ERP Implementations: It’s a Matter of Complexity

 

There are many factors to blame for implementation process’ inefficiency, however many of the factors can be associated with the complexity of the project itself, respectively of the application(s) involved. The problem of complexity can be addressed by either answering to complexity with complexity, building a complex team to handle the tasks, which is seldom feasible even if many organizations do it, respectively by simplifying the implementation process and/or the application.

In what concerns the project, the complexity starts with requirement’s elicitation, the iterative transformations they suffer until the final functional requirements document is finalized, their evaluation and mapping to features, respectively gap’s identification. It’s a complex task because it involves understanding the business as well the functionality available in the target system(s). Then comes the effort estimation, which, as the name suggests, is just a guess based on available historical numbers and/or experts’ opinion. High-level requirements are easier to manage than low-level requirements, however they allow for more gaps in understanding. The more detailed the specifications, the more they should help in the estimation process, though that’s the theory. A considerable number of factors can impact the process.

Even if there are standard activities in the implementation process, the number of resources involved from the customer as well from the partner(s) side makes the whole planning process a nightmare for any Project Manager, no matter how experienced he/she is.

Ideally, each member of the team should behave like a trooper, knowing by instinct when and what needs to be done, which are the expectations, etc. This might be close to expectation on the partner side as the resources more likely participated in similar projects, though there’s always a mix between levels of expertise, resources migrating between projects. Unfortunately, that’s seldom (never) the case on the customer side as the gap between reality and expectation is considerable.

Each team member requires a minimum of information/knowledge so he/she can perform the activities assigned. Moreover, the volume of coordination and cooperation is considerably higher than in other projects, complexity that increases with organization’s size and is inverse proportional with organization’s maturity in managing projects and implementation-related activities. There’s thus a minimum of initial communication needed, and furthermore communication needs to occur between the parties involved. Moreover, the higher the lack of cohesion between the parties, the higher the need for communication and this applies especially when multiple organizations are involved in the project.

The triple constraint of Project Management between scope, cost, and time, respectively on quality has an important impact on the project. Resources need to be available when the project needs them and, especially on the partner side, only when they are needed. The implementation project to be feasible for the partner, its resources must work on several projects in parallel or the timing must be perfect, that no waiting times are involved, respectively the effort is concentrated only when needed. Such precision is possible maybe at project’s beginning, though the further the project evolves, the more challenging becomes the coordination of resources. Similar considerations apply to the customer as well.

Thus, a more realistic expectation is to have resources available only at certain points in time, and the resources should be capable of juggling between projects, respectively between project and other activities. Prioritizing is a must, and sometimes the operations or other projects have higher priority. When the time is not available, resources need to compromise by reducing the level of quality.

On the other side, it would be great if most of the effort could be concentrated at the beginning of the project, the later interactions being minimal.  

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Notes: Team Data Science Process (TDSP)

Team Data Science Process (TDSP)
• an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently [1]
• {goal} help customers fully realize the benefits of their analytics program [1]
• {component} data science lifecycle definition
  • {description} a framework to structure the development of data science projects [1]
  • {goal} designed for data science projects that ship as part of intelligent applications that deploy ML & AI models for predictive analytics [1]
  • {benefit} can be used in the context of other DM methodologies as they have common ground [1]
    • e.g. CRISP-DM, KDD
  • {benefit} exploratory data science projects or improvised analytics projects can also benefit from using this process [1]
• {component} standardized project structure
  • {description} a directory structure that includes templates for project documents
    • ⇒makes it easy for team members to find information [1]
    • ⇐templates for the folder structure and required documents are provided in standard locations [1]
    • all code and documents are stored in an agile VCS tracking repository [1]
      • {recommendation} create a separate repository for each project on the VCS for versioning, information security, and collaboration [1]
  • {benefit} organizes the code for the various activities [1]
  • {benefit} allows tracking the progress [1]
  • {benefit} provides checklist with key questions for each project to guarantee process and deliverables’ quality [1]
  • {benefit} enables team collaboration [1]
  • {benefit} allows closer tracking of the code for individual features [1]
  • {benefit} enables teams to obtain better cost estimates [1]
  • {benefit} helps build institutional knowledge across the organization [1]
• {component} recommended infrastructure
  • {description} a set of recommendations for the infrastructure and resources needed for analytics and storage [1]
  • {benefit} addresses cloud and/or on-premises requirements [1]
  • {benefit} enables reproducible analysis [1]
  • {benefit} avoids infrastructure duplication [1]
    • ⇒minimizes inconsistencies and unnecessary infrastructure costs [1]
  • {tools} tools are provided to provision the shared resources, track them, and allow each team member to connect to those resources securely [1]
  • {good practice} create a consistent compute environment [1]
    • ⇐allows team members replicate and validate experiments [1]
• {component} recommended tools and utilities
  • {description} a set of recommendations for the tools and utilities needed for project’s execution [1]
  • {benefit} help lower the barriers and increase the consistency of their adoption [1]
  • {benefit} provides an initial set of tools and scripts to jump-start methodology’s adoption [1]
  • {benefit} helps automate some of the common tasks in the data science lifecycle [1]
    • e.g. data exploration and baseline modeling [1]
  • {benefit} well-defined structure provided for individuals to contribute shared tools and utilities into their team's shared code repository [1]
    • ⇐ resources can then be leveraged by other projects [1]
• {phase} 1: business understanding
  • {goal} define and document the business problem, its objectives, the needed attributes, and the metric(s) used to determine project’s success
  • {goal} identify and document the relevant data sources
  • {step} 1.1: define project’s objectives
    • elicit together with the stakeholders the requirements, define and document the problem and its objectives, respectively the metric(s) used to determine project’s success
      • requires a good understanding of the business processes, data and further characteristics
  • {step} 1.2: identify data sources
    • identify the attributes and the data sources relevant to the problem under study
  • {step} 1.3: define project plan and team*
    • develop a high-level milestone plan and identify the resources needed for executing it
  • {tool} project charter
    • standard template that documents the business problem, the scope of the project, the business objectives and metric(s) used to determine project’s success
• {phase} 2: data acquisition & understanding
  • {goal} prepare the base dataset(s) as needed by the modeling phase into the target repository
  • {goal} build the data ETL/ELT architecture and processes needed for provisioning the basis data
  • {step} 2.1: ingest data
    • make the required data available for the team in the repository where the analytics operations take place
  • {step} 2.2: explore data
    • understand data’s characteristics by leveraging specific tools (visualization, analysis)
    • prepare the data as needed for further processing
  • {step} 2.3: set up pipelines
    • build the pipelines needed for data actualization and qualitative assessment [3]
    • set up a process to score new data or refresh the data regularly [3]
  • {step} 2.4: feasibility analysis*
    • reevaluate the project to determine whether the value expected is sufficient to continue pursuing it
  • {tool} data quality report
    • report that includes data summaries, data mappings, variable ranking, data qualitative assessment(s) and further information [3]
  • {tool} solution architecture
    • diagram and/or textual-based description of the data pipeline(s), technical assumptions and further aspects
  • {tool} data reports
    • document the structure and statistics of the raw data
  • {tool} checkpoint decision
    • decision template document that
      • summarizes the findings of the feasibility analysis step
      • includes a set of choices and recommendations for the next steps
      • serves as basis for the decision on whether to continue or not the project, respectively what the next steps are
• {phase} 3: modeling
  • {goal} create a machine-learning model that addresses the prediction requirements and that's suitable for production
  • {step} 3.1: feature engineering
    • the inclusion, aggregation, and transformation of raw variables to create the features used in the analysis [4]
      • ⇐requires a good understanding of how the features relate to each other and how the ML algorithms use those features [4]
  • {step} 3.2: model selection*
    • choose one or more modeling algorithms that address problem’s characteristics the best
  • {step} 3.3: model training
    • involves the following steps:
      • split the input data into training and test datasets
      • build the models by using the training dataset
      • evaluate the training and the test data set
      • determine the optimal setup and methods
  • {step} 3.4: model evaluation
    • evaluate the performance of the model(s)
  • {step} 3.5: feasibility analysis*
    • evaluate the readiness of the models for use into production, respectively on whether they fulfill project’s objectives
  • {tool} feature sets
    • describe the features developed for the modeling and how they were generated
    • contains pointers to the code used to generate the features
  • {tool} model report
    • a standard, template-based report that provides details on each experiment’s outcomes
    • created for each model tried
  • {tool} checkpoint decision
  • {tool} model performance metrics
    • e.g. ROC curves or MSE
• {phase} 4: deployment
  • {goal} deploy the models and the data pipelines to the environment used for final user acceptance
  • {step} 4.1: operationalize architecture
    • prepare the models and data pipelines for use into production
    • {best practice} expose the models over an open API interface
      • enables models’ consumption from various applications
    • {best practice} build telemetry and monitoring into the models and the data pipelines [5]
      • helps in monitoring and troubleshooting [5]
  • {step} 4.2: deploy solution*
    • deploy the architecture into production
  • {tool} status dashboard
    • displays data on system’s health and key metrics
  • {tool} model report
    • the report in its final form with deployment information
  • {tool} solution architecture
    • the document in its final form
• {phase} 5: customer acceptance
  • {goal} confirm that project’s objectives were fulfilled and get customer’s acceptance
  • {step} 5.1: system validation
    • validate system’s performance and outcomes and confirm that it fulfills customer’s needs
  • {step} 5.2: project signoff*
    • finalize and review documentation
    • handover the solution and afferent documentation to customer
    • evaluate the project against the defined objectives and get customer’ signoff
  • {tool} exit report
  • {tool} technical report
    • contains all the details of the project that are useful for learning about how to operate the system [6]

