Showing posts with label Management. Show all posts
Showing posts with label Management. Show all posts

26 April 2026

(AI) Technology doesn't democratize efficiency. It benefits early adopters

Technology doesn't democratize things. Initially, it benefits early adopters. Those with the resources and education to adopt and adapt fast. 


The new tech buzzword is: AI agents make everybody more efficient.

Yes, it does. But not equally.


It's a good punchline and buzzword, saying that technology democratizes things - information, knowledge, education, transportation, resources, communication, or, with contemporary AI agents: work efficiency.


But this is rarely true.

I don't remember any technological innovation who really democratized things - at least at first. 

Technology tends to benefit first the rich and the educated - those who already have the capacity to invest, absorb and adapt faster. 


Yes, technology trickles down, eventually. But not immediately. 


It was the same with automobiles - first adopters were rich people; later beneficiaries were industrialists who sold (Ford Model T) affordable cars to the working class. 

Same with writing, printing, books, and newspapers: for hundreds of years, the beneficiaries were still the upper and middle classes, who were already literate, or had the resources to educate their children. For hundreds of years after Gutenberg, the majority of the population remained illiterate and didn't really benefit. Intergenerational mobility was significantly lower at the time, when education was a luxury, and most children had to start working VERY early in factories or agriculture. 


Same with the internet and email: first few years, it was reserved for universities, researchers, top tech entrepreneurs, academia, financial magnates - not everybody. 


So no, AI is not for everybody, it doesn't democratize first, and it doesn't benefit everybody equally. We have to move and adapt fast.


New relevant study (from Marius Comper): 

https://www.ft.com/content/0873e3cb-cb02-4b47-941f-14da74149670?fbclid=IwdGRjcARa7tVjbGNrBFruyGV4dG4DYWVtAjExAHNydGMGYXBwX2lkDDM1MDY4NTUzMTcyOAABHtvpmg9E9NL4vg8-W7XJcFqyz5I2YLwqWuTkFra4mwAO2yOhKZZzgCDBPoPh_aem_WAnPyG78hQSzOTJtuOIDVw


See also discussion 

https://www.facebook.com/share/p/18YjsXCRPB/


14 February 2026

AI replaces software programmers and composers, yet?

After three years of AI revolution, I still believe that major technological revolutions create jobs and businesses. They don’t create unemployment. The invention of the plow didn’t put gatherers out of work. The printing press sparked a phenomenal boom in the book industry and jobs: printing houses, publishers, libraries, writers, translators.


AI won’t lead to the disappearance of software and technology companies either. It will multiply them. 


(Rant triggered by very cool discussions with Vlad Coroama, Mihai Amariutei, Alexander Kruel).


As for how much software programming will actually change, I have reservations. 


I first said it 25 years ago: Software developers are cheap, and replaceable. But software is much more than that. We need architects and engineers; not coders. We need people who understand mathematics, algorithms, data, patterns, design, methodology. "Writing code" is a low-level job, and requires low-level qualifications.


This is even more true today - when AI replaces more and more low-level jobs.



But:

DOES AI REPLACE REAL DEVELOPERS?


=> NO.


We write code every day, at Hermix. With AI. 

From our experience, AI isn’t miraculous, yet. Surely it helps. But it doesn’t replace us - it just helps us. It is a fast, limited assistant.



SO WHAT ABOUT ALL THE STORIES ABOUT VIBE CODING?


Every day, we read about a new AI agent writing an entire application from scratch. 

An AI agent wrote a C compiler. Another migrated an Nintendo game to run in the browser. A non-tech guy built an entire web app in 10 minutes. An AI invented a new game (ask for links).


Are these true?

Well, some are true, but they are anecdotic, exaggerated, not replicable.


If you examine them closely, none is a genuine example of truly innovative real-world application, built from scratch without human help.


It is trivial to write a C compiler in 2026. We already have thousands of open-source C compilers. 

It is trivial to rewrite an application from one language to another.

AI-generated code has security / architecture /design problems, needs huge corrections and rewrites to make it viable.



I keep an open mind, I read and follow developments, I test new stuff (never enough). But I haven’t seen a reliable case of a real, original application entirely generated by AI - yet.





That being said:


I STRONGLY BELIEVE IN AI and the AI REVOLUTION.


It is already transformative, it revolutionizes the society and economy.


As of now, AI doesn't operate independently. Doesn't replace teams of developers, nor music composers, painters, translators.


It only helps them.


AI remains an assistant, for those programmers / architects / composers / managers who are smart enough to control an army of AI agents. Essentially the same visionaries who already knew how to coordinate teams of human agents, to achieve original, impressive results.


The reality is that we now have (many) agents that can quickly solve deterministic computational problems. But we’re still talking about armies of junior agents — unoriginal, not very smart, and very fast. 

19 July 2025

What I Learned Losing a Million Dollars – A Modern Fairy Tale About Gambling in Business

I don’t read business books much anymore. I used to. Obsessively. At one point, reading felt like a compulsion—an intellectual sugar rush I couldn’t resist. These days, I prefer something sharper: peer-reviewed science, niche blogs, curated newsletters, specialized courses, and a healthy dose of GPT-fueled learning. Less time consuming, more frictionless, and far more adaptable to what I’m actually trying to do—learn and build.

But sometimes a book sneaks through the firewall.


What I Learned Losing a Million Dollars, by Jim Paul, came recommended by Sabin Gilceava.

