Beyond Material Flow with Cognitive Waste Theory: Formalizing the Ninth Waste of Lean Manufacturing Through Quantitative Models of Cognitive Inefficiency

Abstract

Lean manufacturing has historically focused on eliminating waste from physical production processes; however, increasing digitalization has shifted a substantial portion of operational effort toward information processing and decision making. Existing Lean frameworks lack formal mechanisms to model and quantify inefficiencies arising within these cognitive processes. This paper introduces Cognitive Waste Theory, a mathematical extension of Lean manufacturing that defines cognitive inefficiency as a distinct form of operational waste. Cognitive waste is conceptualized as non-value-adding mental effort generated by misaligned information flow, task structure, and organizational learning dynamics. The framework decomposes cognitive waste into five analytically separable categories: Information Overload, Context Switching, Knowledge Fragmentation, Cognitive Load, and Learning Lag, each expressed through formal mathematical representations grounded in cognitive and operations theory. To enable quantitative assessment, the study proposes normalized waste functions and develops two composite indices: the Cognitive Efficiency Index (CEI), capturing the ratio of effective decision output to cognitive load, and Information Flow Efficiency (IFE), structured analogously to Overall Equipment Effectiveness. Furthermore, classical Lean instruments are reformulated for analytical application in the cognitive domain through Information Value Stream Mapping and Cognitive 5S. By embedding cognitive constructs within a measurable Lean framework, this work provides an attempt to establish a rigorous foundation for analyzing, comparing, and improving cognitive performance in digitally intensive manufacturing systems.

1. Introduction

Despite growing scholarly interest in the integration of Lean manufacturing with Industry 4.0 and digital production systems, existing research has largely concentrated on the optimization of physical processes, automation architectures, and information technologies, with cognitive phenomena treated only implicitly or as secondary effects. Studies in Lean Industry 4.0 integration acknowledge increased information intensity and operator involvement [1] but do not conceptualize cognitive inefficiency as a distinct form of operational waste. Parallel research in cognitive science and human factors rigorously examines mental workload, information overload, and task switching; however, these insights remain analytically detached from Lean theory and lack operational mechanisms compatible with continuous improvement practice [2]. Consequently, no existing framework provides a systematic method for identifying, categorizing, measuring, and eliminating cognitive inefficiencies within Lean production systems. The present work addresses this gap by formally introducing cognitive waste as a distinct and measurable extension of Lean waste theory, thereby integrating cognitive science principles directly into the core logic of Lean manufacturing. By offering a structured taxonomy, mathematical formulations, and Lean-compatible implementation tools, this study advances beyond prior literature that treats cognition as an auxiliary concern, establishing it instead as a central determinant of operational performance in digitally intensive manufacturing environments [3].

The Unseen Bottlenecks in Modern Manufacturing

The principles of Lean manufacturing have profoundly transformed industrial operations in the last fifty years, yielding significant enhancements in efficiency, quality, and cost-effectiveness through the continuous identification and eradication of waste (muda) in material flows. The seven (or eight) traditional wastes—transportation, inventory, motion, waiting, overproduction, over-processing, and defects—constitute a robust and lasting framework for process improvement [4]. Nonetheless, the continuous digital revolution of production, commonly referred to as Industry 4.0, has established a novel operational paradigm in which the principal limitations on performance are increasingly cognitive rather than exclusively physical [5].

In today’s smart factories, human operators are bombarded with real-time data from sensors, dashboards, and integrated technologies. They are anticipated to cooperate with intelligent agents, execute intricate decisions under pressure, and continuously adjust to emerging technologies and methodologies. In this context, the efficacy of information processing, the caliber of decision making, and the management of cognitive load are essential determinants of total system performance [6]. However, the fundamental notions of Lean manufacturing, conceived during a time of mechanical and manual processes, are devoid of a formalized vocabulary and a systematic approach to tackle these cognitive issues. This establishes an invisible bottleneck: although material flows may be meticulously controlled, the cognitive flows that govern and regulate them frequently exhibit significant inefficiency [7]. In addition to these cognitive constraints, the increasing digitalization of manufacturing systems introduces a parallel layer of vulnerability through cybersecurity risks, which further intensify cognitive and operational strain on human operators [8]. As Industry 4.0 environments become more interconnected via industrial IoT, cloud-based control systems, and cyber-physical production networks, operators and engineers are required not only to interpret complex streams of operational data but also to continuously assess system trustworthiness and detect potential cyber intrusions [9]. This dual responsibility expands cognitive load by adding security monitoring and incident response considerations to already demanding decision-making tasks. Moreover, cybersecurity events such as data manipulation, ransomware attacks, or unauthorized access can directly distort the informational integrity of manufacturing systems, leading to flawed decisions, production disruptions, and increased uncertainty in control processes [10]. From a Lean perspective, these disruptions represent a compounded form of inefficiency, where both physical flow and cognitive flow are simultaneously degraded. Despite this, cybersecurity is rarely integrated into Lean manufacturing frameworks as a systemic operational factor, leaving a critical gap in understanding how digital risk environments shape cognitive waste in modern production systems [11].

This research contends that a range of well-established inefficiencies—previously studied in domains such as information overload, task switching, cognitive load, knowledge management, organizational learning, and human factors—collectively represent an important form of operational inefficiency in modern Lean environments. In this work, we integrate these cognitive phenomena within a unified Lean-compatible framework and refer to them collectively as cognitive waste. Cognitive waste is defined as any mental activity or information-processing demand that does not contribute to value creation in the final product or service, unnecessarily consumes limited cognitive resources, or impairs effective decision making. The increasing presence of data-intensive and digitally mediated manufacturing systems has amplified the relevance of these cognitive inefficiencies. However, they remain insufficiently formalized within traditional Lean frameworks, which primarily focus on physical and process-level waste. To address this gap, we propose Cognitive Waste Theory as an integrative extension of Lean thinking that systematically organizes cognitive inefficiencies into a structured framework aligned with the Lean waste paradigm. Figure 1 illustrates the relationship between traditional operational inefficiencies and the resulting mental strain on workers within a manufacturing environment.

Building on this perspective, we introduce five categories of cognitive waste—Information Overload, Context Switching, Knowledge Fragmentation, Cognitive Load, and Learning Lag—which synthesize and structure prior findings from multiple research streams into a coherent Lean-oriented classification system.

