(Software AG 2016), or âTIBCO ActiveMatrix BPM Spotfireâ (TIBCO Software ...... base/download/313690/K05162_PCF_Ver_6 1_1.pdf; retrieved September 12,.
PROCESS VISIBILITY FIT AND IMPACT: EXAMINATION OF PROCESS MONITORING SUCCESS
Inauguraldissertation zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften der Universität Mannheim
vorgelegt von Martin Berner, Dipl.-Hdl. Mannheim, im November 2016
Prof. Dr. Dieter Truxius (Dekan) Prof. Dr. Alexander Mädche (Referent) Prof. Dr. Christoph Bode (Korreferent)
Tag der mündlichen Prüfung: 07.12.2016
Table of Contents
I
Table of Contents List of Figures ............................................................................................................... III List of Tables ................................................................................................................. IV List of Abbreviations .................................................................................................... VI 1
2
3
Introduction ............................................................................................................1 1.1
Motivation.......................................................................................................1
1.2
Research Goals ...............................................................................................4
1.3
Research Project .............................................................................................7
1.4
Structure of the Thesis ..................................................................................11
Foundations ..........................................................................................................12 2.1
Related Work ................................................................................................12
2.2
State-of-the-Art in Process Monitoring Technologies ..................................21
2.3
Conceptualization of Process Visibility........................................................28
Process Visibility Fit ............................................................................................35 3.1
Introduction of Study One “Assessing Process Visibility Requirements and Capabilities – Development and Quantitative Evaluation of a Fit Framework” ..................................................................................................35
4
3.2
The Process Visibility Fit Framework ..........................................................36
3.3
Method of Study One....................................................................................47
3.4
Results of Study One ....................................................................................54
3.5
Discussion of Study One ..............................................................................65
Process Visibility Impact .....................................................................................68 4.1
Introduction of Study Two “The Impact of Process Visibility on Process Performance – A Multiple Case Study of Operations Control Centers in ITSM” ...........................................................................................................68
5
4.2
Process Monitoring Benefits and Influencing Factors..................................70
4.3
Method of Study Two ...................................................................................77
4.4
Results of Study Two ...................................................................................81
4.5
Discussion of Study Two ..............................................................................95
Conclusions ...........................................................................................................98 5.1
Summary of the Work ..................................................................................98
5.2
Contributions to Theory..............................................................................100
5.3
Contributions to Practice ............................................................................101
Table of Contents
II
5.4
Limitations and Future Research ................................................................102
5.5
Concluding Remarks ..................................................................................105
References .................................................................................................................. XIII Appendix A: Publications ..................................................................................... XXXII Appendix B: Supplements of Study One ........................................................... XXXIII Appendix C: Supplements of Study Two ....................................................... XXXVIII Appendix D: Reference to Data ............................................................................... XLII Appendix E: Curriculum Vitae ............................................................................. XLIII
List of Figures
III
List of Figures Figure 1: Research Project ...............................................................................................7 Figure 2: BPM Conference Papers from 2003-2014 by Lifecycle Phase ......................14 Figure 3: Support for IS Success Interrelationships in Existing Literature at the Organizational Level ......................................................................................20 Figure 4: The BI&A Trend towards Process-centricity and Operational Decision Support ...........................................................................................................23 Figure 5: Scope and Focus of the Process Visibility Concept .......................................29 Figure 6: The Process Visibility Fit Framework ............................................................44 Figure 7: Process Visibility Fit Assessment Matrix .......................................................44 Figure 8: High-level Phases of the Airport Hub Operations Process.............................46 Figure 9: PLS Structural Model Results ........................................................................58 Figure 10: Subgroup Performance Differences of Indicator per_1 (Quality) ..................62 Figure 11: Subgroup Performance Differences of Indicator per_2 (Cost) .......................63 Figure 12: Subgroup Performance Differences of Indicator per_3 (Time)......................63 Figure 13: Control Center Examples ...............................................................................69 Figure 14: Process Monitoring Benefits ..........................................................................70 Figure 15: Process Monitoring Benefits Framework .......................................................95 Figure 16: Overall PLS-SEM Results from SmartPLS ......................................... XXXVII Figure 17: Final Code System in MAXQDA ....................................................... XXXVIII
List of Tables
IV
List of Tables Table 1: Process Monitoring in BPM Lifecycle Models ..............................................13 Table 2: Comparison of BI&A Software Package Categories for Process Monitoring ......................................................................................................21 Table 3: Dimensions of Process Visibility ...................................................................31 Table 4: Dimensions of Process Visibility Requirements ............................................40 Table 5: Dimensions of Process Visibility Capabilities ...............................................41 Table 6: Process Visibility Requirements and Capabilities of the Airport Hub Operations Process .........................................................................................47 Table 7: Stages and Activities of Study One ................................................................48 Table 8: Survey Items ...................................................................................................51 Table 9: Sample Characteristics ...................................................................................53 Table 10: Evaluation of Reflective Measures .................................................................55 Table 11: Indicator Loadings for the Evaluation of Discriminant Validity....................56 Table 12: Latent Variables Correlations for the Evaluation of Discriminant Validity ...56 Table 13: Evaluation of Formative Measures .................................................................58 Table 14: Computed Index Variables .............................................................................59 Table 15: Multicollinearity Evaluation of Endogenous Variables .................................60 Table 16: Data Groups ....................................................................................................61 Table 17: Comparing Process Performance Mean Values .............................................62 Table 18: Process Classification .....................................................................................64 Table 19: Dimensions of Process Performance in ITSM ...............................................71 Table 20: Levels of Situation Awareness (SA) ..............................................................72 Table 21: Dimensions of Bottleneck Identification ........................................................74 Table 22: Stages and Activities of Study Two ...............................................................77 Table 23: Descriptive Case Data ....................................................................................78 Table 24: Inter-coder Reliability.....................................................................................80 Table 25: Coding Examples of Process Visibility ..........................................................82 Table 26: Coding Examples of Situation Awareness (SA).............................................85 Table 27: Coding Examples of Bottleneck Identification ..............................................86 Table 28: Coding Examples of Process Performance .....................................................88 Table 29: High-level Summary of Case by Case Analysis .............................................89 Table 30: Coding Examples of Influential Factors .........................................................90 Table 31: Card Sorting Result of Item Pool “Process Variety” ............................. XXXIII Table 32: Card Sorting Result of Item Pool “Process Interdependence” .............. XXXIV
List of Tables
V
Table 33: Card Sorting Result of Item Pool “Process Importance” ....................... XXXV Table 34: Pilot Test Indicator Loadings of Process Visibility Requirements........ XXXVI Table 35: Pilot Test Indicator Loadings of Process Visibility Capabilities ........... XXXVI Table 36: Used Weightages for Code Segments in MAXQDA ............................ XXXIX
List of Abbreviations
List of Abbreviations ABPMP..........Association of Business Process Management Professionals ANA ..............Process Information Analysis APQC ............American Productivity & Quality Center AVE ...............Average Variance Extracted BAM ..............Business Activity Monitoring BI ...................Business Intelligence BI&A .............Business Intelligence & Analytics BPM ...............Business Process Management BPMS ............Business Process Management Systems CAP ...............Process Visibility Capabilities CEP ................Complex Event Processing CI ...................Continuous Improvement CRM ..............Customer Relationship Management D&M ..............DeLone and McLean DIS .................Process Information Dissemination ERP ................Enterprise Resource Planning GAT ...............Process Information Gathering H1 – H2 .........Hypothesis 1 – Hypothesis 2 iBPMS ...........intelligent Business Process Management Systems IMP ................Process Importance INT ................Process Interdependence IPV .................Information Processing View IS ....................Information Systems IT ...................Information Technology ITIL................Information Technology Infrastructure Library ITSM ..............Information Technology Service Management KPI .................Key Performance Indicator N ....................Number of Data Records OCC ...............Operations Control Center OGC ...............British Office of Government Commerce OpBI ..............Operational Business Intelligence OS ..................Outsourcing
VI
List of Abbreviations
P1 – P8 ...........Proposition 1 – Proposition 8 PER ................Process Performance REQ ...............Process Visibility Requirements PLS-SEM .......Partial Least Squares Structural Equation Modeling RQ..................Research Question RSLF..............RunSAP like a Factory TL ..................Team Lead (of OCC) TPS ................Toyota Production System TQM ..............Technical Quality Manager (of SAP) SA ..................Situation Awareness SCM ...............Supply Chain Management VAR ...............Process Variety VEL ...............Process Velocity VIF .................Variance Inflation Factor VOL ...............Process Volume
VII
1 Introduction
1
1 Introduction1 1.1 Motivation The creation of transparency on activities that had been opaque before has been expected since the early implementations of information technology (IT). Zuboff (1988) states in her seminal work “In the Age of the Smart Machine” that IT not only automates processes, the unique capacity of IT is to produce information: “Activities, events, and objects are […] made visible” (Zuboff 1988, p. 10). She calls organizations with such transparency informated organizations, which “can operate very differently from the traditional assumption of imperative control” (Zuboff 1988, p. 411). Thus, it is not surprising that business process management (BPM) researchers have identified process measurement and monitoring as essential capabilities for process management (Hammer and Stanton 1999; Rosemann and vom Brocke 2010; Willaert et al. 2007). It tracks process statuses, guides process improvements, aligns activities, and reinforces teamwork due to visibility of process information (Hammer and Stanton 1999; Willaert et al. 2007). Accordingly, BPM lifecycle models, which describe the crucial practices of process management, classify process monitoring as a key activity (Morais et al. 2014). Process monitoring is a viable activity in BPM that has process visibility as an outcome. Process visibility is a characteristic of a business process, referring to the quality of information for day-to-day process operation as well as for continuous process improvement. Today’s business processes are characterized by growing complexity (e.g. due to outsourcing) and higher speed (thanks to information technology). Therefore, process errors accelerate and there is an increasing need for end-to-end process control (Fleming 2009). Processes typically span across multiple packaged and custom applications. They cross location boundaries, functional unit boundaries, and company boundaries. In such distributed systems without end-to-end process views, it is challenging to identify and resolve process failures and measure process performance. To utilize the integration
1
Parts of this section are based on published articles of the author (Berner et al. 2012, 2016; Graupner, Berner et al. 2014b).