This is an easy read - more of a business fable than a textbook. The story is compelling, a tale of gambling your way into (and out of) trading and business. It follows a tried-and-true formula: tell the reader simple but intriguing truths, sprinkle in some elementary insights from psychology and statistics, and package it all in a way that makes the reader feel smart. It’s accessible. It's a modern fairy tale - i.e. it’s about money.


It dances with ethical ambiguity. You keep wondering: is the author reflecting or justifying? It is not about value creation, nor business. It's about money, connections, bluffing, image, cheating, misrepresentation, risk, gambling, trading, speculation, money.

In that context, the introductory references to Edison or Ford are ironic. Please. Those men were engineers; they built things. 


That said, the book serves as a good reminder of foundational, state-of-the-art scientific and educational literature from psychology, economics, and statistics. The application of the five stages of grief (from pain management) to business loss is actually quite interesting. But like many books in this genre, it overstays its welcome, and sometimes exagerates with elementary truth until they become false. There’s a point where you realize you’re reading another 10-page explanation of why having a plan is better than not having a plan. And surely, both experience and research suggest that rigidly following an initial plan is usually a mistake, something the author simply ignores. Likewise, the value of objective over subjective decision-making is a repeated theme, in literature as well as in this book. Even though, surprisingly, it’s contradicted in the book’s very conclusion.


One quote stands out as a neat summary of the entire work:

“Most people who think they are investing are speculating. And most people who think they are speculating are gambling.”

Simple. Sharp. That wraps it up.

25 December 2024

Analysis and decision-making tools

I am passionate about analysis and decision-making tools. I am a collector.

When I estimate next year's sales or revenues (or anything uncertain), I use a triangular distribution over 3-4 scenarios.

When I choose holiday locations, or prioritize features on a roadmap, I draw a decision matrix.

analysis and decision tools

Here is my collection of core tools and techniques for analysis and decision-making.

  • Brainstorming
  • Decomposition (e.g. X-BS), divide-et-impera
  • Decision matrices
  • Scenario analysis
  • Triangular distributions (Worst case + 4 x Probable + Best ) ÷ 6
  • Dependency modelling (DSM, DMM, MDM)
  • Cost-benefit analysis
  • Checklists
  • SWOT
  • PEST / STEEP
  • Mind-maps
  • Stakeholder maps
  • Decision trees
  • Cause-effect / Ishikawa
  • Pareto (the 80-20 rule, sometimes 90-10)
  • Delphi
  • Focus groups
  • Monte Carlo
  • Causal-loop, systems thinking
  • Process, workflow, component diagrams
  • Toyota way
  • 5 why-s
  • WWWWWH (Who, What, When, Where, Why, How)

What tools do you use?


    *) I have a different collection of methodologies and frameworks, e.g. here https://blog.stefanmorcov.com/2021/11/frameworks-and-methodologies-for-it.html)

    **) Image generated by ChatGPT.

    22 December 2024

    Online training, MIT: Fundamentals of Entrepreneurial Finance (Entrepreneurship 104).

    Finished a new online training, this week, MIT: Fundamentals of Entrepreneurial Finance (Entrepreneurship 104).

    The general level was a bit basic for me. But I enjoyed a lot some topics such as the VC valuation method, choosing multiples and yearly discount rates according to various factors such as industry and investment type, or convertible preferred stock.


    It was video only, which is, as usual, a minus for me. Usually slow, and including boring, irrelevant parts, such as irrelevant anecdotes and life stories. So, I skipped heavily, and also went directly to transcripts.


    The transcripts were not easy to read, automatically generated, verbose. The course could have benefited from actual handouts. 


    I used ChatGPT heavily to complement and clarify the course materials. GPT is an amazing personal trainer and assistant. Great at maths, calculations, explaining formulas or concepts, giving practical industry examples, great at web search.


    I tested Gemini in parallel with ChatGPT. Disappointing. Google AI is way behind its competition.


    Didn't request the certificate. Don't need one.


    Training and personal development: never stop. Finishing the year with a few hours of training feels great.

    21 December 2024

    Transparency, Traceability, and Accountability in politics, public procurement, and spending

    Transparency, traceability, and accountability are essential for a healthy society and organization.


    Opaque government is dangerous, as it enables mistakes and accidents, some with extraordinary consequences.


    These principles must first be applied to the financing of political parties and election campaigns. It is critical to know who funds a party and where the money goes.


    That’s the basic step.  

    Then, transparency must also apply to:

    - Public spending, through sound governance principles in public procurement and spending.

    - Hiring public servants and officials.

    - Lobby - a functional registry of organizations and individuals that try to influence public policies, laws, taxes, subsidies, including lobbying channels and funding sources.


    Democracy is not a natural phenomenon. It must be built and protected.



    20 October 2024

    A good salesman is someone who sells

    I like simple definitions. 

    There are tons of articles and books on this topic. They talk about what makes a good salesperson. Methods. Processes. Spin selling, solution selling. Pipelines, funnels, inbound, outbound, outdoors, network. Brand. Reputation, awareness, recognition. Tools. CRMs, content marketing, analytics, automation, lead generation. Skills. Empathy, curiosity, NLP, guts, courage, feeling, structure, negotiation.


    I even wrote a couple of papers myself. About online digital communication and collaboration, and about my approach to B2G/public sector and B2E / enterprise sales. We develop ourselves tools for lead generation, market intelligence and bid automation.


