Showing posts with label GBU. Show all posts
Showing posts with label GBU. Show all posts

10 December 2023

💫🧮☯ERP: Microsoft Dynamics 365's Invoice Capture (The Good, the Bad and the Ugly)

Enterprise Resource Planning
ERP Systems

At the last meeting of the Microsoft Dynamics Meetup Germany group there were about 20 Microsoft experts invited to expose in two minutes their favorite new feature from the Microsoft ecosystem. None of them though mentioned Invoice Capture, which I think deserves its place on the list. 

Invoice Capture is a Power Apps-based application deeply integrated with Dynamics 365 which allows the semi-automatic processing of Vendor invoices received over the various channels (Outlook, SharePoint, OneDrive) or via manual upload. The Power App listens on the configured channels, imports the documents as they arrive and uses optical character recognition capabilities to extract the standard textual information needed to create a Vendor invoice record with header, lines and further information. In a first phase the Accountant Clerk classifies, reviews, corrects and transfers the Invoice to Dynamics 365, from where the Invoice follows the standard process being enriched, posted to the Subledger and further booked to General Ledger. Of course, several changes were done also in Dynamics 365, especially in what concerns the parametrization and Invoices' automatic processing.

The Good: Thus, Invoice Capture attempts to provide end-to-end invoice automation and probably with further changes to cover at least the most common scenarios it will be able to do so. Since it was released as a minimal viable product (MVP), besides bug fixing several features were added - the search of Vendors and their automatic synchronization, the entry of Cost Center and Department Code financial dimensions upfront in the Power App, the support for multiple tax codes and for custom fields, just to mentioned the most important features. However, more changes are needed to provide customers more flexibility in automating the process and in handling other complex scenarios. 

Through automation and further features like the continuous learning from manual input and previous value retention, Invoice Capture decreases the volume of manual work and increases the financial cycle time, making the overall process more efficient. Moreover, the invoices are available almost as soon as they came into Dynamics 365, allowing better overview and thus better spend control. Features like Invoice approval via workflows and extrinsic automating features can offer further opportunities for improvement. Last but not the least, Invoice Capture allows achieving a paperless AP, helping organizations' effort on their road to digital transformation. 

The Bad: It's natural in Software Development to start with a MVP and built upon, however the gap between the MVP and what customers need involves certain challenges when evaluating and implementing the feature(s). Some hiccups are inherent as a piece of software needs time to stabilize and mature, however with better transparency and communication about the roadmap the respective processes would have been a better experience. On the other side, Microsoft was quite helpful in the process, welcoming the feedback and integrating it in the plan, and in time even provided more transparency. However, there seem to be still many unknowns, especially in what concerns the integration with old and new features from the roadmap (e,g. e-Invoicing, recurring Vendor invoices).

The truth is that customers have different needs, their processes have degrees of complexity that may go way beyond the features provided by the MVP and subsequent versions. Some customers were happy with the MVP, some had to compromise while others maybe went for alternatives. 

The Ugly: It's time consuming to evaluate and implement a new feature, to fill the gaps and find alternatives, especially when the organizational setup is not optimal. However all these are normal challenges from the life of an ERP consultant.

Despite the current and maybe future challenges, Invoice Capture can become in time an important product on the Microsoft roadmap.

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27 December 2020

🧊☯Data Warehousing: Data Vault 2.0 (The Good, the Bad and the Ugly)

Data Warehousing
Data Warehousing Series

One of the interesting concepts that seems to gain adepts in Data Warehousing is the Data Vault – a methodology, architecture and implementation for Data Warehouses (DWH) developed by Dan Linstedt between 1990 and 2000, and evolved into an open standard with the 2.0 version.

According to its creator, the Data Vault is a detail-oriented, historical tracking and uniquely linked set of normalized tables that support one or more business functional areas [2]. To hold data at the lowest grain of detail from the source system(s) and track the changes occurred in the data, it splits the fact and dimension tables into hubs (business keys), links (the relationships between business keys), satellites (descriptions of the business keys), and reference (dropdown values) tables [3], while adopting a hybrid approach between 3rd normal form and star schemas. In addition, it provides a two- or three-layered data integration architecture, a series of standards, methods and best practices supposed to facilitate its use.

It integrates several other methodologies that allow bridging the gap between the technical, logistic and execution parts of the DWH life-cycle – the PMI methodology is used for the various levels of planning and execution, while the Scrum methodology is used for coordinating the day-to-day project tasks. Six Sigma is used together with Total Quality Management for the design and continuous improvement of DWH and data-related processes. In addition, it follows the CMMI maturity model for providing a clear baseline for benchmarking an organization’s DWH capabilities in development, acquisition and service areas.

The Good: The decomposition of the source data models into hub, link and satellite tables provides traceability and auditability at raw data level, allowing thus to address the compliance requirements of Sarabanes-Oxley, HIPPA and Basel II by design.

The considered standards, methods, principles and best practices are leveraged from Software Engineering [1], establishing common ground and a standardized approach to DWH design, implementation and testing. It also narrows down the learning and implementation paths, while allowing an incremental approach to the various phases.

Data Vault 2.0 offers support for real-time, near-real-time and unstructured data, while new technologies like MapReduce, NoSQL can be integrated within its architecture, though the same can be said about other approaches as long there’s compatibility between the considered technologies. In fact, except business entities’ decomposition, many of the notions used are common to DWH design.

The Bad: Further decomposing the fact and dimension tables can impact the performance of the queries run against the tables as more joins are required to gather the data from the various tables. The further denormalization of tables can lead to higher data storage needs, though this can be neglectable compared with the volume of additional objects that need to be created in DWH. For an ERP system with a few hundred of meaningful tables the complexity can become overwhelming.

Unless one uses a COTS tool which automates some part of the design and creation process, building everything from scratch can be time-consuming, increasing thus the time-to-market for solutions. However, the COTS tools can introduce restrictions of their own, which can negatively impact the overall experience with the methodology.

The incorporation of non-technical methodologies can have positive impact, though unless one has experience with the respective methodologies, the disadvantages can easily overshadow the (theoretical) advantages.

The Ugly: The dangers of using Data Vault can be corroborated as usual with the poor understanding of the methodology, poor level of skillset or the attempt of implementing the methodology without allowing some flexibility when required. Unless one knows what he is doing, bringing more complexity in a field which is already complex, can easily impact negatively projects’ outcomes.

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References:
[1] Dan Linstedt & Michael Olschimke (2015) Building a Scalable Data Warehouse with Data Vault 2.0
[2] Dan Linstedt (?) Data Vault Basics [source]
[3] Dan Linstedt (2018) Data Vault: Data Modeling Specification v 2.0.2 [source]

13 June 2020

🧭☯Business Intelligence: Self-Service BI (The Good, the Bad and the Ugly)

Business Intelligence

Self-Service BI
(SSBI) is a form of Business Intelligence (BI) in which the users are enabled and empowered to explore and analyze the data, respectively build reports and visualizations on their own, with minimal IT support. 

The Good: Modern SSBI tools like PowerBI, Tableau or Qlik Sense provide easy to use and rich functionality for data preparation, exploration, discovery, integration, modelling, visualization, and analysis. Moreover, they integrated the advances made in graphics, data storage and processing (e.g. in-memory processing, parallel processing), which allow addressing most of data requirements. With just a few drag-and-drops users can display details, aggregate data, identify trends and correlations between data. Slice-and-dice or passthrough features allow navigating the data across dimensions and different levels of details. In addition, the tools can leverage the existing data models available in data warehouses, data marts and other types of data repositories, including the rich set of open data available on the web.

With the right infrastructure, knowledge and skills users can better understand and harness the business data, using them to address business questions, they can make faster and smarter decisions rooted in data. SSBI offers the potential of increasing the value data have for the organization, while improving the time to value for data products (data models, reports, visualizations). 

The Bad: In the 90s products like MS Excel or Access allowed users to build personal solutions to address gaps existing in processes and reporting. Upon case, the personal solutions gained in importance, starting to be used by more users to the degree that they become essential for the business. Thus, these islands of data and knowledge started to become a nightmare for the IT department, as they were supposed to be kept alike and backed-up. In addition, issues like security of data, inefficient data processing, duplication of data and effort, different versions of truth, urged the business to consolidate such solutions in standardized solutions. 

