A Framework to Capture and Share Knowledge Using Storytelling and Video Sharing in Global Product Development

In global engineering enterprises, information and knowledge sharing are critical factors that can determine a project’s success. This statement is widely acknowledged in published literature. However, according to some academics, tacit knowledge is derived from a person’s lifetime of experience, practice, perception and learning, which makes it hard to capture and document in order to be shared. This project investigates if social media tools can be used to improve and enable tacit knowledge sharing within a global engineering enterprise. This paper first provides a brief background of the subject area, followed by an explanation of the industrial investigation, from which the proposed knowledge framework to improve tacit knowledge sharing is presented. This project’s main focus is on the improvement of collaboration and knowledge sharing amongst product development engineers in order to improve the whole product development cycle.

Storytelling and story testing in domestication

The domestication of plants and animals marks one of the most significant transitions in human, and indeed global, history. Traditionally, study of the domestication process was the exclusive domain of archaeologists and agricultural scientists; today it is an increasingly multidisciplinary enterprise that has come to involve the skills of evolutionary biologists and geneticists. Although the application of new information sources and methodologies has dramatically transformed our ability to study and understand domestication, it has also generated increasingly large and complex datasets, the interpretation of which is not straightforward. In particular, challenges of equifinality, evolutionary variance, and emergence of unexpected or counter-intuitive patterns all face researchers attempting to infer past processes directly from patterns in data. We argue that explicit modeling approaches, drawing upon emerging methodologies in statistics and population genetics, provide a powerful means of addressing these limitations. Modeling also offers an approach to analyzing datasets that avoids conclusions steered by implicit biases, and makes possible the formal integration of different data types. Here we outline some of the modeling approaches most relevant to current problems in domestication research, and demonstrate the ways in which simulation modeling is beginning to reshape our understanding of the domestication process.