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The authors discuss the technology and guidance required to achieve good KM in a biopharmaceutical company.
Knowledge Management (KM) is one of the most important systems for any biopharmaceutical company. KM is considered to be a vital connection between other management subsystems in an organization. This article focuses on the steps needed for successful implementation of KM in a biopharmaceutical company. The KM implementation discussed here enables new possibilities of effective usage and allows exploration of valuable information existing in a company. The article also emphasizes the use of an electronic document management system (EDMS) and the implementation of other such innovative information technolgy tools. Case studies from the biopharmaceutical industry are used to illustrate the KM implementation methodology.
The biopharmaceutical sector is a knowledge-intensive domain where the emphasis is on continuous product enhancement to meet the current market demand. Organizations are discovering that they need to do a better job of capturing, distributing, sharing, preserving, securing, and valuing their knowledge to stay ahead of their competition (1). The ability of companies to exploit their intangible assets has become far more decisive than their ability to invest and manage their physical assets (2). By managing its knowledge assets, an enterprise can improve its competitiveness and adaptability and increase its chances of success. With an increasing elderly population that consumes three times as many drugs as younger consumers, expansion into developing regions, and an overall increase in population and lifespans, the annual sales of the pharmaceutical industry have increased. Equally encouraging for drug companies is an evolving product pipeline. Process development of novel drugs, improved technology and laboratory research techniques, genomics, proteomics, and increasing R&D investments are shaping sophisticated research data systems. However, there are regulatory concerns, branding issues, impending patent expirations, escalating R&D and operations costs, and an increased complexity in research data that can result in information overload.
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Opportunities in the pharmaceutical industry have never been brighter, but only if companies can harness their knowledge to make better decisions faster. Knowledge management (KM) is a crucial component of any life-science research company. Without an effective knowledge management strategy, it is difficult for a company to quickly respond to current market demand. KM assists in improving research methodologies, maintaining process flow, and ultimately cutting overall costs. This article focuses on the technology and guidance required to achieve good KM in a biopharmaceutical company.
According to Davenport and Prusak, knowledge is located at the apex of the three-story pyramid (see Figure 1) (2). At the first level of pyramid is data, which expresses objective statements in terms of a transaction record (3). For example, the reading of a pressure gauge in a depth filtration process is a datum. The second level of pyramid is information, also called the message. To transmit a message, there must be a sender, a receiver, and a package of information created by the sender. For example, the reading of the pressure gauge can be converted into information by comparing it with standard values and pressure, and can be thus be attributed as high or low pressure. Knowledge is located at the third level of the pyramid. Obviously, it is more general than data or information, but still needs these two as a foundation. Knowledge stems from information just as information originates from data. For example, consistent high pressure above a certain value, for example, 3 psi, gives the user knowledge that a given depth filter will fail as soon as pressure reaches 3 psi.
Figure 1: Identification of data and information in the company.
Knowledge management has emerged as an area of interest in organizational practice. According to Malhotra,
"KM embodies organizational processes that seek synergetic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings (4)."
Backman defines KM as
"the formalization of and access to experience, knowledge, and expertise that create new capabilities, enable superior performance, encourage innovation, and enhance customer value (5)."
Thus, KM can be defined as a systematic management of all activities and processes referring to development, storage, sharing, and utilization of knowledge for an organization's competitive edge.
Reasons for knowledge management implementation
The amount of data that a person in pharmaceutical company handles is extremely large and is rapidly growing. Table I shows data encountered by different people in a typical biopharmaceutical company. Looking at the complexity of data faced by people at different levels, adoption of KM in the organization becomes imperative. A successful KM approach helps to better organize data, which further facilitates data analysis and interpretation. Furthermore, the business environment is getting more demanding because of a number of factors, including:
This complexity has made it important for an organization to respond quickly and effectively to changing environmental conditions. To maintain a competitive advantage, a company's data must be structured in a traceable way. This can be achieved through the implementation of KM in an organization.
Table I: Data encountered by key personnel in the biopharmaceutical industry.
Crucial factors for success
There are several key variables for successful implementation of KM. They are as follows:
Phase 1: Identification of data and information
In a typical biophamaceutical company there are various business lines as shown in Figure 2. The first phase of the KM implementation process includes conducting brainstorming sessions at several randomly selected meetings at different levels with different business lines such as R&D research group meetings, individual personal dialogue, or meeting with production officers. Through these meetings, information and data that are not yet recognized and systemized can be identified. For example, meeting with a production officer to discuss various process parameters of a chromatography process may help to monitor and record these process parameters in a systematic way, or meeting with a research group to discuss the characterization process of a particular molecule may aid in the documentation of the characterization process in a systematic way.
