Purpose
As program managers seek to improve the quality, speed and financial benefits of the programs they manage, many are turning to process improvement methodologies, such as Lean Six Sigma (LSS). However, although existing literature includes multiple studies that apply the methodology to non-manufacturing environments, there is no specific framework for applying LSS within program management (PM). Therefore, the purpose of this paper is to examine the relationships between LSS tools, project scope, program phase and functional area and project outputs, in PM organizations.
Design/methodology/approach
The study uses archival data from 511 LSS projects completed from 2006 to 2015 by a large government agency in the USA composed of 13 PM organizations. The study focuses on four types of input factors: LSS tools, project scope, program phase and functional area; and two output variables: LSS project average financial benefits and percentage of improvement. Multiple regressions are applied to determine what relationships exist between the input and output variables, as well as the nature of such relationships.
Findings
The results of this study show LSS is beneficial to PM and also indicate which tools and organizational contexts have positive and negative associations with project outcomes, serving as guide for future applications. In addition, this study can provide clarity and confidence to program managers who are currently skeptical of LSS, by showing that it can provide cost, schedule and performance improvements beneficial to their programs.
Research limitations/implications
Limitations of this research include the use of a single government agency in the USA, the non-experimental design of the study and limitations associated with the nature and data collection process of the archival data. Future studies should include additional PM organizations, input variables and research designs.
Originality/value
There is no specific framework formalizing the concept of LSS application within PM. The literature includes several studies that apply the methodology to non-manufacturing environments, but not to PM specifically. Furthermore, the existing literature on PM does not explicitly cite any continuous improvement methodology as a critical success factor or provide any detailed guidelines for the application of LSS in PM. This paper contributes by studying the relationships between LSS tools, project scope, program phase and functional area, and project outputs, in a PM environment.
In this study, we postulate that forecasters desire to improve their performance by studying their past forecasting errors. To improve performance, forecasters may measure their past mistakes and revise their forecasts by forecast revision techniques. In an empirical test, forecasts of fifty firms' EPS were prepared by seven forecasting models. The initial forecasts were corrected by the Theil optimal linear correction technique and a Bayesian revision adjustment. Results indicate that the optimal linear correction technique is superior to not correcting for past forecast error.forecasting: applications, finance: securities
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