Acronyms:

Artificial Intelligence (AI)

Cross-Industry Standard Process for Data Mining (CRISP-DM)

Data Mining (DM)

Knowledge Discovery in Databases (KDD)

Team Data Science Process (TDSP) 

Version Control System (VCS)

Visual Studio Team Services (VSTS)

Resources:

[1] Microsoft Azure (2020) What is the Team Data Science Process? [source]

[2] Microsoft Azure (2020) The business understanding stage of the Team Data Science Process lifecycle [source]

[3] Microsoft Azure (2020) Data acquisition and understanding stage of the Team Data Science Process [source]

[4] Microsoft Azure (2020) Modeling stage of the Team Data Science Process lifecycle [source] 

[5] Microsoft Azure (2020) Deployment stage of the Team Data Science Process lifecycle [source]

[6] Microsoft Azure (2020) Customer acceptance stage of the Team Data Science Process lifecycle [source]

ERP Implementations: It’s all about Planning

Ideally the total volume of work can be uniformly distributed for all project’s duration though in praxis the curve representing the effort has the form of a wave or aggregation of waves that tend to reach the peak shortly before or during the Go-Live(s). More important, higher fluctuations occur in the various areas of the project on whole project’s duration, as there are dependencies between the various functional areas, as one needs to wait for decisions to be made, people are not available, etc. Typically, the time wasted on waiting, researching or other non-value-added activities have the potential of leading to such peaks. Therefore, the knowledge must be available, and decisions must be taken when required, which can be challenging but not impossible to achieve. 

To bridge the time wasted on waiting, the team members need to work on other topics. If on customer’s side the resources can handle maybe other activities, on vendor’s side the costs can be high and proportional with the volume of waiting. Therefore, vendor’s resources must be involved at least in two projects or do work in other areas in advance, which is not always possible. However, when vendor’s resources are involved in two or more projects, unless the planning is perfect or each resource can handle the work as it comes, there are further waiting times added. The customer is then forced either to book the resources exclusively, or to wait and carry the costs associated with it. 

On the other side ERP Implementations tend to become exploration projects, especially when the team has only partial knowledge about the system, or the requirements have a certain degree of specialization that deviates from the standard processes. The more unknowns an ERP implementation has, the more difficult is to plan. To be able to plan one must know the activities ahead, how long they take, and of course, one must adhere to the delivery dates, because each delay can have a cascading effect that can impact project’s schedule considerably. 

Probably the best approach to planning is to group the activities into packages and plan the packages, being in each subteam’s duty to handle the planning for each package, following to manage at upper level only the open issues, risks or opportunities. This shifts the burden from Project Manager’s shoulders within the project. Moreover, even if in theory the plan can consider each activity, it will become obsolete as soon it’s updated given the considerable volume of work requested to maintain it. Periodically, one can still revise the whole plan to identify opportunities and risks. What the team can do is to plan for a certain time interval (e.g. 4-6 weeks) and build from there. This allows focusing on the most important activities. 

To further shift the burden, activities like Data Migration, Data Cleaning or the integrations with third party systems should be treated when possible as subprojects. Despite having interdependencies with the main project (e.g. parameters, master data, decisions) and share same resources, they have their own schedule whose deadlines need to be aligned with main project’s milestones. 

Unless the team and business put all effort to respect the plan and, as long the plan is realistic, the initial plan can seldom be respected – it’s anyway just a sketch of the road ahead that can change as the project progresses – and this aspect needs to be understood by the business otherwise will lead to false expectations. On the other side, the team must try respecting the deadlines and communicate in time inability to do so. It’s an interplay in which communication is more important than ever.

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Project Management: Planning Correctly Misundersood IV

The relatively big number of Project Management (PM) methodologies considered nowadays makes it more and more difficult to understand the world of PM and make oneself understood, in the context in which terminology is used in explanations that defy the logic, in which people are stubborn in persisting that their understanding is the ultimate truth and, that between white and black there are no degrees of gray. Between all PM concepts project planning seems to be the most misunderstood, and this probably because all the activities revolve around it, while each methodology brings its own planning philosophy. Each methodology comes with its own story, its own imaginative description of what a perfect plan is about.

Independently of the methodology used there are three levels of planning. At highest level, the strategic one, the project is put in the context of other strategic activities – other projects and initiatives, as well business operations, competing altogether for the same financial and human resources.  At this level are the goals identified and put the basis for the successful execution of the project, including establishing the ground and integrating the main aspects of a project – risk, quality and communication. Here is decided which projects will be considered, in which sequence, how and when resources will be assigned. 

A project plan is typically written and further executed by having the tactical horizon in mind – the individual engagement of resources and actions, the actual means to reach the objectives set at strategic level. It’s the level where the actual project plan is detailed, where activities are sequenced and prioritized. Here each methodology has its own approach – whether the planning is done per deliverable, work package or any other approach used to partition the activities. It’s the level at which the various teams are coordinated toward specific targets. Thus the manageable unit is the team and not the individual, the deliverables or the work packages and not the individual tasks.