    But these are methods.

    I like simple definitions.


    A good salesperson is someone who sells.

    12 May 2024

    Don't win any contest

    Some competitions are not worth winning, or participating. You shouldn't compete in lower categories; winning would disgrace you.

    You know, the lion doesn't need to win competitions to be king.

    This is why some competitions have only 30-40 applicants, the winner is unknown; winning is a sign of weakness.


    In other news: no Eurovision song winner ever made the top10 US charts in 50 years, since Abba's 1974 Waterloo. There were 3 songs that made it before.

    Eurovision is of course a solid event and contest, that has rewarded incredible songs and singers such as Abba, Céline Dion, Lara Fabian or Gigliola Cinquetti. Great many other songs/artists participated and lost, such as Domenico Modugno, France Gall, and even Abba.


    Surely, you wouldn't see Queen, Beatles or Michael Jackson compete in Eurovision.

    20 February 2024

    Soft-skills and formal education

    Xteen years ago, I graduated from university, got a job as a young software engineer, and discovered that school hadn't given me any of the soft-skills needed to function as an adult. I had no public speaking experience, presentation skills, negotiation, planning, team management, project organization know-how.

    Sure, I had the vocabulary, I had read thousands of books, but I had no practical experience whatsoever. I didn't even know how to collaborate, because in college we were taught to solve problems alone, it was always a competition. In 5 years of engineering school, I don't remember even one group project, or a public speech (only the final master dissertation, also without guidance).


    My first boss (and mentor) once told me that he needed time to trust me because I wouldn't look him in the eye when speaking, and he often felt  I was hiding something.

    I needed software engineering and project management courses from Open University UK, to learn how to organize and collaborate.

    I needed patient managers and mentors to teach me how to speak in public, lead meetings, motivate and organize projects and teams, create a strategy, a Gantt, an organizational chart or a budget.

    I discovered project-based research and study only during the MBA (with American professors).


    Soft skills are part of basic education in the Anglo-American world, in northern and western Europe. A few years ago, I met a student from Sweden who gave a phenomenal speech at a European conference, and he had very little domain expertise, but he spoke with a poise and style that left me speechless. In Netherlands, Belgium, Denmark, these skills are taught directly in elementary school. Project-based learning, team collaboration, creating and using slides and props, communication and oral presentation.

    Digital bureaucracy

    There's an IT system for digital communication with government authorities (MySmis).

    To send an answer to any simple question, you need 5-7 overlapping digital signatures. 


    You get an email telling you that you have a new message. You log in (electronic signature 1, not qualified). You download the question (usually a short text, very nicely formatted, header, footer, etc, and saved as pdf document, digitally signed, etc). You write the answer, save it as pdf, and sign the pdf with a digital qualified signature (2). You log in (3). Upload the answer. Optionally log in again. Download the receipt. Sign the receipt with a qualified signature (sic) (4). You log in (5). Upload the signed receipt. Receive a confirmation.

    All this, for an answer that often consists of a standard text: "We confirm the budget, and the eligibility".


    The entire process could be replaced by 1 email.

    Digitization is not only about tools, it is also about processes.

    I said a few years ago that if they would leave me unsupervised 3 days in a public institution, I would eliminate 90% of the processes and 90% of the jobs. And move staff to something better.


    But that's how bureaucracies work. The purpose of bureaucracy is to grow the level of bureaucracy. Bureaucracy is power. Processes, forms, and approvals: are sources of power.

    Parkinson said that officials have 2 major career goals: to multiply subordinates, and to create work for each other.


    See also this post.

    11 February 2024

    Bullying, professors, managers, and insecurity

    Bullying is quite common among professors and teachers. And it has always been.


    MY MOTHER was telling me about her professors, frustrated probably about their positions, shouting at pupils that they are barefoot peasants, polenta-eaters (mămăligari, opincari).

    We all had professors calling us stupid. Professors also used to be violent - this is now prohibited.

    The objective of many teachers is to demonstrate that their students are stupid. They fail to recognize the paradox that their students are largely the result of their own work.

    Laughing at students, saying they are incompetent, diminishing them, insulting them, is a form of bullying. It is not pedagogy, it is destructive.


    AND THE SOURCE of bullying is mostly frustration. Hiding a sense of inferiority, a lack of personal accomplishments. Especially when directed at people in a subordinate position, with significantly less power.

    Because teachers hold a unique position of power, over a significant number of (young) people. The fact that they need to express this power through bullying signals insecurity. 


    MAYBE some professions are more likely to attract or cultivate bullies. Teachers, doctors, soldiers, managers, and Hollywood producers.

    Or, this simply reflects the overall maturity of society and its members.

    24 April 2023

    Hermix - public sector sales analytics, B2G software

    Hermix is the first analytics platform for public sector sales. 

    We help companies understand & win public sector projects, with tender monitoring and market intelligence.


    How we started, more than a year ago? We looked at our personal, direct experience, of more than 20 years, in doing sales for the public sector; mostly tenders to EU institutions and the European Commission, but also to national authorities. The Business-to-Government B2G sector is great, and it worked very well for us: stable market, lots of money, and lots of information - if you know where to look and how to read.


    We asked ourselves: What worked, How, and Why it worked for us? And we noticed that data analysis and market intelligence are key success factors, and yet completely under-exploited. Everything is done manually: tender monitoring, market research, forms, papers, CVs, technical proposals, prices. 

