







































C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 
267-281 

267 

 

 

 

Article 

Research on risk control and sustainability 

strategies of AI-driven big data analytics in LEAN 

manufacturing equipment R&D 
ChengHsien Tsai1, Oyyappan Duraipandi1*, Dhakir Abbas Ali1 

1Faculty of Business and Accountancy, Lincoln University College, 47301 Petaling Jaya, Selangor, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 29 June 2025  
Received in revised form 
20 August 2025 
Accepted 04 September 2025 
 
Keywords:  
AI-LEAN integration,  
Risk management,  
Sustainability metrics, 
Industry 4.0, Operational excellence 
 
*Corresponding author 
Email address: 
aa0929771849@gmail.com 
 
 
DOI: 10.55670/fpll.futech.4.4.22 

A B S T R A C T 
 

The convergence of artificial intelligence (AI) and LEAN manufacturing principles presents 
unprecedented opportunities for operational excellence while introducing complex risk 
management and sustainability challenges. Addressing the critical research gap in 
quantitative AI-LEAN integration models. This research develops an integrated framework 
for implementing AI-driven big data analytics in LEAN manufacturing equipment R&D, 
addressing the critical gap between technological capabilities and sustainable manufacturing 
practices. We used three research methods: theoretical modelling, empirical validation with 
the SECOM semiconductor dataset, and 12-month field testing across three manufacturing 
facilities. This mixed-methods approach quantifies the synergistic effects of AI-LEAN 
integration. The framework incorporates hierarchical risk taxonomy, real-time anomaly 
detection algorithms achieving 93.5% accuracy, and multidimensional sustainability metrics. 
Results demonstrate substantial improvements: 36.1% increase in overall equipment 
effectiveness, 58.9% reduction in setup times, and 31.4% decrease in carbon footprint, 
energy intensity reduced by 30%, employee safety incidents decreased by 67%, and job 
satisfaction increased by 15%, achieving synergistic optimization of environmental benefits 
and social value. Risk prediction models achieved 91-96% accuracy across different 
categories, while maintaining sub-50ms inference times for real-time applications. The AI-
enhanced system outperformed traditional LEAN implementations by 1.81x in continuous 
improvement rates and achieved payback in 13 months versus 23 months for conventional 
approaches. Financial analysis reveals 319.4% ROI over five years, validating the economic 
viability alongside environmental benefits. This research establishes a replicable paradigm 
for sustainable smart manufacturing, demonstrating that advanced analytics can 
simultaneously enhance operational efficiency, risk management, and environmental 
stewardship while preserving LEAN's human-centric values. 

1. Introduction 

The manufacturing industry is undergoing a 
transformation, where the fusion of Industry 4.0 technologies 
and traditional Lean manufacturing is fundamentally 
changing production concepts. The introduction of lean 
manufacturing equipment research and development (R&D) 
with artificial intelligence (AI) and big data Analytics marks a 
significant paradigm shift with potential unprecedented 
efficiency gains, and an associated set of remarkably complex 
risk management and sustainability challenges [1]. With 
more companies targeting AI-based solutions, recent 
research shows that 78% of companies have implemented AI 
in at least one business function, compared to 55% a year 
ago.1 The rise of AI has made it essential to consider holistic 
frameworks that strike a proper balance between innovation 
and minimizing risk, alongside ensuring sustainable practices 
[2]. The rise of LEAN manufacturing in the era of Industry 4.0 
has led to an overhaul of traditional waste reduction and 

continuous improvement approaches. Although LEAN has 
dominated industrial improvement methodologies since the 
1990s, the combined integration of AI and digital technologies 
is creating what researchers call Lean Industry 4.0 —a socio-
technical model that involves humans and AI in a system, 
along with various digital technologies [3]. This integration 
has enabled manufacturers to achieve productivity 
increments of 6% or greater per year when appropriately 
implemented; however, the rapid pace of technology 
adoption often outpaces the development of necessary 
governance [4]. The difficulty then is not just to implement 
new technology and products productively in interaction with 
AI and LEAN methods, but to make AI and these LEAN 
methods work together profitably, while still working with 
the environment. Integrating AI and big data into the field of 
manufacturing equipment R&D is challenging, and the issues 
extend beyond technical concerns. An accompanying study 
reveals that 45% of manufacturers believe a lack of 

 

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November 2025| Volume 04 | Issue 04 | Pages 267-281  

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C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 267-281 

268 

 

knowledge is the primary obstacle, and 44% encounter 
difficulties integrating AI solutions with their production 
facilities [5]. They become even more complex when 
addressing modern manufacturing, where AI-based demand 
forecasting systems interact with just-in-time production 
systems, reducing forecast errors by 20% to 50% while 
mitigating the risks associated with small buffers and short 
response lead times [6]. Additionally, when introducing AI 
into equipment development processes, it is essential to 
consider the quality of data input, the system's 
interoperability, and the preservation of tacit knowledge that 
is inherently encoded in conventional LEAN activities [7]. 
Despite growing interest in AI-LEAN integration, current 
research reveals significant limitations that hinder both 
theoretical advancements and practical implementations. 
The existing literature lacks comprehensive mathematical 
models that can quantify the synergistic effects between AI-
LEAN and dynamic learning. Existing frameworks merely 
treat risk control, sustainability assessment, and operational 
optimization as discrete problems rather than integrated 
dimensions. Furthermore, there remains a dearth of long-
term studies featuring rigorous controlled experimental 
comparisons across diverse manufacturing environments. 

The increasing focus on risk management and 
sustainability in the context of smart manufacturing mirrors 
the more general changes in society and regulation. The EU’s 
AI Act, which commenced in August 2024, represents the 
world's first-ever comprehensive legal framework for AI, 
where systems have been classified according to risk levels 
and specific requirements have been set for high-risk uses [8]. 
Likewise, sustainability reporting has evolved from a 
voluntary to a mandatory framework, with the Corporate 
Sustainability Reporting Directive mandating extensive 
environmental impact assessments [9]. Manufacturers are 
under more scrutiny than ever to prove that we are not just 
efficient, but we are also socially responsible, and we can be 
ethical in AI deployment [10]. 

New AI-based analytics in manufacturing is a fast-paced, 
high-investment area of research, but not without its 
challenges. Predictive maintenance, quality control, and 
supply chain optimization are becoming popular tasks of 
machine learning algorithms. On some systems, about 30% of 
equipment downtime is being reduced by AI-driven 
predictive analytics [11]. Deep learning methods in pattern 
recognition for defect detection and process optimization, 
and reinforcement learning are being further investigated for 
dynamic production scheduling in complex manufacturing 
scenarios [12]. However, the literature also emphasizes 
continued concerns with respect to model interpretability, 
patient privacy, and the possibility of algorithmic bias in the 
decision-making process [13]. LEAN principles in the 
equipment development process have long been centered on 
waste reduction, standard work, and continuous 
improvement. Recent works show that AI can support these 
principles with real-time optimization of the value stream and 
data-driven kaizen [14]. The connection of AI to LEAN has 
given rise to hybrid mechanisms that preserve the human-
based approach of LEAN and combine it with the analytics of 
AI systems [15]. It is essential to emphasize that AI visual 
management systems and digital and on-board systems have 
shown a significant improvement in response time and 
problem-solving [16]. The risk assessment for smart 
manufacturing systems has been developed to address the 
specific issues associated with AI integration. The NIST AI 
Risk Management Framework, released in 2023 and 
subsequently expanded by specific profiles for generative AI, 

offers structured paths for identifying, assessing, and 
mitigating AI-related risks [17]. These models stress the 
importance of ongoing monitoring, stakeholder engagement, 
and having clear lines of accountability [18]. Centralized 
governance, risk, and compliance software is used in 
manufacturing institutions, as organizations have numerous 
departments in which the AI is deployed [19]. Sustainability 
measures and strategies in industrial R&D aren’t just about 
the environment anymore – the scope has broadened to 
include social and economic aspects as well. New studies are 
suggesting combined sustainability indicators that include 
energy consumption, utilization of resources, carbon 
footprint, and social impact [20]. AI is being used to optimize 
these multiple objectives at once, but trade-offs exist between 
competing sustainability goals and production efficiency 
goals [21]. Notwithstanding these advances, the review of the 
literature identifies some research gaps where AI meets LEAN 
manufacturing and sustainable development. The studied 
fields are usually handled separately, which dismisses the 
intricate connections and possible synergies [22]. There is a 
paucity of empirical data regarding the long-term effects of AI 
implementation on the LEAN culture and culture-related 
workforce dynamics, as well as of integrated frameworks that 
facilitate the handling of technical, operational, and strategic 
risk factors in AI-based manufacturing contexts [23]. 

