Archive for the ‘Complexity’ Category

Research Featured in Harvard Business Review

Donnerstag, Juli 26th, 2012

After 2 years of researching ICT projects the on-going research has been picked up by the Harvard Business Review and is on the cover of their September 2011 issue.

„Why your IT projects may be riskier than you think?“

By now, I collected a database of nearly 1,500 IT projects – in short we argue that the numbers in the hotly debated Standish Report are wrong, but their critics don’t get it quite right either. We found that while IT projects perform reasonably well on average the risk distribution has very fat tails in which a lot of Black Swan Events hide. 1 in 6 IT projects turned into a Black Swan – an event that can take down companies and cost executives their jobs.

Enjoy the read!

More background reading on the HBR article can be found in this working paper.

Human Effort Dynamics and Schedule Risk Analysis (Barseghyan, 2009)

Dienstag, April 21st, 2009


Barseghyan, Pavel: Human Effort Dynamics and Schedule Risk Analysis; in:  PM World Today, Vol. 11(2009), No. 3.

Barseghyan researched extensively the human dynamics within project work.  He has formulated a system of intricate mathematics quite similar to Boyle’s law and the other gas laws.  He establishes a simple set of formulas to schedule the work of software developers.

T = Time, E = Effort, P = Productivity, S = Size, and D = Difficulty
Then W = E * P = T * P = S * D, and thus T = S*D/P

But and now it gets tricky S~D~P are correlated!

The author has collected enough data to show the typical curves between Difficulty –> Duration and Difficulty –> Productivity.  To schedule and synchronise two tasks the D/P ratio has to be constant between these two tasks.


Barseghyan then continues to explore the details between Difficulty and Duration.  He argues that the common notion of bell-shaped distributions is flawed because of the non linear relationship between Difficulty –> Duration  [note that his curves have a segment of linearity followed by some exponential part.  If the Difficulty bell curve is transformed into the Diffculty–>Duration probability function using that non-linear transformation formula it looses is normality and results in a fat-tail distribution.  Therefore Barseghyan argues, the notion of using bell-shaped curves in planning is wrong.  

Uncertainty Sensitivity Planning (Davis, 2003)

Donnerstag, Januar 8th, 2009


Davis, Paul K.: Uncertainty Sensitivity Planning; in: Johnson, Stuart; Libicki, Martin; Treverton, Gregory F. (Eds.): New Challenges – New Tools for Defense Decision Making, 2003, pp. 131-155; ISBN 0-8330-3289-5.

Who is better than planning for very complex environments than the military?  On projects we set-up war rooms, we draw mind maps which look like tactical attack plans, and sometimes we use a very militaristic language.  So what’s more obvious than a short Internet search on planning and military.

Davis describes a new planning method – Uncertainty Sensitivity Planning.  Traditional planning characterises a no surprises future environment – much like the planning we usually do.  The next step is to identify shocks and branches.  Thus creating four different strategies

  1. Core Strategy = Develop a strategy for no-surprises future
  2. Contingent Sub-Strategies = Develop contingent sub-strategies for all branches of the project
  3. Hedge Strategy = Develop capabilities to help with shocks
  4. Environmental Shaping Strategy = Develop strategy to improve odds of desirable futures

Uncertainty Sensitivity Planning combines capabilities based planning with environmental shaping strategy and actions. 
Capabilities based planning plans along modular capabilities, i.e., building blocks which are usable in many different ways.  On top of that an assembly capability to combine the building blocks needs to be planned for.   The goal of planning is to create flexibility, adaptiveness, and robustness – it is not optimisation.  Thus multiple measurements of effectiveness exist. 
During planning there needs to be an explicit role for judgements and qualitative assessments.  Economics of choice are explicitly accounted for. 
Lastly, planning requirements are reflected in high-level choices, which are based on capability based analysis.

Hierarchy of inquiring systems in meta-modelling (Gigch & Pipino, 1986)

Mittwoch, Januar 7th, 2009


This concludes our little journey into constructivism, complex system thinking, and the big question: "What do we really really really know?"

Inputs   Philosophy of Science   Outputs
Evidence, epistemological questions —> Epistemology —> Paradigm
Evidence, scientific problems —> Science —> Theories & Models
Evidence, managerial problems —> Practice —> Solution to problems

System of Systems (Flood & Jackson, 1991) Decision making process (Simon, 1976)

Mittwoch, Januar 7th, 2009

Not really a summary of two article, but rather a summary of two constructivists‘ concepts.

Firstly, Flood and Jackson propose a System of Systems and point out the modelling approaches suitable for these specific systems:

  Unitary Pluralist Coercive
Simple Operation Research, Systems Analysis, Systems Engineering, System Dynamics Social Systems Design, Strategic Assumption Surfacing and Testing Critical Systems Heuristics
Complex Viable Systems Model, General Systems Theory, Socio-Technical Systems Thinking, Contingency Theory Interactive Planning, Soft Systems Methodology  

Secondly, because at some point in time I just had to write it down again, Simon’s constructivist process of decision-making, originally published in 1979:

Intelligence (Is vs. Ought situation) —> Design (Problem Solving) —> Choice —> Implementation —> Evaluation

With the extension of decision loops if no choice can be made as proposed by Le Moigne, revisiting the Design, Intelligence, or even the Initial step:

  • Re-Design – the How
  • Re-Finalisation – the What
  • Re-Justification – the Why

A Principal Exposition of Jean-Louis Le Moigne’s Systemic Theory (Eriksson, 1997)

Dienstag, Januar 6th, 2009


Eriksson, Darek: A Principal Exposition of Jean-Louis Le Moigne’s Systemic Theory; in: „Cybernetics and Human Knowing“. Vol. 4 (1997), No. 2-3.

