand forecasting can then be used to predict inventory levels. Companies can predict how long a certain drug may stay on the shelf before it is sold and how much inventory stays in stock longer than its shelf life. With this information, more efficient use of production resources can be performed.
Transportation
Data analysis can significantly improve the efficiency of pharmaceutical transportation. By analyzing the data, companies can make informed decisions about route and load optimization, choose the right carrier for the job, and optimize transportation methods.
For example, models can be created to help identify the shortest possible routes between suppliers, warehouses, and distribution centers. Carrier performance metrics can also be used to select the most suitable carriers, and considering factors like product sensitivity and urgency can influence the mode of transportation to be used. By utilizing this information, pharmaceutical companies can improve the efficiency of their supply chains while saving money.
Manufacturing
Analytics can also improve efficiency in the manufacturing process and prepare for unplanned outages, such as equipment failure. Manufacturers can utilize advanced sensors to help maintain quality standards and predict when equipment needs to be serviced before it breaks down.
Computer vision has become more prevalent in the pharmaceutical industry, allowing for even more advanced quality control in a vertical where strict compliance to standards is paramount. Using this data to detect quality issues and equipment failures early on ensures inventory isn’t disrupted by these issues.
Moving Pharmaceutical Data into Snowflake
The most difficult part of analyzing supply chain data is that it’s normally in many different systems that can be hard to bring together. To really analyze the data, you need it to be centralized in a data warehouse built for analytics, such as Snowflake.
Snowflake provides a simple, scalable data platform to manage your data and mission-critical workloads to drive better business decisions. Built for integration, scalability, governance, and industry-leading security, Snowflake optimizes how you can leverage your supply chain data.
SAP
For example, SAP, one of the largest ERP systems for supply chain data, can be integrated with Snowflake to deliver better insights.
The experts at phData recently developed an entire solution to quickly get your SAP data into Snowflake to easily create data analysis and AI models.
This is an example of what the solution looks like:
LandingLens by LandingAI
As discussed earlier, computer vision is an emerging technology for the pharmaceutical industry that can be used in manufacturing and other pharmaceutical areas. However, computer vision can be intimidating, as it usually requires advanced machine learning and data science knowledge. That’s where having your data in Snowflake is an advantage, as they’ve partnered with LandingAI to implement their LandingLens computer vision software directly in Snowflake.
LandingLens is the industry-leading AI Computer Vision software platform from Landing AI. It is an end-to-end software platform designed for domain professionals and AI experts to develop and deploy visual classification systems quickly. With the ability to create and test computer vision AI projects in minutes with low/no code, the most impressive part of LandingLens is how accessible and intuitive the platform is for users. There is no need for complex programming or AI knowledge to begin building a custom computer vision model.
Here is an example of what LandingLens can look for within pill manufacturing:
Closing
By landing their supply chain data in Snowflake, pharmaceutical companies can use Snowflake’s advanced analytics capabilities to enhance efficiencies significantly. By analyzing historical data to identify patterns, demand can be forecasted, and potential supply disruptions can be handled before they happen. This proactive approach improves patient medication access and strengthens the organization’s financial health.
Snowflake’s cloud-based data warehouse provides an ideal platform for storing and analyzing pharmaceutical supply chain data. With its architecture built for analytics and key partnerships, Snowflake allows organizations to extract insights and make the right decisions regarding their supply chain.
If you have questions or need assistance with using Snowflake for your supply chain data, contact phData. We’re here to help you unlock the platform’s full potential and optimize your data strategy.