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Revisiting automated project management in the digital age - a survey of AI approaches

  • Meissen University of Applied Sciences
  • Meissen University of Applied Sciences (HSF)

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In this decade, remarkable progress has been made in the field of artificial intelligence (AI). Inspired by well-known services of cognitive assistance systems such as IBM Watson, Apple's Siri or Google Duplex, AI concepts and algorithms are widely discussed regarding their automation potentials in business, politics and society. At first glance, project management (PM) seems to be less suitable for automation due to the inherent uniqueness of projects by definition. However, AI is also creating new application possibilities in the PM area, which will be explored in this contribution by involving an extensive literature review as well as real-world examples. The objective of this article is to provide a current overview of AI approaches and available tools that can be used for automating tasks in business project management.
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Revisiting automated project management in the
digital age a survey of AI approaches
Gunnar Auth, Leipzig University of Telecommunications, Germany,
Oliver Jokisch, Leipzig University of Telecommunications, Germany,
Christian Dürk, Corivus AG, Germany,
In this decade, remarkable progress has been made in the field of artificial intelligence (AI).
Inspired by well-known services of cognitive assistance systems such as IBM Watson, Apple's
Siri or Google Duplex, AI concepts and algorithms are widely discussed regarding their
automation potentials in business, politics and society. At first glance, project management (PM)
seems to be less suitable for automation due to the inherent uniqueness of projects by definition.
However, AI is also creating new application possibilities in the PM area, which will be
explored in this contribution by involving an extensive literature review as well as real-world
examples. The objective of this article is to provide a current overview of AI approaches and
available tools that can be used for automating tasks in business project management.
Keywords: Artificial intelligence, project management, automation, machine learning, bot.
The recent rise in expectations of Artificial Intelligence (AI) performance in automating complex
activities (e.g., PwC, 2018a) is also reflected in the discussion on the future of project
management (PM). In addition to the various possible uses of AI methods in PM such as Support
Vector Machines (Wauters & Vanhoucke, 2014) or Predictive Analytics (Fauser,
Schmidthuysen, & Scheffold, 2015), the discussion also revolves around the question of whether
or when the human project manager will be replaceable by AI (e.g., Bailey, 2017; Burger, 2017;
& PwC, 2018b). Particularly optimistic authors are already predicting a revolution in PM, driven
by AI (e.g., Birch, 2018; Branscombe, 2018).
As a generic term Automated Project Management (APM) summarizes all approaches for the
most complete automation of PM tasks and activities. The use of the term can already be found
in the early 1980s (Smith & Mills, 1983). From the available literature, we identified two
different meanings of APM: as part of the related term Automated Project Management System
(APMS) it focuses on special software systems supporting project scheduling and controlling in
terms of time, resources and cost (Nicols, 1986). A second, narrower meaning of APM can be
located in software engineering where it describes the automation of software development tasks,
which are typically organized as a software project (Campbell & Terwilliger, 1986). In parallel,
expert systems had evolved and become popular, leading to a growing supply of commercial
software solutions (Liebowitz, 1997). Nevertheless, APMS were not designed as expert systems.
The use of AI was not a central characteristic of early APMS designs. Still, the discussion of AI
potentials for project management also started around that time (e.g., Hosley, 1987) and has
recently become vital again (e.g., Wang, 2019).
The high expectations of AI technology in earlier times, which can be observed in a similar way
today (see Gartner, 2017; PwC, 2018a), lead to ambitious development plans for knowledge-
based decision support along the entire project lifecycle (Hosley, 1987; Levitt & Kunz, 1987).
However, the so-called AI winter (Russel & Norvig, 2010) made such early approaches
disappear from practical use a little later due to unfulfilled expectations. With the recently
rekindled AI enthusiasm, the term APM has been rediscovered and loaded with a tighter
relationship to AI (Jordan, 2018). The latest advances in AI development increasingly pose the
question of substitutability of the human project leader (Bailey, 2017; Pielmeier & Lommel,
2017; PwC, 2018b). With the extended possibilities of AI-based automation through novel
procedures and extensive data availability (Bitkom & DFKI, 2017), the question again arises in
project management, which potentials for automation can be realized in view of the current state
of development and which future development trends are already emerging. For this purpose,
new term creations such as Data-driven Project Management, Predictive Project Analytics or
Project Management Bot need to be clarified and evaluated.
