2015),"A framework for Six Sigma project selection in higher educational institutions, using a weighted scorecard approach", Quality Assurance in Education, Vol. 23 Iss 1 pp. -Permanent link to this document: http://dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

Purpose -The six sigma methodology has been successfully implemented in many organizations leading to tremendous quality improvements in products manufactured and services delivered. However, academic institutions have lagged other organizations in implementing six sigma. The purpose of this paper is to examine the challenges of implementing the methodology in academia and proposes a framework that serves as a guide for implementing six sigma in academic institutions. Design/methodology/approach -Several unique aspects that differentiate an academic environment from a manufacturing setting for six sigma are identified. A three-tiered framework to organize the six sigma improvement methodology and related academic performance indicators into a hierarchy fitting academic institution governance levels is proposed. Examples of strategic objectives and performance indicators by levels of implementation for the DMAIC process are also provided. Findings -The findings suggest that the unique structure of an academic institution make it an interesting candidate for implementing six sigma. The three-tiered framework for six sigma can be used by administrators, faculty, staff and students as an implementation guide.Research limitations/implications -The paper shows that significant differences between the environments make implementation in many areas within an academic institution challenging. However, there are limitations to the application of six sigma in an academic organization. The six sigma methodology has been more thoroughly developed and refined in manufacturing environments than in service systems such as in a university. Practical implications -This paper helps to stimulate thinking about the application of a proven quality management methodology to academic settings where structured formal improvement programs such as six sigma are not commonly found. Originality/value -The value of this paper is to provide a three-tiered hierarchical structure for applying six sigma in academic organizations.

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The development of computer technology, artificial intelligence, and simulation modelling has become increasingly complex, and yet the application of these techniques is necessary for a company to be effective and competitive. However, the end users may not have the necessary sophistication to apply these technologies effectively on their own. Combining these technologies to provide intelligent interfaces may be beneficial to the non‐expert user. Discusses a conceptual knowledge‐based simulation system and illustrates its applicability using a hypothetical manufacturing example. The example focuses on interfacing knowledge from an expert with a simulation model to make scheduling decisions in a manufacturing environement. The knowledge system is constructed using the M.1 expert system package and the simulation is performed using SLAM II.

Dawes and Corrigan have asserted that linear decision rules using random or unit coefficients "yield predictions that are superior to those of human judges." Their assertion is based on the large correlations between the decisions from random or unit rules and actual decisions. This paper reports an experiment based on the scheduling production problem in which the correlations coefficient is compared with actual costs. In spite of high correlations with the optimal decision, the unit and random rules yielded much higher costs than human judgment.