When thinking about complexity and systems one sooner or later comes across Le Moigne.  Departing point is the dilemma of simplification vs. intelligence.  Therefore systems have to be distinguished to be either complicated = that is they are reducible, or to be complex = show surprising behaviour.

Complicated vs. Complex
This distinction follows the same lines as closed vs. open systems, and mono- vs. multi-criteria optimisation.  Closed/mono-criteria/complicated systems can be optimised using algorithms, simplifying the system, and evaluating the solution by its efficacy.  On the other hand, open/multi-criteria/complex systems can only be satisfied by using heuristics, breaking down the system into modules, and evaluating the solution by its effectivity.

In the case of complex systems simplification only increases the complexity of the problem and will not yield a solution to the problem.  Instead of simplification intelligence is needed to understand and explain the system, in other words it needs to be modelled.  As Einstein already put it – defining the problem is solving it.
Secondly, to model a complex system is to model a process of actions and outcomes. The process definition consists of three transfer functions – (1) temporal, (2) morphologic, and (3) spatial transfer.  In order to make the step from modelling complicated system to modelling complex systems some paradigms need to change:

  • Subject –> Process
  • Elements –> Actors
  • Set –> Systems
  • Analysis –> Intelligence
  • Separation –> Conjunction
  • Structure –> Organisation
  • Optimisation –> Suitability
  • Control –> Intelligence
  • Efficacy –> Effectiveness
  • Application –> Projection
  • Evidence –> Relevance
  • Causal explanation –> Understanding

The model itself follows a black box approach.  For each black box, its function, its ends = objective, its environment, and its transformations need to be modelled.  Furthermore the modelling itself understands and explains a system on nine different levels.  A phenomena is

  1. Identifiable
  2. Active
  3. Regulated
  4. Itself
  5. Behaviour
  6. Stores
  7. Coordinates
  8. Imagines
  9. Finalised

Managerial complexity in project-based operations – A grounded model and its implications for practice (Maylor et al., 2008)

Montag, November 3rd, 2008

 Managerial complexity in project-based operations - A grounded model and its implications for practice (Maylor et al., 2008)

Maylor, Harvey; Vidgen, Richard; Carver, Stephen: Managerial complexity in project-based operations – A grounded model and its implications for practice; in: Journal of Project Management, Vol. 39 (2008), No. S1, pp. S15-S26.
DOI: 10.1002/pmj.20057

Maylor et al. investigate the question – What makes a project complex? More specifically this question asks for managerial complexity of projects, which is neither technical nor environmental complexity which has been looked at in depth in research surrounding the whole areas of function point estimation.

The literature review finds several previous approaches to measure complexity

  • Number of physical elements and interdependencies (Baccarini, 1996)
  • Structural uncertainty (number of project elements), uncertainty of goals and objectives (Williams, 1999)
  • Static dimension – assembly-system-array (Shenhar, 2001)
  • Organisational complexity, technical novelty, scale complexity (Maylor, 2003)
  • Observer-dependent, time-dependent, problem-dependent projects (Jaafari, 2003)
  • Organisational x technological complexity (Xia & Lee, 2004)
  • Communication and power relationships, amibguity, change (Cicmil & Marshall, 2005)

The authors then propose the MODeST model with the dimensions of mission, organisation, delivery, stakeholder, and team. In this qualitative focus group based research, the authors break down the dimesions into

– Objectives
– Scale
– Uncertainty
– Constraints

– Time & Space
– Organisational setting

– Process
– Resources

– Stakeholder attributes
– Inter-stakeholder relationships

– Project staff
– Project manager
– Group

This Complexity Measurements Table shows their full set of questions with the questions stricken out that were not mentioned sufficiently in the focus group discussions.

The Complexity of Self–Complexity: An Associated Systems Theory Approach (Schleicher & McConnell, 2005)

Dienstag, Oktober 28th, 2008

The Complexity of Self–Complexity: An Associated Systems Theory Approach (Schleicher & McConnell, 2005)

Schleicher, Deidra J.; McConnell, Allen R.: The Complexity of Self–Complexity: An Associated Systems Theory Approach; in: Social Cognition, Vol. 23 (2005), No. 5, pp. 387-416.
doi: 10.1521/soco.2005.23.5.387

In my search for complexity measurements of intangible projects I came across this approach to measure the most complex thing I could think of – our beautiful mind.

In this article Schleicher & McConnell describe the commonly used trait sorting exercise to measure self-complexity. Participants are presented 25-40 traits or roles on cards. Then they are asked to group them so that they best describe the aspects of their selfs. For example a participant might group well-dressed, anxious, mature into as traits describing the student aspect of her self.
To measure the self-complexity redundancy and relatedness of the groupings need to be assessed using following formula:

H = log2n – ( ∑i ni log2ni ) / n
n = total number of attributes for sorting, ni = number of attributes in each group/self-aspect, i = number of groups/self-aspects

Studies have confirmed that participants with a higher self-complexity are better in managing stress, well-being, physical illness, and depression.

Schleicher & McConnell propose a two dimensional concept of self-complexity – (1) target-reference: concrete vs. abstract, (2) self-reference: public vs. private self.

