research articles on business process management

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• Published: 18 October 2016

Business process performance measurement: a structured literature review of indicators, measures and metrics

• Amy Van Looy   ORCID: orcid.org/0000-0002-7992-1528 1 &
• Aygun Shafagatova 1  

SpringerPlus volume  5 , Article number:  1797 ( 2016 ) Cite this article

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Measuring the performance of business processes has become a central issue in both academia and business, since organizations are challenged to achieve effective and efficient results. Applying performance measurement models to this purpose ensures alignment with a business strategy, which implies that the choice of performance indicators is organization-dependent. Nonetheless, such measurement models generally suffer from a lack of guidance regarding the performance indicators that exist and how they can be concretized in practice. To fill this gap, we conducted a structured literature review to find patterns or trends in the research on business process performance measurement. The study also documents an extended list of 140 process-related performance indicators in a systematic manner by further categorizing them into 11 performance perspectives in order to gain a holistic view. Managers and scholars can consult the provided list to choose the indicators that are of interest to them, considering each perspective. The structured literature review concludes with avenues for further research.

Since organizations endeavor to measure what they manage, performance measurement is a central issue in both the literature and in practice (Heckl and Moormann 2010 ; Neely 2005 ; Richard et al. 2009 ). Performance measurement is a multidisciplinary topic that is highly studied by both the management and information systems domains (business process management or BPM in particular). Different performance measurement models, systems and frameworks have been developed by academia and practitioners (Cross and Lynch 1988 ; Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Kueng 2000 ; Neely et al. 2000 ). While measurement models were initially limited to financial performance (e.g., traditional controlling models), a more balanced and integrated approach was needed beginning in the 1990s due to the challenges of the rapidly changing society and technology; this approach resulted in multi-dimensional models. Perhaps the best known multi-dimensional performance measurement model is the Balanced Scorecard (BSC) developed by Kaplan and Norton ( 1996 , 2001 ), which takes a four-dimensional approach to organizational performance: (1) financial perspective, (2) customer perspective, (3) internal business process perspective, and (4) “learning and growth” perspective. The BSC helps translate an organization’s strategy into operational performance indicators (also called performance measures or metrics) and objectives with targets for each of these performance perspectives. Even today, the BSC is by far the most used performance measurement approach in the business world (Bain Company 2015 ; Sullivan 2001 ; Ulfeder 2004 ).

Equally important for measuring an organization’s performance is process-oriented management or business process management (BPM), which is “about managing entire chains of events, activities and decisions that ultimately add value to the organization and its customers. These ‘chains of events, activities and decisions’ are called processes” (Dumas et al. 2013 : p. 1). In particular, an organization can do more with its current resources by boosting the effectiveness and efficiency of its way of working (i.e., its business processes) (Sullivan 2001 ). In this regard, academic research also suggests a strong link between business process performance and organizational performance, either in the sense of a causal relationship (Melville et al. 2004 ; Smith and Reece 1999 ) or as distinctive indicators that co-exist, as in the BSC (Kaplan and Norton 1996 , 2001 ).

Nonetheless, performance measurement models tend to give little guidance on how business (process) performance indicators can be chosen and operationalized (Shah et al. 2012 ). They are limited to mainly defining performance perspectives, possibly with some examples or steps to derive performance indicators (Neely et al. 2000 ), but without offering concrete indicators. Whereas fairly large bodies of research exist for both performance models and business processes, no structured literature review of (process) performance measurement has been carried out thus far. To the best of our knowledge, existing reviews cover one or another aspect of performance measurement; for instance, reviews on measurement models or evaluation criteria for performance indicators (Heckl and Moormann 2010 ; Neely 2005 ; Richard et al. 2009 ). Despite the considerable importance of a comprehensive and holistic approach to business (process) performance measurement, little is known regarding the state of the research on alternative performance indicators and their operationalization with respect to evaluating the performance of an organization’s work routines. To some extent, this lack of guidance can be explained by the fact that performance indicators are considered organization-dependent, given that strategic alignment is claimed by many measurement models such as the BSC (Kaplan and Norton 1996 , 2001 ). Although the selection of appropriate performance indicators is challenging for practitioners due to the lack of best practices, it is also highly relevant for performance measurement.

The gap that we are studying is the identification and, in particular, the concretization/operationalization of process-related performance indicators. This study enhances the information systems literature, which focuses on the design and development of measurement systems without paying much attention to essential indicators. To fill this gap, our study presents a structured literature review in order to describe the current state of business process performance measurement and related performance indicators. The choice to focus on the business process management (BPM) discipline is motivated by the close link between organizational performance and business process performance, as well as to ensure a clear scope (specifically targeting an organization’s way of working). Accordingly, the study addresses the following research questions.

RQ1. What is the current state of the research on business process performance measurement?

RQ2. Which indicators, measures and metrics are used or mentioned in the current literature related to business process performance?

The objective of RQ1 is to identify patterns in the current body of knowledge and to note weaknesses, whereas RQ2 mainly intends to develop an extended list of measurable process performance indicators, categorized into recognized performance perspectives, which can be tailored to diverse purposes. This list could, for instance, serve as a supplement to existing performance measurement models. Practitioners can use the list as a source for best practice indicators from academic research to find and select a subset of performance indicators that fit their strategy. The study will thus not address the development of specific measurement systems but rather the indicators to be used within such systems. To make our intended list system-independent, we will begin with the BSC approach and extend its performance perspectives. Given this generic approach, the research findings can also be used by scholars when building and testing theoretical models in which process performance is one of the factors that must be concretized.

The remainder of this article is structured as follows. “ Theoretical background ” section describes the theoretical background of performance measurement models and performance indicators. Next, the methodology for our structured literature review is detailed in “ Methods ” section. The subsequent sections present the results for RQ1 (“ Results for RQ1 ” section) and RQ2 (“ Results for RQ2 ” section). The discussion of the results in provided in “ Discussion ” section, followed by concluding comments (“ Conclusion ” section).

Theoretical background

This section addresses the concepts of performance measurement models and performance indicators separately in order to be able to differentiate them further in the study.

Performance measurement models

According to overviews in the performance literature (Heckl and Moormann 2010; Neely 2005 ; Richard et al. 2009 ), some of the most cited performance measurement models are the Balanced Scorecard (Kaplan and Norton 1996 , 2001 ), self-assessment excellence models such as the EFQM ( 2010 ), and the models by Cross and Lynch ( 1988 ), Kueng ( 2000 ) and Neely et al. ( 2000 ). A distinction should, however, be made between models focusing on the entire business (Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Cross and Lynch 1988 ) and models focusing on a single business process (Kueng 2000 ; Neely et al. 2000 ).

Organizational performance measurement models

Organizational performance measurement models typically intend to provide a holistic view of an organization’s performance by considering different performance perspectives. As mentioned earlier, the BSC provides four perspectives for which objectives and performance indicators ensure alignment between strategies and operations (Fig.  1 ) (Kaplan and Norton 1996 , 2001 ). Other organizational performance measurement models provide similar perspectives. For instance, Cross and Lynch ( 1988 ) offer a four-level performance pyramid: (1) a top level with a vision, (2) a second level with objectives per business unit in market and financial terms, (3) a third level with objectives per business operating system in terms of customer satisfaction, flexibility and productivity, and (4) a bottom level with operational objectives for quality, delivery, process time and costs. Another alternative view on organizational performance measurement is given in business excellence models, which focus on an evaluation through self-assessment rather than on strategic alignment, albeit by also offering performance perspectives. For instance, the EFQM ( 2010 ) distinguishes enablers [i.e., (1) leadership, (2) people, (3) strategy, (4) partnerships and resources, and (5) processes, products and services] from results [i.e., (1) people results, (2) customer results, (3) society results, and (4) key results], and a feedback loop for learning, creativity and innovation.

An overview of the performance perspectives in Kaplan and Norton ( 1996 , 2001 )

Since the BSC is the most used performance measurement model, we have chosen it as a reference model to illustrate the function of an organizational performance measurement model (Kaplan and Norton 1996 , 2001 ). The BSC is designed to find a balance between financial and non-financial performance indicators, between the interests of internal and external stakeholders, and between presenting past performance and predicting future performance. The BSC encourages organizations to directly derive (strategic) long-term objectives from the overall strategy and to link them to (operational) short-term targets. Concrete performance measures or indicators should be defined to periodically measure the objectives. These indicators are located on one of the four performance perspectives in Fig.  1 (i.e., ideally with a maximum of five indicators per perspective).

Table  1 illustrates how an organizational strategy can be translated into operational terms using the BSC.

During periodical measurements using the BSC, managers can assign color-coded labels according to actual performance on short-term targets: (1) a green label if the organization has achieved the target, (2) an orange label if it is almost achieved, or (3) a red label if it is not achieved. Orange and red labels thus indicate areas for improvement.

Furthermore, the BSC assumes a causal or logical relationship between the four performance perspectives. An increase in the competences of employees (i.e., performance related to “learning and growth”) is expected to positively affect the quality of products and services (i.e., internal business process performance), which in turn will lead to improved customer perceptions (i.e., customer performance). The results for the previous perspectives will then contribute to financial performance to ultimately realize the organization’s strategy, mission and vision (Kaplan and Norton 1996 , 2001 ). Hence, indicators belonging to the financial and customer perspectives are assumed to measure performance outcomes, whereas indicators from the perspectives of internal business processes and “learning and growth” are considered as typical performance drivers (Kaplan and Norton 2004 ).

Despite its widespread use and acceptance, the BSC is also criticized for appearing too general by managers who are challenged to adapt it to the culture of their organization (Butler et al. 1997 ) or find suitable indicators to capture the various aspects of their organization’s strategy (Shah et al. 2012 ; Vaivio 1999 ). Additionally, researchers question the choice of four distinct performance perspectives (i.e., which do not include perspectives related to inter-organizational performance or sustainability issues) (EFQM 2010 ; Hubbard 2009 , Kueng 2000 ). Further, the causal relationship among the BSC perspectives has been questioned (Norreklit 2000 ). To some degree, Kaplan and Norton ( 2004 ) responded to this criticism by introducing strategy maps that focus more on the causal relationships and the alignment of intangible assets.

Business process performance measurement models

In addition to organizational models, performance measurement can also focus on a single business process, such as statistical process control, workflow-based monitoring or process performance measurement systems (Kueng 2000 ; Neely et al. 2000 ). The approach taken in business process performance measurement is generally less holistic than the BSC. For instance, in an established BPM handbook, Dumas et al. ( 2013 ) position time, cost, quality and flexibility as the typical performance perspectives of business process performance measurement (Fig.  2 ). Similar to organizational performance measurement, concrete performance measures or indicators should be defined for each process performance perspective. In this sense, the established perspectives of Dumas et al. ( 2013 ) seem to further refine the internal business process performance perspective of the BSC.

