Performance Management: First Time Right (The Aim toward Operational Excellence)

 

Rooted in Six Sigma methodology as a step toward operational excellence, First Time Right (FTR) implies that any procedure is performed in the right manner the first time and every time. It equates to minimizing the waste in its various forms (inventory, motion, overprocessing, overproduction, waiting, transportation, defects). Like many quality concepts from the manufacturing industry, the concept was transported in the software development process as principle, process, goal and/or metric. Thus, it became part of Software Engineering, Project Management, Data Science, and any other similar endeavors whose outcome results in software products. 

Besides the quality aspect, FTR is rooted also in the economic imperative – the need to achieve something in the minimum amount of time with the minimum of effort. It’s about being efficient in delivering a product or achieving a given target. It can be associated with continuous improvement, learning and mastery, the aim being to encompass FTR as part of organization’s culture. 

Even if not explicitly declared, FTR lurks in each task planned. It seems that it became common practice to plan with the FTR in mind, however between this theoretical aim and practice there’s as usual an important gap. Unfortunately, planners, managers and even tasks' performers often forget that mistakes are made, that several iterations are needed to get the job done. It starts with the communication between people in clarifying the requirements and ends with the formal sign off. All the deviations from the FTR add up in the deviations between expected and actual effort, though probably more important are the deviations from the plan and all the consequences deriving from it. Especially in complex projects this adds up into a spiral of issues that can easily reinforce themselves. 

Many of the jobs that imply creativity, innovation, research or exploration require at least several iterations to get the job done and this is independent of participants’ professionalism and experience. Moreover, the more quality one needs, the higher the effort, the 80/20 being sometimes a good approximation of the effort needed. In extremis, aiming for perfection instead of excellence can make certain tasks a never-ending story. 

Achieving FTR requires practice - the more novelty, the higher the complexity, the communication or the synchronization needs, the more practice is needed. It starts with the individual to master the individual tasks and ends with the team, where communication, synchronization and other aspects need to be considered. The practice is usually achieved on hands-on work as part of the daily duties, project work, and so on. Unfortunately, it’s based primarily on individual experience, and seldom groomed in advance, as preparation for future tasks. That’s why sometimes when efficiency is needed in performing critical complex tasks, one also needs to consider the learning curve in achieving the required quality. 

Of course, many organizations demand from job applicants experience and, when possible, they hire people with experience, however the diversity, complexity and changing nature of tasks require further practice. This aspect is somehow recognized in the implementation in organizations of the various forms of DevOps, though how many organizations adopt it and enforce it on a regular basis? Moreover, a major requirement of nowadays businesses is to be agile, and besides the mere application of methodologies, being agile means to have also a FTR mindset. 

FTR starts with the wish for mastery at individual and team level and, with the right management attention, by allocating time for learning, self-development in the important areas, providing relevant feedback and building an infrastructure for knowledge sharing and harnessing, FTR can become part of organization’s culture. It’s up to each of us to do it!

ERP Implementations: It’s all about Scope II (Nonfunctional Requirements & MVP)

Nonfunctional Requirements

In contrast to functional requirements (FRs), nonfunctional requirements (NFRs) have no direct impact on system’s behavior, affecting end-users’ experience with the system, resuming thus to topics like performance, usability, reliability, compatibility, security, monitoring, maintainability, testability, respectively other constraints and quality attributes. Even if these requirements are in general addressed by design, the changes made to the system have the potential of impacting users’ experience negatively.  

Moreover, the NFRs are usually difficult to quantify, and probably that’s why they are seldom made explicit in a formal document or are considered eventually only at high level. However, one can still find a basis for comparison against compliance requirements, general guidelines, standards, best practices or the legacy system(s) (e.g. the performance should not be worse than in the legacy system, the volume of effort for carrying the various activities should not increase). Even if they can’t be adequately described, it’s recommended to list the NFRs in general terms in a formal document (e.g. implementation contract). Failing to do so can open or widen the risk exposure one has, especially when the system lacks important support in the respective areas. In addition, these requirements need to be considered during testing and sign-off as well. 

Minimum Viable Product (MVP)

Besides gaps’ consideration in respect to FRs, it’s important to consider sometimes on whether the whole functionality is mandatory, especially when considering the various activities that need to be carried out (parametrization, Data Migration).

For example, one can target to implement a minimum viable product (MVP) - a version of the product which has just enough features to cover the mandatory or the most important FRs. The MVP is based on the idea that implementing about 80% of the needed functionality has in theory the potential of providing earlier a usable product with a minimum of effort (quick wins), assure that project’s goals and objectives were met, respectively assure a basis for further development. In case of cost overruns, the MVP assures that the business has a workable product and has the opportunity of deciding whether it’s worth of investing more into the project now or later. 

