Learning and technological capability building in emerging economies: the case of the biomass power equipment industry in Malaysia

There is increasing recognition that the transfer of foreign technology to developing countries should be considered in light of broader processes of learning, technological capability, formation and industrial development. Previous studies that have looked at this in the context of cleantech industries in emerging economies tend to overlook firm-level specifics. This paper contributes to filling this gap by utilising in-depth qualitative firm-level data to analyse the extent to which the use of different learning mechanisms can explain differences in the accumulation of technological capabilities. This is explored via an examination of eight firms in the biomass power equipment industry in Malaysia during the period 1970-2011. The paper finds that firms relying on a combination of learning from foreign technology partners and internal learning by planned experimentation make most progress in terms of technological capability. Nevertheless, local spill-over effects were found to be important for some firms who learned principally from imitation of local competitors, although significantly, firms learning from local spillovers failed to advance beyond extra basic operating technological capabilities. Those firms who proactively pursued learning from foreign partners, on the other hand, advanced further, reaching basic innovative levels of technological capabilities. These findings are relevant for a wider range of industrial sectors in emerging economies

Customer knowledge transfer challenges in a co-creation value network:Toward a reference model

In today’s interconnected global marketplace, where customers have become increasingly knowledgeable and empowered, a customer-centric view is becoming a prominent differentiating strategy for firms. Accordingly, firms with the aim of delivering a seamless customer experience strive to offer integrated solutions. This quite often relies on inter-organizational collaboration in the context of a value network In this context, customer-related knowledge is regarded as one of the primary sources in the provisioning process of integrated solutions. This, in turn, implies the importance of effective sharing of customer knowledge among actors of a value network. Customer knowledge transfer is difficult due to some recognizable challenges such as a lack of trust. Because of the added complexity of a value network, achieving a shared understanding among actors about customer knowledge transfer challenges in a value network setting (VN-CKTC) might be more difficult. A systematic and comprehensive overview of the VN-CKTC (in the form of a reference model) might support this by providing additional structure. Although scholars have long studied knowledge transfer challenges within business network settings, they are usually limited in scope and their resulting challenges differ widely. Therefore, they provide insufficient coverage of the possible challenges. A more comprehensive view is thus needed. Our research aims at designing and validating a reference model that provides a systematic and wider spectrum of possible VN-CKTC. To this end, a design science research approach is followed. In the design phase, by conducting a systematic literature review followed by a structured classification, a reference model of VN-CKTC is designed. In the evaluation phase, the validation of this designed artifact is evaluated in a value network setting by conducting multiple case studies. The results of this study give us both theoretically and context-specific descriptions of the significant relevant of these challenges. The proposed reference model provides a rich picture of VN-CKTC. Decision makers of value networks can use this reference model as a means to achieve a shared understanding about customer knowledge transfer challenges and to come to an agreement on these challenges. They can also apply it to be aware of which challenges to focus on, so they are provided with a much stronger basis to make better-informed decisions to address and mitigate these challenges

A systems thinking approach for modelling supply chain risk propagation

Supply Chain Risk Management (SCRM) is rapidly becoming a most sought after research area due to the influence of recent supply chain disruptions on global economy. The thesis begins with a systematic literature review of the developments within the broad domain of SCRM over the past decade. Thematic and descriptive analysis supported with modern knowledge management techniques brings forward seven distinctive research gaps for future research in SCRM. Overlapping research findings from an industry perspective, coupled with SCRM research gaps from the systematic literature review has helped to define the research problem for this study. The thesis focuses on a holistic and systematic approach to modelling risks within supply chain and logistics networks. The systems thinking approach followed conceptualises the phenomenon of risk propagation utilising several recent case studies, workshop findings and focus studies. Risk propagation is multidimensional and propagates beyond goods, finance and information resource. It cascades into technology, human resource and socio-ecological dimensions. Three risk propagation zones are identified that build the fundamentals for modelling risk behaviour in terms of cost and delay. The development of a structured framework for SCRM, a holistic supply chain risk model and a quantitative research design for risk assessment are the major contributions of this research. The developed risk assessment platform has the ability to capture the fracture points and cascading impact within a supply chain and logistics network. A reputed aerospace and defence organisation in UK was used to test the experimental modelling set up for its viability and for bridging the gap between theory and practice. The combined statistical and simulation modelling approach provides a new perspective to assessing the complex behavioural performance of risks during multiple interactions within network