This article contributes to the discussion of using AI for automation in project management from
an application-oriented perspective. The objective of the presented survey is to provide a current
overview of AI approaches and available real-world applications that can be used for automating
tasks in business project management. In order to understand and to conceptualize the role of AI
in project management, we started our research with a literature review focused on research
outcomes and applications following Brocke et al. (2009). The search engines and electronic
databases we used included Google Scholar, SpringerLink, IEEE Xplore, and ReserachGate.
From the review results we defined a conceptual framework for AI applications in PM, which is
presented in the next section. The data for the application-oriented part of our study is mainly
based on vendor-side and third-party information resulting from a second search process based
on the conceptual framework and using Google Web search as well as the academic search tools
mentioned before. In subsequent section, we summarize the search results in an overview of
current AI applications in PM structured into the three main categories 1) Data-driven project
management, 2) AI platforms for PM, and 3) Project management bots. With the last section we
conclude the paper.
From Process to Project Automation with AI
In the era of digitalization with its transformation processes in business and society, driven by
rapid developments and high processing speed in information and communication technology
(ICT), the feasibility limits of automating human tasks seem to soar. This was impressively
demonstrated 2016 by Google’s software AlphaGo, which won four of five matches against the
18-time world champion of the board game Go, Lee Sedol. Such an AI performance was not
expected before, due to the complexity of Go, although already in 1997, IBM’s super computer
DeepBlue had beaten the world champion of Chess, Garry Kasparov. In 2018, the system Google
Duplex was presented, which can automatically make appointments via telephone with human
agents using natural language. In the given examples, the persons did not even seem to notice
that they were talking to a machine (Leviathan & Matias, 2018).
Requirements of Progressive Automation
Automation can be defined as the technology by which a process or procedure is performed with
a minimum of human assistance (e.g., Groover, 2008). For a long time, the according technical
system could only be constructed as a finite state machine. Program-controlled automation
technology, such as looms with punch card control, considerably expanded the automation
capability of processes and tasks at the beginning of the 19th century. Throughout time, the limits
of automation were always determined by the current state-of-the-art in technology. At the end
of the 20th century, the amount of information processing required to perform a task was
considered as a limiting factor. Recently, the available ICT has progressed so much through
advances in AI that the general automation capability has also expanded enormously (Bitkom &
DFKI, 2017). Therefore, it is hardly surprising that high expectations are currently being made of
the potential of automation. For the German economy alone, the accounting firm
PricewaterhouseCoopers (PwC) estimated the added value potential of AI by the year 2030 at a
total of around 430 billion euros (PwC, 2018a). PwC was not only focusing on work processes,
which are more easily accessible to automation due to their repetitive nature on the basis of
previously defined solutions and fixed decision parameters. Moreover, the PwC (2018a) study
highlighted learning AI systems that can adapt to new situations and act without human support.
This allows for bringing projects into the focus of application. A project can be compared to a
Go game, which demands creativity, intuition and strategic thinking from the player (or project
manager). As prerequisites basic cognitive skills are necessary which are characteristic of human
intelligence, such as audiovisual cognition, memory, learning, planning and problem solving.
A Conceptualization of AI for Project Automation
According to Russell and Norvig (2010), AI is concerned with the development of intelligent
agents, which can perceive their environment and carry out derived actions. Furthermore, such
artificial systems have the ability to (1) act autonomously, (2) persist for longer, (3) adapt to
changes, and (4) set and track objectives (Russel & Norvig, 2010). With regard to the successful
planning and execution of projects, so-called rational agents suggest a particular potential: their
extended capabilities enable them to strive for the best result in their actions or the most valued
result under uncertainty. Regarding its application-oriented scientific context, AI is utilizing
models and methods of mathematics, statistics/stochastics, computer science, psychology and of
cognition and neuroscience. For more complex applications, agents often process large amounts
of formalized knowledge, which in turn is derived from data (Rowley, 2007). Through this
connection between knowledge and data, concepts from the field of data management are also
relevant in the discussion about AI, but their references to AI are not always clearly demarcated.