Figure 2: Various business lines in the biopharmaceutical industry.
Phase 2: Identification of data storage process
This phase involves identification of various processes being followed by various people at different business lines. The process includes going through existing standard operating procedures for different processes, or examining process flow sheets and finding how the data is being stored.
Phase 3: Identification of data storage location
Based on findings from phases 1 and 2 the data locations are identified and listed. Storage locations can include corporate databases where relevant data is stored in the organization. These locations can be computer hard drives, USB drives, CD-ROMs, paper files, or corporate databases.
Phase 4: Classification of structured and unstructured assets
Data from various locations can be categorized as structured and unstructured assets. The structured assets are data that are stored in several database tables within specific applications according to the need of a particular business line. Typical examples are chromatography system software for storage of process information, chromatograms and reports of a particular chromatography process, or ERP systems for materials management. The data that are not stored in several database tables within specific applications are unstructured assets. Unstructured assets can be divided into documented and undocumented assets. Documented assets are unstructured assets stored in various process templates, or spreadsheets. Undocumented assets are unstructured assets which are not stored in anywhere in an enterprise. A typical example of an undocumented asset is expert knowledge that a process consultant stores in his brain.
Phase 5: Transformation of data and information into knowledge
This phase deals with organizing the data and information and converting them to knowledge-rich information systems. An example of such an information system is an electronic document management system (EDMS). The EDMS manages electronic documents, scanned documents, pictures, tables, and other types of data. It enables efficient search, controlled storage, data security, data sharing, and data nonredundancy. Furthermore, these management systems enable secured access through different business lines. Consider a case as shown in Figure 3, where data is to be shared between quality control, R&D, and production managers. In this typical case the R&D manager can directly access the R&D data but can't access the production data. If he needs to consult any production data, he can access the data via access procedures through a production officer. A similar interaction can be described between a quality head and a production head.
Figure 3: Implementation of data access control strategy.
Phase 6: Proposing the system structure
This phase includes providing IT solution by creating a KM framework. This stage mainly deals with proposing the the system structure for KM. A typical example of such a framework is shown in Figure 4, where the user (e.g. R&D person, Finance officer etc.) interacts with a web-based interface through the internet or intranet. The Interface is designed in such a way that it is connected to all information management systems (e.g., enterprise resource planning, project management systems, learning and development modules, etc.) All information management systems, project management systems, learning and development modules, literature modules, and so forth, are connected to a database at the backend. This approach helps in effective data mining and prevents data redundancy and data overload.
Figure 4: System structure of knowledge management.
Phase 7: Implementing the system structure
This phase includes the development of the system structure of KM. The development is carried out taking software development life cycle models in consideration. After development, the software is tested vigorously followed by maintenance. To implement the software solution of KM, various training sessions need to be conducted where people in the organization can understand the importance of KM, learn to implement KM through software solutions, and explore other issues regarding KM.
A KM initiative is a major concern for biopharmaceutical companies worldwide. The major objective of this article is to present the conceptual implementation of a KM framework for the biopharmaceutical industry. The framework, when implemented, will enable effective storage and handling of knowledge developed within the organization. This will also lead to more efficient process implementation within an organization as the knowledge thus achieved can be applied synergically throughout the organization when needed. This could go a long way in dealing with the various challenges being faced by biopharmaceutical companies.
Tarun Jain* is an assistant systems engineer and Bipul Pandey is an assistant systems engineer at Tata Consultancy Services, New Delhi, India. *To whom correspondence should be addressed, email@example.com.
Article submitted: Oct. 25, 2011.
Article accepted: Dec. 5, 2011.
1. J. Liebowitz and T. Beckman, Knowledge organizations: What every Manager Should Know, (St. Luice Press, Boca Raton, FL, 1998).
2. T. Davenport and L. Prusak, Working Knowledge: How Do Organizations Manage What They Know?, (Harvard Business School Press, Boston, MA, 1998).
3. C.H. Tsai, C.L. Chang, and L. Chen, International Journal of the Computer, the Internet and Management 14 (3), 60–78 (2006).
4. Y. Malhotra, Information Strategy: The Executive's Journal, 16 (4), 5–16 (2000).
5. M. Lytras, A. Pouloudi, and A. Poulymenakou, J. Knowledge Management, 6 (1), 40–51 (2002).