Abstract
Along with enterprise transformation, enterprise re-engineering is essential for maintaining the competitiveness of an enterprise. Enterprise re-engineering addresses (emergent) changes, re-organizing, outsourcing and re-aligning alike. Re-engineering itself has drawn traction in both academia and business. Most scholarly work in this area is confined to model-driven analysis, holistic frameworks for analyzing as-is/to-be enterprise models, and a few other conceptualization techniques. The practice of process redesign understandably takes the stage in re-engineering. Yet algorithmic techniques that insightfully point out how a process might be improved for proactively re-engineering process-intensive enterprise architecture are missing. Data science and business intelligence have brought a refreshingly new analysis to this mainstream problem by studying the operational history of a business process to facilitate most plausible changes. In this article, we investigate enterprise process redesign taking into account enterprise’s high-level strategy and data warehouse. More specifically, we propose an approach to reasoning about an enterprise’s strategy together with data mining rules extracted from the data warehouse of the enterprise. Our redesign algorithms suggest design-time changes to be made to its business processes, primarily by eliminating redundant tasks and re-ordering inefficiently-located tasks. We analyze the effectiveness of candidate to-be business processes with regard to business intelligence indicators. We report our work on the enterprise architecture developed for a retailer of low-cost domestic flights.
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Notes
Until 2017, the company achieved net operating revenue of $11 millions for the same year, gross profit of more than $350 thousands. They gradually became passenger’s first choice in hunting for cheap flights.
ArchiMate modeling language http://www.archimate.nl/.
The specification of BPMN is managed by the Object Management Group http://www.bpmn.org/.
The process returns to regular tickets if she does not have enough e-balance.
Company AD has to wait for customer’s confirmation sent via email. Latency caused by any delay in making such a confirmation by the customer could result in a final charge for rescheduling her airfare that is actually higher than the reschedule fees previously confirmed by the provider. For this reason, Company AD puts a reasonable surcharge to cover this potential payment gap. The customer is entitled for a refund in case her actual reschedule fees finally go below the surcharge she paid.
Goals might logically be refined into sub-goals, thereby their satisfaction relies on that of their sub-goals. A goal might be achieved in several ways if it or its sub-goals are OR-refined (Horkoff et al. 2014).
A large-sized companies typically has more than 300 employees; medium-sized 200–300 employees; small-sized 10–200 employees; micro-sized—no more than 10 employees.
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Acknowledgements
Work presented in this article is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under Grant number C2019-20-15. The authors are thankful to our colleague Khuong Nguyen-An for correcting and commenting on the mathematical formalization based on which re-design algorithms could have been devised.
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Appendices
A Classification rules
B Process representation in ACP
In this appendix, we walk the readers through the ACP representation of the rescheduling process. We proceed by pointing out the relevant artifacts of this process: Flight, CustomerSupport and Booking. For each of them, we then list all possible states both informally and formally. There are a range of visual modeling techniques available for informally investigating the states of a given artifact (or an object). A domain expert might employ a UML statechart, finite state machine or Harel state chart to work out the states. Regardless of what modeling technique being employed, a state should be grounded in the attribute domain of the artifact in question.
We rigorously describe the states of CustomerSupport, Flight and Booking in Tables 14, 15, 16, respectively. Note that we ground them in logical predicates that are formulated over the attributes of the said artifacts in an attempt to formally describe their states. Based on this, we should fundamentally be able to produce an ACP model of the rescheduling process as described in Table 17 where the pre- and post-conditions of all the tasks of this process are formulated.
C Business process measurements
In this appendix, we offer a non-trivial elaboration of the process measurements for the (to-be) selling and rescheduling processes with respect to the following indicators: time, cost and transparency.
1.1 C.1 Time
As a recap, we describe a flow analysis technique in Subsection 6.1.1 and present the unit time of each process task in Table 18 and Table 19. We now proceed in calculating the cycle time of the selling process as presented in Table 20 (for its as-is version) and Table 21 (for its to-be version). The cycle time of the rescheduling process is given in Table 22 (for its as-is version) and Table 23 (for its to-be version).
1.2 C.2 Cost
As recap, we present activity-based costing theory in Subsection 6.1.2. We work out the activity cost of each task of the selling process and rescheduling process in Table 24 and Table 25, respectively.
1.3 C.3 Transparency
Customer service department’s view of the as-is process of selling tickets
Customer’s view of the as-is process of selling tickets
Finance department’s view of the as-is process of selling tickets
Customer service department’s view of the to-be process of selling tickets
We articulate four different views that correspond to the as-is version of the selling process as shown in Figure 4, Figure 14, Figure 15 and Figure 16. Similarly, views for the to-be version of the same process are depicted in Figure 12, Figure 17, Figure 18 and Figure 19. We shall work out the transparent levels of these views using a formula presented previously in Subsection 6.1.5. The transparent levels of these views are presented in Table 26 (for the as-is version) and Table 27 (for the to-be version).
In the same way, we could list all possible views for the rescheduling process. However, we opt to skip this trivial elaboration to go straight ahead with the transparent levels of the rescheduling process, as presented in Table 28 (for the as-is version) and Table 29 (for the to-be version).
Customer’s view of the to-be process of selling tickets
Finance department’s view of the to-be process of selling tickets
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Truong, TM., Lê, LS., Paja, E. et al. A data-driven, goal-oriented framework for process-focused enterprise re-engineering. Inf Syst E-Bus Manage 19, 683–747 (2021). https://doi.org/10.1007/s10257-021-00523-6
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DOI: https://doi.org/10.1007/s10257-021-00523-6