    We also made this astounding observation: public sector sales doesn't use big data analytics. Information is managed manually in emails and Excel files.

    But in B2C/B2B, retail and consumer, data is king! Marketers rely heavily on billions of data points and on hundreds of tools: Google Analytics, Facebook Analytics, LinkedIn Sales Navigator, Plausible, Indicative, etc etc.


    So we started to automate B2G, providing services such as:

    * Tender monitoring, smart market watch, notifications.

    * Big data analytics, deep market intelligence, actionable insights: where is the money, who buys & sells, what, where, how.


    We've gone a long way since we started out in 2022.

    We gathered a great team. We developed the technical platform. We launched Hermix.com. We tested, validated and evaluated the concept and tools.

    We had ~350 meetings. We enrolled 217 users.

    We signed quite a few contracts with serious, solid customers. We have reliable partners, such as Amazon, Tremend, Zetta, Westpole, Brayton, Hubspot. We received in kind contributions and support.

    We won the EU Datathon award from the European Commission, and a prize of 25k. We received the Deloitte Impact Star. We signed a 250k regional R&D grant.


    We listen to our customers and partners, every week. We get their feedback and requirements. We aim to understand their real needs. We focus on ergonomics, usability and on key user scenarios: Which are the daily pains of the sales managers and commercial directors, of bid managers and presales architects?

    And then we design crucial pain-killers for these needs.


    We improve our data algorithms continuously. We analyzed millions of historical government contracts, tenders and payments, hundreds of thousands of authorities and contractors. We import and clean new data daily.

    We release 2-3 new major features per month. We use the most modern & fancy technology out there, but we remain function-driven.


    We make sense of public sector sales.

    Contact me for your test drive.

    31 March 2023

    Post-rationalization of decisions

    We often take subconscious decisions, that we then try to rationalize, by finding or inventing logical arguments.

    It is what Daniel Kahneman called "thinking fast". 

    The first example comes from marketing, i.e the post rationalization of the buying decision. Studies show that people make decisions subconsciously, after which they try to rationalize them, with logical arguments such as "it's cheaper", or "it's expensive, but the quality is higher", or "it's worth taking care of myself", or "it's the best quality-price ratio", or the infamous "I know I don't need this, but it's on sale".


    A similar process is described by Radu Umbres for ethical norms. Apparently, we don't have moral principles from which we derive ethical norms, but instead we have ethical norms, and then we create principles to support these norms. With examples such as: because of the principle that women should have the right to dispose of their own body, we support abortion, but we don't apply the same principle to support surrogate motherhood or prostitution. Similarly, we consider morally acceptable that men should donate sperm, but not women donating ovules.


    The 3rd example comes from business.

    Although in this area we try to use objective decision-making tools (decision matrix, decision tree, balanced scorecard, Cocomo, risk analysis, etc.), in practice we adjust these tools to fit our intuition. 

    20 years ago, when I was a young manager, I built and used a tool for calculating the salaries of staff, using a set of variables such as experience, skills, performance, education, foreign languages, etc. At a certain moment, I went to my boss and told him that I have a problem: the tool suggests to increase the salary of an incompetent colleague. The manager said: a good instrument should help you take the right decisions. If it doesn't, i.e. if the results of the instrument don't match your expected results, then you need to recalibrate the instrument. Accordingly, I adapted the tool by introducing a new "over-ride" variable to get the desired result.


    Same happened during my PhD, when I discovered that an instrument is "correct" if its results are useful. Utility is the best measure of the scientific validity of a tool. In fact, my research consisted of designing and calibrating a set of useful tools for the management of complexity.


    The same subjectivity affects all tools, including those for deciding investments or acquisitions. I recently heard people talk about "gut feeling" in investment decisions. The "gut" factor is in fact embedded in the whole decision process. Business tools are always recalibrated to produce desired results. 


    Even when we over-rationalize the decision process, when we design tools that are highly objective, when we try to isolate personal bias from decisions - we will never eliminate subjectivity completely.


    Subconscious decisions work a lot of times. 

    Calibrating instruments to match desired results is not a bad thing, if it works. But it is important to be aware of how this process works, even when it works. And the calibration process should be as controlled as possible.


    24 December 2022

    Accidents, problem solving and systematic approaches to social issues

    Accidents happen. E.g. a bus crashes into a metallic protection and people die.


    When accidents happen 

    it's important:

    1. To be outraged, in order to mobilize resources.

    But then it's also important:

    2. To cool down, 

    3. Identify root causes, 

    4. Identify potential solutions,

    5. Analyze the impact and cost of potential solutions, 

    6. Prioritize and plan the implementation of these solutions,

    7. Implement selected solutions.


    Because there is no single cause, and no single solution

    This is why:

    3. for root-cause analysis we use tools like Ishikawa / fish-bone analysis.

    Because each problem has multiple causes, and each cause has other causes.


    Direct causes include driver fatigue, architectural and design problems, construction issues, signage issues.


    Underlying causes include lack of professional standards and processes in construction and road design, lack of professional expertise and training of those responsible, societal cultural issues (disrespect of laws, misapplication of penalties, low standards and expectations), corruption, legislation, and political populism.


    4. Identifying potential solutions can be done with solution solving techniques. Decomposition, brainstorming, Delphi, design thinking, design cycle.

    There are lots of possible solutions for each of the causes.


    5. You cannot solve all causes by implementing all possible solutions. But also, solving only one cause is probably not enough.