The MVP allows also to get early users’ feedback and integrate it into further enhancements and developments. Often the users understand the capabilities of a system, respectively implementation, only when they are able using the system. As this is a learning process, the learning period can take up to a few months until adequate feedback is available. Therefore, postponing implementation’s continuation with a few months can have in theory a positive impact, however it can come also with drawbacks (e.g. the resources are not available anymore). 

A sketch of the MVP usually results from requirements’ prioritization, however then requirements need to be regarded holistically, as there can be different levels of dependencies existing between them. In addition, different costs can incur if the requirements will be handled later, and other constrains may apply as well. Considering an MVP approach can be a sword with two edges. In the worst-case scenario, the business will get only the MVP, with its good and bad characteristics. The business will be forced then to fill the gaps by working outside the system, which can lead to further effort and, in extremis, with poor acceptance of the system. In general, users expect having their processes fully implemented in the system, expectation which is not always economically grounded.

After establishing an MVP one can consider the further requirements (including improvement suggestions) based on a cost-benefit basis and implement them accordingly as part of a continuous improvement initiative, even if more time will be maybe required for implementing the same.

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Strategic Management: Simplicity V (ERP Implementations' Story I)

Strategic Management

Probably ERP Implementations are one of the most complex type of projects one deals with in the IT world, however their complexity seldom resides in technologies themselves, but in the effort that needs to be made by organizations before, during and post-implementations. Through their transformative nature ERP implementations have the potential of changing the whole organization if their potential is exploited accordingly, which is unfortunately not always the case. Therefore, the challenges don’t resume only to managing a project or implementing a technology, but also in managing change, and that usually happens or needs to happen at several levels. 

Typically, the change is considered mainly at IT infrastructure and processual level, because at these levels most of the visible changes happen – that’s what steals the show. For the whole project duration is about replacing one or more legacy systems, making sure that the new infrastructure works as expected. The more an organization deviates from the standard the more effort is needed, and this effort can exhaust an organization’s resources to the degree that will need some time to recover after that, financially, but maybe more important from a vital point of view.

Even if the technological and processual layers are important, as they form the foundation on which an organization builds upon, besides the financial and material flow there are also the data, informational and knowledge flows, which seems to be neglected. Quite often that’s where the transformational potential resides. If an organization is not able to change positively these flows, on the long term the implementation will deal with problems people wished to be addressed much earlier, when the effort and effect would have met the lowest resistance, respectively the highest impact. 

An ERP implementation involves the migration of data between source(s) and target(s), the data requirements, including the one of appropriate quality, being regarded in respect to the target system(s). As within the data migration steps the data are extracted from the various sources, enriched, and prepared for import into the target system(s), there is the potential of bringing data quality to a level which would help the organization further. It’s probably simpler to imagine the process of taking the data from one place, cleaning and enriching the data to bring it to the needed form, and then putting the data into the new system. It’s a unique chance of improving data quality without touching the source or target system(s) while getting a considerable value.

Unfortunately, many organizations’ efforts to improve the quality of their data stop after the implementation. If there’s no focus and there are no structures in place to continue the effort, sooner or later data’s quality will decrease despite the earlier made efforts. Investing for example in a long-term data quality improvement or even a data management initiative might prove to be an exploratory and iterative process in which mistakes are maybe made, the direction might need to be changed, though, as long learning is involved, in this often resides the power of changing for the better.

When one talks about information there are two aspects to it: how an organization arrives from data to actionable information that reach timely the people who need it, respectively how information is further aggregated, recombined, shared, and harnessed into knowledge. These are the first three layers of knowledge (aka DIKW) pyramid, and an organization’s real success story is in how can manage these flows together, while increasing the value they provide for the organization. It’s an effort that must start with the implementation itself, or even earlier, and continue after the implementation, as an organization seems fit.

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Written: Sep-2020, Last Reviewed: Mar-2024

Project Management: Some Thoughts on Planning II

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

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

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

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

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

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

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

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

Performance Management: The Need for Perfection vs. Excellence

A recurring theme occurring in various contexts over the years seemed to be corroborated with the need for perfection, need going sometimes in extremis beyond common sense. The simplest theory attempting to explain at least some of these situations is that people tend to confuse excellence with perfection, from this confusion deriving false beliefs, false expectations and unhealthy behavior. 

Beyond the fact that each individual has an illusory image of what perfection is about, perfection is in certain situations a limiting force rooted in the idealistic way of looking at life. Primarily, perfection denotes that we will never be good enough to reach it as we are striving to something that doesn’t exist. From this appears the external and internal criticism, criticism that instead of helping us to build something it drains out our energy to the extent that it destroys all we have built over the years with a considerable effort. Secondarily, on the long run, perfection has the tendency to steal our inner peace and balance, letting fear take over – the fear of not making mistakes, of losing the acceptance and trust of the others. It focuses on our faults, errors and failures instead of driving us to our goals. In extremis it relieves the worst in people, actors and spectators altogether. 

