Ina S. Markham

Self-management and self-leadership reexamined: A levels-of-analysis perspective

Abstract This article critiques the concepts of self-management and self-leadership from a levels-of-analysis perspective. Conceptual and methodological problems in identifying the most pertinent levels of analysis are noted. We articulate the ways in which the individual, the dyad, the group, and the organization can be theoretically melded into existing self-leadership theory. Suggestions for future research are explored, as well as practical applications in the areas of self-managed work teams and high-performance organizations.

The effect of sample size and variability of data on the comparative performance of artificial neural networks and regression

This research explores the robustness of simple linear regression and artificial neural networks with respect to varying sample size and variance of the error term by comparing their predictive abilities. The comparison is made using the root mean square difference between the predicted output from each technique and the actual output.

Kanban setting through artificial intelligence: a comparative study of artificial neural networks and decision trees

Determining the number of circulating kanban cards is important in order effectively to operate a just‐in‐time with kanban production system. While a number of techniques exist for setting the number of kanbans, artificial neural networks (ANNs) and classification and regression trees (CARTs) represent two practical approaches with special capabilities for operationalizing the kanban setting problem. This paper provides a comparison of ANNs with CART for setting the number of kanbans in a dynamically varying production environment. Our results show that both methods are comparable in terms of accuracy and response speed, but that CARTs have advantages in terms of explainability and development speed. The paper concludes with a discussion of the implications of using these techniques in an operational setting.

A rule induction approach for determining the number of kanbans in a just-in-time production system

Abstract A procedure based on rule induction is presented which can be used to determine the number of kanbans while simultaneously determining the critical factors in a just-in-time (JIT) production system. In particular, the classification and regression tree (CART) technique developed by Brieman et al. [Breiman, L., Friedman, J., Olshen, R. and Stone, C. J., Classification and Regression Trees . Wadsworth, Belmont, CA, 1984.] is used to automatically generate rules from dynamic shop floor data. An example application of the methodology is presented and the advantages of a rule induction approach are explained. The paper concludes with a discussion of future research directions.

An approach to learning from both good and poor factory performance in a Kanban-based just-in-time production system

In a JIT manufacturing environment it may be desirable to learn from an archived history of data that contains information that reflects less than optimal factory performance. The purpose of this paper is to use rule induction to predict JIT factory performance from past data that reflects both poor (saturated or starved) and good (efficient) factory performance. Inductive learning techniques have previously been applied to JIT production systems (Markham et al. , Computers and Industrial Engineering, 34 , 717-726, 1998; Markham et al. , International Journal of Manufacturing Technology Management, 11 (4), 239-246, 2000), but these techniques were only applied to data sets that reflected a well-performing factory. This paper presents an approach based on inductive learning in a JIT manufacturing environment that (1) accurately classifies and predicts factory performance based on shop factors, and (2) identifies the important relationships between the shop factors that determine factory performance. An example application is presented in which the classification and regression tree (CART) technique is used to predict saturated, starved or efficient factory performance based on dynamic shop floor data. This means that the relationship between the variables that cause poor factory performance can be discovered and measures to assure efficient performance can then be taken.

Management Science in U.S. AACSB International-Accredited Core Undergraduate Business School Curricula

In 2003, accreditation standards were revised to require coverage of management science (MS) after previously removing it in 1991. Meanwhile, increasing awareness of the value of business analytics stimulated a renewed interest in MS. To examine its present status in undergraduate core business curricula, the authors conducted two studies to review quantitative course requirements at top-ranked schools and to survey MS course content. The results indicate limited visibility of MS as a discipline and significant variation in MS topic coverage across institutions. These findings raise serious concerns about the ability of business schools to produce future graduates with the skills needed to support industry adoption of advanced analytics.

Improving the prediction of employee productivity: a comparison of ordinary least squares versus genetic algorithms coupled with artificial neural networks

This research compares the results of utilising an Ordinary Least Squares (OLS) approach versus a combined Genetic Algorithm (GA) with an Artificial Neural Network (ANN) for the task of selecting high-productivity employees. Demographic and piece-rate performance data were collected from 378 employees of a large garment manufacturer. While the OLS model showed only 3 of 11 predictors to be significant, a combined GA procedure coupled with an ANN model found seven determinants to be important in identifying the most productive employees. The ANN models R² of 0.30 was significantly better at predicting hourly productivity than the OLS model (R² = 0.14). The accuracy of the classification results showed that the two techniques were very different; the ANN results were significantly more accurate for identifying and classifying high-performance employees. The implications of this for the field of productivity and employee selection are discussed.

Assessing the prediction of employee productivity: a comparison of OLS vs. CART

This research compares the results of utilising an ordinary least squares (OLS) approach vs. a classification and regression tree (CART) approach for identifying employees with a high likelihood of being productive. Relevant performance data were collected from 378 employees of a large garment manufacturer. Past research (Markham et al., 2006) has shown that a combined genetic algorithm with an artificial neural network substantially outperformed (R² = 0.30) an equivalent OLS solution (R² = 0.14) when predicting individual level productivity. The current research compares the use of CART to OLS using the same data set. With an R² of 0.43, the CART results were even more powerful in identifying and classifying high performance employees. The implications of this finding for the field of productivity research and employee selection are discussed.