Concrete ← target-reference → Abstract
Visual System Verbal System Public Self
Visual appearance Social Categories Personality Traits
Behavioural Observations Evaluations self-reference
Behavioural Responses Orientations Affective Responses
Action System Affective System Private Self

Learning and acting in project situations through a meta-method (MAP) a case study: Contextual and situational approach for project management governance in management education (Bredillet, 2008)

Dienstag, Oktober 28th, 2008


Bredillet, Christophe N.: Learning and acting in project situations through a meta-method (MAP) a case study – Contextual and situational approach for project management governance in management education; in: International Journal of Project Management, Vol. 26 (2008), No. 3, pp. 238-250.

[This is a relatively complex post that follows – the article goes into epistemology quite deep (What is knowledge? How do we acquire it?) without much explanation given by the author. I tried to put together some explanatory background to make the rationale for the article more accessible. If you are just interested in the curriculum Bredillet proposes for learning project management on the job, skip these parts and jump right to the end of the post.]

In this article Bredillet outlines his meta-method used to teach project management. This method’s goal is to provide a framework in terms of processes and structure for learning in situ, namely on projects, programmes and alike. Bredillet argues that this method is best in accounting for complex, uncertain and ambiguous environments.

[Skip this part if you’re only interested in the actual application of the method.]

The authors starts with reviewing the three dominant project perspectives. a) Instrumental Perspective, which defines a project as a temporary endeavour to create something. b) Cognitive Perspective, which defines projects as exploitation of constraints and human/monetary capital in order to achieve an outcome. c) Political Perspective, which define projects as spatial actions which are temporarily limited, thus interacting with their environment. Bredillet argues that project management education does not reflect these perspectives according to their importance in the real world.

Bredillet argues that project management, knowledge creation and production (epistemology) have to integrate classical scientific aspects (Positivism) as well as fuzzy symbolisms (Constructivism). He says: „that the ‚demiurgic‘ characteristic of project management involves seeing this field as an open space, without ‚having‘ (Have) but rather with a raison d’être (Be), because of the construction of Real by the projects“ (p. 240).
Without any prior indulgence into epistemology (‚What is knowledge?‘ E. v. Glaserfeldt, Simon, Le Moigne etc.) this sentence is rather cryptic. What Bredillet wants to achieve is to unify the Positivist and Constructivist epistemology. Positivist epistemology can shortly be summarised to be our approach to understand the world quantitatively (= have = materialism, with only few degrees of freedom, e.g., best practices, OR, statistical methods). On the other hand Constructivist epistemology tries to understand the world with a qualitative focus (=be = immateriality, with many degrees of freedom, e.g., learning, knowledge management, change management). Bredillet summarises the constructivist epistemology citing Comte as „from Science comes Prevision, from Prevision comes Action“, and the positivist epistemology according to Le Moigne’s two hypothesis of reference – phenomenological („an existing and knowledgeable reality may be constructed by its observers who are then its constructors“) and teleological („knowledge is what gets us somewhere and that knowledge is constructed with an aim“).

Bredillet then argues that most research follows the positivist approach, valuing explicit over tacit knowledge, individual knowledge over team/organisational knowledge. To practically span the gap between Constructivism and Positivism Bredillet suggests to acknowledge tacit, explicit, team and individual knowledge as „distinct forms – inseparable and mutually enabling“ (p. 240).

How to unify Constructivsm and Postivitsm in Learning of Project Management?
Practically he explores common concepts always from both views, from the positivist and the constructivist standpoint, for instance, Bredillet describes concepts of organisational learning using the single-loop model (Postivism) vs. double-loop model, and system dynamics theory (Constructivsm).  Secondly, Bredillet stresses that learning and praxis are integrated, which is what the MAP method is all about:

„The MAP method provides structure and process for analysing, solving and governance of macro, meso, and micro projects. It is founded on the interaction between decision-makers, project team, and various stakeholders.“ (p. 240)

The three theoretical roots for the map method are (1) Praxeological epistemology, (2) N-Learning vs. S-Learning, (3) Theory of Convention. Thus the map method novelty is that it

  • Recognises the co-evolution of actor and his/her environment,
  • Enables integrated learning,
  • Aims at generating a convention (rules of decision) to cope with the uncertainty and complexity in projects.

Ad (1): The basic premises of Praxeological epistemology [in Economics] taken from Block (1973):

  • Human action can only be undertaken by individual actors
  • Action necessarily requires a desired end and a technological plan
  • Human action necessarily aims at improving the future
  • Human action necessarily involves a choice among competing ends
  • All means are necessarily scarce
  • The actor must rank his alternative ends
  • Choices continually change, both because of changed ends as well as means
  • Labour power and nature logically predate, and were used to form, capital
  • Technological knowledge is a factor of production

Ad (2): I don’t know whether n-Learning in this context stands for nano-Learning (constantly feeding mini chunks of learning on the job) or networked learning (network over the internet to learn from each other – blogs, wikis, mail etc.). Neither could I find a proper definition of S-Learning. Generally it seem to stand for supervised learning. Which can take place most commonly when training Neural Networks, and sometimes on the job.
Sorry – later on in the article Bredillet clarifies the lingo: N-Learning = Neoclassical Learning = Knowledge is cumulative; and S-Learning = Schumpeterian Learning = creative gales of destruction.