In its proximate semantics though at diametral side through its implications, excellence focuses on our goals, on the aspiration of aiming higher without implying a limit to it. It’s a shift of attention from failure to possibilities, on what matters, on reaching our potential, on acknowledging the long way covered. It allows us building upon former successes and failures. Excellence is what we need to aim at in personal and professional life. Will Durant explaining Aristotle said that: “We are what we repeatedly do. Excellence, then, is not an act, but a habit.” 

People who attempt giving 100% of their best to achieve a (positive) goal are to admire, however the proximity of 100% is only occasionally achievable, hopefully when needed the most. 100% is another illusory limit we force upon ourselves as it’s correlated to the degree of achievement, completeness or quality an artefact or result can ideally have. We rightly define quality as the degree to which something is fit for purpose. Again, a moving target that needs to be made explicit before we attempt to reach it otherwise quality envisions perfection rather than excellence and effort is wasted. 

Considering the volume of effort needed to achieve a goal, Pareto’s principles (aka the 80/20 rule) seems to explain the best its underlying forces. The rule states that roughly 80% of the effects come from 20% of the causes. A corollary is that we can achieve 80% of a goal with 20% of the effort needed altogether to achieve it fully. This means that to achieve the remaining 20% toward the goal we need to put four times more of the effort already spent. This rule seems to govern the elaboration of concepts, designs and other types of documents, and I suppose it can be easily extended to other activities like writing code, cleaning data, improving performance, etc. 

Given the complexity, urgency and dependencies of the tasks or goals before us probably it's beneficial sometimes to focus first on the 80% of their extent, so we can make progress, and focus on the remaining 20% if needed, when needed. This concurrent approach can allow us making progress faster in incremental steps. Also, in time, through excellence, we can bridge the gap between the two numbers as is needed less time and effort in the process.

Performance Management: Pareto Principle (Definitions)

"A rule that posits that 80 percent of business activity comes from about 20 percent of the customers or clients. Named for Vilfredo Pareto, an Italian economist." (Robert McCrie, "Security Operations Management 2nd Ed.", 2006)

"The general observation that a small amount of effort can derive a great amount of rewards. Also known as the 80/20 rule because it often is stated as 80 percent of the results come from 20 percent of the effort." (Craig S Mullins, "Database Administration: The Complete Guide to DBA Practices and Procedures" 2nd Ed., 2012)

"Also known as the 80/20 rule, Pareto’s principle holds that a small number of causes may account for the vast majority of observed instances. For example, a small number of rich people account for the majority of wealth. Likewise, a small number of diseases account for the vast majority of human illnesses. A small number of children account for the majority of the behavioral problems encountered in a classroom. A small number of states or provinces contain the majority of the population of a country. A small number of books, compared with the total number of published books, account for the majority of book sales. Sets of data that follow Pareto’s principle are often said to follow a Zipf distribution, or a power law distribution. These types of distributions are not tractable by standard statistical descriptors. For example, simple measurements, such as average and standard deviation, have virtually no practical meaning when applied to Zipf distributions. Furthermore, the Gaussian distribution does not apply, and none of the statistical inferences built upon an assumption of a Gaussian distribution will hold on data sets that observe Pareto’s principle." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)

"In the Dynamic Systems Development Method, the assumption that 80-percent of an application’s features will take 20-percent of the project’s total time to implement. (The 80/20-rule often applies to other situations, too. For example, 80-percent of the bugs are usually contained in 20-percent of the code.)" (Rod Stephens, "Beginning Software Engineering", 2015)

"Better known as the 80/20 rule, this observation is that 20% of things will make 80% of difference, i.e. 20% of customers account for 80% of profits (and vice versa)." (Duncan Angwin & Stephen Cummings, "The Strategy Pathfinder" 3rd Ed., 2017)

"Doctrine which shows that approx. 20% of causes create 80% of problems. Also known as 80/20 rule." (Albert Lester, "Project Management, Planning and Control" 7th Ed., 2017)

"Sometimes called the Pareto distribution, the notion that to be strategic organisations should focus on the 20% of the business/customers/suppliers/stakeholders that make 80% of the difference to the business. The potential weakness of using this logic is that it may not adequately reflect dynamic situations." (Duncan Angwin & Stephen Cummings, "The Strategy Pathfinder" 3rd Ed., 2017)

"A general rule of thumb that suggests that 80 percent of the cost comes from 20 percent of the cost factors, or that 80 percent of the value is generated by 20 percent of the people. Also called the 80/20 rule. Used to guide system designers to focus on the aspects that matter most to outcome." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)