Predictive Analytics for Life Sciences

Abstract

Business process of Life Sciences demands lot of information and analysis for efficient delivery of product and services. Currently industry has IT applications and solutions which merely act as reactive systems helping in functioning of day to day business process. These systems help to retrieve reports and data but lag in providing crucial insights prior to occurrence of any business event.

Today business organizations have to anticipate future business changes and should take measures to get ready to efficiently tackle ever changing business situations. Organizations have to shift their gear from reactive to proactive managing of situations and should execute operations with clinical precision all the time.

This article focuses on utilizing predictive analytics in various business functions of a Life science industry for proactively increasing service efficiency levels thus gaining competitive edge.

Introduction

Organizations have amassed transactional data relevant to forecasting, new product launches, Quality management, purchase, production, maintenance, sales etc. This data makes organization unique in itself and shows its experience of past history. This data is the greatest asset to be possessed by any company. There is loads of information in this data which might show that a particular event has been occurred some n number of times.

Predictive analytics is the tool which will help in predicting possible business events using this rich source of information. These analytics will provide information of a possible business event using which business can pro-actively plan their activities to improve their process productivity. Algorithms used in predictive analytics perform systematic reasoning and utilizes varied quantitative techniques to derive insights from data to improve business efficiency.

Entire idea is to avoid inimical business events and process failures by quantitative anticipating.

Leading towards A Proactive Failure Management System, which avoids failures by utilizing predictive analytics

Predictive Analytics Overview

Predictive Analyticsincorporates a range of activities including visualizing data, developing assumptions and data models, overlaying modelling theory and mathematics, then estimating/predicting future outcomes. At its core it relies on capturing relationships between the explanatory variables and the predicted variable from past data points, and exploiting those relationships to predict future outcomes.

Predictive analysis solutions are delivered by using data mining technologies that use analytic models to discover hidden patterns and apply them to predict future trends and behaviors.Data mining has many components, but the most significant components that are required to ensure meaningful and actionable insight are the following:

• Identify and define business problem.
• Data selection
• Data Preparation.
• Run Algorithms.
• Data Visualization.
• Test and save model.
• Decision Making.

Following section describes about common failures faced across business functions of a Life Science Industry and the required variables to create a Predictive model.

Preventive Maintenance

It’s evident that production line has to be stopped either due to scheduled maintenance or line/equipment break down. A maintenance engineer would be interested in reducing the down time of resource to increase its available capacity. To achieve this he has to perform just in time preventive maintenance, which does not require long hauls, to repair or replace a part of resource and also has should possess information about probability of resource failure.

Data which can be analyzed to retrieve predictive analytics can be:

1. Resource type
2. Last serviced part number
3. Campaign during line/resource breakdown
4. Days Running
5. Service due date
6. Power utilization
7. Capacity Load during line/resource breakdown

Quality Management

Quality Analyst may want to predict whether the in process product will meet the norms before it is finally packed. This will help him to proactively take action to avoid critical product failure

There may be various parameters for which define quality failure. These parameters can be studied relatively to predict quality failures of a product.

These parameters can be: 

1. Source of supply (raw material)
2. Product batch size
3. Supply source batch size
4. Production equipment used
5. Changes to equipment part which comes direct contact with product (wall of reactor/anchor)

Sales Forecast

Predictive analytics is widely used by organization for sales forecasting. Predictive forecast brings scientific and objective analysis of sales forecasting thus enables rationalized planning.

Predictive analytics coupled with traditional forecasting tool will improve forecast values by optimizing statistical model parameters to fit historical demand patterns. It can also incorporate external demand factors to improve forecast.

Apart from the parameters used in traditional forecasting techniques, following few are to be considered modelling predictive analytics

1. Point of Sales (POS) information
2. Customer purchasing patterns
3. Competitors pricing
4. Customer demographic consumption behavior

Resource Scheduling

Predictive Analytics is required in production planning and detailed scheduling of resources to predict events related to break downs, campaign delay or reschedules, campaign yields. This will help business in taking measures to improve productivity.

Predictive scheduling is done using set of algorithms which will considers few parameters below

1. Past details of campaign performance
2. Resource usage and its characteristics
3. product characteristics and relation with resource
4. Campaign batch size

Clinical Trials

Predictive modeling plays a critical role in successful clinical trial patient recruitment planning and completion. Using predictive modelling, clinical study teams can estimate the time needed to recruit the required number of enrolled subjects using a set number of sites.

Predictive analytics provide crucial information to study manager of deviations from the plan before they occur. The study manager and recruitment vendor have the opportunity to adjust recruitment efforts so that enrollment never falls off track.

Few Parameters to be considered during modelling for clinical trial patient enrolling:

1. Historical performance  of study
2. Uncertainties during enrollment
3. Number of centers required

Conclusion

For a dynamically oriented business of Life Sciences predictive analytics comes as a boon to decision makers. Often we see Life Sciences deals with multitude of Mergers and acquisition activities, patent fights, delay in drug filling, patent expiries, etc. Considering these kinds of humungous processes and business activities predictive analytical models can be generated to bring insights to process for proactively managing of business operations.

Benefits of utilizing predictive analytics are already proven in sales forecasting. Companies which are applying predictive analytics to their traditional forecasting already claiming to reduce their forecast deviations and also could able to successfully launch their new products range.

This technology cannot be undermined for other areas of business function, especially predictive analytics in areas of manufacturing and supply chain can yield major cost and resource savings over a period.

This article was just to show how predictive analytical models can be derived using certain variable in few of business functions, but this is not limited to those areas only. Predictive analytics can be applied in others like procurement, human resources, molecule design, incident management etc.

Companies have started investing to build capabilities necessary to become predictive in various business areas. Focus has shifted from customer to inherent business activity. For organizations, looking to be a step ahead in this world of competition then Predictive analytics is the way to march with.