1 Introduction
2
potential of enterprise systems it is required to overcome technological information silos – “it is necessary to make processes visible to people and to make process performance relevant to them” (Beretta 2002, p. 261), because you need to understand and measure processes in order to operate and improve them. Nevertheless, it seems that companies have not reached the required process visibility level. Recent surveys show that most enterprises are still lacking transparency. Müller et al. (2011) found that the majority of companies neither document nor measure nor regularly improve their processes. Moreover, a regularly repeated survey inside the BPM Web Community “BPTrends” shows that companies partially analyze processes at the enterprise level, but the majority of the polled organizations never or only occasionally use performance data to manage their processes (Wolf and Harmon 2012). In conclusion, visibility of end-to-end processes seems to be highly relevant but still not available in most enterprises. This conflict calls for further investigation of how to support organizations with enablement of process visibility. Lately, new technological opportunities and challenges arise around process monitoring, which bring the topic further to the center of attention of BPM practitioners, software vendors, analysts, and researchers. First, an unprecedented and permanently expanding amount of data is created by smart machines, sensors, robots, cameras, smartphones, social networks, etc. (Manyika et al. 2011). Process monitoring potentially can leverage this big data to create new insights for process operations and improvement. Second, the current separation of data for the purpose of analysis and process execution in different systems is vanishing, because advanced database technology allows transactional and decision-related data to be managed together (Loos et al. 2011). Hence, new kinds of analytics will become feasible for process monitoring, which can guide decisions of process participants based on real-time analysis of historical data and forecast models. Additionally, analysts predict that today’s process monitoring technologies, which are mostly based on business intelligence (BI), will be increasingly combined with complementary technologies like complex event processing (CEP), business rules management (BRM), big data analytics, etc. (Fleming 2009; Gartner 2012; Russom 2013). These combined software packages for process monitoring, enable real-time analytics during process execution. Considering these blurring boundaries of today’s
1 Introduction
3
BPM-related software categories, Gartner (2015a) reports a market size of $2.7 Billion for BPM systems (BPMS). But this is only one segment of the process monitoring market, because process monitoring goes beyond BPMS and has to take into account also data from other enterprise systems like enterprise resource planning (ERP) or customer relationship management (CRM). In line with the analysts, also software vendors increasingly offer advanced, combined process monitoring solutions – for example “SAP Operational Process Intelligence” (SAP 2013a), “webMethods Operational Intelligence” (Software AG 2016), or “TIBCO ActiveMatrix BPM Spotfire” (TIBCO Software 2012). However, none of these new monitoring technologies comes for free, and adapting them is associated with significant investments (Bantleman 2012; Evans 2014). In times of constant, or even decreasing, IT budgets (Capgemini 2013) the questions of where and how to deploy these new technologies require careful assessment. It is important to understand, what benefits can be expected from process monitoring, and what influences these benefits. BPM research literature as of today is not contributing a lot to these questions. There is a lack in empirical studies exploring process management, especially the impact of single capabilities like measuring and monitoring (Kohlbacher 2010). The important topic of process monitoring is strongly underrepresented in BPM literature (van der Aalst 2013). Therefore, Recker and Mendling (2015) call for further research of process monitoring. First, computer science researchers should study efficient processing techniques and big data analytics for process monitoring (Recker and Mendling 2015). Second, from an information systems research perspective, there is the call for an empirical research agenda regarding process monitoring, where “viable concepts have to be judged in terms of the utility they can provide for the organization, [including] systematic evaluation of empirical data” (Recker and Mendling 2015, p. 60). Our work contributes to the second by the identification and empirical examination of process monitoring key concepts and their relations. It provides practical guidance on where process monitoring improvements are required and what factors should be considered for the implementation of process monitoring technology.
1 Introduction
4
In summary, process monitoring is a highly relevant topic that gets growing attention due to technological advances. Empirical research is required to help to understand conditions of successful process monitoring that creates appropriate process visibility.
1.2 Research Goals The concept of visibility is coined by supply chain management (SCM) literature. Supply chain visibility is the outcome of information sharing in a supplier network (Barratt and Oke 2007). Research shows that this visibility of process information in the SCM process is positively related with supply-chain performance (Barratt and Oke 2007; Wei and Wang 2010; Williams et al. 2013), but these findings have not been generalized to business processes beyond SCM. Furthermore, lean production literature discusses the importance of transparency and visual controls for the identifications of problems that require improvement (Monden 2012; Womack and Jones 2003). Similarly, as supply chain visibility relates to SCM processes, the lean transparency concept is focusing on production processes. Both related research streams seem to be promising foundations to investigate the phenomenon of process visibility. In summary, it is not clear what determines process visibility in general, and what factors influence the success of process monitoring. Therefore, in our research we conceptualize process visibility in a generic process-context and examine the concept with two research goals in mind, that are described in the following two subsections.
1.2.1 Understand the Needs and the Opportunities of Process Monitoring: Examination of Process Visibility Fit The implementation of advanced process monitoring is relatively cost-intensive and requires a conscious evaluation (Bantleman 2012; Evans 2014). However, project prioritizations are often politically driven, which means that projects are funded independent of their potential business value (Alter 2014). Therefore, a framework that supports an objective prioritization of business processes regarding their process monitoring needs and potential opportunities would be very beneficial.
1 Introduction
5
Current frameworks from BPM research regarding process prioritization “are either of very high level and hence not of much assistance when attempting to implement BPM initiatives, or on the contrary, are so detailed that it can take a significant effort to simply identify the critical processes” (Bandara et al. 2014). An example refers to process maturity models – often based on the capability maturity model (SEI 2010). Such models allow a classification of business processes, but they provide limited guidance for identifying desirable maturity levels as well as for implementation of improvement measures and they often lack theoretical as well as empirical foundation (Röglinger et al. 2012). Other process prioritization methods from design science researchers (Bandara et al. 2014; Ohlsson et al. 2014) focus too generically on process improvements, and are not applicable for process prioritization concerning process visibility. These gaps in current BPM research call for conceptual and empirical work regarding process classifications. The lens of process monitoring is a highly relevant application area in this regard, and it is studied insufficiently. To assess appropriate information supply, IS research has applied the concept of fit between information requirements and capabilities (Mani et al. 2010; Premkumar et al. 2005). Accordingly, we examine the fit between visibility requirements and visibility capabilities of business processes as the baseline for the classification of processes regarding their process monitoring needs and opportunities. Our research activities in this regard are guided by the following research questions: RQ 1.1: What factors determine the demand of process visibility (“process visibility requirements”)? RQ 1.2: What technological capabilities need to be established to cope with the visibility requirements of business process (“process visibility capabilities”)? Process performance is the ultimate objective of process monitoring, and therefore also the most relevant dependent variable for the validation of our proposed framework. Consequently, we study the empirical relevance of process visibility fit based on the general research question: RQ 1:
Does the fit between process visibility requirements and process visibility capabilities positively affect the performance of a business process?
1 Introduction
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1.2.2 Understand the Benefits of Process Monitoring: Examination of Process Visibility Impact The understanding of process monitoring benefits is essential for the justification of corresponding technology investments. Furthermore, factors that influence these potential benefits should be identified to help practitioners with the enablement of valuable process monitoring. BPM literature falls short in outlining benefits of its use cases. Therefore, BPM researcher are encouraged to increase the value-sensitivity of their research, because “a discipline that is more focused on how it conducts its work rather than gathering evidence for the existence of its value propositions faces compromises to its credibility” (Rosemann 2014, p. 9). This is in line with the call for a more value-based approach in BPM (Afflerbach et al. 2014; Buhl et al. 2011). Rosemann (2014, p. 11) mentions the relevance of such research explicitly in our research context regarding the area of “big process data”, where without “a sound understanding of the value… this community lacks a direction”. In IS research outside the BPM discipline, the phenomenon of IS success and benefits is studied intensively (DeLone and McLean 2003). However, there is a gap of research on the organizational level concerning the impact of success categories like information quality on organizational performance (Petter et al. 2008). For IS success research above the individual level, the processes level is highly relevant, because business processes translate firm strategies to the operational level (Norton and Russell 2009), and on the process level effects of information technology can be observed directly (Melville et al. 2004). With these gaps of BPM and IS success research in mind, we investigate the benefits of process monitoring on the process level under the general research question: RQ 2:
How does process monitoring influence process performance?
This comprehensively addresses the question of process monitoring benefits – including the downstream impacts of process visibility and environmental factors that influence the success of process monitoring. Therefore, more precisely, we investigate the subquestions:
1 Introduction
7
RQ 2.1: What are benefit dimensions of process monitoring? RQ 2.2: What factors influence the benefits of process monitoring?
1.3 Research Project In response to the outlined research gaps and questions, the author has performed a research project Process Visibility to which this thesis belongs. The research activities of the project are structured along the implementation phases of process monitoring technologies (Figure 1). These monitoring technologies are either dedicated enterprise systems for process monitoring (e.g. BAM) or are embedded in enterprise systems with a broader scope (e.g. ERP). The implementation phases of Figure 1 are based on the Enterprise Systems Experience Cycle (Markus and Tanis 2000; Soh and Markus 1995). This framework explains enterprise systems implementations as a series of linked phases. First, the chartering phase that ends with the decision whether an implementation project will be started or not. Second, the configure and rollout phase which is the core project implementation phase where the activities to get the system up and running are executed. Third, the shakedown phase that refers to the initial usage phase – characterized by bug fixing and tuning activities. Forth, the onward and upward phase represents the normal operation mode with continuous improvement of the system.
Figure 1: Research Project
1 Introduction
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The needs and opportunities of organizations relative to enterprise systems are important parameters for the chartering phase and are defined by implementation starting conditions like size, structure, experience, competitive and financial position, etc. (Markus and Tanis 2000). In our case, visibility requirements and visibility capabilities of the processes are such starting conditions that define the needs and opportunities of process monitoring implementations. Accordingly, the first research activity regarding Process Visibility Fit addresses the identification of potential misalignment of process visibility requirements and process visibility capabilities base on research question RQ1. The second research activity Process Visibility Impact focuses on the effects of process visibility, which is a quality of business processes and the outcome of process monitoring. It studies monitoring benefits and success in the post-adoption phases of process monitoring technology implementations along research question RQ2. The research project was conducted in close cooperation with SAP SE. Based on the annual revenue of 20.8 billion EURO, SAP is the world’s third largest independent software vendor. The headquarters of our industry partner is in Walldorf, Germany. SAP has more than 320,000 customers in 190 countries and nearly 80,000 employees in 130 countries (SAP 2016a). In the past, SAP coined the ERP concept particularly for large enterprises. Recently, the company developed a new software package based on inmemory database technology with the dedicated goal to create real-time process visibility: “SAP Operational Process Intelligence Powered by SAP HANA” (Berner and Jegadeesan 2014; SAP 2013a). The industry partner’s main interest in our joint research project was to gain scientific insights concerning the business effects of such software implementation and appropriate approaches for starting such projects. SAP’s huge customer and employee base enables comprehensive data collection across various industries, countries and organizational sizes. In this research project, multiple journal and conference articles have been written to present and discuss our research in the corresponding research communities (Appendix A). Quantitative and qualitative research approaches were adopted inside our research activities. The following two subsections introduce our research activities, their research methods, and their contributions.
1 Introduction
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1.3.1 Research Activity 1: “Process Visibility Fit” Novel information technology can enable real-time visibility of relevant information during process execution. Organizations need to answer the question of where to utilize new, but also cost-intensive developments in process monitoring. Theoretically grounded in the Information Processing View of the Firm (IPV) (Galbraith 1973; Tushman and Nadler 1978) this research activity develops a Process Visibility Fit Framework that supports the identification of process visibility gaps. Such gaps potentially indicate opportunities and needs for advanced process monitoring that justifies investments in these technologies. The framework considers both, visibility requirements of processes as well as existing visibility capabilities of information technology. We developed a survey instrument to assess the visibility fit of business processes. The measurement instrument and the hypotheses of our framework were empirically evaluated by conducting a survey study with 115 process experts. Further, we applied our framework and classified processes of a large company in alignment to their process visibility requirements and capabilities. Our examination shows that process visibility fit is positively associated with process performance. The classification of a company’s process landscape based on our framework demonstrated its usefulness to identify misfits between process visibility requirements and capabilities, where investment in process monitoring technology might be valuable. Besides the practical usability of our framework for the classification of processes concerning process visibility fit, our study contributes to empirical BPM research in the area of process monitoring, which despite its importance of practice, is largely underrepresented in literature. It outlines conditions for the need and success of process monitoring. Furthermore, we adopted the IPV in a new and beneficial manner by applying it on the process level and not on the organizational level. Our conceptualization, operationalization, and application of our framework add to the theorizing of the fit concept on the process level.