An overview of the performance perspectives in Dumas et al. ( 2013 )

Neely et al. ( 2000 ), on the other hand, present ten steps to develop or define process performance indicators. The process performance measurement system of Kueng ( 2000 ) is also of high importance, which is visualized as a “goal and performance indicator tree” with five process performance perspectives: (1) financial view, (2) customer view, (3) employee view, (4) societal view, and (5) innovation view. Kueng ( 2000 ) thus suggests a more holistic approach towards process performance, similar to organizational performance, given the central role of business processes in an organization. He does so by focusing more on the different stakeholders involved in certain business processes.

Performance indicators

Section “ Performance measurement models ” explained that performance measurement models typically distinguish different performance perspectives for which performance indicators should be further defined. We must, however, note that we consider performance measures, performance metrics and (key) performance indicators as synonyms (Dumas et al. 2013 ). For reasons of conciseness, this work will mainly refer to performance indicators without mentioning the synonyms. In addition to a name, each performance indicator should also have a concretization or operationalization that describes exactly how it is measured and that can result in a value to be compared against a target. For instance, regarding the example in Table  1 , the qualitative statements to measure customer satisfaction constitute an operationalization. Nonetheless, different ways of operationalization can be applied to measure the same performance indicator. Since organizations can profit from reusing existing performance indicators and the related operationalization instead of inventing new ones (i.e., to facilitate benchmarking and save time), this work investigates which performance indicators are used or mentioned in the literature on business process performance and how they are operationalized.

Neely et al. ( 2000 ) and Richard et al. ( 2009 ) both present evaluation criteria for performance indicators (i.e., in the sense of desirable characteristics or review implications), which summarize the general consensus in the performance literature. First, the literature strongly agrees that performance indicators are organization-dependent and should be derived from an organization’s objectives, strategy, mission and vision. Secondly, consensus in the literature also exists regarding the need to combine financial and non-financial performance indicators. Nonetheless, disagreement still seems to exist in terms of whether objective and subjective indicators need to be combined, with objective indicators preferred by most advocates. Although subjective (or quasi-objective) indicators face challenges from bias, their use has some advantages; for instance, to include stakeholders in an assessment, to address latent constructs or to facilitate benchmarking when a fixed reference point is missing (Hubbard 2009 ; Richard et al. 2009 ). Moreover, empirical research has shown that subjective (or quasi-objective) indicators are more or less correlated with objective indicators, depending on the level of detail of the subjective question (Richard et al. 2009 ). For instance, a subjective question can be made more objective by using clear definitions or by selecting only well-informed respondents to reduce bias.

We conducted a structured literature review (SLR) to find papers dealing with performance measurement in the business process literature. SLR can be defined as “a means of evaluating and interpreting all available research relevant to a particular research question, topic area, or phenomenon of interest” (Kitchenham 2007 : p. vi). An SLR is a meta study that identifies and summarizes evidence from earlier research (King and He 2005 ) or a way to address a potentially large number of identified sources based on a strict protocol used to search and appraise the literature (Boellt and Cecez-Kecmanovic 2015 ). It is systematic in the sense of a systematic approach to finding relevant papers and a systematic way of classifying the papers. Hence, according to Boellt and Cecez-Kecmanovic ( 2015 ), SLR as a specific type of literature review can only be used when two conditions are met. First, the topic should be well-specified and closely formulated (i.e., limited to performance measurement in the context of business processes) to potentially identify all relevant literature based on inclusion and exclusion criteria. Secondly, the research questions should be answered by extracting and aggregating evidence from the identified literature based on a high-level summary or bibliometric-type of content analysis. Furthermore, King and He ( 2005 ) also refer to a statistical analysis of existing literature.

Informed by the established guidelines proposed by Kitchenham ( 2007 ), we undertook the review in distinct stages: (1) formulating the research questions and the search strategy, (2) filtering and extracting data based on inclusion and exclusion criteria, and (3) synthesizing the findings. The remainder of this section describes the details of each stage.

Formulating the research questions and search strategy

A comprehensive and unbiased search is one of the fundamental factors that distinguish a systematic review from a traditional literature review (Kitchenham 2007 ). For this purpose, a systematic search begins with the identification of keywords and search terms that are derived from the research questions. Based on the research questions stipulated in the introduction, the SLR protocol (Boellt and Cecez-Kecmanovic 2015 ) for our study was defined, as shown in Table  2 .

The ISI Web of Science (WoS) database was searched using predetermined search terms in November 2015. This database was selected because it is used by many universities and results in the most outstanding publications, thus increasing the quality of our findings. An important requirement was that the papers focus on “business process*” (BP). This keyword was used in combination with at least one of the following: (1) “performance indicator*”, (2) “performance metric*”, (3) “performance measur*”. All combinations of “keyword in topic” (TO) and “keyword in title” (TI) have been used.

Table  3 shows the degree to which the initial sample sizes varied, with 433 resulting papers for the most permissive search query (TOxTO) and 19 papers for the most restrictive one (TIxTI). The next stage started with the most permissive search query in an effort to select and assess as many relevant publications as possible.

Filtering and extracting data

Figure  3 summarizes the procedure for searching and selecting the literature to be reviewed. The list of papers found in the previous stage was filtered by deleting 35 duplicates, and the remaining 398 papers were further narrowed to 153 papers by evaluating their title and abstract. After screening the body of the texts, 76 full-text papers were considered relevant for our scope and constituted the final sample (“Appendix 1 ”).

Exclusion of papers and number of primary studies

More specifically, studies were excluded if their main focus was not business process performance measurement or if they did not refer to indicators, measures or metrics for business performance. The inclusion of studies was not restricted to any specific type of intervention or outcome. The SLR thus included all types of research studies that were written in English and published up to and including November 2015. Furthermore, publication by peer-reviewed publication outlets (e.g., journals or conference proceedings) was considered as a quality criterion to ensure the academic level of the research papers.

Synthesizing the findings

The analysis of the final sample was performed by means of narrative and descriptive analysis techniques. For RQ1, the 76 papers were analyzed on the basis of bibliometric data (e.g., publication type, publication year, geography) and general performance measurement issues by paying attention to the methodology and focus of the study. Details are provided in “Appendix 2 ”.

For RQ2, all the selected papers were screened to identify concrete performance indicators in order to generate a comprehensive list or checklist. The latter was done in different phases. In the first phase, the structured literature review allowed us to analyze which performance indicators are mainly used in the process literature and how they are concretized (e.g., in a question or mathematical formulation), resulting in an unstructured list of potential performance indicators. The indicators were also synthesized by combining similar indicators and rephrasing them into more generic terms.

The next phase was a comparative study to categorize the output of phase 1 into the commonly used measurement models in the performance literature (see “ Theoretical background ” section). For the purpose of this study, we specifically looked for those organizational performance models, mentioned in “ Theoretical background ” section, that are cited the most and that suggest categories, dimensions or performance perspectives that can be re-used (Kaplan and Norton 1996 , 2001 ; EFQM 2010 ; Cross and Lynch 1988 ; Kueng 2000 ). Since the BSC (Kaplan and Norton 1996 , 2001 ) is the most commonly used of these measurement models, we began with the BSC as the overall framework to categorize the observed indicators related to business (process) performance, supplemented with an established view on process performance from the process literature (Dumas et al. 2013 ). Subsequently, a structured list of potential performance indicators was obtained.

In the third and final phase, an evaluation study was performed to validate whether the output of phase 2 is sufficiently comprehensive according to other performance measurement models, i.e., not included in our sample and differing from the most commonly used performance measurement models. Therefore, we investigated the degree to which our structured list covers the items in two variants or concretizations of the BSC. Hence, a validation by other theoretical models is provided. We note that a validation by subject-matter experts is out of scope for a structured literature review but relates to an opportunity for further research.

Results for RQ1

The final sample of 76 papers consists of 46 journal papers and 30 conference papers (Fig.  4 ), indicating a wide variety of outlets to reach the audience via operations and production-related journals in particular or in lower-ranked (Recker 2013 ) information systems journals.

The distribution of the sampled papers per publication type (N = 76)

When considering the chronological distribution of the sampled papers, Fig.  5 indicates an increase in the uptake of the topic in recent years, particularly for conference papers but also for journal publications since 2005.

The chronological distribution of the sampled papers per publication type (N = 76)

This uptake seems particularly situated in the Western world and Asia (Fig.  6 ). The countries with five or more papers in our sample are Germany (12 papers), the US (6 papers), Spain (5 papers), Croatia (5 papers) and China (5 papers). Figure  6 shows that business process performance measurement is a worldwide topic, with papers across the different continents. Nonetheless, a possible explanation for the higher coverage in the Western world could be due to its long tradition of measuring work (i.e., BSC origins).

The geographical distribution of the sampled papers per continent, based on a paper’s first author (N = 76)

The vast majority of the sampled papers address artifacts related to business (process) performance measurement. When looking at the research paradigm in which the papers are situated (Fig.  7 ), 71 % address design-science research, whereas 17 % conduct research in behavioral science and 12 % present a literature review. This could be another explanation for the increasing uptake in the Western world, as many design-science researchers are from Europe or North America (March and Smith 1995 ; Peffers et al. 2012 ).

The distribution of the sampled journal papers per research paradigm (N = 76)

Figure  8 supplements Fig.  7 by specifying the research methods used in the papers. For the behavioral-science papers, case studies and surveys are equally used. The 54 papers that are situated within the design-science paradigm explicitly refer to models, meta-models, frameworks, methods and/or tools. When mapping these 54 papers to the four artifact types of March and Smith ( 1995 ), the vast majority present (1) methods in the sense of steps to perform a task (e.g., algorithms or guidelines for performance measurement) and/or (2) models to describe solutions for the topic. The number of papers dealing with (3) constructs or a vocabulary and/or (4) instantiations or tools is much more limited, with 14 construct-related papers and 9 instantiations in our sample. We also looked at which evaluation methods, defined by Peffers et al. ( 2012 ), are typically used in the sampled design-science papers. While 7 of the 54 design-science papers do not seem to report on any evaluation effort, our sample confirms that most papers apply one or another evaluation method. Case studies and illustrative scenarios appear to be the most frequently used methods to evaluate design-science research on business (process) performance measurement.

The distribution of the sampled journal papers per research method (N = 76)

The sampled design-science research papers typically build and test performance measurement frameworks, systems or models or suggest meta-models and generic templates to integrate performance indicators into the process models of an organization. Such papers can focus on the process level, organizational level or even cross-organizational level. Nonetheless, the indicators mentioned in those papers are illustrative rather than comprehensive. An all-inclusive list of generic performance indicators seems to be missing. Some authors propose a set of indicators, but those indicators are specific to a certain domain or sector instead of being generic. For instance, Table  4 shows that 36 of the 76 sampled papers are dedicated to a specific domain or sector, such as technology-related aspects or supply chain management.