Critical success factors and risk mitigation strategy for new product development

”Success in new product development (NPD) offers a competitive and comparative advantage in the marketplace. A primary objective in an NPD project is to launch world class products with minimal risk. To deliver the superior quality and performance customers require, a company must develop the right NPD structure and framework for seamless execution by the NPD project teams throughout the product lifecycle. Companies must understand how to identify and mitigate risk to enable the success of their NPD projects. The costs to develop new products are often a considerable portion of an organization’s budget; however, studies have shown only 60 percent of new products making it to the market are commercially successful. Therefore, NPD project teams need to have a risk mitigation strategy, methodology, or framework to help with the identification and mitigation of risks in the product development process. This research conducted a systematic literature review to document the current research in the development of a risk mitigation framework tied to critical success factors (CSFs) that can be applied in the NPD process. The purpose of this research was to 1) determine the top CSFs that enable successful NPD through a worldwide multi-industry survey and 2) develop an NPD framework to mitigate risk. The survey responses were analyzed using the Kruskal-Wallis non-parametric statistical analysis to determine statistical differences in the CSFs based on rank. The top CSFs were then grouped to provide a conceptual highlevel view for managers to consider when developing or continuously improving their NPD execution structure, methods, and processes. An NPD framework was proposed based on the CSFs in order to mitigate risk”--Abstract, page iv

Data management and Data Pipelines: An empirical investigation in the embedded systems domain

Context: Companies are increasingly collecting data from all possible sources to extract insights that help in data-driven decision-making. Increased data volume, variety, and velocity and the impact of poor quality data on the development of data products are leading companies to look for an improved data management approach that can accelerate the development of high-quality data products. Further, AI is being applied in a growing number of fields, and thus it is evolving as a horizontal technology. Consequently, AI components are increasingly been integrated into embedded systems along with electronics and software. We refer to these systems as AI-enhanced embedded systems. Given the strong dependence of AI on data, this expansion also creates a new space for applying data management techniques. Objective: The overall goal of this thesis is to empirically identify the data management challenges encountered during the development and maintenance of AI-enhanced embedded systems, propose an improved data management approach and empirically validate the proposed approach.Method: To achieve the goal, we conducted this research in close collaboration with Software Center companies using a combination of different empirical research methods: case studies, literature reviews, and action research.Results and conclusions: This research provides five main results. First, it identifies key data management challenges specific to Deep Learning models developed at embedded system companies. Second, it examines the practices such as DataOps and data pipelines that help to address data management challenges. We observed that DataOps is the best data management practice that improves the data quality and reduces the time tdevelop data products. The data pipeline is the critical component of DataOps that manages the data life cycle activities. The study also provides the potential faults at each step of the data pipeline and the corresponding mitigation strategies. Finally, the data pipeline model is realized in a small piece of data pipeline and calculated the percentage of saved data dumps through the implementation.Future work: As future work, we plan to realize the conceptual data pipeline model so that companies can build customized robust data pipelines. We also plan to analyze the impact and value of data pipelines in cross-domain AI systems and data applications. We also plan to develop AI-based fault detection and mitigation system suitable for data pipelines

Application of a Blockchain Enabled Model in Disaster Aids Supply Network Resilience

