This paper describes the development of a new computational model to predict the desirability of decision consequences in an organization, and the development of a prototype tool to enable real-time interaction and decision support when changes occur simultaneously. A tool, called Decision Propagation System, is developed in response to the needs of BT Group plc in understanding the most effective set of interventions in the organization where the high degree of connectivity between system components and the uncertainty in connectivity data are two critical issues. Designed on a case study of the Fields Operations Engineering, this research demonstrates that a knowledge of overlapping decision propagation paths can direct the organizational decisions towards mitigating the risk of unintended consequences.

This paper employs the concept of real options to quantitatively assess resilience. First, the definitions of resilience are distilled from literature in the fields of engineering, management and ecology to give requirements for further assessment. From this, it was found that resilience requires a system to be robust, adaptable and flexible in the face of uncertainty. The main contribution of the paper is to connect these requirements to real options valuation and demonstrate the evaluation of the robust and flexible cases through real options methods. Specifically, Least Squares Monte Carlo method is used to value each option with the robust case being the benchmark and flexibility representing upgrades to the system. This is applied to an illustrative telecommunications case and the properties of the model assessed. The results show that uncertainties on the system can be captured and valued through this method so that it can aid a decision maker to assess which technology option or investment to select for future planning.

Today's market conditions such as globalisation and digitalisation have made it challenging to design an effective service system that can efficiently balance organisational service capacity and customer service quality, hence ensure achieving business growth. One key reason might be related to the complex structure of relationships within and across functional disciplines that in often cases are dynamic and uncertain while occurring in multiple layers. Therefore, effective understanding of these interrelationships might be a significant step towards understanding the dynamics of a complex service system. In response to this challenge, this paper presents development and application of a systematic modelling and analysis framework that uses functionality of Change Prediction Method and System Dynamics to integrate multiple levels of relationships. The objective is to help decision makers understand key influencing factors and their underlying risk and impact on the system behaviour, i.e., customer experience, thus making organisation more adaptive in responding to changes and uncertainties. The ideas are illustrated through an expanded case study in British Telecom company.

Although modelling tools are intensively used within companies, the modelling process itself is still scarcely researched. The few related works focus on the steps encompassed when developing a model, without taking into consideration the context surrounding it. Nevertheless understanding this context is crucial since this influences the modelling process in terms of objectives, available data and tools. A survey conducted among expert modellers in 1994 provided insights into this context by establishing a profile of the modeller and highlighting the qualities needed to improve modelling practice. However software, technology and businesses have evolved over twenty years, which may have impacted the modelling practice. Twenty years later, we conduct a similar survey. Comparing the results enables studying the evolution of modelling practice over time. The findings are discussed in the light of potentially impacting technological progress and provide insight for future research concerned with improving the modelling process.

The development of models, especially simulation models of both products and processes, has increased in industry and now offer substantial competitive advantages in decision support across many fields. Even so, little is known about the structures of applied modelling processes as the focus so far has primarily been on improving modelling tools and software, methodologies, and modelling outcomes. In this paper, we gain insights into the value creation activities in modelling practice through the analysis of activity structures from 12 different modelling processes across two large UK companies. The results show that modelling process structures can be divided into three distinct process types; ad-hoc modelling for decision support, new model development, and model change management. Existing research mainly considers new model development and therefore it is suggested that the other two types are also part of modelling practice, and therefore should be included in modelling process management. The process types are categorized from a modelling management perspective and a tentative modelling process management toolbox is suggested for further research.