Exploring how ANF and SETDS can complement Agile, Kanban, and established management practices through statistical analysis and capacity forecasting
Introduction: From Technical Systems to Human Organizations
Throughout my career in IT Performance and Capacity Management, I have worked on a recurring problem: how to identify meaningful changes in complex systems without overwhelming administrators with unnecessary information.
This problem led me to develop the Statistical Exception and Trend Detection System (SETDS), along with methods for exception detection, change-point analysis, trend recognition, and forecasting.
The underlying management principle was relatively simple:
Do not intervene merely because something changes. Intervene when the change is meaningful.
This principle became central to my approach to Exception-Based Management.
More recently, while developing the broader concept of the Area of Normal Functioning (ANF), I began considering whether the same statistical principles could help us better understand corporate teams and organizational workloads.
What if we treated a team's workload as a dynamic system with its own normal operating range?
Could we distinguish ordinary fluctuations from meaningful structural changes? Could we anticipate emerging capacity problems? And could we reduce unnecessary managerial intervention while preserving team autonomy?
Initially, these possibilities appeared to offer a substantially different approach to organizational management.
However, examining established management frameworks reveals an important fact: many of these ideas already have significant precedents.
Agile emphasizes self-management and adaptation. Kanban provides quantitative flow metrics and forecasting. Statistical Process Control distinguishes common variation from exceptional conditions. PRINCE2 explicitly incorporates management by exception.
This raises a more interesting research question.
Rather than attempting to replace these approaches, could ANF provide an additional analytical framework that integrates statistical exception detection, structural change recognition, business-demand modeling, and proactive capacity forecasting?
I believe this possibility deserves further investigation.
1. Not Every Change Requires Management Intervention
Consider a software engineering team.
During one week, the team completes 25 tasks. During another, it completes only 19.
A manager looking at a conventional productivity dashboard might immediately question the decline.
But is there really a problem?
Perhaps the second week involved more complex tasks, unexpected technical difficulties, or necessary architectural improvements.
A single measurement rarely tells the whole story.
In technical systems, distinguishing normal variation from meaningful change has been studied for decades.
Statistical Process Control (SPC), originating in Walter Shewhart's pioneering work, introduced the distinction between common-cause and special-cause variation.
Thomas Nolan and Lloyd Provost, in their 1990 article Understanding Variation, emphasized the importance of this distinction for management decisions. They also explained why unnecessary adjustments to stable processes can actually increase variation.
The same principle applies to corporate teams.
Not every temporary decline in output represents a productivity problem. Not every increase in workload means that a team is approaching exhaustion.
We need to understand behavior over time.
This is where the ANF concept becomes relevant.
Instead of comparing every measurement against a fixed target or simple historical average, we can estimate a range of normal functioning based on observed behavior.
Suppose a team historically completes between 18 and 28 comparable work items per week.
Completing 19 items may be entirely consistent with normal variation.
A sustained decline to 12 items, however, could indicate a meaningful change requiring investigation.
The management principle becomes:
Normal variation should generally be tolerated. Meaningful deviations should be investigated, not automatically punished or corrected.
This is not a new management philosophy. What interests me is whether ANF and SETDS could provide an integrated statistical mechanism for implementing it.
2. What Existing Management Frameworks Already Offer
Before discussing the potential contribution of ANF, it is important to recognize the existing approaches.
Agile and Scrum: Autonomy and Adaptation
Agile principles emphasize sustainable development, teamwork, and responsiveness to change.
Scrum builds on transparency, inspection, and adaptation. It also recognizes teams as self-managing.
This philosophy is compatible with the objectives of ANF-based management.
Both approaches recognize that continuous external intervention is not necessarily productive.
However, Scrum does not prescribe a particular statistical method for calculating normal workload boundaries, detecting structural changes, or forecasting when team capacity constraints may emerge.
Such analytical methods could complement Scrum without changing its underlying philosophy.
Kanban: Managing the Flow of Work
Kanban is particularly relevant to the proposed application.
The May 2025 Kanban Guide identifies four fundamental flow metrics:
Work in Progress (WIP).
Throughput.
Work Item Age.
Cycle Time.
Kanban also supports probabilistic Service Level Expectations based on historical observations.
These metrics already provide valuable information about workload, predictability, and bottlenecks.
Furthermore, tools such as Jira include control charts displaying cycle times, rolling averages, and variability.
Therefore, introducing statistical charts into team management is not, by itself, a new contribution.