This study constructs an integrated framework that aims 
to integrate risk control and sustainability strategies into the 
research and development environment of AI-LEAN 
integration equipment. It establishes a hierarchical risk 
classification system, identifying key risk factors inherent in 
the technological, operational, strategic, and ethical 
dimensions of AI-lean manufacturing convergence. 
Concurrently, the research develops a multidimensional 
sustainability assessment indicator system to quantify the 
environmental, economic, and social impacts of AI integration 
initiatives on performance. The study also develops proactive 
risk mitigation strategies that leverage the technological 
advantages of AI systems while adhering to the core principle 
of continuous improvement within the lean manufacturing 
philosophy. The scope is confined to the R&D phase of 
manufacturing equipment, employing a mixed-methods 
approach that integrates theoretical modelling, empirical 
validation using the SECOM semiconductor dataset, and 
comprehensive 12-month field implementation verification 
across multiple manufacturing environments. This study 
makes an innovative contribution to the manufacturing 
science literature through its multidimensional approach, 
advancing both theoretical understanding and practical 
application of intelligent manufacturing systems. It proposes 
a comprehensive theoretical framework that explicitly 
integrates risk management and sustainability perspectives, 
addressing a key gap in existing literature where these 
domains are typically treated separately. The investigation 
quantifies AI-Lean synergies through mathematical 
modelling, establishing an operational performance 
assessment model for systematically evaluating and 
optimizing integrated systems. It provides empirically 
validated implementation guidelines that demonstrate how 
advanced analytical techniques can simultaneously enhance 
operational efficiency, risk control capabilities, and 
environmental management standards while upholding the 
core human-centered values of lean manufacturing. The 
framework's replicability and 319.4% return on investment 
within five years establish a new paradigm for sustainable 
smart manufacturing transformation, achieving a balance 



C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 267-281 

269 

 

between technological advancement and organizational and 
environmental responsibility. 

2. Literature review 

2.1 AI-LEAN integration in industry 4.0 
Recent investigations have demonstrated significant 

progress in integrating artificial intelligence with LEAN 
manufacturing principles within Industry 4.0 paradigms. 
Powell [1] explored the emerging roles of artificial 
intelligence in lean manufacturing, emphasizing the need for 
digitalization with a human touch. Tashkinov [21] proposed 
an interdisciplinary approach combining lean manufacturing 
principles with artificial intelligence to improve production 
system efficiency. Shahin [7] demonstrated that integrating 
Lean Manufacturing tools with artificial intelligence 
represents a revolutionary approach to optimizing 
production processes, reducing waste, and enhancing 
efficiency, where AI algorithms excel in pattern recognition, 
data analysis, and decision-making, offering more precise, 
data-driven solutions for manufacturing challenges. Saad [24] 
conducted a systematic review of the literature on Industry 
4.0 and Lean Manufacturing integration, providing scholars 
with a better understanding of existing research and 
contributing to the definition of clear topics for future 
research opportunities. Saraswat [3] investigated the 
technological integration of lean manufacturing with Industry 
4.0 toward lean automation through a systematic review. 

2.2 Predictive maintenance and smart manufacturing 
applications 
Predictive maintenance represents a critical 

convergence area where AI capabilities complement LEAN 
total productive maintenance principles. Ucar [25] reviewed 
recent developments in AI-based predictive maintenance, 
focusing on key components, trustworthiness, and future 
trends. Recent systematic multi-sector mapping reveals that 
within smart manufacturing contexts, predictive 
maintenance approaches can decrease downtimes, reduce 
operational costs, and increase productivity, improving 
system performance and decision-making across diverse 
manufacturing sectors. Achouch [26] provided a 
comprehensive overview of predictive maintenance in 
Industry 4.0, examining models and challenges while 
highlighting that data-driven predictive maintenance 
constitutes a cutting-edge solution with growing interest in 
modern manufacturing. Recent advances in smart 
manufacturing have demonstrated unified predictive 
maintenance platforms that leverage data warehousing, 
Apache Spark, and machine learning, addressing the 
heightened complexity in machinery and equipment used 
within collaborative manufacturing landscapes while 
presenting significant risks associated with equipment 
failures.  

2.3 Sustainability integration and research gaps 
Despite growing emphasis on sustainable 

manufacturing, systematic integration of sustainability 
metrics with AI-LEAN frameworks remains limited. Ghaithan 
[27] investigated the integrated impact of circular economy, 
Industry 4.0, and lean manufacturing on sustainability 
performance. Ciliberto [28] presented a sustainable lean 
manufacturing recipe for Industry 4.0 that enables a 
transition to a circular economy. Machado [29] identified 
interlinks between Industry 4.0 technologies and sustainable 
operations, discussing influences on sustainable business 
models and effects on lean manufacturing practices, while 
noting convergence about desirable features relating to being 

flexible, reconfigurable, low cost, adaptive, agile, and lean. 
Recent studies examining relationships between lean 
manufacturing, Industry 4.0, and sustainability reveal that 
while Industry 4.0 shows a strong correlation with 
sustainability pillars, the relationship between lean 
manufacturing and sustainability dimensions is not 
conclusive. Kipper [30] demonstrated that Industry 4.0 and 
lean manufacturing practices contribute to sustainable 
organizational performance in Indian manufacturing 
companies, achieving improvements in operational metrics 
while supporting environmental objectives. However, studies 
in the Mexican maquiladora industry reveal that while lean 
manufacturing tools are being applied in production lines, 
few investigations have examined the relationships with 
comprehensive sustainability dimensions that encompass 
social, economic, and environmental aspects. Research gaps 
identified include the absence of comprehensive risk 
taxonomies specific to AI-LEAN integration, limited 
quantification of sustainability synergies, and a lack of long-
term empirical validation across multiple manufacturing 
contexts. Buer [31] demonstrated complementary effects of 
lean manufacturing and digitalization on operational 
performance. 

3. Methodology 

3.1 Theoretical framework development 
We developed a theoretical framework for AI-LEAN 

integration. This framework combines proven LEAN 
principles with advanced AI methodologies. This integration 
necessitates careful consideration of how traditional 
continuous improvement paradigms can be enhanced 
through machine learning capabilities while preserving the 
human-centric values fundamental to LEAN philosophy. Our 
framework construction begins with the mathematical 
formalization of LEAN-AI synergies, proceeds through risk 
categorization specific to intelligent manufacturing systems, 
and culminates in a multidimensional sustainability 
assessment model. The integration of LEAN principles with 
AI-driven analytics represents a paradigm shift from reactive 
to predictive operational management. Traditional LEAN 
methodologies focus on waste elimination through visual 
management and standardized work. The traditional value 
stream efficiency (VSE) formula: ηVSM = Value-added time 
(VAT)/Total lead time (LT) [32]. While AI introduces 
capabilities for pattern recognition and optimization at scales 
beyond human cognitive capacity. We propose an enhanced 
value stream efficiency model that incorporates AI 
optimization factors: 

𝑉𝑆𝐸𝐴𝐼 =
𝑉𝐴𝑇

𝐿𝑇
× (1 + 𝛾) × 𝜃 × 𝜙                       (1) 

where 𝑉𝑆𝐸𝐴𝐼  denotes the AI-enhanced value stream 
efficiency, VAT represents value-added time in the 
production process, LT indicates total lead time including 
processing and waiting periods, 𝛾  is the AI-driven 
improvement factor ranging from 0 to 0.8, 𝜃 represents the 
data quality coefficient (0 to 1), and 𝜙 denotes the human-AI 
collaboration effectiveness factor (0.5 to 1.5). 
The AI-driven improvement factor 𝛾  captures the 
incremental efficiency gains achieved through machine 
learning applications [33] and is calculated through the 
weighted sum across k=1 to 5 dimensions: 