Without an adequate strategy and a certain control over the outcomes of the SSBI initiatives, organization risk of reaching to the same deplorable state, with SSBI initiatives having the potential to bring more damage than the issues they can solve. Insufficient data quality and integration, unrealistic expectations, the communication problems between business and IT, as well insufficient training and support have the potential of making SSBI’s adoption more difficult.

The investment in adequate SSBI tool(s) might be small compared with the further changes that need to be done within the technical and logistical BI infrastructure. In addition, even if the role of IT is minimized, it doesn’t mean that IT needs to be left out of the picture. IT is still the owner of the IT infrastructure, it still needs to oversight the self-service processes and the flow of data, information and knowledge within the organization. From infrastructure to skillset, there are aspects of the SSBI that need to be addressed accordingly. The BI professional can’t be replaced entirely, though the scope of his work may shift to address new types of challenges.

Not understanding that SSBI initiatives are iterative, explorative in nature and require time to bring value, can put unnecessary pressure on those being part of it. Renouncing to SSBI initiatives without attempting to address the issues and stir them in the right direction hinder an organization and its employees’ potential to grow, with all the implication deriving from it.

The Ugly: Despite the benefits SSBI can bring, its adoption within organizations remains low. Whether it’s business’ credibility in own forces, or the inherent technical or logistical challenges, SSBI follows the BI trend of being a promise that seldom reaches its potential.

11 June 2020

🧭🪄☯Business Intelligence: SQL Server Reporting Services (The Good, the Bad and the Ugly)

Business Intelligence

SQL Server Reporting Services (SSRS) is the oldest solution from the modern Microsoft BI stack. Released as add-on to SQL Server 2000, it allows covering most of an organization's reporting requirements, either if we talk about tables, matrices or crosstab displays, raw data, aggregations, KPIs or visualizations like charts, gauges, sparklines, tree maps or sunbursts.

The Good: Once you have a SQL query based on any standard data sources (SQL Server, Oracle, SharePoint, OData, XML, etc.), it can be used in just a few minutes to create a report with the help of a wizard. Sure, adding the needed formatting, parameters, custom code, drilldown and drill-through functionality might take some effort, though in less than an hour you have a running report. The use of templates and a custom branding allows providing a common experience across the enterprise. 

The whole service is available once you have a SQL Server license, fact that makes from the SSRS a cost-effective tool. The shallow learning curve and the integration with SharePoint facilitates the development and consumption of reports.

With its pixel-accurate display of data, SSRS is ideal for printing business documents. This was probably one of the reasons why SSRS become with Microsoft Dynamics AX 2009 also the main reporting platform for the further versions. One can use an AX 2009 class as source for the report, or directly use the base tables, which can increase reports’ performance in the detriment of reengineering the logic from AX 2009. With a few exceptions in finance area the reporting logic is easy to build.  

With SQL Server 2016 it got a HTML5 rendering engine, while with SSRS 2017 it supports a responsive web design. The integration of the SSRS and Power BI environments has the chance to further extend the value provided by this powerful combination, however it depends also in which direction Microsoft will develop this idea.   

The Bad: One of the important downsides of SSRS is that it doesn’t allow custom authentication. Even if some examples exist on the Web, it’s hard to understand Microsoft’s stubbornness of not providing this by design. 

Because SSRS still uses an older MS Office driver, it allows exporting only 65536 records to Excel, fact that makes data consumption more complicated. In addition, the pixel-perfect isn’t that perfect, the introduction of empty columns when exporting to Excel, adds some unnecessary burden.

In total, the progress made by SSRS between the various releases is small when compared with the changes suffered by SQL Server. Even if the visualization capabilities cover most of the requests, it loses field when compared with Power BI and similar visualization tools. 

The Ugly: SSRS, as the typical BI developer knows it, is different than the architecture frameworks provided when working with Business Central, respectively Dynamics 365 and CRM. Even if there are maybe entitled reasons, Microsoft failed to unite the three architectures into a flexible solution. Almost all the examples available on the Web target CRM, and frankly it’s hard to understand that. It feels like Microsoft wants to sabotage their own product?! What’s hard to understand is that besides SSRS and Power BI Microsoft has several other reporting tools for Dynamics 365. Building reports for Business Central or Dynamics 365 requires certain skills, while the development time increased considerably, thus SSRS losing from the appeal it previously had, allowing other tools to join the landscape (e.g. electronic documents).

SSRS can’t be smoothly integrated with Office 365 Online, remaining mainly a solution for on-premise architectures.  This can become a bottleneck when the customers move to the cloud, the BI strategy needing to be eventually rethought as well. 

24 May 2020

🧊🎡☯Data Warehousing: SQL Server Integration Services (The Good, the Bad and the Ugly)

Data Warehousing

Microsoft SQL Server Integration Services (SSIS) is a platform for building (enterprise-level) data integrations and data transformations solutions by using a rich set of built-in tasks and transformations, graphical tools for building packages, respectively a catalog for storing the packages. Formally called Data Transformation Services (DTS), it was introduced with SQL Server 2000 and with SQL Server 2005 it was rebranded as SSIS.

The Good: Since its introduction it was adopted by DBAs and (database) programmers because it allowed the import and export of data on the fly from and to SQL Server, flat files, other relational data sources, in fact any resource exposing a driver for ODBC or OLEDB libraries. The extract/load functionality was extended by a basic set of transformations, making from DTS the ideal ETL tool for data warehousing and integrations. The data from multiple sources and targets could be processed in parallel or sequentially, the ETL logic being encapsulated in one or more packages that could be run manually or scheduled via the SQL Server agent flexibly.

With SQL Server 2005 and further versions the SSIS framework was extended to support further data sources including XML, CAML-based SharePoint lists, OData, Hadoop or Azure Bloob. It allowed the storage of packages on the local storage or within the built-in catalog.

One could thus develop rich ETL functionality without writing a single line of code. In theory the packages could be run and modified also by non-IT users, which can be a plus in certain scenarios. On the other side one could build custom packages programmatically from the beginning, and thus extend the available data processing logic as seemed fit, being able to using existing code and whole libraries embedded into the packages or run via dlls calls .

The Bad: Despite the rich functionality, a data pipeline usually has a lower performance and is more difficult to troubleshoot compared with the built-in RDBMS functionality for data processing. Most, if not all transformations can be handled over SQL-based queries more efficiently as long the data are available on the same SQL Server instance. In addition, SQL provides better code reuse, maintainability, chances for refactoring, scalability and the solutions are easier to deploy. Therefore, one practice resumes in using SSIS only for import/export, the further logic being encapsulated into stored procedures and further database objects. This isn’t necessarily bad, on contrary, though specific expertise is needed then to modify the code.

The Ugly: SSIS is in general suitable for data warehousing and integrations solutions whose logic is ideally stable and well-defined. Therefore, SSIS is less suitable for ERP data migrations or similar task which at least at the beginning have an exploratory nature and an overwhelming complexity, multiple iterations being needed before the requirements were fully identified and understood. In extremis each iteration can involve a redesign, which can prove to be time-consuming. One could in theory attempt first understanding all the data, though this could mean starting the development late in the process, while the data for testing are required much earlier. One can still use SSIS for specific tasks, though implementing a whole solution could imply certain challenges that otherwise could have been avoided.

SSIS is not suitable for real-time complex data integrations which require the processing of a considerable amount of data, when specific architectures like SOA, Restful calls or other solution could be more efficient. When not adequately implemented a data integration can lead to more problems than it can solve. Best example is the increase in execution time with the volume of data, fact that can easily lead to time-outs and locking of data.

01 February 2020

#️⃣☯Software Engineering: Concept Documents (The Good, the Bad and the Ugly)

Software Engineering

A concept document (simply a concept) is a document that describes at high level the set of necessary steps and their implications in order to achieve a desired result, typically making the object of a project. In other words, it describes how something can be done or achieved, respectively how a problem can be solved.

The GoodThe main aim of the document is to give all the important aspects and to assure that the idea is worthy of consideration, that the steps considered provide a good basis for further work, respectively to provide a good understanding for the various parties involved, Therefore, concepts are used as a basis for the sign-off, respectively for the implementation of software and hardware solutions.

 A concept provides information about the context, design, architecture, security, usage, purpose and/or objectives of the future solution together with the set of assumptions, constraints and implications. A concept is not necessarily a recipe because it attempts providing a solution for a given problem or situation that needs a solution. Even if it bears many similarities in content and structure a concept it also not a strategy, because the strategy offers an interpretation of the problem, and also not a business case, because the later focuses mainly on the financial aspects.