This study makes the following original contributions:

• It integrates established cognitive and human-factor inefficiencies into the Lean waste framework, extending the traditional muda concept beyond physical and organizational domains to include cognitive dimensions of work.
• It develops a structured taxonomy of cognitive waste, comprising Information Overload, Context Switching, Knowledge Fragmentation, Cognitive Load, and Learning Lag, each grounded in and mapped to existing research in cognitive science and operations management.
• It proposes mathematical formulations for each cognitive waste category, enabling quantitative representation and comparative analysis of cognitive inefficiencies in operational settings.
• It adapts established Lean tools—such as Value Stream Mapping, 5S, and Kaizen—into cognitive-oriented analytical instruments while preserving methodological continuity with Lean practice.
• It introduces composite performance metrics, including the Cognitive Efficiency Index (CEI) and Information Flow Efficiency (IFE), to support the evaluation of cognitive performance within a Lean-compatible measurement structure.

These contributions establish a new theoretical and practical foundation for integrating cognitive science into Lean manufacturing, addressing a critical gap in existing Industry 4.0 and operations management research.

2. Background

A review of existing literature reveals three key domains that inform the development of Cognitive Waste Theory: the historical evolution of Lean manufacturing, foundational principles of cognitive science, and the emerging recognition of cognitive factors within industrial engineering. The intersection of these fields highlights a critical theoretical and practical gap that this paper aims to address.

2.1. The Evolution of Lean Manufacturing Principles

Lean manufacturing, derived from the Toyota Production System (TPS), is essentially a concept focused on the eradication of waste to enhance customer value, quality, and cost efficiency [12]. The foundational architecture, established by innovators such as Taiichi Ohno, defined seven principal wastes (muda): transportation, inventory, motion, waiting, overproduction, over-processing, and faults. This taxonomy offered a robust framework for examining and enhancing physical production processes. Over time, numerous practitioners and researchers have championed the incorporation of an eighth waste: the underutilization of human ability, creativity, and abilities [13,14,15]. This addition signified a critical, though restricted, acknowledgment of the human aspect beyond simply physical movement [16].

Investigations into Lean theory demonstrate a domain abundant with varied viewpoints yet devoid of a cohesive, singular theory. A critical examination by prominent Lean researchers determined that Lean is most effectively comprehended as a multifaceted notion, perceived in diverse ways as a socio-technical system, a business reality, or a meta-theory for continuous improvement. This theoretical diversity, although a source of strength, has also hindered the prompt integration of non-physical notions. The major emphasis has consistently been on the efficiency of material and process flows, whereas information flows are frequently regarded as a secondary, supportive component rather than an independent value stream [17,18]. Figure 2 illustrates how traditional physical wastes, like those identified in the TPS, funnel into “unseen bottlenecks” that create cognitive overload and decision fatigue for workers in modern industrial environments.

2.2. Cognitive Science and Human Information Processing

Alongside the advancement of Lean, cognitive science has generated substantial ideas elucidating the mechanics, capacities, and constraints of human information processing. The core of this research is the concept of working memory, a limited-capacity system that briefly retains and manipulates information for complicated activities, including learning, reasoning, and comprehension [19].

Cognitive Load Theory (CLT), formulated by John Sweller, offers a notably pertinent approach. CLT asserts that learning and performance are maximized when the cognitive load of a task remains within the learner’s working memory capacity [20,21]. The hypothesis differentiates among three categories of cognitive load:

• Intrinsic Load: The inherent difficulty of the subject matter.
• Extraneous Load: The load generated by the way information is presented or the tasks required, which does not contribute to learning or performance.
• Germane Load: The load dedicated to the processing, construction, and automation of schemas (i.e., learning).

From a manufacturing efficiency standpoint, superfluous cognitive load is fundamentally equivalent to waste—it represents effort exerted that fails to contribute value. Studies in human factors and ergonomics have consistently demonstrated that inadequate interface design, ambiguous instructions, and frequent interruptions—sources of superfluous cognitive load—result in elevated error rates, diminished performance, and decreased employee well-being [22,23]. A key enabling technology for addressing both cognitive inefficiencies and cybersecurity-related disruptions in modern manufacturing systems is anomaly detection using artificial intelligence-based models. In Industry 4.0 environments, where sensor data streams are high-dimensional, noisy, and continuously evolving, traditional rule-based monitoring approaches are often insufficient for identifying subtle deviations from normal operational behavior. Deep learning methods, particularly generative models, have emerged as powerful tools for unsupervised anomaly detection. They can effectively capture complex dependencies across multivariate time-series data, enabling early detection of equipment faults, process deviations, and even cyber-intrusions that manifest as atypical data patterns [24]. Figure 3 connects the relationship between human information processing and operational efficiency in a manufacturing setting.

2.3. The Emerging Gap: Cognitive Factors in Manufacturing

Although Lean manufacturing and cognitive science have developed independently, an increasing amount of research on their convergence indicates a rising recognition of cognitive elements in achieving operational excellence. Research on “Lean automation” and Industry 4.0 has observed that the implementation of digital technology, whilst addressing certain issues, frequently engenders new, unexpected cognitive obstacles for operators. The extensive data generated by smart industrial systems can result in information overload, a condition in which the quality of decision making declines due to the incapacity to efficiently absorb all available information [25,26].

Moreover, studies on multitasking and task switching in intricate work settings have empirically established the presence of a “switch cost.” This refers to a quantifiable time lag and elevated error rate that arises when an individual transitions from one task to another. In a contemporary production environment, where an operator may need to oversee a control panel, react to an alert from a collaborative robot, and reference a digital work instruction handbook in rapid succession, these switch costs can aggregate into a substantial source of inefficiency [27,28,29]. Figure 4 outlines the cognitive framework essential for Operator 4.0, illustrating how human-centric factors influence performance in a smart manufacturing environment.

Notwithstanding these findings, the incorporation of cognitive principles into the foundation of Lean theory is still in its infancy and lacks systematic structure. Current Lean frameworks lack a formalized method for identifying, quantifying, and eradicating waste that arises solely within the cognitive domain [30,31,32]. The notion of “unused talent” as the eighth waste addresses human potential, however, lacks a detailed framework for examining the processes of cognition, decision making, and learning [33,34,35,36]. This article argues that, in the absence of such a framework, Lean manufacturing would fail to adequately tackle the principal sources of inefficiency in 21st-century industry. Cognitive Waste Theory is presented to address this significant gap, offering an essential extension to Lean concepts for the digital era.

Table 1. Dimensions of cognitive waste framework.