𝛾 = ∑ 𝛼𝑘
5
𝑘=1 × 𝛽𝑘 × (1 − 𝑒−𝜆𝑘𝑡)                       (2) 

where 𝛼𝑘  represents the potential improvement in LEAN 
waste category k (overproduction, waiting, transport, 



C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 267-281 

270 

 

overprocessing, inventory), 𝛽𝑘  denotes the AI applicability 
factor for waste type k, 𝜆𝑘 is the learning rate coefficient, and 

t represents the time since AI implementation. 1 − 𝑒−𝜆𝑘𝑡  is 
the index convergence term, modelling the temporal 
evolution of learning effects. The core distinction between AI-
enhanced models and traditional LEAN efficiency 
assessments lies in dynamic modelling capabilities. This 
formulation introduces learning effect modelling through the 

exponential convergence term 1 − 𝑒−𝜆𝑘𝑡 , capturing the AI 
system's progressive improvement trajectory over time, 
whereas traditional LEAN methods rely on static efficiency 
level assumptions. Simultaneously, its five-dimensional 
summation structure provides a multifaceted comprehensive 
evaluation, offering greater breadth than conventional single-
metric approaches. The parameterized learning rate 𝜆𝑘  for 
each dimension permits heterogeneous convergence speeds 
across different improvement aspects, grounded in empirical 
observational data rather than uniform theoretical 
assumptions. Mathematically, this method constitutes a 
multidimensional extension of empirical learning curve 
models, incorporating temporal dynamics beyond traditional 
static computations. However, it fundamentally represents a 
parametric refinement of existing LEAN efficiency 
assessment approaches rather than a foundational 
theoretical breakthrough. 

Based on 12 months of manufacturing site validation, 
this modeling approach demonstrated significant 
improvements over traditional LEAN efficiency assessments 
across three manufacturing plants: predictive accuracy 
increased from 78% using conventional methods to 89%, 
while response times were reduced from several hours for 
manual evaluations to real-time computation. However, the 
method requires a minimum of six months' historical data for 
parameter 𝜆𝑘 calibration to operate effectively. The practical 
efficacy of this mathematical extension is highly contingent 
upon the digital maturity of the manufacturing environment 
and the caliber of available data.  

 

In settings with inadequate digital infrastructure or 
limited data acquisition capabilities, its advantages over 
conventional methods may be markedly diminished or even 
negligible. Consequently, its applicability is subject to clear 
technical and environmental constraints. As illustrated in 
Figure 1, the integrated framework creates synergistic value 
through the convergence of traditional LEAN methodologies 
and AI capabilities. This framework validates the core 
research hypothesis across three tiers. The traditional lean 
layer (value stream mapping, 5S visual management, 
continuous flow, standardized work, improvement culture) 
preserves fundamental lean production principles. The AI-
Augmented layer (predictive analytics, computer vision 
quality control, process mining, optimization algorithms, 
machine learning) delivers intelligent analytical capabilities. 
While the synergistic integration zone (data-driven 
improvement, AI-enhanced VSM, predictive maintenance) 
achieves its organic combination. The enhanced 
manufacturing performance formula (1) directly quantifies 
this synergy. The prominent role of the human-machine 
collaboration factor φ underscores the study's key argument 
that human-machine collaboration is pivotal to the system's 
success. The human-AI collaboration factor 𝜙 plays a crucial 
role in determining overall system effectiveness [34] and is 
modeled as: 

𝜙 = 0.5 + 0.5 × tanh(𝑘 × (𝑇 + 𝐸 + 𝐴 − 1.5))                      (3) 

where 𝑇  represents the trust level in AI systems (0-1), 𝐸 
denotes employee engagement with AI tools (0-1), 𝐴 
indicates the adequacy of AI training programs (0-1), and 𝑘 is 
a scaling constant typically set to 2. 1.5 is the threshold 
parameter, when the sum of T, E and A exceeds 1.5, the 
collaborative effect begins to increase significantly.  
 
 
 
 
 

 

                      

                                          

                                

              

                    

                                      

                                     

                

                            

           

      

           

   

          

           

                                  

                                  

                                                 

Figure 1. Integrated LEAN-AI framework for manufacturing excellence 

 



C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 267-281 

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This formula employs the hyperbolic tangent function (tanh) 
to model the nonlinear characteristics of human-machine 
collaboration, yielding an output range approximately 
between [0,1]. When the sum of the three input variables is 
low, the collaboration factor approaches 0.5 (neutral state); 
when the sum of the input variables is high, the collaboration 
factor gradually approaches 1 (optimal collaboration state). 
This S-shaped curve characteristic aligns with the observed 
critical point effect in human-machine collaboration. The risk 
taxonomy for AI-enabled manufacturing systems extends 
beyond traditional operational hazards to encompass 
emerging vulnerabilities specific to intelligent systems. Our 
comprehensive risk assessment framework categorizes 
threats across multiple dimensions, as presented in Table 1. 
This classification encompasses 12 specific risk categories 
across four dimensions: technical, operational, strategic, and 
ethical. Each risk category is quantitatively assessed through 
severity (S), probability of occurrence (P), and an AI 
amplification factor (F). Cybersecurity threats within 
technical risks exhibit the highest AI amplification factor (1.9), 
reflecting the unique security challenges faced by AI systems. 
Algorithm bias within ethical risks demonstrates the highest 
AI amplification factor (2.0), highlighting the critical 
importance of transparency in AI decision-making. This 
classification system directly validates the scientific rigor of 
the risk aggregation model in Formula (4). The distribution of 
AI amplification factors Fi within the range of 1.3–2.0 
confirms the dual impact characteristic of AI technology on 
traditional manufacturing risks. The aggregate risk score [35] 
for an AI-enabled manufacturing system incorporates both 
traditional risk factors and AI-specific amplification effects: 

𝑅𝑎𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒 = ∑ 𝑤𝑖
𝑛
𝑖=1 × 𝑆𝑖 × 𝑃𝑖 × 𝐹𝑖 × (1 + 𝜎𝑖)                      (4) 

where 𝑅𝑎𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒 represents the total risk score, 𝑤𝑖  denotes 

the weight assigned to risk category i, 𝑆𝑖 is the severity rating 
(1-5 scale), 𝑃𝑖  indicates probability of occurrence (0-1), 𝐹𝑖  
represents the AI amplification factor (1-2), and 𝜎𝑖  is the 
interconnectedness coefficient capturing risk propagation 
effects.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

This formula quantifies the amplification or mitigation effects 
of AI technology on different risk categories through the 𝐹𝑖  
factor (ranging from 1 to 2), while capturing risk propagation 
characteristics within intelligent manufacturing systems via 
the 𝜎𝑖  coefficient. This approach, which simultaneously 
incorporates the impact of AI technology and system 
interconnection effects into quantitative risk assessment, 
remains relatively underutilized in existing LEAN risk 
management literature. It provides manufacturing 
enterprises with a more comprehensive risk quantification 
tool; however, its effectiveness hinges on the accurate 
calibration of parameters and the digital maturity of the 
manufacturing environment. The sustainability assessment 
model construction addresses the triple bottom line of 
environmental, economic, and social performance within the 
context of AI-LEAN integration. Our multidimensional 
sustainability index incorporates both direct and indirect 
impacts of AI implementation: 

𝑆𝐼𝐴𝐼−𝐿𝐸𝐴𝑁 = 𝛼𝐸 × 𝐸𝑠𝑐𝑜𝑟𝑒 + 𝛼𝐸𝑐 × 𝐸𝑐𝑠𝑐𝑜𝑟𝑒 + 𝛼𝑆 × 𝑆𝑠𝑐𝑜𝑟𝑒 +

∆𝑠𝑦𝑛𝑒𝑟𝑔𝑦                                                                                               (5) 

where 𝑆𝐼𝐴𝐼−𝐿𝐸𝐴𝑁  represents the integrated sustainability 
index, 𝛼𝐸 , 𝛼𝐸𝑐 , and 𝛼𝑆  are weighting factors for 

environmental, economic, and social dimensions, respectively 
(summing to 1), and ∆𝑠𝑦𝑛𝑒𝑟𝑔𝑦 captures the synergistic effects 

of AI-LEAN integration. 
The environmental sustainability score incorporates energy 
efficiency, waste reduction, and carbon footprint metrics: 

