A Risk Modeling Framework for the Pharmaceutical Industry

By Adis, Warren | Communications of the IIMA, January 2007 | Go to article overview

A Risk Modeling Framework for the Pharmaceutical Industry


Adis, Warren, Communications of the IIMA


ABSTRACT

This conceptual paper seeks to advance a theoretical discussion on risk modeling and how it is used within the context of business process modeling. It discusses developments in risk modeling and then shows how they have been applied to the USA pharmaceutical industry. The pharmaceutical industry is a particularly interesting example in that it is bound on one side by stringent USA government mandates, and on the other by a risk adverse consumer population. A third aspect, the expanding cost structure of drug production and compliance, adds to the complexity of the problem. The discussion of risk in this paper applies mainly to regulated industries, and may be less applicable to more unregulated industry sectors. The important lesson for researchers is that a risk framework can play a significant part in business process modeling. The format for this paradigm may very well resemble a process repository, similar to those found in knowledge management systems.

INDUSTRIAL STRENGTH SYSTEMS

One of the principal tasks of business process modeling (BPM) is to develop what Booch broadly calls "industrial strength" systems (Booch, 1994). In complex environments one of the fundamental features of an industrial strength system is that it can manage the forces of internal complexity and external variability throughout the life cycle of the system. To build industrial strength systems researchers and practitioners often turn to the 'best practices' of industry leaders. These best practices are accepted standards which have usually been developed over time and have proven themselves through benchmarking and quality assurance tests. Yet risk analysis has often been an unexamined premise that is fundamental to the development of best practices.

When a technical system fails, a reasonable conclusion is that the system was not stable enough to survive the internal and external forces that caused the system to degrade (Scott, 2000). The failure may be ascribed to an incomplete or faulty process modeling technique, weak implementation, or similar problems. Though when a system fails and a disaster occurs or is narrowly avoided, the business and technical community also belatedly conclude that not enough attention was paid to possibilities outside the predicted range of events. In these instances the industry best practices and other benchmarks are found to be lacking. Based on this failure scenario, the business process model can be modified and a different set of best practices can evolve. The designer's basic objective would be to further minimize and constrain unnecessary risk.

This paper's focus will be to highlight steps taken by the pharmaceutical industry to incorporate risk modeling in their system development. It is important to note that while the U.S. pharmaceutical industry's experience is well documented, these findings may not apply to other similar industrial sectors that are not so rigorously regulated. The pharmaceutical industry in some senses may be considered unique in that it has a fiducial responsibility in management and production functions.

USA PHARMACEUTICAL INDUSTRY

The USA pharmaceutical industry is an excellent example of a business sector that is incorporating risk planning into their BPM. It is particularly useful to study this industry because it faces the complex tasks of developing, testing and manufacturing of drugs, has a rigorous oversight agency in the U.S. Food and Drug Administration (FDA), and serves a marketplace with an exceptionally low tolerance for variability in pharmaceutical products (FDA, 2003a; FDA, 2004).

Yet it is common knowledge that the pharmaceutical industry, like many mature industries, is built around traditional manufacturing processes and legacy information systems. Each is based on rigid work flow patterns that have been optimized for efficiency and cost reductions, rather than for data integration and compliance. …

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