Dimension	Lean–Industry 4.0 Literature	Cognitive Load/Human Factors	Knowledge Management Studies	This Work
Primary focus	Cloud-Based Manufacturing [37]	Mental workload, error, performance [7]	Knowledge transfer and learning [38]	Cognitive inefficiency as operational waste
Treatment of cognition	Implicit, indirect [1]	Explicit but isolated from operations [39]	Organizational, non-processual [40]	Explicit, process-embedded
Lean waste integration	Limited to classic wastes [41]	Not addressed [7]	Cyber Deception Strategies [42]	Formal ninth waste (cognitive waste)
Waste taxonomy	Lean-healthcare [43]	Not framed as waste [7]	Not framed as waste [40]	Five distinct cognitive waste categories
Quantification	OEE, cycle time, throughput [41]	Subjective/experimental metrics [39]	Qualitative or learning curves [38]	Mathematical waste models, CEI, IFE
Lean tool adaptation	VSM, 5S (physical) [41]	Not applicable	Not applicable	IVSM, Cognitive 5S, Cognitive Kaizen
Managerial actionability	High (physical domain) [44]	Low–moderate [7]	Moderate [45]	High, Lean-compatible
Theoretical integration	Operations-centric [46]	Cognition-centric [7]	Organization-centric [40]	Operations–cognition synthesis

shows how prior research contributed valuable partial insights yet was lacking a unifying Lean-based cognitive waste framework. This work uniquely consolidates these dimensions into a single operational theory.

3. Cognitive Waste Theory: The Ninth Waste of Lean

To rectify the deficiency noted in the literature, we explicitly propose the augmentation of Lean manufacturing principles by introducing a novel category of waste. This addition does not supplant or undermine the significance of the original seven or eight wastes; instead, it enhances them by offering a framework to address the non-physical, information-centric inefficiencies that are common in contemporary, technologically sophisticated production settings.

3.1. Defining Cognitive Waste

Traditional Lean identifies seven wastes associated with physical operations and material flow, with an additional eighth sometimes included to address human potential. We advocate for the official incorporation of a ninth waste.

Cognitive Waste is defined as any mental processing or information-handling activity that consumes finite cognitive resources without adding value to the end product, the customer, or the organization. It represents the friction and inefficiency within the cognitive value stream that parallels the physical production value stream.

This waste is especially problematic because of its invisibility. Cognitive waste, in contrast to surplus inventory or unnecessary movement, is not observable on the shop floor. It manifests indirectly through consequences such as delayed decisions, increased errors, operator stress, and an inability to fully utilize the promise of Industry technology. By assigning a designation and a legal framework to this waste, we render it visible, quantifiable, and controllable [47].

3.2. Defining and Modeling the Five Categories of Cognitive Waste

To operationalize the concept of cognitive waste, we have created a taxonomy that breaks it down into five separate, quantifiable categories. This classification offers a pragmatic foundation for diagnosis and enhancement.

For Cognitive Waste Theory to be a practical extension of Lean, its central concepts must be measurable. While the precise quantification of cognitive states is inherently challenging, we can establish mathematical formulations that provide a basis for relative measurement, trend analysis, and the evaluation of countermeasures. These models are intended not for absolute precision but as tools for making cognitive waste visible and manageable. Therefore, each of the five categories of cognitive waste can be represented by a conceptual mathematical model that helps clarify its components and drivers. Each cognitive waste is modeled in a domain-appropriate way, then transformed into a normalized inefficiency measure that enables principled aggregation and comparison.

3.2.1. Information Overload Waste (IOW)

Information overload refers to the condition of being inundated by abundant, frequently irrelevant material, resulting in challenges with concentration, decision-making paralysis, heightened stress, and diminished productivity, arising from incessant digital stimuli.

Therefore, IOW occurs when the volume and complexity of information presented to an operator exceed their capacity to process it effectively. Critically, IOW arises not from information availability per se, but from the presentation of information that is not directly required to support an imminent operational decision within a given task context. This leads to a degradation in decision-making quality, increased response times, and the potential for critical signals to be missed amidst the noise. As a result, finite attentional resources are consumed by non-value-adding cognitive processing, reducing the salience of decision-critical signals and increasing decision latency.

OIW manifests itself in Lean manufacturing in various ways, such as excessive dashboards that present operators with large volumes of data points that are often not relevant to their immediate tasks, alarm fatigue caused by a high frequency of non-critical system alerts that reduces sensitivity to critical warnings, and redundant reporting where multiple systems generate overlapping or conflicting information that requires additional effort to reconcile. Overall, these conditions contribute to delayed recognition of decision-critical signals and increased decision latency.

From a Lean perspective, OIW can be interpreted as the cognitive equivalent of Overproduction Waste. Just as producing more physical output than is required creates inventory and obscures process inefficiencies, excessive information inflow generates cognitive “inventory” that obscures relevant signals and degrades decision efficiency.

To enhance clarity and support practical implementation,

Table 2. Operationalization of variables used in cognitive waste models and indices.

Variable	Definition	Data Source	Measurement Method	Unit
$I(t)$	Information inflow rate	System logs (MES/ERP/dashboards)	Event frequency per time window	events/time
$C$	Cognitive processing capacity	Workload studies, calibration tests	Performance threshold estimation	normalized/events/time
${\lambda }_k(t)$	$\mathrm{Arrival} \mathrm{rate} \mathrm{of} \mathrm{info} \mathrm{type} k$	System/event logs	Count per category over time	events/time
$w_k$	Cognitive cost weight	Expert judgment, regression	AHP or statistical weighting	dimensionless
$S_i$	Cost of switch event	Interaction logs	Time delay or error increase per switch	s
$D(T_i^{-},T_i^{+})$	Cognitive distance	Task analysis, expert rating	Similarity scoring (0–1 scale)	dimensionless
$T_{search}$	Knowledge search time	System logs, observation	Timestamp-based duration	s
$T_{integrate}$	Knowledge integration time	Task tracking	Time difference estimation	s
$T_{ideal}$	Ideal access time	Benchmark/simulation	Best-case reference measurement	s
$L_{total}$	Total cognitive load	NASA-TLX, sensors	Standardized workload scoring	index
$P_{actual}$	Observed performance	MES/KPI systems	Time-series performance tracking	output units
$P_{potential}$	Ideal performance	Benchmark data	Reference modeling	output units
$D_{effective}$	Effective decisions	Operational logs	Count of correct/timely decisions	count/time
$Q,S$	Quality & speed factors	Error logs, timestamps	Normalization vs. benchmark	0–1
${\varphi }_k$	Normalized indicator	Derived metrics	Min–max scaling	0–1
$w_k$	Composite weight	Regression/AHP	Coefficient normalization	dimensionless
$A,P,Q$	IFE components	System logs	Normalized ratios	0–1

provides a structured overview of all variables used in the proposed cognitive waste formulations and composite indices. It summarizes each variable’s definition, data source, and measurement method in a compact format to facilitate operational interpretation and future empirical application in industrial settings.