A concept proves thus to be a good basis for implementing the described solution, being often an important enabler. On the other side, a written concept is not always necessary, even if conceptualization must exist in implementers’ head.

The Bad: From these considerations projects often consider the elaboration of a concept before further work can be attempted. To write such a document is needed to understand the problem/situation and be capable of sketching a solution in which the various steps or components fit together as the pieces of a puzzle. The problem is that the more complex the problem to be solved, the fuzzier the view and understanding of the various pieces becomes, respectively, the more challenging it becomes to fit the pieces together. In certain situations, it becomes almost impossible for a single person to understand and handle all the pieces. Solving the puzzle becomes a collective approach where the complexity is broken in manageable parts in the detriment of other aspects.

Writing a concept is a time-consuming task. The more accuracy and details are needed, the longer it takes to write and review the document, time that’s usually stolen from other project phases, especially when the phases are considered as sequential. It takes about 20% from the total effort needed to write a ‘perfect’ concept for writing a concept that covers only 80% of the facts, while 80% from the effort to consider the remaining 20% of the facts as the later involve multiple iterations. In extremis, aiming for perfection will make one start the implementation late or not start at all. It’s a not understandable pedantry with an important impact on projects'
 timeline and quality in the hope of a quality increase, which is sometimes even illusory.

The Ugly: The concept-based approach is brought to extreme in ERP implementations where for each process or business area is needed to write a concept, which often carries fancy names – solution design document, technical design document, business process document, etc. Independently how it is called, the purpose is to describe how the solution is implemented. The problem is that the conceptualization phase tends to take much longer than planned given the dependencies between the various business area in terms of functionality and activities. The complexity can become overwhelming, with an important impact on project’s budget, time and quality.

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31 January 2020

💫🧮☯ERP: Microsoft Dynamics 365 (The Good, the Bad and the Ugly)

ERP Implementation

The Good: The shift made by Microsoft by porting their Dynamics AX ERP solution to a web-based application (aka D365) hosted in the Microsoft cloud, offered them a boost on the ERP market. The integration with the Office and BI stack, as well Microsoft’s One Version strategy of pushing new features almost on a monthly basis, and of having customers at a maximum 2 releases from the current version, makes from D365 a solution to consider for small to big organizations that span over business sectors and geographies.

The Bad: Currently the monthly release cycle seems to be a challenge for the customers and service providers altogether. Even if the changes in existing functionality are minor, while the functionality is thoroughly tested before releases, the customers still need to test the releases in several systems, especially to assure that the customizations and integrations still work. This can prove to be quite a challenge in which automatic or semiautomatic tools can help when adequately used. Even then, a considerable effort needs to be addressed by the parties involved.
The burden is bigger for the service providers that build their own solutions for D365 as they need to assure in advance that after each release the applications still work. From customers’ perspective, the more such applications they use, the higher the risks of delays in adopting a release or, in extremis, to look for similar solutions. In theory, with good planning and by following best practices the risks are small, though that’s just the theory speaking.
If in the past 2-3 instances were enough to support the ERP during and post implementation, currently the requirements for the cloud-based solution more than doubled, an organization arriving to rent 5-7 D365 instances for the same. Moreover, even if the split between the main blocks (Finance, Supply Chain, Retail and Talent), plus the various Customer Engagement packages, provides some flexibility when thy are combined, this leads to a considerable price increase. Further costs are related to the gaps existing in the available functionality. More likely Microsoft will attempt closing some of the gaps, however until then the customers are forced to opt for existing solutions or have the functionality built. Microsoft pretends that their cloud-based ERP solution provides lower ownership costs, however, looking at the prices, it’s questionable on whether D365 is affordable for small and average organizations. To put it bluntly – think how many socks (aka products) one needs to sell just to cover the implementation, the licensing and infrastructure costs!
One important decision taken by Microsoft was to not allow the direct access to the D365 production database, decision that limits an organization’s choices and flexibility in addressing reporting requirements. Of course, the existing BI infrastructure can still be leveraged with a few workarounds, though the flexibility is lost, while further challenges are involved.
The Ugly: ERP implementations based on D365 make no exceptions from the general trend – given their complexity, they are predisposed to fail achieving the set objectives, and this despite Microsoft’s attempts of creating methodologies, tools and strong communities to support the service providers and customers in such projects. The reasons for failure reside with the customers and service providers altogether, the chains of implications forming a complex network of causalities with multiple levels of reinforcement. When the negative reinforcements break the balance, it can result a tipping point where the things start to go wrong – escalations, finger-pointing, teams’ restructuring, litigations, etc. In extremis, even if the project reaches the finish, the costs can easily reach an overrun of 50-150% from the initial estimation, and that’s a lot to bear.

30 January 2020

💼☯Project Management: Methodologies (The Good, the Bad and the Ugly)

Mismanagement

The Good
: Nowadays there're several Project Management (PM) methodologies to choose from to address a project’s specifics and, when adapted and applied accordingly, a methodology can enable projects to be run and brought under control.

The Bad: Even if the theoretical basis of PM methodologies has been proved and perfected over the years, projects continue to fail at a disturbing rate. Of course, the reasons behind their failure are multiple, though often the failure reasons are rooted in how PM methodologies are taught, understood and implemented.

Same as a theoretical course in cooking won’t make one a good cook, a theoretical course in PM won’t make one a good Project Manager or knowledgeable team member in applying the learned methodology. Surprisingly, the expectation is exactly that – the team member got a training and is good to go. Moreover, people believe that managing a software project is like coordinating the building of a small treehouse. To some degree there are many similarities though the challenges typically lie in details, and these details often escape a standard course.

To bridge the gap between theory and practice is needed time for the learner to grow in the role, to learn the does and don’ts, and, most important, to learn how to use the tools at hand efficiently. The methodology is itself a tool making use of further tools in its processes – project plans, work breakdown structures, checklists, charters, reports, records, etc. These can be learned only through practice, hopefully with some help (aka mentoring) from an experienced person in the respective methodology, either the Project Manager itself, a trainer or other team member. Same as one can’t be thrown into the water and expected to traverse the Channel Tunnel, you can’t do that with a newbie.

There’s a natural fallacy to think that we've understood more than we have. We can observe our understanding's limits when we are confronted with the complexities involved in handing PM activities. A second fallacy is not believing other people’s warnings against using a tool or performing an activity in a certain way. A newbie’s mind has sometimes the predisposition of a child to try touching a hot stove even if warned against it. It’s part of the learning process, though some persist in such behavior without learning much. What’s even more dangerous is a newbie pretending to be an expert and this almost always ends badly.

The Ugly appears when the bad is brought to extreme, when methodologies are misused for the wrong purposes to the degree that they destroy anything in their way. Of course, a pool can be dug by using a spoon but does it make sense to do that? Just because a tool can be used for something it doesn’t mean it should be used for it as long there are better tools for the same. It seems a pretty logical thing though the contrary happens more often than we’d like. It starts with the preconception that one should use the tool one knows best, ignoring in the process the fit for purpose condition. What’s even more deplorable is breaking down a project to fit a methodology while ignoring the technical and logistical aspects.

Any tool can lead to damages when used excessively, in wrong places, at the wrong point in time or by the wrong person. Like the instruments in an orchestra, when an instrument plays the wrong note, it dissonates from the rest. When more instruments play wrongly, then the piece is unrecognizable. It’s the role of the bandmaster to make the players touch the right notes at the right time.

25 December 2018

♟️Strategic Management: The Good (Just the Quotes)

"Present opportunities are neglected, and attainable good is slighted, by minds busied in extensive ranges and intent upon future advantages." (Samuel Johnson, "The Idler", 1801)

"For any manager to utilize graphic methods for visualizing the vital facts of his business, in the first place it must be impressed upon his that the method will produce the results for him and then he must know how to get up a chart correctly, and last, but far from least, he must know what the essential facts of his business are. Charts, in themselves, mean little and like many another force for the accomplishment of good, if misdirected, may result unprofitably." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"The fine art of executive decision consists in not deciding questions that are not now pertinent, in not deciding prematurely, in not making decision that cannot be made effective, and in not making decisions that others should make. Not to decide questions that are not pertinent at the time is uncommon good sense, though to raise them may be uncommon perspicacity. Not to decide questions prematurely is to refuse commitment of attitude or the development of prejudice. Not to make decisions that cannot be made effective is to refrain from destroying authority. Not to make decisions that others should make is to preserve morale, to develop competence, to fix responsibility, and to preserve authority.