Ad (3): Convention Theory (as explained in this paper) debunks the notion that price is the best coordination mechanism in the economy. It states that there are collective coordination mechanisms and not only bilateral contracts, whose contingencies can be foreseen and written down.
Furthermore Convention Theory assumes Substantive Rationality of actors, radical uncertainty (no one knows the probability of future events), reflexive reasoning (‚I know that you know, that I know‘). Thus Convention Theory assumes Procedural Rationality of actors – actors judge by rational decision processes & rules and not by rational outcome of decisions.
These rules or convention for decision-making are sought by actors in the market. Moreover the theory states that

  • Through conventions knowledge can be economised (e.g., mimicking the behaviour of other market participants);
  • Conventions are a self-organising tools, relying on confidence in the convention
  • Four types of coordination exist – market, industry, domestic, civic

[Start reading again if you’re just interested in the application of the method.]

In the article Bredillet then continues to discuss the elements of the MAP meta model:

  • Project situations (entrepreneurial = generating a new position, advantage) vs. operations situations (= exploiting existing position, advantage)
  • Organisational ecosystem [as depicted on the right of my drawing]
  • Learning dynamics and praxis, with the three cornerstones of knowledge management, organisational learning, and learning organisation

Thus learning in this complex, dynamic ecosystem with its different foci of learning should have three goals – (1) individual learning, e.g., acquire Prince 2/PMP methodology; (2) Team learning, e.g., acquire team conventions; and (3) organisational learning, e.g., acquire new competitive position.

The MAP model itself consists of the several project management theories and concepts [theories are depicted on the left side of my drawing], the concepts included are

  • Strategic Management
  • Risk Management
  • Programme Management
  • Prospective Analysis
  • Projects vs. Operations
  • Ecosystem project/context
  • Trajectory of projects/lifecycles
  • Knowledge Management – processes & objects; and individual & organisational level
  • Systems thinking, dynamics
  • Organisational design
  • Systems engineering
  • Modelling, object language, systems man model
  • Applied sciences
  • Organisational Learning (single loop vs. double loop, contingency theory, psychology, information theory, systems dynamics)
  • Individual learning – dimensions (knowledge, attitudes, aptitudes) and processes (practical, emotional, cognitive)
  • Group and team learning, communities of practice
  • Leadership, competences, interpersonal aspects
  • Performance management – BSC, intellectual capital, intangible assets, performance assessments, TQM, standardisation

The praxeology of these can be broken down into three steps, each with its own set of tools:

  1. System design – social system design (stakeholder analysis, interactions matrices), technical system design (logical framework, e.g., WBS matrix, and logical system tree)
  2. System analysis – risk analysis (technical/social risk analysis/mapping), scenario analysis (stakeholder variables & zones)
  3. System management – scheduling, organisation & planning, strategic control

As such, Bredillet describes the MAP method trajectory as

  1. Strategic choice with a) conception, b) formulation
  2. Tactical alternatives with a) alternatives analysis and evaluation, b) decision
  3. Realisation with a) implementation, b) reports and feedback, c) transition into operations, c) post-audit review

In praxis the learning takes part in form of simulations, where real life complex situations have to be solved using the various concepts, methods, tools, and techniques (quantitative and psycho-sociological) which are included in the MAP-method. To close the reflective learning loop at the end two meta-reports have to be written – use of methods and team work, and how learning is transferred to the workplace. Bredillet says that with this method his students developed case studies, scenario analysis, corporate strategy evaluation, and tools for strategic control.

The balance between order and chaos in multi-project firms: A conceptual model (Geraldi, 2008)

Donnerstag, Oktober 23rd, 2008

 The balance between order and chaos in multi-project firms: A conceptual model (Geraldi, 2008)

Geraldi, Joana G.: The balance between order and chaos in multi-project firms – A conceptual model; in: International Journal of Project Management, Vol. 26 (2008), No. 4, pp. 348-356.

Geraldi takes a deeper look into multi-project settings at the ‚Edge of Chaos‘. Geraldi describes the Edge of Chaos as that fine line between chaos and order. Wikipedia (I know I shouldn’t cite it) has something else to say about the Edge of Chaos:

In the sciences in general, the phrase has come to refer to a metaphor that some physical, biological, economic and social systems operate in a region between order and either complete randomness or chaos, where the complexity is maximal. The generality and significance of the idea, however, has since been called into question by Melanie Mitchell and others. The phrase has also been borrowed by the business community and is sometimes used inappropriately and in contexts that are far from the original scope of the meaning of the term.

Geraldi defines the Edge of Chaos as a match between complexity and flexibility.  Complexity can either be located within faith or facts. Flexibility, on the other hand, is either high or low, whilst it is measured along the dimensions of scope + goals, processes + tools, and roles + staffing. Geraldi argues that only two of these archetypes represent a fit (highlighted below):

Complexity Faith Bureaucratisation of Chaos Creative Reflective
Fact Mechanic-Structured Chaotification of order
Low High

Relating, reflecting and routinizing: Developing project competence in cooperation with others (Söderlund et al., 2008)

Montag, Oktober 20th, 2008

Relating, reflecting and routinizing: Developing project competence in cooperation with others (Söderlund et al., 2008)

Söderlund, Jonas; Vaagaasar, Anne Live; Andersen, Erling S.: Relating, reflecting and routinizing – Developing project competence in cooperation with others; in: International Journal of Project Management, Vol. 26 (2008), No. 5, pp. 517-526.