Furthermore, the reviewed literature was analyzed with regard to its (1) scope, (2) functionalities, (3) terminology, and (4) foundations.

Starting with scope, it is observed that nearly two-thirds of the sampled papers can be categorized as dealing with process-oriented performance measurement, whereas one-third focuses more on general performance measurement and management issues. Nonetheless, most of the studies of process performance also include general performance measurement as a supporting concept. A minor cluster of eight research papers specifically focuses on business process reengineering and measurement systems to evaluate the results of reengineering efforts. Furthermore, other researchers focus on the measurement and assessment of interoperability issues and supply chain management measurements.

Secondly, while analyzing the literature, two groups of papers were identified based on their functionalities: (1) focusing on performance measurement systems or frameworks, and (2) focusing on certain performance indicators and their categorization. Regarding the first group, it should be mentioned that while the process of building or developing a performance measurement system (PMS) or framework is well-researched, only a small number of papers explicitly address process performance measurement systems (PPMS). The papers in this first group typically suggest concrete steps or stages to be followed by particular organizations or discuss the conceptual characteristics and design of a performance measurement system. Regarding the second group of performance indicators, we can differentiate two sub-groups. Some authors focus on the process of defining performance indicators by listing requirements or quality characteristics that an indicator should meet. However, many more authors are interested in integrating performance indicators into the process models or the whole architecture of an organization, and they suggest concrete solutions to do so. Compared to the first group of papers, this second group deals more with the categorization of performance indicators into domains (financial/non-financial, lag/lead, external/internal, BSC dimensions) or levels (strategic, tactical, operational).

Thirdly, regarding terminology, different terms are used by different authors to discuss performance measurement. Performance “indicator” is the most commonly used term among the reviewed papers. For instance, it is frequently used in reference to a key performance indicator (KPI), a KPI area or a performance indicator (PI). The concept of a process performance indicator (PPI) is also used, mainly in the process-oriented literature. Performance “measure” is another prevalent term in the papers. The least-used term is performance “metric” (i.e., in only nine papers). Although the concepts of performance indicators, measures and metrics are used interchangeably throughout most of the papers, the concepts are sometimes defined in different ways. For instance, paper 17 defines a performance indicator as a metric, and paper 49 defines a performance measure as an indicator. On the other hand, paper 7 defines a performance indicator as a set of measures. Yet another perspective is taken in paper 74, which defines a performance measure as “a description of something that can be directly measured (e.g., number of reworks per day)”, while defining a performance indicator as “a description of something that is calculated from performance measures (e.g., percentage reworks per day per direct employee” (p. 386). Inconsistencies exist not only in defining indicators but also in describing performance goals. For instance, some authors include a sign (e.g., minus or plus) or a verb (e.g., decrease or increase) in front of an indicator. Other authors attempt to describe performance goals in a SMART way—for instance, by including a time indication (e.g., “within a certain period”) and/or target (e.g., “5 % of all orders”)—whereas most of the authors are less precise. Hence, a great degree of ambiguity exists in the formulation of performance objectives among to the reviewed papers.

Finally, regarding the papers’ foundations, “ Performance measurement models ” section already indicated that the BSC plays an important role in the general literature on performance management systems (PMS), while Kueng ( 2000 ) also offers influential arguments on process performance measurement systems (PPMS). In our literature review, we observed that the BSC was mentioned in 43 of the 76 papers and that the results of 19 papers were mainly based on the BSC (Fig.  9 ). This finding provides additional evidence that the BSC can be considered the most frequently used performance model in academia as well. However, the measurement model of Kueng ( 2000 ) was also mentioned in the sampled papers on PPMS, though less frequently (i.e., in six papers).

The importance of the BSC according to the sampled papers (N = 76)

Interestingly, the BSC is also criticized by the sampled papers for not being comprehensive; for instance, due to the exclusion of environmental aspects, supply chain management aspects or cross-organizational processes. In response, some of the sampled papers also define sector-specific BSC indicators or suggest additional steps or indicators to make the process or business more sustainable (see Table  4 ). Nonetheless, the majority of the papers agree on the need for integrated and multidimensional measurement systems, such as the BSC, and on the importance of directly linking performance measurement to an organization’s strategy. However, while these papers mention the required link with strategy, the prioritization of indicators according to their strategic importance has been studied very little thus far.

Results for RQ2

For RQ2, the sampled papers were reviewed to distinguish papers with performance indicators from papers without performance indicators. A further distinction was made between indicators found with operationalization (i.e., concretization by means of a question or formula) and those without operationalization. We note that for many indicators, no operationalization was available. We discovered that only 30 of the 76 sampled papers contained some type of performance indicator (namely 3, 5, 6, 7, 11, 16, 17, 18, 20, 22, 26, 27, 30, 35, 37, 40, 43, 46, 49, 51, 52, 53, 55, 57, 58, 59, 60, 66, 71, 73). In total, approximately 380 individual indicators were found throughout all the sampled papers (including duplicates), which were combined based on similarities and modified to use more generic terms. This resulted in 87 indicators with operationalization (“Appendix 3 ”) and 48 indicators without operationalization (“Appendix 4 ”).

The 87 indicators with operationalization were then categorized according to the four perspectives of the BSC (i.e., financial, customer, business processes, and “learning and growth”) (Kaplan and Norton 1996 , 2001 ) and the four established dimensions of process performance (i.e., time, cost, quality, and flexibility) (Dumas et al. 2013 ). In particular, based in the identified indicators, we revealed 11 sub-perspectives within the initial BSC perspectives to better emphasize the focus of the indicators and the different target groups (Table  5 ): (1) financial performance for shareholders and top management, (2) customer-related performance, (3) supplier-related performance, (4) society-related performance, (5) general process performance, (6) time-related process performance, (7) cost-related process performance, (8) process performance related to internal quality, (9) flexibility-related process performance, (10) (digital) innovation performance, and (11) employee-related performance.

For reasons of objectivity, the observed performance indicators were assigned to a single perspective starting from recognized frameworks (Kaplan and Norton 1996 , 2001 ; Dumas et al. 2013 ). Bias was further reduced by following the definitions of Table  5 . Furthermore, the authors of this article first classified the indicators individually and then reached consensus to obtain a more objective categorization.

Additional rationale for the identification of 11 performance perspectives is presented in Table  6 , which compares our observations with the perspectives adopted by the most commonly used performance measurement models (see “ Theoretical background ” section). This comparison allows us to highlight similarities and differences with other respected models. In particular, Table  6 shows that we did not observe a dedicated perspective for strategy (EFQM 2010 ) and that we did not differentiate between financial indicators and market indicators (Cross and Lynch 1988 ). Nonetheless, the similarities in Table  6 prevail. For instance, Cross and Lynch ( 1988 ) also acknowledge different process dimensions. Further, Kueng ( 2000 ) and the EFQM ( 2010 ) also differentiate employee performance from innovation performance, and they both add a separate perspective for results related to the entire society.

Figure  10 summarizes the number of performance indicators that we identified in the process literature per observed performance perspective. Not surprisingly, the initial BSC perspective of internal business process performance contains most of the performance indicators: 29 of 87 indicators. However, the other initial BSC perspectives are also covered by a relatively high number of indicators: 16 indicators for both financial performance and customer-related performance and 26 indicators for “learning and growth”. This result confirms the close link between process performance and organizational performance, as mentioned in the introduction.

The number of performance indicators with operationalization per performance perspective

A more detailed comparison of the perspectives provides interesting refinements to the state of the research. More specifically, Fig.  10 shows that five performance perspectives have more than ten indicators in the sample, indicating that academic research focuses more on financial performance for shareholders and top management and performance related to customers, process time, innovation and employees. On the other hand, fewer than five performance indicators were found in the sample for the perspectives related to suppliers, society, process costs and process flexibility, indicating that the literature focuses less on those perspectives. The latter remains largely overlooked by academic research, possibly due to the newly emerging character of these perspectives.

We must, however, note that the majority of the performance indicators are mentioned in only a few papers. For instance, 59 of the 87 indicators were cited in a single paper, whereas the remainder are mentioned in more than one paper. Eleven performance indicators are frequently mentioned in the process literature (i.e., by five or more papers). These indicators include four indicators of customer-related performance (i.e., customer complaints, perceived customer satisfaction, query time, and delivery reliability), three indicators of time-related process performance (i.e., process cycle time, sub-process turnaround time, and process waiting time), one cost-related performance indicator (i.e., process cost), two indicators of process performance related to internal quality (i.e., quality of internal outputs and deadline adherence), and one indicator of employee performance (i.e., perceived employee satisfaction).

Consistent with “ Performance indicators ” section, the different performance perspectives are a combination of financial or cost-related indicators with non-financial data. The latter also take the upper hand in our sample. Furthermore, the sample includes a combination of objective and subjective indicators, and the vast majority are objective indicators. Only eight indicators explicitly refer to qualitative scales; for instance, to measure the degree of satisfaction of the different stakeholder groups. For all the other performance indicators, a quantifiable alternative is provided.

It is important to remember that a distinction was made between the indicators with operationalization and those without operationalization. The list of 87 performance indicators, as given in “Appendix 3 ”, can thus be extended with those indicators for which operationalization is missing in the reviewed literature. Specifically, we found 48 additional performance indicators (“Appendix 4 ”) that mainly address supplier performance, process performance related to costs and flexibility, and the employee-related aspects of digital innovation. Consequently, this structured literature review uncovered a total of 135 performance indicators that are directly or indirectly linked to business process performance.

Finally, the total list of 135 performance indicators was evaluated for its comprehensiveness by comparing the identified indicators with other BSC variants that were not included in our sample. More specifically, based on a random search, we looked for two BSC variants in the Web of Science that did not fit the search strategy of this structured literature review: one that did not fit the search term of “business process*” (Hubbard 2009 ) and another that did not fit any of the performance-related search terms of “performance indicator*”, “performance metric*” or “performance measur*” (Bronzo et al. 2013 ). These two BSC variants cover 30 and 17 performance indicators, respectively, and are thus less comprehensive than the extended list presented in this study. Most of the performance indicators suggested by the two BSC variants are either directly covered in our findings or could be derived after recalculations. Only five performance indicators could not be linked to our list of 135 indicators, and these suggest possible refinements regarding (1) the growth potential of employees, (2) new markets, (3) the social performance of suppliers, (4) philanthropy, or (5) industry-specific events.