With regard to the practical focus of this article, these terms include in particular Business
Intelligence (BI), Analytics and Big Data. In contrast to AI, BI pursues a specific application
reference, which is centered around the support of human decision-makers in operational
decision-making processes through appropriate information provision (Schieder, 2016). The term
BI is strongly influenced by certain technical implementation concepts such as multidimensional
analysis, dashboards and reporting. However, the term analytics has a more neutral, technically
oriented focus, but nevertheless aims at more specific “local” decision support from a technical
point of view (Winter, 2016). Big Data, probably the most recent term in this series, extends the
already mentioned concepts by “new” technological possibilities for processing and using
extremely large (feature volume) or rapidly growing (velocity) as well as strongly heterogeneous
(variety) data sets (Dittmar, 2016). In turn, AI systems or methods can be used in all three
concepts as tools to achieve advanced purposes. The combination of methods of AI with data
analysis methods in the field of the above-mentioned concepts (e.g., data mining) has led to the
development of new conceptual creations such as predictive analytics, advanced analytics or
data-driven AI (Bencke, 2017; Gluchowski, 2016). To create a conceptual framework for AI in
the PM domain, we followed the organization of AI topics, related concepts and procedures by
Russel and Norvig (2010) as well as combined it with the AI taxonomy by Davis, Hoffert, and
Vanlandingham (2016). We also made adaptations by leaving out nonrelevant elements and
aggregating other elements according to our research objectives based on personal judgment.
The result of this conceptualization is shown in Table 1. Guided by this framework, the search
for AI applications in PM was carried out – summarized in the according section.
Table 1. Conceptual Framework for This Study Based on Russel and Norvig (2010) as well as
Davis, Hoffert, and Vanlandingham (2016)
1. Problem solving
2. Knowledge representation
and deduction
3. Machine learning
Algorithmic search
Data/text mining
Learning with or w/o supervisor
Blind search
Reinforcement learning
Heuristic search
Expert system
Support vector machines
Adversarial search
(Multi-)agent system
Neural network
Constraint satisfaction
Bayes network
Deep learning
Decision tree or forest
Predictive analytics
4. Communication
5. Perception & recognition
6. Robotics
Natural language processing
Text recognition
Image or audio classification
Sensor elements
Text generation
Speech recognition
Actuating elements
Speech synthesis
3D-world reconstruction
A Unified Understanding of Project Management
So far, it has become clear that AI is a multi-layered and diverse term. However, the second
central term in the focus of this article – project management – is hardly different in this regard.
Although the basic meaning is largely consensual, closer examination opens up a very broad and
dynamic spectrum of content-related conceptual components and their characteristics, such as
sub-areas, process models, roles and structures, methods, techniques or types of projects. This
diversity is offset by many years of standardization efforts in the PM discipline that provided
accepted definitions of terms. From a task-oriented perspective, the ISO 21500 and ANSI/IEEE
PMI PMBOK international standards consistently identify ten subfields of PM (ISO, 2012; PMI,
2017): (1) integration, (2) stakeholders, (3) scope, (4) (human) resource, (5) Time, (6) Cost, (7)
Quality, (8) Risk, (9) Procurement, (10) Communication, which are integrated into a five-phase
project lifecycle in which the two standards also agree (except for marginal differences in phase
designations): (1) Initiating, (2) Planning, (3) Implementing / Executing, (4) Monitoring and
Controlling, (5) Closing.
AI Applications in The Domain of Project Management
Ever since the beginning of the development of AI systems, the key question that has remained
immanent to date was: Can a human-developed technical system have human intelligence? Alan
Turing already investigated this question in 1950 with the still relevant Turing test. Due to the
close relationship between AI, knowledge and data processing, the limitations of AI have always
been defined by the power of Information Technology (IT) in terms of data volume and
processing speed. Rich (1983) put it in a nutshell as early as 1983 with her AI definition:
“Artificial Intelligence is the study of how to make computers do things at which, at the moment,
people are better” (p. 1). In the following, three categories are presented, which were identified
as content-related focal points within the results of our investigation on the current state of AI
development in project management. The succession of the categories results from an increasing
proportion of ‘strong’ AI.