Söderlund et al. explore the question – How do organisations build project management capabilities? They analyse a focal project to show how the specific competence, project management, is build in an ever changing environment. As such comptence creation is situated and recursive.

The authors use a process view to explain the capabilities building. The process is three-fold – (1) Relating, (2) Reflecting, and (3) Routinising.

First step – Relating to expand the resource base, in this step the organisation

  • Acknowledges the situated character of project competence
  • Expands the resource base, which builds social capital
  • Engages in boundary spanning activites to cooperate with stakeholders and act against de-coupling, which decreases the overall resources needed for the authority system as coordination mechanism
  • Creates interdependencies

Second step – Reflecting to improve use of resource base, in this step the organisation highlights actions of importance for institutionalising a common frame of reference and stimulating shared reflection in the project. As such it:

  • Improves the resource base
  • Engages in problem solving
  • Shifts from exploitation based to experimentation learning, which re-uses previous processes, and recycles old solutions
  • Detects system-wide errors & generates new associations

Third step – Routinising to secure resource base and improve relational activity, in this step the organisation tries to ensure the best use of its resource base. Therefore it

  • Codifies new knowledge
  • Triggers reflecting
  • Exploits what is known
  • Emphasises and builds project-level comptence

Images as action instruments in complex projects (Taxén & Lilliesköld, 2008)

Montag, Oktober 20th, 2008

Images as action instruments in complex projects (Taxén & Lilliesköld, 2008)

Taxén, Lars; Lilliesköld, Joakim: Images as action instruments in complex projects; in: International Journal of Project Management, Vol. 26 (2008), No. 5, pp. 527-536.

Images are quite powerful. I hate motivational posters which a distant corporate HQ decorates every meeting room with, but I once saw the department strategy visualised by these folks, they include all employees and the group dynamic is unbelievable. Later on they cleaned the images, blew them up, and posted them around the company – of course, meaningless for an outsider but a powerful reminder for everyone who took part.

Taxén & Lilliesköld analyse the images typically used in project management. They find that these common images, such as PERT/CPM, Gantt charts, or WBS are increasingly difficult to use in complex projects, in this case the authors look into a large-scale IT project.

Based on Activity Domain Theory they develop alternative images better suited for complex projects. Activity Domain Theory, however, underlines that all tasks on a project (= each activity domain) have a motive, fulfils needs, modifies objects, and has actors. Outcomes are produced by activity domains and are at the same time prerequisites for activity domains. Activity domains have activity modalities, which can be either manifested as resources or as communal meaning. These activity modalities are

  • Contextualisation = situation of human action
  • Spatialisation = need for spatial orientation in human action
  • Temporalisation = need for certain order in human action
  • Stabilisation = need for certain rules and norms in human action
  • Transition = need for interaction between activity domains

Useful images, the authors argue, need to fulfil these needs while being situated in the context of the activity. Traditional images focus on optimisation and control, rather than on coordination and action. Thus alternate images need to focus on dependencies and integration; on value comprehensibility and informality over formality and rigour.

Alternative images suited for complex project management are

  • Anatomies – showing modules, work packages and their dependencies of the finished product, e.g., functional node diagrams
  • Dependency diagrams – showing the incremental assembly of the product over a couple of releases, e.g. increment plan based on dependencies (a feature WBS lack)
  • Release matrices – showing the flow of releases, how they fit together, and when which functionality becomes available, e.g., integration plan
  • Information flow diagrams – showing the interfaces between modules, e.g. DFD

Tailored task forces: Temporary organizations and modularity (Waard & Kramer, 2008)

Montag, Oktober 20th, 2008

Tailored task forces: Temporary organizations and modularity (Waard & Kramer, 2008)

Waard, Erik J. de; Kramer, Eric-Hans: Tailored task forces – Temporary organizations and modularity; in: International Journal of Project Management, Vol. 26 (2008), No. 5, pp. 537-546.

As a colleague once put it: Complex projects should be organised like terrorist organisations – Autonomous cells of highly motivated individuals.

Waard & Kramer do not analyse projects but it’s fast paced and short lived cousin – the task force. The task force is THE blueprint for an temporary organisation. The authors found that the more modularised the parent company is, the easier it is to set-up a task force/temporary organisations. Waard & Kramer also found that the temporary organisations are more stable if set-up by modular parent companies. They explain this with copying readily available organisational design principles and using well excercised behaviours to manage these units.

The more interesting second part of the article describes how a company can best set-up task forces. Waard & Kramer draw their analogy from Modular Design.

„Building a complex system from smaller subsystems that are independently designed yet function together“

The core of modular design is to establish visible design rules and hidden design parameters. The authors describe that rules need to be in place for (1) architecture, (2) interfaces, and (3) standards. The remaining design decisions is left in the hands of the task force, which is run like a black box.
In this case Architecture defines which modules are part of the system and what each modules functionality is. Interface definition lays out how these modules interact and communication. Lastly, the Standards define how modules are tested and how their performance is measured.