This structured literature review culminated in an extended list of 140 performance indicators: 87 indicators with operationalization, 48 indicators without operationalization and 5 refinements derived from two other BSC variants. The evaluation of our findings against two BSC variants validated our work in the sense that we present a more exhaustive list of performance indicators, with operationalization for most, and that only minor refinements could be added. However, the comprehensiveness of our findings can be claimed only to a certain extent given the limitations of our predefined search strategy and the lack of empirical validation by subject-matter experts or organizations. Notwithstanding these limitations, conclusions can be drawn from the large sample of 76 papers to respond to the research questions (RQs).

Regarding RQ1 on the state of the research on business process performance measurement, the literature review provided additional evidence for the omnipresence of the BSC. Most of the sampled papers mentioned or used the BSC as a starting point and basis for their research and analysis. The literature study also showed a variety of research topics, ranging from behavioral-science to design-science research and from a focus on performance measurement models to a focus on performance indicators. In addition to inconsistencies in the terminology used to describe performance indicators and targets, the main weakness uncovered in this literature review deals with the concretization of performance indicators supplementing performance measurement systems. The SLR results suggest that none of the reviewed papers offers a comprehensive measurement framework, specifically one that includes and extends the BSC perspectives, is process-driven and encompasses as many concrete performance indicators as possible. Such a comprehensive framework could be used as a checklist or a best practice for reference when defining specific performance indicators. Hence, the current literature review offers a first step towards such a comprehensive framework by means of an extended list of possible performance indicators bundled in 11 performance perspectives (RQ2).

Regarding RQ2 on process performance indicators, the literature study revealed that scholars measure performance in many different ways and without sharing much detail regarding the operationalization of the measurement instruments, which makes a comparison of research results more difficult. As such, the extended list of performance indicators is our main contribution and fills a gap in the literature by providing a detailed overview of performance indicators mentioned or used in the literature on business process performance. Another novel aspect is that we responded to the criticism of missing perspectives in the original BSC (EFQM 2010 ; Hubbard 2009 ; Kueng 2000 ) and identified the narrow view of performance typically taken in the process literature (Dumas et al. 2013 ). Figures  1 and 2 are now combined and extended in a more exhaustive way, namely by means of more perspectives than are offered by other attempts (Table  6 ), by explicitly differentiating between performance drivers (or lead indicators) and performance outcomes (or lag indicators), and by considering concrete performance indicators.

Our work also demonstrated that all perspectives in the BSC (Kaplan and Norton 1996 , 2001 ) relate to business process performance to some degree. In other words, while the BSC is a strategic tool for organizational performance measurement, it is actually based on indicators that originate from business processes. More specifically, in addition to the perspective of internal business processes, the financial performance perspective typically refers to sales or revenues gained while doing business, particularly after executing business processes. The customer perspective relates to the implications of product or service delivery, specifically to the interactions throughout business processes, whereas the “learning and growth” perspective relates to innovations in the way of working (i.e., business processes) and the degree to which employees are prepared to conduct and innovate business processes. The BSC, however, does not present sub-perspectives and thus takes a more high-level view of performance. Hence, the BSC can be extended based on other categorizations made in the reviewed literature; for instance, related to internal/external, strategic/operational, financial/non-financial, or cost/time/quality/flexibility.

Therefore, this study refined the initial BSC perspectives into eleven performance perspectives (Fig.  11 ) by applying three other performance measurement models (Cross and Lynch 1988 ; EFQM 2010 ; Kueng 2000 ) and the respected Devil’s quadrangle for process performance (Dumas et al. 2013 ). Additionally, a more holistic view of business process performance can be obtained by measuring each performance perspective of Fig.  11 than can be achieved by using the established dimensions of time, cost, quality and flexibility as commonly proposed in the process literature (Dumas et al. 2013 ). As such, this study demonstrated a highly relevant synergy between the disciplines of process management, organization management and performance management.

An overview of the observed performance perspectives in the business process literature

We also found out that not all the performance perspectives in Fig.  11 are equally represented in the studied literature. In particular, the perspectives related to suppliers, society, process costs and process flexibility seem under-researched thus far.

The eleven performance perspectives (Fig.  11 ) can be used by organizations and scholars to measure the performance of business processes in a more holistic way, considering the implications for different target groups. For each perspective, performance indicators can be selected that fit particular needs. Thus, we do not assert that every indicator in the extended list of 140 performance indicators should always be measured, since “ Theoretical background ” section emphasized the need for organization-dependent indicators aligned with an organization’s strategy. Instead, our extended list can be a starting point for finding and using appropriate indicators for each performance perspective, without losing much time reflecting on possible indicators or ways to concretize those indicators. Similarly, the list can be used by scholars, since many studies in both the process literature and management literature intend to measure the performance outcomes of theoretical constructs or developed artifacts.

Consistent with the above, we acknowledge that the observed performance indicators originate from different models and paradigms or can be specific to certain processes or sectors. Since our intention is to provide an exhaustive list of indicators that can be applied to measure business process performance, the indicators are not necessarily fully compatible. Instead, our findings allow the recognition of the role of a business context (i.e., the peculiarities of a business activity, an organization or other circumstances). For instance, a manufacturing organization might choose different indicators from our list than a service or non-profit organization (e.g., manufacturing lead time versus friendliness, or carbon dioxide emission versus stakeholder satisfaction).

Another point of discussion is dedicated to the difference between the performance of specific processes (known as “process performance”) and the performance of the entire process portfolio (also called “BPM performance”). While some indicators in our extended list clearly go beyond a single process (e.g., competence-related indicators or employee absenteeism), it is our opinion that the actual performance of multiple processes can be aggregated to obtain BPM performance (e.g., the sum of process waiting times). This distinction between (actual) process performance and BPM performance is useful; for instance, for supplementing models that try to predict the (expected) performance based on capability development, such as process maturity models (e.g., CMMI) and BPM maturity models (Hammer 2007 ; McCormack and Johnson 2001 ). Nonetheless, since this study has shown a close link between process performance, BPM performance, and organizational performance, it seems better to refer to different performance perspectives than to differentiate between such performance types.

In future research, the comprehensiveness of the extended list of performance indicators can be empirically validated by subject-matter experts. Additionally, case studies can be conducted in which organizations apply the list as a supplement to performance measurement models in order to facilitate the selection of indicators for their specific business context. The least covered perspectives in the academic research also seem to be those that are newly emerging (namely, the perspectives related to close collaboration with suppliers, society/sustainability and process flexibility or agility), and these need more attention in future research. Another research avenue is to elaborate on the notion of a business context; for instance, by investigating what it means to have a strategic fit (Venkatraman 1989 ) in terms of performance measurement and which strategies (Miller and Friesen 1986 ; Porter 2008 ; Treacy and Wiersema 1993 ) are typically associated with which performance indicators. Additionally, the impact of environmental aspects, such as market velocity (Eisenhardt and Martin 2000 ), on the choice of performance indicators can be taken into account in future research.

Business quotes such as “If you cannot measure it, you cannot manage it” or “What is measured improves” (P. Drucker) are sometimes criticized because not all important things seem measurable (Ryan 2014 ). Nonetheless, given the perceived need of managers to measure their business and the wide variety of performance indicators (i.e., ranging from quantitative to qualitative and from financial to non-financial), this structured literature review has presented the status of the research on business process performance measurement. This structured approach allowed us to detect weaknesses or inadequacies in the current literature, particularly regarding the definition and concretization of possible performance indicators. We continued by taking a holistic view of the categorization of the observed performance indicators (i.e., measures or metrics) into 11 performance perspectives based on relevant performance measurement models and established process performance dimensions.

The identified performance indicators within the 11 perspectives constitute an extended list from which practitioners and researchers can select appropriate indicators depending on their needs. In total, the structured literature review resulted in 140 possible performance indicators: 87 indicators with operationalization, 48 additional indicators that need further concretization, and 5 refinements based on other Balanced Scorecard (BSC) variants. As such, the 11 performance perspectives with related indicators can be considered a conceptual framework that was derived from the current process literature and theoretically validated by established measurement approaches in organization management.

Future research can empirically validate the conceptual framework by involving subject-matter experts to assess the comprehensiveness of the extended list and refine the missing concretizations, and by undertaking case studies in which the extended list can be applied by specific organizations. Other research avenues exist to investigate the link between actual process performance and expected process performance (as measured in maturity models) or the impact of certain strategic or environmental aspects on the choice of specific performance indicators. Such findings are needed to supplement and enrich existing performance measurement systems.

Abbreviations

behavioral science

business process management

balanced scorecard

design-science

research question

structured literature review

keyword in topic

keyword in title

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Authors’ contributions

AVL initiated the conception and design of the study, while AS was responsible for the collection of data (sampling) and identification of performance indicators. The analysis and interpretation of the data was conducted by both authors. AVL was involved in drafting and coordinating the manuscript, and AS in reviewing it critically. Both authors read and approved the final manuscript.

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We thank American Journal Experts (AJE) for English language editing.

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Appendix 2: The mapping of the structured literature review

The mapping details per sampled paper can be found here.

https://drive.google.com/file/d/0B_2VpjwsRLrlRHhfRHJ4ZFBWdEE/view?usp=sharing .

See Table  8 .

See Table  9 .

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Van Looy, A., Shafagatova, A. Business process performance measurement: a structured literature review of indicators, measures and metrics. SpringerPlus 5 , 1797 (2016). https://doi.org/10.1186/s40064-016-3498-1

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The research field of Business Process Management (BPM) has gradually developed as a discipline situated within the computer, management and information systems sciences. Its evolution has been shaped by its own conference series, the BPM conference. Still, as with any other academic discipline, debates accrue and persist, which target the identity as well as the quality and maturity of the BPM field. In this paper, we contribute to the debate on the identity and progress of the BPM conference research community through an analysis of the BPM conference proceedings. We develop an understanding of signs of progress of research presented at this conference, where, how, and why papers in this conference have had an impact, and the most appropriate formats for disseminating influential research in this conference. Based on our findings from this analysis, we provide conclusions about the state of the conference series and develop a set of recommendations to further develop the conference community in terms of research maturity, methodological advance, quality, impact, and progression.