Data-Driven Project Management
The core idea of data-driven project management (DdPM) is well-known – the more relevant
information about a decision problem is available, the more reliable the best decision alternative
can be selected (Sullivan III, 2016). Since information is based on data, any PM decision should
be founded on a solid data basis (L & George, 2004). In the DdPM notion, this database needs to
be combined with experience and intuition of a human project manager to actually make
decisions (L & George, 2004; Vanhoucke, 2018). DdPM's focus is initially on the classic
problem of resource-constrained project scheduling and thus the planning and controlling
functions in terms of time, costs, risks and quality (Vanhoucke, 2012). The repertoire of methods
includes known mathematical-statistical methods such as Program Evaluation and Review
Technique (PERT), Critical Path or Chain, Earned Value Management (EVM), Analytical
Hierarchy Process (AHP), and (Lean) Six Sigma.
In the course of the digitalization, however, more and more data as well as high-performance IT
infrastructure is available for processing. Against this background, DdPM increasingly uses
analytical methods. These pick up the results of classical, past or present-oriented methods to
derive predictions about future developments (hence predictive analytics). For example, Singh
(2015) described processes and application examples based on linear regression for the
prediction of cost changes through extension of project scope and duration for the PM area.
Since the informational value of predictive analytics results depends heavily on the amount of
data and the number of variables, special data analysis tools are indispensable for practical
application. Some authors (Duggal, 2018; Ou, 2007; Rechenthin, 2013) summarized this new
development under the term Project Intelligence (based on Business Intelligence), which has not
yet been widely accepted.
AI Platforms for Project Management
AI platforms for PM can be understood as an evolution stage of DdPM, targeted to unlocking
new potential through AI in the context of big data and analytics (Ruchi & Srinath, 2018).
Because of the combination of high implementation effort for a single company on the one hand
and high user expectations on the other hand, certain vendors have developed cloud-based
service platforms that provide AI-based services.
As a well-known company, for example, the consulting firm Deloitte offers a consulting service
under the name Predictive Project Analytics, which is based on a special analytics engine
combined with a comprehensive database, which was obtained from more than 2,000 projects
(Fauser, 2015). Furthermore, neural networks and generic algorithms are used (Fauser, 2015),
extending the conventional DdPM approach. Key areas of application include complexity and
success analyses, risk assessments and employee selection for project teams (Deloitte, 2016).
Team member selection is also featured by other application examples based on the platform
approach. E.g., startup Cloverleaf develops software for the compilation of project teams using
employee data, which, in addition to characteristics such as experience and qualifications, also
takes into account “invisible facets of a person”, e.g., the ability to adapt to the desired working
model or the agreement with (work) cultural values (Cloverleaf, 2018). A more comprehensive
approach is pursued by the Californian vendor TARA with its eponymous platform. Originally
designed to automate the recruiting process for external software developers, the focus has now
greatly expanded towards project planning and monitoring (TARA, 2018). TARA uses machine
learning to automate the initial definition of the project focus, task and time planning, creating
the project team as well as monitoring and forecasting for the current project.
Project Management Bots
The term Project Management Bots (PMB) was coined in 2017 by consulting firm Gartner in the
Hype Cycle for Project and Portfolio Management (Schoen, 2017), meaning a class of intelligent
software agents specializing in project management. In contrast to RPA bots, however, the focus
on graphical user interfaces is missing. PMB are more likely to be equipped with speech or text
interfaces for communicating with humans, and thus have features of chatbots (Gaton, 2017).
