A Business Process Improvement framework for Knowledge-Intensive Entrepreneurial Ventures

Pushed by the transition towards the knowledge economy, as well as several other change drivers, an ever-increasing number of knowledge intensive ventures are relying on operational knowledge intensity in order to generate value. Through their interaction with their varied stakeholders -- from actors within their supply chains to educational and financial institutions -- knowledge intensive enterprises are increasingly becoming a key component of regional economic stability. Within their complex environment, these organisations lack the support of suitable frameworks to inform their efforts to optimise, adapt and improve their underlying business processes in order to maximise the efficiency of their performance and pursue growth ambitions. This paper examines the distinct nature of knowledge intensive entrepreneurial ventures (KIEs) and the applicability of current Business Process Improvement (BPI) frameworks to their setting. Finally, a KIE-oriented business process improvement framework is developed through an integrative adaptation of the concepts of knowledge intensity and knowledge management to the principles of business process redesign and re-engineering reported in existing literature. The proposed framework contributes to the existing literature in the subject of BPI modelling for knowledge intensive entrepreneurial ventures by addressing a distinct set of improvement concerns that this type of organisations face at a process level

The Nature of Knowledge and its Sharing through Models

Enterprise Modelling has been repeatedly proposed as a way to share knowledge within and among companies. However, industry practitioners especially in Small and Medium Enterprises are slow to take up this practice, and models are usually only built to support the development of application programs, databases or other information technology artefacts, rather then for the broader purpose of knowledge sharing. The article examines knowledge categories previously proposed in the literature and proposes an extension of previous work in order to better understand the nature of knowledge sharing processes and the role of models in these.No Full Tex

An engineering design knowledge reuse methodology using process modelling.

This paper describes an approach for reusing engineering design knowledge. Many previous design knowledge reuse systems focus exclusively on geometrical data, which is often not applicable in early design stages. The proposed methodology provides an integrated design knowledge reuse framework, bringing together elements of best practice reuse, design rationale capture and knowledge-based support in a single coherent framework. Best practices are reused through the process model. Rationale is supported by product information, which is retrieved through links to design process tasks. Knowledge-based methods are supported by a common design data model, which serves as a single source of design data to support the design process. By using the design process as the basis for knowledge structuring and retrieval, it serves the dual purpose of design process capture and knowledge reuse: capturing and formalising the rationale that underpins the design process, and providing a framework through which design knowledge can be stored, retrieved and applied. The methodology has been tested with an industrial sponsor producing high vacuum pumps for the semiconductor industry

Analysing Supply Chain Strategies Using Knowledge-Based Techniques

Abstract. We are experiencing a digital revolution that is rapidly changing the way business is conducted. In this new economy, information is shared speedily and many participants are collaborating. Effective and seamless collaboration between distributed supply chain members is therefore crucial. To understand how this may be done, we describe a knowledge based framework for abstracting, enriching, analysing and improving supply chain models. We employ business process modelling as a useful methodology for capturing and analysing supply chain strategies. In addition, we use a meta-interpreter and workflow engine for simulating business processes to help understand business scenarios. Its declarative approach makes business rationale more transparent. Our work is demonstrated by looking at a case study of Dell’s supply chain management that depicts its PC supply chain operation logics and strategies