The operational level equates with the execution of a project’s activities. Even if the project manager oversights the activities, it’s in team’s duties to plan the activities having the set deliveries in mind. The project manager doesn’t need to know all the details, though he should be updated on a timely manner on the progress, the eventual risks and opportunities that arise in each area. This requires continuous coordination on vertical as well horizontal level.

The project manager typically oscillates between the strategic and tactical views of a project, while the operational level appears in the view only when operational themes are escalated or further coordination is needed. Even if this delimitation is clear in big projects, in the small projects the three levels melt into each other. Therefore the sprung from small to big projects and vice-versa can create issues when the approach is not tailored to project’s size and its further characteristics.

Attempting to plan each activity in the project at the lowest level of detail obscures the view, the complexity of the project kicking back sooner or later. Maintaining such a detailed plan can become a waste of time on the long term. In extremis a resource is used to update a plan, which easily can become obsolete by the time all activities were reviewed. This doesn’t mean that the project plan doesn’t need to be updated regularly, though the pace can be decided on each project’s specifics.

Therefore, one of the most important challenges in projects is finding the appropriate level of detail for planning, and there’s no general rule that works for all projects. Typically the choices alternate between work packages and deliverables. 

Project Management: Planing Correctly Misundersood III

One of the most misunderstood topics in Project Management seems to be the one of planning, and this probably because everyone has a good idea of what it means to plan an activity – we do it daily and most of the times (hopefully) we hit a bull’s-eye (or we have the impression we did that). You must do this and that, you have that dependency, you must coordinate with a few people, you must first reach that milestone before going further, you do one step at a time, and so on. It’s pretty easy, isn’t it?

From a bird’s eyes view project planning is like planning every other activity though there are several important differences. The most important one is of scale – the number of activities and resources involved, the level of coordination and communication, as well the quality with which occur, the level of uncertainty and control, respectively manageability. All these create a complexity that is hardly manageable by just one person. 

Another difference is the detail needed for the planning and targets’ reachability. Some believe that the plan needs to be done down to the lowest level of detail, which even if possible can prove to be an impediment to planning. Projects’ environment share some important characteristics with a battle field in terms of complexity of interactions, their dynamics and logistical requirements. Within an army’s structure there are levels of organization that require different mindsets and levels of planning. A general thinks primarily at strategic level in which troops and actions are seen as aggregations at the needed level of abstraction that makes their organization and planning manageable. The strategy is done however in collaboration with other generals and upper structures, while having defined the strategic goals the general must devise together with the immediate subalterns the tactics. In theory the project manager must regard the project from the same perspective. Results thus three levels of planning – strategic, done with the upper management, tactical done with the team members, respectively logistical, done within the team. That’s a way of breaking the complexity and dividing the responsibilities within the project. 

Projects’ final destination seem to have the character of a wish list more or less anchored in reality. From a technical point the target can be achievable though in big projects the most important challenges are of organizational nature – of being able to allocate and coordinate effectively the resources as needed by the project. The wish-like character is reflected also by the cost, scope, time triangle in respect to the expected quality – to some point in time one is forced to choose between two of them. On the other side, there’s the tendency to see the targets and milestones as fixed, with little room for deviation. One can easily forget that a strategic plan’s purpose is to set the objectives, identify the challenges and the possible lines of action, while a tactical plan’s objective is to devise the means to reach the objectives. Bringing everything together can easily obscure the view and, in extremis, the plan loses its actuality as soon was created (and approved). 

The most confusing aspect is probably the adherence of a plan to a given methodology, one dicing a project and thus a plan to fit a methodology by following blindly the rules and principles imposed by it instead of fitting the methodology to a project. Besides the fact that the methodologies are best practices but not necessarily good practices, what fits for an organization, they tend to be either too general, by specifying the what and not the how, or too restrictive (interpreted). 

Project Management: Some Thoughts on Planning II

A project’s dependency on resources’ (average) utilization time (UT) and quality expectations expressed as a quality factor (QF) doesn’t come as a surprise, as hopefully one is acquainted with project’s triangle which reflects the dependency between scope, cost and time in respect to quality. Even if this dependency is intuitive, it’s difficult to express it in numbers and study the way it affects the project. That was the purpose of the model built previously.

From the respective model there are a few things to ponder. First, it’s a utopia to plan with 90% UT, unless one is really sure that the resources have enough work to bring the idle time close to zero. A single person can achieve maybe a 90% UT if he works alone on the project, though even then there are phases in which the input or feedback from other people is necessary. The more people involved into the project and the higher the dependency between their activities, the higher the chances that the (average) UT will decrease considerably.

When in addition there’s also a geographical or organizational boundary between team members, the UT will decrease even more. In consequence, in big projects like ERP implementations the team members from customer and vendor side are allocated fully to the project; when this is not possible, then on the vendor side the consultants need to be involved in at least two projects to cover the idle time. Of course, with good planning, communication, and awareness of the work ahead one can try minimizing the idle time, though that’s less likely to happen.

Probably, a better idea would be planning with 75% or even 60% UT though the values depend on team's experience in handling similar projects. If the team members are involved also in operational activities or other projects, then a 50% UT is more realistic.

Secondly, in the previous post was considered in respect to quality the 80%-20% rule which applies to the various deliverables, though the rule has a punctual character. Taken on the average the rule is somehow attenuated. Therefore, in the model was considered a sprung between factors of 1 to 2 with a step of 0,25 for each 5% quality increase. It's needed to prove whether the values are realistic and how much they depend on project's characteristics.

On the other side, quality is difficult to quantify, and 100% quality is hypothetical. One discusses in theory about 3 sigma (the equivalent of 93,3 accuracy) or 4 sigma (99,4 accuracy) in respect to the number of errors found in the code, though from there on everything is fuzzy. In software projects each decision has the potential of leading to an error, and there’s lot of interpretability as long there’s no fix basis against to compare the deviations. One needs precise and correct specification for that.

I think that one should target in a first phase 80% quality (on average) and further build from there, try to improve the quality iteratively as the project goes on and as lessons are learned. In other words, a project plan, a concept, a design document doesn’t need to be perfect from the beginning but should be good enough to allow working with it. One can detail them as progress is made into the project, and hopefully their quality should converge to a value that is acceptable for the business.

Thirdly, in case a planning tool was used, one can use the model backwards to roughly prove timeline’s feasibility, dividing the planned effort by the estimated effort and the number of resources involved to identify the implied utilization time.  

Project Management: Some Thoughts on Planning I

One of the issues in Project Management (PM) planning is that the planner idealizes a resource and activities performed by it much like a machine. Unlike machines whose uptime can approach 100%, a human resource can work at most 90% of the available time (aka utilization time), the remaining 10% being typically associated with interruptions – internal emails and meetings, casual communications, pauses, etc. For resources split between projects or operations the utilization time can be at most 70%, however a realistic value is in general between 40% and 60% on average. What does it mean this for a project?

So, if a resource has a volume of work W, the amount of time needed to complete the work would be at best W/UT, where UT is the utilization time of the respective resource. “At best” because in each project there are additional idle time resulted from waste related activities – waiting for sign off, for information, for other resource to complete the time, etc.

