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Product Features

Feature One

The batch data analytics in ProcessPad specifically cater to the analysis needs of batch manufacturing processes that involve lot of quantitative (e.g. process parameters) as well as qualitative data (e.g. events and textual information). Our engineers with years of batch manufacturing data analysis experience have taken special attention in designing various charts and visualizations, a special focus has been given to displaying events and textual information related to the particular batch. Some of the analysis types are shown below:


Lot Genealogy helps investigators to instantly find feed/raw materials, intermediates or finished products corresponding to defect lot

Compare intra or inter batch time-series data for comparing batch profiles within the processing times


Align unit operations on a time line to correlate execution time context with the process event under investigation

Perform correlations and regressions on process parameters for inter or intra unit operations


Trend batch data on a control chart for statistical process control for understanding process variability and perform CPV

Create ad-hoc data groups on-the-fly (for inter and intra products or processes) to perform statistical comparison of groups for identifying significant differences



These analysis types provides rich context to the various trends/charts/visualizations bringing together all the necessary information in one window that is required to draw useful conclusions about the process and the product. The browser based access to these visualizations gives this an edge over desktop tools by making the right information available to right people at the right time. This on demand accessibility of useful information proves of real significance in timely and efficient handling of various kinds of analysis requirements for:

  • Root Cause Investigations
  • Process Monitoring and Trending
  • Tracing and tracking of buffer/media and product batches
  • Outlier batch detection
  • Cell Culture Profiles Comparison and Trend Analysis
  • Process Capability assessment

Feature Two

ProcessPad’s real time module provides direct web-based access to streaming data from process equipment for real-time machine and batch data analysis. Process engineers will have easy access to data for performing the routine monitoring of batch profiles or process troubleshooting. List below entails a few tasks for process engineers that becomes a breeze with ProcessPad

  • Live plant, machine or batch status
  • Determine current stage or phase of the process batch
  • Tabular access to execution stats like batch start time, end times and batch duration
  • Compute and compare equipment utilization for OEE estimations
  • Easily find process tags within or across equipment to overlay or compare parameter values within the time window of the non-conformance event or investigation
  • Overlay current and historical batch profiles on a common time scale for easy comparison and bench marking
  • On-the-fly extract data within batch phases and compare phase data across batches
  • Perform correlation of streaming parameters
  • Align multiple batch events to compare and troubleshoot batch differences arising due to operational shifts in scheduled batch events
  • Generate batch excursion report on both quantitative (e.g., pH, concentration, DO) as well as non-numeric qualitative parameters (e.g.,operators, phases, events)

All the analysis tools provided within this real time module were carefully designed with years of experience of our engineers and scientists working on the plant floor investigating on process issues. These are designed to minimize the time from discovery to insights for faster root cause investigations and CAPA implementation. Readers interested can also visit few of our blogs (links below) on suggestions on how to properly make use of online streaming machine data and offline batch data for effective and timely process troubleshooting.


Feature Three

ProcessPad’s advanced data connection tool can aggregate data for various batches, parameters and unit operations from variety of external data sources that includes external data repositories like legacy Excel sheets, any standard database like SQL-Server, MySQL, Oracle or via third party applications’ web-services. Data can also be captured from manual paper records via ProcessPad’s data capture templates that have been designed specifically to capture batch process or sample test data and are flexible to capture various kinds of data such as

  • time based observations
  • end‐point performance data
  • textual observations and events
  • qualitative batch attributes (e.g.: column re-pack details, resin lot ID etc)
  • assay results
  • assay background (reference assay, calibration or system suitability details)
  • stability sample results
  • qualitative assay attributes (e.g.: test instrument ID, internal reference lot ID etc)

While doing so, the assembly engine keeps intact the relationships that exist between batches, unit operations and parameters. The data capture template design is flexible to enable on‐the‐fly addition of any number of parameters by the system user without the need of any special IT skills and expertise.

Feature Four

Testing for stability and trending for shelf life prediction is an important activity of engineers and scientists involved in pharmaceutical or biologics drug development and manufacturing. With the stability module within ProcessPad platform, scientists can perform this important aspect of process data management and analysis without the need of exiting (or exporting the data) to an external system. Within ProcessPad’s existing process sample test data management capability, stability module extends the capability of the software to capture and trend drug stability data. The stability data capture templates mimic the in-process test/assay data capture format to ensure no additional training for lab and QA personnel entering, verifying, and approving data.


The stability management console supports specification limits management for each marketing region and tracks both quantitative and qualitative specifications. Users can flag protocol events/addendums or method changes for better correlation of data and quick compilation of protocol data, events, or addendums throughout the entire life-cycle of the protocol execution.


The module also comes embedded with an advanced statistics engine for predicting shelf-life for long term storage condition. All the shelf-life prediction models designed for poolability of batches conforms to statistical approaches mentioned in ICH-Q1E (Appendix B2.2)

Feature Five

Create report templates within ProcessPad to run or schedule reports for user defined data sets. Download the reports in MS-Excel or MS-Word for further analysis or processing or simply download in PDF format. Reports templates can easily be configured by users to generate periodic process summary reports or process/assay data reports in support of annual product reviews (APR).


Or simply create adhoc reports from all the analysis performed within ProcessPad in support of an investigation. These adhoc datasets and analysis often need to be shared among various stakeholders in order to discuss process and product issues and problems. With ProcessPad, analysis can be shared with just a click with the relevant people. The shared analysis is then available for the people (among whom the analysis is shared) to discuss and comment providing a forum to share process insights about the problem. The sharing feature is helpful in sharing with external parties where only particular information needs to be shared and access to the whole application is not required.

3 Reasons to Implement ProcessPad at your Organization

1
Saves Time

Currently organizations spend 80% time looking for data and 20% time doing actual analysis. Reverse this with ProcessPad

2
Easy Access

Needing just a browser everyone in the organization can access relevant batch process information in full context when needed

3
Low TCO

Being a zero client web-server application and with deployment time in weeks rather than months the total cost of ownership is very low.