The research opportunity lies in investigating whether ANF/SETDS could add value through the integration of change-point detection, dynamically estimated operating boundaries, and forecasting of future operational constraints.
PRINCE2: Management by Exception
Another particularly relevant predecessor is PRINCE2.
Its Manage by Exception principle allows authority to be delegated within agreed tolerances.
When a project is expected to exceed these tolerances, the appropriate management level becomes involved.
This is conceptually close to the approach I have described as Exception-Based Management.
PRINCE2 also recognizes the importance of forecasting potential tolerance breaches before they occur.
An important distinction, however, is that project management tolerances are generally defined according to agreed objectives and constraints.
The ANF concept introduces a different type of boundary: one statistically estimated from observed system behavior.
These two types of boundaries should not be confused.
Statistical ANF boundaries describe what has historically occurred. Management tolerances describe what is considered acceptable.
Combining these perspectives could provide useful additional information for management decisions.
Statistical Process Control: A Common Foundation
Perhaps the closest methodological predecessor is Statistical Process Control.
SPC already provides methods for recognizing significant deviations from stable process behavior.
My SETDS work belongs to this broader tradition of statistical performance analysis, with particular emphasis on exceptions, change points, trends, and forecasting.
The proposed organizational application should therefore be understood as an extension and integration of established statistical ideas rather than a claim that statistical management itself is new.
The central research question is whether this particular combination can provide useful capabilities beyond those already available in conventional management analytics.
A Note About Terminology
There is also a terminology issue worth clarifying.
I have used Exception-Based Management to describe my management approach. However, Scrum.org already uses the abbreviation EBM for its distinct Evidence-Based Management framework.
Although the concepts are related through their reliance on information for management decisions, they are not the same methodology.
To avoid confusion, I will use ANF-based management or the full phrase Exception-Based Management when discussing my proposed approach.
3. From Reactive Management to Dynamic ANF Analysis
Having recognized these predecessors, we can formulate the proposed ANF application more precisely.
I envision it as an analytical layer that could complement existing organizational management frameworks.
Its potential value would come from combining four capabilities.
First: Estimating the Area of Normal Functioning.
Historical observations can be used to estimate a typical range of operating behavior.
Depending on the analytical method, boundaries might be represented by statistical control limits or distribution quantiles, such as P05 and P95, with P50 representing the median.
These methods have different statistical interpretations and should not be treated as interchangeable.
The purpose is to understand expected variation rather than using a single fixed target as the only acceptable value.
Second: Detecting meaningful changes.
A team may experience an abrupt shift following a product launch, organizational restructuring, or significant change in business demand.
Change-point analysis could help identify when the underlying behavior has changed.
The normal operating range may then need to be reassessed.
Third: Recognizing developing trends.
Not all important changes happen suddenly.
Incoming work, cycle time, or backlog may increase gradually while individual measurements remain within their historical ranges.
Trend analysis can help distinguish persistent movement from short-term fluctuations.
Fourth: Forecasting future constraints.
When a persistent trend is identified, forecasting methods may estimate when an operational threshold will be approached or crossed.
This is an extension of the time-to-threshold forecasting approach I have used in technical Capacity Management.
Together, these capabilities could support a more proactive form of workload analysis.
Importantly, none of these techniques individually constitutes a new invention in organizational management.
The proposed contribution lies in their integration through ANF and SETDS, and in evaluating whether that integration improves management decisions.
3.1. A Practical Illustration: IT-Control Chart of a Team's Weekly Workload
To illustrate this idea, I created a hypothetical example using synthetic workload observations for a software engineering team.
The example covers 60 weeks: 52 observed weeks and an eight-week forecasting horizon.
Rather than using completed story points as a proxy for workload, I use estimated effort-hours associated with incoming work.
This distinction is important.
Completed story points describe delivery estimates, not necessarily incoming demand, actual employee working hours, or sustainable team capacity.
Estimated effort-hours are also imperfect measurements, but they allow us to illustrate how changing demand might be compared with a separately estimated processing-capacity limit.
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| Figure 1. ANF-based IT-Control Chart of a hypothetical software engineering team's weekly incoming workload. The chart shows observed demand (black), retrospectively estimated statistical ANF boundaries (P05 in blue, P50 in green, P95 in red), a shaded normal-functioning range, a simulated change in operating conditions, and a linear forecast. The orange dashed line represents an independently assumed processing-capacity limit of 185 estimated effort-hours per week. All observations are synthetic. |
Figure 1. ANF-based IT-Control Chart of a hypothetical software engineering team's weekly incoming workload. The chart shows observed demand (black), retrospectively estimated statistical ANF boundaries (P05 in blue, P50 in green, P95 in red), a shaded normal-functioning range, a simulated change in operating conditions, and a linear forecast. The orange dashed line represents an independently assumed processing-capacity limit of 185 estimated effort-hours per week. All observations are synthetic.