The utilization time is not the only factor to consider. Upon case, the delivered work can reach maybe on average 80% of the expected quality. This applies to documentation and concepts as well for written code, bug testing and other project activities. To reach in the range of 100% one more likely will need 4 times of the effort associated with reaching 80% of the expected quality, however this value is dependent also on people’s professionalism and the degree with which the requirements were understood and possibly achievable. Therefore, the values vehiculated can be regarded as “boundary” values.

Let’s consider a quality factor (QF) which has a value of 1 for 80%, with an increase of 0,25 for each 5% of quality increase. Thus, with an initial effort estimation of 100 days, this is how the resulted effort modifies for various UT and QF values:

Considering that a project can target between 60% and 95% UT, and between 80% and 95% quality, for an initial estimation of 100 days the actual project duration can range between 117 and 292 days, where the lowest, respectively the right bound values are more realistic.

The model is simplistic as it doesn’t reflect the nonlinear aspect of the factors involved and the dependencies existing between them. It also doesn’t reflect the maturity of an organization to handle the projects and the tasks involved. However, it can be used to increase the awareness in how the utilization time and expected quality can affect a project’s timeline, and to check on whether one’s planning is realistic.

For example, at project’s start one can target an UT of 70% and a quality of 85%, which for 100 days of estimated effort will result in about 178 days of actual effort. Now diving the value by the number of resources involved, e.g. 4, it results that the project could be finished in about 44,5 days. This value can be compared then with the actual plan in which the activities are listed.

During the project it would be useful to look on how the UT changed and by how much, to understand the impact the change has on the project. For example, a decrease of 5% in utilization time can delay the project with 2,5 days which is not much, though for a project of 1000 days with talk already about one month. Same, it will be helpful to check how much the quality deviated from the expectation, because a decrease in quality by 5% can result in an additional effort of extra 8 days, which for 1000 days would mean almost 4 months of delay.

Software Engineering: Programming (Misconceptions about Programming II)

Continuation

One of the organizational stereotypes is having a big room full of cubicles filled with employees. Even if programmers can work in such settings, improperly designed environments restrict to a certain degree the creativity and productivity, making more difficult employees' collaboration and socialization. Despite having dedicated meeting rooms, an important part of the communication occurs ad-hoc. In open spaces each transient interruption can easily lead inadvertently to loss of concentration, which leads to wasted time, as one needs retaking thoughts’ thread and reviewing the last written code, and occasionally to bugs.

Programming is expected to be a 9 to 5 job with the effective working time of 8 hours. Subtracting the interruptions, the pauses one needs to take, the effective working time decreases to about 6 hours. In other words, to reach 8 hours of effective productivity one needs to work about 10 hours or so. Therefore, unless adequately planned, each project starts with a 20% of overtime. Moreover, even if a task is planned to take 8 hours, given the need of information the allocated time is split over multiple days. The higher the need for further clarifications the higher the chances for effort to expand. In extremis, the effort can double itself.

Spending extensive time in front of the computer can have adverse effects on programmers’ physical and psychical health. Same effect has the time pressure and some of the negative behavior that occurs in working environments. Also, the communication skills can suffer when they are not properly addressed. Unfortunately, few organizations give importance to these aspects, few offer a work free time balance, even if a programmer’s job best fits and requires such approach. What’s even more unfortunate is when organizations ignore the overtime, taking it as part of job’s description. It’s also one of the main reasons why programmers leave, why competent workforce is lost. In the end everyone’s replaceable, however what’s the price one must pay for it?

Trainings are offered typically within running projects as they can be easily billed. Besides the fact that this behavior takes time unnecessarily from a project’s schedule, it can easily make trainings ineffective when the programmers can’t immediately use the new knowledge. Moreover, considering resources that come and go, the unwillingness to invest in programmers can have incalculable effects on an organization performance, respectively on their personal development.

Organizations typically look for self-motivated resources, this request often encompassing organization’s whole motivational strategy. Long projects feel like a marathon in which is difficult to sustain the same rhythm for the whole duration of the project. Managers and team leaders need to work on programmers’ motivation if they need sustained performance. They must act as mentors and leaders altogether, not only to control tasks’ status and rave and storm each time deviations occur. It’s easy to complain about the status quo without doing anything to address the existing issues (challenges).

Especially in dysfunctional teams, programmers believe that management can’t contribute much to project’s technical aspects, while management sees little or no benefit in making developers integrant part of project's decisional process. Moreover, the lack of transparence and communication lead to a wide range of frictions between the various parties.

Probably the most difficult to understand is people’s stubbornness in expecting different behavior by following the same methods and of ignoring the common sense. It’s bewildering the easiness with which people ignore technological and Project Management principles and best practices. It resides in human nature the stubbornness of learning on the hard way despite the warnings of the experienced, however, despite the negative effects there’s often minimal learning in the process...

To be eventually continued…

Software Engineering: Programming (Misconceptions about Programming - Part I)

Software Engineering Series

Besides equating the programming process with a programmer’s capabilities, minimizing the importance of programming and programmers’ skills in the whole process (see previous post), there are several other misconceptions about programming that influence process' outcomes.

Having a deep knowledge of a programming language allows programmers to easily approach other programming languages, however each language has its own learning curve ranging from a few weeks to half of year or more. The learning curve is dependent on the complexity of the languages known and the language to be learned, same applying to frameworks and architectures, the scenarios in which the languages are used, etc. One unrealistic expectation is that the programmers are capablle of learning a new programming language or framework overnight, this expectation pushing more pressure on programmers’ shoulders as they need to compensate in a short time for the knowledge gap. No, the programming languages are not the same even if there’s high resemblance between them!

There’s lot of code available online, many of the programming tasks involve writing similar code. This makes people assume that programming can resume to copy-paste activities and, in extremis, that there’s no creativity into the act of programming. Beside the fact that using others’ code comes with certain copyright limitations, copy-pasting code is in general a way of introducing bugs in software. One can learn a lot from others’ code, though programmers' challenge resides in writing better code, in reusing code while finding the right the level of abstraction.  

 

There’s the tendency on the market to build whole applications using wizard-like functionality and of generating source-code based on data or ontological models. Such approaches work in a range of (limited) scenarios, and even if the trend is to automate as much in the process, is not what programming is about. Each such tool comes with its own limitations that sooner or later will push back. Changing the code in order to build new functionality or to optimize the code is often not a feasible solution as it imposes further limitations.

Programming is not only about writing code. It involves also problem-solving abilities, having a certain understanding about the business processes, in which the conceptual creativity and ingenuity of design can prove to be a good asset. Modelling and implementing processes help programmers gain a unique perspective within a business.

For a programmer the learning process never stops. The release cycle for the known tools becomes smaller, each release bringing a new set of functionalities. Moreover, there are always new frameworks, environments, architectures and methodologies to learn. There’s a considerable amount of effort in expanding one's (necessary) knowledge, effort usually not planned in projects or outside of them. Trainings help in the process, though they hardly scratch the surface. Often the programmer is forced to fill the knowledge gap in his free time. This adds up to the volume of overtime one must do on projects. On the long run it becomes challenging to find the needed time for learning.

In resource planning there’s the tendency to add or replace resources on projects, while neglecting the influence this might have on a project and its timeline. Each new resource needs some time to accommodate himself on the role, to understand project requirements, to take over the work of another. Moreover, resources are replaced on project with a minimal or even without the knowledge transfer necessary for the job ahead. Unfortunately, same behavior occurs in consultancy as well, consultants being moved from one known functional area into another unknown area, changing the resources like the engines of different types of car, expecting that everything will work as magic.