    This is why causes and solutions must be prioritized. This is done with tools such as Paretto (80/20) and cost-benefit analysis.


    Follow me for additional management and engineering tips.

    18 September 2022

    Models are (useful) simplifications of reality

    The only true measure of a model is if it works.

    Applies to Systems Thinking, as well as to Cynefin, or to my Positive Complexity model.


    Because all models are simplifications of reality.

    Yes, we need models, i.e. simplifications, so that we understand reality. But it is important to know that they are limited, and to know their limitations.


    Some are too simple, some are too complicated or complex for a specific problem.


    The simplest example is Newton's laws. They were super useful for hundreds of years, and still are. 

    We know that they do not model exactly the reality - because no model does. You cannot fly to the moon using only Newtonian physics. Einstein's théories are a better model for space navigation. But still a model. Einstein's model is more advanced, more complex. It is nevertheless less practical for designing bicycles (you only need classic mechanics for bicycles).


    So, there is no such thing as a correct or wrong model. Especially in social sciences, in management, or in engineering.

    Thus models are better measured by appropriateness: useful or less useful.


    The only true measure of a model or theory is if it works.

    05 September 2022

    Parkinson's law applied to the digitalisation of public services

    Digital services available. 2nd floor, on the right. 

    Bucharest 2nd district (Veranda mall, 2nd floor) just implemented a ancient-old law forbidding public servants to ask citizens to provide copies of documents they already own.

    Now, they only ask for the originals.

    Then, they copy these originals, using a xerox machine down the hall. Which they sign, stamp, and file.

    Digitalization at best.


    But hear me out: this is a great exemplification of Parkinson's Law, on how bureaucratic organizations function and grow.


    Parkinson was a historian. He studied the functioning of the British government for decades. He even wrote a book about it.

    In short, he says (these are excerpts from some seminars I gave at an Erasmus master program at KU Leuven), that work expands so as to fill the time available.

    This law is true for both private and public organizations;

    He exemplified with case studies from the UK Ministry of Naval Forces and the UK Colonial Office.


    The staff of the UK Ministry of Naval Forces rose by 5%, and UK Colonial Office’s staff rose 7% per year - regardless of any variation in the amount of work (if any) to be done.

    Because, you know, the UK ministry of Naval Forces lost all their ships in ’45, and the UK lost all its colonies after ’45. The ministry for colonies practically disappeared. BUT their staff continued to augment.


    Parkinson says that the driving forces were:

    * An official wants to multiply subordinates, not rivals.

    * Officials work for each other.


    IN DETAIL

    Parkinson noticed that the number of people employed by the British navy increased continuously, regardless of activity. It was also a magic number, like a 5% growth constant. What intrigued him was that the growth rate was the same even though the British navy declined dramatically after 1945. Due to the economic crisis after the war, the British scrapped most of its war fleet. But it kept and even increased the number of employees !


    Intrigued, Parkinson started to study the phenomenon systematically. The most interesting case was the Ministry of Colonies. He studied the evolution of the number of its employees over a period of several decades, from 1918 to the 1950s.

    After 1918 Britain gained a lot of new colonies (from the Germans). But of course, after 1945, it lost all its colonies, one by one. The Ministry of Colonies therefore lost its purpose. Finally, it was ended, being reduced to the level of a department within the Ministry of Foreign Affairs. Well, quite surprisingly, although it no longer had a purpose, its team not only did not diminish, but continued to grow at the same rate as in the interwar period.


    After studying this phenomenon thoroughly, Parkinson summarized that it had 2 causes:

    1. An official wants to multiply subordinates, not rivals. The effect is that an official would never employ only one public servant. Because he/she would become his colleague, thus diminishing his responsibilities. Instead, he/she would  hire 2 people - so that they become a team, and he/she would becomes their boss.


    2. Officials make work for each other. The effect is the creation of procedures and processes. There will always be a need for an extra stamp, an approval, a commission, a review.

    The phenomenon of comitology is quite interesting on this topc. The European Commission even has a procedure that explains how commissions and committees should work. That's what it's called: comitology. PM me for details.


    The phenomenon is also well documented in private companies. Businesses are also marred by departments without purpose. Monsters that appeared once upon a time, initially had a clear purpose, but over time evolved independently, turned into absolutely useless, but increasingly larger departments.


    THE REVERSE PHENOMENON

    I developed a theory of my own about the drivers that limit the growth of bureaucracy. Because bureaucracy has limits. There is a set of reverse forces that work against Parkinson's law.

    The pressure to limit the number of employees and unnecessary processes is always top-down, strategic, political.


    In the private sector, the driver pushing down on bureaucracy: is competition. If a company becomes too fat, too bureaucratic, then it is no longer competitive, and it dies or is restructure. We’ve seen the phenomenon in huge companies, such as HP or Intel. When these have a bad financial year, their board of directors, under the pressure of the shareholders, decides to change the managers and fire ~10k employees. Regardless of their duties or processes. They’re out. 


    In the public sector, the only driver for pushing down on bureaucracy: is elections. In a functioning democracy, elected officials must demonstrate value to voters. So an elected official should have a program to eliminate bureacracy.

    But this needs a functional democracy. E.g. elections “by list” negate this driver - because the elected are not accountable to the voters, but to the party.

    If the political system is not democratic, then the reverse factor is only revolutions or coups d'état. These force a top-down reset to the entire bureaucratic system.


