Quantifying the value of knowledge within the context of product development

Abstract

Knowledge management (KM) is recognized as a significant activity in order for enterprises to survive in today’s competitive environment, and one of the most challenging topics in KM is how to analyze the value of knowledge in a quantitative way. In order to avoid descriptive arguments, this paper restricts knowledge to the context of product development, and proposes some effective definitions and measurements of knowledge value. Based on these, the values of both tacit and explicit knowledge can be quantified, and finally, Knowledge Integrated Production System (KIPS) is introduced, which encompasses product development and knowledge evolution. In particular, KIPS enables the quantitative analysis of knowledge value, and the mutual impact of value addition between knowledge and product is revealed.

Introduction

Although the notion that “knowledge is power” emerged more than 400 years ago, it is still far from being applied, and people are not able to control and measure knowledge like they can for electricity or finance. Meanwhile, knowledge management is playing an important role in almost every aspect of our lives, especially in production activities.

Since knowledge management (KM) became a hotly discussed issue in the last century, a set of KM related concepts, activities and procedures have been developed. Meanwhile, different definitions, paradigms, frameworks, assumptions, perspectives and measurements have been described to investigate the core question: what is knowledge and how to evaluate it. Bassi [1] considers KM as a process by which a company creates and leverages intellectual capital and it is the primary source of competitive advantage in many industries. Demarest [2] suggests that KM is the systematic underpinning, observation, instrumentation, and optimization of a firm’s knowledge. Quintas et al. [3] argue that KM is a process of continually managing knowledge of all kinds and requires a company-wide strategy which comprises policy, implementation, monitoring and evaluation. Jordan and Jones [4] focus more on intellectual capital to create competitive advantage and develop a framework that can be used to describe the dominant knowledge modes within an organization, including knowledge acquisition, problem-solving, dissemination, ownership, and storage/memory. Wiig [5] gives some illustration of KM from a managerial perspective and points out that systematic and explicit KM covers four areas of emphasis: top–down monitoring and facilitation of knowledge-related activities; creation and maintenance of knowledge infrastructure; renewing, organizing and transforming knowledge assets; using knowledge assets to realize their value. Birkinshaw and Sheehan [6] regard KM as a thing that has a life cycle by developing a model showing that new knowledge is born as something fairly nebulous and that it takes shape as it is tested, matures through application in various settings, is diffused to a growing audience and eventually becomes widely understood and recognized as common practice.

In order to improve the possibility of success, a variety of knowledge management and knowledge integrated manufacturing models have emerged one after another in recent years [7].

Some researchers pay more attention to the manufacturing aspect. For example, Paiva et al. [8] explored the role of manufacturing knowledge as a key strategic resource and showed the interactive process between manufacturing knowledge and cross-functional activities. Karacapilidis et al. [9] have presented a web-based knowledge management system for assisting the manufacturing strategy process, which aims at capturing the strategists’ rationale and stimulating knowledge elicitation, so as to help the users during the decision making process. Elgh [10] has introduced an approach for modelling manufacturing requirements in design automation, which integrates knowledge execution and information management into one system and thus enables a high level of product adaptation in an engineer-to-order approach.

Other people focus more on the knowledge aspect. For example, Lin et al. [11] have constructed an industry layer knowledge management (ILKM) model which includes knowledge clustering, knowledge enlarging, knowledge exchanging and knowledge initiating. Nunes et al. [12] have described a context-based KM model by considering the representation of context information, capture and storage of context information and retrieval and presentation of contexts. Han and Park [13] have introduced a process centered knowledge model, and they have analyzed its three sub-systems: project management sub-system based on process knowledge, knowledge management sub-system for maintaining task support knowledge, and infrastructure sub-system which supports the above two sub-systems. McGinnis and Huang [14] have incorporated knowledge management into enterprise resource planning (ERP) implementation and generated a self-sufficient model. Bernard and Xu [15] have proposed an integrated knowledge reference system which could serve as a base to characterize product development and knowledge evolution process.

Besides knowledge-based system modelling, some people are interested in surveys on measurement issues, which is indispensable in KM application. Wen et al. [16] have presented a knowledge-based decision support system for measuring enterprise performance, using both neural network forecasting and knowledge reasoning, so that it could help managers better understand current and future situations of the enterprise. Wen [17] has developed a model to measure the effectiveness of knowledge management activities by using focus groups, analytical hierarchy processes and questionnaire analysis. Hsieh et al. [18] have constructed a knowledge navigator model (KNM™) which consists of an evaluation and calculation framework. Xu and Bernard [19] have built a maximum entropy model for knowledge classification to evaluate their correlativity for a specific purpose.

Those ideas and methods introduced by former researchers all have insightful contributions to the modelling and analysis of knowledge management in industrial productions. However, they mainly describe or analyze the knowledge integrated systems in a qualitative way and there is a lack of direct discussions on knowledge value that could be quantified. Therefore, there is a growing need to specify the concrete impact of knowledge on the product development process and also to analyze the value of knowledge in a quantitative way.

Knowledge is such an intangible thing that it is difficult to give a definition of knowledge value that is widely recognized. An effective definition could be a base for knowledge value quantification and help to assess the “true” value of knowledge. Knowledge is quite different from traditional resources, for example, knowledge could be used without been consumed, the cost of knowledge reproduction could be ignored compared to the cost of knowledge production, the use of tacit knowledge is almost uncertain, the value of knowledge is usually difficult to judge before it is applied, etc.

Furthermore, measuring knowledge faces the following difficulties:

• Knowledge is related to individual cognitive behavior, in other words, different people have different points of view, so it is difficult to define a unit for knowledge value, such as meter or kilogram.

• Knowledge usually has multiple objective effects which cannot be broken down as easily as mechanical decomposition.

• Knowledge performance is not always revealed explicitly, so “a part” of knowledge value may be hidden.

• Knowledge effect may have time delays, in other words, knowledge acquired today may seem to have no value today, but could be quite valuable in the future.

Although these are difficult problems, this paper attempts to study KM issues in a quantitative way. In order to avoid too broad a discussion, knowledge is constrained within a certain context – the context of product development.

The paper is organized as follows. Section 2 discusses how knowledge is regarded and represented in the context of product development. Section 3 firstly analyzes the issues about “what knowledge value is” which is concluded by a newly proposed definition, then, both the values of tacit and explicit knowledge are studied and quantified. Section 4 introduces a knowledge-based system called KIPS, which characterizes the mutual value adding processes between product and knowledge. Finally, the paper concludes with Section 5.