Continued…

Project Management: Agility under Eyeglasses II

Continuation

Employees are used to follow procedures and processes, and when they aren’t available insecurity rules - each day there’s another idea advanced how things are supposed to work. Practically, the Agile approaches (incl. Agile Prince2) focus on certain aspects and ignore specific Project Management activities that need to be performed inside of a project – releasing resources for the project, getting users on-board, getting management’s buy-in, etc. Therefore, they need to be used with a methodology that offers the lacking processes. Problematic is when is considered that the Agile approaches are self-consistent and the Project Management practices and principles don’t apply anymore.

It’s true that the Agile methods attempt reconciling disciplined project execution with creativity and innovation, however innovation is needed typically in design (incl. prototyping) , while in programing there isn’t lot of room for creativity per se. The real innovation appears when the customer lists the functionality it needs from a system and the vendor, after analyzing all the related requirements, is capable to evaluate and propose a solution from the industry trending technologies. That’s innovation and not changing controls in user interfaces!

User stories are good for situations in which an organization doesn’t know what’s doing or the tasks have a deep segmentation and specialization. Starting from user stories and building upwards to processes can prove to be a waste of time the customer pays for, while the approach leaves few room for innovation. In big projects it’s also difficult to sense the contradictions from user stories or their duplication. Even if the user stories allow maybe (but not necessarily) a better effort estimate the level of detail can become overwhelming for any skilled solution architect.

It’s also true that an agile approach needs a culture with certain characteristics. A culture can’t be changed with one project or several projects running in parallel. Typically, is recommended to start with a pilot test, assert organization’s readiness, disseminate knowledge, start several small to medium projects and build from there. For sure starting a big project with an agile methodology  will involve more challenges to the extent the challenges will push back.

One sign of agility is when self-organizing teams emerge within projects, however it takes time and training to build such teams. The seeds must be planted long before, for such teams to emerge. The key is being able of working in such teams. In extremis, conflicts appear when multiple self-organizing teams appear, each with its own political agenda, agendas that don’t necessarily match project manager or stakeholders’ agendas, and from here a large range of potential conflicts.

The psychological effect of tight sprints (iterations) and daily status meetings for the whole duration of a project is not to neglect. It builds unnecessary stress and, unless the planning reaches perfection, the programmer or consultant will often find himself in the position to be in defensive. The frequent meetings can easily become a source for nuisance and in extremis can lead to extreme behavior that can easily affect the productivity and involved persons’ health.

Personally, I wouldn’t recommend using an Agile methodology for a big project like an ERP implementation unless it was adequately adapted to organization’s needs. This doesn’t necessarily mean that the Agile methods aren’t suitable for big projects, it means that the risks are high because in big projects there’s the chance for all these mentioned issues to occur.

Despite the weak points of the Agile methods, when adequately applied, they have the chance of better performing than the “traditional” approaches. Even if people tend to see more the negative sides there’s lot of potential in being agile.

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Project Management: Agility under Eyeglasses I

There are more and more posts in the cyberspace voicing against the agile practices, the way they are understood and implemented by organizations. Some try to be hilarious [5]; others try to keep the scholastic seriousness [1] [2] [3] [4], and all of them make some valid points. In each remark there’re some seeds of truth, even if context-dependent.

Personally, I embrace an agile approach when possible, however I find it difficult to choose between the agile methodologies available on the market because each of them introduces some concepts that contradict what it means to be agile – to respond promptly to business needs. It doesn’t mean that one must consider each requirement, but that’s appropriate to consider those which have business justification. Moreover, organizations need to adapt the methodologies to their needs, and seldom vice-versa.

Considering the Agile Manifesto, it’s difficult to take as serious statements that lack precision, formulations like “we value something over something else” are more of a wish than principles. When people don’t understand what the agile “principles” mean, one occasionally hears statements like “we need no documentation”, “we need no project plan”, “the project plan is not important”, “Change Management doesn’t apply to agile projects” or “we need only high-level requirements because we’ll figure out where we’re going on the way”. Because of the lack of precision, a mocker can variate the lesser concept to null and keep the validity of the agile “principles”.

The agile approaches seem to lack control. If you’re letting the users in charge of the scope then you risk having a product that offers a lot though misses the essential, and thus unusable or usable to a lower degree. Agile works good for prototyping something to show to the users or when the products are small enough to easily fit within an iteration, or when the vendor wants to gain a customer’s trust. Therefore, agile works good with BI projects that combine in general all three aspects.

An abomination is the work in fix sprints or iterations of one or a few weeks, and then chopping the functionality to fit the respective time intervals. If you have the luck of having sign-offs and other activities that steal your time, then the productive time reduces up to 50% (the smaller the iterations the higher the percentage). What’s even unconceivable is that people ignore the time spent with bureaucracy. If this way of working repeats in each iteration then the project duration multiplies by a factor between 2 or 4, the time spent on Project Management increasing by the same factor. What’s not understandable is that despite bureaucracy the adherence to delivery dates, budget and quality is still required.

Sometimes one has the feeling that people think that software development and other IT projects work like building a house or like the manufacturing of a mug. You choose the colors, the materials, the dimensions and voila the product is ready. IT projects involve lot of unforeseen and one must react agilely to it. Here resides one of the most important challenges.   

Communication is one important challenge in a project especially when multiple interests are involved. Face-to-face conversation is one of the nice-to-have items on the wish list however in praxis isn’t always possible. One can’t expect that all the resources are available to meet and decide. In addition, one needs to document everything from meeting minutes, to Business Cases and requirements. A certain flexibility in changing the requirements is needed though one can’t change them arbitrarily, there must be a concept behind otherwise the volume of overwork can easily make the budget for a project explode exponentially.

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Resources:
[1] Harvard Business Review (2018) Why Agile Goes Awry - and How to Fix It, by Lindsay McGregor & Neel Doshi (Online) Available from: https://hbr.org/2018/10/why-agile-goes-awry-and-how-to-fix-it
[2] Forbes (2012) The Case Against Agile: Ten Perennial Management Objections, by Steve Denning  (Online) Available from:
https://www.forbes.com/sites/stevedenning/2012/04/17/the-case-against-agile-ten-perennial-management-objections/#6df0e6ea3a95 
[3] Springer (2018) Do Agile Methods Work for Large Software Projects?, by Magne Jørgensen  (Online) Available from:
https://link.springer.com/chapter/10.1007/978-3-319-91602-6_12
[4] Michael O Church (2015) Why “Agile” and especially Scrum are terrible  (Online) Available from:
https://michaelochurch.wordpress.com/2015/06/06/why-agile-and-especially-scrum-are-terrible/
[5] Dev.to (2019) Mockery of agile, by Artur Martsinkovskyi (Online) Available from: https://dev.to/arturmartsinkovskyi/mockery-of-agile-5bdf

Project Management: Planning Correctly Misundersood I

It is sometimes helpful to take a step back, observe, and then logically generalize the extremes of the observed facts; if possible, without judging people’s behavior as there’s more to it as the eyes can perceive. In some cases however one can feel that the observed situations are really close to extreme. It’s the case of some tendencies met in project planning - not planning, planning for the sake of planning, expecting a plan to be perfect, setting a plan as fix, without the possibility of changing it in utile time, respectively changing the plan too often.

