Design of a Knowledge-enabled

DESIGN OF A KNOWLEDGE-ENABLED INNOVATION MANAGEMENT SYSTEM
VINCENT M. RIBIERE
Institute for Knowledge and Innovation, South-East Asia - IKI-SEA
Bangkok University, Bangkok, Thailand
vince@vincentribiere.com

PROF. FRANCIS D. (DOUG) TUGGLE
Argyros School of Business and Economics,
Chapman University, Orange, CA USA
tuggle@chapman.edu
This paper presents some initial thoughts (research in progress) on the design of a Knowledge-enabled Innovation Management System (KIMS).
1. Introduction
Technological innovation is central to the progress of civilizations and economic and societal prosperity, yet most innovations fail. The primary reason most innovations fail is that end users do not adopt them. The reasons for lack of adoption are subtle, but often revolve around insufficient knowledge of end users’ preferences and requirements (these factors may also not be consciously known to the end users themselves—these may have to be revealed during “forced choice” situations). Henry Ford was fond of saying that if he had performed market research prior to developing the automobile, the responses he would have received would not have pointed toward the need to develop automobiles but rather toward the invention of faster horses that ate less hay. Improved marketing research per se will probably not lead to higher rates of successful innovation adoption.
Over the past five years innovation has become one of the top priorities for organizations that want to remain competitive in a knowledge/creative economy. Various studies report the importance given by executives to the implementation of innovation management initiatives (AMA, 2006; Andrew, Haanaes, Michael, Sirkin, & Taylor, 2009; Capgemini, 2008; IBM, 2006). Even during a time of financial crisis, organizations continue to strongly believe that innovation can be a solution to prepare them to bounce back once the crisis is over (Andrew et al., 2009). Innovation can be described through a variety of different lenses—application (product, service, process, paradigm or business concept), level (incremental, substantial, or radical), target (consumer, business, or procedure), etc. Different technologies can be used to support the various phases of the innovation process but very few are fully integrated and provide the features necessary to support the new managerial approaches and models of innovation. Since knowledge is considered as a catalyst of innovation we believe that a knowledge-enabled system could be of a great value to support and leverage the innovation process, which is currently rarely automated and very often not clearly defined.
This paper presents some initial thoughts regarding the design of a Knowledge-enabled Innovation Management System (KIMS). We first present the current state of the art in term of innovation management processes and we present how knowledge management practices and tools can enable and improve such process. We then present some of the core salient features that the KMS should possess in order to benefit designers and managers inside the firm as well as the customers and clients who might use the innovation.
2. Background regarding the innovation process
Before describing the type of KMS system that we envision, it is useful to understand the innovation process in greater detail. Various authors have recently developed innovation models/frameworks (Desouza et al., 2009; Dobni, 2006; Gambardella, 2006); each of them has a particular value but we decided to base our research on what we consider being the most comprehensive one. Rothwell (1992, 1994) describes five generations of innovation processes. The five generations illustrate the evolution of the innovation process over time, starting by the “technology push” model (1950/60s) with an emphasis on R&D, followed by the “Market pull” model (1970s) with an emphasis on marketing, followed by the “Coupling” model with an emphasis on integrating R&D and Marketing, followed by the “interactive” model (1980/90s) with a combination of push and pull and ending by the “network” model (2000s) focusing on knowledge accumulation and external linkages (Rothwell, 1992, 1994). Figure 1 illustrates the “coupling” model of innovation.



Figure 1: The “coupling” model of innovation (third generation) (Rothwell, 1992, 1994)
The coupling model described remains the basis for most models of the innovation process. Organizations might apply it to various extents based on how critical and on how structured their innovation strategy and processes are. The fourth generation added the concepts of supplier’s integration and parallel development of activities (e.g., concurrent engineering) making the innovation less linear (less sequential). The fifth generation focused on ways to become a “fast innovator” by introducing the concepts of system integration and networking models (Rothwell, 1994). Closer integration with stakeholders, higher quest for flexibility and a high use of technology can summarize the evolution to the 5th generation that Rothwell described as “lean innovation” (1994). It is interesting to notice that Rothwell created this typology of innovation processes in 1992, and that his forecast of what the fifth generation will be ended up being very accurate. The fact that technology is becoming one of the key enablers to innovation management cannot be contested, but few systems can currently be labeled as Innovation management systems. Later on in this paper we describe an aspect of such system that could support the interaction with the customer all along the innovation process.