𝐸𝑠𝑐𝑜𝑟𝑒 =
1

3
[
𝐸𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒−𝐸𝐴𝐼

𝐸𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒
+

𝑊𝑟𝑒𝑑𝑢𝑐𝑒𝑑

𝑊𝑡𝑜𝑡𝑎𝑙
+

𝐶𝑎𝑣𝑜𝑖𝑑𝑒𝑑

𝐶𝑝𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑
] × 100             (6) 

where 𝐸𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒  and 𝐸𝐴𝐼  represent energy consumption 
before and after AI implementation, 𝑊𝑟𝑒𝑑𝑢𝑐𝑒𝑑  denotes waste 
eliminated through AI optimization, 𝑊𝑡𝑜𝑡𝑎𝑙 is the total waste 
generated, 𝐶𝑎𝑣𝑜𝑖𝑑𝑒𝑑  represents carbon emissions prevented, 
and 𝐶𝑝𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑  indicates projected emissions without 

intervention. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Table 1. Comprehensive risk taxonomy for AI-enabled manufacturing systems 

Risk category Risk type Description Severity (S) Probability (P) AI factor (F) 

Technical risks Data integrity Corrupted, incomplete, or biased 

training datasets affecting model 

performance 

5 0.4 1.8 

Model drift Degradation of AI model accuracy 
over time due to changing conditions 

4 0.6 1.6 

System integration Compatibility issues between AI 

systems and legacy infrastructure 

3 0.5 1.3 

Cybersecurity Adversarial attacks, model poisoning, 
unauthorized data access 

5 0.3 1.9 

Operational risks Process disruption False positives/negatives leading to 

unnecessary interventions 

3 0.4 1.4 

Quality variance Inconsistent product quality due to AI 
decision variability 

4 0.3 1.5 

Maintenance errors Incorrect predictive maintenance 

scheduling causing failures 

4 0.2 1.4 

Strategic risks Technology 
obsolescence 

Rapid AI advancement rendering 
current systems outdated 

3 0.7 1.7 

Regulatory compliance Non-compliance with emerging AI 

governance regulations 

5 0.5 1.8 

Vendor dependency Over-reliance on specific AI 
technology providers 

3 0.6 1.5 

Ethical risks Algorithmic bias Discriminatory outcomes affecting 

workforce or product allocation 

4 0.4 2.0 

Transparency deficit Lack of explainability in AI decision-
making processes 

3 0.8 1.9 

Workforce displacement Job losses due to AI automation 

without reskilling programs 

5 0.5 1.6 

 
 



C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 267-281 

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As depicted in Figure 2, the sustainability assessment 
framework captures the interconnected nature of 
environmental, economic, and social dimensions. The 
multidimensional sustainability assessment framework 
systematically illustrates the integrated AI-LEAN 
methodology's combined impact across environmental, 
economic, and social dimensions, directly validating this 
study's core proposition that AI-enhanced LEAN 
manufacturing achieves synergistic optimization of multiple 
sustainability objectives. Through its clear visual design, this 
framework reveals the interconnections between dimensions: 
the environmental dimension's energy efficiency, waste 
reduction, and carbon footprint metrics correspond to the 
environmental sustainability score 𝐸𝑠𝑐𝑜𝑟𝑒 , the economic 
dimension's ROI enhancement, cost reduction, and 
productivity gains reflect the economic sustainability 
modelling, while the social dimension's employee wellbeing, 
safety improvements, and skills development mirror the 
social impact assessment. Of particular significance are the 
two Synergy indicators in the diagram, directly 
corresponding to the synergy term, quantifying the additional 
benefits generated by the integrated approach. This 
framework provides a theoretical explanation for multiple 
outcomes observed in empirical validation—including a 31.4% 
reduction in carbon footprint, a 319.4% five-year ROI, and a 
significant increase in employee satisfaction—demonstrating 
that AI-LEAN integration transcends the limitations of 
traditional single-objective optimization to achieve systemic 
improvements in sustainable manufacturing. 

 
Figure 2. Multidimensional sustainability assessment framework 

The synergy term   quantifies the additional benefits 
arising from the integrated approach: 

∆𝑠𝑦𝑛𝑒𝑟𝑔𝑦= 𝜆 × √𝐸𝑠𝑐𝑜𝑟𝑒 × 𝐸𝑐𝑠𝑐𝑜𝑟𝑒 × 𝑆𝑠𝑐𝑜𝑟𝑒 × (1 − 𝑒−𝜇𝑡)        (7) 

where 𝜆 represents the synergy coefficient (typically 0.1-0.3), 
𝜇  is the maturity rate constant, and 𝑡  denotes time since 
implementation. 
The economic sustainability score incorporates return on 
investment, operational cost reduction, and productivity 
improvements: 

𝐸𝑐𝑠𝑐𝑜𝑟𝑒 = 𝑤𝑅𝑂𝐼 ×
𝑁𝑃𝑉𝐴𝐼

𝐼𝑖𝑛𝑖𝑡𝑖𝑎𝑙
+𝑤𝑐𝑜𝑠𝑡

𝐶𝑠𝑎𝑣𝑒𝑑

𝐶𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒
+ 𝑤𝑝𝑟𝑜𝑑 ×

𝑃𝑔𝑎𝑖𝑛

𝑃𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒
     (8) 

where 𝑁𝑃𝑉𝐴𝐼  represents the net present value of AI 
investment, 𝐼𝑖𝑛𝑖𝑡𝑖𝑎𝑙  is the initial investment, 𝐶𝑠𝑎𝑣𝑒𝑑 denotes 

cost savings achieved, 𝐶𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒  is the baseline operational 
cost, 𝑃𝑔𝑎𝑖𝑛  indicates productivity improvement, and 𝑤𝑅𝑂𝐼 , 

𝑤𝑐𝑜𝑠𝑡 , 𝑤𝑝𝑟𝑜𝑑  are the respective weighting factors. The social 

sustainability dimension encompasses workforce impact, 
safety improvements, and community benefits: 

𝑆𝑠𝑐𝑜𝑟𝑒 =
1

𝑛
∑ (𝑊𝑗 × 𝑆𝑗 × 𝐷𝑗 × (1 + 𝐶𝑗))

𝑛

𝑗=1
                                            (9) 

where 𝑊𝑗  represents workforce wellbeing metrics for the 

stakeholder group 𝑗 , 𝑆𝑗  denotes safety improvement 

indicators, 𝐷𝑗  indicates skill development and employability 

enhancement, 𝐶𝑗 captures community benefit factors, and   is 

the number of stakeholder groups considered. 
This theoretical framework provides the foundation for 
empirical investigation and practical implementation of AI-
LEAN integration systems that balance operational excellence 
with risk management and sustainability imperatives. The 
mathematical formulations enable quantitative assessment 
and optimization of system performance across multiple 
dimensions, supporting evidence-based decision-making in 
the digital transformation of manufacturing operations. 

3.2 Data collection and processing 
Multi-source data acquisition in AI-LEAN integration 

integrates heterogeneous streams from sensor networks, 
historical repositories, quality systems, and energy monitors. 
Manufacturing equipment sensors generate high-frequency 
vibration, temperature, and pressure data following adaptive 
sampling [36] protocols: 

𝑓𝑠(𝑡) = 𝑓𝑏𝑎𝑠𝑒 × (1 + 𝛼 × |∇𝑥(𝑡)|)                                                  (10) 

where 𝑓𝑠(𝑡)  denotes sampling frequency, 𝑓𝑏𝑎𝑠𝑒  represents 
baseline rate, 𝛼  is the adaptation coefficient, and |∇𝑥(𝑡)| 
indicates signal gradient magnitude. 
Historical R&D data encompassing CAD models, simulation 
results, and testing reports provides longitudinal insights for 
predictive modelling. Quality control metrics from automated 
inspection systems and energy consumption records enable a 
comprehensive performance assessment. Data preprocessing 
addresses heterogeneity through standardized pipelines 
incorporating outlier detection, missing value imputation, 
and noise filtering. Feature engineering [37] extracts domain-
specific representations: 

𝐅 = [𝑓𝑡𝑖𝑚𝑒 ⊕𝑓𝑓𝑟𝑒𝑞 ⊕𝑓𝑠𝑡𝑎𝑡] ∈ ℝ𝑑                                                              (11) 

where 𝐅 represents a feature vector, 𝑓𝑡𝑖𝑚𝑒 , 𝑓𝑓𝑟𝑒𝑞 , 𝑓𝑠𝑡𝑎𝑡  denote 

time-domain, frequency-domain, and statistical features, 
respectively, and ⊕ indicates concatenation. ℝ𝑑 denotes a d-
dimensional real vector space, indicating the mathematical 
properties of the final eigenvector. 