There are situations in which it’s better to be spontaneous and go with the flow. Managing a project isn’t one of these situations. As Lakein’s Law formulates it succinctly: “failing to plan is planning to fail”, or paraphrasing Eisenhower (1) and Clausewitz (2) - plans are useless as no plan ever survived contact with the enemy (reality), but planning is indispensable - as a plan increases awareness about project’s scope, actions, challenges, risks and opportunities, and allows devising the tactics and logistics needed to reach the set goals. Even if the plan doesn’t reflect anymore the reality, it can still be adapted to fit the new requirements. The more planning experience one has the more natural it becomes to close the gap between the initial plan and reality, and of adapting the plan as needed.

There’s an important difference between doing something because one is forced to do it and doing it because one sees and understands the value of planning. There's the tendency to plan for the sake of planning, because there's the compel to do it. Besides the fact that it documents the what, when, why and who, and that is used as a basis for action, the plan must reflect project’s current status and the activities planed for the next reporting cycle. As soon a plan is not able to reflect these aspects it becomes thus in time unusable.

The enemy of a good plan can prove to be the dream of a perfect plan (3). Some may think that the holy grail of planning is the perfect plan, that the project can’t start until all the activities were listed to the lowest detail and the effort thoroughly assigned. Few plans actually survive the contact with the reality and there can be lot of energy lost by working on the perfect plan.

Another similar behavior,  rooted mainly in the methodologies used, is that of not allowing a plan to be changed for a part or whole duration of the project. Publilius Syrus recognized more than two millennia ago that a plan that admits no modification is a bad plan (4) per se. Methodologies and practices that don’t allow a flexible way of changing the plan make no service to projects. Often changes need to occur immediately and not at an ideal point in time, when maybe the effect is lost.

Modern Project Management tools allow building the dependencies between the various activities and it’s inevitable that a change in one place will cause a chain reaction and lead to a contraction or dilatation of the plan, and this can happen with each planning iteration. In extremis the end date will alternate as the lines of a seismograph during an earthquake. It’s natural for this to happen in projects in a first phase, however it’s in Project Manager’s attribution to mitigate such variations.

The project plan is a reflection of the project and how it’s managed, therefore, one needs to give it the proper focus, how often and how detailed required.

Referenced quotes:
(1) “In preparing for battle I have always found that plans are useless, but planning is indispensable” (Eisenhower quoted by Nixon)
(2) “No plan ever survived contact with the enemy. ” (Carl von Clausewitz)
(3) “The enemy of a good plan is the dream of a perfect plan.” (Carl von Clausewitz)
(4) "It's a bad plan that admits of no modification." (Publilius Syrus)

Project Management: Planning Correctly Misundersood II

Even if planning is the most critical activity in Project Management it seems to be also one of the most misunderstood concepts. Planning is critical because it charters the road ahead in terms of what, when, why and who, being used as a basis for action, communication, for determining the current status in respect to the initial plan, as well the critical activities ahead.

The misunderstandings derive maybe also from the fact that each methodology introduces its own approach to planning. PMI as traditional approach talks about baseline planning with respect to scope schedule and costs, about management plans, which besides the theme covered in the baseline, focus also on quality, human resources, risks, communication and procurement, and separate plans can be developed for requirements, change and configuration management, respectively process improvement. To them one can consider also action and contingency planning.

In Prince2 the product-based planning is done at three levels – at project, stage, respectively team level – while separate plans are done for exceptions in case of deviations from any of these plans; in addition there are plans for communication, quality and risk management. Scrum uses an agile approach looking at the product and sprint backlog, the progress being reviewed in stand-up meetings with the help of a burn-down chart. There are also other favors of planning like rapid application planning considered in Extreme Programming (XP), with an open, elastic and undeterministic approach. In Lean planning the focus is on maximizing the value while minimizing the waste, this being done by focusing on the value stream, the complete list of activities involved in delivering the end-product, value stream's flow being mapped with the help of visualization techniques such as Kanban, flowcharts or spaghetti diagrams.

With so many types of planning nothing can go wrong, isn’t it? However, just imagine customers' confusion when dealing with a change of methodology, especially when the concepts sound fuzzy and cryptic! Unfortunately, also the programmers and consultants seem to be bewildered by the various approaches and the philosophies supporting the methodologies used, their insecurity bringing no service for the project and customers’ peace of mind. A military strategist will more likely look puzzled at the whole unnecessary plethora of techniques. On the field an army has to act with the utmost concentration and speed, to which add principles like directedness, maneuver, unity, economy of effort, collaboration, flexibility, simplicity and sustainability. It’s what Project Management fails to deliver.

Similarly to projects, the plan made before the battle seldom matches the reality in the field. Planning is an exercise needed to divide the strategy in steps, echelon and prioritize them, evaluate the needed resources and coordinate them, understand the possible outcomes and risks, evaluate solutions and devise actions for them. With a good training, planning and coordination, each combatant knows his role in the battle, has a rough idea about difficulties, targets and possible ways to achieve them; while a good combatant knows always the next action. At the same time, the leader must have visibility over fight’s unfold, know the situation in the field and how much it diverged from the initial plan, thus when the variation is considerable he must change the plan by changing the priorities and make better use the resources available.

Even if there are multiple differences between the two battlefields, the projects follow the same patterns of engagement at different scales. Probably, Project Managers can learn quite of a deal by studying the classical combat strategists, and hopefully the management of projects would be more effective and efficient if the imperatives of planning, respectively management, were better understood and addressed.

Data Migrations (DM): Approaches to Planning a Data Migration for an ERP Implementation

Data Migrations Series

Introduction

ERP implementations are one of the most complex projects to plan as they often imply changes/transformations at different levels (e.g. strategic, processes, data, cultural, technological), span over one or more years, involve many resources that need to be efficiently managed, and often come with important costs for the organization.

One way of handling complexity is to ignore the nonessential in planning by focusing on the important activities/phases, following to go deeper as the project progresses. Another way to handle complexity is to split it at manageable parts – identifying and grouping together components. For example, Data Migration (DM) and Data Quality (DQ) are managed as subprojects, with their own planning. The two strategies can be combined to increase the effect.

Planning a DM cannot be done without looking at the timelines of the ERP implementation and considering the various interfaces to the DQ, however in this post I will focus only on the first two.

The Context

In the context of an ERP implementation there are three main approaches to the planning of a DM – pushing the activities toward the end of the implementation, pushing most activities toward the beginning of the implementation, or splitting the various activities over the whole timeline of the ERP implementation. Borrowing a term from statistics, we can talk about a left-skewed plan, a right-skewed plan, respectively a uniform-distributed plan.

For exemplification I will use a set of Lego pieces grouped together in 3 rows and representing the main phases of an ERP implementation, DM, respectively DQ:

The Lego pieces are a good tool for representing the phases, even they can be mischievous because there’s often no clear delimitation between certain phases as they overlap or repeat over several iterations, and bricks’ length doesn’t necessarily represent the actual duration of the phases. In addition, the phases are oversimplified in order not to clutter the diagrams. The detailed phases will be considered in further posts. The color changes gradually as the activities get closer toward the end.