While a bot externally presents itself as one actor through one or more central communication
interfaces, in the case of bots with extended capabilities, it is mostly multi-agent systems. These
are characterized by the fact that the associated agents interact with each other in order to
achieve a common goal (Olfati-Saber, Fax, & Murray, 2007). For example, interaction can take
the form of negotiation and is based on communication between the agents. Considerations to
apply multi-agent systems in project management are already much older than the new term
creation PMB (de Medeiros Baia, 2015; Petrie, Goldmann, & Raquet, 1999; Yan, Kuphal, &
Bode, 2000). What is new, however, is that today not only research prototypes but also
commercial products are available for practical use, due to the technological developments of
recent years. PMB solutions are often based on a proprietary cloud platform that enables server-
side storing and processing of data as well as communication with and between client-side bot
components. The current product range for PMB can be divided into three categories:
(1) Independent products specialized in PM such as PMOtto, which is offered by a Danish
startup of the same name and is also referred to as the “Personal Project Management
Assistant” (PMOtto, 2018). PMOtto assists human users in working with conventional
PM software (Currently, only Microsoft Project online, which is part of the Office365
cloud software package, is supported). For this purpose, the bot understands natural
language, which it transforms into operating steps for the PM software and executes it.
The system continues to learn with machine learning and is thus able to improve
(2) Vendor-side extensions of established products to support project teams. Currently, these
are mainly found in the field of modern collaboration and communication tools such as
Microsoft Teams. This product was launched in 2017 and is a communications service
for teams integrated in the Office365 product family. Teams includes two preinstalled
chatbots, T[each]-bot and Who-bot (McDonald, 2016). T-bot supports new users in
learning system operations. Who-bot can answer questions of the type “Who knows
about x” and analyse communication via teams. Beyond the relatively simple
functionality, the two bots demonstrate the integrated functionality for developing custom
(3) Extensions for established third-party products. Especially for the Atlassian products Jira,
Confluence and HipChat/Stride a larger range of bot extensions has been developed. For
example, the company Stratejos offers a Project Assistant Bot for Atlassian products that
supports project teams in data entry and editing, risk analysis and project monitoring
(Baldassarre, 2018).
Further AI-Related Aspects in Project Management
In addition to the previous AI examples in the domain of project management, a few close-knit
areas of development can be identified, such as Intelligent Information Management (IIM),
which emphasizes the integration of current AI processes and technology into information
management (e.g., Bailin & Truszkowski, 2001). The potential of IIM is closely linked to project
management information systems (PMIS) (PMI, 2017). Furthermore, Robotic Process
Automation (RPA) is suited for well-structured, less complex routine tasks, in which the term
“robotic” refers to software agents (“robots”) that are able to learn manual activities and then
perform them automatically (van der Aalst, Bichler, & Heinzl, 2018). PM is not within the
primary focus, but RPA may include monitoring and controlling of projects (e.g., to keep
thresholds) (Sharma, 2017), reporting and documentation (which is similar to IIM) or even
planning and optimization (Branscombe, 2018).
With regard to the initial question about the substitutability of the human project leader by AI,
based on our investigation of the current state of research and development, the first all-clear can
be given. The expectations exceed (still) today’s possibilities. In particular, the solutions
available in practice hardly meet the requirements of ambitious terms such as Automated Project
Management or Project Management Bot. The broad and dynamic field of tasks of a project
manager can currently only be automated in small, clearly defined areas. In the metaphor of an
autopilot, today's situation is more like a car with early assistance systems such as ABS and ESP
than a Tesla or Google Driverless Car. However, the development is very fast and prototypes
like Google Duplex give an impression of realistic potential. Much is already technically
feasible, but still needs to be brought to product maturity. So, if the technical feasibility fades
into the background, is it only a matter of time until APM becomes reality?
The results of our study point the way to further research. In general, we found a number of
interesting approaches and use cases for AI in PM. To understand the potential of AI for PM
even better, a business process perspective seems to be appropriate. Nowadays, PM is widely
considered as a set of specific business processes; standards like ISO and PMI describe
normative process models. This allows for evaluating new methods for process automation and
digitalization (e.g., Denner, Püschel, & Röglinger, 2018) whether they could be adapted for
identifying and exploiting the potential of PM processes for AI-based automation.
