Abstract
The modeling and management of business processes often leads to the definition of several variants of the same process. This variability can be reflected in different process perspectives such as control-flow, data, resources or performance. The management of process variants can be a laborious, time-consuming and error-prone task since they require a high coordination in the management of each variant and in most cases this management is done manually. For this reason, many proposals have been developed to deal with the variability of business processes. However, none of them covers in detail the variability in the performance perspective, which is concerned with the definition of performance requirements usually specified as a set of Process Performance Indicators (PPIs). This variability can be reflected in the form of repetitive and redundant PPI definitions, and can lead to errors and inconsistencies in PPI definitions. To address this problem, in this article we propose a detailed PPI variability classification and a formalization of how PPIs can be modeled together with the variability of other process perspectives. To this end, we considered variability management approaches, called by restriction and by extension, and we illustrated our proposal by integrating it with existing variability modeling languages. An evaluation conducted in two scenarios shows the feasibility and usefulness of our proposal.
Highlights
T HE definition and modeling of business processes may vary slightly from one context to another within an organization, for example, to adapt to new requirements [1] and business strategies [2], by regulations in different countries or regions [3] and/or to reflect new resource allocations and responsibilities [2].In general, variability has been defined as the ability to change or customize a system to make a set of changes easier [4] and as the ability of software and artifacts “to be extended, changed, customized or configured in a specific context" [5]
The reason is that Supply Chain Operation Reference model (SCOR) is a reference model and it does not include information related to other attributes required to define Process Performance Indicators (PPIs) like target value or responsible resource
When all PPIs/measures were defined over all variants (DimC-1 = NO), we found variability in the elements to which PPI measures were connected (DimC-2.M1) for 8 measures, 4 measures were calculated using different values depending on the variant (DimC-2.M2), and by 7 measures both subdimensions were identified DimC-2
Summary
T HE definition and modeling of business processes may vary slightly from one context to another within an organization, for example, to adapt to new requirements [1] and business strategies [2], by regulations in different countries or regions [3] and/or to reflect new resource allocations and responsibilities [2].In general, variability has been defined as the ability to change or customize a system to make a set of changes easier [4] and as the ability of software and artifacts “to be extended, changed, customized or configured in a specific context" [5]. The lack of control over multiple variants usually causes an increase of the time required to design, configure and modify each variant, and may introduce errors from their definitions to the evaluation of its performance [12], [13]. To deal with these issues, many approaches have been proposed to manage variability in business processes. They are aimed at avoiding duplication and redundancy of information through the reuse of some parts of the process control flow, by identifying the common parts of the flow and modeling them only once, reducing design time and model maintenance time [14]
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