Left-Skewed Planning

One way to plan a DM is backwards from the Go-Live, the DM activities flowing continuously backwards (the DM bricks are arranged from the end over the implementation bricks) and thus accumulating toward the end of the ERP implementation. This approach is natural considering that the requirements stabilize in the second half of the implementation, the code freeze occurring toward the end. Stabilizing means that the most important code changes were performed, and only minor changes need to be performed, typically bug fixing, refactoring or last-minute changes.

The DM starts with a set of requirements in what concerns the business processes, the data and configuration. Each change to these requirements equate with overwork that need to be performed. Typically, this happens only at entity level, however there can be changes that have impact for the whole or important parts of the migration. Additionally, after each set of changes another dry-run needs to be performed in order test the changes. Therefore, to minimize the volume of rework a DM needs a stable environment – in other words a stabile data model, configuration and requirements.

Thus, the conceptualizing of the migration including the prototyping can start in the first half of the implementation or, for long-running projects, even in the second half of the implementation. Performing the DM without interruptions assures an optimal use and planning of the resources – the resources are continuously working on the project, they are focused toward the end.

On the other side, the accumulation of the activities toward the end can easily lead to problems in what concerns resources’ availability. This type of approach needs a good planning, otherwise the project runs into the risk of having the Go-Live delayed until the stability of the DM is assured, or of going Live with data that don’t have the expected quality. These risks can be alleviated by adding a puffer to DM’s timeline, or by considering one of the other two approaches.

This approach minimizes the various types of waste associated with software projects, and thus the costs associated with waste.

Right-Skewed Planning

To release the pressure existing toward the end of the project, some of the DM activities can be performed toward the beginning of the project – the conceptualization and prototyping, as well the data mapping. One can in theory build also an important part of the DM for the standard functionality, following to address the changes in data model, processes and configuration in subsequent iterations. This could involve a higher volume of rework and more dry-runs, however this depends on the complexity and number of the changes. If a small number of customizations are expected, then this approach may be the best approach. Even in the case of many customization this approach might be something to consider, however the DM costs increase with the number of customization made, and in certain contexts the increase can be exponential.

This approach pushes some of the costs toward the beginning of the implementation, and this can have positive as well negative aspects. For example, it is well-known that ERP implementations involve cost overruns. With this approach the DM costs are assured toward the beginning and one can better get a hold of the budget, at least in theory. As negative aspect could be considered the cases in which an ERP implementation is stopped toward the middle of the project, the incurred costs being thus higher. In the end the main cost-driver are the volume of customizations.

Breaking a DM in two can have several other negative aspects. The data cleaning needs to be broken eventually as well, most probably in the second phase more data enrichment activities need to be considered.

The resources that worked on the first phase might not be available for the second phase. An adequate knowledge transfer might be hard to make, so the second team might need good documentation or time to understand the solution. This can lead to other type of behavior, e.g. rewriting unnecessarily the code, the push for a redesign, and so on.

As the environment stabilizes much later, there is the risk that an important part of the migration need to be reworked/redesigned. In extreme cases might be needed to start from zero. The chances for this to happen are small, though such a case can occur. Probably some of the code, transformation can be reused, though this depends from case to case. Without knowing implementation’s details it’s difficult to estimate the chances for something like this to happen. Sometimes is enough to invalidate a premise considered in design phase. Usually the interplay between several new requirements lead to redesign.

Uniform-distributed Plan

To alleviate the risks from the first two approaches, some of the activities could be uniformly distributed over the whole duration of the ERP implementation. This approach works well when same resources from the vendor side are involved in activities from all the three layers, the nature of the tasks allowing them to work continuously in the project. For example, the consultants working on the DM concept are helping on the mapping of the attributes, as well on data cleansing. When the work on multiple activities isn’t possible then the vendor(s) more likely will have problems in assigning resources to the project. Either the same resources will be assigned for big parts from the projects, incurring thus higher costs, or the resources will be replaced by others, additional learning being involved. In either case the costs are higher.

One of the main dangers of this approach is that certain activities will expand taking the time available, incurring thus higher costs. When the Implementation time is much higher than DM’s duration, the distance between DM’s phases can increase dramatically, being almost impossible to manage resources adequately. Keeping the metaphor of the Lego pieces, it will be thus also more difficult to build a structure on which an edifice can be built. With proper planning and adequate use of resources and knowledge the empty spaces can be incorporated in the structure for project’s advantage.

Even if this approach attempts to even the DM effort over the whole duration of the ERP implementation, performing the activities too early, before requirements stabilized can have an adverse effect.

Personal Approach

Looking back at the projects I worked on, I think I used a hybrid between the 3 approaches. The DM was planed backwards from the Go Live, however the first draft of the DM concept and prototyping was performed at the beginning of the implementation. This assured that the technical solution was working. Being involved in the creation of the data mappings as well in data cleansing, the jumps between activities allowed me to smoothly switch between the various activities, however toward the end of the project this became a bottleneck, the activities being harder to synchronize, and the volume of work could be addressed at that time only with overtime.

With a few exceptions I worked mainly alone on the technical activities, being responsible for the data mappings, design, prototyping, implementation, testing, protocolling, and execution of the DM. I think that more resources would have removed some of the burden but made the planning more complicated and the synchronization even harder. Probably a team of 2-3 people that could cover these activities would provide the optimum balance between costs, effort and quality.

Conclusion

I suppose there is no best solution that will work for all. The three approaches are more an attempt to highlight some of the extreme usages of planning. In an ERP implementation there are so many factors, so many chances for a decision to be an opportunity or a threat. My advice – ponder the various aspects/constraints, choose an approach, and adjust it as the project advances.

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Project Management: Project Planning (Just the Quotes)

"Planning starts usually with something like a general idea. For one reason or another it seems desirable to reach a certain objective, and how to reach it is frequently not too clear. The first step then is to examine the idea carefully in the light of the means available. Frequently more fact-finding about the situation is required. If this first period of planning is successful, two items emerge: namely, an 'over-all plan' of how to reach the objective and secondly, a decision in regard to the first step of action. Usually this planning has also somewhat modified the original idea. The next period is devoted to executing the first step of the original plan." (Kurt Lewin, "Action research and minority problems", 1946)

"Every company has beloved projects on which if prices had held up, if the contractors had finished on time (or finished at all), if the plans hadn't been altered, if the thing had actually worked, the planned return would have been earned. But since some or all of these calamities [things that don't go as expected] usually happen, any manager who neglects to allow for them is not planning - merely thinking wishfully. Desire for the project has, as usual, overtaken desire for profit." (Ernest Dale, "Planning and developing the company organization structure", 1952)

"Project management is the process by which it is assured that the objective is achieved and resources are not wasted. Planning is one of the two parts of project management. Control is the other. [...] Each project must first be planned in detail. Control is involved with comparing actual progress with the plan and taking corrective action when the two do not correspond. Without the plan, true control is not possible; the need for corrective action, its nature, extent, and urgency cannot he accurately determined." (Robert D Carlsen & James A Lewis, "The Systems Analysis Workbook: A complete guide to project implementation and control", 1973)

"Since software construction is inherently a systems effort - an exercise in complex interrelationships - communication effort is great, and it quickly dominates the decrease in individual task time brought about by partitioning [increasing the workers]. Adding more people then lengthens, not shortens, the schedule." (Frederick Brook, "The Mythical Man-Month", 1975)

"Because one has to be an optimist to begin an ambitious project, it is not surprising that underestimation of completion time is the norm." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