The chart illustrates three periods.
Period 1: Normal Functioning (Weeks 1–20)
During the initial period, incoming workload fluctuates around a relatively stable level.
The retrospective statistical baseline is:
Lower ANF boundary (P05): approximately 127.5 effort-hours per week.
Median (P50): approximately 133.8 effort-hours per week.
Upper ANF boundary (P95): approximately 140.4 effort-hours per week.
Most observations remain within the estimated normal-functioning range.
This variation does not necessarily require corrective intervention.
The team can continue operating autonomously while managers maintain regular communication, support, and oversight.
Period 2: A New Operating Regime (Weeks 21–40)
Beginning around Week 21, the synthetic example introduces an increase in incoming demand.
This represents a hypothetical business event, such as a product launch or the acquisition of additional customers.
The team begins operating at a higher workload level.
Using a sample from the new regime (Weeks 24–39), the retrospectively estimated ANF becomes:
P05: approximately 143.4 effort-hours per week.
P50: approximately 150.7 effort-hours per week.
P95: approximately 160.2 effort-hours per week.
This illustrates an important feature of dynamic systems: their normal operating conditions can change.
However, we must distinguish a statistically established new baseline from a healthy or sustainable one.
A manager should investigate whether the team has adapted successfully through automation, process improvements, or additional resources—or whether it is absorbing the extra demand through excessive effort.
The statistical change does not answer that question by itself.
Period 3: Increasing Demand and Capacity Forecasting (Weeks 41–60)
After Week 40, the synthetic workload begins increasing more rapidly.
Several observations exceed the previously estimated upper ANF boundary.
The repeated deviations suggest that the earlier operating regime may no longer adequately describe current conditions.
For illustration, I fitted a simple linear trend to the observations from Weeks 41–52.
The resulting trend increases by approximately 1.84 estimated effort-hours per week.
If that trend continues without intervention, the forecast reaches the assumed processing-capacity threshold of 185 effort-hours per week around Week 57.
This is a conceptual example of Time-to-Threshold Forecasting applied to an organizational system.
Instead of waiting until backlogs accumulate or delivery deadlines are missed, management could investigate the trend and consider adjusting priorities, improving processes, or increasing available resources.
However, several methodological limitations must be emphasized.
First, the change in demand was deliberately introduced into the synthetic dataset. It was not independently discovered by SETDS.
Second, the ANF boundaries are empirical percentiles calculated retrospectively from selected operating periods. They are not classical Shewhart control limits, nor do individual percentile exceedances automatically establish statistical significance.
Third, the forecast is a simple linear regression extrapolation. It does not include uncertainty intervals, future changes in demand, or the effects of management intervention.
Finally, the 185-hour threshold is an assumed operational planning limit. It is not a medically or psychologically defined burnout threshold.
What Does This Example Demonstrate?
Despite its simplified nature, the example illustrates three different analytical questions:
Normal variation: Is the observed workload consistent with an established operating regime?
Structural change: Has the behavior of the system changed enough to justify reassessing its statistical baseline?
Future constraints: Does the developing trend suggest that operational demand may exceed available processing capacity?
The example also highlights why analyzing incoming workload alone is insufficient.
A practical organizational application would need to examine incoming demand together with completed throughput, backlog, cycle time, available resources, and relevant quality indicators.
The real opportunity for ANF-based management is to integrate these perspectives, not to replace them with a single workload chart.
4. Connecting Business Demand to Team Capacity
Another possible extension comes directly from my earlier work in IT Capacity Management.
In technical systems, resource requirements often depend on measurable business drivers.
For example, we may model how transaction volume affects CPU utilization, memory consumption, or response time.
The objective is to establish empirical relationships between business activity and technical resource demand.
A similar approach could be investigated for corporate teams.
Consider a customer onboarding department.
Its workload may depend on several factors:
Number of new customers.
Complexity of onboarding requirements.
Number of products or services involved.
Amount of manual processing.
Availability of automation.
Experience and skills of the team.
Suppose historical observations suggest that onboarding ten additional customers is associated with approximately 15 extra hours of team effort under comparable conditions.
