A Knowledge Management (KM) system refers to a system for managing knowledge in organizations to support the creation, capturing, storage and the dissemination of information (Fuller 2002). In as much as KM has become a widely accepted business practice, many companies are still struggling to measure the gains it purports to offer. In these uncertain economic times, there is the urgent need for KM to show the value that knowledge sharing and its re-use bring to the company. There is the need to use measures to derive metrics that show performance and efforts. The measures outline the information that a person wants to gather which may include customer satisfaction, worker’s productivity or cost savings. If GM is able to build effective measures, it would be able to track the successful implementation, identify the key milestones achieved and show the return on their investments. Measures would also lead to a change of behavior in GM.
There are two issues that came up with regards to GM’s knowledge management valuation. First, it was extremely difficult to create any measure of knowledge sharing that would have shown an absolute correlation between the actions of knowledge sharing and business results. The measuring of knowledge sharing always requires correlation and assumptions. Second, to truly understand the impact of KM, it was important for GM to understand its baseline and the process performance before embarking on the KM system. It was important for GM to know where it came from so as to know what it aimed at.
According to Jerry Ash (2007), “the early outcome metrics had already begun validating the product engineering initiatives”. During the first 36 months of service, and for the vehicles sold between the years 2000 and 2003, the actual warranty costs had gone down by a whopping 20% which was way below the forecasts. The catalogues on engineering solutions, closed-learning and technical memory were identified as the key factors that enabled the driving down of the warranty costs. Other results involved the reduction of the structural costs, improvement of the product quality and the reduction of time taken to the markets.
The intangible metrics of GM’s KM system include both the social capital and the intellectual capital. The social capital consists of relationships, goodwill and the networks. Intellectual capital on the other hand, is the agglomeration of tacit and explicit knowledge, codified information and the intrinsic knowhow. Intellectual capital is rooted in the experience and expertise of the individuals in the organization. The goal of the project was to capture intellectual capital but interestingly, it resulted in the improvement of the flow of tacit-to-tacit knowledge and the engineers became more connected than ever. GM found out that the teams became more like communities of practice and knowledge sharing was not just through flow of documents but through the human networks (Ash, 2007).
Since the year 2000, GM had put up a vibrant knowledge management system that produced over 5000 best practices, reduced costs and impacted on quality and schedule. Coming to the year 2009, GM filed for bankruptcy (Dixon, 2014). This meant that its KM system could not save the company. Most KM systems fall as a result of factors that fall within the categories of culture, technology, content and management. GM was brought down by a flawed strategy which is a product of the knowledge in the organization. James March wrote that exploitation of new knowledge must be balanced with exploration of new knowledge (Dixon, 2014). GM focused on exploitation of the existing knowledge which is much cheaper. The downside of this exploitation led to a closed loop that led to the sub-optimization of the organization’s knowledge. GM could not move beyond within the best practices that existed within its walls. GM failed to note that the global market was rapidly changing due to technology but still relied on the existing knowledge (Dixon, 2014). GM could not convene the organization conversation in a way that would allow new thinking. The KM system also lacked transparency and cognitive diversity.
A number of factors are associated with GM’s knowledge management system. First, the teams looked like communities of practice (CoP). Communities of practice are groups of individuals with shared interests that come together to share and discuss opportunities and problems, talk over lessons learnt and discuss the best practices (Fuller, 2002). CoP’s often emphasize the social nature of learning across companies. The teams in GM usually met to discuss issues and chat the way forward which was a success factor. The sharing of knowledge was not only done through the flow of documents but also through the networking between the individuals in the organization. This built the social capital. At GM, there was the closed loop learning that ensured valuable lessons and learning were inserted into the three categories of intellectual capital that include technical excellence, intellectual property and technical exchanges (Ash, 2007). This closed loop learning ensured that the lessons learnt from previous mistakes were avoided. Closed loop learning also ensured that the knowledge-sharing culture captured more individuals as it engaged more than 10000 individuals from the initial of 1000. It also ensured that anyone could bring a problem or a solution at the appropriate time and it was worked upon (Ash, 2007). Another success factor of GM’s knowledge management system is that it identified SMRE’s (Subject Matter Responsible Engineers) that were responsible for problem identification, mentorship, refining and developing of new product technologies (Ash, 2007).
Knowledge Management systems have a number of potential drawbacks. It is difficult for KM systems to deliver the expected performance outcomes (Thierauf, 1999). Most KM systems are automated and hence there comes the problem of usability. Some of the users of the user may not be capable to use the automated tools to get the required information and hence it becomes a major drawback. Another major issue is the need to retain the knowledge retirees (Rubenstein & Geisler, 2003). Like in the cases of lessons learned idea, retirees may come in handy in giving the right knowledge and the solutions. With these retirees leaving the picture, this leaves behind huge information gap that might be detrimental to the company. Retirees need to be retained in CoP’s for example so as they can interact with the current employees. Sometimes the knowledge might be shared but most people might take a lot of time in acting on the information hence presenting another major drawback. There is also the problem of keeping up with the pace of changing knowledge due to technological advancement while also taking note of what needs to be retained. KM systems also deal with the issue of appropriate motivation and the management of knowledge workers.
An expert location system identifies and locates the people in the organization that have expertise in particular areas (Rao, 2005). GM does have an expert location system. The engineering executives identify subject matter responsible engineers (SRME’s) who are usually the resource persons in case of a problem or solution. They are usually drawn from car and truck engineering with experience in various aspects including the design, the developing and manufacturing of a vehicle. GM needs an expert location system as it would be responsible for problem identification, developing and refining of new products, implementation of solutions and mentorship of the other employees.
References
Ash, Jerry. "Changing Gear." Case Report: General Motors. N.p., n.d. Web. 15 Apr. 2014. Retrieved
http://www.ikmagazine.com/xq/asp/sid.0/articleid.628AE717-8E87-4379-849C-DBB6CBC38293/eTitle.Case_report_General_Motors/qx/display.htm
Fuller, S. (2002). Knowledge management foundations. Boston: Butterworth-Heinemann.
Dixon, Nancy. "Conversation Matters." 'conversation Matters' N.p., n.d. Web. 15 Apr. 2014. Retrieved
http://www.nancydixonblog.com/2012/05/-why-knowledge-management-didnt-save-general-motors-addressing-complex-issues-by-convening-conversat.html
Rao, M. (2005). Knowledge management tools and techniques: Practitioners and experts evaluate KM solutions. Amsterdam: Elsevier Butterworth-Heinemann.
Rubenstein, A. H., & Geisler, E. (2003). Installing and managing workable knowledge management systems. Westport, Conn: Praeger.
Thierauf, R. J. (1999). Knowledge management systems for business. Westport, Conn: Quorum Book.