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Towards successful business process improvement – An extension of change acceleration process model

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Business and Engineering Management, Sir Syed CASE Institute of Technology, Islamabad, Pakistan

Roles Formal analysis, Methodology, Validation, Writing – review & editing

Affiliation School of Management (AUSOM), Air University, Islamabad, Pakistan

Roles Software, Visualization

Affiliation Department of Engineering Management, NUST College of E&ME, Islamabad, Pakistan

Roles Supervision

Affiliation Faculty of Management Sciences, Foundation University Islamabad, Islamabad, Pakistan

• Maha Syed Ibrahim, 
• Aamer Hanif, 
• Faheem Qaisar Jamal, 

• Published: November 27, 2019
• https://doi.org/10.1371/journal.pone.0225669
• Peer Review
• Reader Comments

Change Acceleration Process model (CAP) emerged in early 90's as a set of principles for accelerating change management efforts in organizations. Business Process Improvement (BPI) projects open avenues of opportunity and success for organizations in this highly competitive era. However, most of these projects fail due to lack of commitment, communication, scope creep and inadequate resources. This research attempts to study industry relevant factors most critical to success of a BPI Project in the highly competitive telecom sector. Modified Delphi technique employing a panel of telecom professionals was adopted in order to determine the critical success factors (CSFs) after a thorough review of the literature. Exploratory factor analysis was performed to map extracted factors to the five agents of change. Research outcome defines the relevant CSFs in terms of vision, skills, incentives, resources and action plan. A significant contribution of this research is an extended CAP model for implementation of BPI projects. Practical implications of this research are utilization of the proposed model for BPI project success.

Citation: Syed Ibrahim M, Hanif A, Jamal FQ, Ahsan A (2019) Towards successful business process improvement – An extension of change acceleration process model. PLoS ONE 14(11): e0225669. https://doi.org/10.1371/journal.pone.0225669

Editor: Amira M. Idrees, Fayoum University Faculty of Computers and Information, EGYPT

Received: July 16, 2019; Accepted: November 8, 2019; Published: November 27, 2019

Copyright: © 2019 Syed Ibrahim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Business Process Management (BPM) is the science that ensures consistent outcomes and the practice to seize improvement opportunities by overseeing performance of cross-functional work in organizations [ 1 ]. This highly challenging era demands organizations to improve continuously just to stay competitive in the run. It is a perennial responsibility of the management to analyze their business processes and improve them to be more efficient and productive. Complex nature of the business environment demands rapid and significant changes. To survive in such environments, managers are compelled to respond to these changes swiftly by revising their business processes. The identification and improvement of business processes comes under the umbrella of BPM. Telecommunication industry is a rapidly growing industry worldwide and faces many challenges. This requires these organizations to be more responsive to change [ 2 ]. Advancement in management sciences have brought various tools and techniques which help organizations to be more receptive and adaptive e.g. Business Process Improvement (BPI) lets organizations to improve their business processes gradually and continuously [ 3 ]. Successful implementation of BPI interventions is really challenging, as a BPI project demands attention from various business functions making it an expensive proposition. It has been seen that 60–70% of the BPI projects fail and are not completed [ 4 , 5 ]. The key problems identified behind failure of BPI projects are the lack of acknowledgment of the risks that potentially confront organizations for successful implementation. The identification of risks lie in the people, process and product paradigm [ 6 ]. Specifically for the services sector, the problem has some additional dimensions like the quality of service, service response time and service performance enhancement [ 7 , 8 ].

Generally, implementation of BPI projects has been focusing primarily on process design and system configurations while the areas involving soft factors and intangibles related to employees and customers have not been extensively explored in literature [ 9 ]. Moreover, the impact of culture, motivation, and people side of BPI need further exploration specifically with respect to the services sector [ 10 ]. A few models have been brought forward that consider culture and role of leadership as the pillars for successful implementation, however there is no known model for BPI implementation focusing on BPI implementation success dictated by project champions and people management [ 10 , 11 , 12 , 13 ].

Pakistani telecom industry

In recent years, telecom companies across the globe have faced difficulties and challenges for Pakistan have been no different. With more than 150 million users, the industry employs about 1.36 million persons in the five major players in the market. Although the consumption of mobile data has increased overall revenues, cash flows have declined. This is mainly attributed to the fact that telecom companies have made heavy investments for their wireless 3G, 4G and 5G networks. Pakistan will continue to face challenges in the fiscal year 2019–20 as the companies will participate in the competition for market share in a market that has reached its saturation. The consumers have now become more aware and now the need has arisen for the companies to continuously improve their business processes and innovate in order to exceed the customer expectations.

Change accelerated process analysis

In 2001, General Electric came up with a practical and less complex model named as Change Accelerated Process (CAP). CAP is a widely used change management tool successfully used for the implementation of change in many organizations. The CAP model ( Fig 1 ) illustrates the elements that are common to all successful change initiatives as an organization moves from its current state, through the transition, and to the improved or future state. The model is presented in Fig 1 .

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https://doi.org/10.1371/journal.pone.0225669.g001

The objective of this research is to bridge the identified gap by extension of General Electric's CAP for implementation of BPI Projects in telecommunication industry of Pakistan. The proposed extension of CAP identifies industry specific CSF's for BPI, enabling the practitioners to make informed decisions that actually yield results, thus making a significant addition to existing literature.

Research contribution and novelty

This paper proposes a comprehensive framework for BPI implementation including the key elements of change encompassing the critical success factors. In doing so, the paper addresses a clear gap in literature that calls for a comprehensive framework to assist the BPI implementation for achieving desired results. The main contribution of this paper is an extension of the CAP model for successful implementation of BPI projects in the telecom sector. Some other related contributions of this research are:

• Identification of industry specific CSFs.
• Definition of CSFs in terms of the 5 key agents of change namely: vision, skill, incentives, resources, and action plan
• Research in the area of BPI, and the factors critical to its implementation success in context of Pakistan.

The expected outcome of this research is an extension of CAP model for BPI project implementation, introducing a fresh perspective for implementation.

Literature review

Over the last couple of decades, implementation of BPI projects has been studied, through both theoretical and practical lenses. Various studies have identified Critical Success Factors (CSFs) as well as critical failure factors (CFFs) of successful implementation of a BPI project [ 15 ]. This provided a rich basis from which the researchers can get deeper understanding of the contributing factors. The authors have attempted to summarize the CSFs found in the literature. The CSFs have evolved since the conception of BPI due to global competition, rapidly changing business environment and developing technologies. This evolution has played a role in fine-tuning the CSFs of BPI projects. The refinement of literature over the years is described below:

One of the earliest studies suggests that BPI initiatives are successful if they are aligned with organizational strategy [ 16 ]. Literature subsequently focused on the Business Process Management (BPM). It identified CSFs that revolved around project actors, BPM teams, organizational leaders/ leadership, communication, commitment and politics [ 17 , 18 , 19 , 20 ].

As BPI matured, CSFs were categorized into organization, process and technology specific factors [ 21 , 22 , 23 , 24 ]. Different studies resulted into addition of multiple CSFs like learning (amongst employees), organizational culture (that is more apt and adaptable to change) [ 6 , 25 , 26 , 27 ], resource management [ 28 ] and a more structured approach [ 29 ].

When customer needs claimed much attention, some papers also referred to it as a CSF [ 30 , 31 ] With the increasing dependency on technology, BPI and business process redesign (BPR] tools were identified as CSFs [ 32 , 33 , 34 ]. Environment has emerged as a CSF in recent studies [ 27 , 35 , 36 ]. The organization dimension for BPI success has also been explored. Study of effectiveness of a BPI framework for a particular industry or organization and its applicability to another organization is also a recent research trend [ 37 ]. Keeping in mind the organizational dimension, a comprehensive list of CSFs that have been found reliable in literature are presented in S1 Appendix .

With a threshold of at least four citations (during the exhaustive review of the literature) for a success factor, the authors have formulated a list of 22 CSFs. Although the CSFs were validated in previous researches, further reliability of correct formulation of the list was ensured by showing it to more than 25 field experts of telecom industry and then through Delphi technique. Since CAP is widely used in the industry as a change management tool and has proven results [ 14 ], the authors have proposed an extension of CAP for BPI project implementation. Another viewpoint for change management exists in form of Knoster’s model which identifies five key elements required to govern the process of change [ 38 ]. These five elements are shown in Fig 2 , which also depicts the result of missing any one of these elements. For example, missing vision will result into confusion, lack of skills will cause anxiety, lack of incentives will result in gradual change, lack of resource will cause frustrations and absence of action plan will result into false starts.

https://doi.org/10.1371/journal.pone.0225669.g002

The need for a more robust and people driven approach for BPI projects implementation is time and again established in studies [ 8 , 11 ]. Recently identified top ten reasons for process improvement project failures include “lack of commitment and support from top management; poor communication practices; incompetent team; inadequate training and learning; faulty selection of process improvement methodology and its associated tools/techniques; inappropriate rewards and recognition system/culture; scope creepiness; sub-optimal team size and composition; inconsistent monitoring and control; and resistance to change” [ 39 ]. Although success factors have been identified in many researches, there is a need for a framework to facilitate successful implementation of BPI projects. The authors have recognized this gap and have made use of the elements of change as the foundations of this research. The authors have analyzed and established a link between the industry relevant CSFs and the elements of change.

Materials and methods

The ethical considerations are stated prior to proceeding with the research design.

Ethical considerations

The autonomy of individual respondents for this research was given due consideration by the researchers and all participation in the survey was voluntary. Confidentiality of participants and informed consent were specifically ensured. All participants were informed that their identity and individual responses were to be treated as anonymous and utilized only for the purpose of this research.

This is a mixed method study and is conducted in two phases. First phase is qualitative in nature while the second phase is quantitative in nature. The element of bias from the qualitative part (Delphi technique) was addressed by maintaining control over the process and by following a structured approach involving judgment of a number of experts from the field.

Phase 1: The Delphi technique

Delphi technique has been used to finalize the CSFs for BPI in Telecom Industry of Pakistan. Delphi is used "to explore or expose underlying assumptions or information leading to differing judgments; and to seek out information which may generate a consensus on the part of the respondent group;" [ 40 ]. Delphi technique is used to gain clarity and arrive at a consensus on an area where there is extensive literature that makes the problem under discussion haphazard[ 41 , 42 ]. Therefore, this technique is appropriate for our study since there is lack of consensus on the CSFs that are relevant to the telecom industry.

Delphi technique is used for gaining consensus from a panel of experts by undergoing multiple rounds where information is fed back to the panel after empirical analysis of the data from each round [ 43 ]. The cardinal aspects of Delphi are:

• Sampling and use of experts
• Anonymity of Delphi participants
• Controlled Feedback of responses
• Statistical aggregation of group responses

Sampling and use of experts for Delphi

For Delphi technique, individuals with adequate knowledge of the topic under study are used as subjects and their opinions are requested to reach a consensus. In a study by McKenna, these subjects are referred to as a panel of informed individuals called 'experts' [ 44 ]. The selection of 'experts' is used as recommended by Keeney [ 41 ]. Delphi method is considered appropriate and used for BPI projects in organizations. Its structured and academically rigorous approach maintains controls over any expected bias [ 45 ]. For this study, the 'experts' chosen are Business Process Managers (BPMs) who have the experience of undertaking BPI projects for more than six years in the telecom industry of Pakistan. From this point forward, the experts are denoted as BPMs in this research.