Project management approaches for dynamic environments (Collyer, 2009)

Donnerstag, Oktober 9th, 2008

 Project management approaches for dynamic environments (Collyer, in press)Collyer, Simon: Project management approaches for dynamic environments; in: International Journal of Project Management, in press (2008). this article has been published in: International Journal of Project Management, Vol. 27 (2009), No. 4, pp. 355-364. There it is again: Complexity, this time under the name of Dynamic Project Environments. I admit that link is a bit of a stretch. Complexity has been described as situations, where inputs generate surprising outputs. Collyer on the other hand focuses special project management strategies to succeed in changing environments. The author’s example is the IT project, which inherently bears a very special dynamic.He discusses eight different approaches to cope with dynamics. (1) Environment manipulation, which is the attempt to transform a dynamic environment into a static environment. Examples commonly employed are design freezes, extending a systems life time, and leapfrogging or delaying new technology deployment.(2) Planning for dynamic environments. Collyer draws a framework where he classifies projects on two dimensions. Firstly, if their methods are well defined or not, and secondly if the goals are well defined or not. For example he classifies the System Development project as ill-defined and ill-defined. This is a point you could argue about, because some people claim that IT projects usually have well-defined methodologies, but lack clear goals. Collyer suggest scaling down planning. Plan milestones according to project lifecycle stages, and detail when you get there. He recommends spending more time on RACI-matrices than on detailed plans.(3) Control scope, which is quite the obivious thing to try to achieve – Collyer recommends to always cut the project stages along the scope and make the smallest possible scope the first release.(4) Controlled experimentation. The author suggest that experimentation supports sense-making in a dynamic environment. Typical examples for experimentation are prototyping (Collyer recommends to always develop more than one prototype), feasibility studies, and proofs of concept.(5) Lifecycle strategies, although bearing similarities to the scope control approaches he proposes this strategy deals with applying RuP and agile development methods, to accelerate the adaptability of the project in changing environments.(6) Managment control, as discussed earlier in this post every project uses a mix of different control techniques. Collyer suggest deviating from the classical project management approach of controlling behaviour by supervision, in favour for using more input control, for example training to ensure only the best resources are selected. Besides input control Collyer recommend on focussing on output control as well, making output measurable and rewarding performance.Collyer also discusses a second control framework, which distinguishes control by the abstract management principle. Such as diagnostic control (=formal feedback), control of beliefs (=mission, values), control of interactions (=having strategic, data-based discussions), and boundary control (=defining codes of conduct).Lastly the author discusses two more approaches to succeeding with dynamic environments which are (7) Categorisation and adaptation of standards and (8) Leadership style.

Preparing project managers to deal with complexity (Thomas & Mengel, 2008) and Preparing the mind for dynamic management (Hartman, 2008)

Dienstag, Oktober 7th, 2008

 Preparing project managers to deal with complexity (Thomas & Mengel, 2008) and Preparing the mind for dynamic management (Hartman, 2008)

Thomas, Janice; Mengel, Thomas: Preparing project managers to deal with complexity – Advanced project management education; in: International Journal of Project Management, Vol. 26 (2008), pp. 304-315.

Hartman, Francis: ; in: International Journal of Project Management, Vol. 26 (2008), pp. 258-267.

Complexity is a meme that just doesn’t want to die. I wrote before about articles on the foundamentals of complexity theory and project management, about the use of autonomous cells in project organisations and how they prevent complex project systems from failing, and the complex dynamics of project entities in a programme. Not surprisingly this meme has spread into the coaching and project management education world where there is some money to make of it.

Thomas & Mengel argue that the current project manager education is not suited at all to prepare for complex projects. The focus on standardisation, control, and hard systems thinking stands in direct opposition to the actuality of projects, which show high complexity in roles, high degrees of chaos and uncertainty.
Theoretically Thomas & Mengel base their discussion on three complexity/chaos theory concepts

  • Chaos theory – explaining the behaviour of dynamic and unstable systems
  • Dissipative structures – explaining moment of dynamic stability and instability
  • Complex adaptive systems – explaining behaviour of systems with a large number of independent agents, and organisational evolution and learning

So what does it take to be a Complex Project’s Manager?
Thomas & Mengel propose that understanding the complex environment is far more important than using tools and techniques of project management. Furthermore they outline three key capabilities to manage complexity

  • IQ – possessing the self-knowledge to adapt existing tools
  • EQ – possessing the emotional skills to coach and manage people
  • SQ – possessing the capacity for finding meaning

In their framework Thomas & Mengel see most of today’s project managers as Experts, these are project managers heavy on the IQ side of their IQ-EQ-SQ-Triangle. The authors see two developmental strategies. One is coping with uncertainty by moving towards the sense-making SQ corner of the triangle and becoming a Leader. The other developmental direction is coping with complexity by strengthening the EQ corner and becoming a Manager.

Similar ideas are discussed in the paper by Hartman. Altough he does not call the elephant on the table complex project management but he names it dynamic management. Blink or not Blink – Hartman argues that wisdom and intuition are the two desired qualities in a leader with a mind for dynamic management. Furthermore he identifies three traits absolutely necessary

  • Pattern recognition & decision-making
  • Relationship building & communication
  • Integrity & trust

The effectiveness in managing a group of multiple projects: Factors of influence and measurement criteria (Patanakul & Milosevic, in press)

