A Framework for Enterprise Knowledge Discovery from Databases

Knowledge discovery from large databases has become an emerging research topic and application area in recent years primarily because of the successful introduction of large business information systems to enterprises in the electronic business era. However, transferring subjects/problems from managerial perspective to data mining tasks from information technology perspective requires multidisciplinary domain knowledge. This paper proposes a practical framework for enterprise knowledge discovery in a systematical manner. The six-step framework employs the cause-andeffect diagram to model enterprise processes, tasks and attributes corresponding diagram to define data mining tasks, and multi-criteria method to assess the mined results in the form of association rules. This research also applied the proposed framework to a real case study of knowledge discovery from service records. The mining results have been proven useful in product design and quality improvement and the framework has demonstrated its applicability of guiding an enterprise to discover knowledge from historical data to tackle existing problems

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

On the versatility of Little’s Law in operations management: a review and classification using vignettes

Despite Little’s Law being considered as one of the ‘laws’ of operations management, evidence of its application in an empirical context is diverse and diffuse. Hence, this paper aims to identify, classify and consolidate published empirical applications of Little’s Law in a systematic manner to better understand its versatility. This paper undertakes a systematic literature review of the databases of the five main publishers of operations management journals and snowball sampling for additional papers. A final sample of 128 empirical journal articles is identified and categorized. Tactical, medium-term decisions relating to capacity dynamics and operations re-engineering are the most popular categories. To give further insights into versatility, vignettes for each category are developed. The review and vignettes confirm Little’s Law as a highly relevant paradigm to operations management decisions due to its empirical versatility across levels, sectors and time domains. The paper suggests four factors to underline the empirical versatility of Little’s Law in operations management: applicability, utility, simplicity and visibility