Such a relationship could help estimate future resource requirements.
However, organizational systems are more complex than technical infrastructure.
The relationship may change when new software is introduced, employees gain experience, processes are redesigned, or customer requirements become more complicated.
Therefore, a useful model should not assume that historical relationships remain constant indefinitely.
This is where change-point detection and dynamic model reassessment could become particularly valuable.
Instead of relying on a permanently fixed relationship between business demand and team effort, we could investigate how that relationship evolves.
I see this as a possible continuation of my earlier exception-based capacity modeling work.
The objective would not be to mechanically determine how many employees a company must hire.
It would be to provide quantitative evidence to support resource planning and managerial judgment.
5. The Human Difference: Statistical Normality Is Not Sustainability
At this point, an essential distinction must be made.
Corporate teams are not server clusters.
Although statistical methods may be applied to observable organizational behavior, people cannot be managed as interchangeable technical resources.
Human performance depends on motivation, skills, fatigue, interpersonal relationships, organizational culture, and many other factors.
A team might maintain high output because its members have become more experienced and efficient.
The same output might also be sustained through excessive overtime and growing exhaustion.
Operational statistics alone may not distinguish these situations.
An additional problem arises when historical behavior itself is unhealthy.
Imagine a department whose employees have consistently worked excessive hours for several years.
A statistical model might identify this workload as normal because it has become the established operating pattern.
But statistically normal does not necessarily mean healthy, desirable, or sustainable.
This distinction is fundamental to extending ANF into human systems.
I would therefore separate two types of boundaries.
Statistical ANF boundaries describe the range of observed behavior under a particular operating regime.
Organizational sustainability boundaries represent conditions considered acceptable based on available resources, operational goals, working conditions, employee feedback, and relevant research.
These boundaries are not necessarily identical.
This also connects ANF to existing research in organizational psychology.
For example, Job Demands–Resources (JD-R) theory examines relationships among workplace demands, available resources, burnout, and engagement.
Similarly, the SPACE framework for developer productivity emphasizes that productivity cannot be represented adequately by a single metric. It includes dimensions such as satisfaction, well-being, performance, communication, and efficiency.
These frameworks provide perspectives that a purely statistical ANF model would not capture on its own.
A meaningful organizational application should therefore complement established human-centered research.
It should also prioritize team-level measurements where practical, protect employee privacy, and avoid turning exception detection into individual surveillance.
6. A Different Role for the Manager
What would this mean for a corporate manager?
I do not believe the goal should be automated management in which statistical models decide when human intervention is permitted.
A manager has responsibilities that cannot be reduced to quantitative monitoring.
These include coaching, resolving conflicts, supporting professional development, understanding employee concerns, and creating an effective working environment.
However, statistical analysis could help managers devote less attention to routine fluctuations and more attention to meaningful issues.
Three activities become particularly important.
Understanding Exceptions
When a significant deviation occurs, the first response should be investigation rather than blame.
Is the change caused by business demand, staffing, task complexity, process inefficiency, or an improvement in productivity?
An exception should initiate a question, not automatically trigger a corrective action.
Anticipating Constraints
Trend analysis and forecasting could help identify emerging problems before they become operational crises.
If incoming demand consistently grows faster than throughput, a manager may have time to adjust priorities or request additional resources.
Supporting Adaptation
When structural changes occur, teams may need to adapt.
Management should help determine whether the team can establish a sustainable new operating regime or whether the organization must change its expectations, resources, or processes.
In this interpretation, Exception-Based Management is not about reducing leadership to statistical alerts.
It is about using statistical evidence to support better judgment while preserving team autonomy.
7. Toward an Integrated Organizational ANF Framework
The organizational extension of ANF remains a research proposal.
My previous work provides a technical foundation in statistical exception detection, change-point analysis, trend recognition, and capacity forecasting.
The next challenge is to investigate whether combining these methods adds practical value to existing organizational management approaches.
A possible framework could contain five stages.
Stage 1 — Select meaningful operational measurements.
Use existing team-level information such as incoming demand, throughput, backlog, cycle time, and available processing capacity.
Avoid treating a single productivity metric as a complete description of team performance.
Stage 2 — Establish a statistical ANF.
Estimate expected operating behavior from historical observations, taking into account staffing changes, work complexity, seasonality, and other relevant conditions.
Stage 3 — Detect exceptions and structural changes.
Apply statistical methods to distinguish ordinary variation from potentially significant changes in system behavior.
