Application of information technology and statistical process control in pharmaceutical quality assurance & compliance

Recently, the FDA issued new quality guidelines (Q10) encouraging drug manufacturers to improve their quality monitoring procedures. This renewed focus on quality and risk management has prompted Novartis to re-evaluate their systems and procedures to ensure compliance with the proposed guidelines. The company has chosen to respond by introducing more advanced statistical analysis of the data they share with regulatory bodies through the Annual Product Review (APR). However, procedural changes alone cannot bring about the needed innovation. Currently, too much time is spent on data consolidation and other non-value added tasks allowing less time for analysis. The solution is an Information Technology system with new procedures that will both improve process quality and increase productivity. The design proposed in this thesis utilizes statistical software that can analyze data securely, automatically generate graphs, and display alerts through an online dashboard. This Decision Support System will be integrated into Novartis's Global APR Automation project which aims to automate the generation of the entire APR document. A dashboard feature will allow processes to be monitored continuously instead of annually. The final version of the system will also include content management systems, business warehousing, audit validation and business intelligence tools. In addition to software, alternate statistical methods are proposed for evaluating critical processes that are either not in statistical control or lack normal distributions. These methods together with the new IT tools should help Novartis address process exceptions and reduce process variation without overloading the organization.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Srinivasan, Asvin

Advisor dc:contributor.advisor

• Roy E. Welsch and Duane S. Boning.

• Sloan School of Management.
• Electrical Engineering and Computer Science.
• Leaders for Global Operations Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/66047

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/66047