There have been previous attempts to quantify information overload in different environmental contexts [7,48,49,50]. However, what is novel about the proposed formulation is not the idea of quantification itself, but the Lean-compatible, cumulative “waste” framing and the explicit integration with capacity exceedance over time. IOW can be modeled as the cumulative excess of information volume over an individual’s processing capacity over a given period [51]:

$$\mathrm{I}\mathrm{O}\mathrm{W}(T)={\int }_0^T\mathrm{m}\mathrm{a}\mathrm{x}\{I(t)-C, 0\} dt, \mathrm{for} \mathrm{all} t \mathrm{where} I(t) > C$$

(1)

The integral represents accumulated exposure to information overload over time rather than an instantaneous condition, capturing the persistence and compounding effects of sustained excess information flow. The integral is calculated over the time periods where the information rate exceeds capacity, where:

• I(t) is the rate of information inflow at time t (e.g., bits/sec, data points/min). In practice, I(t) may be operationalized using observable indicators such as alarm frequency, dashboard update rates, or message arrival density.
• C is the effective cognitive processing capacity of the operator, a constant that can be empirically estimated for a given context (e.g., under stable workload, fatigue, training level).

If different information items differ in cognitive demand, then:

$$I(t)={\sum }_k{w}_k {\lambda }_k(t)$$

(2)

The integration adjusts for information (messages) being not equally demanding to the operator, where:

• ${\lambda }_k(t)$ is the arrival rate of item type $k$ (alarms, robot alerts, work-instruction lookups, etc.);
• $w_k$ is a weight reflecting processing cost.

Then IOW is the cumulative time-integral of excess information demand above processing capacity, i.e., the area where information inflow exceeds effective capacity [51]:

$$\mathrm{I}\mathrm{O}\mathrm{W}(T)={\int }_0^T\mathrm{m}\mathrm{a}\mathrm{x}\left\{{\sum }_k{w}_k{\lambda }_k(t)-C, 0\right\}dt$$

(3)

3.2.2. Context Switching Waste (CSW)

Context switching refers to the act of transitioning focus or effort from one task to another. This concept, which originated in computing where a CPU preserves the state of one process to initiate another, facilitates multitasking [52]. It is also applicable to humans, as their cognitive processes alternate between applications, projects, or mental frameworks for various activities, frequently diminishing productivity and inducing mental fatigue. Multitasking is crucial, but it incurs a cost, as the brain requires time and energy to acclimate to new contexts, hence impeding its efficiency [53,54].

Therefore, CSW refers to the loss of efficiency and focus that occurs when an operator is required to frequently switch between different tasks, systems, interfaces, or mental models. CSW arises specifically when such transitions are not intrinsic to value creation but are imposed by system fragmentation, poor interface integration, or uncoordinated information flows. Each switch incurs a cognitive reorientation cost as the operator disengages from one task schema and activates another, temporarily reducing attentional focus and working-memory efficiency.

Manifestations in Manufacturing:

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Fragmented Workflows: An operator must stop a physical task to input data into a terminal, then consult a separate tablet for the next instruction, each requiring a mental re-calibration.

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Multiple Logins: Accessing different systems (e.g., MES, ERP, QMS) requires separate authentication processes, interrupting the flow of work.

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Frequent Interruptions: Operators are frequently interrupted by pages, alerts, or supervisor queries that are unrelated to their current task.

These conditions force repeated cognitive reconfiguration, increasing task completion time, elevating error probability, and degrading sustained concentration even when individual tasks are themselves well designed.

Lean Connection: CSW is analogous to Motion Waste. While motion waste involves unnecessary physical movement, context switching involves unnecessary mental movement, both of which consume time and energy without adding value. Just as unnecessary physical motion interrupts material flow and increases cycle time, unnecessary cognitive motion disrupts mental flow, fragments attention, and lengthens decision lead time within Lean systems.

While context switching costs have been extensively studied in cognitive psychology and human–computer interaction, existing research conceptualizes them as local performance effects [55,56,57,58,59]. This work is the first to formalize context switching as a cumulative, non-value-adding operational waste, explicitly incorporating switching frequency and cognitive dissimilarity into a Lean-compatible efficiency metric. CSW represents the cumulative inefficiency arising from repeated transitions between distinct tasks, systems, or cognitive states within a given operational period. Each transition incurs a measurable cost that depends on both the frequency of switching and the degree of cognitive dissimilarity between the tasks involved [60]. We can attempt to express it as:

$$\mathrm{C}\mathrm{S}\mathrm{W}={\displaystyle {\sum }_{i=1}^n}{S}_i D \left(T_i^{-},T_i^{+}\right)$$

(4)

The formulation emphasizes that CSW accumulates over time as a function of both switching frequency and cognitive dissimilarity, rather than arising from isolated interruptions, where:

• $S_i$ ($S_i$ ≥ 0) denotes the cost associated with the $i$-th context switch, quantified in terms of additional time consumption, increased error likelihood, or both. Empirically, $S_i$ may be estimated using observed task delays, error rate changes, or recovery time following a switch. $S_i$ has units of time (seconds) or expected error cost. Higher per-switch penalties ($S_i$) increase CSW.
• $D \left(T_i^{-},T_i^{+}\right)$ represents a dimensionless cognitive distance function (0 ≤ D ≤1) capturing the degree of dissimilarity between $T_i^{-}$, the task before the $i$-th switch, and $T_i^{+}$, the task after. Cognitive distance may be approximated using task similarity metrics, interface modality differences, or required shifts in mental representation. D = 0 when there is no “distance” when staying on the same task. Switching between more dissimilar tasks increases CSW.
• n is the total number of context switching tasks observed within a defined timeframe. More frequent switching (larger n) increases CSW.

The summation extends over all context-switching events occurring within the defined timeframe. This formulation reflects the principle that switching between cognitively distant tasks imposes disproportionately higher costs than transitions between closely related tasks, thereby constituting a non-value-adding source of cognitive inefficiency.

3.2.3. Knowledge Fragmentation Waste (KFW)

Knowledge fragmentation refers to the dispersion of information, expertise, and insights across disparate systems, departments, or disciplines, hindering accessibility, connectivity, and utilization for comprehensive understanding or effective action, resulting in inefficiency, redundant efforts, and lost opportunities for innovation, akin to having library books dispersed without a catalog. It is not merely an absence of information, but a failure in synthesis and communication, prevalent in intricate domains and organizations utilizing numerous tools [61].

Therefore, KFW arises when the information and expertise required to perform a task are scattered across multiple, disconnected sources, forcing operators to spend time searching for and integrating knowledge rather than applying it. KFW also arises specifically when this dispersion is not inherent to task complexity but results from organizational silos, inconsistent information governance, or poor integration of knowledge systems. As a result, cognitive effort is diverted from execution and problem solving toward information foraging and mental synthesis, increasing task duration and cognitive load without adding operational value.

Manifestations in Manufacturing:

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Siloed Information: Process specifications are in one system, quality standards in another, and maintenance logs in a third.