The processes described by Rothwell are linear and from the 4th generation the process becomes parallel (overlap of linear sub-processes) but the flow of information and knowledge is not, as depicted in Figure 1. More recently, Berkhout, Hartmann, van der Duin and Ortt (2006) developed a new innovation model which is no longer linear but cyclic (Cyclic Innovation Model or CIM) as depicted in Figure 2. This model is built on four main components: technological research, product development, market transitions, and scientific exploration. Each component influences and is influenced by its adjacent components. For example, technological research is driven by new scientific insights (science push) and by new functional requirements (function pull) (Berkhout et al., 2006). Such models add to the previous models by increasing the level of interaction of the various parties involved and by making the process more dynamic allowing organizations to start quickly, adjust quickly, and learn quickly (Berkhout et al., 2006). And, the CIM model demonstrates that successful innovation requires contributions from all four quadrants and that the inputs from the four quadrants mesh with one another.


Figure 2: The Cyclic Innovation Model (CIM) (Berkhout et al., 2006)
In particular, this model reinforces the importance of the required interaction of science (hard and soft) with product development (technical and social aspects) in the innovation process (Berkhout et al., 2006). Both the Rothwell and Berkhout et al. models show the importance of constant and continuous interaction between the different sub-processes of the overall innovation process (internal) but also between the internal sub-processes and their environment (market, technology, research/science).
The current practice of technological innovation is for design engineers, often operating in isolated silos, to propose, craft, and create new products, services, processes, and practices based upon scientific, engineering, and technological advances, with the presumption that the new device, service, process, or practice will in fact be put to use. After the innovation has been developed, estimates are generated regarding costs to put it into large-scale production, pricing to recover development and production costs and generate surplus revenues, modes of use of the innovation by adopters, and marketing campaigns built around those modes to entice prospective end users to acquire and use the innovation. Recent technological innovations that have succeeded in widespread adoption include the iPod, and recent technological innovations that have failed to be widely adopted include the Segway personal travel device.
3. Research aim
This project aims at using the technologies of knowledge management (KM) to facilitate the flow of communication and collaboration between innovators and prospective end users in order to enhance the probability of successful innovation. We envision a two-way flow of knowledge. Innovators could query end users to discover what their pressing needs are—what tasks do they need the most assistance with; what features do they desire to see in their tools; how are they frustrated by existing constraints; etc.? Innovators could place prototypes and/or hypothetical prototypes in the “hands” of end users and ask the end users to examine them, test them, experiment with them, explain how they would use them, suggest improvements and refinements to them, etc. The latest innovation trends consist of providing toolkits to customers so they can build what they want/need so the user requirements phase becomes almost nonexistent and risks are reduced to a minimum. Customers become the innovators (Thomke & von Hippel, 2002).
KM is shorthand for a set of technologies and practices emphasizing the capture, organization, reuse, and adroit deployment of knowledge generated by people, typically in organizational settings. Part of KM is an outgrowth of expert system methodology, in the sense that one of its purposes is to elicit, capture, organize, and reuse the tacit knowledge developed by human experts in some task domain. Thus, learning how master diagnosticians quickly pinpoint problems and identify solutions with tricky malfunctions in a large production plant (e.g., offset printing machines) is a KM issue. Once that tacit knowledge has been elicited and stored, that knowledge base can be used by others to solve similar problems elsewhere and that knowledge base can be used to design training programs and to design the machinery itself.
The aim of this project is to create systems to accelerate communications between innovators and prospective end users, so that innovators quickly focus on projects and features in areas of greatest need by end users, and so that end users are obliged to clearly articulate their likes and dislikes of prospective innovations and to provide feedback for how to improve the nascent innovations. As “agile programming” has reformed the way in which large computer programs are developed, the theme of this project might be best described as an effort to promulgate “agile innovation”—rapid prototyping of desired innovations and features coupled with rapid and precise feedback from prospective end users.