The studies indicate that the sample size may vary from 4 to 3000 [ 46 ]. A recommendation has been found in the literature for usage of minimal number of subjects that would seek to verify the responses and would commit their attention throughout the Delphi process [ 42 ]. The authors selected a panel of 30 BPMs from the telecom industry for the application of the Delphi technique. Out of these only 26 responded in the first and 22 responded in subsequent rounds. Among the 26 who responded, there were 21 males and 5 females. All participants held masters degree and had an average work experience of at least seven years in the telecom industry. The sample size of 26 for this study is considered appropriate as similar studies with less sample sizes have been conducted and published [ 47 ].

Questionnaire design for Delphi

In Delphi technique, a questionnaire is tweaked and modified in all rounds. A panel of experts who validated the process ensures content validity. Delphi concludes with a questionnaire which is then used for Exploratory Factor Analysis.

Use of structured questionnaire, for the first round created after exhaustive study of the literature is more effective in driving the full potential of Delphi technique [ 42 ]. A questionnaire, with an exhaustive list of CSFs from the literature, was presented in Round 1. The BPMs were asked for their opinion on whether they considered these factors to be critical for the success of BPI Projects. They were also given the opportunity, if they would deem important to add any other CSF to the list.

The BPMs were asked to rate the CSFs on a Likert scale from 1–5 (5 = most critical to success, and 1 = not critical to success). The Likert scale has been indicated as a standard way to analyze the relative importance of issues for Delphi iterations [ 48 ]. A 5-point Likert scale has been adopted as it has been used as an apt scale for deriving consensus on CSFs in various similar studies [ 38 , 49 , 50 ]

The BPMs were provided with the panel mean rating and standard deviation for each CSF, and the BPMs were given the opportunity to review and revise the rating for each CSF

Results of Delphi analysis

The results and analysis of subsequent rounds of Delphi technique are described below in a stepwise manner. Note that the results of Round 1 are used in Round 2 and similarly Round 2 results are used in Round 3. Detailed description of results and analysis is provided below:

Round 1 CSF identification, results & analysis.

Modified Delphi technique was used for establishing round 1. Moreover, BPMs were also given the opportunity to add any CSF to the list provided. However, the respondents agreed that the CSFs presented to them were exhaustive and did not need any further addition. Therefore, none of the participants added any other CSF to the list.

The results collected from BPMs were analyzed. The agreement among 70–80% of the experts on a factor is generally considered as the selection criterion[ 43 ] while some researchers have opted for as low as 51% of agreement [ 40 ]. However, the BPMs consulted for this research suggested that 80% agreement amongst the BPMs should be considered as the selection criterion.

The factors over which more than 80% of the BPMs had agreed upon being the most critical for success were then passed on to Round 2 for further iterations. Subsequently application of BPI toolbox (56%), project initiation and completion (0.76%), level of IT investment (0.64%), standardization of the process (0.76%), use of external support (0.44%), learning organizational culture (0.72%), were discarded as the CSFs, as the percentage of agreements is less than 80%.

Round 2 Ranking, results & analysis.

The BPMs were asked to identify the relative importance of the CSFs for successful BPI on a Likert scale from 1 to 5 (1 = not at all critical to success, 2 = slightly critical, 3 = neutral, 4 = critical to success, 5 = very critical to success of BPI projects). Out of the 26 respondents that participated in Round 1, only 22 responded in Round 2. The data from these questionnaires was then analyzed in Statistical Package for Social Sciences (SPSS). The reliability statistic Cronbach's Alpha value of 0.779 (signifying the reliability [ 51 ] is also computed to measure the consistency of responses over successive rounds. The respondents were divided amongst two groups 'Leadership' and 'Management' to further analyze the results. This division into two groups was made on the basis of their designation in the organization. Out of the 22 respondents, 11 managers were classified as “management” and remaining 11 directors were classified as “leadership” category. The means of the responses were computed separately and arranged into descending order. The CSFs were "ranked" depending on the order of the means. Ranking was done to statistically analyze the consensus agreements [ 47 ].

Two statistical tests Spearman's rank correlation (rho) and Kendall's rank order correlation (tau-b) were calculated using SPSS for the two groups, 'Leadership' and 'Management'. The computed values of both 0.795 and 0.934 (for rho and tau-b respectively) exceeded the critical values. It was concluded that a statistical significant relationship existed amongst expert responses (at a significance level of 5%, 2-tailed) [ 51 ]. The value of 3.5 for overall mean was considered as a cut-off point [ 47 ]. Consequently, the top 13 CSFs with more than 3.5 overall mean score were selected for re-evaluation in Round 3.

Round 3 Ranking, results & analysis.

The results of Round 2 helped in identifying the factors that were most critical to success of a BPI project. The reduced list of CSFs was again passed on to the 22 respondents for further reaching towards a group consensus. In Round 3 respondents were provided their previous ratings and mean and standard deviation (SD) of the group consensus was also provided. The panel was also given the provision to further evaluate and restate the rating on the same Likert scale for the CSFs. The means were again calculated and CSFs were arranged in descending order. The CSFs were also ranked according to their order. Ranking is done to statistically analyze the consensus agreements [ 47 ]. The responses of the 22 respondents depicted in Table 1 gives an overview of their opinions.

https://doi.org/10.1371/journal.pone.0225669.t001

Same test procedures that were applied in Round 2 were applied again, as shown in Table 2 , and it was found again that the values exceeded the critical values.

https://doi.org/10.1371/journal.pone.0225669.t002

The consistency in results of the two rounds is calculated and compared as shown in Table 3 .

https://doi.org/10.1371/journal.pone.0225669.t003

Concluding Delphi

The results of the two rounds were collated and the percentage improvement over the rounds is calculated as shown in Table 3 . The results show the values of Kendall's coefficient had a 6% improvement and Spearman's rank correlation coefficient had an improvement of 1%. The results depict that there will be no substantial change in the results if the Delphi is iterated further. Since both the leadership and management have arrived at a consensus, we can safely move ahead with these 13 CSFs. With the help of Delphi analysis, we were able to narrow down from our list of 22 critical success factors to 13 factors that are considered most critical to success of a BPI project. The final list nominates the CSFs to be:

• Involvement of organizations stakeholders and leadership
• Performance measurement
• Supporting organizational structure
• People training and empowerment
• Appointment of process owners
• Communication
• Customer focus
• Understanding of the process
• Process Improvement road map
• People change management
• Value realization
• Scope change management
• Resources allocation

Phase 2: Exploratory factor analysis

Exploratory factor analysis (EFA) was conducted to consolidate the results of Delphi rounds. EFA is the most appropriate technique when there is no prior hypothesis about factors or patterns of measured variables [ 52 ]. In this case, the authors have applied EFA to the 13 CSFs to determine if the existence of any underlying relationships among CSFs. The four assumptions of EFA are: normal variables, linear relations, minimum correlation and sample size with a cases/items ratio of at least 5:1 (for the 13 CSFs determined, it implies about 65 responses).

Sample and sample size for EFA

The questionnaire finalized as the result of Delphi was used for EFA and floated to the BPI departments in the five major telecom organizations in Pakistan. The telecom organizations in Pakistan have central formal or informal BPI departments, whilst for nation-wide BPI projects, geographically distributed project managers are appointed as projects actors. The employees that are assigned the BPI projects are trained accordingly. Going forward we can notify them as the BPI teams for the sake of simplicity in this research. These BPI teams are the stratification characteristic of this population. This survey was administered to the employees that work on the BPI projects. The BPI teams are spread across the organization and contain 4–6 members. Proportionate stratified sampling technique was used to collect data. The BPI employees from 5 telecom companies are divided into 5 stratum corresponding to the population size of each strata, different clusters are assigned. Among the 29 clusters a total of 268 observations were deduced as the sample size.

EFA questionnaire

Delphi technique resulted in a questionnaire that asked the respondents to rate each of the 13 CSFs from 1–5 on a Likert scale (5 = most critical, 4 = critical. 3 = neutral, 2 = slightly critical, 1 = Not critical at all). This questionnaire was floated among the designated sample.

Results of exploratory factor analysis

The questionnaire formalized at the conclusion of Delphi technique was then floated within the designated clusters. Among the 268 designated respondents, 247 responded. Among the 247 who responded, there were 176 males and 71 females. Average work experience of the respondents was between 4 and 6 years. 45 respondents held a bachelors degree whereas 202 participants had masters degree. The reliability of the responses was checked by calculating Cronbach alpha which was 0.77 signifying the reliability of responses [ 53 ]. The data was then analyzed using exploratory factor analysis so that the factors that have similar contribution in the model could be grouped together. This will give us the knowledge that how these CSFs of a BPI intervention participate for the successful implementation of the project. The exploratory factor analysis was performed on SPSS; Varimax (orthogonal) rotation technique with Kaiser Normalization was used [ 52 ].

It was observed that five extracted components explained 79.77% variability with the loss of information of less than 21%. The components thus derived had 41.05%, 14.46%, 11.08%, 8.15% of the total variance explained for components 1, 2, 3, 4 & 5, named as vision, skills, incentive, resources and action plan respectively. The loadings of the components are given in Table 4 .

https://doi.org/10.1371/journal.pone.0225669.t004

Extension of CAP model

The Knoster model for managing complex change provides a framework with five basic elements to facilitate change management. This research makes an effort to integrate the elements of that model with the CAP model for BPI project implementation. The five elements of change and their proposed mapping to CAP model are given below:

• “Vision” mapped to “shaping vision”
• “Skills” mapped to “creating a shared need”
• “Incentives” mapped to “monitoring progress”
• “Resources” mapped to “making change last”
• “Action Plan” mapped to “mobilizing commitment”

Fig 2 clearly explains that change is only possible when the five elements of change are present. The five key components derived from EFA were mapped on the agents of change. The conceptual basis of this mapping is described in Table 5 .

https://doi.org/10.1371/journal.pone.0225669.t005

Business process improvement is a continuous process allowing organizations to improve gradually. There are numerous BPI projects being carried out in an organization, focusing on reducing costs, delays and redundancies. It has been established that the implementation of these initiatives is a challenging task [ 54 ]. The research has answered this by providing an extension of CAP for BPI success. It was found that only two, one, and two factors are loaded under elements of Skill, Incentives and Resources respectively. Although these elements had two or less than two factors loaded, yet they were considered appropriate under the procedure carried out and furthermore the results are in-line with a number of previous researches as indicated in Table 5 . This directs that for any BPI initiative the project actor must take account of the five change accelerators defined in terms of the industry relevant CSFs for the successful implementation of BPI projects.