    03 December 2021

    Synopsis of management frameworks

    I studied a number of fascinating management frameworks, as model for the IT Project Complexity Management IT-PCM framework. 

    A typical management process structure consists of the following main phases, or processes:

    1. Planning.
    2. Identification.
    3. Analysis.
    4. Developing response plans (strategies, actions).
    5. Implementation, monitoring, control, and lessons learned.

    Some widely recognized and accepted frameworks in project management and IT/software engineering are presented below.

    Risk management

    Risk management consists of the following processes (PMI, 2017):

    1. Plan Risk Management - defining how to conduct risk management activities.
    2. Identify Risks - identifying individual project risks as well as sources.
    3. Perform Qualitative Risk Analysis - prioritizing individual project risks by assessing probability and impact.
    4. Perform Quantitative Risk Analysis - numerical analysis of the effects.
    5. Plan Risk Responses - developing options, selecting strategies and actions.
    6. Implement Risk Responses - implementing agreed-upon risk response plans. In the 4th Ed. of PMBoK, this process was included as an activity in the Monitor and Control process, but was later separated as a distinct process in PMBoK 6th Ed. 
    7. Monitor Risks - monitoring the implementation. This process was known as Monitor and Control in the previous PMBoK 4th Ed., when it also included the “Implement Risk Responses” process.

    Vulnerability management

    Project vulnerability is the project's susceptibility to being subject to negative events, the analysis of their impact, and the project's capability to cope with negative events (Marle & Vidal, 2016). Based on Systems Thinking, project systemic vulnerability management takes a holistic vision, and proposes the following process:

    1. Project vulnerability identification.
    2. Vulnerability analysis.
    3. Vulnerability response planning.
    4. Vulnerability controlling – which includes implementation, monitoring, control, and lessons learned.

    Coping with negative events is done, in this model, through:

    • resistance – the static aspect, referring to the capacity to withstand instantaneous damage, and
    • resilience – the dynamic aspect, referring to the capacity to recover in time.

    Redundancy is a specific method to increase resistance and resilience (Taleb, Goldstein, & Spitznagel, 2009).

    Antifragility is the capacity of systems to not only resist or recover from adverse events, but also to improve because of them (Taleb, 2012).

    Complexity management in systems engineering

    A proposed model for managing complexity in systems engineering consists of (Maurer, 2017):

    1. Define the system.
    2. Identify the type of complexity.
    3. Determine the strategy.
    4. Determine the method.
    5. Model the system.
    6. Implement the method.

    Software engineering: Systems development life cycle (SDLC), Waterfall, OOAD, Agile

    Software engineering proposes models such as the Systems development life cycle (SDLC); Waterfall software development methodology / Structured systems analysis and design method SSAD, or OOAD (Satzinger, Jackson, & Burd, 2007).

    The six core processes required in the development of a software application - SDLC are:

    1. Identify the problem or need and obtain approval to proceed.
    2. Plan and monitor the project—what to do, how to do it, and who does it.
    3. Discover and understand the details of the problem or the need.
    4. Design the system components that solve the problem or satisfy the need.
    5. Build, test, and integrate system components.
    6. Complete system tests and then deploy the solution.

    The Waterfall model is infamous for not providing for iterations, and in general for lack of flexibility. It consists of:

    1. Initiation.
    2. Planning.
    3. Analysis.
    4. Design.
    5. Implementation.
    6. Deployment
    7. Maintenance, support.

    The Scrum Agile software development framework (Schwaber & Sutherland, 2020) proposes short iterative-incremental sprints, where each sprint includes the following events:

    1. Sprint Planning – performed at the beginning of the sprint. 
    2. Daily Scrum (or stand-up) meetings.
    3. Sprint Review, and Sprint Retrospective – at the end of a sprint.
    4. In Scrum, Backlog Refinement is an ongoing activity, not a discrete event.

    ADDIE and SAM models for instructional design

    The ADDIE model (Allen & Sites, 2012) consists of:

    1. Analysis.
    2. Design.
    3. Development.
    4. Implementation.
    5. Evaluation.

    The Successive Approximation Model - SAM:

    OODA loop: observe, orient, decide, act

    The OODA loop for problem-solving and decision-making is formed of: observe, orient, decide, act (Boyd, 2018). It was designed in a military context by United States Air Force Colonel John Boyd.

    PDCA: plan, do, check, act

    The PDCA management method (also known as OPDCA, or the Deming Cycle) is formed of: observe, plan, do, check, act (or adjust) (Liker & Franz, 2011).

    Jolly frameworks!

    22 November 2021

    Frameworks and methodologies for IT governance and management

    IT governance is the system that ensures that the use of ICT is directed and controlled at the level of an organization, sustaining and extending the organization's strategies and objectives (ISO/IEC, 2015). COBIT - Control Objectives for Information and Related Technology is one of the most used enterprise governance of information and technology (EGIT) frameworks (ISACA, 2019).

    COBIT specifically differentiates between governance and management. Management is the group of processes that ensures the execution of the organizational activities, in alignment with the direction set as part of the Governance processes.

    IT Management processes cover activities such as building, implementation, maintenance, operation, and support of IT systems, as well as transversal processes referring to risk, security, and data protection management. 

    In the IT industry, project management tools and techniques are used in conjunction with IT-specific frameworks and tools, such as software development, maintenance, IT support, quality assurance and control, security, or data protection frameworks, guides, and standards. 

