Customer Success Story
Pharma Expense Forecasting Solution Saves USD 1 Million
Automated expense forecasting has enabled a pharmaceutical company to reduce manual efforts by 95%, errors by more than 50%, and save over $1M in costs.
InfoCepts’ advanced, predictive analytics solution for accurate and unbiased forecasts helps our client’s global finance team eliminate manual budget planning efforts.
The Challenge
Our client used its legacy forecasting system to plan quarterly budgets for three business functions across eight regions. Its finance team faced several business and technical challenges:
- Its antiquated system lacked flexibility and didn’t offer users control over its modelling implementation and parameters. The model consistently yielded incorrect results by not considering all data (i.e., underfitting), thereby generating inaccurate trends and overestimating budgets.
- Being time consuming and effort intensive, forecast development took approximately seven hours per region. Scaling the model across new divisions and regions consumed a lot of resources; hence the company sought to automate repetitive tasks to improve efficiency.
- Manual forecasting required looking at 480 tags of time-series data across each division and function. This effort consumed hundreds of hours every month.
- Existing system processes included collation of input data, pre-processing, outlier handling, selecting the best model based on error, creation of validation metrics, and visualizing forecasting results.
The Solution
InfoCepts developed an automated solution to help our client obtain highly accurate expense forecasts across all functions and regions. It uses predictive analytics and follows a unique model selection and forecast combination. The solution automates the following:
- Forecasting – Generates forecasts for the subsequent 24 months using updated actuals data.
- Validation – Tests the accuracy of forecasts against legacy forecasting cycles/models. This helps the business assess expansion of the forecasting solution to newer divisions and regions.
InfoCepts built this end-to-end solution using the Dataiku platform, seamlessly integrating it with existing applications to enable process automation. Eight predictive time-series models (using various machine learning algorithms) help create forecasts using three approaches:
- Root mean square error (RMSE) – Uses RMSE for most recent year actuals and forecasts.
- Trend, seasonality, and RMSE weights – Users provide weightage for these three components to calculate against forecasted values. Relative importance given to individual trend and seasonality components from the training data provides better forecasts without compromising on the RMSE.
- Forecast combinations – Obtaining the correct trend and seasonality of underlying data isn’t always possible; it typically contains small or rather finite historical data points. Combining forecasts hedges against this problem. InfoCepts’ solution uses 15 popular estimation methods (e.g., simple, regression-based, eigenvector-based) to create a combined forecast.
These three approaches let users select the most accurate and best-fit model for unique time-series data, then finalize and share forecasts across the business for budget planning.
The Customer
Our client provides innovative pharmaceutical products across the globe. Through ongoing scientific research and innovations, it positions itself at the forefront of healthcare by delivering greater value to patients.
Industry
Pharma
Business Functions
Finance
Solutions
Data Science-as-a-Service
Technologies
R, Python, Dataiku, Machine Learning, Data Science
Geography
North America
Year of Deployment
2021
Let me start with an appreciation of the amount of effort you’ve put into code modularization and output quality. The code is immaculate and completely thought through. Thank you! This enabled us to enhance and completely automate the forecasting process for M&D finance, while supporting three separate functions in parallel. Finance stakeholders are using the model forecasts for quarterly budget planning and are keen on expanding the solution to more functions. Your stellar process and code documentation is already starting to benefit us."
The Result
After rolling out the predictive analytics solution, all stakeholders now use the model forecasts for quarterly budget planning. InfoCepts’ solution helps them reduce execution time, thereby freeing up the finance team to carefully review and optimize projected outcomes.
Automated forecasting enables the finance team to make on-the-go budget planning decisions. The solution’s scalability and ability to generalize means it’s easy to add variables and prediction parameters for forecasting, such as registration costs and other departmental expenses. Additionally, our client uses this new solution to forecast use cases across business functions.
InfoCepts’ end-to-end forecasting solution has enabled our client to realize:
- Cost savings of over $1M, coupled with top-down budgeting, improved planning, and revenue.
- Improved decision-making (e.g., easily defined monthly, quarterly, and yearly forecasts).
- 50% reduction in errors, with increased accuracy and reduced bias from predictive analytics models.
- 95+% reduction in manual effort—average time for regional forecasts now 20 minutes rather than ~7 hours; overall time down to ~2.5 hours from the original 2.5 days.
Our automated forecasting solution has improved efficiencies, thus enabling the company to focus on improving its overall business rather than just forecasting and expense planning.
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