From this it may be seen that decisions fall into two major classes, positive decisions - to do something, to direct action, to cease action, to prevent action; and negative decisions, which are decisions not to decide. Both are inescapable; but the negative decisions are often largely unconscious, relatively nonlogical, instinctive, 'good sense'. It is because of the rejections that the selection is good."" (Chester I Barnard, "The Functions of the Executive", 1938)"

"Good management are rarely overcompensated to an extent that makes any significant difference with respect to the stockholder's position. Poor management are always overcompensated, because they are worth less than nothing to the owners." (Benjamin Graham, "The Intelligent Investor", 1949)

"If charts do not reflect actual organization and if the organization is intended to be as charted, it is the job of effective management to see that actual organization conforms with that desired. Organization charts cannot supplant good organizing, nor can a chart take the place of spelling out authority relationships clearly and completely, of outlining duties of managers and their subordinates, and of defining responsibilities." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"While good charting will attempt, as far as possible, to make levels on the chart conform to levels of importance in the business enterprise, it cannot always do so. This problem can be handled by clearly spelling out authority relationships." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"To say a system is 'self-organizing' leaves open two quite different meanings. There is a first meaning that is simple and unobjectionable. This refers to the system that starts with its parts separate" (so that the behavior of each is independent of the others' states) and whose parts then act so that they change towards forming connections of some type. Such a system is 'self-organizing' in the sense that it changes from 'parts separated' to 'parts joined'. […] In general such systems can be more simply characterized as 'self-connecting', for the change from independence between the parts to conditionality can always be seen as some form of 'connection', even if it is as purely functional […]  'Organizing' […] may also mean 'changing from a bad organization to a good one' […] The system would be 'self-organizing' if a change were automatically made to the feedback, changing it from positive to negative; then the whole would have changed from a bad organization to a good." (W Ross Ashby, "Principles of the self-organizing system", 1962)

"The successful manager must be a good diagnostician and must value a spirit of inquiry." (Edgar H Schein, "Organizational Psychology", 1965)

"Good mission statements focus on a limited number of goals, stress the company's major policies and values, and define the company's major competitive scopes." (Philip Kotler, "Marketing Management", 1967)

"In most management problems there are too many possibilities to expect experience, judgement, or intuition to provide good guesses, even with perfect information." (Russell L Ackoff, "Management Science", 1967)

"Good results without good planning come from good luck, not good management." (David Jaquith, "The Time Trap", 1972

"To be productive the individual has to have control, to a substantial extent, over the speed, rhythm, and attention spans with which he is working […] While work is, therefore, best laid out as uniform, working is best organized with a considerable degree of diversity. Working requires latitude to change speed, rhythm, and attention span fairly often. It requires fairly frequent changes in operating routines as well. What is good industrial engineering for work is exceedingly poor human engineering for the worker." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"Any approach to strategy quickly encounters a conflict between corporate objectives and corporate capabilities. Attempting the impossible is not good strategy; it is just a waste of resources." (Bruce Henderson, Henderson on Corporate Strategy, 1979)

"Executive stress is difficult to overstate when there is a conflict among policy restrictions, near-term performance, long-term good of the company, and personal survival." (Bruce Henderson, "Henderson on Corporate Strategy", 1979)

"[Organizational] change is intervention, and intervention even with good intentions can lead to negative results in both the short and long run. For example, a change in structure in going from application of one theory to another might cause the unwanted resignation of a key executive, or the loss of an important customer. [...] the factor of change, acts as an overriding check against continual organizational alterations. It means that regardless of how well meant a change is, or how much logic dictates this change, its possible negative effects must be carefully weighed against the hoped-for benefits." (William A Cohen, "Principles of Technical Management", 1980)

"A leader is one who, out of madness or goodness, volunteers to take upon himself the woe of the people. There are few men so foolish, hence the erratic quality of leadership in the world." (John Updike, "They Thought They Were Better", TIME magazine, 1980)

"Because the importance of training is so commonly underestimated, the manager who wants to make a dramatic improvement in organizational effectiveness without challenging the status quo will find a training program a good way to start." (Theodore Caplow, "Managing an Organization", 1983)

"It seems to me that we too often focus on the inside aspects of the job of management, failing to give proper attention to the requirement for a good manager to maintain those relationships between his organization and the environment in which it must operate which permits it to move ahead and get the job done." (Breene Kerr, Giants in Management, 1985) 

"Managers who are skilled communicators may also be good at covering up real problems." (Chris Argyris, Harvard Business Review, 1986)

"Operating managers should in no way ignore short-term performance imperatives [when implementing productivity improvement programs.] The pressures arise from many sources and must be dealt with. Moreover, unless managers know that the day-to-day job is under control and improvements are being made, they will not have the time, the perspective, the self-confidence, or the good working relationships that are essential for creative, realistic strategic thinking and decision making." (Robert H Schaefer, Harvard Business Review, 1986)

"Some management groups are not good at problem solving and decision making precisely because the participants have weak egos and are uncomfortable with competition." (Chris Argyris, Harvard Business Review, 1986)

"The chain of command is an inefficient communication system. Although my staff and I had our goals, tasks, and priorities well defined, large parts of the organization didn't know what was going on. Frequent, thorough, open communication to every employee is essential to get the word out and keep walls from building within the company. And while face-to-face communication is more effective than impersonal messages, it's a good idea to vary the medium and the message so that no one (including top management) relies too much on ''traditional channels of communication." (William H Peace, Harvard Business Review, 1986)

"The source of good management is found in the imagination of leaders, persons who form new visions and manifest them with a high degree of craft. The blending of vision and craft communicates the purpose. In the arts, people who do that well are masters. In business, they are leaders." (Henry M. Boettinger, Harvard Business Review on Human Relations, 1986)

"Employees are most apt to deal with their problems when they believe that they will be helped in good faith." (Paul V Lyons, "Management", 1987)

"Good people can fix a lot of flaws in poor planning, but it's never the other way around." (Roland Shmitt, "Government Executive", 1987)

"Some people are excited about learning a new piece of software. Other people get very depressed. Good managers anticipate both situations they involve the persons to be affected in the process of selecting a particular program, and they provide time and resources for training. Training is the key in both cases." (Jonathan P Siegel, "Communications", 1988)

"Pressure can also make managers act out of character. Degrees of panic will cause a normally good manager to lose self-confidence and focus. Under stress, even a good plan can be abandoned." (Wheeler L Baker, "Crisis Management: A Model for Managers", 1993)

"Managers must clearly distinguish operational effectiveness from strategy. Both are essential, but the two agendas are different. The operational agenda involves continual improvement everywhere there are no trade-offs. Failure to do this creates vulnerability even for companies with a good strategy. The operational agenda is the proper place for constant change, flexibility, and relentless efforts to achieve best practice. In contrast, the strategic agenda is the right place for defining a unique position, making clear trade-offs, and tightening fit. It involves the continual search for ways to reinforce and extend the company’s position. The strategic agenda demands discipline and continuity; its enemies are distraction and compromise." (Michael E Porter, "What is Strategy?", Harvard Business Review, 1996)

"There's a fundamental distinction between strategy and operational effectiveness. Strategy is about making choices, trade-offs; it's about deliberately choosing to be different. Operational effectiveness is about things that you really shouldn't have to make choices on; it's about what's good for everybody and about what every business should be doing. " (Michael E Porter, "What is Strategy?", Harvard Business Review, 1996)

"An effective leader leaves a legacy; they leave their footprints on the road for others to follow. A good leader develops themselves and they develop others. They bring people together rather than divide them." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"Good leaders are ethical, responsible and effective. Ethical because leadership connects you to others through shared values. Responsible because leadership means self-development and not simply giving orders, however charismatically, to get others to do what you want. Effective because shared values and goals give the strongest motivation for getting tasks done. There are no guarantees, but this sort of leadership will bring you closer to people and give you the greatest chance of success." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"Managing [...] used to be about planning and control. Top management decided what was to be done, middle management worked out how to do it and everyone else did as they were told. This model assumed, of course, that top management knew what needed to be done, that the orders had time to percolate their way down and that, like a good army, the lower ranks would obey." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"Good leaders make people feel that they're at the very heart of things, not at the periphery. Everyone feels that he or she makes a difference to the success of the organization. When that happens, people feel centered and that gives their work meaning." (Warren Bennis, "Managing People Is Like Herding Cats", 1999)