3.3 AI-Driven analytics architecture 
The AI-driven analytics architecture for LEAN 

manufacturing employs hierarchical machine learning 
models selected based on data characteristics and 
computational constraints. Model selection follows multi-
criteria [38] optimization: 

[𝑀∗ = arg⁡𝑚𝑎𝑥𝑀∈ℳ[𝜔1𝐴𝐶𝐶(𝑀) + 𝜔2
1

𝑇(𝑀)
+ 𝜔3𝐼𝑁𝑇(𝑀)]  (12) 

where 𝑀∗  represents the optimal model, 𝑀  denotes model 
space, 𝐴𝐶𝐶(𝑀)  indicates accuracy, 𝑇(𝑀)  represents 

inference time, 𝐼𝑁𝑇(𝑀)  measures interpretability, and 
1

𝑇(𝑀)
 

denotes reasoning efficiency. In addition, 𝜔1 , 𝜔2  and 𝜔3 

       

              

             

       

                 

               

                

        

        

               

              

                  

      

       

                                                      

              



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273 

 

correspond respectively to the weighting coefficients for 
accuracy, computational efficiency, and interpretability. 
Deep learning architectures leverage convolutional neural 
networks for visual inspection and recurrent networks for 
temporal pattern recognition. The CNN architecture [39] 
processes manufacturing images through: 

ℎ(𝑙+1) = 𝜎(𝑊(𝑙) ∗ ℎ(𝑙) + 𝑏(𝑙))                                                       (13) 

where ℎ(𝑙)  denotes layer 𝑙  activations, 𝑊(𝑙)  represents 
convolutional kernels, 𝑏(𝑙)  is the bias term for layer 𝑙 , ∗ 
indicates a convolution operation, and 𝜎 is activation function. 
Predictive risk analytics employs ensemble methods [40] 
combining gradient boosting and neural networks for robust 
forecasting: 

𝑅
^

(𝑡 + 𝜏) = ∑ 𝛼𝑘𝑓𝑘(𝑋𝑡)
𝐾

𝑘=1
+ 𝛽 ⋅ 𝑔𝑁𝑁(𝑋𝑡)                                  (14) 

where 𝑅
^

(𝑡 + 𝜏) predicts risk at time 𝑡 + 𝜏, 𝑓𝑘  represents 𝑘-th 
base learner, 𝑔𝑁𝑁  denotes neural network predictor, 𝐾 
denotes the total number of base learners, 𝛼𝑘  denotes the 
weight coefficient of the kth basic learner, 𝛽  is the weight 
coefficient of the neural network predictor, and 𝑋𝑡  indicates a 
feature vector. 
Real-time anomaly detection utilizes adaptive thresholding 
with statistical process control [41]: 

𝐴(𝑥𝑡) = {
1 if |𝑥𝑡 − 𝜇𝑡| > 𝑘𝜎𝑡

0 otherwise
                                                      (15) 

where 𝐴(𝑥𝑡)  is an anomaly indicator, 𝜇𝑡 , 𝜎𝑡  represent the 
dynamic mean and standard deviation, 𝑥𝑡  denotes the current 
observed value, and 𝑘 is the control limit coefficient. 
Based on 12 months of field validation and SECOM dataset 
analysis, the mathematical extension developed in this study 
demonstrates performance improvements over traditional 
LEAN models under specific conditions. Empirical validation 
results indicate: Overall equipment effectiveness increased 
by 36.1% (compared to the traditional LEAN baseline), setup 
time decreased by 58.9% (achieved through AI optimization), 
and risk prediction accuracy reached 91-96% (across 
different risk categories). These improvements provide 
quantitative evidence supporting the practical value of AI-
enhanced models. 

The effectiveness of this mathematical extension hinges 
on five critical technical conditions: the data quality 
coefficient θ must exceed 0.6 to ensure input data integrity 
and accuracy; the human-machine collaboration effect factor 
φ must remain within the [0.5, 1.5] range; at least six months 
of historical data accumulation is required for accurate 
calibration of the learning rate parameter 𝜆𝑘 ; the 
manufacturing environment must possess foundational 
sensor networks and data acquisition capabilities; and 
operators must receive appropriate training in AI tool usage. 
Failure to meet these conditions will directly impact the 
model's predictive accuracy and optimization effectiveness. 

From a methodological perspective, this represents a 
parametric refinement of existing LEAN efficiency 
assessment methods rather than a fundamental theoretical 
breakthrough. In manufacturing environments lacking the 
aforementioned technical conditions, its advantages over 
traditional methods may significantly diminish or even 
vanish, indicating clear technical boundaries and 
environmental constraints on its applicability. Figure 3 
illustrates the hierarchical architecture integrating multiple 
AI paradigms. This architecture supports the parallel 
deployment of four AI methods through multi-source fusion 
at the data input layer (sensor streams, image data, time 

series, event logs): classical machine learning provides high 
interpretability, deep learning handles complex patterns, 
ensemble methods enhance robustness, and statistical 
methods ensure real-time responsiveness.  

3.4 Risk control framework 
The risk control framework for AI-LEAN integration 

employs systematic identification, quantification, and 
mitigation strategies addressing technical, operational, and 
strategic dimensions. Risk identification methodology 
integrates failure mode analysis with AI-specific 
vulnerabilities, encompassing system failures, data quality 
degradation, process variations, human errors, market 
volatility, and regulatory compliance challenges. The 
comprehensive risk score incorporates probability, impact, 
and AI amplification factors: 

𝑅𝑡𝑜𝑡𝑎𝑙 = ∑ 𝑤𝑖
3
𝑖=1 ∑ 𝑃𝑖𝑗

𝑛𝑖

𝑗=1
× 𝐼𝑖𝑗 × (1 + 𝛼𝐴𝐼,𝑖𝑗)                        (16) 

where 𝑅𝑡𝑜𝑡𝑎𝑙 represents the aggregate risk score, 𝑤𝑖  denotes 
category weight (technical, operational, strategic), 𝑃𝑖𝑗 

indicates the probability of risk 𝑗 in category 𝑖, 𝐼𝑖𝑗 represents 

impact severity (1-5 scale), and 𝛼𝐴𝐼,𝑖𝑗 is the AI amplification 

factor (0-1). 
Risk assessment employs Monte Carlo simulation [42] for 
uncertainty quantification: 

𝑉𝑎𝑅𝜏 = 𝑖𝑛𝑓𝑥: 𝑃(𝐿 > 𝑥) ≤ 1 − 𝜏                                                       (17) 

where 𝑉𝑎𝑅𝜏  represents Value-at-Risk at confidence level 𝜏 , 
and 𝐿 denotes loss distribution. 
Mitigation strategies follow hierarchical control 
implementation, prioritizing prevention over detection. The 
risk reduction effectiveness is modeled as: 

𝑅𝑅 = 1 −∏ (1 − 𝜂𝑘 × 𝑐𝑘)
𝑚

𝑘=1
                                                        (18) 

where 𝑅𝑅  indicates risk reduction ratio, 𝜂𝑘  represents the 
effectiveness of control 𝑘 , and 𝑐𝑘  denotes implementation 
completeness. 
Figure 4 presents the hierarchical risk control framework, 
which integrates the identification, assessment, and 
mitigation phases. This three-tiered architecture forms a 
complete risk management loop, spanning from risk 
identification (across technical, operational, and strategic 
dimensions) to risk assessment quantification (probability 
analysis P (0-1), impact severity (1-5), AI amplification factor 
(0-1)), and risk mitigation strategies (preventive, detective, 
corrective, adaptive), forming a complete risk management 
closed loop. This directly corresponds to the implementation 
framework of the risk aggregation model in Formula (4).  

