Towards improved simulation process capabilities : A simulation process maturity model

Abstract: Purpose – The purpose of this thesis is to present the result of simulation process maturity (SPM) assessments in the pharmaceutical and manufacturing industry using a process maturity model derived from empirical research. The model is used to benchmark an organisations simulation capability and to apply the results of the assessment to develop a road map for implementing simulation process improvement as well as integration initiatives within the organisation. Methodology approach – This is a survey-based research on benchmarking simulation process in the industry. The SPM model was adopted as the reference model and data were collected through interviews with key simulation staff within each organisation with the aim of identifying anomalies or conformities in the way simulation projects are performed. A pragmatic scoring system was introduced based on the fulfilment of defined requirements to score findings from the interviews based on defined SPM tenets and the sample profiles SPM tier calculated. Findings – Each tenet of the SPM model consists of 3 maturity tiers with increasing level of complexity of the process. The results of the assessment showed that the overall average of SPM of the sample profile is at tier 2. Key discrepancies representing the lowest and highest ratings are found to be mostly related to process understanding and management commitment.  A higher mean score is recognised for the samples displaying a deeper integration with top management and alignment with organisational strategic objectives. These maturity tiers and tenets reflect the extent of the implementation of contracting best practices within the studied industries. Research limitations/implications – This thesis uses a purposeful sampling approach designed at acquiring data on an organisational current simulation process. The assessment survey was conducted solely on qualified simulation personal within pharmaceutical and manufacturing. It is as such not clear whether the proposed SPM model will work within other types of industrial settings and if so how to organise the simulation activities in that setting. By purposively selecting the sample profile there are limitations concerning generalizability. Still, the conclusion based on the analysis of these benchmarking assessments may offer key take-aways in the context of process management. Practical implications – The findings suggest that benchmarking can be effective in measuring and improving simulation process capabilities within the pharmaceutical and manufacturing industry. The use of these benchmarking assessment can be instrumental in tracking the achievements of this process and enable management to measure the quality of the simulation activities in addition to offer guidance on what development actions to prioritise. By improving the simulation process, organisations will work towards furthering the integrity and credibility of its simulation studies. Originality/value – The existing literature does properly not present adequate empirical research in the field on maturity on the simulation process.  Also, the analysis method used in this study will further help organisation to perform self-assessment and determine their respective SPM. This value is reflected in using the results for implementing simulation process improvement initiatives that will ensure that the process is conducted in the most effective and efficient way.