Boosting efficiency and speed with a pricing data integration solution
OVERVIEW
Glenmark Arzneimittel GmbH (Glenmark) faced a challenge in the German market, where regulations heavily influence pricing decisions. To ensure it could properly adjust prices, Glenmark needed rapid access to a key information source. Our collaboration helped the company secure a robust data integration solution to gain better market insights while reducing manual processing work. With this new solution, Glenmark has improved its decision-making process, helping it boost efficiency.
SCOPE
Data Engineering, Data Architecture, Data Importing, Data Pipeline Optimization, Cloud Architecture, Cloud Enablement, Cloud Infrastructure, Analytics, Customer/User Experience
TECHNOLOGY
Amazon Aurora PostgreSQL, Amazon Web Services (AWS) Lambda, AWS Step Functions, Amazon DynamoDB, Amazon Simple Queue Service (SQS), Amazon S3, Amazon Simple Notification Service (SNS), Python
Navigating complex pricing regulations
Glenmark Pharmaceuticals Ltd. is a research-led, global pharmaceutical company with a presence across branded, generics, and over-the-counter (OTC) segments. Glenmark Arzneimittel GmbH (Glenmark) is the German subsidiary.
The company is ranked among the top 20 pharmaceutical companies in the German generic segment, where regulations heavily influence pricing decisions. Besides profitability, these regulations determine the probability that a pharmacist will dispense or substitute a specific product to a patient or that a doctor will prescribe it. Pharmacists may substitute a prescribed drug with a cheaper, equivalent alternative, promoting cost efficiency.
Generic drug manufacturers must respect a specific, time-sensitive process when setting prices during regular pricing rounds. They have a short timeframe of one to two days, twice a month, to adapt and submit their prices to a leading pharmaceutical data provider based on current market conditions. If an off-patent drug manufacturer misses the deadline to set its prices, it must wait two weeks to change these details.
The pitfalls of inefficient data processes
Glenmark accessed pricing data from an external source but often received this information too late. Beyond this challenge, the data source it relied on for setting prices for its roughly 500 products was less comprehensive than the information offered by a different pharmaceutical pricing data provider.
The pharmaceutical company manually processed information from this external data source using Excel, resulting in issues with timing, potential errors, and extra internal effort. Its team of data analytics engineers was highly skilled at interpreting information, but the company lacked the ability to create a solution to obtain the most up-to-date pricing data in its systems in a structured and analytics-friendly way. Achieving this goal would help the company be more efficient and speedier, leading the way for increased sales and margin growth.
That’s when Glenmark reached out to Slalom.
Building an efficient data processing solution
We began our collaboration with a small workshop and discovery period to understand the use case, where data was coming from, and where it should go. Equipped with this knowledge, we set up an automatic pipeline in the cloud using Amazon Aurora Serverless. We used Amazon Web Services (AWS) Lambda and AWS Step Functions to schedule calls for web scraping from the pharmaceutical pricing data provider’s page.
As soon as detailed information is published on the pricing data provider’s portal, the data pipeline takes over and does all the work. Depending on the size of the data set published, this process can be completed within a few minutes, sometimes half an hour.
“What we particularly appreciate is the professional, flexible, and solution-oriented partnership we have with Slalom.”
— Michael Schroepel, Business Planning & Analysis Manager, Glenmark Arzneimittel GmbH
Increased speed and efficiency for a promising future
Glenmark can now immediately jump on information from the pricing data provider as soon as it’s available. The information is much more robust, offering the company more detailed analysis options.
Gaining deeper and faster insights into market prices earlier improves the company’s decision-making on medication pricing and leads to better top- and bottom-line results. The company has become more competitive and agile in responding to market developments.
Updating reports with the latest data is simplified and accelerated. Glenmark’s employees are saving an estimated two days a month on automated data processing, which they can now spend doing more meaningful work.
Speaking of transforming the employee experience, we’ve helped train Glenmark’s data analytics engineers to work with a cloud environment. They’re now deploying changes, doing source control and in-depth testing, and deploying to production, among other tasks.
“This implementation significantly improves the processing and use of data. It allows us to automate certain steps, enhancing our employees’ productivity.”
— Michael Schroepel, Business Planning & Analysis Manager, Glenmark Arzneimittel GmbH
Charting the future with prescriptive insights
Glenmark has transformed its approach to navigating the complex German pharmaceutical market. By implementing a sophisticated data pipeline, the company has enhanced its ability to adapt pricing strategies swiftly, leveraging insights with unprecedented speed and efficiency.
This technological advancement not only streamlines Glenmark’s operations but empowers it with deeper market analysis capabilities, significantly boosting its competitive edge. The project marks a pivotal step towards embracing digital innovation within Glenmark, setting a new standard for operational excellence and strategic agility in the pharmaceutical industry.
“Our team’s previous Excel-focused way of working meant that this project was a significant first step towards advanced data processing and automation. It opens new possibilities in our organization, where we use the power of technology to increase our efficiency and accuracy in data analysis, optimize workflows, and improve decision-making processes.”
— Michael Schroepel, Business Planning & Analysis Manager, Glenmark Arzneimittel GmbH
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