Creating a shared vision that is deeply understood amongst all the team members is a hallmark for success of a BPI intervention [ 55 ]. Understanding of process is identified as an integral part of vision setting that transform processes from mobilization to implementation of change [ 56 ]. Some other researches have also established the link between understanding of process and vision [ 57 , 58 , 59 ]. It has been emphasized that when the value of the change initiatives are realized and aligned with vision, the change initiatives have a better chance of completion [ 60 , 61 ]. Kotter introduced an eight step transformation process that impedes the transformation process [ 59 ]. Numerous other papers were found that established the importance of communicating vision and its impact on successful completion of improvement projects [ 62 , 63 , 64 , 65 ].

Allocation of resources should be in alignment with the vision in order to lead the organization to the successful implementation of an improvement project [ 55 , 56 , 57 , 60 ] Other studies have also emphasized the role of organizational stake holder and leadership in vision setting [ 55 , 56 , 58 , 61 ].

It is evident from the literature that when process owners are equipped with the skills to manage the improvement project, the project has better chances of successful completion. It has been found that along with skill development for the tasks on job, organizations should develop managers and equip them with change management and implementation skills. These skills should be sought after both before and after their appointment as process owners of the BPI projects. With rapidly changing current demands, it is high time that organizations be proactive in developing the relevant skills [ 64 , 65 ].

Management of change is an emerging area of study that is getting recognition widely in all sectors of businesses. People management through change requires ample skills. There are a number of frameworks that have emerged and organizations have spent a large amount of their budgets on consultants and trainers that educate the employees in this competitive environment. Impact of skill development on the employees for people change management has been studied to facilitate successful completion of BPI projects [ 4 , 66 , 67 ].

It has been found that proper performance measurement and fair incentives positively influence in successful completion of the improvement initiative [ 68 ]. Performance measurement also serves as an incentive as it acknowledges the highly performing employees and also instills controls and checks on the employees [ 69 ].

Supporting organizational structure serves to be very crucial resource when it comes to implementation of an improvement project [ 70 ]. There have been numerous studies that discuss the impact of specific organizational structures and their impact on the BPI projects. It has been found that a flexible organizational structure is more supportive of the success of BPI projects [ 2 ].

Development of the employees on the undergoing improvement projects is very crucial for success. The employees should be trained and empowered to deliver the newly developed projects [ 71 ]. Employee training and empowerment is discussed in the literature in following two perspectives:

• Training and empowerment on the proposed process
• Training and empowerment over critical decision making and management of the change management process, impeding the successful completion of the BPI project [ 72 ].

Action plan.

The contribution of a complete and concise roadmap in successful contribution of BPI projects is indubitable and well established. Customer focus is an integral part of all types of action plans [ 73 ]. Literature references suggest that process improvement roadmap is integral to develop a concrete action plan [ 74 ]. For an action plan to be comprehensive, it is imperative that it does not deviate from the scope. The evolution of business process management sheds light on the importance of scope change management for successful completions of the improvement projects [ 75 , 76 ].

The extended model shown in Fig 3 thus contributes by drawing out the five keys agents of change- the "Change Accelerators" signifying their presence for successful implementation of a BPI Project. The extension defines the elements of change in context of the telecom industry BPI projects. Given realistic time and budget, project actor ensures project success, after taking into account these five key agents of change.

https://doi.org/10.1371/journal.pone.0225669.g003

Validity analysis

The following analysis were conducted in order to ensure the validity of the proposed model

Content validity.

Validation of the model was done firstly, by showing the model and results to subject matter experts (BPMs)[ 53 ].

Construct validity.

The performed EFA explains he contribution of the vision, skills, incentives, resources and action plan to the total variance explained thus ensuring the construct validity [ 53 ].

External validity.

Two tailed test with α = .05, df = 98 is applied for which the critical value is 1.9845. The results of the t-test are presented below in Table 6 :

https://doi.org/10.1371/journal.pone.0225669.t006

The group that participated in model design (M = 3.78, SD = 0.492) was not significantly different than the group that validated the model (M = 3.65, SD = 0.572), t (98) = 1.259, p = 0.211. As there is no significant difference among the two groups (people who participated in EFA, people who did not participate in EFA), hence we can conclude that the extension of the CAP model can be successfully used to ensure the success of BPI project.

The purpose of this research was to design an extension of CAP model that ensures the success of BPI initiatives. To achieve this, the authors have first developed a thorough understanding of what factors impact the success of BPI projects. The study of literature has lead the authors to develop a list of CSFs that have been identified and tested in studies over last decades (attached as S1 Appendix ). The authors then conducted a Delphi study with the involvement of business process managers that are experienced in BPI projects deemed as subject matter (BPI) experts. A consensus was derived from the experienced business process managers and was statistically tested using Delphi analysis.

During rounds of Delphi analysis the authors came across interesting observations. It is to be noted that when the ranks of the CSFs were calculated from the average score of each CSF [ 47 ] it was observed that there was a difference in opinion of the two groups. The "leadership" is the decision maker. This group decides why, when and how the improvement intervention takes place. The 'management' is the action actor as it makes the project happen. The difference in the point of view of the groups is evident as the leadership has a bird's eye view of the whole situation whilst the managers have the look and feel of the total picture. The differences in the point of view of the two groups are shown in detail in the radar diagram shown in Fig 4 .

https://doi.org/10.1371/journal.pone.0225669.g004

The derived most critical success factors were further analyzed using the exploratory factor analysis. EFA was used to explore the underlying theoretical structural details of the CSFs with respect to BPI project implementation success. The conduction of this analysis was a crucial and a challenging task with the involvement of BPI teams constituting 267 respondents from all the major telecom organizations. The responses generated interesting results.

Understanding of the process (with factor loading 0.865), value realization (0.834), communication (0.856), resource allocation (0.810) and involvement of stakeholders and leadership (0.765) showed most influence on the component 1. Similarly, appointment of process owners (0.802) and people change management (0.776) influenced component 2. While performance measurement (0.843) influenced component3 and supporting organizational structure (0.875), people training and empowerment (0.627) explained component 4. Scope change management (0.691), process improvement road map (0.548) and customer focus (-0.786) have most influence on component 5. It should be noted that for some components 2 or less than two factors have been loaded, this finding has been backed up by literature [ 79 , 80 , 81 , 82 ], indicating that for some narrowly defined constructs, single-item measures may suffice. Another interesting finding is that customer focus is loaded onto the fifth component with a negative value which only represents the direction of the eigenvector and has no bearing on the interpretation of its magnitude. Customer focus had the highest loading which represents its importance relative to other elements in the factor. This finding is in compliance with latest research that directs that customer focus must be pragmatically addressed in BPI projects, and when not thoroughly looked after may hinder the successful implementation of BPI projects [ 83 ]. The authors thus needed to have a keen consideration on this while moving towards the next step.

Next step was to put under the microscope each identified component and establish an extension of the CAP model based on the five agents of change identified by CAP. The authors meticulously mapped each factor on the agents of change in light of the literature presented over the recent years. The paper contributes to the existing body of knowledge by presenting an extension of CAP for the BPI projects in the telecom industry of Pakistan. The extension presents a list of factors that must be taken into account for successful implementation of BPI projects.

The focus on the customer should not drive the BPI project away from the desired and intended results. Making use of the cardinal aspects of CAP for successful implementation is a distinctive approach in this area. The proposed CAP extension model is based upon the iterative and exploratory characteristics of Delphi technique used to meet research objectives. To understand the CSFs of BPI projects from the vast available success factors and to study their relevance to the telecom industry, the exploratory research method is considered appropriate [ 54 ]. The proposed model has twofold significance. It is prepared using critical factors from literature which were then ascertained by experienced experts from telecom industry. Moreover, the time tested CAP model integrated with BPI projects success is likely to improve chances of meeting the desired objectives.

Validity analysis was conducted on the model and content. Construct as well as the external validity has been ensured. The collaboration of literature and experience of industry personnel, who have practically implemented BPI projects and have endured the challenges is also a novel contribution to the existing body of knowledge.

Utility of extended model

The proposed augmented model is designed with the help of telecom industry BPMs and teams making it applicable and relevant to this industry. Although the model has been designed by keeping telecom sector in view, it is of relevance to other related sectors as well where change management projects for process improvement are executed. The telecom industry is unique in terms of the technological advances and fierce competition it faces due to multiple external and internal factors. This extension directs BPM teams to ensure and expedite BPI project success. The biggest challenges in telecom sector relate to making change last and mobilizing commitment. The proposed model identifies critical factors to address these specific concerns.

Conclusions

The extension of CAP for BPI success is a comprehensive tool that guides the business process manager with complete directions that ensure success of a BPI project. It directs that BPI should be made a part of the organization’s vision. It defines the skill for BPI projects is the appointment of process owners and practicing people change management. The performance during the implementation of BPI projects should be measured for the successful implementation. Organizational structure that supports the improvement initiative and empowers employees are the key resource that contributes towards success. It is emphasized that improvement roadmap must be outlined with control over the scope changes, focusing on the customer needs.

The paper contributes by presenting industry relevant extension of CAP for BPI implementation that give the practitioners a crisp list of action items, when followed pave the way for successful implementation of BPI projects. The extension of CAP by defining the five key elements of change in context of BPI in Telecom industry, which is novel and unique.

Limitations & future work direction

There are limitations of the research and some pose as future work opportunities. The research is limited to the telecom Industry of Pakistan which is a rapidly evolving industry, facing challenges on technological, political, economical, financial and human resource aspects. The adoption of the proposed extension in different industries facing similar challenges and for different geographical locations can be an interesting topic of a future study. Exploration of CSFs in other industries could be another area for future work. Exploring the relationship of customer focus and BPI action plans can be an interesting area of study. A case study analysis of the proposed model is also encouraged. This research directs practitioners by probing into the pitfalls of BPI implementation with a fresh perspective, encouraging them to make informed decisions. This is likely to expedite and enhance the chances of success of BPI initiatives.

Supporting information

S1 appendix. list of critical factors from the literature along with references [ 84 – 94 ]..

https://doi.org/10.1371/journal.pone.0225669.s001

S1 Dataset.

https://doi.org/10.1371/journal.pone.0225669.s002

S2 Dataset.

https://doi.org/10.1371/journal.pone.0225669.s003

S1 Questionnaires. Selection of most critical success factors.

https://doi.org/10.1371/journal.pone.0225669.s004

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Business Process Management (BPM) is a systematic approach to managing and streamlining business processes . BPM is intended to help improve the efficiency of existing processes, with the goal of increasing productivity and overall business performance.