1 Introduction
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1.3.2 Research Activity 2: “Process Visibility Impact” Successful monitoring is essential for managing security-critical or business-critical processes. This research activity seeks to understand and empirically evaluate benefits of process monitoring in the post-adoption phase of Operations Control Centers (OCCs). OCCs create visibility of critical events and statuses in very sensitive processes. In IT Service Management (ITSM) they support the event management process with real-time monitoring and event analysis of critical systems in complex system landscapes. This special focus of OCCs on visibility is a promising context to study fundamentals of process visibility. We propose a Process Monitoring Benefits Framework that draws on the Situation Awareness Theory (Endsley 1995) and the Theory of Constraints (Goldratt and Cox 1992). We conceptualize process visibility and suggest that it is positively related with process performance. We have carried out a multiple case study in seven organizations to examine the framework and its propositions. The case study indicates that the impact of process visibility on process performance is mediated by situation awareness of the process participants as well as the identification of bottlenecks in processes. Moreover, we identified factors that potentially influence process visibility outcome – namely continuous improvement culture, outsourcing quality, and maturity of the software tool used for monitoring. Beyond process monitoring benefits, we also examine the impact of process visibility on employee empowerment and control in this research activity (Berner et al. 2014). On the one hand, increased process visibility may help to empower employees (Sia and Neo 2008). On the other hand, visibility can also increase the surveillance of individuals, which might have negative effects on employees’ job satisfaction, performance, and motivation. However, these aspects of process visibility impact are not in scope of the thesis at hand, as they have so far not been empirically validated inside the research project.
1 Introduction
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1.4 Structure of the Thesis After this introduction of the thesis, the remainder of the thesis is structured as follows: Section 2 describes the common foundations of the studies in this thesis. First outlined are: related work regarding process monitoring in BPM literature, the concept of supply chain visibility in SCM literature, the concept of transparency in lean production literature, and the concept of information quality in IS success literature. Second are presented: related technologies for process monitoring and their latest advances. Third: process visibility is conceptualized based on the related work as a process characteristic that defines the information quality of a process for its operation and improvement. Section 3 outlines the research activity about “process visibility fit” and its findings. A Process Visibility Fit Framework is derived and operationalized. The framework aims to support the identification of processes with potential process visibility deficiencies, where process visibility should be improved. Furthermore, the quantitative evaluation of the created survey instrument and the Process Visibility Fit Framework is discussed. The practical usability of the framework is demonstrated by applying it to real-world process landscape of a large company. Section 4 provides the results of the qualitative examination of “process visibility impacts”. A Process Monitoring Benefits Framework is proposed and evaluated in a multiple case study in seven firms. Section 5 summarizes the conclusions of our research. Theoretical contribution and practical relevance of our studies are discussed, as well as limitations and promising future research activities are outlined.
2 Foundations
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2 Foundations 2.1 Related Work2 2.1.1 Process Monitoring in BPM Literature Process-orientation refers to the management of work and budget around end-to-end business processes instead of functional units (Hammer and Stanton 1999; Willaert et al. 2007). A business process is defined as “a collection of inter-related events, activities and decision points that involve a number of actors and objects, and that collectively lead to an outcome that is of value to at least one customer” (Dumas et al. 2013, p. 5). In this thesis we use the terms business process and process equally. Business Process Management (BPM) is a “a body of methods, techniques and tools to discover, analyze, redesign, execute, and monitor business processes” (Dumas et al. 2013, p. 5). The change towards process-orientation has been discussed as highly beneficial for organizations: “In virtually every industry, companies of all sizes have achieved extraordinary improvements in cost, quality, speed, profitability, and other key areas by focusing on, measuring and redesigning their customer-facing and internal processes” (Hammer 2007, p. 1). These benefits have been confirmed in various studies (Ittner and Larcker 1997; Kueng and Hagen 2007; Ongaro 2004). An important characteristic of “process enterprises” is the existence of dedicated process roles like process owners (Hammer and Stanton 1999). These roles require information about process quality, process cycle time, process cost, and process flexibility to manage and optimize processes (Heckl and Moormann 2010). Such visibility is needed for a micro level referring to individual process instances as well as for an aggregation on a macro level referring to entire processes. Accordingly, process monitoring is a crucial managerial practice in BPM, which refers to the activity of data measurement for decision support during process execution (van der Aalst 2013). This monitoring of business processes creates information to support continuous improvement as well as day-to-day operations.
2
Parts of this section are based on published articles of the author (Berner et al. 2012, 2016).
2 Foundations
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Process monitoring is an essential and common element in lifecycle models that define the managerial practices of BPM, whereas in these models process monitoring is sometimes also referred to as process control, evaluation, or diagnosis (Morais et al. 2014). Examples of BPM lifecycle models and their definitions of process monitoring related activities are illustrated in Table 1. However, while all lifecycle models have an element that considers the continuous improvement aspect of monitoring, only some explicitly include real-time operational monitoring in their definitions (zur Muehlen and Ho 2005). Our understanding of process monitoring covers both purposes, supporting process improvement as well as process operation. van der Aalst et al. (2003)
Diagnosis: “the operational processes are analyzed to identify problems and to find things that can be improved” (van der Aalst et al. 2003, p. 5)
zur Muehlen and Ho (2005)
Weske (2007)
Dumas et al. (2013)
Process Monitoring: “performance is monitored in real-time” Evaluation: “data from multiple process instances is aggregated to discover temporal trends and design flaws” (zur Muehlen and Ho 2006, p. 457)
Evaluation: “uses information available to evaluate and improve business process models and their implementations” (Weske 2007, p. 15)
Process monitoring and controlling: “relevant data are collected and analyzed to determine how well is the process performing” (Dumas et al. 2013, p. 22)
Table 1: Process Monitoring in BPM Lifecycle Models
Despite its relevance in the BPM lifecycles, process monitoring as a research topic is greatly underrepresented in the BPM literature. A literature review by van der Aalst (2013) recognizes an over-proportional emphasis on BPM use cases concerning process modeling at the expenses of other equally relevant use cases – such as process monitoring. Recently, a similar analysis by Recker and Mendling (2015) of published papers at the BPM conference series, which is regarded as the leading forum for BPM researchers,
2 Foundations
14
found that between 2003 and 2014 only 2% (8 papers) addressed the topic of process monitoring (Figure 2). Out of these eight papers, three empirically study BPM practices in general and thereby process monitoring as a subtopic (Patig et al. 2010; Reijers et al. 2010; Siegeris and Grasl 2008) and five address specific technical questions of process monitoring such as integration via SOA architectures or configuration of tasks for predictive monitoring (Brahe 2007; Cabanillas et al. 2014; De Giacomo et al. 2014; Khalaf and Leymann 2010; Rouached et al. 2006). In summary, there were no empirical studies published at the BPM conference series with a primary focus on process monitoring.
Figure 2: BPM Conference Papers from 2003-2014 by Lifecycle Phase (based on Recker and Mendling 2015)
Existing work in BPM literature primarily examines technical implementation options of process monitoring in the context of business intelligence (e.g. Bucher et al. 2009), eventdriven architectures (e.g. Janiesch et al. 2012; Krumeich et al. 2014), or in a generic fashion (e.g. zur Muehlen and Shapiro 2010). Beside the more technical BPM research, another research stream in the process monitoring context relates to process performance management. It applies concepts of corporate performance measurement to the process level and studies questions about key performance indicators (KPIs) and performance measurement of business processes (e.g. Cleven et al. 2011; Heckl and Moormann 2010; Kueng 2000; Robson 2004). Research in
2 Foundations
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the area of process performance management focuses on the design of artifacts for process performance metrics. In this research context, Balasubramanian and Gupta (2005) introduce a process visibility metric that measures to which degree process states are visible to process stakeholders. Furthermore, Pidun and Felden (2012) define process performance visibility as appropriate information supply regarding process performance. However, as outlined above, there are not many empirical studies about constraints and impact of process monitoring. An exception is the work of Sia and Neo (2008), who investigated the impact of process monitoring on empowerment of process participants and found positive effects for non-routine processes.
2.1.2 Supply Chain Visibility in SCM Literature The concept of visibility is well-established in supply chain management (SCM) research. Supply chain visibility 3 is the outcome of information sharing between supply chain partners regarding demand and supply conditions – such as stock levels or good flows (Barratt and Oke 2007; Wei and Wang 2010; Williams et al. 2013). It is “the ability to access information across the supply chain in real time” (Swaminathan and Tayur 2003, p. 1391). Today’s supply chains are becoming more complex (Bartlett et al. 2007; Christopher and Lee 2004). New data sources and technologies such as radio frequency identification (RFID) tags offer additional opportunities to enhance supply chain visibility (Angeles 2005; Hardgrave et al. 2008). Therefore, SCM professionals consistently rate the implementation of supply chain visibility as one of their most important challenges and top priorities (Francis 2008; Williams et al. 2013). Some supply chain visibility researchers focuses their attention more on the ability and amount of information that is shared across the supply chain (Lamming et al. 2001; Swaminathan and Tayur 2003). While others focus on the quality of the exchanged information in order to avoid that data only gives an “illusion of visibility” (Bartlett et al. 2007). In the latter case, the level of supply chain visibility is defined by the extent to
3
Instead of supply chain visibility, some authors use the term information visibility in the supply chain (Goswami et al. 2013; Wang and Wei 2007).
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which the information is accurate, trusted, timely, useful and in a readily usable format (Barratt and Oke 2007; Caridi et al. 2010; Williams et al. 2013). These information properties determine the level of supply chain visibility. The purpose of increasing visibility into suppliers and/or customers’ processes is primarily to improve internal decision making and operating performance (Barratt and Oke 2007). Supply chain visibility is frequently positioned as a counter measure against the “bullwhip-effect”, which is the phenomenon of increasing swings in demands and inventories along a supply chain (Dejonckheere et al. 2004; Lee et al. 1997, 2012). The further a supplier is upstream in a supply chain, the more distorted and amplified is its received demand information. Supply chain visibility has been identified to be essential for effective SCM in several empirical studies (Barratt and Oke 2007; Wei and Wang 2010; Williams et al. 2013; Zee and Vorst 2005). Increased visibility improves operational performance, customer service, and solution development (Swaminathan and Tayur 2003). Information sharing can allow firms to reduce buffers and thereby reduce inventory costs as well as stock-out costs, synchronize their production, improve forecasts, and develop a common understand of supply chain performance (Goswami et al. 2013). Findings regarding information sharing from SCM research might be relevant also for business processes in general, because process standardization and current outsourcing trends result in increased involvement of external parties in process execution (Davenport 2005). Therefore, in most cases, comprehensive information about business processes also has to include information from third parties of the supply chain network. Information systems play an important role in enabling supply chain visibility (Goswami et al. 2013). Accordingly, a growing body of information systems research in the context of SCM integration has also recognized the important role of information sharing and the resulting supply chain visibility (Goswami et al. 2013; Li and Lin 2006; Rai et al. 2006; Subramani 2004). The extent to which operational, tactical, and strategic information are shared between supply chain partners, is the most influential process integration capability in a supply chain network that influences performance (Rai et al. 2006).