    The main project management frameworks are: 

    PMBoK, proposed by PMI (PMI, 2017)
    PM² - the Project Management methodology developed by the European Commission (European Commission, 2018)
    Prince 2
    APM BoK
    IPMA International Competence Baseline (ICB)
    Project Planning and Project Management (P2M) developed by the PM Association of Japan (PMAJ)
    ISO 21500:2012 Guidance on Project Management
    Global Alliance for Project Performance Standards (GAPPS)
    Procedures for Project Formulation and Management (PPFM) by the Indian Ministry of Defence (Mohindra & Srivastava, 2019)

    An overview of specific IT frameworks and methodologies is presented below.

    Area Frameworks & methodologies
    IT governance and information management COBIT (Control Objectives for Information and Related Technology), developed by ISACA (Information Systems Audit and Control Association)
    ISO/IEC 38500:2015 Information technology - Governance of IT for the organization
    (ISACA, 2019) (De Haes, Van Grembergen, Joshi, & Huygh, 2020) (ISO/IEC, 2015)
    IT Service Management - ITSM ISO/IEC 20000 family of standards – Information technology — Service management
    ITIL - IT Infrastructure Library
    The Open Group Architecture Framework TOGAF
    Microsoft Operations Framework MOF
    (ISO/IEC, 2018b) (Shiff, 2021) (Ohlinger, Sharkey, & Cai, 2017) (The Open Group, 2018)
    IT Security CIS Controls V8
    ISO/IEC 27001 Information security management
    ISO/IEC 27002 Information technology - Security techniques - Code of practice for information security controls
    (CIS, 2021)  (ISO/IEC, 2018)   (ISO/IEC, 2013)
    Software development and maintenance Systems development life cycle SDLC
    Waterfall
    Rational Unified Process RUP
    Spiral development
    Object Oriented Analysis and Design OOAD
    Scrum Agile, Kanban
    Feature driven development FDD
    Extreme Programming XP
    Rapid Application Development RAD
    (Satzinger, Jackson, & Burd, 2007) (Schwaber & Sutherland, 2020)
    Software estimation Function Point Analysis FPA
    The Constructive Cost Model for cost estimation COCOMO
    (Albrecht, 1979) (Longstreet, 2012) (Pressman, 2001) (Jørgensen, 2007)
    IT Quality assurance and control ISO/IEC 9001:2015 Quality management systems
    ISO/IEC/IEEE 90003:2018 Software engineering - Guidelines for the application of ISO 9001:2015 to computer software
    ISO/IEC/IEEE 29119 family of standards – Software and systems engineering - Software testing
    Total Quality Management TQM
    Capability Maturity Model Integration (CMMI)
    Six-sigma
    International Software Testing Qualifications Board ISTQB
    (ISO/IEC/IEEE, 2013) (ISO/IEC, 2015) (Godfrey, 2004) (ISTQB, 2012)

    Other lists of software/project management and decision making tools are in these slides.

    References

    Albrecht, A. J. (1979). Measuring application development productivity. Proceedings of the Joint SHARE, GUIDE, and IBM Application Development Symposium (pp. 83–92). Monterey, California: IBM Corporation.

    CIS. (2021). CIS Controls Version 8. Center for Internet Security. Retrieved from https://www.cisecurity.org/controls/v8/

    De Haes, S., Van Grembergen, W., Joshi, A., & Huygh, T. (2020). Enterprise Governance of Information Technology: Achieving Alignment and Value in Digital Organizations. Cham, Switzerland: Springer Nature Switzerland AG.

    European Commission. (2018). PM2 Project Management Methodology Guide 3.0. Brussels, Luxembourg: Publications Office of the European Union. doi:10.2799/755246

    Godfrey, S. (2004). What is CMMI? NASA. Retrieved Oct. 3, 2021, from https://ses.gsfc.nasa.gov/ses_data_2004/040601_Godfrey.ppt

    ISACA. (2019). COBIT 2019 Framework: Introduction and Methodology. ISACA.

    ISO/IEC. (2013). ISO/IEC 27002:2013 Information technology — Security techniques — Code of practice for information security controls. International Organization for Standardization, International Electrotechnical Commission. Retrieved from https://www.iso.org/standard/54533.html

    ISO/IEC. (2015). ISO 9000:2015 Quality management systems — Fundamentals and vocabulary (4 ed.). International Organization for Standardization, International Electrotechnical Commission. Retrieved from https://www.iso.org/standard/45481.html

    ISO/IEC. (2015). ISO/IEC 38500:2015 Information technology - Governance of IT for the organization. International Organization for Standardization/International Electrotechnical Commission. Retrieved from https://www.iso.org/standard/62816.html

    ISO/IEC. (2018). ISO/IEC 27000:2018 Information technology — Security techniques — Information security management systems — Overview and vocabulary (5 ed.). International Organization for Standardization, International Electrotechnical Commission. Retrieved from https://www.iso.org/standard/73906.html

    ISO/IEC. (2018b). ISO/IEC 20000-1:2018 Information technology — Service management — Part 1: Service management system requirements (3 ed.). International Organization for Standardization, International Electrotechnical Commission. Retrieved from https://www.iso.org/standard/70636.html

    ISO/IEC/IEEE. (2013). ISO/IEC/IEEE 29119-1:2013 Software and systems engineering — Software testing — Part 1: General concepts (1 ed.). International Organization for Standardization/International Electrotechnical Commission/Institute of Electrical and Electronics Engineers. Retrieved Oct. 10, 2021, from https://www.iso.org/standard/45142.html

    ISTQB. (2012). ISTQB in a Nutshell. ISTQB Marketing Working Group. Retrieved Oct 3, 2021, from https://www.istqb.org/documents/ISTQB_201202_v10.pdf

    Jørgensen, M. (2007). Forecasting of software development work effort: evidence on expert judgment and formal models. International Journal of Forecasting, 23(3), 449.