Apart from implementation questions, AI technology in particular raises questions of acceptance,
reliability, transparency and legal as well as ethical and moral responsibility. While these have
been discussed in other areas of application such as autonomous driving for some time, the
discussion in project management is still in its infancy. Here, too, the wheel does not have to be
re-invented. Scientifically, for example, information ethics deals with relevant questions and
provides answers (Bendel, 2016). In practice, first companies have begun to create guidelines
and framework conditions on this basis. For example, Deutsche Telekom has recently issued
“Guidelines for the Use of Artificial Intelligence” (Fulde, 2018). In addition to the further
development of the technical possibilities, the respective implications for the ethics of project
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Authors’ Biographies
Dr. Gunnar Auth is a full professor for Business Information Systems at
Leipzig University of Telecommunications (HfTL), Germany. He completed his
master’s degree in business information systems at the University of Bamberg,
Germany, and received a Ph.D. in economics from the University of St. Gallen,
Switzerland. He started his professional career as an internal consultant at
DaimlerChrysler where he worked in several management positions in logistics,
operations and quality management. Before assuming his current position, he
was IT director and representative of the CIO board at Leipzig University. His research focuses
on IT project management, IT service management and information management.
Dr.-Ing. Oliver Jokisch is teaching as a professor for signal and system theory
at the Leipzig University of Telecommunications (HfTL), Germany. He studied
information technology at TU Dresden in Germany as well as at the
Loughborough University in United Kingdom. Oliver graduated as a diploma
engineer and holds a PhD degree in information technology from TU Dresden.
His research is dedicated to different AI areas and audio/speech communication
such as audio coding, speech prosody and synthesis or language learning
systems. Oliver has co-founded or mentored the IT-oriented companies voiceINTERconnect
GmbH, Linguwerk GmbH, COSEDA Technologies GmbH and ambisone GmbH as well as the
education and knowledge management firm IBWM GmbH.
Christian Dürk is Managing Director of the consulting firm CORIVUS AG and
one of the company’s very first employees. Graduated in industrial engineering
he has more than 15 years of experience in managing complex IT and
organizational projects. One of his specialties is the balancing act between
turning a project at a short notice and its long-term organization – in particular,
sustainable personnel planning that ensures the more permanent functioning of a
business area. He lives with his wife and three children on the German
... The continuously rapid advancement in technology is changing almost every aspect of organizational and managerial activities. The fast-growing discipline of Artificial Intelligence (AI) gets more and more attention from practitioners (Gartner, 2020;McKinsey Analytics, 2019;Ransbotham et al., 2017) and academics (Iansiti & Lakhani, 2020;Raisch & Krakowski, 2020) in different fields of management, and is expected to disrupt the field of project management (PM) as well (Auth et al., 2019;Lahmann, 2019;Parsi, 2019;PMI, 2019;Q. Wang, 2019). ...
... The 'soft' practices, such as communications, stakeholder, and integration management, are yet to be developed to benefit from future AI developments. There is an uncertainty with regard to the ways in which AI will impact the PM discipline in general (Auth et al., 2019;Holzmann et al., 2022;Munir, 2019;Parsi, 2019;PMI, 2019), and there is no agreement on the future of AI in the construction industry in particular (Pan & Zhang, 2021;Turner et al., 2021). ...
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have a huge impact on projects and project management practices in the forthcoming years. The purpose of this paper is to contribute to project management theory and practice in the construction industry by analyzing the expectations of project professionals. A mixed method based on an international survey and semi-structured interviews was applied. The results show that construction project practitioners are looking for AI solutions to support the quantitative processes mainly related to scope, schedule, cost, quality, and risk management. However, the human-related processes, such as communication and stakeholder management, are not expected to be directly enhanced by AI, although might benefit from it indirectly. The findings also demonstrate a difference between amplifying and accelerating countries, where somewhat surprisingly the latter are more ready to adopt AI in their projects.
... Very recently, some surveys have been conducted in order to investigate the use of AI in project management [35,36,37,38]. The initial applications were mainly concerned with project information, project tasks, critical path method, and program evaluation and review technique where noticeably most of them are related to project scheduling [39]. ...
