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American Journal of  Financial 
Technology and Innovation (AJFTI)

Enhancing Decision-Making Efficiency Through Production Process Diagnostics
Jiaqi Yang1*, Oksana Kudriavtseva1

Volume 3 Issue 1,  Year 2025
ISSN: 2996-0975 (Online)

DOI: https://doi.org/10.54536/ajfti.v3i1.4038
https://journals.e-palli.com/home/index.php/ajfti

Article Information ABSTRACT

Received: January 15, 2025

Accepted: February 17, 2025

Published: June 28, 2025

In response to fragmented approaches in green manufacturing research, this study proposes 
an integrated decision-support framework that unifies production process diagnosis, multi-
resource optimization, and data-driven analytics to enhance sustainability in complex 
manufacturing systems. Combining theoretical modeling (e.g., dynamic resource-element 
networks), empirical case studies (12 cross-industry cases in automotive, electronics, and 
textiles), and systematic diagnostics, the research addresses inefficiencies in traditional 
ERP-MES-PCS architectures, where manual decision-making and disconnected data flows 
hinder holistic optimization. Key results demonstrate that integrating green manufacturing 
principles—such as renewable energy adoption, AI-driven logistics, and circular resource 
strategies—reduces carbon emissions by 15–20%, cuts material waste by 25%, and achieves 
10–15% long-term cost savings. For instance, solar-powered equipment in automotive 
plants lowered emissions by 18%, while AI-optimized routing in electronics reduced 
transportation pollution by 22%. The framework establishes actionable benchmarks (e.g., 
emission thresholds, energy-resource efficiency ratios) and enables real-time coordination 
between production planning, process control, and sustainability goals. By bridging gaps 
between ERP, MES, and PCS systems through automated data aggregation and knowledge 
deduction, this work provides a scalable pathway for manufacturers to align operational 
decisions with global standards like the UN SDGs, advancing both ecological stewardship 
and competitive resilience.

Keywords
Green Manufacturing, 
Management Decision-Making, 
Production Process Diagnosis

1 Kharkiv National Automobile and Highway University, Kharkiv 61002, Ukraine
* Corresponding author’s e-mail: y99381@qq.com

INTRODUCTION
In the complex environment of  modern manufacturing, 
management decisions in the production process are 
particularly important. The decision-making content of  
the production process includes production planning, 
processing equipment, process flow, production logistics, 
and raw material procurement. With the introduction 
of  the concept of  green manufacturing, these decisions 
must not only consider economic benefits, but also take 
into account factors such as resource consumption, 
environmental impact, and occupational health and 
safety, making the decision information and content 
richer and the decision process more complex (Liu & 
Cao, 2005). Green manufacturing emphasizes reducing 
resource consumption and environmental pollution 
throughout the production process to achieve sustainable 
development. In recent years, many experts and scholars 
at home and abroad have begun to pay attention to 
the application of  green manufacturing in production 
decisions (Munoz & Sheng, 1995).
MUNOZ (Munoz & Sheng, 1995) proposed an 
analysis model for the environmental impact of  the 
cutting process, quantitatively analyzed the energy 
utilization, processing speed, and raw material logistics 
in the processing process, and gave some quantitative 
relationships between indicators and cutting parameters, 
providing important decision support. Gutowski et al. 
(2006) compared the energy consumption of  aluminum 
and steel materials processed on different machine tools 
and found that by selecting a suitable machine tool, 