3.5 Sustainability evaluation metrics 
Sustainability evaluation in AI-LEAN integration 

encompasses environmental, economic, and social 
dimensions through quantitative metrics, enabling 
comprehensive performance assessment [43]. 
Environmental impact indicators measure resource 
efficiency, emissions reduction, and waste minimization 
achieved through AI optimization: 

𝐸𝑒𝑛𝑣 =∑ 𝑤𝑖(
𝐵𝑖−𝐴𝑖

𝐵𝑖
) × 100

𝑛

𝑖=1
                                                     (19) 

where 𝐸𝑒𝑛𝑣 represents the environmental performance score, 
𝑤𝑖  denotes weight for the indicator 𝑖 , 𝐵𝑖  and 𝐴𝑖  indicate 
baseline and AI-optimized values, respectively. 

 



C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 267-281 

274 

 

 
Figure 3. Hierarchical AI analytics architecture for manufacturing 

 
 

 
 
 
 
 
 
 
 
 

 
 
 
 

 
 
 
 
 
 
 
 
 

                

                                             

            

               

         

     

               

                

             

              

                 

             

               

                  

        

          

        

          

        

                  

           

       

      

                

           

             

                 

                  

              

                    

                 

               

                     

               

                   

                

                  

                       

                      

                   

                  

                                  

                                                    

                   

               

                             
                          

                 

                                
                            

               

                          
                       

                                

           

        

       

      

        

      

         

             

      

                          

          

               
          

         

          
      

          

              
        

        

                
       

Figure 4. Integrated risk control framework for AI-LEAN manufacturing 

 



C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 267-281 

275 

 

Economic sustainability [44] measures incorporate 
return on investment, operational cost reduction, and 
productivity gains: 

𝑆𝑒𝑐𝑜𝑛 = 𝛼 × 𝑅𝑂𝐼 + 𝛽 ×
Δ𝐶

𝐶0
+ 𝛾 ×

Δ𝑃

𝑃0
                                          (20) 

where 𝑆𝑒𝑐𝑜𝑛  represents the economic sustainability score, 
𝑅𝑂𝐼  indicates return on AI investment, Δ𝐶  denotes cost 
savings, Δ𝑃 represents productivity improvement, 𝐶0 , 𝑃0 are 
baseline values, and 𝛼, 𝛽, 𝛾 are weighting factors. 
Social sustainability factors encompass workforce wellbeing, 
skill development, and safety improvements. The integrated 
sustainability index synthesizes all dimensions: 

𝑆𝐼𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑒𝑑 = √𝐸𝑒𝑛𝑣 × 𝑆𝑒𝑐𝑜𝑛 × 𝑆𝑠𝑜𝑐𝑖𝑎𝑙
3 × (1 + 𝜆 × 𝜌)            (21) 

where 𝑆𝐼𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑒𝑑  represents the holistic sustainability index, 

𝑆𝑠𝑜𝑐𝑖𝑎𝑙  denotes social performance score, 𝜆  is synergy 
coefficient, and 𝜌 represents inter-dimensional correlation. 
Table 2 demonstrates the improvements achieved by AI-
LEAN integration in environmental and social sustainability. 
Environmental metrics reveal a 30.0% reduction in energy 
consumption per unit of output, a 31.4% decrease in carbon 
footprint, and a 23.6% increase in waste recycling rates. 
Regarding social indicators, the safety incident rate decreased 
by 66.7%, employee satisfaction increased by 14.7%, and staff 
turnover rate fell by 50.4%. These figures validate that AI-
LEAN integration can simultaneously achieve operational 
optimization and sustainable development objectives. 

 
4. Experiment 

4.1 Experimental setup 

The experimental validation utilized the publicly 
available SECOM dataset from semiconductor manufacturing, 
containing 1567 instances with 591 sensor measurements 
collected from actual production processes. This dataset, 
widely used in manufacturing analytics research, captures 
real-time sensor data from semiconductor fabrication, 
including temperature, pressure, and flow measurements 
across multiple production stages. Additionally, we 
incorporated the steel plates fault dataset from Northeastern 
University, containing 1941 samples with 27 features 
describing manufacturing defects in steel production, 
providing comprehensive quality control scenarios. The 
selection of the SECOM dataset was based on three technical 
considerations: providing sufficient feature dimensions for 
complex AI algorithms, meeting the training sample 
requirements for deep learning, and serving as an established 
benchmark for manufacturing analysis research. However, 
significant limitations exist: the high-precision cleanroom 
environment of semiconductor manufacturing, its complex 
multi-stage processes, and its inherent differences from 
typical LEAN manufacturing characteristics—such as high-
mix low-volume production, rapid changeovers, and human-
machine collaboration.  

 

 

 

 

 

 

 

Whilst the steel plate defect dataset supplements 
discrete manufacturing scenarios, it fails to adequately 
represent the continuous flow and pull-based production 
principles characteristic of LEAN manufacturing. The 
specificity of these datasets constitutes a significant 
methodological constraint affecting the model's 
generalization capability across manufacturing 
environments. To mitigate this limitation, we implemented a 
transfer learning approach, fine-tuning and calibrating the 
base model trained on SECOM features using on-site data 
from three manufacturing plants. This involved feature 
mapping between SECOM sensor data and industrial process 
parameters, model calibration based on facility-specific 
failure modes, and validation assessments across 
manufacturing environments. For real-time manufacturing 
data, collaboration with three medium-scale manufacturing 
facilities in the Midwest region provided access to production 
data streams under non-disclosure agreements. These 
facilities, producing automotive components, electronic 
assemblies, and metal fabrication products respectively, 
contributed 18 months of historical data encompassing 
sensor readings, quality inspection results, and energy 
consumption records. Data collection followed standard 
industrial protocols with sampling rates matching typical 
manufacturing environments: vibration sensors at 1-10 kHz, 
temperature monitors at 1 Hz, and quality measurements at 
batch completion intervals. 

The hardware environment consisted of standard 
industrial computing infrastructure commonly deployed in 
manufacturing settings. Edge devices included Siemens 
SIMATIC IPC547G industrial computers for data collection 
and preliminary processing at the machine level. Central 
processing utilized Dell PowerEdge R750 servers with dual 
Intel Xeon Gold processors and 256GB RAM, reflecting typical 
on-premise manufacturing IT deployments. The software 
stack comprised open-source tools, including Apache Spark 
3.2.0 for distributed processing, scikit-learn 1.0.2, and 
TensorFlow 2.8.0 for machine learning implementations, 
ensuring reproducibility without proprietary dependencies. 
Baseline establishment involved analyzing six months of 
historical data to capture normal operating conditions and 
seasonal variations. Performance metrics included standard 
manufacturing KPIs: Overall Equipment Effectiveness (OEE), 
First Pass Yield (FPY), Mean Time Between Failures (MTBF), 
and energy consumption per unit produced. The control 
group selection utilized production lines that manufactured 
similar products but maintained traditional operations, with 
matching performed based on historical performance 
variance to ensure statistical validity. The experimental 
design accounted for common manufacturing variables, 
including shift patterns, operator experience levels, and 
preventive maintenance schedules, documenting these 
factors to enable accurate performance attribution and 
ensure research reproducibility. 

 

 

 

 

 

 

 

Table 2. Detailed environmental and social sustainability indicator system 

Key indicator Baseline value Actual achievement Improvement rate 

Environmental metric 

Energy consumption per unit (kWh/unit) 4.20 2.94 -30.0% 

Carbon footprint (kg CO2/unit) 2.80 1.92 -31.4% 

Waste recycling rate (%) 72.0 89.0 +23.6% 

Social Indicators 

Safety incident rate (incidents/1000h) 1.2 0.4 -66.7% 

Employee satisfaction (score) 6.8 7.8 +14.7% 

Employee turnover rate (%) 12.5 6.2 -50.4% 

 



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Basic details of the three partner factories: Factory A 
specializes in automotive component manufacturing (daily 
output: 2,400 units; baseline OEE: 72.3%), Factory B in 
electronic assembly manufacturing (daily output: 8,500 units; 
baseline OEE: 69.8%), and Factory C in metal processing 
manufacturing (daily output: 1,200 units; baseline OEE: 
74.1%). Each facility established three experimental lines and 
three control lines, matched by equipment age (±2 years), 

historical performance variance (σ ≤ 5%), and operational 

shifts to ensure the validity of the control groups. 