    Longstreet, D. H. (2012, Feb). Function Points Analysis training course. Retrieved Mar 13, 2012, from Software Metrics: http://www.softwaremetrics.com/Function%20Point%20Training%20Booklet%20New.pdf

    Mohindra, T., & Srivastava, M. (2019). Comparative Analysis of Project Management Frameworks and Proposition for Project Driven Organizations. PM World Journal, VIII(VIII). Retrieved from https://pmworldlibrary.net/wp-content/uploads/2019/09/pmwj85-Sep2019-Mohindra-Srivastava-comparative-analysis-of-project-management-frameworks.pdf

    Ohlinger, M., Sharkey, K., & Cai, S. (2017, Aug 6). High Availability and the Microsoft Operations Framework. Retrieved Oct 3, 2021, from Microsoft Docs: https://docs.microsoft.com/en-us/biztalk/core/high-availability-and-the-microsoft-operations-framework

    PMI. (2017). A Guide to the Project Management Body of Knowledge (PMBOK Guide), 6th Ed. Pennsylvania: Project Management Institute.

    Pressman, R. S. (2001). Software Engineering - A Practitioner's Approach. New York: McGraw-Hill.

    Satzinger, J. W., Jackson, R. B., & Burd, S. (2007). Systems Analysis & Design In A Changing World. Boston: Thomson Course Technology.

    Schwaber, K., & Sutherland, J. (2020, Nov). The 2020 Scrum Guide. Retrieved Dec 27, 2020, from ScrumGuides.org: https://www.scrumguides.org/scrum-guide.html

    Shiff, L. (2021, Jun 14). Popular IT Service Management (ITSM) Frameworks. Retrieved Oct 3, 2021, from BMC Blogs: https://www.bmc.com/blogs/itsm-frameworks-popular/

    The Open Group. (2018). The TOGAF® Standard, Version 9.2 (9.2 ed.)

    07 November 2021

    PhD thesis published: Managing positive and negative complexity

    My PhD thesis is finally published: Managing Positive and Negative Complexity. Design and Validation of an IT Project Complexity Management Framework. 

    Also poster and presentation slides.

    I am highly interested in your opinion on the new proposed concepts of positive and appropriate complexity.

    This project was about understanding IT project complexity and contributing to its theoretical foundations and practice. It proposes a holistic view, and provides insights into its Positive, Appropriate (requisite), and Negative effects. It proposes a structured framework for IT Project Complexity Management (IT-PCM), composed of formal processes: plan, identify, analyze, plan responses, monitor and control. These are defined and described in terms of inputs and outputs, and with an inventory of available tools and techniques. Anchored in this framework, new practical tools are proposed, for: measuring complexity; analyzing its sources and effects; planning and monitoring complexity mitigation strategies. 

    06 April 2021

    Project success vs. project management performance

    “Project success” has 2 perspectives: 

    • the perspective of the process, i.e. delivering efficient outputs; typically called project management performance or project efficiency.
    • the perspective of the result, i.e. delivering beneficial outcomes; typically called project performance (sometimes just project success) [1, 2].

    The established (project) management school, and even practice, puts more priority on processes, rather than on results. We measure screens, workflows, processes, APIs, requests, forms, sometimes even lines of code (remember the beautiful KLOC paradox). Yes, some of these are reasonable proxies for measuring results. But even if they can be measured, they are still proxies.

    Interestingly, this is not the case for engineering; it is mostly the management view. Quality management is famous for example for its obsession for documenting and measuring processes. Continuous improvement of processes becomes sometimes a religion, with fanatics following blindly 7-sigma, Deming, or Kanban.

    Engineering and IT are more focused on the end-product, to its benefits to the organization and to the stakeholders, rather than on measuring methods and processes [3].

    Managers fear complexity, so they try to simplify and reduce it.
    But engineers need complexity in their products, so they find tools to manage it [4].


    This is why the mission of the engineering manager is not to simplify technology at all cost; but to design and build successful, useful products. Even if the products are complex.


    [1] Daniel, P. A., & Daniel, C. (2018). Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in project management. International Journal of Project Management, 36, 184–197.

    [2] Pinto, J. K., & Winch, G. (2016). The unsettling of “settled science:” The past and future of the management of projects. International Journal of Project Management, 34, 237–245.

    [3] Locatelli, G., Mancini, M., & Romano, E. (2014). Systems Engineering to improve the governance in complex project environments. International Journal of Project Management, 32, 1395–1410.

    [4] Morcov, S., Pintelon, L., & Kusters, R. J. (2021). A Framework for IT Project Complexity Management. IADIS IS 2021 : 14th IADIS International Conference Information Systems (pp. 61-68).

    The engineering view: AI is amazing, but not human. It's a tool. It's not perfect. It works

    The engineering view: AI is amazing, but not human. It's a tool. It's not perfect. It works. This text by Andrew Ng is so good, that...