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Tribal Knowledge: Critical operational knowledge resides only in the minds of a few senior operators and has not been documented or made accessible to others.

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Inconsistent Formats: Work instructions are a mix of paper documents, PDF files on a server, and videos on a separate platform.

These conditions impose repeated search and integration cycles that delay task execution, increase the likelihood of misinterpretation, and amplify dependence on individual experience rather than standardized knowledge.

Lean Connection: KFW is the cognitive parallel to Transportation Waste. Just as moving materials unnecessarily adds no value, moving one’s attention between disparate knowledge sources is a non-value-added activity that delays the core task. In this sense, fragmented knowledge creates cognitive transportation loops in which attention is repeatedly shifted across sources, increasing lead time and obscuring process abnormalities in direct analogy to excessive material movement.

It is worth noting that there is no canonical construct called “knowledge fragmentation” with a unified metric in the literature. However, different disciplines quantify fragments of the phenomenon, each incompletely, such as the Information Foraging Theory [62,63], Human–Computer Interaction (HCI) and multitasking research [64,65], Distributed Cognition [66,67], Knowledge Management & Organizational Silos [68,69], and Safety-Critical and Healthcare IT Studies [70,71]. Therefore, the existing literature quantifies fragments of knowledge dispersion, including information search costs and coordination overhead, yet lacks an operational construct that captures the cumulative cognitive inefficiency imposed by fragmented knowledge during task execution. This work is the first to formalize and quantify KFW, defining it as the excess time required to search for and cognitively integrate task-relevant knowledge relative to an ideal unified-access condition, thereby making KFW evaluated at the level of task execution rather than individual knowledge components.

Let task episode index $e$ require $n_e$ knowledge components in that respective episode e, and i is knowledge component index within the episode e [72]:

$${\mathrm{K}\mathrm{F}\mathrm{W}}_e=\mathrm{m}\mathrm{a}\mathrm{x}({\displaystyle {\sum }_{i=1}^{n_e}}(T_{\mathrm{search},e,i}+T_{\mathrm{integrate},e,i})-T_{\mathrm{ideal},e}, 0)$$

(5)

Aggregate over a period:

$$\mathrm{K}\mathrm{F}\mathrm{W}={\displaystyle {\sum }_{e=1}^E}{\mathrm{K}\mathrm{F}\mathrm{W}}_e where e, i, n are dimensionless$$

(6)

This formulation captures the cumulative time penalty imposed by fragmented knowledge structures relative to an idealized, fully integrated access state, thereby rendering knowledge fragmentation observable as a system-level inefficiency, where:

• T_search,e,i is the time spent locating knowledge component i during task episode e such as navigation, query formulation, screen switching, document hunting.
• T_{integrate,e,i} is the time spent reconciling that component with others such as format translation, terminology alignment, mental synthesis, cross-checking. Integration time reflects the cognitive effort required to reconcile formats, terminology, or contextual assumptions across sources.
• T_ideal,e is the total knowledge-access time for a task episode e under unified access.
• Max is the positive part operator which was added, since in real data, some items might be faster than the benchmark; then a term becomes negative, which contradicts “waste” being nonnegative.

3.2.4. Cognitive Load Waste (CLW)

Cognitive load refers to the aggregate mental effort exerted in working memory to comprehend information and accomplish a job. This idea is crucial in learning psychology and UX design, as surpassing this capacity (cognitive overload) results in confusion, errors, and failure [73]. CLW occurs when the design of a task or system imposes mental demands that are unnecessarily high (extraneous load), thereby consuming working memory capacity that could be better used for problem solving and learning (germane load). CLW arises specifically when excess mental demand is attributable to task or system design rather than to the inherent complexity of the work itself (intrinsic load). Under such conditions, limited working-memory resources are depleted by non-value-adding processing, reducing the operator’s capacity for accurate execution, adaptation, and learning.

Manifestations in Manufacturing:

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Poorly Designed Interfaces: A user interface on a machine controller is cluttered, uses inconsistent terminology, or requires a long sequence of steps for a simple function.

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Complex Calculations: Operators are required to perform manual calculations that could easily be automated.

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Ambiguous Instructions: Work instructions are poorly written, lack clear visuals, or are open to multiple interpretations, forcing operators to engage in mental problem solving for a routine task.

These design deficiencies increase extraneous cognitive processing, elevate error likelihood, and slow task execution even when operators possess adequate skill and experience.

Lean Connection: CLW is directly related to Over-processing Waste. Over-processing involves doing more work than is necessary to produce a physical part; Cognitive Load Waste involves imposing more mental work than is necessary to complete a task. In both cases, effort is expended beyond what is required to achieve the intended outcome, increasing cycle time and obscuring opportunities for flow improvement.

CLW is the portion of total cognitive load that is extraneous and does not contribute to task performance or learning [74].

CLW = L_extraneous = L_total − (L_intrinsic + L_germane)

(7)

This decomposition explicitly distinguishes value-adding cognitive effort from wasteful load, enabling a targeted reduction in extraneous demands without diminishing task understanding or learning potential.

• L_total is the total measured cognitive load (e.g., using NASA-TLX or physiological sensors). Total cognitive load may be estimated using validated subjective workload instruments or objective physiological indicators. L_total is an aggregated scalar quantity and analytical approximation based on cognitive load theory.
• L_intrinsic is the inherent difficulty of the task → unavoidable.
• L_germane is the load associated with learning and schema formation → value-adding.
• L_extraneous is the waste to be eliminated. Extraneous load represents the portion of cognitive demand that can be reduced through improved task, interface, or information design → non-value-adding.
• All L-terms must be expressed on the same measurement scale and must be ≥0.

Existing cognitive load theory research [75,76,77] conceptualizes extraneous load in the context of instructional effectiveness. This work is the first to formalize extraneous cognitive load as a measurable waste within operational systems, integrating cognitive load decomposition into Lean logic and enabling systematic elimination of non-value-adding cognitive demand.

3.2.5. Learning Lag Waste (LLW)

Learning lag denotes a deceleration or disparity in an operator’s learning advancement, frequently resulting from disruptions or inconsistencies in the learning context. It differs from “learning loss” (when skills are forgotten) and entails a sustained learning process, albeit at a reduced rate [78].

Therefore, LLW is the inefficiency in the process of knowledge acquisition, skill development, and adaptation. LLW arises specifically when delays in learning are attributable to organizational, informational, or structural barriers rather than to the inherent time required for human skill acquisition. It is the time lost between when an improvement opportunity is identified and when the organization effectively learns and standardizes the new best practice. During this lag, the organization continues to operate below its attainable performance level despite possessing the knowledge required to improve.