4. Initial thoughts on designing a Knowledge-enabled Innovation Management System (KIMS).
Various “traditional” technologies can be used to support the innovation process; Technologies of design such as Computer Aided Design (CAD). Artificial Intelligence (AI), Database and data mining tools, Expert systems, Simulation, and Optimization tools; Technologies of Manufacturing such as Computer Integrated Manufacturing (CIM), Computer Integrated Production (CIP), and Knowledge-based software; Technologies of co-ordination such as Materials Requirement Planning (MRP), Enterprise Resource Planning (ERP), and Electronic Data Interchange (EDI) (Dodgson, Gann, & Salter, 2002). These tools have proven to be very effective in supporting and enabling the first to fourth generation of innovation processes (Rothwell, 1992), but they need to be supplemented with more collaborative and interactive technologies to support the most recent models of innovation.
The recent trends/models in term of innovation management put a strong emphasis on involving external parties to be part of the innovation process. The concepts of open innovation and outside innovation are gaining more and more interest. In the old “closed innovation” paradigm, companies developed their own ideas, built them, marketed them, distributed them, serviced them, financed them, and supported them on their own (Chesbrough, 2006). The open innovation paradigm assumes that “firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology. Open Innovation combines internal and external ideas into architectures and systems whose requirements are defined by a business model. The business model utilizes both external and internal ideas to create value, while defining internal mechanisms to claim some portion of that value. Open Innovation assumes that internal ideas can also be taken to market through external channels, outside the current businesses of the firm, to generate additional value” (Chesbrough, 2006). This paradigm supports the innovation management models previously presented by reinforcing the need to collaborate and to share knowledge inside and outside the firm.
The concept of “outside innovation” fits into the paradigm of open innovation. Seybold gives a good definition of it: “It is when customers lead the design of your business processes, products, services, and business models. It’s when customers roll up their sleeves to co-design their products and your business. It’s when customers attract other customers to build a vital customer-centric ecosystem around your products and services” (Seybold, 2006). This paradigm works particularly well when the customer needs are highly varied or not fully understood (Boudreau & Lakhani, 2009). Furthermore, firms are limited by the ability/capacity of innovation of their own employees. Crowdsourcing consists of making an open online call for a creative idea, or problem solving, or evaluation (Bonabeau, 2009), or any other type of business issues, and to let anyone (in the crowd) submit solutions. The winning idea will often be rewarded. The collective intelligence (wisdom) and the background diversity of the crowd will often offer companies very creative and innovative ideas for a relatively low cost. Number of large “traditional” companies have already successfully used such an approach, such as Starbucks, Procter and Gamble, IBM, Google (through “Google labs”), and Cisco (Jouret, 2009). Some relatively new companies based their business model on crowdsourcing like Innocentive.com , threadless.com, and even Wikipedia.
Even though such new innovation approaches cannot be applied to all types of businesses and will not fully replace the traditional innovation processes, they demonstrate the value of high collaboration, communication, interaction among networks of people coming from different horizons (inside and outside the organization). Such a pattern is the foundation of Web 2.0 technologies supporting social networking and computing. We believe that such technologies should drive the design of a Knowledge-enabled Innovation Management System (KIMS) that could be used to involve innovators and customers all along the innovation process in order to better meet their evolving needs and to reduce any risk of failure.
We represented our vision of a Knowledge-enabled Innovation Management System (KIMS) in integrating the various models and ideas previously presented (Figure 3).


Figure 3: Vision of a Knowledge-enabled Innovation Management System (KIMS)
At the core of our system we can find the customers and the crowd who are the main actors and drivers of the innovation process. The customers and the crowd have a direct interaction with all the phases of the innovation process and all along the product life cycle (including customer service and maintenance). The type of interaction they can have may vary based on the phase of the process they interact with. We listed some of them as examples in Table 1:
Table 1: Examples of actions enabling the interactivity between
the customers and the crowd with the internal innovation process
Submit idea (new product, new feature, new process, problem solving, …) Learn and share knowledge and expertise
Comment on idea, products, features, strategy … Entertain themselves and play
Evaluate, rank , assess, judge, test Make money and get recognition
Experience (simulation, virtual reality) and experiment Advise / recommend
Share (multimedia documents) Design, build, test their own prototypes
Communicate, discuss and interact with others (internal and external actors of the innovation process) Ask for assistance
Compare Complain!