Investigate whether a detected change represents improvement, deterioration, or adaptation to new conditions.
Stage 4 — Forecast operational constraints.
Analyze developing trends and estimate when relevant planning limits might be approached.
Keep these limits distinct from the statistical ANF boundaries.
Stage 5 — Support decisions and evaluate outcomes.
Combine statistical evidence with managerial experience, employee feedback, and organizational context.
Measure whether the approach improves the quality or timeliness of decisions.
The final stage is particularly important.
It would not be sufficient to demonstrate that an ANF model produces visually interesting charts.
A meaningful study should compare its results with existing methods.
For example, we could evaluate conventional fixed thresholds, established SPC techniques, Kanban-based flow analysis, and an integrated ANF/SETDS implementation using identical datasets.
Potential evaluation criteria could include detection delay, false-alert frequency, forecast accuracy, and the usefulness of the resulting information for management decisions.
Only such comparisons could establish whether the proposed integration offers measurable advantages.
8. What Might Be the Actual Contribution of ANF?
After examining existing management practices, I would formulate the research opportunity more carefully than I initially did.
Management by exception is not new.
Statistical control charts are not new.
Agile autonomy, Kanban flow metrics, and workload forecasting are not new either.
However, these concepts are frequently implemented in different contexts and for different purposes.
The possibility I would like to investigate is whether an ANF-based analytical layer can bring together:
Statistical estimation of normal operating behavior.
Detection of meaningful exceptions and structural changes.
Recognition of evolving workload trends.
Modeling relationships between business demand and operational capacity.
Forecasting the time remaining before relevant constraints may be reached.
The specific contribution would depend on the methods implemented and their demonstrated performance compared with existing alternatives.
The central hypothesis is that integrating these analytical capabilities could improve the recognition of meaningful workload changes and provide earlier, more actionable information for management decisions.
This remains a research hypothesis, not an empirically validated conclusion.
The broader significance may lie in exploring how methods developed for technical performance management can be adapted to more complex organizational systems without ignoring their human characteristics.
Conclusion: ANF as a Complement to Existing Management Practices
My work on the Area of Normal Functioning began long before its recent applications to IT performance analysis.
The original concept emerged from my research into robotic grasping and passive adaptation.
Later, my work on SETDS and IT-Control Charts provided statistical approaches for examining normal behavior, detecting exceptions, identifying structural changes, and analyzing trends in technical systems.
Today, I am exploring whether this accumulated experience can contribute to understanding human and organizational systems.
Reviewing existing management practices shows that many of the underlying ideas already have well-established foundations.
Rather than diminishing the relevance of ANF, I believe this provides a more meaningful direction for further work.
ANF does not need to replace Agile, Kanban, PRINCE2, or Statistical Process Control.
It may instead provide a complementary analytical perspective, particularly where operating conditions change over time and future capacity constraints are important.
The objective is not to turn people into measurable components of a mechanical system.
It is to help leaders distinguish routine variation from meaningful change, recognize emerging constraints, and support organizational adaptation.
The principle remains consistent with my earlier technical work:
Do not react to every fluctuation. Understand what is normal, recognize when conditions meaningfully change, and anticipate future constraints before they become serious problems.
Whether an integrated ANF approach can improve corporate workload management remains an open research question.
But it is one I believe is worth investigating.
References and Further Reading
Nolan, T. W., & Provost, L. P. (1990). Understanding Variation. Quality Progress, 23(5), 70–78. American Society for Quality.
Schwaber, K., & Sutherland, J. (2020). The Scrum Guide. Official Scrum Guide.
Kanban Guides. (2025). The Kanban Guide. Official Guide.
Atlassian. View and Understand the Control Chart. Jira Software Documentation.
PRINCE2. Understanding Tolerances in PRINCE2 Project Management. PRINCE2 Resource.
Scrum.org. (2024). Evidence-Based Management Guide. Official Guide.
Forsgren, N., Storey, M.-A., Maddila, C., Zimmermann, T., Houck, B., & Butler, J. (2021). The SPACE of Developer Productivity: There's More to It Than You Think. ACM Queue, 19(1). DOI: 10.1145/3454122.3454124.
Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. (2023). Job Demands–Resources Theory: Ten Years Later. Annual Review of Organizational Psychology and Organizational Behavior, 10, 25–53. DOI: 10.1146/annurev-orgpsych-120920-053933.
This article is part of my ongoing research and book project on the Area of Normal Functioning (ANF), exploring its applications across technical, AI, human, and hybrid systems.