Manifestations in Manufacturing:

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Slow Onboarding: New operators take an excessively long time to reach proficiency due to ineffective training materials and lack of structured on-the-job training.

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Recurring Errors: The same process errors are made repeatedly by different shifts or operators because the lessons from the initial error were not effectively disseminated and integrated into standard work.

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Delayed Improvement: A successful Kaizen event on one production line is not replicated on other lines for months or years due to a lack of a systematic process for sharing and adapting improvements.

These conditions prevent experiential knowledge from being converted into standardized practice, allowing inefficiencies and errors to persist across time, shifts, and organizational boundaries.

Lean Connection: LLW is a form of Waiting Waste. In this case, the organization is waiting for the value that comes from improved knowledge and skills. It is the delay in capitalizing on the organization’s own learning potential. In this context, value creation is delayed not by material or information unavailability, but by postponed organizational learning and standardization.

LLW can be modeled as the integral of the performance gap between the current state and the potential state if a known improvement was instantly adopted [79].

$$\mathrm{L}\mathrm{L}\mathrm{W}={\int }_{t_0}^{t_1}\mathrm{m}\mathrm{a}\mathrm{x}\{P_{\mathrm{potential}}(t)-P_{\mathrm{actual}}(t), 0\} dt$$

(8)

The integral captures the cumulative opportunity cost of delayed learning, representing the total performance potential foregone while improved practices remain unapplied, where:

• $t_0$: time the improvement becomes known/available.
• $t_1$: time the improvement is fully adopted/standardized.
• P_potential(t) is the performance level achievable with a known best practice at time t. Potential performance may be estimated using benchmark data, pilot implementations, or demonstrated improvements in comparable processes.
• P_actual(t) is the actual performance level at time t. Actual performance reflects observed operational outcomes prior to full adoption and standardization of the improvement.

The integral is calculated from the time the improvement is known until it is fully adopted. The formula assumes higher P is better (e.g., throughput, yield, OEE). If a lower performance metric is better (e.g., cycle time, defect rate), then transform performance into a “higher is better” form (e.g., use yield instead of defect rate) or invert the sign inside the integral (e.g., use cycle time).

While organizational learning and improvement diffusion have been extensively studied [80,81,82], existing approaches do not treat delayed learning as an explicit source of waste. This work is the first to formalize learning lag as a waste and a time-integrated operational inefficiency, enabling direct measurement of the performance potential foregone while known best practices remain unapplied. Figure 5 provides a rigorous classification of five distinct types of cognitive waste in Lean 4.0, pairing each with a specific mathematical formula for quantification.

3.3. Measurement Foundations and Index Construction

Cognitive waste is seen as a latent construct that cannot be directly observed but can be inferred by observable indications, like task delays, error rates, interaction logs, and verified workload assessments [7]. The proposed mathematical formulations aim to facilitate relative, comparative, and longitudinal assessment, in alignment with known techniques in operations management, ergonomics, and human factors research [83]. The paradigm deliberately refrains from asserting absolute cognitive quantification and instead highlights construct validity, internal coherence, and practical observability.

3.3.1. Cognitive Efficiency Index (CEI)

Ratio-Based Cognitive Efficiency Index (RCEI)

Efficiency logic is the cognitive performance as a ratio of output to cognitive effort [7]. Effective cognitive performance requires both correctness and timeliness [84]. Human performance metrics often penalize raw output by quality and timeliness factors [85]. Human efficiency metrics routinely balance performance output vs. cognitive cost [86]. While prior research has independently examined cognitive workload, decision accuracy, and response timeliness, these dimensions are typically assessed in isolation. This study introduces the RCEI as a comprehensive metric for assessing cognitive performance. RCEI is a composite, dimensionless metric that correlates the productive output of cognitive labor with the cognitive effort necessary to produce it, while explicitly including decision quality and timeliness.

$$\mathrm{RCEI}=\left({\displaystyle \frac{D_{\mathrm{effective}}}{L_{\mathrm{total}}}}\right)\cdot Q_{\mathrm{factor}}\cdot S_{\mathrm{factor}}$$

(9)

Workload instruments are interpreted comparatively, not absolutely [87]. The index is intended for comparative and longitudinal evaluation rather than absolute measurement [88], where:

• $D_{\mathrm{effective}}$ denotes the number of effective decisions—defined as decisions that are both correct and timely—made per unit time.
• $L_{\mathrm{total}}$ represents the average total cognitive load experienced during the same period.
• The quality multiplier $Q_{\mathrm{factor}}\in [0, 1]$ captures decision accuracy.
• The speed multiplier $S_{\mathrm{factor}}\in [0, 1]$ reflects decision timeliness relative to operational requirements.

The RCEI produces a singular index value that can be monitored over time or contrasted across tasks, systems, or organizational divisions. An escalating RCEI signifies that the system is producing enhanced effective cognitive output per unit of mental exertion, therefore indicating a decrease in cognitive inefficiency. Crucially, the RCEI is not designed as a definitive measure of cognition, but rather as a normalized efficiency metric appropriate for trend analysis and managerial decision-making assistance.

The novel idea of RCEI resides in its integration of decision efficacy, cognitive load, and temporal constraints into a unified efficiency indicator designed for operational management rather than cognitive measurement.

Formative Composite-Based Cognitive Efficiency Index (FCCEI)

The CEI here is characterized based on formative composite index rather than a reflecting psychometric instrument [89]. Because each waste operates independently, they cannot be treated as interchangeable indicators of a single construct. Additionally, internal correlation is unnecessary [89] since correlation between wastes is contingent, not structural, and each component uniquely enhances cognitive efficiency since no linear combination of any two or more wastes can reconstruct the effect of the others [90]. A generalized weighted formulation [91] can thus be articulated as:

$$\mathrm{FCCEI}={\displaystyle {\sum }_{k=1}^n}{w}_k\cdot {\varphi }_k \mathrm{subject} \mathrm{to} \sum w_k=1$$

(10)

FCCEI is produced by multiple distinct inefficiency mechanisms, each of which must be modeled, weighted, and managed explicitly rather than inferred indirectly, where:

• ${\varphi }_k$ represents normalized indicators of decision productivity, quality, and timeliness.
• Weights $w_k$ may be determined through expert elicitation, analytic hierarchy processes [92], or empirical calibration.