... Automated project management (APM) is the automation of software development tasks, typically organised as software projects (Campbell and Terwilliger, 1986). In general terms, APM contains all approaches for automating project management tasks and activities (Auth, Jokisch and Dürk, 2019). The expectations of what AI can do still exceed the current possibilities that lie within the technology, and the broad and dynamic field of tasks of a project manager can currently only be automated in limited, clearly defined areas. ...
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Much research is conducted on the importance of success factors. This study contributes to the body of knowledge by using artificial intelligence (AI), specifically machine learning (ML), to analyse success factors through data from construction projects. Previously conducted studies have explored the use of AI to predict project success and identify important success factors in projects; however, to the extent of the authors’ knowledge, no studies have implemented the same method as this study. This study conducts quantitative analysis on a sample of 160 Norwegian construction projects, with data obtained from a detailed questionnaire delivered to relevant project team members. The method utilises ML through a Random Forest Classifier (RFC). The findings obtained from the analysis show that it is possible to use AI and ML on a limited dataset. Furthermore, the findings show that it is possible to identify the most important success factors for the projects in question with the developed model. The findings suggest that a group of selected processes is more important than others to achieve success. The identified success factors support the theoretically acknowledged importance of thorough and early planning and analysis, complexity throughout the project, leadership involvement, and processes supporting project success.
... Lechner and Heck, 2014;PMI, 2017). However, project management (PM) is often investigated independently from the available technological realisations such as BIM (Chan et al., 2018) and may not be suitable for automation (Auth et al., 2019). The investigation of the literature shows that the PM domain provides a high-level terminology (e.g. ...
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Numerous stakeholders contribute to building projects during the design phase, prevailingly dealing with digital objects. The design phase is characterised by project-specific heterogeneous workflows which are not standardised; available software tools still do not sufficiently support digital management of these building design workflows across the industry. However, single activities within the workflows are similar and constitute patterns that could allow for modularisation and eventual standardisation. An analysis of design protocols found on a document exchange platform is performed in order to identify the processes within the workflows, including information about actors, activities and assets, and subsequently recognising the patterns. High digitalisation potential is recognised on the activity level, due to numerous similarities and iterations detected between the analysed processes, mainly depending on the constellation of stakeholders. The results reveal information granularity which suffices for digitalisation of communication process flows. Proposed modular patterns represent the first step towards design workflow automation, facilitating the use of technologies such as blockchain and smart contracts.
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The stage-gate method was initially developed as a description of the new product development practices within high-performing firms. At its heart the concept is simple: and the flow of activity of a stage-gate includes project action, information generation, analysis, and decision. Research has shown that the stage-gate method has been extremely successful in many contexts. The question of whether the approach is suitable for all projects in all situations is a principal fault line within the literature. Proponents argue that adaptations and evolutions of the stage approach enable it to be universally applied. This paper provides a critical review of the literature and we identify chronic limitations of stage-gate when evaluated against contemporary challenges, including VUCA (volatility, uncertainty, complexity and ambiguity), environment, digitisation and open innovation. We remain critical about whether these contemporary currents are best approached by yet another re-configuration of stage-gate building blocks. We argue that the high uncertainty (caused by these currents) requires the flexibility to change fundamental elements of a project, including the underlying concept and the target market, which means that stage-gate is not well suited to innovation processes addressing these contemporary challenges. We propose a typology to show its suitability.
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Process improvement is the most value-adding activity in the BPM lifecycle. Despite ma-ture knowledge, many approaches have been criticized to lack guidance on how to put process improvement into practice. Given the variety of emerging digital technologies, organizations not only face the process improvement black box, but also high uncertainty regarding digital technologies. We thus propose a method that supports organizations in exploiting the digitalization potential of their processes. To do so, we adopted action de-sign research (ADR) and situational method engineering (SME). We conducted two de-sign cycles involving practitioners (i.e., managers and BPM experts) and end-users (i.e., process owners and participants). In the first cycle, we evaluated our method’s alpha version by interviewing practitioners from five organizations. In the second cycle, we evaluated the beta version via real-world case studies. In this paper, we include detailed results of one case study, which we conducted at a semiconductor manufacturer.
