Fig. 5.10 A loan application process structured model. Finally, several activities do not follow the naming conventions of G6.The model can be reworked and simplified to the one shown in Fig. 5.9. 5.7

Fig. 5.10 A loan application process structured model. Finally, several activities do not follow the naming conventions of G6.The model can be reworked and simplified to the one shown in Fig. 5.9. 5.7 Further Exercises Exercise 5.11 Imagine you are responsible for the human resources department of a leading consultancy. Which characteristics would you check when hiring new process analysts? Exercise 5.12 As responsible for human resources department of a consultancy, how would you develop the skills of your junior process analysts? Exercise 5.13 How would you as a process analyst prepare for an interview with a domain expert? Exercise 5.14 Analyze the loan application process model of Fig. 5.10 for soundness-related problems. Exercise 5.15 Look up tools on the internet that offer a soundness check for process models. Exercise 5.16 Consider again the loan application process model of Fig. 5.10. What are indications that it would not be complete? Exercise 5.17 Have a look at the activity labels of the loan application model of Fig. 5.10 and propose improved labels where appropriateExercise 5.18 Have a look at the process model of Fig. 5.10 showing a sales campaign process for one of our industry partners. Describe which 7PMG guidelines can be used to improve this model. Have a look at the process model of Fig. 5.11 showing a sales campaign process for one of our industry partners5.8 Further Reading The general topic of process discovery is well covered in the book on workflow modeling by Sharp and McDermott [86]. This book gives detailed advice on all phases of process discovery, specifically data gathering and workshop organization. Other practical advice is summarized by Verner [101] and by Stirna, Persson, and Sandkuhl [88]. Interview techniques are widely discussed as a social science research method for instance in the book by Berg and Lune [7] or the book by Seidman [85]. Frederiks and van der Weide [18] discuss the skills required from process analysts, particularly when engaging in process discovery efforts. In a similar vein, Schenk, Vitalari and Davis [83] and Petre [66] discuss the capabilities that expert process analysts (as opposed to novice ones) generally display when engaging in process discovery. In this chapter, we emphasized “manual” process discovery techniques, wherein process models are manually constructed based on data collected from various process stakeholders by means of interviews, workshops and related techniques. As mentioned in Sect. 5.2.1, there is also a whole range of complementary techniques for automatic discovery process models from event logs. These automatic process discovery techniques are part of a broader set of techniques for analyzing event logs, collectively known as process mining [94]. We will discuss several process mining techniques later in Chap. 10. The modeling method introduced in Sect. 5.3 revolves around the discovery of activities and control-flow relations between activities. This family of approaches is usually called activity-based modeling [68]. An alternative approach to process modeling is known as artifact-centric modeling [59] or object-centric modeling [68]. In artifact-centric modeling the emphasis is not on identifying activities, but rather artifacts (physical or electronic objects or documents) that are manipulated within a given process. For example, in an order-to-cash process, typicalartifacts are the purchase order, the shipment notice and the invoice. Once these artifacts have been identified, they are analyzed in terms of the data that they hold and in terms of the phases they go through during the process. For example, a purchase order typically goes through the phases received, accepted, manufactured, shipped and invoiced. These phases and the transitions between these phases are called the artifact lifecycle. The main emphasis in artifact-centric process modeling is put on identifying these artifact lifecycles. Several industrial applications of artifact-centric process modeling have shown that it is quite suitable when discovering processes that exhibit significant amounts of variation, for example variation between business units, geographical regions or types of customer as discussed for example by Caswell et al. [59] and Redding et al. [68]. The quality of conceptual models in general, and of process models specifically, has received extensive attention in the research literature. The Sequal framework introduced by Lindland, Sindre, and Sølvberg adapts semiotic theory, namely the three perspectives of syntax, semantics and pragmatics, to the evaluation of conceptual model quality [45]. An extended version of this framework is presented by Krogstie, Sindre, and Jørgensen [42]. Validation and verification of process models has also received extensive attention in the literature. Mendling [51] for example provides numerous pointers to related research. The verification of Workflow nets specifically is investigated by van der Aalst [93] who connects soundness analysis of process models with classical Petri nets notions of liveness and boundedness. The 7PMG guidelines discussed in Sect. 5.4.4 are by Mendling, Reijers, and van der Aalst in [53]. These guidelines build on empirical work on the relation between process model metrics on the one hand and error probability and understandability on the other hand [50, 54, 55, 63, 73, 74]. Specifically, the impact of activity label quality on process model understanding is investigated by Mendling, Reijers, and Recker [52]. Another set of modeling guidelines are the Guidelines of Process Modeling by Becker et al. [5]. As a complement to process modeling guidelines and conventions, it is usefulto also keep in mind potential pitfalls to be avoided in process modeling projects. For example, Rosemann [78, 79] draws a list of 22 pitfalls of process modeling, including a potential lack of strategic connection, l’art pour l’art, to name but a few. His bottom line is that modeling success does not directly equate with process success.Analyzing business processes is both an art and a science. In this respect, qualitative analysis is the artistic side of process analysis. Like fine arts, such as painting, there is not a single way of producing a good process analysis, but rather a range of principles and techniques that tell us what practices typically lead to a “good” process analysis. When learning to paint, you learn how to hold the brush, how to produce different types of brushstroke, how to mix colors, etc. The rest of the art of painting is up to you to acquire by means of practice, discernment and critical self-assessment. In this chapter, we introduce a few basic principles and techniques for qualitative process analysis. First, we present principles aimed at making the process leaner by identifying unnecessary parts of the process in view of their elimination. Next, we present techniques to identify and analyze the weak parts of the process, meaning the parts that create issues that negatively affect the performance of the process. In particular, we discuss how to analyze the impact of issues in order to prioritize redesign efforts. 6.1 Value-Added Analysis Value-added analysis typically consists of two stages: value classification and waste elimination. Below we discuss each of these stages in turn. 6.1.1 Value Classification Value-added analysis is a technique aimed at identifying unnecessary steps in a process in view of eliminating them. In this context, a step may be a task in the process, or part of a task, or a handover between two tasks. For example, if a task “Check