Each ${\varphi }_k$ is a normalized indicator (dimensionless) derived from an observable metric [93]:

$${\varphi }_k\equiv {\tilde{X}}_k = {\displaystyle \frac{X_k-X_k^{\mathrm{m}\mathrm{i}\mathrm{n}}}{X_k^{\mathrm{m}\mathrm{a}\mathrm{x}}-X_k^{\mathrm{m}\mathrm{i}\mathrm{n}}}}\mathrm{so} \mathrm{that} {\varphi }_k\in \left[0,1\right]$$

(11)

The standard approach in the framework is min–max normalization within a defined operational context, where:

• Raw metric $X_k$ for component $k$ (e.g., decision throughput, accuracy, timeliness, or a waste magnitude).
• Define bounds $X_k^{\mathrm{m}\mathrm{i}\mathrm{n}}$ and $X_k^{\mathrm{m}\mathrm{a}\mathrm{x}}$ empirically (e.g., observed best/worst in the same line, shift, or month, or a benchmark standard).

Some metrics are “higher is better” (e.g., accuracy), while others are “higher is worse” (e.g., overload, switch cost, error rate). If the component is a waste or penalty where higher values mean worse performance, use an inverted normalization [94] so that ${\varphi }_k$ always preserves the same interpretation (higher = better) [93]:

$${\varphi }_k = 1-{\displaystyle \frac{X_k-X_k^{\mathrm{m}\mathrm{i}\mathrm{n}}}{X_k^{\mathrm{m}\mathrm{a}\mathrm{x}}-X_k^{\mathrm{m}\mathrm{i}\mathrm{n}}}}$$

(12)

This ensures all ${\varphi }_k$can be aggregated consistently. Weights $w_k$ quantify the relative importance of each component in the composite index. Empirical calibration or regression-based weights derive weights from observed operational outcomes [95]. It starts with selecting an outcome measure $Y$ that the index should predict (e.g., defect rate, downtime, throughput, safety incidents). Then a regression model is fitted using the normalized components ${\varphi }_k$ as predictors [96]:

$$Y={\beta }_0+{\displaystyle {\sum }_{k=1}^n}{\beta }_k{\varphi }_k+\epsilon$$

(13)

Convert the estimated coefficients into weights using absolute values (to avoid sign issues) [96]:

$$w_k={\displaystyle \frac{\mid {\beta }_k\mid }{{\displaystyle {\sum }_{j=1}^n}\mid {\beta }_j\mid }}$$

(14)

This produces weights based on data and performance impact, which is compelling in operations research settings.

3.3.2. Information Flow Efficiency (IFE)

We build on the established Availability–Performance–Quality decomposition used in Overall Equipment Effectiveness (OEE) [97]. Information flow is critical to Lean value creation [98,99]. This study proposes IFE as an analogous construct for cognitive value streams. Prior research has independently demonstrated the importance of information availability, processing speed, and decision accuracy for operational performance; however, these dimensions have not previously been integrated into a unified efficiency metric for cognitive work.

$$\mathrm{FE}=\mathrm{Availability} (A)\cdot \mathrm{Performance} (P)\cdot \mathrm{Quality} (Q),\mathrm{where} A,P,Q\in [0,1]$$

(15)

IFE assesses the efficiency of information dissemination, processing, and its conversion into accurate actions and decisions, where:

• Availability refers to the ratio of time during which necessary information is accessible and cognitive resources are ready for processing. It is diminished by system downtime, prolonged knowledge search time due to KFW, and operator unavailability resulting from interruptions typical of CSW.
• Performance measures the speed of information processing and decision making against an optimal standard, hindered by IOW and CLW.
• Quality denotes the ratio of judgments or activities executed correctly on the initial attempt and is adversely influenced by all types of cognitive inefficiency.

Existing metrics assess information quality, workload, or decision performance in isolation [98,99]. This work is the first to formalize information flow as a value stream governed by availability, performance, and quality losses, introducing an OEE-like framework. IFE offers a clear and communicable measure that connects conventional operations management techniques with the growing necessity to oversee cognitive efficiency in digitally demanding industrial processes. IFE adopts the multiplicative structure of OEE as a normalized loss decomposition; it is intended as a monitoring index rather than a claim that availability, processing speed, and decision quality are independent causal factors. In this sense, the five cognitive waste models explain where and why inefficiencies arise, while CEI and IFE quantify how much those inefficiencies matter for overall cognitive performance.

3.3.3. Measurement Philosophy and Construct Validity

Scale Type and Permissible Operations

The scale features of the principal conceptions are clearly acknowledged to ensure theoretical coherence. Information input rates and temporal metrics, such as search duration or switching costs, are classified as ratio-scale variables, allowing for both additive and proportional operations. Subjective cognitive load assessments, like NASA-TLX [100], are regarded as approximately interval-scale constructs, where differences hold significance but ratios do not. Composite measures like CEI and IFE are defined as dimensionless indices, appropriate for comparative and trend analysis rather than absolute interpretation.

Normalization and Cross-Context Comparability

Normalization is required when aggregating or comparing indicators with different scales and units [94]. Composite indicators are designed for relative comparison and trend analysis [91].

To enable comparison across tasks, systems, and organizational settings, cognitive waste components are expressed in normalized form using a min–max transformation, yielding bounded indices in $[0, 1]$ [93]:

$${\tilde{W}}_j={\displaystyle \frac{W_j-W_j^{\mathrm{m}\mathrm{i}\mathrm{n}}}{W_j^{\mathrm{m}\mathrm{a}\mathrm{x}}-W_j^{\mathrm{m}\mathrm{i}\mathrm{n}}}}$$

(16)

This normalization approach aligns the framework with practices used in OEE, data envelopment analysis, and composite productivity indices, where:

• $W_j$ denotes a specific cognitive waste component and the bounds are empirically determined within a defined operational context.
• ${\tilde{W}}_j\in [0,1]$, bounds are empirically determined within a given operational context.

The normalization procedure itself follows established practices in performance measurement; however, its application to cognitive waste components enables systematic comparison of otherwise incommensurable cognitive inefficiencies across tasks and organizational contexts.

While formative composite indices and normalization techniques are well established, their application to cognitive efficiency in operational contexts is novel. FCCEI formalizes cognitive efficiency as a composite of independently acting inefficiency mechanisms, enabling systematic diagnosis and management within Lean production systems.

4. Pathway to Integrating Cognitive Waste Reduction into Lean Practices

The goal is to integrate cognitive waste awareness into the existing continuous improvement culture of a Lean organization.

4.1. The Information Value Stream Mapping (IVSM) Process

Value Stream Mapping (VSM) is fundamental to Lean methodology, utilized to illustrate the flow of materials and information necessary for delivering a product to the client [101]. Traditional VSM typically features a superficial information flow line. An advancement of this tool comes in the form of IVSM, where IVSM prioritizes the cognitive and informational processes as the principal value stream under consideration. The IVSM Process consists of the following steps:

• Identify the Cognitive Value Stream: Select a key decision-making or problem-solving process that runs parallel to a physical production process (e.g., responding to a quality alert, re-planning a production schedule).
• Map the Process Steps: Document each step in the information flow, from data acquisition to final decision or action.
• Add Cognitive Waste Metrics: For each step, collect data related to the five categories of cognitive waste. This includes:

  -

  Data volume and sources (for IOW).

