Towards the Design of a Business Architecture Simulation Technique

Simulation techniques support the redesign of busin es processes by analyzing the effect of possible changes on operational performance indi cators that focus on the correctness, effectiveness, and efficiency of processes [6, 7]. However, the impact of these process changes on the overall business performance is not explicitly taken into account during this analysis [2]. This can result in operational i mprovements that are not in line with the organizational strategy, which leads to a suboptima l allocation of resources inside the organization. The goal of this work is to solve this issue by the d velopment of a business architecture simulation model, which employs the ex isting Process-Goal Alignment (PGA) modeling technique to provide a coherent view on the impact of process changes on the other business architecture elements [4]. Th is modeling language is extended by a simulation mechanism to test the effect of operatio n l adaptations on performance indicators that reflect the overall business perfor mance. The new business architecture simulation model, which is built according to the D sign Science Methodology, can be applied by the following steps: (i) building the bu siness architecture hierarchy, (ii) executing the operational performance measurement, (iii) determining how performance indicators can be propagated throughout the busines s architecture hierarchy, (iv) executing the simulation runs, and (v) analyzing how strategi c fit can be improved. 1. Building the business architecture hierarchy. PGA employs eight different modeling constructs to analyze how value is hierarchically created throughout the business arch ite ture: Goals, Financial Structure, Value Proposition, Competence, Process, and Activit y [3]. As these constructs are included in an integrative modeling language, a coh erent view can be provided of how operational decisions affect the business architect ur . Therefore, it explicitly needs to be specified how operational elements (i.e., Activitie s and Processes) support the value creation of the higher-level elements (i.e., Compet ence, Value Proposition, Financial Structure, and Goal). This is realized in the PGA m odeling language by the identification of valueStream relations, which can be used to conn ect elements that are on different levels in the business architecture hierarchy. 2. Executing the operational performance measurement. To execute the performance measurement for the oper ational business architecture elements (i.e., activities and processes), the foll owing data need to be collected: • Measure type: to account for positive (e.g., profit), negative (e.g., loss) or qualitative indicators (e.g., a satisfied criterion) [4] • Measure description: the textual description of the performance indica tor [4] • Performance goal: the desired value that the company wants to achie ve [4] • Allowed deviation percentage: to be used in case of uncertainty about the desired value of a quantitative performance goal [4] • Stochastical distribution with according parameters: extension that is needed to support the creation of simulated performance resul ts. Parameters can be estimated based on historical data about the past performance inside or outside the organization. 3. Determine how performance indicators can be propaga ted throughout the business architecture hierarchy. The purpose of this step is to determine how the op erational performance can be further propagated to the higher-level business architectur elements. Based on the available information, either business formulae (with convers ion factors) [1] or the AHP measurement with normalized values [1, 5] can be us ed. Business formulae are relevant if there is a clear m thematical relation between the performance indicators of two elements that are dir ctly connected by a valueStream relation in the business architecture hierarchy (se e t p 1). In some cases, conversion factors (e.g., in monetary terms) can be useful to enable the addition or subtraction of performance indicators that are measured in differe nt units. If it is impossible to identify a mathematical rela tion, performance indicators can be propagated by determining the weight of each valueS tream relation using the AHP mechanism. In the original PGA technique, this mech anism was already employed for this purpose by executing pairwise comparisons of all el em nts that are connected to the same higher-level element in the business architecture h ierarchy. This enables us to calculate the performance of a higher-level element as a weig ht d sum of the lower-level elements that support this element in the business architect ur . In order to use this mechanism, the performance of a business architecture element firs t needs to be normalized with respect to their performance goal and allowed deviation per centage (see step 2). 4. Executing the simulation runs. Once it is clear how the operational performance wi ll affect the other business architecture elements, simulated data can be produc ed based on the stochastical distribution of these operational indicators (see s tep 2). Afterwards, these simulated data are propagated throughout the business architecture by using the relevant mechanism (see step 3). 5. Strategic fit improvement analysis The strategic fit improvement analysis can be appli ed as originally proposed by the PGA technique. This step includes the identification of a critical path, which combines the weight of the valueStream relations in the business architecture hierarchy (see step 3) with the propagation of the simulated performance (see s t p 4). This allows the end-user to identify operational adaptations, of which the impa ct can be simulated by reapplying the different steps of the business architecture simula tion model. As such, we explicitly acknowledge the impact of operational changes on th e overall business performance during the redesign of business processes.