This high degree of interaction and collaboration is made possible by the use of the Web 2.0 technology modules of the KIMS. Among the Web 2.0 technologies we envision the use of social networking tools, wikis, blogs, communities but also the use of virtual environments like “second life” particularly for the use of simulation and experimentation to gain feedback from users.
Other sources of information and knowledge are required for the innovation process, involving environmental factors like: Needs of society, Marketplace, State of the art in term of technology and production and scientific exploration. By constantly monitoring and capturing such type of information and making it available to the various actors of the innovation process, it will help creating, leveraging and selecting the best ideas and solutions. The remaining challenge will consist of organizing and presenting the right information at the right time to the right people in the right format so they can, not only be aware of its existence, but they can also appropriately act on it (knowledge). A combination of various KM technologies will facilitate such tasks by facilitating the capture of external knowledge (Intelligent agents, content syndication, search engines), its selection and organization (expert systems and taxonomies), its storage (database (multimedia) and content management systems), and its sharing (push technologies, knowledge mapping, awareness system, search engines).
The goal of this paper was not to present the architecture of the KIMS but more to introduce the main ideas and concepts that drove us to identify the need and the current feasibility of developing such system relying partly on KM and Web 2.0 technologies. Innovation is becoming critical for companies to compete in a knowledge/creative economy so not only new innovation management processes and models must be developed but also some IT tools to support and enable them. Future research papers will mature the idea presented in this paper as well as present the architecture of the KIMS. We hope to be able to present a working prototype in a near future. Any comment or feedback is most welcome.
References
AMA. (2006). The Quest for Innovation. A global study of innovation management 2006-2016
Andrew, J. P., Haanaes, K., Michael, D. C., Sirkin, H. L., & Taylor, A. (2009). Innovation 2009. Making hard decisions in the downturn. Boston, MA, USA: The Boston Consulting Group
Berkhout, A. J., Hartmann, D., van der Duin, P., & Ortt, R. (2006). Innovating the innovation process. International Journal of Technology Management, 34(3/4), 390-404.
Bonabeau, E. (2009). Decisions 2 0: The Power of Collective Intelligence. MIT Sloan Management Review, 50(2), 45-52.
Boudreau, K. J., & Lakhani, K. R. (2009). How to Manage Outside Innovation. MIT Sloan Management Review, 50, 69-76.
Capgemini. (2008). Global CIO Survey 2008. The Role of IT function in Business Innovation. Innovator vs Operator: Capgemini consultingo. Document Number)
Chesbrough, H. (2006). Open Innovation: The New Imperative for Creating And Profiting from Technology Harvard Business Press.
Desouza, K., Dombrowski, C., Awazu, Y., Baloh, P., Papagari, S., Jha, S., et al. (2009). Crafting organizational innovation processes Innovation: Management, Policy & Practice, 11(1), 6-33.
Dobni, C. B. (2006). The innovation blueprint. Business Horizons, 49(4), 329-339.
Dodgson, M., Gann, D. M., & Salter, A. J. (2002). The Intensification of Innovation. International Journal of Innovation Management, 6(1), 53.
Gambardella, P. (2006). Innovation Frameworks. CSC.
IBM. (2006). Expanding the Innovation Horizon - The Global CEO Study 2006
Jouret, G. (2009). Inside Cisco's Search for the Next Big Idea. Harvard Business Review, 87(9), 43-45.
Rothwell, R. (1992). Successful Industrial Innovation: Critical Factors for the 1990s R & D management, 22(3), 221-239.
Rothwell, R. (1994). Towards the Fifth-generation Innovation Process. International Marketing Review, 11(1), 7.
Seybold, P. B. (2006). Outside Innovation: How Your Customers Will Co-Design Your Company's Future: HarperBusiness.
Thomke, S., & von Hippel, E. (2002). Customers as Innovators: A New Way to Create Value. Harvard Business Review, 80(4), 74-81.