Freitag, Oktober 3rd, 2008

The effectiveness in managing a group of multiple projects: Factors of influence and measurement criteria (Patanakul & Milosevic, in press)Patanakul, Peerasit; Milosevic, Dragan: The effectiveness in managing a group of multiple projects: Factors of influence and measurement criteria; in: International Journal of Project Management, in press, corrected proof. This article has been published inInternational Journal of Project Management, Vol. 27 (2009), pp. 216–233. Multi-project management. I covered a similar topic yesterday looking at it from a Complexity Theory viewpoint. The authors argue that multi-project management is increasingly used in the industry mainly for reasons of better utilisation. [Having worked as a multi-project manager for marketing projects back in the old days, I don’t know if that is really true. My projects always culminated in a week with crazy workload, followed by dry spells, where I bored myself to death.]Patanakul & Milosevic analyse the critical success factors for managing multi-projects from a ‚bundle of projects‘-perspective, i.e., by interviewing a multi-project manager and his/her supervisor. Thus they build six case studies of organisations using multi-project-management. Accordingly the authors define multi-project as the middle state in the project continuum, where single projects are on one end, and programmes are on the other end of the scale.What do Patankul & Milosevic find? The antecedents of multi-project management effectiveness are

  • Organisational field
    • Project assignments
      • Projects‘ strategic importance
      • Required fit to managers‘ competencies
      • Organisational & personal limitations
    • Resource allocation
      • Sufficient resource allocation
      • Sustainable resource allocation
    • Organisational culture
      • Commitment
      • Communication
      • Teamwork
      • Rewards
  • Operational field
    • Project management processes
      • Individual processes
      • Inter-project processes
      • Interdependency management
    • Competencies of the multi-project manager
      • Competencies for leading individual projects
      • Competencies for coordinating between projects

Finally the authors identify the consequences of multi-project management effectiveness as

  • Organisation
    • Resource productivity
    • Organisational learning
  • Project
    • Time-to-Market
    • Customer satisfaction
  • Personal
    • Personal growth
    • Personal satisfaction

Integrating diverse knowledge through boundary spanning processes – The case of multidisciplinary project teams (Ratcheva, in press)

Freitag, Oktober 3rd, 2008

Integrating diverse knowledge through boundary spanning processes – The case of multidisciplinary project teams (Ratcheva, in press)

Ratcheva, Violina: Integrating diverse knowledge through boundary spanning processes – The case of multidisciplinary project teams; in: International Journal of Project Management, in press, corrected proof.

The author argues that diverse, multi-disciplinary teams have knowledge boundaries which make information sharing difficult. An issue even more difficult if the team is geographically separated.

Ratcheva conceptualises the diverse project team as being embedded in the macro environment and organisational environment. The team itself is characterised at its starting point by three factors – (1) interpersonal, interactions & relational capital, (2) knowledge diversity, and (3) establishing workpractice. These three factors influence each other. Starting with this diverse team context or setting the team goes on to integrate it’s knowledge which ultimately leads to a project outcome.

Which knowledge boundaries exist in such a project team? Ratcheva identifies three different knowledge domains and at the edge of these knowledge boundaries. First of all there is the project team, surrounded by it’s projectation boundary, outside this boundary lies the occupational knowledge. Which simply means that each project team member is rooted in a broader knowledge of his profession which goes beyond the boundaries of the current project.
Secondly, the team has contextual knowledge which is confined by the project knowledge boundary. Thirdly, the broader project relevant knowledge lies inside the project’s social boundary.

How does the concept look like in motion? Which boundary spanning activities does the team perform? Ratcheva describes a four step process which combines all knowledge related and boundary spanning activities.

  1. The project core team works on the project, solves problems and issues = understanding occupational knowledge, and realising and spanning the projectation boundary
  2. The team understands the context knowledge, e.g., customer needs, stakeholder requirements = realising and spanning the project knowledge boundary
  3. The team understands  it’s personal diversity, thus understanding which personal knowledge is project relevant knowledge = realising and spanning the project social boundary
  4. The team integrates all knowledge, a knowledge which then feeds back into the first step

Construction client multi-projects – A complex adaptive systems perspective (Aritua et al., in press)

Donnerstag, Oktober 2nd, 2008

 Construction client multi-projects – A complex adaptive systems perspective

Aritua, Bernard; Smith, Nigel J.; Bower, Denise: ; in: International Journal of Project Management, Article in Press, Corrected Proof.

The meme of Complexity Theory is unstoppable in recent project management research. On the other hand it does make sense. Research such as this and, of course, common sense, indicate that the project’s context is a field better not left unmanaged. Since reality is quite complex  and peoples‘ behaviour is anyway – a view like this increases the pre-existing complexity of projects management.

In this article Aritua et al. analyse the special complexity which presents itself in multi-project environments. I posted about complexity theory before and in more detail here.
A quick recap. Complexity theory has 6 distinctive features, which make the outcomes of decisions, actions, and events increasingly unpredictable

  • Inter-relationships
  • Adaptability
  • Self-organisation
  • Emergence
  • Feedback
  • Non-linearity

Aritua et al. model the multi-project environment as being two-fold – (1) strategic environment and (2) tactical environment. The strategic environment builds the business context for the projects, programmes, or portfolio. The authors conceptualise the typical strategic cycle as consisting of vision – mission- strategic blueprint & objectives.

The tactical environment is the project portfolio/programme itself. Consisting of a couple of projects, it does provide a Risk:Value ratio for each project, which leads to an overall risk:value ration for the whole portfolio/programme, as such it feeds back into the strategic cycle in the business context environment.

In a second step the authors analyse the dynamics of such a system – what happens to a mulit-project portfolio if its external environment changes?
First of all, the boundary spanning activity in this conceptualisation is the information exchange with the environment. The information exchange into and out of the project portfolio triggers dynamics inside and between each project. Self-organising local relationships emerge into complex adaptive behaviour which feeds back, positively or negatively into the self-organising relationships. Huh?

