Variability management in process families through change patterns

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Context: The increasing adoption of process-aware information systems together with the high variability in business processes has resulted in collections of process families. These families correspond to a business process model and its variants, which can comprise hundreds or thousands of different ways of realizing this process. Managing process variability in this context can be very challenging, labor-intensive, and error-prone, and new approaches for managing process families are necessary. Objective: We aim to facilitate variability management in process families, ensure process family correctness, and reduce the effort needed for such purposes. Method: We have derived a set of change patterns for process families from variability-specific language constructs identified in the literature. For validation, we have conducted a case study with a safety standard in which we have measured the number of operations needed to model and evolve the variability of the standard with and without the patterns. Results: We present 10 change patterns for managing variability in process families and show how they can be implemented. The patterns support the modeling and evolution of process families and ensure process family correctness by automatically introducing and deleting modeling elements. The case study results show that the application of the defined change patterns can reduce the number of operations when modeling a process family by 34% and when evolving it by 40%. Conclusions: The application of the change patterns can help in effectively modeling and evolving large and highly-variable process families. Their application can also considerably reduce variability management effort. (C) 2016 Elsevier B.V. All rights reserved.This work has been developed with the financial support of Spanish Ministry of Economy and Competitiveness under the project SMART-ADAPT TIN2013-42981-P. We also want to thank Barbara Weber and Manfred Reichert for their valuable input and feedback on the design and development of the set of change patterns for process families.Ayora Esteras, C.; Torres Bosch, MV.; De La Vara González, JL.; Pelechano Ferragud, V. (2016). Variability management in process families through change patterns. Information and Software Technology. 74:86-104. https://doi.org/10.1016/j.infsof.2016.01.007S861047

VIVACE: A Framework for the Systematic Evaluation of Variability Support in Process-Aware Information Systems

CONTEXT The increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. OBJECTIVE The goal of this paper is to develop a fundamental understanding of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process lifecycle (i.e., process analysis and design, configuration, enactment, diagnosis, and evolution). METHOD We conducted a systematic literature review (SLR) in order to discover how process variability is supported by existing approaches. RESULTS The SLR resulted in 63 primary studies which were deeply analyzed. Based on this analysis, we derived the VIVACE framework. VIVACE allows assessing the expressiveness of a process modeling language regarding the explicit specification of process variability. Furthermore, the support provided by a process-aware information system to properly deal with process model variants can be assessed with VIVACE as well. CONCLUSIONS VIVACE provides an empirically-grounded framework for process engineers that enables them to evaluate existing process variability approaches as well as to select that variability approach meeting their requirements best. Finally, it helps process engineers in implementing PAISs supporting process variability along the entire process lifecycle

VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems

Context: The increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. Objective: The goal of this paper is to develop a fundamental understand-ing of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process life

VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems

Context: The increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. Objective: The goal of this paper is to develop a fundamental understanding of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process lifecycle (i.e., process analysis and design, configuration, enactment, diagnosis, and evolution). Method: We conducted a systematic literature review (SLR) in order to discover how process variability is supported by existing approaches. Results: The SLR resulted in 63 primary studies which were deeply analyzed. Based on this analysis, we derived the VIVACE framework. VIVACE allows assessing the expressiveness of a process modeling language regarding the explicit specification of process variability. Furthermore, the support provided by a process-aware information system to properly deal with process model variants can be assessed with VIVACE as well. Conclusions: VIVACE provides an empirically-grounded framework for process engineers that enables them to evaluate existing process variability approaches as well as to select that variability approach meeting their requirements best. Finally, it helps process engineers in implementing PAISs supporting process variability along the entire process lifecycle. (C) 2014 Elsevier B.V. All rights reserved.This work has been developed with the support of MICINN under the project EVERYWARE TIN2010-18011.Ayora Esteras, C.; Torres Bosch, MV.; Weber, B.; Reichert, M.; Pelechano Ferragud, V. (2015). VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems. Information and Software Technology. 57:248-276. https://doi.org/10.1016/j.infsof.2014.05.009S2482765

Alamprotsessidest, protsesside variatsioonidest ja nendevahelisest koosmõjust: Integreeritud “jaga ja valitse” meetod äriprotsesside ja nende variatsioonide modelleerimiseks