"Making good judgments when one has complete data, facts, and knowledge is not leadership - it's bookkeeping." (Dee Hock, "Birth of the Chaordic Age", 1999)

"Data have to be filtered in some manner to make them intelligible. This filtration may be based upon a person's experience plus his presuppositions and assumptions, or it may be more formalized and less subjective, but there will always be some method of analysis. If experience is the basis for interpreting the data, then the interpretation is only as good as the manager's past experience. If the current situation is outside the manager’s experience, then his interpretation of the data may well be incorrect. Likewise, flawed assumptions or flawed presuppositions can also result in flawed interpretations. However, in the absence of formal and standardized data, most managers use the scat-of-the-pants approach. and in the end, about all they can say that some days appear to be better than others." (Donald J Wheeler," Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"I've learned that mistakes can often be as good a teacher as success." (Jack Welch, "Jack: Straight from the Gut", 2001)

"Project failures are not always the result of poor methodology; the problem may be poor implementation. Unrealistic objectives or poorly defined executive expectations are two common causes of poor implementation. Good methodologies do not guarantee success, but they do imply that the project will be managed correctly." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"The key to good decision making is not knowledge. It is understanding. We are swimming in the former. We are desperately lacking in the latter." (Malcolm Gladwell, "Blink: The Power of Thinking Without Thinking", 2005)

"Wisdom and good governance require more than the consistent application of abstract principles." (Anthony Daniels, "Romancing Opiates: Pharmacological Lies and the Addiction Bureaucracy", 2006)

"Enterprise architecture is the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of the company's operating model. The operating model is the desired state of business process integration and business process standardization for delivering goods and services to customers." (Peter Weill, "Innovating with Information Systems Presentation", 2007)

"Whereas strategy is abstract and based on long-term goals, tactics are concrete and based on finding the best move right now. Tactics are conditional and opportunistic, all about threat and defense. No matter what pursuit you’re engaged in - chess, business, the military, managing a sports team - it takes both good tactics and wise strategy to be successful." (Garry Kasparov, "How Life Imitates Chess", 2007)

"A bad strategy will fail no matter how good your information is and lame execution will stymie a good strategy. If you do enough things poorly, you will go out of business." (Bill Gates, "Business @ the Speed of Thought: Succeeding in the Digital Economy", 2009)

"A good strategy is one that takes into account not only the requirements of the position, but also the opponent's strategy and tactics. Strategy lies between science and art. It supports the ability to evaluate positions, recognize patterns and imagine adequate plans." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"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)

"Almost by definition, one is rarely privileged to 'control' a disaster. Yet the activity somewhat loosely referred to by this term is a substantial portion of Management, perhaps the most important part. […] It is the business of a good Manager to ensure, by taking timely action in the real world, that scenarios of disaster remain securely in the realm of Fantasy." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Clearly, total feedback is Not a Good Thing. Too much feedback can overwhelm the response channels, leading to paralysis and inaction. Even in a system designed to accept massive feedback" (such as the human brain), if the system is required to accommodate to all incoming data, equilibrium will never be reached. The point of decision will be delayed indefinitely, and no action will be taken." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Despite the roar of voices wanting to equate strategy with ambition, leadership, 'vision', planning, or the economic logic of competition, strategy is none of these. The core of strategy work is always the same: discovering the critical factors in a situation and designing a way of coordinating and focusing actions to deal with those factors." (Richard Rumelt, "Good Strategy Bad Strategy", 2011)

"Having conflicting goals, dedicating resources to unconnected targets, and accommodating incompatible interests are the luxuries of the rich and powerful, but they make for bad strategy. Despite this, most organizations will not create focused strategies. Instead, they will generate laundry lists of desirable outcomes and, at the same time, ignore the need for genuine competence in coordinating and focusing their resources. Good strategy requires leaders who are willing and able to say no to a wide variety of actions and interests. Strategy is at least as much about what an organization does not do as it is about what it does." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"It is hard to avoid the conclusion that while strategy is undoubtedly a good thing to have, it is a hard thing to get right. […] So what turns something that is not quite strategy into strategy is a sense of actual or imminent instability, a changing context that induces a sense of conflict. Strategy therefore starts with an existing state of affairs and only gains meaning by an awareness of how, for better or worse, it could be different." (Lawrence Freedman, “Strategy: A history”, 2013)

"You can only look so far, and so you better just keep looking frequently. That’s the most important element of strategy: You understand the direction you’re going, but you also know what you’re going to do in the next six months. Most companies will do a pretty good job many times about the direction, but then they never break it down to shorter metrics. Intel did a super job on that. When you ask why [we] succeeded, this is one of the reasons." (Les Vadasz, 2013)

"Good decision-making is like playing chess and you must avoid making hasty decisions without thinking of how that particular decision will impact on different aspects of your work and organization. The worst kind of decision-making is to decide to delay a difficult decision until later or to pass it to someone else to have to make. You will never excel and be valued by your colleagues if you get into these habits of procrastination and passing responsibility to others." (Nigel Cumberland, "Secrets of Success at Work: 50 techniques to excel", 2014)

"Good governance is less about structure and rules than being focused, effective and accountable." (Pearl Zhu,  "Digitizing Boardroom: The Multifaceted Aspects of Digital Ready Boards", 2016)

"Good mission statements have five major characteristics. (1) They focus on a limited number of goals. (2) They stress the company’s major policies and values. (3) They define the major competitive spheres within which the company will operate. (4) They take a long-term view." (5) They are as short, memorable, and meaningful as possible." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016)

"No methodology can guarantee success. But a good methodology can provide a feedback loop for continual improvement and learning." (Ash Maurya, "Scaling Lean: Mastering the Key Metrics for Startup Growth", 2016)

"Decision trees are considered a good predictive model to start with, and have many advantages. Interpretability, variable selection, variable interaction, and the flexibility to choose the level of complexity for a decision tree all come into play." (Ralph Winters, "Practical Predictive Analytics", 2017)

"Random forests are essentially an ensemble of trees. They use many short trees, fitted to multiple samples of the data, and the predictions are averaged for each observation. This helps to get around a problem that trees, and many other machine learning techniques, are not guaranteed to find optimal models, in the way that linear regression is. They do a very challenging job of fitting non-linear predictions over many variables, even sometimes when there are more variables than there are observations. To do that, they have to employ 'greedy algorithms', which find a reasonably good model but not necessarily the very best model possible." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"If you do not conduct sufficient analysis and if you do not have firm technical knowledge, you cannot carry out improvement or standardization, nor can you perform good control or prepare control charts useful for effective control." (Kaoru Ishikawa)

"The traditional approach to leadership values decision-making conviction and consistency; good leaders 'stick to their guns'. By contrast, the emerging approach recognizes that in fast-changing environments, decisions often need to be reversed or adapted, and that changing course in response to new information is a strength, not a weakness. If this tension is not managed wisely, leaders run the risk of seeming too rigid, on the one hand, or too wishy-washy on the other." (Jennifer Jordan et al, "Every Leader Needs to Navigate These 7 Tensions", Harvard Business Review, 2020)


24 December 2018

♟️Strategic Management: The Bad (Just the Quotes)

"To say a system is 'self-organizing' leaves open two quite different meanings. There is a first meaning that is simple and unobjectionable. This refers to the system that starts with its parts separate" (so that the behavior of each is independent of the others' states) and whose parts then act so that they change towards forming connections of some type. Such a system is 'self-organizing' in the sense that it changes from 'parts separated' to 'parts joined'. […] In general such systems can be more simply characterized as 'self-connecting', for the change from independence between the parts to conditionality can always be seen as some form of 'connection', even if it is as purely functional […]  'Organizing' […] may also mean 'changing from a bad organization to a good one' […] The system would be 'self-organizing' if a change were automatically made to the feedback, changing it from positive to negative; then the whole would have changed from a bad organization to a good." (W Ross Ashby, "Principles of the self-organizing system", 1962)

"The definition of a problem and the action taken to solve it largely depend on the view which the individuals or groups that discovered the problem have of the system to which it refers. A problem may thus find itself defined as a badly interpreted output, or as a faulty output of a faulty output device, or as a faulty output due to a malfunction in an otherwise faultless system, or as a correct but undesired output from a faultless and thus undesirable system. All definitions but the last suggest corrective action; only the last definition suggests change, and so presents an unsolvable problem to anyone opposed to change." (Herbert Brün, "Technology and the Composer", 1971)

