Towards a Contingency Theory perspective of Quality Management in Enabling Strategic Alignment

The aim of this paper is to explore the role of Quality Management (QM) theory and practice using a contingency theory perspective. The study is grounded in the role of QM in improving strategic alignment within Small and Medium Sized Enterprises (SMEs) using Contingency Theory rather than adopting best practice approaches. An inductive theory building research methodology was used involving multiple case analyses of five SMEs, involving repeat interviews (n=45), focus groups (n=5) and document analysis. From the findings, it was found that Contingency Variables (strategy, culture, lifecycle and customer focus) and their respective typologies were found to interact with QM practices in helping to shape strategic alignment between the SMEs and their environments. This shaping process based on contingency approaches occurred in a manner unique to each SME and their respective environments rather than in an overarching best practice manner.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.