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Despite the relevance of supply chain visibility for our work, the focus and the unit of analysis of this research stream is different. SCM research about information sharing (and supply chain visibility as its outcome) focuses on differences in information provisioning in the collaboration between organizations. Whereas our research about process monitoring (and process visibility as its outcome) concentrates on differences between business processes regarding information quality for process operation and improvement.
2.1.3 Transparency in Lean Production Literature Beside BPM and SCM research, lean production literature stresses the importance of making information visible during process execution. Lean production is based on philosophy and practices of the Toyota Production System (TPS), which got huge attention in the 1990s in the automotive industry and beyond, because its just-in-time production approach was contrary and superior to Europe and America’s buffered production (Holweg 2007; Krafcik 1988; Ohno 1988; Womack et al. 1990). Lean production literature places a strong emphasis on the standardization of work and its continuous improvement (Pettersen 2009; Shah and Ward 2007). In this regard, lean production recognizes transparency as essential for continuous improvement. “The most important spur to perfection is transparency, the fact that in a lean system everyone […] can see everything” (Womack and Jones 2003, p. 26) 4 . Visual controls that create immediate transparency about abnormalities are a crucial part of lean production systems (Shingo 1989), and they are essential for banishing waste to continuously improve processes (Monden 2012; Womack and Jones 2003). Visual controls pursues the goal that every person involved must be able to fully understand the different aspects of a process and its status at any time (Bauch 2004). Lean practitioners developed simple visual communication tools to visualize problems. For example, Andon boards – a display that can be seen by everyone and shows where workers call for help or stopped the line (Monden 2012). Such visual aids include
4
Transparency is the dominant term in Lean Production literature in the context of making problems visible. However, there are also a few authors, that use the term process visibility – for example Hall (2004, p. 25) says “Toyota's logical pattern is to introduce tools to increase process visibility, gradually stepping up the effectiveness of problem seeing and problem solving.”
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graphical representations like symbols and color coding as well as audio signals (Bilalis et al. 2002). The visual controls of the TPS can be applied as an effective tool also to business processes beyond the production line (Parry and Turner 2006). Lean production focuses on end-to-end processes from the sub-suppliers to the end customers – even if within lean literature the term value stream is preferred instead of business process (Pettersen 2009). Accordingly, BPM researchers also study lean production concepts in a general business processes context. For example, Klotz et al. (2008) examines whether process maps are an effective communication tool to increase transparency of processes. In conclusion, the transparency concept of lean production shares aspects with supply chain visibility and process monitoring. It particularly stresses the importance of open information communication, and lean production literature propose diverse, easy consumable visual communication tools. However, it should be considered that from an academic perspective there is a lot of inconsistency and ambiguity regarding lean production definitions and its components (Pettersen 2009; Shah and Ward 2007).
2.1.4 Information Quality in IS Success Literature Nowadays, process monitoring is always facilitated by IS. The production of information is the unique output of IS in general (DeLone and McLean 1992; Zuboff 1988) – and for process monitoring technology, information creation is even the only direct purpose. Accordingly, the quality of this information is an important criterion to assess the outcome of process monitoring. The dependent variables of many IS studies revolve around the created benefits of information systems (Urbach and Müller 2012). The dominant framework to categorize these benefits is the DeLone and McLean (D&M) IS Success Model (DeLone and McLean 1992, 2003). Therein, information quality is one success category that describes the desirable characteristics of the IS’ output – for example “the importance and usableness of information presented in reports” (DeLone and McLean 1992, p. 64). The updated D&M IS Success Model treats IS success as a multidimensional concept with six, interdependent success categories: The three quality categories (information quality, system quality, and service quality) influence use and user satisfaction, which
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subsequently affect the net benefits of IS such as individual or organizational impact (DeLone and McLean 2003). Researchers conceptualized information quality differently. The definitions either take an intrinsic or contextual view of the concept (Nelson et al. 2005; Wang and Strong 1996) 5. The intrinsic view assesses information based on the degree to which it correctly reflects the reality. Accordingly, this perspective focuses on information characteristics like accuracy, consistency, and currency (Levitin and Redman 1998; Seddon 1997). The contextual view describes information quality relative to its usefulness in a specific context, which depends on the consumer of the information and the tasks to be supported. Consequently, properties like usability, relevance, and completeness shape the perception of information quality (Fisher and Kingma 2001; Pipino et al. 2002; Wang and Strong 1996). In this regard, the representation format of information determines the usefulness of information (Nelson et al. 2005; Rai et al. 2002; Wang and Strong 1996), and has long been a research topic of human-computer interaction studies in the IS discipline (e.g. Benbasat and Dexter 1985; Jarvenpaa 1989; Tractinsky and Meyer 1999). As this contextual perspective depends a lot on user perceptions, studies on the individual level often not distinguish information quality as a unique construct but measure it as a key component of user satisfaction (Petter et al. 2008). Nelson et al. (2005) considered both perspectives and distilled a core set of information quality dimensions from IS success literature. According to their work, the degree of information quality is defined by the accuracy, completeness, currency, and format of the information. In opposite to the originally suggested relations in the D&M IS Success Model, several studies indicate that there is an interrelationship between information quality and system
5
Nelson et al. (2005) suggest based on Wang and Strong (1996) that the dimension representation format belongs to an own “representational” perspective of information quality. However, we subsume this dimension here under the contextual view, because its quality can be evaluated only in relation to a user or task (“context”).
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quality (e.g. Ding and Straub 2008; Gorla et al. 2010; Xu et al. 2013). System quality reflects the system that produces information and can be determined by the dimensions accessibility, reliability, response time, flexibility, and integration (Nelson et al. 2005)6. Problems with the system’s quality can degrade information quality, and consequently, system quality significantly influences information quality (Xu et al. 2013). An IS success study by Gorla et al. (2010) even identified that information quality is the key mediator between system quality and organizational impact. This study of Gorla et al. (2010) is one of the few that examines the impact of information quality at the organizational level. The majority of IS success studies deal with individual impact rather than organizational impact (Petter et al. 2008; Sabherwal et al. 2006). Petter, DeLone, and McLean (2008) asked for conscious research efforts to study net benefits of IS beyond the individual level, as existing literature provides insufficient data to generalize the interrelations between the success constructs of the D&M IS Success Model at higher levels of analysis – e.g. the relation of information quality to organizational net benefits (Figure 3).
Figure 3: Support for IS Success Interrelationships in Existing Literature at the Organizational Level (Petter et al. 2008, p. 255; based on the updated D&M IS Success Model)
6
Caused by the large amount of research related to the Technology Acceptance Model (Davis 1989), perceived ease of use is often used as a measure for system quality (Urbach and Müller 2012). However, Nelson et al. (2005) suggest that perceived ease of use should be rather seen as a consequence of system quality, than as a proxy for it.
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In summary, the examination of information quality and its impact beyond the individual level is a promising and important research field that can leverage existing work from IS success literature.
2.2 State-of-the-Art in Process Monitoring Technologies7 2.2.1 Process Monitoring Technologies Originally, process monitoring was only a functionality inside ERP systems. Over time, dedicated technologies evolved to support process monitoring. Looking at these technologies for process monitoring, we believe that three current package categories can be differentiated (Table 2). These software packages belongs to the technological area of Business Intelligence and Analytics (BI&A), which
are “techniques, technologies,
systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand its business and market and make timely business decisions” (Chen et al. 2012).
BAM Purpose
BPI
OpBI
Process design & redesign
Process design & redesign
Managing and optimizing daily operations
Management Level
Strategic & tactical
Strategic & tactical
Operational
Range of Users
Small
Small
Broad
Time Relevance
Real-time
Historical
Right-time
Information Sources
Runtime data from modeled processes in BPM systems
Structured and unstructured data
Structured and unstructured data
Reference
McCoy (2002, 2004)
Felden et al. (2010)
White (2005)
Table 2: Comparison of BI&A Software Package Categories for Process Monitoring
Business Activity Monitoring (BAM) refers to a software package category for process monitoring and reporting. BAM is often seen as the monitoring component of Business
7
Parts of this section are based on published articles of the author that was presented and discussed at the International Conference on Information Systems (ICIS) and Multikonfernz Wirtschaftsinformatik (MKWI) (Berner et al. 2012; Graupner, Berner et al. 2014a).
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Process Management Systems (BPMS) (McCoy 2004). BPMS is defined as “a generic software system that is driven by explicit process designs to enact and manage operational processes” (van der Aalst et al. 2003, p. 1). This is in contrast to transactional software packages like ERP, where processes are not modeled. Furthermore, BPMS focus on the integration and visualization of processes. However, it should be considered that only a minority of business processes are managed in a BPMS – in reality, Office Software, ERP, CRM, Procurement, Content Management, and Product Data Management systems are more relevant as runtime environments for business processes than BPMS (Patig et al. 2010). Business Process Intelligence (BPI) refers to analytical software that focuses on “identifying, defining, modeling, and improving value creating business processes in order to support the tactical and strategic management” (Linden et al. 2011, p. 212). BPI supports the analysis (of completed process executions), prediction (of exceptions and undesired behavior), monitoring (of process instances), control (via interaction with BPM), and optimization (of identified areas of improvements) (Grigori et al. 2004). It marks the trend towards process-centric analytics and integration of several data sources towards an end-to-end process view, and it sets data into the process context by linking pre-occurring, co-occurring and post-occurring sets of data (O’Leary 2013). In contrast to BAM, BPI is not bound to a BPMS and it is based on data warehouse technology. Operational Business Intelligence (OpBI) describes one of the latest trends in Business Intelligence & Analytics (BI&A). It targets regular line-of-business users and focuses on operations support by identifying opportunities and detecting problems in daily business. Like all data warehouses, OpBI has the challenge to provide up-to-date information from transactional systems with minimal latency. Thus, discussions linked to OpBI focus on the architecture of “right-time data integration” with transactional systems (White 2005). As right-time is often linked to (near) real-time, the development towards real-time BI&A “blurs the line between decision support and operational systems“ (Anderson-Lehman et al. 2004, p. 173). The shift from managerial task support towards operational decision support marks one important movement in BI&A (White 2005). Frontline workers require analytical information for identifying opportunities and detecting problems in daily business. Organizations which provide analytical information to their operational decision makers perform better than those without analytical information provisioning on
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the operational level (Lock 2010). Therefore, OpBI is designed to support process workers with their operational tasks – in contrast to BAM and BPI it is falling short in relating analytical information to its process-context (Bucher et al. 2009), but it offers decision support while process execution based on multiple data sources. In summary, initially BI&A was data-centric and focused on strategic and tactical decision support based on historical data (Bucher et al. 2009; White 2005). Traditional data analysis and provisioning is not or is only loosely coupled to the process execution. The vast majority of data is therefore used without considering its process context. Furthermore, latest analytical data are not available for day-to-day decision making. Currently, BI&A moves to overcome both limitations. The development towards processcentricity and enhanced operational decision support is outlined in Figure 4. None of the outlined process monitoring software packages addresses the process-centric and operational decision support challenges altogether: BPI claims process-centricity but has little focus on the operational level. BAM is closely linked to BPMS and therefore lacks to address processes that are not explicitly under the ambit of a single BPM system. Finally, OpBI provides operational decision support, but lacks process-centricity.
processcentric
Information Focus
Business Process Intelligence (BPI)
Business Activity Monitoring (BAM)
datacentric
Operational BI (OpBI)
Data Warehouse
operational
strategic Decision Type
Figure 4: The BI&A Trend towards Process-centricity and Operational Decision Support
Whereas the monitoring software packages develop either towards process-centricity or operational decision support, analysts predict that the software packages are combined
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with additional technologies and their boundaries are vanishing. These latest technological developments are described in the next section.