"The dogma of delegation is simple - the Sixth Truth of Management again: either the delegatee is capable of running the operation successfully by himself or he isn't. This handy formula relieves the top executive of any responsibility except that of finding, supervising, and" (at the appropriate time) moving the men who are doing all the work. He Can then truly manage by exception: he does not get worked up over operations that are going well, but concentrates on the plague spots, where everything, including the management, is going badly." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"Managers sometimes justify the stick by pointing to better results, with the assumption that the threats caused the improvements. Alas, this is unlikely. One event coming before another does not automatically mean that the first is the cause of the second; the rooster does not make the sun rise every morning, although it may think it does. Bad results are much more likely to improve than get worse due to the simple law of statistics known as regression: results average out over time. Poor performance will eventually improve even when left to itself." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"Managers are incurably susceptible to panacea peddlers. They are rooted in the belief that there are simple, if not simple-minded, solutions to even the most complex of problems. And they do not learn from bad experiences. Managers fail to diagnose the failures of the fads they adopt; they do not understand them. […] Those at the top feel obliged to pretend to omniscience, and therefore refuse to learn anything new even if the cost of doing so is success." (Russell L Ackoff, "A Lifetime Of Systems Thinking", Systems Thinker, 1999)

"It’s tempting to view the multitude of monster projects gone bad as anomalies, excrescences of corporate and government bureaucracies run amok. But you will find similar tales of woe emerging from software projects big and small, public and private, old and new. Though details differ, the pattern is depressingly repetitive: Moving targets. Fluctuating goals. Unrealistic schedules. Missed deadlines. Ballooning costs. Despair. Chaos." (Scott Rosenberg, "Dreaming in Code", 2007)

"A bad strategy will fail no matter how good your information is and lame execution will stymie a good strategy. If you do enough things poorly, you will go out of business." (Bill Gates, "Business @ the Speed of Thought: Succeeding in the Digital Economy", 2009)

"A leader’s most important job is creating and constantly adjusting this strategic bridge between goals and objectives." (Richard Rumelt, "Good Strategy Bad Strategy", 2011)

"A strategy coordinates action to address a specific challenge. It is not defined by the pay grade of the person authorizing the action." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"Having conflicting goals, dedicating resources to unconnected targets, and accommodating incompatible interests are the luxuries of the rich and powerful, but they make for bad strategy. Despite this, most organizations will not create focused strategies. Instead, they will generate laundry lists of desirable outcomes and, at the same time, ignore the need for genuine competence in coordinating and focusing their resources. Good strategy requires leaders who are willing and able to say no to a wide variety of actions and interests. Strategy is at least as much about what an organization does not do as it is about what it does." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"Our minds, especially our intuitions, are not equipped to deal with a probabilistic world. Risk and prediction are widely misunderstood, […] All decision making in a probabilistic world involves estimating the likelihood of an event and how much we will value it" (affective forecasting). Humans are bad at both - ​​​​​ particularly at the former. […] In business, understanding the psychology of risk is more important than understanding the mathematics of risk." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

"The bad news is that companies tend to focus on three out of the four elements of the balanced scorecard and emphasis is skewed away from the customer component, which is the least understood and believed by many to be the least quantifiable." (Alan Pennington, "The Customer Experience Book", 2016)


19 December 2018

♟️Strategic Management: The Ugly (Just the Quotes)

"The concept of leadership has an ambiguous status in organizational practice, as it does in organizational theory. In practice, management appears to be of two minds about the exercise of leadership. Many jobs are so specified in content and method that within very broad limits differences among individuals become irrelevant, and acts of leadership are regarded as gratuitous at best, and at worst insubordinate." (Daniel Katz & Robert L Kahn, "The Social Psychology of Organizations", 1966)

"Planning and management by objectives have their point as devices for compelling thought, so long as executives don't forget that any plan worth making is inaccurate; the longer a plan takes to write, the worse it is - just because of its consumption of time. And the more they change plans to suit events, the better they will manage - if you've made a mistake, you had better admit it." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"[...] when a variety of tasks have all to be performed in cooperation, synchronization, and communication, a business needs managers and a management. Otherwise, things go out of control; plans fail to turn into action; or, worse, different parts of the plans get going at different speeds, different times, and with different objectives and goals, and the favor of the 'boss' becomes more important than performance." (Peter F Drucker, "People and Performance", 1977)

"The first rule is that a measurement - any measurement - is better than none. But a genuinely effective indicator will cover the output of the work unit and not simply the activity involved. […] If you do not systematically collect and maintain an archive of indicators, you will have to do an awful lot of quick research to get the information you need, and by the time you have it, the problem is likely to have gotten worse." (Andrew S Grove, "High Output Management", 1983)

"The obsession with methodologies in the workplace is another instance of the high-tech illusion. It stems from the belief that what really matters is the technology. [...] Whatever the technological advantage may be, it may come only at the price of a significant worsening of the team's sociology." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"Managers sometimes justify the stick by pointing to better results, with the assumption that the threats caused the improvements. Alas, this is unlikely. One event coming before another does not automatically mean that the first is the cause of the second; the rooster does not make the sun rise every morning, although it may think it does. Bad results are much more likely to improve than get worse due to the simple law of statistics known as regression: results average out over time. Poor performance will eventually improve even when left to itself." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"A blame culture is corrosive, eroding the team ethos that is vital for success. If they fear that they will be pilloried or punished for their mistakes, your colleagues will start worrying more about how to protect their back than doing what’s best for the team and wider organization. In the worst cases, this can even lead to lying, setting up fall guys, and other dysfunctional behavior." (Paul Butcher, "Debug It! Find, Repair, and Prevent Bugs in Your Code", 2009)

"It is hard to avoid the conclusion that while strategy is undoubtedly a good thing to have, it is a hard thing to get right. […] So what turns something that is not quite strategy into strategy is a sense of actual or imminent instability, a changing context that induces a sense of conflict. Strategy therefore starts with an existing state of affairs and only gains meaning by an awareness of how, for better or worse, it could be different." (Lawrence Freedman, “Strategy: A history”, 2013) 

"Good decision-making is like playing chess and you must avoid making hasty decisions without thinking of how that particular decision will impact on different aspects of your work and organization. The worst kind of decision-making is to decide to delay a difficult decision until later or to pass it to someone else to have to make. You will never excel and be valued by your colleagues if you get into these habits of procrastination and passing responsibility to others." (Nigel Cumberland, "Secrets of Success at Work: 50 techniques to excel", 2014)

"The passage of time and the action of entropy bring about ever-greater complexity - a branching, blossoming tree of possibilities. Blossoming disorder" (things getting worse), now unfolding within the constraints of the physics of our universe, creates novel opportunities for spontaneous ordered complexity to arise." (D J MacLennan, "Frozen to Life", 2015)

26 December 2014

🕸Systems Engineering: Perfection (Just the Quotes)

"The concept of an independent system is a pure creation of the imagination. For no material system is or can ever be perfectly isolated from the rest of the world. Nevertheless it completes the mathematician’s ‘blank form of a universe’ without which his investigations are impossible. It enables him to introduce into his geometrical space, not only masses and configurations, but also physical structure and chemical composition." (Lawrence J Henderson, "The Order of Nature: An Essay", 1917)

"Knowledge is not something which exists and grows in the abstract. It is a function of human organisms and of social organization. Knowledge, that is to say, is always what somebody knows: the most perfect transcript of knowledge in writing is not knowledge if nobody knows it. Knowledge however grows by the receipt of meaningful information - that is, by the intake of messages by a knower which are capable of reorganising his knowledge." (Kenneth E Boulding, "General Systems Theory: The Skeleton of Science", Management Science Vol. 2 (3), 1956)

"The hardest problems we have to face do not come from philosophical questions about whether brains are machines or not. There is not the slightest reason to doubt that brains are anything other than machines with enormous numbers of parts that work in perfect accord with physical laws. As far as anyone can tell, our minds are merely complex processes. The serious problems come from our having had so little experience with machines of such complexity that we are not yet prepared to think effectively about them." (Marvin Minsky, 1986)

"Nature behaves in ways that look mathematical, but nature is not the same as mathematics. Every mathematical model makes simplifying assumptions; its conclusions are only as valid as those assumptions. The assumption of perfect symmetry is excellent as a technique for deducing the conditions under which symmetry-breaking is going to occur, the general form of the result, and the range of possible behaviour. To deduce exactly which effect is selected from this range in a practical situation, we have to know which imperfections are present" (Ian Stewart & Martin Golubitsky, "Fearful Symmetry: Is God a Geometer?", 1992)