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  Number of systems/interfaces used (for CSW).

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  Time spent searching for information (for KFW).

  -

  Operator-reported cognitive load (for CLW).

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  Time taken to complete the cognitive task (for LLW and overall efficiency).

• Create the Future State Map: Just as in traditional VSM, the team brainstorms a future state that dramatically reduces or eliminates the identified cognitive wastes. This might involve redesigning interfaces, integrating systems, or automating information gathering.
• Develop an Action Plan: Create a tangible plan to move from the current state to the future state, with clear responsibilities and timelines.

4.2. The 5S Methodology

The 5S (Sort, Set in Order, Shine, Standardize, Sustain) is a systematic approach to workplace organization, primarily focused on the physical environment. It can be effectively modified to structure the cognitive environment:

• Sort (Seiri): Eliminate unnecessary information. Review dashboards, reports, and alerts. If a piece of information is not used for a specific, value-adding decision, remove it. This directly combats IOW.
• Set in Order (Seiton): Organize information for ease of use. Design user interfaces so that the most frequently needed information is the most accessible. Group related data together. This reduces KFW and CLW.
• Shine (Seiso): Keep the information environment clean. This means regularly validating data accuracy, archiving old and irrelevant information, and ensuring that information systems are running correctly. A clean information environment builds trust and reduces CLW.
• Standardize (Seiketsu): Create standards for how information is presented and managed. Use consistent terminology, color codes, and layouts across all systems. Standardize reporting formats and communication protocols. This reduces CSW and CLW.
• Sustain (Shitsuke): Make cognitive organization a habit. Regularly audit dashboards and reports (a “Cognitive 5S Audit”). Make information management a part of standard work and daily accountability.

4.3. Kaizen

Kaizen, or continuous improvement, is the engine of Lean. A Cognitive Kaizen event is a focused, short-term workshop aimed at improving a specific cognitive process. An example of a potential Cognitive Kaizen scenario is presented below:

• Theme: Reduce the time it takes for an operator to diagnose and respond to a machine stoppage.
• Team: A cross-functional team including the operator, a supervisor, an engineer, and an IT specialist.
• Process: The team uses IVSM to map the current diagnostic process. They identify significant waste: the operator has to check three different screens (CSW), the error codes are cryptic (CLW), and the troubleshooting guide is a 300-page PDF file (KFW).
• Countermeasures: The team designs a single, integrated diagnostic screen that presents the error code in plain language, displays relevant sensor data leading up to the stoppage, and provides a direct link to the specific page in the troubleshooting guide. They also implement a system for operators to add their own notes to the guide (reducing LLW).
• Possible Results: The average diagnostic time is reduced, and the cognitive load on the operator is significantly lowered, freeing up their mental capacity to focus on solving the root cause of the problem.

4.4. Potential Implications

The introduction of Cognitive Waste Theory as an extension of Lean manufacturing has considerable ramifications for academic research and industrial practice. It urges scholars and practitioners to transcend the physical realm and prioritize the cognitive aspect as a new frontier for operational excellence.

Cognitive Waste Theory provides multiple contributions to the theoretical framework of Lean. It offers a timely revision of the idea of muda, enhancing its applicability to the data-intensive, cognitively challenging contexts of Industry 4.0. Formally designating cognitive waste as the ninth waste establishes a valid and systematic subject for scholarly investigation.

Furthermore, it establishes a conceptual connection between operations management and cognitive science. This facilitates new opportunities for multidisciplinary study, enabling Lean researchers to leverage decades of studies in cognitive load, decision making, and human factors to create more advanced models of factory performance.

Ultimately, it aids in clarifying the persistent discourse on the function of information in Lean methodology. Treating information flow as a value stream susceptible to its own types of waste offers a more comprehensive framework than only considering information as a supplementary component of material flow. It asserts that in numerous contemporary systems, the cognitive value stream serves as the principal catalyst for performance, while the physical value stream represents its outcome.

The implications for practitioners are immediate and actionable. The framework offers a novel perspective on their operations and a new array of tools to facilitate enhancement. Managers can now pose inquiries that were once challenging to express:

“Are we overloading our operators with data?” (IOW)

“How much time do our people lose switching between systems?” (CSW)

“Is our critical knowledge accessible, or is it fragmented?” (KFW)

“Is this interface making the task harder than it needs to be?” (CLW)

“How quickly are we learning from our mistakes?” (LLW)

The adoption of IVSM and Cognitive 5S can result in significant enhancements in decision-making speed, error reduction and employee engagement. By paying attention to reducing unnecessary cognitive load, organizations may unlock their employees’ mental capacity, allowing for greater engagement in essential cognitive tasks such as problem solving, innovation, and continuous improvement—the activities that the eighth waste of “unused talent” aims to activate.

Moreover, an emphasis on cognitive waste offers a more human-centered perspective on digital transformation. Rather than merely implementing technology for its own merit, it compels companies to evaluate the influence of such technology on the cognitive comfort and efficiency of its human users. This may result in improved system design, increased user adoption rates, and a more sustainable and effective integration of humans and technology.

5. Conclusions

Lean manufacturing has established itself as one of the most influential management concepts of the last century, largely due to its emphasis on the systematic identification and elimination of waste. This study argues that, as organizations increasingly operate within digitally intensive environments, the traditional Lean conception of waste may benefit from expansion to account for cognitive challenges faced by modern workers. To this end, we propose Cognitive Waste as a potential ninth waste of Lean and present a theoretical framework that categorizes it into five dimensions: Information Overload, Context Switching, Knowledge Fragmentation, Cognitive Load, and Learning Lag.

In addition, this work introduces conceptual mathematical formulations for measuring these dimensions and discusses how established Lean tools may be adapted to incorporate cognitive considerations. While these contributions provide a foundation for extending Lean thinking toward both material and cognitive processes, their practical applicability and effectiveness have not yet been empirically validated. Consequently, the framework should be viewed as a conceptual starting point for future investigation rather than a demonstrated solution for improving organizational performance.

Several avenues for future research emerge from this work. Empirical studies are needed to validate the proposed mathematical models and assess whether reducing different forms of cognitive waste is associated with measurable operational benefits across diverse production environments. Future research may also explore the relationships between cognitive wastes and traditional physical wastes to better understand their interactions and combined effects. Furthermore, the development of software-based approaches for identifying and quantifying cognitive waste through system logs, user interaction data, and digital workflow analysis represents a promising direction for translating the proposed framework into practical assessment tools. Such efforts would help determine the validity, reliability, and practical value of the concepts introduced in this study.