2.2.2 Latest Advances in Process Monitoring Technologies Recent technological advances enable new opportunities for process monitoring. Particularly, big data technologies and in-memory databases are of importance for process monitoring and discussed subsequently. 8 2.2.2.1 Big Data Technologies Process monitoring is facing new challenges and opportunities by large data amounts. In a digitalized process, “things” like smart electric meters, smoke alarms, security cameras, geospatial devices, door locks, machine sensors, and a lot more are integrated to the business process and produce data to be leveraged for process operation and improvement (Gartner 2015a). New technologies enable to overcome the challenge of correlating millions of events, that are increasingly coming from sensors and machines, to its underlying processes (Russom 2013). Furthermore, instead of reporting about historical data, prediction models can be derived to forecast future process events (Cabanillas et al. 2014; van Grondelle 2013). Big data technologies deal with datasets and sources which exceed the abilities of classical databases in terms of capturing, storing, managing, and analyzing (Manyika et al. 2011). Consequently, big data is not only huge in terms of volume, but also in terms of velocity and variety (called the “3Vs of Big Data” (Laney 2001; Russom 2011)9). Subsequently, we adapt this 3V-terminology and outline related challenges in the process monitoring context.
8
In-memory databases can be seen as a supporting technology for big data analytics (Russom 2011). However, as this section outlines, big data technologies and in-memory databases follow different purposes. Furthermore, in-memory databases are not suited for the storage of large data amounts. Accordingly, software vendors offer different products for both technologies – for example SAP recently launched “Vora” as its big data offering in addition to its in-memory data base HANA (SAP 2016b).
9
Some authors see veracity as the fourth characteristic of big data (Gupta et al. 2012; Schroeck et al. 2012). It emphasizes the unreliability of some data types and sources associated with big data. We agree to this, but argue that variety, volume, and velocity implicitly result in lower data reliability. Thus, veracity can be seen as a consequence of the 3Vs rather than a fourth characteristic.
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First, data volume in today’s economy has become increasingly huge and continues to grow steadily. Organizations double their total data volume approximately every 18 months (Forrester Research 2010). From the perspective of a single enterprise data quantities above one terabyte are considered big today (Schroeck et al. 2012). Whereas the data-centric notion referred to volume as amount of data, the process-centric notion extends the meaning towards the number of process instances and the amount of information per process instance. Accordingly, the correlation of the huge amount of events to its process context is a significant technological challenge (Ferreira and Gillblad 2009). Second, high data stream velocity is a characteristic of big data. Velocity refers to the “speed at which data is created, accumulated, ingested, and processed” (Minelli et al. 2013, p. 10). Whereas the data-centric view identifies data latency, reporting latency, and analysis latency as challenges, the process-centric view puts emphasis on the decision latency (Davenport and Snabe 2011). An example refers to algorithmic trading: It is crucial to finish the decision making process and take a buy or sell decision within 0.5 milliseconds. Otherwise, value of the information declines – a phenomenon generally referred to as the “time value of information” (Inmon 2007). Third, the huge variety is a defining characteristic of big data. Variety characterizes the diversity in terms of data types, sources, as well as entities represented (Russom 2011). Putting the variety characteristic into a process context, it is particularly the variety of systems that creates challenges. Establishing end-to-end visibility of processes may embrace numerous organizational functions and involve multiple application systems. Functions may even exceed organizational boundaries making cross organizational process mining particularly relevant (van der Aalst 2011). From a technology perspective the processing, management, and analysis of big data is associated with diverse classical and advanced BI&A technologies. Specific software tools connected to the three characteristics of big data include parallelization platforms (Hadoop), advanced data visualization, CEP engines, as well as NoSQL databases (Chen et al. 2012; Manyika et al. 2011; Russom 2011). Whereas classical BI&A technology requires the preparation and optimization of potential queries already at design time, new database technologies and massively distributed architectures allow full flexibility at
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query execution time (van Grondelle 2013). Big data analytics is based on statistical techniques and machine learning. 2.2.2.2 In-Memory Databases The separation of data analysis and transactional process execution is blurring, because new in-memory databases allow that transactional and decision-related data is managed in an integrative manner (Loos et al. 2011). This technology makes the “main memory the new disk” (Plattner and Zeier 2011, p. 14), which allows efficient data storage and access. Enterprise application systems (like SAP R/3) originally combined transactional process execution and information needs in one system. However, in the late 1990s both layers were separated due to sole technical reasons, as rising process coverage and data volumes led to performance issues (French 1995). On one site there are the content-rich and information-centric business intelligence (BI) systems with historical data for strategic decision management. On the other site there are the process-centric real-time systems like enterprise resource planning (ERP) or customer relationship management (CRM) used by the maturity of enterprise users for their daily operational business (Maedche and Mueller 2012). Process control requires up-to-date information for tasks like failure resolution or ensuring customer service levels. So real-time solutions are required, which keep away the environment and process complexity from business users, and help them to discover, understand, analyze, and evaluate end-to-end processes. In other words, create real-time visibility into business processes. In future we expect that the separation of enterprise systems between process execution and information analysis will vanish again, because with new in-memory databases the separation of transactional and analytical data are anticipated to become obsolete (Loos et al. 2011; Plattner and Zeier 2011). Therefore, we suppose that in-memory technology will enable and accelerate the development towards integrated, less complex, and realtime process monitoring. 2.2.2.3 Combination of Advanced Technologies for Process Monitoring Analysts predict that the process monitoring software packages will be combined with additional technologies and their boundaries are vanishing. Reflecting the latest
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technological advances, they increasingly address the topic of process monitoring and apply numerous buzzwords: Gartner (2012) introduces Intelligent Business Operations as generic term for real-time usage of BI&A technologies enhanced with complementary software packages like complex event processing (CEP), and business rules management (BRM). Furthermore, Gartner (2015) stresses the importance of integrating advanced analytics into BPMS under the label intelligent Business Process Management Suites (iBPMS). IDC analysts use an analogy from the car industry and label such combined and enhanced solutions Business Navigation Systems (Fleming 2009). TDWI defines Realtime Operational Intelligence as “an emerging class of analytics that provides visibility into business processes, events, and operations as they are happening” (Russom 2013). Accordingly, software vendors increasingly focus on new process monitoring solutions where the boundaries between the software packages vanish, and they incorporate big data as well as in-memory database technology in their existing offerings. Examples include, but are not limited to the following: Vitria Technology (2013) integrates Hadoop and NoSQL databases in their process monitoring offering. Progress Software (2011) stresses the significance of real-time information for process workers and emphasizes end-to-end visibility. TIBCO Software (2012) also confirms the relevance of analytics for operational business users. SAP renews its architecture towards in-memory databases where operational and analytical data are merged (Färber et al. 2011), and they launched “SAP Operational Process Intelligence” based on the in-memory database HANA (SAP 2013a). Software AG (2013) announced In-Genius as their new in-memory platform that they use for process intelligence in combination with their BI, BAM, BPM, and CEP software packages (Software AG 2011). In conclusion, software vendors and analysts increasingly focus on process monitoring by leveraging new technologies. Although the product names and analyst terms vary, their promises ultimately link to equivalent characteristics, which we aggregate under the concept of process visibility. The conceptualization of process visibility is done in the next subsection, based on the introduced related work and in the context of the outlined related technologies.
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2.3 Conceptualization of Process Visibility10 Related scholarly work and the current technological trends of process monitoring can be synthesized in a new concept: process visibility. Process monitoring is the activity in BPM that enables process visibility – analogously like the SCM activity information sharing has supply chain visibility as its outcome (Barratt and Oke 2007; Wei and Wang 2010). We suggest that process visibility is a characteristic of a process that describes the quality of information to support process operation and improvement. Information for process operation is relevant for broad range of users on the operational level. In contrast to strategic levels, information in such a context is characterized by a well-defined scope, is frequently used, very current, and provides high levels of detail (Gorry and Morton 1989). It should consider internal and external data sources to support decisions of process workers by predictions (Cabanillas et al. 2014; van Grondelle 2013). Information for process improvement is mainly relevant for the tactical management level and requires access to a wide range of historical data by ongoing data acquisition (Cotteleer and Bendoly 2006). Figure 5 summarizes our conceptual frame of process visibility in a morphological box, which is derived from a comprehensive review of literature (Urbitsch 2014). The grey highlighted attribute values in Figure 5 describe the scope and focus of the process visibility concept.
10
Parts of this section are based on published articles of the author (Berner et al. 2012, 2016; Graupner, Berner et al. 2014b).
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Attribute
Attribute Value
Management Level (Felden et al. 2010)
Operational
Tactical
Strategic
Integration Level (Felden et al. 2010; zur Muehlen 2001)
Instance
Model / Multiple Instances
Meta Model
BPM Lifecycle Phase (van der Aalst et al. 2003)
Process Design
System Configuration
Process Enactment
Diagnosis
Kind of Process (Felden et al. 2010; zur Muehlen 2001)
Business
Technical
Time Relevance (Felden et al. 2010; zur Muehlen 2001)
Real-time (live)
Historical (ex-post)
Range of Users (Felden et al. 2010)
Small
Data Sources (Felden et al. 2010)
Middle
Internal (intra-organizational)
Broad
External (inter-organizational)
Figure 5: Scope and Focus of the Process Visibility Concept
Our conceptualization of process visibility refers to information quality in respect to operating and improving a process. Information quality is one of the key constructs of the D&M IS Success Model (section 2.1.4). Therefore, we derive dimensions that determine the level of process visibility from IS success literature. The D&M IS Success Model describes information quality as a characteristic of an information system whereas process visibility is defined as a characteristic of a process. Processes as unit of analysis are beneficial because the organizational benefits of IT are mediated by business processes (Melville et al. 2004). Therefore, we suggest deriving the process visibility dimensions from information quality dimensions by putting them in a process information context. Information that plays a supporting role in process operation and improvement is called process information (Davenport 1993). Information quality is a multi-dimensional construct determined by accuracy, completeness, currency, and format of information (Nelson et al. 2005). In this regard, visibility should not be confused with visualization, because the representation format of information is only one aspect of it. In addition, IS success literature identified that information quality is influenced and interlinked with system quality (Xu et al. 2013). Analogously, also properties of the monitoring system might determine the level of process visibility. Hence, also several system quality dimensions of the monitoring system may contribute to the level of process visibility – namely accessibility, flexibility,
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and integration (Nelson et al. 2005), because process monitoring technologies intend to improve these dimensions explicitly. Other system qualities (reliability and response time) are of generic relevance and therefore no specific dimensions of process visibility. In summary, process visibility is a characteristic of a business process defined by the information quality to support process operation and improvement. The degree of process visibility depends on the level to which process information is accurate, complete, current, accessible, flexible, integrated, and usable (in an understandable as well as interpretable format). This definition of process visibility is complementary to the rare, other explicit definitions of process visibility in BPM literature (Balasubramanian and Gupta 2005; Bhat and Goel 2011; Klotz et al. 2008; Pidun et al. 2011)11. Furthermore, it covers the qualities of supply chain visibility, where its level is defined by the extent to which the shared information is accurate, trusted, timely, useful, and in a readily usable format (Barratt and Oke 2007; Caridi et al. 2010; Williams et al. 2013). Table 3 outlines the identified dimensions of process visibility, which are discussed in the following. They are linked from a practical perspective to the analysis results of related technologies as well as from a scholarly perspective to related process monitoring, supply chain visibility, and lean transparency literature.