"Skewness is a measure of symmetry. For example, it's zero for the bell-shaped normal curve, which is perfectly symmetric about its mean. Kurtosis is a measure of the peakedness, or fat-tailedness, of a distribution. Thus, it measures the likelihood of extreme values." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"Swarm systems generate novelty for three reasons: (1) They are 'sensitive to initial conditions' - a scientific shorthand for saying that the size of the effect is not proportional to the size of the cause - so they can make a surprising mountain out of a molehill. (2) They hide countless novel possibilities in the exponential combinations of many interlinked individuals. (3) They don’t reckon individuals, so therefore individual variation and imperfection can be allowed. In swarm systems with heritability, individual variation and imperfection will lead to perpetual novelty, or what we call evolution." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Bounded rationality simultaneously constrains the complexity of our cognitive maps and our ability to use them to anticipate the system dynamics. Mental models in which the world is seen as a sequence of events and in which feedback, nonlinearity, time delays, and multiple consequences are lacking lead to poor performance when these elements of dynamic complexity are present. Dysfunction in complex systems can arise from the misperception of the feedback structure of the environment. But rich mental models that capture these sources of complexity cannot be used reliably to understand the dynamics. Dysfunction in complex systems can arise from faulty mental simulation-the misperception of feedback dynamics. These two different bounds on rationality must both be overcome for effective learning to occur. Perfect mental models without a simulation capability yield little insight; a calculus for reliable inferences about dynamics yields systematically erroneous results when applied to simplistic models." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"Yet, with the discovery of the butterfly effect in chaos theory, it is now understood that there is some emergent order over time even in weather occurrence, so that weather prediction is not next to being impossible as was once thought, although the science of meteorology is far from the state of perfection." (Peter Baofu, "The Future of Complexity: Conceiving a Better Way to Understand Order and Chaos", 2007)

"The word ‘symmetry’ conjures to mind objects which are well balanced, with perfect proportions. Such objects capture a sense of beauty and form. The human mind is constantly drawn to anything that embodies some aspect of symmetry. Our brain seems programmed to notice and search for order and structure. Artwork, architecture and music from ancient times to the present day play on the idea of things which mirror each other in interesting ways. Symmetry is about connections between different parts of the same object. It sets up a natural internal dialogue in the shape." (Marcus du Sautoy, "Symmetry: A Journey into the Patterns of Nature", 2008)

"[...] a high degree of unpredictability is associated with erratic trajectories. This not only because they look random but mostly because infinitesimally small uncertainties on the initial state of the system grow very quickly - actually exponentially fast. In real world, this error amplification translates into our inability to predict the system behavior from the unavoidable imperfect knowledge of its initial state." (Massimo Cencini et al, "Chaos: From Simple Models to Complex Systems", 2010)

"Because the perfect system cannot be designed, there will always be weak spots that human ingenuity and resourcefulness can exploit." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

See also: Failure, Good, Bad, Ugly


25 December 2014

🕸Systems Engineering: The Good (Just the Quotes)

"Plasticity, then, in the wide sense of the word, means the possession of a structure weak enough to yield to an influence, but strong enough not to yield all at once. Each relatively stable phase of equilibrium in such a structure is marked by what we may call a new set of habits." (William James, "The Laws of Habit", 1887)

"The engineer must be able not only to design, but to execute. A draftsman may be able to design, but unless he is able to execute his designs to successful operation he cannot be classed as an engineer. The production engineer must be able to execute his work as he has planned it. This requires two qualifications in addition to technical engineering ability: He must know men, and he must have creative ability in applying good statistical, accounting, and 'system' methods to any particular production work he may undertake." (Hugo Diemer, "Industrial Engineering", 1905)

"A system is said to be coherent if every fact in the system is related every other fact in the system by relations that are not merely conjunctive. A deductive system affords a good example of a coherent system." (Lizzie S Stebbing, "A modern introduction to logic", 1930)

"Stability is commonly thought of as desirable, for its presence enables the system to combine of flexibility and activity in performance with something of permanence. Behaviour that is goal-seeking is an example of behaviour that is stable around a state of equilibrium. Nevertheless, stability is not always good, for a system may persist in returning to some state that, for other reasons, is considered undesirable." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"To say a system is 'self-organizing' leaves open two quite different meanings. There is a first meaning that is simple and unobjectionable. This refers to the system that starts with its parts separate (so that the behavior of each is independent of the others' states) and whose parts then act so that they change towards forming connections of some type. Such a system is 'self-organizing' in the sense that it changes from 'parts separated' to 'parts joined'. […] In general such systems can be more simply characterized as 'self-connecting', for the change from independence between the parts to conditionality can always be seen as some form of 'connection', even if it is as purely functional […]  'Organizing' […] may also mean 'changing from a bad organization to a good one' […] The system would be 'self-organizing' if a change were automatically made to the feedback, changing it from positive to negative; then the whole would have changed from a bad organization to a good." (W Ross Ashby, "Principles of the self-organizing system", 1962)

"The idea of knowledge as an improbable structure is still a good place to start. Knowledge, however, has a dimension which goes beyond that of mere information or improbability. This is a dimension of significance which is very hard to reduce to quantitative form. Two knowledge structures might be equally improbable but one might be much more significant than the other." (Kenneth E Boulding, "Beyond Economics: Essays on Society", 1968)

"Perhaps the most important single characteristic of modern organizational cybernetics is this: That in addition to concern with the deleterious impacts of rigidly-imposed notions of what constitutes the application of good 'principles of organization and management' the organization is viewed as a subsystem of a larger system(s), and as comprised itself of functionally interdependent subsystems." (Richard F Ericson, "Organizational cybernetics and human values", 1969)  

"Indeed, except for the very simplest physical systems, virtually everything and everybody in the world is caught up in a vast, nonlinear web of incentives and constraints and connections. The slightest change in one place causes tremors everywhere else. We can't help but disturb the universe, as T.S. Eliot almost said. The whole is almost always equal to a good deal more than the sum of its parts. And the mathematical expression of that property - to the extent that such systems can be described by mathematics at all - is a nonlinear equation: one whose graph is curvy." (M Mitchell Waldrop, "Complexity: The Emerging Science at the Edge of Order and Chaos", 1992)

"Reliable information processing requires the existence of a good code or language, i.e., a set of rules that generate information at a given hierarchical level, and then compress it for use at a higher cognitive level. To accomplish this, a language should strike an optimum balance between variety (stochasticity) and the ability to detect and correct errors" (memory).(John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"System dynamics models are not derived statistically from time-series data. Instead, they are statements about system structure and the policies that guide decisions. Models contain the assumptions being made about a system. A model is only as good as the expertise which lies behind its formulation. A good computer model is distinguished from a poor one by the degree to which it captures the essence of a system that it represents. Many other kinds of mathematical models are limited because they will not accept the multiple-feedback-loop and nonlinear nature of real systems." (Jay W Forrester, "Counterintuitive Behavior of Social Systems", 1995)

"Fuzzy systems are excellent tools for representing heuristic, commonsense rules. Fuzzy inference methods apply these rules to data and infer a solution. Neural networks are very efficient at learning heuristics from data. They are 'good problem solvers' when past data are available. Both fuzzy systems and neural networks are universal approximators in a sense, that is, for a given continuous objective function there will be a fuzzy system and a neural network which approximate it to any degree of accuracy." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Our simplistic cause-effect analyses, especially when coupled with the desire for quick fixes, usually lead to far more problems than they solve - impatience and knee-jerk reactions included. If we stop for a moment and take a good look our world and its seven levels of complex and interdependent systems, we begin to understand that multiple causes with multiple effects are the true reality, as are circles of causality-effects." (Stephen G Haines, "The Managers Pocket Guide to Systems Thinking & Learning", 1998)

"The internet model has many lessons for the new economy but perhaps the most important is its embrace of dumb swarm power. The aim of swarm power is superior performance in a turbulent environment. When things happen fast and furious, they tend to route around central control. By interlinking many simple parts into a loose confederation, control devolves from the center to the lowest or outermost points, which collectively keep things on course. A successful system, though, requires more than simply relinquishing control completely to the networked mob." (Kevin Kelly, "New Rules for the New Economy: 10 radical strategies for a connected world", 1998)