4.2 Implementation process 

The implementation of the AI-LEAN integration 
framework followed a systematic approach spanning 12 
months, progressing through data integration, model 
development, risk system deployment, and sustainability 
tracking. Initial activities focused on establishing a 
comprehensive data collection infrastructure across the 
manufacturing facility. Edge computing devices were 
installed at 47 critical production points, establishing secure 
data pipelines that connected legacy equipment with modern 
analytics platforms. Integration challenges arose from 
heterogeneous communication protocols, necessitating 
custom adapter development for Modbus, OPC-UA, and 
proprietary interfaces from equipment manufacturers such 
as Siemens, Fanuc, and Mitsubishi. 

 
(a) 

 

 
(b) 

Figure 5. Implementation progress and performance evolution (a) 
Implementation progress by component (b) Cumulative performance 
improvements throughout implementation 

Figure 5 illustrates the progressive implementation 
timeline with corresponding performance improvements 
achieved at each stage. The data integration stage achieved 
97.3% completion within 2.5 months, exceeding the target of 
a 95% data capture rate. Subsequently, AI model training 
commenced using accumulated data streams, with parallel 
development of quality prediction, anomaly detection, and 
process optimization algorithms. The overlapping nature of 
the implementation stages, as shown in Figure 5a, reflects the 
iterative approach adopted to ensure continuous 
improvement while maintaining production stability. Figure 
5b illustrates the corresponding cumulative effects: OEE and 
quality improvements steadily rose to 134% and 132%, 
respectively, while energy consumption decreased to 72%. 
The temporal alignment between the two figures 
demonstrates the causal relationship between 

implementation progress and performance enhancements, 
directly supporting core experimental findings such as the 
36.1% OEE improvement. 

4.3 Performance evaluation 

The performance evaluation of the AI-LEAN integration 
system encompassed a comprehensive assessment across 
risk prediction accuracy, operational efficiency 
improvements, and sustainability metrics. Risk prediction 
models demonstrated robust performance across multiple 
evaluation criteria, with particular emphasis on minimizing 
false negatives that could lead to critical failures. The 
evaluation utilized stratified k-fold cross-validation to ensure 
model generalizability across different operating conditions 
and production scenarios. 

Figure 6 comprehensively illustrates LEAN efficiency 
improvements achieved through AI implementation. The 
waste reduction radar chart (Figure 6a) demonstrates 
substantial reductions across all waste categories, with 
inventory waste reduced by 55% and defects by 62%. Lead 
time distribution analysis (Figure 6b) shows not only a 36.7% 
reduction in average lead time but also significantly reduced 
variability, indicating more predictable and reliable delivery 
performance. The quality improvement trajectory (Figure 6c) 
reveals consistent month-over-month improvements, with 
defect rates declining from 3.2% to 0.3% over the 12-month 
period. Process-wise efficiency analysis (Figure 6d) indicates 
that all manufacturing processes experienced efficiency gains, 
with testing showing the highest improvement at 47% due to 
AI-optimized test sequences and predictive quality 
assessment. 

 
(a)  (b) 

 

 
     (c)                             (d) 

 
Figure 6. LEAN efficiency improvements analysis: (a)Waste 

reduction achievement; (b)Lead time distribution comparison, 

(c)Quality improvement trajectory, (d)Process-wise efficiency 

improvements 

LEAN performance metrics quantitatively validate the 
operational improvements achieved through AI-enhanced 
methods, with all six key indicators significantly surpassing 
industry benchmarks (Table 3). Overall equipment 
effectiveness rose from 68.2% to 92.8% (+36.1%), setup time 
decreased from 45 minutes to 18.5 minutes (-58.9%), 
inventory turnover reached 18.3 times (+123.2%), pre-
production lead time was compressed to 9.5 days (-36.7%), 
while space utilization and labor productivity increased by 
18.3% and 36.3% respectively. The AI system's optimization 



C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 267-281 

277 

 

algorithms simultaneously consider both production 
efficiency and resource consumption, identifying 
improvement opportunities that are often overlooked by 
traditional methods. This achieves synergistic benefits of 
enhanced operational efficiency and reduced environmental 
impact, directly supporting the core hypothesis of this 
research that AI-LEAN integration can overcome the 
limitations of traditional single-objective optimization. 
Statistical analysis indicates that the experimental group 
significantly outperformed the control group across all key 
metrics (p<0.05) (Table 4). Analysis of inter-factory 
variations revealed the most pronounced improvements in 
automotive component factories (OEE increase of 24.7%), 
followed by electronics assembly plants (OEE increase of 
19.8%), with metal processing factories showing a smaller yet 
still significant gain (OEE increase of 17.9%). These 
disparities were primarily attributed to differences in digital 
foundations and implementation challenges across the 
factories. 

Figure 7 presents comprehensive sustainability 

performance metrics demonstrating the environmental 

benefits of AI integration. The daily energy consumption 

profile (Figure 7a) reveals AI-optimized load balancing that 

reduces peak demand by 25% while maintaining production 

output. This optimization is particularly beneficial for shift 

transitions, where energy waste is traditionally prevalent. 

Carbon footprint analysis (Figure 7b) shows reductions 

across all emission sources, with electricity-related emissions 

decreasing by 33% through intelligent equipment scheduling 

and predictive maintenance, preventing energy-intensive 

failures. The resource efficiency trends (Figure 7c) 

demonstrate consistent improvements that exceed initial 

targets, with energy efficiency showing the strongest gains, at 

44% improvement over the baseline. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The comprehensive sustainability impact assessment 

quantified in Table 5 demonstrates the significant 

environmental benefits achieved through the AI-LEAN 

integration, with substantial improvements across all six 

environmental metrics.  

                                       

(a)                                                      (b) 

 

 

 

 

 

 

 

 

 

 

 

 

 

(c) 

Figure 7. Sustainability performance analysis: (a) Daily energy 

consumption profile, (b)Carbon footprint reduction by source, 

(c)Resource efficiency improvement trends 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3 LEAN performance metrics summary 

Metric category Baseline Month 6 Month 12 Improvement Industry Benchmark 
Overall equipment 
Effectiveness (%) 

68.2 81.5 92.8 +36.1% 85.0 

Setup time (minutes) 45.0 32.0 18.5 -58.9% 25.0 

Inventory turns 8.2 12.5 18.3 +123.2% 12.0 
Production lead time 

(days) 
15.0 11.2 9.5 -36.7% 12.0 

Space utilization (%) 72.0 78.5 85.2 +18.3% 80.0 

Labor productivity 
(units/hour) 

24.5 29.8 33.4 +36.3% 28.0 

 

Table 4 Comparison of key indicators between the control group and experimental group at the end of the 12-month implementation period 

Indicator Control group 
mean 

Experimental group 
mean 

Improvement rate Statistical significance 

OEE (%) 76.4 92.8 +21.5% p<0.001 
Setup time (min) 42.0 18.5 -55.9% p<0.001 

Inventory turnover rate 9.1 18.3 +101.1% p<0.001 

Energy consumption 
(kWh/unit) 

4.15 2.94 -29.2% p<0.01 

Defect rate (%) 2.8 0.3 -89.3% p<0.001 

 

 



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Electricity consumption decreased by 31.2% (annual 

savings of 2,847 MWh), natural gas usage fell by 28.5% 

(saving 145,000 terms), water consumption dropped by 29.7% 

(saving 1.2 million gallons), hazardous waste and VOC 

emissions were reduced by 52.3% and 36.7% respectively, 

and solid waste sent to landfill decreased by 48.9%. This 

equates to an annual reduction of 3,136 tons of CO2 equivalent, 

directly supporting the core research statement of a 31.4% 

reduction in carbon footprint. In terms of social sustainability, 

the AI-LEAN integration has significantly enhanced the 

working environment and employee well-being. The safety 

incident rate decreased from 1.2 incidents per thousand 

hours at baseline to 0.4 incidents per thousand hours (a 67% 

reduction), primarily attributable to the real-time monitoring 

provided by the AI early-warning system. Employee job 

satisfaction rose from 6.8 points to 7.8 points (a 15% 

increase), reflecting how intelligent systems have alleviated 

the physical strain of repetitive tasks. Regarding skills 

development, annual training hours per employee increased 

from 32 to 45 hours, with 41% of staff obtaining certification 

in AI tool operation. Employee turnover decreased from 12.5% 

to 6.2%, indicating that technological advancement did not 

trigger mass unemployment but rather enhanced job appeal. 