11
To the best of our knowledge, we are aware of the following existing definitions of process visibility in BPM literature. First, Balasubramanian and Gupta (2005, p. 686) introduce a process visibility metric, that measures “the extent to which process states are visible to specific process stakeholders through process information reporting or recording”. Second, Bhat and Goel (2011, p. 8) understand process visibility as the provisioning of “detailed, real time and accurate information of the business process being executed within the IT systems”. Third, Klotz et al. (2008) use the terms process visibility and transparency equally. They define a transparent process as “everyone can see and understand the necessary aspects and status of an operation at all times” (Klotz et al. 2008, p. 625). Finally, in analogy to supply chain visibility, Pidun et al. (2011, p. 206) define “visibility of process performance as the principal ability to assess process performance information”.
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Dimension
Definition
Accuracy
The degree to which process information is correct, unambiguous, meaningful, consistent, and trustable (perceived to be valid, reliable and objective and a positive attitude is embraced towards the source).
Completeness
The degree to which all possible process states and other information relevant for the process participants and stakeholders are represented.
Currency
The degree to which process information is up-to-date, or the degree to which the information precisely reflects the current state of a process instance.
Format
The degree to which process information is presented in a manner that is useful, readily useable, analytically interpreted, and contextualized (centered on process steps and is set into relation with previous and adjacent process steps).
Accessibility
The degree to which the process participants and stakeholders can access process states and other relevant process information with relatively low effort.
Flexibility
The degree to which process information analysis and representation can adjust to a variety of process participants and stakeholders needs and to changing conditions.
Integration
The degree to which process information is available for the entire process by facilitating the combination of information from various sources to support decisions.
Table 3: Dimensions of Process Visibility12
First, accuracy of process information affects the level of process visibility. Beyond IS success research (Nelson et al. 2005; Rai et al. 2002), literature related to supply chain visibility also stresses the importance of information accuracy (Barratt and Oke 2007; Christopher and Lee 2004). Accuracy describes the degree to which process information is correct, unambiguous, meaningful, consistent, and trustable (Nelson et al. 2005). In this regard, in statistical research, trustworthiness describes the validity and reliability of results (Golafshani 2003; Morse et al. 2002). The trustworthiness and objectivity of the information is ultimately linked to the trust between the partners who share information. Related research in SCM confirms that mutual trust between supply chain partners is a key element to enable information sharing (Klein et al. 2007; Li and Lin 2006). Trust in business partner relations is defined as “the degree to which the trustor holds a positive attitude toward the trustee's goodwill and reliability in a risky exchange situation” (Das and Teng 1998, p. 494).
12
Example quotes from interviews with practitioners that illustrate the dimensions of process visibility are outlined in Table 25.
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Second, completeness of information is an antecedent of process information quality. This also links to the claim of lean production literature, that transparency should be there about “everything” (Womack and Jones 2003). For information systems in general, information completeness is defined as the degree to which all possible states relevant to the user are represented (Nelson et al. 2005). Transformed to our process context, completeness refers to the accessibility of process states and other information that is relevant for process participants and stakeholders. A process state is an achieved milestone in the process progress (Balasubramanian and Gupta 2005). Third, information currency represents the degree to which process information is up to date, or the degree to which the information precisely reflect the current state of the world that it represents (Nelson et al. 2005) – in our case, the current state of a process instance. The very frequent use of information in operational tasks requires high data currency (Gorry and Morton 1989). Thus, currency refers to the freshness of information and stresses that data provisioning in right-time depends on the business need and the associated decision (White 2004). On the operational level right-time generally means real-time or near-real-time information provision (Bruckner et al. 2002). Referring to historical evolution of enterprise systems, this property is closely linked to in-memory computing and the corresponding reconsolidation of process-centric and informationcentric systems. Further support about the importance of timely information provisioning can be found in the related supply chain visibility literature (Angeles 2005). Forth, the quality of process information is defined by the format in which information is presented in a manner that is understandable and interpretable (Nelson et al. 2005). Related work about supply chain visibility underlines that process information must be useful and in a readily useable format (Barratt and Oke 2007; Caridi et al. 2010; Williams et al. 2013). Additionally, in our process monitoring context, the information should be analytical interpreted. Process monitoring technology ''focuses on the analyses of business processes and their connection with analytical information'' (Linden et al. 2011, p. 210). Compared to descriptive approaches, analytical approaches do not not only summarize information but also derive valuable insights. Furthermore, the integration of information into its process context is a key characteristic of process monitoring (Bucher et al. 2009; Grigori et al. 2004). It refers to the degree to which process information is centered on process steps and is seen in relation with previous and adjacent process steps.
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The information needs to be revolved and visualized around processes regardless of whether they are explicitly modeled or not. This not only anticipates that the information itself is valuable, but that the information within a specific context creates even more value. Fifth, accessibility represents the degree to which information can be accessed with relatively low effort (Nelson et al. 2005). For example, the information consumer – in our case a process participant – knows how to retrieve it and has the required authorization to do so (Wang and Strong 1996). Or as lean production literature says, in a lean system “everyone” should have transparency (Womack and Jones 2003). Balasubramanian and Gupta (2005) even understand this aspect as the main characteristic of process visibility, and define process visibility as “the proportion of total number of process states required to be visible to all process stakeholders that are actually reported to or recorded for the relevant stakeholders” (p. 686). Sixth, the flexibility of process information is a dimension of process visibility. It refers to the degree to which process information analysis and representation can adjust to a variety of process participants needs and to changing conditions (adapted to the processlevel from Nelson et al. 2005). The less standardized a process is, the more this flexibility of process information becomes relevant and the individual adjustments of reports is required. In this regard, the trade-off between report standardization (to achieve economic scale) and report individualization (for flexibility) is a challenge for BI&A that need to be balanced out (Kretzer and Maedche 2015). Finally, the integration dimension relates to the degree to which process information is available for the entire process by facilitating the combination of information from various sources to support decisions (adapted to the process-level from Nelson et al. 2005). The idea of integration is omnipresent in all mentioned related technologies. Process information integration is challenging as processes typically span across multiple application systems and organizational functions or even exceed the organizational boundaries and thus involve different geographical locations. Information integration is an essential element of organizations’ process integration capability (Beretta 2002; Rai et al. 2006). Thus, the degree to which information about an entire process is available determines the visibility of the process, too. The relevance of information integration can
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be also noticed by the importance that the software vendors give to the topic “end-to-end visibility” of processes (e.g. Progress Software 2011; Vitria Technology 2012). With this conceptualization of process visibility, we laid the foundation for the subsequently presented two empirical studies that examine process visibility fit and the impact of process visibility on process performance.
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5 Conclusions 5.1 Summary of the Work Process monitoring is one of the key activities in business process management (BPM). However, surveys indicate that there are still fundamental weaknesses in today’s BPM practices regarding collection and availability of relevant process information. Furthermore, current BPM research lacks concepts and empirically grounded recommendations concerning needs, effects, and constraints of process monitoring. Accordingly, it remains vague what most influences the success of process monitoring. Recently, new technological opportunities and challenges brought the topic of process monitoring even more to the focus and priority of BPM practitioners and researchers. These technological novelties in process monitoring link to recent advances in business intelligence and analytics (BI&A) coming from big data technology as well as in-memory databases. Furthermore, process monitoring might benefit from the trend in BI&A technology to move from support of strategic decisions based on historical data towards support of day-to-day operational decisions based on real-time data integration. In parallel, there are more and more process-centric BI&A software packages, resulting in dedicated process monitoring offerings. Business Process Intelligence (BPI) and Business Activity Monitoring (BAM) label such BI&A solutions with an explicit notion of business processes. Nevertheless, process monitoring technology is not limited to specific solutions. On the contrary, analysts predict the future combination of multiple technologies for the purpose of process monitoring while today’s technological borders are vanishing. Although there is a large amount of technology addressing the process monitoring topic, the underlying concepts are unclear and have not been aggregated and conceptualized explicitly. We suggest process visibility as a possible approach and conceptualize it as a characteristic of a business process. It is an outcome of process monitoring and refers to the quality of process information to support process operation as well as improvement. Visibility is an important concept in supply chain management and in lean production literature. In addition information quality is intensively studied in IS success research. Based on the adaptation of the concepts from these related research streams to business
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processes, we define the degree of process visibility as the extent to which process information is accurate, complete, current, accessible, flexible, integrated, and usable. The new process monitoring technologies are associated with substantial investments. This raises the question, where organizations should deploy them. Theoretically grounded in the Information Processing View of the Firm (IPV) we propose a prioritization framework for business processes regarding their process visibility requirements and process visibility capabilities.
Our Process Visibility Fit Framework supports the
identification of process with a mismatch of their process visibility requirements and process visibility capabilities based on a parsimonious survey instrument. Business processes, which show a process visibility gap, potentially would benefit from investments in process monitoring technology to increase their process visibility. The hypotheses of our Process Visibility Fit Framework and the measurement instrument were tested in a quantitative study with 115 process experts from a multinational company. This study confirmed the validity and reliability of the survey instrument. The empirical relevance of our framework could be shown, as the analysis of the survey data identified a positive relation between process visibility fit and process performance. Additionally, the practical applicability of the framework was demonstrated by applying it to the process landscape of the company. A second study examined the impact of process visibility on process performance. The basic belief regarding process monitoring activities and related technologies are that they increase the performance of business processes. The understanding of this fundamental assumption is important for the justification of process monitoring technology investments as well as for the steering of related implementation projects, because it addresses the questions; what are the benefits of process monitoring as well as what contingency factors influence these benefits? We propose a Process Monitoring Benefits Framework, which builds on our conceptualization of process visibility as well as on the Situation Awareness Theory and the Theory of Constraints. It argues that the key benefits of process monitoring refer to process visibility, situation awareness for process operation, bottleneck identification for process improvement, and ultimately, process performance – whereby bottleneck
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identification and situation awareness mediate the effects of process visibility on process performance. For the evaluation of the Process Monitoring Benefits Framework and its propositions, we conducted case studies in seven companies that implemented an Operational Control Center (OCC) for the monitoring inside their IT service management event management process. OCCs with their focus on monitoring of critical infrastructure and processes, offer a great research context to study the impact of process visibility. Our multiple case studies provide preliminary evidence for the proposed process monitoring benefit dimensions and their relations. Additionally, we identified factors that influence these benefits – which are the continuous improvement culture of the organization, the outsourcing quality regarding potentially involved third party partner organizations, and the tool maturity of the used process monitoring technology. Regarding the expected potentially influential factor skills and knowledge of the process participants, our study indicates that process visibility reduces the impact of this factor on process performance.
