In the pursuit of Operational Excellence - Challenges with performance measures and performance estimates

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School of Business | Doctoral thesis (article-based)
Degree programme
82 + app. 134
Aalto University publication series DOCTORAL DISSERTATIONS, 40/2021
Essentially all companies want to outperform their competitors. Operational excellence is thus the goal for most organisations that operate in a competitive environment. However, the ways to achieve operational excellence depend largely on the company's characteristics. Nonetheless, there are two common crucial factors that all firms must face in the pursuit of operational excellence: performance measures and performance estimates. This Dissertation investigates different challenges associated with performance measures and performance estimates and the implications of such complexity in the pursuit of operational excellence. The research problems are formulated as follows: (i) the nuanced and complex nature of performance measures, (ii) the challenges in monitoring numerous performance measures, and (iii) the implications of estimate uncertainty. Essay I investigates the nuanced and complex nature of performance measures by illustrating that performance measures are influenced by numerous factors, many of which are subtle. Specifically, Essay I examines the impacts on performance outcomes of occupational safety perceptions from both managers and workers. Essay I finds significant correlations between various performance outcomes with perceptions of occupational safety. Essay II investigates the additional two challenges: (i) measure intangible performance outcomes and (ii) monitor numerous performance measures when there are numerous stakeholders involved. Essay II also reveals that analytics, combined with a centralised information system, is an effective tool for quantifying otherwise intangible performance measures and tracking numerous performance indicators. Analytics also helps enable data-based decision-making processes, which is imperative for a performance management programme to succeed. Essay III examines two strategies to tackle estimate uncertainty: (i) consider estimate uncertainty with the Bayesian method and (ii) ignore estimate uncertainty by taking estimates at face value. Essay III reveals that the optimal strategy depends on the goal of the decision. Strategy (i) is optimal when the goal is a high expected return and low post-decision disappointment. Strategy (ii) is optimal when the goal is to capture as many big wins as possible. Essay III also provides practical confirmations of the mathematical findings from R&D investment decisions in the pharmaceutical industry. Through investigating three vastly different operations, this Dissertation contributes to the literature on performance and quality management by exploring the common challenges that all firms must face in the pursuit of operational excellence. While the significance of performance measures and performance estimates is often emphasised at the strategic level, this Dissertation proves that it is also vital to understand such complexity to make better-informed decisions at the operational and tactical levels.
Defense is held on 16.4.2021 12:00 – 15:00 Via Zoom
Supervising professor
Kuula, Markku, Prof., Aalto University, Department of Information and Service Management, Finland
Thesis advisor
Finne, Max, Asst. Prof., Aalto University, Finland
operational excellence, performance measures, performance estimates, analytics, estimate uncertainty, Bayesian statistics
Other note
  • [Publication 1]: Tran, Tri; Finne, Max; Kuula, Markku. The linkages between occupational safety and performance outcomes – Empirical evidence from the manufacturing sector. Unpublished manuscript, an earlier version of the literature review part of the essay – Tran, T., & Finne, M. (2019). ‘Revisiting the linkages between safety and quality in manufacturing firms: a thematic literature review and research agenda’ – was presented at the 26th EurOMA Conference in Helsinki, Finland, 2020
  • [Publication 2]: Kuula, Markku; Tran, Tri. Quality Leadership in Higher Education – Measure the Immeasurable with Analytics. Case: Aalto University School of Business. Unpublished manuscript, 2020
  • [Publication 3]: Tran, Tri. Dealing with uncertain performance estimates in project selection: A Bayesian analysis with practical confirmations from thepharmaceutical R&D investment decisions. Working paper, 2020