BPM is often confused with other seemingly similar initiatives. For example, BPM is smaller in scale than business process reengineering (BPR), which radically overhauls or replaces processes. Conversely, it has a larger scope than task management, which deals with individual tasks, and project management, which handles one-time initiatives. And while enterprise resource planning (ERP) integrates and manages all aspects of a business, BPM focuses on its individual functions—optimizing the organization’s existing, repeatable processes end-to-end.

An effective BPM project employs structured processes, uses appropriate technologies and fosters collaboration among team members. It enables organizations to streamline project workflows, enhance productivity and consistently deliver value to stakeholders. Ultimately, the successful implementation of BPM tools can lead to increased customer satisfaction, competitive advantage and improved business outcomes.

3 main types of business process management

Integration-centric BPM focuses on processes that don’t require much human involvement. These include connecting different systems and software to streamline processes and improve data flow across the organization, for example human resource management (HRM) or customer relationship management (CRM)

Human-centric BPM centers around human involvement, often where an approval process is required. Human-centric BPM prioritizes the designing of intuitive processes with drag and drop features that are easy for people to use and understand, aiming to enhance productivity and collaboration among employees.

Document-centric BPM is for efficiently managing documents and content—such as contracts—within processes. A purchasing agreement between a client and vendor, for example, needs to evolve and go through different rounds of approval and be organized, accessible and compliant with regulations.

Business process management examples

BPM can help improve overall business operations by optimizing various business processes. Here are some BPM examples that outline the use cases and benefits of BPM methodology:

Business strategy

BPM serves as a strategic tool for aligning business processes with organizational goals and objectives. By connecting workflow management, centralizing data management , and fostering collaboration and communication, BPM enables organizations to remain competitive by providing access to accurate and timely data. This ensures that strategic decisions are based on reliable insights.

Through BPM, disparate data sources—including spend data, internal performance metrics and external market research—can be connected. This can uncover internal process improvements, strategic partnership opportunities and potential cost-saving initiatives. BPM also provides the foundation for making refinements and enhancements that lead to continuous improvement.

• Enhanced decision-making
• Efficient optimization
• Continuous improvement

Claims management

BPM can be used to standardize and optimize the claims process from start to finish. BPM software can automate repetitive tasks such as claim intake, validation, assessment, and payment processing—using technology such as Robotic Process Automation (RPA ). By establishing standardized workflows and decision rules, BPM streamlines the claims process by reducing processing times and minimizing errors. BPM can also provide real-time visibility into claim status and performance metrics. This enables proactive decision-making, ensures consistency and improves operational efficiency.

• Automated claim processing
• Reduced processing times
• Enhanced visibility

Compliance and risk management

By automating routine tasks and implementing predefined rules, BPM enables timely compliance with regulatory requirements and internal policies. Processes such as compliance checks, risk evaluations and audit trails can be automated by using business process management software, and organizations can establish standardized workflows for identifying, assessing, and mitigating compliance risks. Also, BPM provides real-time insights into compliance metrics and risk exposure, enabling proactive risk management and regulatory reporting.

• Automated compliance checks
• Real-time insights into risk exposure
• Enhanced regulatory compliance

Contract management

Contract turnaround times can be accelerated, and administrative work can be reduced by automating tasks such as document routing, approval workflows and compliance checks. Processes such as contract drafting, negotiation, approval, and execution can also be digitized and automated. Standardized workflows can be created that guide contracts through each stage of the lifecycle. This ensures consistency and reduces inefficiency. Real-time visibility into contract status improves overall contract management.

• Accelerated contract turnaround times
• Real-time visibility into contract status
• Strengthened business relationships

Customer service

BPM transforms customer service operations by automating service request handling, tracking customer interactions, and facilitating resolution workflows. Through BPM, organizations can streamline customer support processes across multiple channels, including phone, email, chat, and social media. With BPM, routine tasks such as ticket routing and escalation are automated. Notifications can be generated to update customers about the status of their requests. This reduces response times and improves customer experience by making service more consistent. BPM also provides agents with access to a centralized knowledge base and customer history, enabling them to resolve inquiries more efficiently and effectively.

• Streamlined service request handling
• Centralized knowledge base access
• Enhanced customer satisfaction and loyalty

Financial management

BPM is used to streamline financial processes such as budgeting, forecasting, expense management, and financial reporting. It ensures consistency and accuracy in financial processes by establishing standardized workflows and decision rules, reducing the risk of human errors and improving regulatory compliance. BPM uses workflow automation to automate repetitive tasks such as data entry, reconciliation and report generation. Real-time visibility into financial data enables organizations to respond quickly to changing market conditions.

• Increased operational efficiency
• Instant insights for informed decision-making
• Enhanced compliance with regulations and policies

Human resources

Using BPM, organizations can implement standardized HR workflows that guide employees through each stage of their employment experience, from recruitment to retirement . The new employee onboarding process and performance evaluations can be digitized, which reduces administrative work and allows team members to focus on strategic initiatives such as talent development and workforce planning. Real-time tracking of HR metrics provides insights into employee engagement, retention rates, and the use and effectiveness of training.

• Reduced administrative work
• Real-time tracking of HR metrics
• Enhanced employee experience

Logistics management

BPM optimizes logistics management by automating processes such as inventory management, order fulfillment, and shipment tracking, including those within the supply chain. Workflows can be established that govern the movement of goods from supplier to customer. Automating specific tasks such as order processing, picking, packing and shipping reduces cycle times and improves order accuracy. BPM can also provide real-time data for inventory levels and shipment status, which enables proactive decision-making and exception management.

• Streamlined order processing and fulfillment
• Real-time visibility into inventory and shipments
• Enhanced customer satisfaction and cost savings

Order management

BPM streamlines processes such as order processing, tracking, and fulfillment. BPM facilitates business process automation —the automation of routine tasks such as order entry, inventory management, and shipping, reducing processing times and improving order accuracy. By establishing standardized workflows and rules, BPM ensures consistency and efficiency throughout the order lifecycle. Increased visibility of order status and inventory levels enables proactive decision-making and exception management.

• Automated order processing
• Real-time visibility into order status
• Improved customer satisfaction

Procurement management

BPM revolutionizes procurement management through the digital transformation and automation of processes such as vendor selection, purchase requisition, contract management, and pricing negotiations. Workflows can be established that govern each stage of the procurement lifecycle, from sourcing to payment. By automating tasks such as supplier qualification, RFx management, and purchase order processing, BPM reduces cycle times and improves efficiency. Also, with real-time metrics such as spend analysis, supplier performance, and contract compliance, BPM enables business process improvement by providing insights into areas suitable for optimization.

• Standardized procurement workflows
• Real-time insights into procurement metrics
• Cost savings and improved supplier relationships

Product lifecycle management

BPM revolutionizes product lifecycle management by digitizing and automating processes such as product design, development, launch, and maintenance. Workflows that govern each stage of the product lifecycle, from ideation to retirement can be standardized. Requirements gathering, design reviews, and change management , can be automated. This accelerates time-to-market and reduces development costs. BPM can also encourage cross-functional collaboration among product development teams, which ensures alignment and transparency throughout the process.

• Accelerated time-to-market
• Reduced development costs
• Enhanced cross-functional collaboration

Project management

In the beginning of this page, we noted that BPM is larger in scale than project management. In fact, BPM can be used to improve the project management process. Business process management tools can assign tasks, track progress, identify bottlenecks and allocate resources. Business process modeling helps in visualizing and designing new workflows to guide projects through each stage of the BPM lifecycle. This ensures consistency and alignment with project objectives. Tasks assignments, scheduling, and progress monitoring can be automated, which reduces administrative burden and improves efficiency. Also, resource utilization and project performance can be monitored in real time to make sure resources are being used efficiently and effectively.

• Streamlined project workflows
• Real-time insights into project performance
• Enhanced stakeholder satisfaction

Quality assurance management

BPM facilitates the automation of processes such as quality control, testing, and defect tracking, while also providing insights into KPIs such as defect rates and customer satisfaction scores. Quality assurance (QA) process steps are guided by using standardized workflows to ensure consistency and compliance with quality standards. Metrics and process performance can be tracked in real time to enable proactive quality management. Process-mapping tools can also help identify inefficiencies, thereby fostering continuous improvement and QA process optimization.

• Automated quality control processes
• Real-time visibility into quality metrics

Business process management examples: Case studies

Improving procure-to-pay in state government.

In 2020, one of America’s largest state governments found itself in search of a new process analysis solution . The state had integrated a second management system into its procurement process, which required the two systems, SAP SRM and SAP ECC, to exchange data in real time. With no way to analyze the collected data, the state couldn’t monitor the impact of its newly integrated SAP SRM system, nor detect deviations during the procurement process. This created an expensive problem.

The state used IBM Process Mining to map out its current workflow and track the progress of the SAP SRM system integration. Using the software’s discovery tool, data from both management systems was optimized to create a single, comprehensive process model. With the end-to-end process mapped out, the state was able to monitor all its process activities and review the performance of specific agencies.

Streamlining HR at Anheuser-Busch

AB InBev wanted to streamline its complicated HR landscape by implementing a singular global solution to support employees and improve their experience, and it selected workday as its human capital management (HCM) software. Working with a team from  IBM® Workday consulting services , part of IBM Consulting™, AB InBev worked with IBM to remediate the integration between the legacy HR apps and the HCM software.

What was once a multi-system tool with unorganized data has become a single source of truth, enabling AB InBev to run analytics for initiatives like examining employee turnover at a local scale. Workday provides AB InBev with a streamlined path for managing and analyzing data, ultimately helping the company improve HR processes and reach business goals.

Business process management and IBM

Effective business process management (BPM) is crucial for organizations to achieve more streamlined operations and enhance efficiency. By optimizing processes, businesses can drive growth, stay competitive and realize sustainable success.

IBM Consulting offers a range of solutions to make your process transformation journey predictable and rewarding.

• Traditional AI and generative AI-enabled Process Excellence practice uses the leading process mining tools across the IBM ecosystem and partners.
• Our patented IBM PEX Value Triangle includes industry standards, benchmarks, and KPIs and is used to quickly identify process performance issues and assess where and how our clients can optimize and automate everywhere possible.
• IBM Automation Quotient Framework and Digital Center of Excellence (COE) platform prioritized and speeds up automation opportunities, ultimately establishing a Process Excellence COE for continuous value orchestration and governance across your organization.

Key improvements might include 60-70% faster procurement, faster loan booking, and reduced finance rework rate, along with risk avoidance, and increased customer and employee satisfaction.

With principles grounded in open innovation, collaboration and trust, IBM Consulting doesn’t just advise clients. We work side by side to design, build, and operate high-performing businesses—together with our clients and partners.

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