"If we decide to plan not to lose, we take a defensive posture in which we expend huge amounts of effort trying to prevent and track errors. This will lead us to a very heavyweight planning process in which we try to plan everything up front in a much detail as possible. Such a process will have many review steps, sign-offs, authorizations, and phase gates. Such a planning process is highly adept at making sure that blame can be assigned when something fails; but takes no direct steps towards making sure that the right system is delivered at a reasonable cost." (Kent Beck & Martin Fowler, "Planning Extreme Programming", 2000)

"One of the purposes of planning is we always want to work on the most valuable thing possible at any given time. We can’t pick features at random and expect them to be most valuable. We have to begin development by taking a quick look at everything that might be valuable, putting all our cards on the table. At the beginning of each iteration the business (remember the balance of power) will pick the most valuable features for the next iteration." (Kent Beck & Martin Fowler, "Planning Extreme Programming", 2000)

"Planning is not about predicting the future. When you make a plan for developing a piece of software, development is not going to go like that. Not ever. Your customers wouldn’t even be happy if it did, because by the time software gets there, the customers don’t want what was planned, they want something different." (Kent Beck & Martin Fowler, "Planning Extreme Programming", 2000)

"Projects sometimes fail long before they deliver anything. At some point they may be determined to be too expensive to continue. Or perhaps they took too long to develop and the business need evaporated. Or perhaps the requirements change so often that the developers can never finish one thing without having to stop and start all over on something new. Certainly these are planning failures." (Kent Beck & Martin Fowler, "Planning Extreme Programming", 2000)

"There are two ways to approach prevention of these planning failures. We can plan not to lose, or we can plan to win. The two are not identical. Planning not to lose is defensive; while planning to win is aggressive. [...] the problem that planning is supposed to solve is simply, to build the right system at the right cost. If we take a defensive posture by planning not to lose, we will be able to hold people accountable for any failures; but at an enormous cost. If we take an aggressive posture and plan to win, we will be unafraid to make errors, and will continuously correct them to meet our goals." (Kent Beck & Martin Fowler, "Planning Extreme Programming", 2000)

"We plan because: We need to ensure that we are always working on the most important thing we need to do. We need to coordinate with other people. When unexpected events occur we need to understand the consequences for the first two." (Kent Beck & Martin Fowler, "Planning Extreme Programming", 2000)

"When we plan to win we take direct steps to ensure that we are building the right system at the best possible cost. Every action we take goes towards that end. Instead of trying to plan everything up front, we plan just the next few steps; and then allow customer feedback to correct our trajectory. In this way, we get off the mark quickly, and then continuously correct our direction. Errors are unimportant because they will be corrected quickly." (Kent Beck & Martin Fowler, "Planning Extreme Programming", 2000)

"If you have no plan, you cannot have control, by definition, because it is your plan that tells where you are supposed to be in the first place. Further, if you don’t know where you are, you can’t have control. This comes from your information system. Most organizations have difficulties with both of these." (James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"No project can succeed when the team members have no commitment to the plan, so the first rule of project planning is that the people who must do the work should help plan that part of the project. You will not only gain their commitment to the plan, but also most likely cover all of the important issues that you may individually have forgotten."(James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"The big fallacy in our assumptions is that the world will stand still while we execute our project plan." (James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"Project planning is the key to effective project management. Detailed and accurate planning of a project produces the managerial information that is the basis of project justification (costs, benefits, strategic impact, etc.) and the defining of the business drivers (scope, objectives) that form the context for the technical solution. In addition, project planning also produces the project schedules and resource allocations that are the framework for the other project management processes: tracking, reporting, and review." (Rob Thomsett, "Radical Project Management", 2002)

"If you've been in the software business for any time at all, you know that there are certain common problems that plague one project after another. Missed schedules and creeping requirements are not things that just happen to you once and then go away, never to appear again. Rather, they are part of the territory. We all know that. What's odd is that we don't plan our projects as if we knew it. Instead, we plan as if our past problems are locked in the past and will never rear their ugly heads again. Of course, you know that isn't a reasonable expectation." (Tom DeMarco & Timothy Lister, "Waltzing with Bears: Managing Risk on Software Projects", 2003)

"The pathology of setting a deadline to the earliest articulable date essentially guarantees that the schedule will be missed." (Tom DeMarco & Timothy Lister, "Waltzing with Bears: Managing Risk on Software Projects", 2003)

"Ending up somewhere entirely different from where you expected to go is the norm in this world. Software projects are prime illustrations of the law of unintended consequences, and their innovations and breakthroughs are more often side effects than planned outcomes." (Scott Rosenberg, "Dreaming in Code", 2007)

"[…] in software development, as in all things, plans get dodgier the farther into the future one looks. Any developer who has been around the block will admit that the cavalcade of methodologies over three decades of software history has left the field richer and given programmers useful new tools and ways of thinking about their work. But finding a developer or team that actually subscribes to a particular methodology isn’t easy." (Scott Rosenberg, "Dreaming in Code", 2007)

"The picture of digital progress that so many ardent boosters paint ignores the painful record of actual programmers’ epic struggles to bend brittle code into functional shape. That record is of one disaster after another, marking the field’s historical time line like craters. Anyone contemplating the start of a big software development project today has to contend with this unfathomably discouraging burden of experience. It mocks any newcomer with ambitious plans, as if to say, What makes you think you’re any different?" (Scott Rosenberg, "Dreaming in Code", 2007)

"Users may be annoyed by bugs, and software developers may be disappointed by their inability to perfect their work, and managers may be frustrated by the unreliability of their plans. But in the end, none of that matters as much as the simple fact that software does not work the way we think, and until it does, it is not worth trying to perfect." (Scott Rosenberg, "Dreaming in Code", 2007)

"And even if we make good plans based on the best information available at the time and people do exactly what we plan, the effects of our actions may not be the ones we wanted because the environment is nonlinear and hence is fundamentally unpredictable. As time passes the situation will change, chance events will occur, other agents such as customers or competitors will take actions of their own, and we will find that what we do is only one factor among several which create a new situation." (Stephen Bungay, "The Art of Action: How Leaders Close the Gaps between Plans, Actions, and Results", 2010)

"A project plan is a prediction. It predicts that a team of N people will complete X amount of work by Y date." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Development is a design process. Design processes are generally evaluated by the value they deliver rather than a conformance to plan. Therefore, it makes sense to move away from plan-driven projects and toward value-driven projects. […] The realization that the source code is part of the design, not the product, fundamentally rewires our understanding of software." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"The planning fallacy is the systematic tendency for project plans and budgets to undershoot. […] The reasons for the planning fallacy are partly psychological, partly cultural, and partly to do with our limited ability to think probabilistically." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"An effort estimate is not complete without including its assumptions. Estimate assumptions include any and all underlying factors the estimate relies upon. Assumptions are especially important in more rigid estimation environments, but they are a good practice even where expectations are more flexible. Explicitly listing all assumptions helps to remove ambiguity and avoid misunderstandings during project delivery." (Morgan Evans, "Engineering Manager's Handbook", 2023)

"Plans allow us to think through objectives beforehand in the hope of being prepared for delivery. Plans are useful when they preempt conflict, direct efforts in harmony, and align expectations. Plans are not useful when they waste valuable build time or provide a false sense of security, for example, by missing unknown unknowns." (Morgan Evans, "Engineering Manager's Handbook", 2023)