Firstly, the project portfolio/programme is a complex system and therefore adapts itself when the environment changes. The one and only pre-requisite for this is, as the authors argue, that information and feedback freely flows inside, into, and out of the system.

Secondly, the self-organising relationships simply imply that individual components of the system affect each other and influence behaviour and actions. That is no project in a portfolio is independent from others. The authors cite the ant colony example, where ants make individual decision based on decisions by their closest neighbours. Thus complex interaction emerges.

Thirdly, self-organisation is the driving force behind creating stability in this open system. As the authors put it: „This aspect of the relationship between complexity theory and multi-project management would imply that programme managers and portfolio managers should not be bogged down with detail and should allow and enable competent project managers to act more creatively and on their own. This understanding also influences multi-project managers to seek a balance between trusting project managers and allowing them to concentrate on details – on the one hand – while seeking the necessary level of control and accountability.“

Fourthly, emergence is the effect that the group behaviour is more than the sum of behaviours of each individual project. Which implies, that risk and value can better be managed and balanced in a portfolio. On the flipside non-linearity shows that small changes in the system have unpredictable outcomes, which might be quite large. Thus management tools which don’t rely on non-linearity are needed. Moreover „it also emphasises the need for the multi-project management to react to the changing business environment to keep the strategic objectives at the fore while providing relative stability for the delivery of individual projects“.

So what shall the manager of a programme/portfolio do?

  • Find a right balance between control and freedom for the individual projects (self-organisation)
  • Enable information flow between the environment and the projects, as well as in between the projects (adaptability)
  • Adapt strategic objectives, while stabilising project deliveries (feedback)
  • Balance risk and value in the portfolio (emergence)
  • Use non-linear management tools (non-linearity), such as AHP, Outranking, mental modelling & simulation

Can Project Management Learn Anything from Studies of Failure in Complex Systems? (Ivory & Alderman, 2005)

Dienstag, August 12th, 2008

Complex Systems and Local Interventionism

Ivory, Chris; Alderman, Neil: Can Project Management Learn Anything from Studies of Failure in Complex Systems?; in: Journal of Project Management, Vol. 36 (2005), No. 3, pp. 5-16.

This article is similar to the Cooke-Davis et al. from 2007. In this article Ivory & Alderman describe complex systems as being tightly coupled thus showing high degrees of interdependencies and creating complex interactions. The authors show that projects as Complex Systems have five distinctive characteristics

  1. Non-linear interactions – surprising/unexpected outputs, non-equilibrium states, tipped by small events
  2. Emergence – multiple causes for failures, sub-systems prevent system melt-down, unpredictability of failures
  3. Conflicting objectives – sub-systems with different and conflicting goals, dominance of trade-off decisions, short-term orientation
  4. Overly centralized management – more than one centre exist, tighter control does not solve problems
  5. Multi-Nodality – open-textured and multi-nodal technologies are managed uniformly despite their dispersed (and often not understood) contexts

To counter-act the shortcomings of classical project management which relies on tight control and standardised processes & policies, Ivory & Alderman recommend what they call „Interventionism“. Interventionism or interventions on the ground is the „flexibility to usurp the chain of command in favour for technical expertise in times of stress“. Especially slack engineered into plans and processes allows local ‚cells‘ to deal with dysfunctions of the central control authority.
If that slack is not used for these corrections it usually is used for self-improvement and learning. In order to make such a system work the authors recommend implementing local empowerment to fix errors and centrally embed processes for organisational learning from mistakes.

Furthermore Ivory & Alderman’s case study is set in an high reliability organisations, which has only few resource constraints, shows a procedure-driven top down management, learns from mistakes, and embraces a safety culture. In their case study complexity arises not from technology but from goal confusion among different customers and is further increased by inexperienced contractors. The project decomposed the final product so it could be build in mixed teams. This multi-nodality showed some major shortcomings, e.g., bad news were withheld, integration problems are created, management of change requests becomes more resource consuming.

In this setting the authors found Interventionism most helpful. They observed how vendor-client task forces were established as autonomous cells. These cells worked in advance of official decisions in order not to delay the plan due to central decision backlogs. They saw increased communication among leaders of cells. Furthermore they found most effective if the project sponsor forces the project to abandon it’s natural short-term view by carrying the concerns of operations and fulfilment of business needs.

Large-scale projects, self-organizing and meta-rules: towards new forms of management (Jolivet & Navarre, 1996)

Montag, August 11th, 2008

New Approach to Manage Large-Scale Projects

Jolivet, F.; Navarre, C.: Large-scale projects, self-organizing and meta-rules: towards new forms of management; in: International Journal of Project Management, Vol. 14 (1996), No. 5, pp. 265-271.

This is one of the few articles dealing with the specifics of large-scale projects. Jolivet & Navarre argue that the traditional approach of pyramidal organisation, centralised control, standardisation of procedures, and reactive management are not suited to successfully execute a large-scale project.

Instead the authors recommend a new approach of autonomy, subsidiarity, and cellular division which is characterized by

  • Maximal individualisation
  • Differentiation of management styles and use of central meta-rules
  • Use of autonomous, self-organising teams
  • Central performance audits

They argue that large scale projects can regain speed if decision power is shifted to people on the ground and is not centrally bundled which creates a bottleneck around the central management team.  All  (sub)-projects in their case study are conducted along a specific and limited set of 12 principles which are all correlated with project success. In all other areas small scale teams have full decisional autonomy.