5.2 Contributions to Theory Our work regarding the phenomenon of process visibility responds to the need of more empirical examinations of values and constraints of BPM practices. Understanding these value propositions is essential for the long-term legitimation of the BPM discipline. Particularly the BPM key activity process monitoring lacks such foundational work. Our empirical study of needs, opportunities, and benefits of process monitoring helps to identify the vital concepts that relate to the success of process monitoring. The conceptualization of process visibility as a multidimensional construct offers a generalization of concepts coming from supply chain management and lean production literature to a broader process context. By doing so, we theorize the concept of information quality on the level of business processes. Our conceptualization aggregates the objectives of a wide range of process monitoring technologies to their essentials. In opposite to most existing process maturity and prioritization models from BPM research, our suggested Process Visibility Fit Framework can provide an efficient process
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classification based on actionable criteria. Thus, it enhances BPM literature with an empirically and theoretically grounded approach to classify and prioritize business processes. In addition, our Process Visibility Fit Framework contributes to the fit concept of IPVrelated literature with respect to multiple aspects. First, whereas the IPV considers capabilities generally as referring to the organizational structure, we adopted a technological perspective. Second, to the best of our knowledge, we are the first who adopt the IPV on a generic process level. Thus, we assess process visibility requirements and capabilities where they are most apparent. Third, our operationalization and evaluation of “fit as moderation” adds to the theorizing of fit in IS research. The proposed Process Monitoring Benefits Framework responds to the call of BPM researchers for more value-sensitivity of the BPM discipline. It identifies and categorizes benefits of process monitoring and its influential factors. By doing this, it provides a conceptual baseline for future research of process monitoring success. Our research regarding the outcome of process monitoring technologies contributes additionally to IS success research beyond the individual level, which still is an insufficiently studied area. Analogous to the D&M IS Success Model that categorizes the success dimensions of IS in general, our Process Monitoring Benefits Framework structures the success dimensions of process monitoring technologies and activities. Business processes as unit of analysis are a promising research level to study IS impact beyond the individual level, because the process level provides the possibility to identify IS impacts without the need for observing the often hard to capture organization-wide impacts.
5.3 Contributions to Practice The synthesis of related scholarly concepts and technological capabilities of process monitoring might help software vendors to focus their offerings around the identified dimensions of process visibility and the connected benefits. In this regard, our work potentially supports the change of the vendor’s product positioning and development efforts from technological features to business impact.
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Furthermore, our analysis and structuring of current process monitoring technologies and trends aims to give practitioners orientation in the wide and confusing range of related software offerings and analyst terms. It simplifies this discussion by linking the technologies and buzzwords to their essential qualities from a BPM perspective. Beyond the theoretical explanations that our Process Visibility Fit Framework suggests, it is also an applicable framework for practical usage. User companies of process monitoring technologies can apply the framework including its survey instrument to classify and benchmark their processes – as it was demonstrated for the business processes of our industry partner company. This enables organizations to classify processes objectively concerning their process visibility deficiencies, which can help to prioritize potentially costintensive investments in process monitoring technologies. Such applicable process classification frameworks support the demand for more data-driven and less politicallydriven IT investment decisions. For the business justification of process monitoring investments, practitioners require a sound understanding of potential process monitoring benefits. Our Process Monitoring Benefits Framework proposes a baseline for such a cost-benefit assessment by outlining the different success dimensions for process operation and improvement. In addition, factors that influence process monitoring benefit realization were identified. Our research proposes several links on how practitioners can increase success of process monitoring in organizations. It describes what influencing factors should be considered for the implementation of process monitoring technologies.
5.4 Limitations and Future Research Beside the contributions of this thesis, the present status of our research is subject of specific limitations, which potentially represent opportunities for future research. Additionally, our work might inspire future research that investigates BPM demand and outcome in other areas than process monitoring with similar research approaches as we followed. The thesis at hand addresses questions regarding the identification of processes with distinct process monitoring needs and opportunities (“process visibility fit”) as well as
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the outcome of process monitoring (“process visibility impact”). The question of “process visibility enablement” is out of scope of this thesis. However, future research should also investigate how to successfully implement process monitoring technologies and what are specific measures in different demand situations. The author is conducting such a research activity concerning process visibility enablement, which will contribute to answer these questions. Generally, we acknowledge the limitation that the close collaboration with our industry partner SAP bears the risk of biases. On the other side, we also see it as an opportunity to ensure the relevance of our work, and has enabled us to get data that would have been inaccessible without this collaboration. The data collection of the first study was done in only one company. Future work should enhance this data basis, which would allow comparison and benchmarking of processes or process groups across companies and industries based on our Process Visibility Fit Framework. However, process benchmarking would require a significant larger amount of survey responses, including assessment of the same processes in different contexts. In our Process Visibility Fit Framework we consider capabilities specially from a technological perspective. However, organizational capabilities like strategic alignment or leadership might be as well relevant counter measures for process visibility gaps. Accordingly, future research should study more intensively non-technological capabilities in the context of process monitoring. Furthermore, our evaluation of the measurement instrument of the Process Visibility Fit Framework identified some opportunities for slight improvements. Future applications of the instrument should adjust the corresponding survey items correspondingly. Although performance is the preferred dependent variable in BPM as well as in IPV research, process performance has many antecedent factors, and one factor (process visibility fit) can explain only a small proportion of its variance. Other process monitoring benefit dimensions that were identified in our second study could serve as alternative dependent variables for the evaluation of the fit framework in future research.
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Our qualitative approach in the second study provides preliminary evidence for the propositions of our Process Monitoring Benefits Framework and allowed an explorative identification of additional factors that influence process monitoring success. Now future research could test the suggested framework also with a quantitative approach and operationalize its constructs for this purpose. One limitation of the second study is that we examined the process monitoring benefits only in the context of IT service management (ITSM). This context is especially relevant for process monitoring and a stable context had advantages for the comparison of differences in monitoring related variables in different cases. However, for the generalizability of our findings it would be beneficial to study our propositions also in other contexts. Particularly, the influencing factors of process monitoring success might be context sensitive. Just as we identified outsourcing quality as important influencing factor in the ITSM context, there might additional factors in different process contexts. Future research should complement our findings regarding the impact of process visibility on situation awareness. This relation should be additionally investigated during process operation by observing process participants in critical situations (e.g. with help of experiments), because our approach to assess situation awareness in retrospect might be influenced by hindsight bias of the interviewees. This thesis discusses the impact of process visibility under performance related aspects on the process level. Potential process monitoring outcomes on other levels, for example the impact of process visibility on empowerment and control of process workers on the individual level (Berner et al. 2014), were not considered and could offer an additional research stream for future work. Furthermore, the two studies of this thesis address the need and the impact of process monitoring technologies. Project monitoring implementation projects were out of the scope of this thesis. In this regard, the author is executing an additional research activity that follows a design science approach to develop a method, which supports process monitoring implementation projects. It particularly focuses on the chartering phase of such projects, because this phase is highly relevant for implementation success, but is understudied in IS research (Berner et al. 2015). A systematic literature review of ES implementation literature and semi-structured expert interviews identified differences in
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the importance of certain success factors regarding implementation of process monitoring technology compared to other enterprise systems (Berner et al. 2015). From these situation-specific success factors design principles were derived, which can guide the creation of a process monitoring implementation method. For the design of such a method this research activity applies a situational method engineering approach (Brinkkemper 1996; Henderson-Sellers et al. 2014). Situational method engineering is a systematic approach to design methods for development and implementation of information systems by considering a particular context or project situation – like process monitoring (Brinkkemper 1996; Bucher et al. 2007; van DeWeerd et al. 2006).
5.5 Concluding Remarks Despite the limitations of our work, we believe to have made significant contributions to theory and practice. From a theoretical perspective, this thesis adds to the body of knowledge related to empirical BPM research in the important domain of process monitoring. Our suggested two frameworks can help guide future research in this area. From a practical perspective, our work offers an applicable instrument to classify business processes regarding their process visibility fit as a potential baseline for investment justifications. Furthermore, our studies propose several anchor points on how to increase the success of process monitoring in organizations. This doctoral thesis focuses exclusively on process monitoring. Other BPM key activities like process discovery or process redesign are out of the scope. However, we believe that our suggested research approaches, for example the creation of practical applicable process classification frameworks and the conceptualization as well as evaluation of BPM benefits, could be also transferred to other BPM activities. In this regard, we hope that our studies encourage researchers to conduct theoretical grounded, empirical research also in other BPM areas.
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Appendix A: Publications
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Appendix A: Publications Publications of the Author regarding Topics of this Thesis Berner, M., Augustine, J., and Maedche, A. 2016. “The Impact of Process Visibility on Process Performance: A Multiple Case Study of Operations Control Centers in ITSM,” Business & Information Systems Engineering (BISE) (58:1), pp. 31–42. Berner, M., Graupner, E., Maedche, A., and Mueller, B. 2012. “Process Visibility – Towards a Conceptualization and Research Themes,” Proceedings of the Thirty Third International Conference on Information Systems (ICIS), pp. 1-13. Berner, M., and Jegadeesan, H. 2014. “SAP Operational Process Intelligence: Empowering Line-of-Business Workers with Real-time Process Visibility,” in Proceedings of the BPM Demo Sessions 2014, Co-located with 12th International Conference on Business Process Management (BPM 2014), Eindhoven, pp. 1–5. Berner, M., and Maedche A., in progress. “Assessing Process Visibility Requirements and Capabilities: Development and Evaluation of a Fit Framework,” planned to be submitted to Business Process Management Journal. Graupner, E., Berner, M., Maedche, A., and Jegadeesan, H. 2014a. “Business Intelligence & Analytics for Processes – A Visibility Requirements Evaluation,” in Proceedings MKWI 2014 - Multikonferenz Wirtschaftsinformatik, Paderborn, February 26-28, 2014, Paderborn, pp. 154–166. Graupner, E., Berner, M., Maedche, A., and Jegadeesan, H. 2014b. “Assessing the Need for Visibility of Business Processes – A Process Visibility Fit Framework,” in 12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014, Springer Lectue Notes in Computer Science 8659, pp. 384–392.
Further Publications of the Author Berner, M., Graupner, E., and Maedche, A. 2014. “The Information Panopticon in the Big Data Era,” Journal of Organization Design (3:1), pp. 14-19. Berner, M., Gansen, J., and Maedche, A. 2015. “Design Principles for an Enterprise Systems Chartering Method,” in At the Vanguard of Design Science: First Impressions and Early Findings from Ongoing Research. 10th International Conference, DESRIST 2015. Dublin, Ireland, 20-22 May. B. Donnellan, R. Gleasure, M. Helfert, J. Kenneally, M. Rothenberger, M. Chiarini Tremblay, D. van der Meer, and R. Winter (eds.), pp. 117–124.
Appendix B: Supplements of Study One
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Appendix B: Supplements of Study One B1: Card Sorting with Practitioners22 Item
Correct Placement
Normalized Priority
Standard Deviation
6
0,52
0,21
6
0,44
0,34
5
0,46
0,30
5
0,45
0,24
5
0,44
0,22
5
0,33
0,25
4
0,33
0,35
4
0,31
0,35
Process steps and process output are difficult to predict (self-created based on construct definition from IPV). The process has many steps to perform (Mukhetha et al. 2010). The process is highly variable (self-created based on construct definition from IPV). For the different process instances, different methods or procedures are required to complete the work (Mani et al. 2006). Process execution can vary a lot from one instance of the process to another (Chang and Tien 2006). There are frequent exceptions expected to arise during process execution (Chang and Tien 2006). Process participants need to follow different process steps from one instance of the process to another (Chang and Tien 2006). The sequence of steps in the process are difficult to understand (Mani et al. 2006). Table 31:
22
Card Sorting Result of Item Pool “Process Variety”
Item pool creation and card sorting was supported by a master thesis (Kalidindi 2015).