Neben den operativen Informationssystemen, welche die Abwicklung des betrieblichen Tagesgeschäftes unterstützen, treten heute verstärkt Informationssysteme für die analytischen Aufgaben der Fach- und Führungskräfte in den Vordergrund. In fast allen Unternehmen werden derzeit Begriffe und Konzepte wie Data Warehouse, On-Line Analytical Processing und Data Mining diskutiert und die zugehörigen Produkte evaluiert. Vor diesem Hintergrund bietet das Buch einen aktuellen Überblick über Technologien, Produkte und Trends in den genannten Bereichen. Als Entscheidungsgrundlage für den Praktiker beim Aufbau und Einsatz derartiger analytischer Informationssysteme können die unterschiedlichen Beiträge aus Wirtschaft und Wissenschaft wertvolle Hilfestellung leisten. Für die Neuauflage wurde der Praxisbezug durch neue Beiträge und die Aktualisierung technologischer Aspekte vergrößert.
Als soziotechnische Systeme verbinden analytische Informationssysteme genauso wie andere Informationssysteme menschliche und technische Aufgabenträger im Kontext von Organisationen. Analytische Informationssysteme sind fast immer ein komplexes Konglomerat aus Komponenten zwischen den beiden Extrema „Entscheidungsunterstützung einzelner Personen für bestimmte Entscheidungen“ einerseits und „Nutzung von Synergien durch den unternehmensweiten Austausch von Daten“ andererseits. Managementansätze müssen diese Vielgestaltigkeit adressieren, d. h. die Eigenheiten der jeweils unterschiedlichen Leistungen, Ziele, Bedarfs- und Produktionsaspekte sowie Planungs- und Steuerungsansätze differenziert berücksichtigen. Als exemplarische (Extrem-)Szenarien für analytische Informationssysteme grenzt dieser Beitrag das „Management dezentraler Entscheidungsunterstützung“ vom „Management unternehmensweiter Informations-Infrastrukturen“ ab. Dabei wird jeweils analysiert, welche Planungs- und Steuerungsherausforderungen mit der Entwicklung und dem Betrieb „im Großen“ und „im Kleinen“ verbunden sind. Soweit jeweils anwendbar, werden zentrale Positionierungs- und Gestaltungsfragen wie strategische Ausrichtung, organisatorische Umsetzung und Alignment mit entsprechenden IT-Systemen angesprochen. Eine besondere Rolle spielen aus Managementsicht finanzielle Aspekte analytischer Informationssysteme wie z. B. Wertbeitrag, Leistungsverrechnung und Finanzierung.
Die Technologien und Konzepte, die sich im Bereich der Analytischen Informationssysteme ausmachen lassen, sind stetigen Entwicklungen und Veränderungen unterworfen. Nicht zuletzt aufgrund der hohen kommerziellen Relevanz des Themas lässt sich die zu beobachtende Volatilität sogar als besonders ausgeprägt bezeichnen. Vor diesem Hintergrund greift der vorliegende Beitrag einige augenfällige Entwicklungstendenzen auf und beschreibt die zentralen Neuerungen.
Der Begriff „Big Data “ ist derzeit als einer der sogenannten Megatrends in aller Munde. In der breiten öffentlichen Diskussion zu Big Data löst die damit verbundene Idee, aus der Vielzahl und Vielfalt der verfügbaren Daten schnell werthalte Informationsschätze zu heben, sehr viele positive aber auch negative Reaktionen aus: Während sich auf der einen Seite ein schier unbegrenztes Spektrum an Anwendungsfeldern mit zum Teil komplett neuen Geschäftsmodellen eröffnet, wird auf der anderen Seite eindringlich auf potenzielle Gefahren hingewiesen, die sich aus der breiten Verknüpfung von Dateninhalten und der damit einhergehenden, vollständigen Transparenz über das Verhalten und Vorlieben Einzelner ergeben. Im vorliegenden Beitrag wird Big Data aus dem Blickwinkel der Analyseorientierten Informationssysteme eingeordnet und anhand der wesentlichen Anwendungsfelder und Technologien konkretisiert. Anschließend wird der resultierende Einfluss von Big Data auf etablierte Architekturparadigmen skizziert.