The disaster area is a dynamic environment. The bottleneck in distributing the supplies may be from the damaged infrastructure or the unavailability of accurate information about the required amounts. The success of the disaster response network is based on collaboration, coordination, sovereignty, and equality in relief distribution. Therefore, a reliable dynamic communication system is required to facilitate the interactions, enhance the knowledge for the relief operation, prioritize, and coordinate the goods distribution. One of the promising innovative technologies is blockchain technology which enables transparent, secure, and real-time information exchange and automation through smart contracts. This study analyzes the application of blockchain technology on disaster management resilience. The influences of this most promising application on the disaster aid supply network resilience combined with the Internet of Things (IoT) and Dynamic Voltage Frequency Scaling (DVFS) algorithm are explored employing a network-based simulation. The theoretical analysis reveals an advancement in disaster-aids supply network strategies using smart contracts for collaborations. The simulation study indicates an enhance in resilience by improvement in collaboration and communication due to more time-efficient processing for disaster supply management. From the investigations, insights have been derived for researchers in the field and the managers interested in practical implementation

Occupational health and safety issues in human-robot collaboration: State of the art and open challenges

Human-Robot Collaboration (HRC) refers to the interaction of workers and robots in a shared workspace. Owing to the integration of the industrial automation strengths with the inimitable cognitive capabilities of humans, HRC is paramount to move towards advanced and sustainable production systems. Although the overall safety of collaborative robotics has increased over time, further research efforts are needed to allow humans to operate alongside robots, with awareness and trust. Numerous safety concerns are open, and either new or enhanced technical, procedural and organizational measures have to be investigated to design and implement inherently safe and ergonomic automation solutions, aligning the systems performance and the human safety. Therefore, a bibliometric analysis and a literature review are carried out in the present paper to provide a comprehensive overview of Occupational Health and Safety (OHS) issues in HRC. As a result, the most researched topics and application areas, and the possible future lines of research are identified. Reviewed articles stress the central role played by humans during collaboration, underlining the need to integrate the human factor in the hazard analysis and risk assessment. Human-centered design and cognitive engineering principles also require further investigations to increase the worker acceptance and trust during collaboration. Deepened studies are compulsory in the healthcare sector, to investigate the social and ethical implications of HRC. Whatever the application context is, the implementation of more and more advanced technologies is fundamental to overcome the current HRC safety concerns, designing low-risk HRC systems while ensuring the system productivity

Distributive justice in the implementation of science-based targets for businesses : a systematic literature review

This thesis examines the integration of distributive justice into science-based targets (SBTs) for climate change mitigation by businesses, addressing concerns over the burden shift from public authorities to private entities. Through a systematic literature review, interdisciplinary research work addressing relevant contributory debates was thus extracted from Scopus, ScienceDirect, and Google Scholar within the scope 2015-2023. The study assesses the state-of-the-art of distributive justice in SBTs, the efficacy of interactions within the knowledge-to-action system and possible improvements for a better inclusion of distributive justice in SBTs. The findings indicate a lack of system-thinking in the research focused on distributive justice within SBTs, despite recognition of its significance for equitable climate action. The thesis reveals a need for enhanced mechanisms in the knowledge-to-action system to ensure equity, transparency, accountability, and inclusivity. Finally, the study calls for a more just and effective approach to corporate climate action, advocating for improvements in the creation and application of SBTs that incorporate distributive justice principles

Need Finding for the Development of a Conceptional, Engineering- Driven Framework for Improved Product Documentation

AbstractEngineering companies that develop advanced products in multi-disciplinary new product development (NPD) teams, have difficulties in managing, communicating, and (re)using knowledge in and between NPD projects. Information is lost due to team dynamics, inappropriate documentation and methods, resulting in unnecessary design iterations, repeated problem-solving, lack of effectiveness and value, and low financial performance. It is, therefore, desirable to develop a documentation model that can be integrated into different engineering processes and used to effectively communicate product information within a single project and between projects, combining strategies from product design methodology, model-based systems engineering, and lean development. It is necessary to combine the most recent product (systems) engineering methods with the understanding of problems and needs in industrial environments where they shall be applied. This paper presents results of need finding in four companies using a semi-structured interview approach to gain insight into problems associated with product documentation. The findings are turned into a conceptual engineering-driven product documentation framework, which links documentation to the product architecture using knowledge-brief (A3) type documentation strategies from lean execution environments