Appendix B: Supplements of Study One
Item Process participants highly depend on others to obtain information (Chang and Tien 2006). The interdependencies of steps in the process is very high (Gribbins et al. 2006). The interdependence of process participants is high (self-created). Process participants highly depend on others in executing the process (Chang and Tien 2006). For a successful process execution, a functional unit must be in constant contact with other functional units (Gattiker and Goodhue 2005). There are high number of departments, functional roles, or information systems that are involved in a process (Sanders and Courtney 1985). The process is very dependent on other process and requires high coordination (Gattiker and Goodhue 2005). The process depends on other processes to obtain information (Karim et al. 2007). Close coordination between process participants is essential for process execution (Gattiker and Goodhue 2005). Frequent information exchanges between process participants happen during process execution (Gattiker and Goodhue 2005). The number of interfaces between process participants is high (self-created). Table 32:
XXXIV
Correct Placement
Normalized Priority
Standard Deviation
6
0,63
0,11
6
0,48
0,29
6
0,42
0,19
6
0,41
0,23
6
0,31
0,27
5
0,74
0,34
5
0,54
0,31
5
0,39
0,32
5
0,33
0,32
5
0,21
0,16
3
0,13
0,15
Card Sorting Result of Item Pool “Process Interdependence”
Appendix B: Supplements of Study One
Item Process is of strategic importance to our company (Yang et al. 2012). The process contributes highly to the competitiveness of our company (Mani et al. 2006). The process drives profitability (Barjis 2008). It would be very costly for our competitors to imitate the process (Tanriverdi et al. 2007). The process has a significant impact on overall firm performance (Chmielarz et al. 2013). The process contributes to create a distinctive brand proposition (Mani et al. 2006). The process enables growth in the organization (Barjis 2008). The process is highly important to the organization (Chmielarz et al. 2013). The process importance is only for few steps and not every step (Jeston and Nelis 2014). The process is very valuable to the company (Tanriverdi et al. 2007). It is very difficult for our competitors to replicate how we do the process (Tanriverdi et al. 2007). The process is common among our competitors (Tanriverdi et al. 2007). Table 33:
XXXV
Correct Placement
Normalized Priority
Standard Deviation
6
0,85
0,11
6
0,69
0,22
6
0,58
0,19
6
0,31
0,24
5
0,55
0,27
5
0,45
0,27
5
0,43
0,21
5
0,41
0,27
5
0,27
0,28
4
0,44
0,34
4
0,14
0,15
1
0,07
0,10
Card Sorting Result of Item Pool “Process Importance”
Appendix B: Supplements of Study One
XXXVI
B.2: Pilot Test Variable
Process Variety (VAR)
Process Interdependence (INT)
Process Importance (IMP) Table 34:
Variable
Item The process varies a lot from case to case. Process steps are difficult to predict as they can change from one instance of the process to another. Process participants need to follow different process steps from one instance of the process to another. Process execution can vary from one instance of the process to another. Process participants highly depend on other participants to get required resources or information. Process participants are highly dependent on other participants for process execution. Process execution requires very close coordination between process participants. The interdependencies between the steps in the process are high. The process is highly valuable to our company. The process is of high strategic importance to our company. The process adds a lot of value to our company. The process contributes significantly to the competitiveness of our company.
INT
IMP
0.713
0.276
0.218
0.469
0.137
0.190
0.816
0.260
0.181
0.893
0.536
0.331
0.369
0.793
0.195
0.260
0.829
0.248
0.480
0.887
0.493
0.399
0.878
0.398
0.091
0.266
0.847
0.314
0.352
0.894
0.140
0.197
0.765
0.473
0.395
0.878
GAT
ANA
DIS
0.742
0.685
0.549
0.881
0.795
0.524
0.848
0.664
0.538
0.932
0.850
0.602
0.829
0.900
0.708
0.759
0.865
0.564
0.709
0.837
0.498
0.698
0.803
0.563
0.680
0.738
0.732
0.375
0.413
0.870
0.383
0.369
0.727
Pilot Test Indicator Loadings of Process Visibility Requirements
Item (Graupner et al. 2015)
Our information systems enable us to integrate process information from all relevant data sources Process Our information systems enable us to capture granular Information (detailed) events in the entire process. Our information systems enable us to collect process Gathering information along the entire process in a timely manner. (GAT) Our information systems enable us to gather process information from all steps (activities) in the process. Our information systems enable us to aggregate process data into all relevant key performance indicators. Process information, such as process-level key performance Process indicators, are continuously calculated by our information Information systems. Analysis Based on preset levels (thresholds), our information systems (ANA) can automatically detect deviations from plans. Our information systems enable us to predict final results of the business process during process execution. Our information systems do notify the concerned process participants regarding all relevant events that may require Process adjustments. Information Process information is widely shared among process Dissemination participants. (DIS) Process information is distributed to process participants (e.g. operational decision makers) along the entire process.
Table 35:
VAR
Pilot Test Indicator Loadings of Process Visibility Capabilities
Appendix B: Supplements of Study One
B.3: PLS-SEM Model
Note: *** indicates that the path relation is significant at the p < 0.001 level. Figure 16: Overall PLS-SEM Results from SmartPLS
XXXVII
Appendix C: Supplements of Study Two
Appendix C: Supplements of Study Two C.1: Code System
Figure 17: Final Code System in MAXQDA
XXXVIII
Appendix C: Supplements of Study Two
XXXIX
C.2: Code Weightages (Excerpt of Codebook) Weightage
Definition
Description
1
very low
Weightage 1 should be assigned to a coded segment, if the statement is an indication that the related category (e.g. process performance) has a “very low” level.
2
low
Weightage 2 should be assigned to a coded segment, if the statement is an indication that the related category (e.g. process performance) has a “low” level.
3
medium
Weightage 3 should be assigned to a coded segment, if the statement is an indication that the related category (e.g. process performance) has a “medium” level.
4
high
Weightage 4 should be assigned to a coded segment, if the statement is an indication that the related category (e.g. process performance) has a “high” level.
5
very high
Weightage 5 should be assigned to a coded segment, if the statement is an indication that the related category (e.g. process performance) has a “very high” level.
0
not applicable
Weightage 0 should be assigned to a coded segment, if from the statement no conclusions can be made about the level of the related category (e.g. process performance).
Table 36:
Used Weightages for Code Segments in MAXQDA
Appendix C: Supplements of Study Two
XL
C.3: Questionnaire Semi-Structured Interviews 1) Interviewee a) Name? b) Role? c) Experience? 2) OCC Implementation Project a) What was the motivation for implementing an OCC in your company? b) When did you start the OCC implementation project? c) When did you go live with the OCC? d) What are your main challenges with the OCC? e) To what extent does the OCC implementation meet your expectations?
3) Visibility of Events and Alerts a) To which degree changed the visibility of events and alerts due to OCC implementation? b) What process information do you trust and what not? (What changed regarding information accuracy due to OCC implementation?) c) What process information do you miss? (What changed regarding information completeness due to OCC implementation?) d) What relevant process information do you get often too late? (What changed regarding information currency due to OCC implementation?) e) What process information is difficult to interpret or not usable for actions? (What changed regarding information format due to OCC implementation?) f) What relevant process information is difficult to access? (What changed regarding information accessibility due to OCC implementation?) g) In what situations should process information analysis be more flexible? (What changed regarding flexibility due to OCC implementation?) h) What systems or data sources should be monitored in addition? (What changed regarding information integration due to OCC implementation?)
4) Situation Awareness a) To what extent do you know what is going on? Do you detect all important events in the observed systems? b) Did you have recently incidents, where situations were misinterpreted? c) To what extent can you differentiate which detected alerts relate to primary/root causes and which are only side effects? d) To what extent can you predict the status of the observed systems for the near future? e) How would you rate the change coming from OCC on your situation awareness?
5) Bottleneck/Impediment Identification a) To what extent did the OCC introduction help to identify sources of recurring failures in the managed systems?
Appendix C: Supplements of Study Two
XLI
b) To what extent did the OCC introduction help to identify bottlenecks and impediments in the execution of the ITSM event management process?
6) Performance Outcomes a) To what extent did the OCC introduction impact stakeholder perception of ITSM regarding professionalism? b) To what extent did the OCC introduction impact SLA compliance of ITSM? c) To what extent did the OCC introduction enable IT employees to deliver prompt services? d) To what extent did the OCC introduction improve the knowledge of IT employees to answer customer questions? e) To what extent did the OCC introduction improve the reliability of the managed systems? f) To what extent did the OCC introduction improve the response time of the managed systems?
7) Skills and Knowledge of OCC Operators a) How many internal resources take care of the OCC b) Have you matched the skill levels of employees with the OCC activities c) How were the training programs conducted? (in detail) i) Classroom trainings ii) Self-learn opportunities iii) One-on-one knowledge transfer d) How do you rate the knowledge level of your OCC Operators i) Back office employees who create incidents from alerts ii) Experienced staff that is able to do some troubleshooting and problem solving. Doubles as second life support. iii) Experts who are able to do deep troubleshooting and problem solving
8) Continuous Improvement Culture a) To what extent does your organization regularly evaluate your ITSM processes for potential improvement? b) To what extent do your managers invite ideas to improve you ITSM processes? c) Do you have processes in place that drive continuous improvement? If yes, can you please describe it? d) Is there a designated owner for continuous improvement activities? e) How is improvement measured? f) Do you have top management support for change initiatives?
Appendix D: Reference to Data
XLII
Appendix D: Reference to Data The dataset of study 1 and the coding guide of study 2 are deposited in the Mannheim Research Data Repository (Link: https://madata.bib.uni-mannheim.de/185/).
Appendix E: Curriculum Vitae
XLIII
Appendix E: Curriculum Vitae Martin Berner 1985 – 1988
Wirtschaftsgymnasium, Böblingen Abschluss mit Abitur
1988 – 1990
Gustav-Werner-Stiftung, Seewald-Schernbach Zivildienst
1990 – 1995
Universität Mannheim Wirtschaftspädagogik mit Doppelwahlpflichtfach Wirtschaftsinformatik, Abschluss als Diplom-Handelslehrer
1995 – 1999
Integrata Unternehmensberatung GmbH, Frankfurt Consultant & Trainer (C++, OOA, UML, QM)
1999 – 2003
SAP Deutschland AG & Co. KG, Mannheim Senior Consultant (SAP CRM)
2003 – 2010
SAP AG, Walldorf Project Manager (SAP NetWeaver Mobile & CE)
2010 – 2015
SAP AG, Walldorf Vice President, Operations (HANA Cloud Integration, SAP NetWeaver BPM, PI and Gateway)
2012 – heute
Universität Mannheim Externer Doktorand an der Fakultät für Betriebswirtschaftslehre Institut für Enterprise Systems, Prof. Dr. Alexander Mädche
2016 – heute
SAP SE, Walldorf Vice President, Engineering Services (User Experience Platform)