"An equilibrium is not always an optimum; it might not even be good. This may be the most important discovery of game theory." (Ivar Ekeland, "Le meilleur des mondes possibles" ["The Best of All Possible Worlds"], 2000)

"Periods of rapid change and high exponential growth do not, typically, last long. A new equilibrium with a new dominant technology and/or competitor is likely to be established before long. Periods of punctuation are therefore exciting and exhibit unusual uncertainty. The payoff from establishing a dominant position in this short time is therefore extraordinarily high. Dominance is more likely to come from skill in marketing and positioning than from superior technology itself." (Richar Koch, "The Power Laws", 2000)

"Most physical systems, particularly those complex ones, are extremely difficult to model by an accurate and precise mathematical formula or equation due to the complexity of the system structure, nonlinearity, uncertainty, randomness, etc. Therefore, approximate modeling is often necessary and practical in real-world applications. Intuitively, approximate modeling is always possible. However, the key questions are what kind of approximation is good, where the sense of 'goodness' has to be first defined, of course, and how to formulate such a good approximation in modeling a system such that it is mathematically rigorous and can produce satisfactory results in both theory and applications." (Guanrong Chen & Trung Tat Pham, "Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems", 2001) 

"A smaller model with fewer covariates has two advantages: it might give better predictions than a big model and it is more parsimonious (simpler). Generally, as you add more variables to a regression, the bias of the predictions decreases and the variance increases. Too few covariates yields high bias; this called underfitting. Too many covariates yields high variance; this called overfitting. Good predictions result from achieving a good balance between bias and variance. […] fiding a good model involves trading of fit and complexity." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"All models are mental projections of our understanding of processes and feedbacks of systems in the real world. The general approach is that models are as good as the system upon which they are based. Models should be designed to answer specific questions and only incorporate the necessary details that are required to provide an answer." (Hördur V Haraldsson &amp; Harald U Sverdrup, "Finding Simplicity in Complexity in Biogeochemical Modelling", 2004)

"The laws of thermodynamics tell us something quite different. Economic activity is merely borrowing low-entropy energy inputs from the environment and transforming them into temporary products and services of value. In the transformation process, often more energy is expended and lost to the environment than is embedded in the particular good or service being produced." (Jeremy Rifkin, "The Third Industrial Revolution", 2011)

24 December 2014

🕸Systems Engineering: The Bad (Just the Quotes)

"The concept of teleological mechanisms however it be expressed in many terms, may be viewed as an attempt to escape from these older mechanistic formulations that now appear inadequate, and to provide new and more fruitful conceptions and more effective methodologies for studying self-regulating processes, self-orienting systems and organisms, and self-directing personalities. Thus, the terms feedback, servomechanisms, circular systems, and circular processes may be viewed as different but equivalent expressions of much the same basic conception." (Lawrence K Frank, 1948)

"[...] the concept of 'feedback', so simple and natural in certain elementary cases, becomes artificial and of little use when the interconnexions between the parts become more complex. When there are only two parts joined so that each affects the other, the properties of the feedback give important and useful information about the properties of the whole. But when the parts rise to even as few as four, if every one affects the other three, then twenty circuits can be traced through them; and knowing the properties of all the twenty circuits does not give complete information about the system. Such complex systems cannot be treated as an interlaced set of more or less independent feedback circuits, but only as a whole. For understanding the general principles of dynamic systems, therefore, the concept of feedback is inadequate in itself. What is important is that complex systems, richly cross-connected internally, have complex behaviours, and that these behaviours can be goal-seeking in complex patterns." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"To say a system is 'self-organizing' leaves open two quite different meanings. There is a first meaning that is simple and unobjectionable. This refers to the system that starts with its parts separate" (so that the behavior of each is independent of the others' states) and whose parts then act so that they change towards forming connections of some type. Such a system is 'self-organizing' in the sense that it changes from 'parts separated' to 'parts joined'. […] In general such systems can be more simply characterized as 'self-connecting', for the change from independence between the parts to conditionality can always be seen as some form of 'connection', even if it is as purely functional […]  'Organizing' […] may also mean 'changing from a bad organization to a good one' […] The system would be 'self-organizing' if a change were automatically made to the feedback, changing it from positive to negative; then the whole would have changed from a bad organization to a good." (W Ross Ashby, "Principles of the self-organizing system", 1962)

"The purpose and real value of systems engineering is [...] to keep going around the loop; find inadequacies and make improvements." (Robert E Machol, "Mathematicians are useful", 1971)

"Systems with unknown behavioral properties require the implementation of iterations which are intrinsic to the design process but which are normally hidden from view. Certainly when a solution to a well-understood problem is synthesized, weak designs are mentally rejected by a competent designer in a matter of moments. On larger or more complicated efforts, alternative designs must be explicitly and iteratively implemented. The designers perhaps out of vanity, often are at pains to hide the many versions which were abandoned and if absolute failure occurs, of course one hears nothing. Thus the topic of design iteration is rarely discussed. Perhaps we should not be surprised to see this phenomenon with software, for it is a rare author indeed who publicizes the amount of editing or the number of drafts he took to produce a manuscript." (Fernando J Corbató, "A Managerial View of the Multics System Development", 1977)

"How can a cognitive system process environmental input and stored knowledge so as to benefit from experience? More specific versions of this question include the following: How can a system organize its experience so that it has some basis for action even in unfamiliar situations? How can a system determine that rules in its knowledge base are inadequate? How can it generate plausible new rules to replace the inadequate ones? How can it refine rules that are useful but non-optimal? How can it use metaphor and analogy to transfer information and procedures from one domain to another?" (John H Holland et al, "Induction: Processes Of Inference, Learning, And Discovery", 1986)

"[…] the complexity of a given system is always determined relative to another system with which the given system interacts. Only in extremely special cases, where one of these reciprocal interactions is so much weaker than the other that it can be ignored, can we justify the traditional attitude regarding complexity as an intrinsic property of the system itself." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"Complex adaptive systems have the property that if you run them - by just letting the mathematical variable of 'time' go forward - they'll naturally progress from chaotic, disorganized, undifferentiated, independent states to organized, highly differentiated, and highly interdependent states. Organized structures emerge spontaneously. [...]A weak system gives rise only to simpler forms of self-organization; a strong one gives rise to more complex forms, like life." (J Doyne Farmer, The Third Culture: Beyond the Scientific Revolution", 1995)

"No plea about inadequacy of our understanding of the decision-making processes can excuse us from estimating decision making criteria. To omit a decision point is to deny its presence - a mistake of far greater magnitude than any errors in our best estimate of the process." (Jay W Forrester, "Perspectives on the modelling process", 2000)

"Remember a networked learning machine’s most basic rule: strengthen the connections to those who succeed, weaken them to those who fail." (Howard Bloom, "Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century", 2000)

"A fundamental reason for the difficulties with modern engineering projects is their inherent complexity. The systems that these projects are working with or building have many interdependent parts, so that changes in one part often have effects on other parts of the system. These indirect effects are frequently unanticipated, as are collective behaviors that arise from the mutual interactions of multiple components. Both indirect and collective effects readily cause intolerable failures of the system. Moreover, when the task of the system is intrinsically complex, anticipating the many possible demands that can be placed upon the system, and designing a system that can respond in all of the necessary ways, is not feasible. This problem appears in the form of inadequate specifications, but the fundamental issue is whether it is even possible to generate adequate specifications for a complex system." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"It is no longer sufficient for engineers merely to design boxes such as computers with the expectation that they would become components of larger, more complex systems. That is wasteful because frequently the box component is a bad fit in the system and has to be redesigned or worse, can lead to system failure. We must learn how to design large-scale, complex systems from the top down so that the specification for each component is derivable from the requirements for the overall system. We must also take a much larger view of systems. We must design the man-machine interfaces and even the system-society interfaces. Systems engineers must be trained for the design of large-scale, complex, man-machine-social systems." (A Wayne Wymore, "Systems Movement: Autobiographical Retrospectives", 2004)

"Synergy is the combined action that occurs when people work together to create new alternatives and solutions. In addition, the greatest opportunity for synergy occurs when people have different viewpoints, because the differences present new opportunities. The essence of synergy is to value and respect differences and take advantage of them to build on strengths and compensate for weaknesses." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"Because the perfect system cannot be designed, there will always be weak spots that human ingenuity and resourcefulness can exploit." (Paul Gibbons, "The Science of Successful Organizational Change",  2015)

See also: Failure, Good, Bad, Ugly, Perfection

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