Igat organisatsiooni võib vaadelda kui süsteemi, mis rakendab äriprotsesse väärtuste loomiseks. Suurtes organisatsioonides on tavapärane esitada äriprotsesse kasutades protsessimudeleid, mida kasutatakse erinevatel eesmärkidel nagu näiteks sisekommunikatsiooniks, koolitusteks, protsesside parendamiseks ja infosüsteemide arendamiseks. Arvestades protsessimudelite multifunktsionaalset olemust tuleb protsessimudeleid koostada selliselt, et see võimaldab nendest arusaamist ning haldamist erinevate osapoolte poolt. Käesolev doktoritöö pakkudes välja integreeritud dekompositsioonist ajendatud meetodi äriprotsesside modelleerimiseks koos nende variatsioonidega. Meetodi kandvaks ideeks on järkjärguline äriprotsessi ja selle variatsioonide dekomponeerimine alamprotsessideks. Igal dekompositsiooni tasemel ning iga alamprotsessi jaoks määratletakse esmalt kas vastavat alamprotsessi tuleks modelleerida konsolideeritud moel (üks alamprotsessi mudel kõikide või osade variatsioonide jaoks) või fragmenteeritud moel (üks alamprotsess ühe variatsiooni jaoks). Sel moel kasutades ülalt-alla lähenemist viilutatakse ja tükeldatakse äriprotsess väiksemateks osadeks. Äriprotsess viilutatakse esmalt tema variatsioonideks ning seejärel tükeldatakse dekompositsioonideks kasutades kaht peamist parameetrit. Esimeseks on äri ajendid variatsioonide jaoks – igal äriprotsessi variatsioonil on oma juurpõhjus, mis pärineb ärist endast ja põhjustab protsesside käivitamisel erisusi. Need juurpõhjused jagatakse viide kategooriasse – ajendid kliendist, tootest, operatiivsetest põhjustest, turust ja ajast. Teine parameeter on erinevuste hulk viisides (tegevuste järjekord, tulemuste väärtused jms) kuidas variatsioonid oma väljundit toodavad. Käesolevas töös esitatud meetod on valideeritud kahes praktilises juhtumiuuringus. Kui esimeses juhtumiuuringus on põhirõhk olemasolevate protsessimudelite konsolideerimisel, siis teises protsessimudelite avastamisel. Sel moel rakendatakse meetodit kahes eri kontekstis kahele üksteisest eristatud juhtumile. Mõlemas juhtumiuuringus tootis meetod protsessimudelite hulgad, milles oli liiasust kuni 50% vähem võrreldes tavapäraste meetoditega jättes samas mudelite keerukuse nendega võrreldes enamvähem samale tasemele.Every organization can be conceived as a system where value is created by means of business processes. In large organizations, it is common for business processes to be represented by means of process models, which are used for a range of purposes such as internal communication, training, process improvement and information systems development. Given their multifunctional character, process models need to be captured in a way that facilitates understanding and maintenance by a variety of stakeholders. This thesis proposes an integrated decomposition-driven method for modeling business processes with variants. The core idea of the method is to incrementally construct a decomposition of a business process and its variants into subprocesses. At each level of the decomposition and for each subprocess, we determine if this subprocess should be modeled in a consolidated manner (one subprocess model for all variants or for multiple variants) or in a fragmented manner (one subprocess model per variant). In this manner, a top-down approach of slicing and dicing a business process is taken. The process model is sliced in accordance with its variants, and then diced (decomposed). This decision is taken based on two parameters. The first is the business drivers for the existence of the variants. All variants of a business process has a root cause i.e. a reason stemming from the business that causes the processes to have differences in how they are executed. The second parameter considered when deciding how to model the variants is the degree of difference in the way the variants produce their outcomes. As such, the modeling of business process variations is dependent on their degree of similarity in regards to how they produce value (such as values, execution order and so on). The method presented in this thesis is validated by two real-life case studies. The first case study concerns a case of consolidation existing process models. The other deals with green-field process discovery. As such, the method is applied in two different contexts (consolidation and discovery) on two different cases that differ from each other. In both cases, the method produced sets of process models that had reduced the duplicity rate by up to 50 % while keeping the degree of complexity of the models relatively stable