How to use lean thinking to improve knowledge management performance of manufacturing supply chains
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This thesis aims to eliminate inefficient knowledge management activities and use Lean Principles as guidance to improve knowledge management performance in manufacturing supply chains. In order to achieve this aim, this research examines the causal relationships between Knowledge Management Processes (KMPs), 4 Lean-KM Wastes and 2 Lean-KM Principles in different countries, industries and company sizes.
This thesis employs a quantitative method. A theoretical model is built on rigorous literature reviews of supply chain knowledge management and Lean thinking studies, in-depth discussions, item review and pilot study with experts to signify ambiguity or misunderstanding with the items and to suggest modifications. The proposed model is empirically tested with survey data using 359 responses from two types of manufacturing industries (i.e. machinery and electronics manufacturing and food and drink industry), two types of business sizes (i.e. SMEs and Large companies), and two countries (i.e. China and the US).
The key output is a framework for Lean-Knowledge Management Processes (Lean-KMPs). With regard to the findings of the empirical research, three main constructs were successfully validated as multi-dimensional constructs. The results from path model analysis shows that most of the sub-hypotheses are supported. Only three of them were rejected in both aggregated-level path model analysis and multi-group analysis. The results have proven the four Lean-KM Wastes and two Lean-KM Principles having negative and positive effects on KMPs, respectively. The detailed findings of this thesis include five parts. Firstly, with respect to Knowledge Acquisition (KA), badly designed information systems are the biggest obstacles for improving the performance of KA. Identification and Usage of Valuable Information and Knowledge (IUVI) and Encouraging Information and Knowledge Flow (EIKF) are two factors that can enhance KA. In addition, big companies should build trustful relationships and improve the accessibility of required information with their supply chain. Secondly, concerning the performance of Knowledge Selection (KS), companies should only retain the most valuable information for avoiding overloaded databases, and information provider need to understand receiver’s requirement and provide the most relevant information, so that could help receivers to store that information more effectively and also make the retrieval of it much easier. Thirdly, for enhancing the performance of Knowledge Generation (KG), companies should gather business information as comprehensive as possible. In addition, Low Quality Information (LQI) and Insufficient Knowledge Inventory (IKI) are two negative factors which could diminish the performance of KG. Moreover, the results also reveal that small or less resourceful companies should focus more on improving the information quality over quantity. Furthermore, well-developed IT systems, IUVI, and EIKF are important positive factors for large and/or machinery and electronics manufacturing’s KG performance. Fourthly, as for Knowledge Internalisation (KI), IUVI and EIKF are two positive factors to the performance of KI. While Inappropriate Information System (IIS) is the biggest obstacle of KI. Lastly, regarding to Knowledge Externalisation (KE), the results indicate that LQI and IKI are two negative factors to KE and IUVI is the only positive factor to KE.
This thesis synthesises Lean thinking, supply chain integration, and knowledge management to develop a comprehensive approach to improve the knowledge management performance of manufacturing supply chains. It has four theoretical contributions: 1) developed Lean-KMPs model and 19 hypotheses to improve the KM performance of manufacturing supply chains; 2) developed 4 Lean-KM wastes and 2 Lean-KM Principles based on the Lean thinking for manufacturing supply chain KM; 3) identified and developed 5 latent constructs for KMPs and 30 corresponding indicators to accurately measure companies’ KM performance; 4) conducted industry-specific empirical studies, collected 359 useful data from different countries, different industries and different sized companies, and conducted three pairs of multi-group analyses based on these different contexts.
Various manufacturing companies in both heavy and light industries would benefit from applying the results of this study to improve their KM performance. The results also suggest that manufacturing practitioners should use a comprehensive approach to improve knowledge management processes in order to make sure that critical information and knowledge flow seamlessly and efficiently among their supply chain members, further to achieve successful supply chain integration.