4.4 Comparative analysis 

The comparative analysis between AI-LEAN integration 

and traditional approaches reveals fundamental differences 

in capability, scalability, and performance outcomes. 

Traditional LEAN implementations rely heavily on human 

observation, manual data collection, and periodic 

improvement cycles, whereas the AI-enhanced system 

enables continuous optimization through real-time data 

analysis and predictive capabilities. This comparison 

encompasses operational metrics, implementation timelines, 

and resource requirements across multiple manufacturing 

environments. Figure 8 provides a comprehensive 

comparison between traditional and AI-driven LEAN 

implementations. The performance improvement trajectories 

(Figure 8a) demonstrate that while traditional LEAN follows 

a logarithmic improvement curve with diminishing returns, 

AI-driven approaches achieve rapid initial gains followed by 

sustained improvement through continuous learning. The 

capability assessment (Fig. 8b) reveals AI's superior 

performance in real-time optimization and predictive 

capabilities, though traditional LEAN maintains advantages in 

human engagement aspects.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                (a)     

  (b) 

 

 

(c)               (d) 

Figure 8. Traditional vs AI-driven LEAN performance comparison 

(a)Performance improvement trajectories; (b)Capability assessment 

comparison (c)Implementation effort comparison; (d)Return on 

investment progression 

Implementation effort distribution (Figure 8c) shows 

that AI-driven systems require greater upfront investment in 

planning and training but significantly reduce ongoing 

optimization efforts. The ROI analysis (Figure 8d) indicates 

that despite higher initial costs, AI-driven implementations 

achieve payback two quarters earlier and deliver 2.7x higher 

returns over three years. 

As shown in Table 6, comparative metrics between 

traditional LEAN and AI-driven LEAN validate the AI-

enhanced approach's significant advantage across all key 

performance dimensions. The AI-driven method achieved 

improvement factors ranging from 1.45x to 30.4x in waste 

identification rate (94% vs 65%), issue response time (8.3 

minutes vs 4.2 hours), cycle time reduction (2–4 weeks vs 3–

6 months), and data utilization (87% vs 15%). All metrics 

demonstrate statistically significant improvements (p<0.001 

to p<0.05). Notably, the continuous improvement rate 

increased from 2.1% per month to 3.8% per month (1.81x 

improvement). 

Figure 9 presents a comprehensive financial analysis of 

the AI-driven LEAN implementation. The cost structure 

analysis (Figure 9a) reveals that while initial hardware and 

software investments are substantial, ongoing operational 

costs remain manageable, accounting for approximately 20% 

of the first-year investment.  

Table 5. Comprehensive sustainability impact assessment 

Environmental Indicator Unit Reduction Achieved Annual Savings CO₂ Equivalent (tons) 

Electricity consumption MWh -31.2% 2,847 1,423 

Natural gas usage Therms -28.5% 145,000 815 

Water consumption Gallons -29.7% 1.2M 45 

Hazardous waste kg -52.3% 8,400 126 

VOC emissions kg -36.7% 3,200 89 
Solid waste to landfill tons -48.9% 425 638 

 



C. Tsai et al. /Future Technology                                                                                      November 2025| Volume 04 | Issue 04 | Pages 267-281 

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Table 6. Traditional LEAN vs AI-driven LEAN comparative metrics 

Performance Metric Traditional LEAN AI-Driven LEAN Improvement Factor Statistical 
Significance 

Waste identification rate 65% 94% 1.45x p < 0.001 

Response time to issues 4.2 hours 8.3 minutes 30.4x p < 0.001 

Improvement cycle time 3-6 months 2-4 weeks 6.5x p < 0.001 

Data utilization 15% 87% 5.8x p < 0.001 

Predictive accuracy N/A 91.3% N/A - 

Continuous improvement 
rate 

2.1%/month 3.8%/month 1.81x p < 0.05 

Employee training hours 40 hrs/year 85 hrs/year 2.13x p < 0.01 

Sustainable improvements 73% 96% 1.32x p < 0.01 

 

Table 7. Five-year financial impact summary 

Financial Metric Year 1 Year 2 Year 3 Year 4 Year 5 5-year total 

Implementation Costs ($K) 1,480 318 318 318 318 2,752 

Operational Savings ($K) 1,020 1,280 1,450 1,580 1,680 7,010 

Quality Benefits ($K) 420 480 520 550 570 2,540 

Risk Mitigation Value ($K) 180 220 250 270 285 1,205 

Sustainability Credits ($K) 130 145 160 170 180 785 

Net Annual Benefit ($K) 270 1,807 2,062 2,252 2,397 8,788 

ROI (%) 18.2 122.1 139.3 152.2 162.0 319.4 
            

 
 

(a) (b) 

  

(c) (d) 

Figure 9. Comprehensive cost-benefit analysis: (a)Implementation cost structure, (b)Cumulative benefit streams, (c)NPV sensitivity analysis, 

(d)Payback period comparison 

 

 



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Benefit stream analysis (Figure 9b) demonstrates 

diversified value creation across multiple categories, with 

productivity improvements contributing the largest share, 

but quality and inventory benefits providing significant 

additional value. NPV sensitivity analysis (Figure 9c) confirms 

robust positive returns across a wide range of discount rates, 

with positive NPV maintained even under pessimistic 

scenarios for discount rates up to 18%. The payback 

comparison (Figure 9d) shows that despite a higher initial 

investment, AI-driven implementation achieves payback in 

13 months compared to 23 months for traditional approaches, 

primarily due to accelerated benefit realization. 

Table 7 validates the economic viability of AI-driven 

LEAN, demonstrating cumulative net benefits of 8,788K over 

five years. The return on investment (ROI) escalates from 

18.2% in the inaugural year to 162.0%, culminating in a five-

year total of 319.4%. This rate of return substantially 

surpasses the typical 150-200% benchmark achieved by 

conventional LEAN methodologies, directly substantiating 

the core economic argument that AI-LEAN integration 

generates synergistic benefits. 

5. Conclusion 

This research demonstrates the transformative potential 

of AI-LEAN integration for manufacturing equipment R&D 

through an integrated framework addressing risk control and 

sustainability. Experimental validation shows AI-enhanced 

systems achieve 91-96% risk prediction accuracy with 30-

fold faster response times, while delivering substantial 

operational improvements: 36.1% increase in equipment 

effectiveness, 36.7% reduction in lead times, and 123.2% 

improvement in inventory turns. Sustainability outcomes 

include 31.4% carbon footprint reduction and 48.9% 

decrease in solid waste, demonstrating that operational 

excellence and environmental stewardship are mutually 

reinforcing. The framework contributes empirical evidence 

for AI-LEAN synergies while balancing technical 

sophistication with human-centric values, addressing 

workforce displacement concerns. The compelling 319% ROI 

over five years validates economic viability alongside 

environmental benefits, presenting a case for industry-wide 

transformation toward AI-driven sustainable manufacturing. 

However, significant limitations exist in the reliance on public 

datasets (SECOM, steel plate defects) that inadequately 

represent authentic LEAN manufacturing environments, 

limiting generalizability to typical LEAN contexts. Future 

research should establish comprehensive LEAN-specific 

datasets encompassing multi-industry environments and 

human-machine collaboration patterns, explore cross-sector 

applicability, investigate integration with emerging 

technologies, and examine long-term societal implications of 

widespread AI-LEAN adoption. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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