energy consumption can be significantly reduced.
Although these studies have achieved remarkable results 
in certain links of  the production process, most of  them 
focus on the greenness of  a single processing element or 
production link, and rarely consider the greenness of  the 
production process from an integrated perspective (Zhang 
et al., 2000). In previous research, the author found that 
the analysis and optimization of  the existing processes 
and resources (such as machine tools, cutting tools and 
cutting fluids) of  traditional manufacturing enterprises 
according to the principles of  green manufacturing 
have achieved significant resource conservation and 
environmental pollution reduction effects (Cao et al., 
2004). Further research found that there is a close 
connection between related resource elements (Cao & Yi, 
2002), multiple resource elements and multiple variables 
in the production process (Tan et al., 2003), and the effect 
of  green manufacturing implementation will be more 
obvious from an integrated perspective (Liu et al., 2003).
Therefore, the purpose of  this study is to provide 
scientific decision-making support for enterprises in the 
implementation of  green manufacturing through in-
depth analysis of  the application of  production process 
diagnosis and green manufacturing factors in management 
decision-making, improve management decision-making 
efficiency, and promote the sustainable development of  
the manufacturing industry. This paper will discuss the 
theory and methods of  production process diagnosis in 
detail, analyze the application of  green manufacturing 
in production decision-making, and verify the impact 



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of  green manufacturing on decision-making efficiency 
through specific case studies.

LITERATURE REVIEW
Research on production process diagnosis and green 
manufacturing decision-making is becoming increasingly 
important in today’s manufacturing industry. Chai Tian-
You and Ding Jin-Liang proposed relevant models 
and methods when studying intelligent optimization 
manufacturing in process industries, which promoted 
the development of  this field (Chai & Ding, 2018). 
They emphasized that efficient operation and resource 
conservation can be achieved through intelligent 
optimization, which provides theoretical support for the 
sustainable development of  the manufacturing industry.
At the same time, Gui Wei-Hua et al. (2018) explored 
the importance of  knowledge automation to intelligent 
manufacturing in their research on the development 
strategy of  big data and manufacturing process knowledge 
automation and provided a theoretical basis for this. They 
pointed out that the use of  big data analysis and knowledge 
automation can significantly improve the efficiency and 
decision-making quality of  the manufacturing process.
In terms of  green manufacturing, Qian et al. (2017) 
proposed the application of  green manufacturing 
in production decision-making and developed 
corresponding analysis models. These models can 
quantify the environmental impact of  the processing 
process and provide scientific references for decision-
makers (Qian et al., 2017). Similarly, Chai Tian-You 
studied the methods of  optimizing control of  the entire 
production process and discussed the important role 
of  control and optimization theory in achieving green 
manufacturing (Chai, 2009). These studies provide a solid 
theoretical basis for the practical application of  green 
manufacturing decision-making.
Improving decision-making efficiency is also a research 
focus. The oil refining process control and real-
time optimization method proposed by Young R E 
significantly improved production efficiency through 
real-time optimization (Young, 1999). Ding Jin-Liang 
studied the optimization decision-making method of  the 
whole process operation index of  mineral processing 
production in a dynamic environment, providing new 
ideas for improving decision-making efficiency in 
complex environments (Ding, 2012). In addition, Chai 
Tian-You et al. (2014) explored the mineral processing 
manufacturing execution system technology based on the 
Internet of  Things, which improved the decision-making 
efficiency of  the production process from an integrated 
perspective (Chai et al., 2018).
However, although these studies have achieved 
remarkable results in certain links of  the production 
process, most of  the studies still focus on the greenness 
of  a single production factor or production link, and rarely 
consider the greenness of  the entire production process 
from an integrated perspective. The author’s previous 
research shows that optimizing the existing processes 

and resources of  traditional manufacturing enterprises 
according to the principles of  green manufacturing can 
significantly save resources and reduce environmental 
pollution (Chai et al., 2014). However, in the production 
process, the close connection between various resource 
elements and variables indicates that the implementation 
effect of  green manufacturing will be more obvious if  
the problem is considered from an integrated perspective 
(Chai, 2013).
In summary, the existing research provides a theoretical 
basis and practical reference for this study. This study will 
further analyze the impact of  production process diagnosis 
and green manufacturing factors on management 
decision-making efficiency, and verify its actual 
application effect through specific cases. This will provide 
scientific decision-making support for enterprises in the 
implementation of  green manufacturing and promote the 
sustainable development of  the manufacturing industry.

MATERIALS AND METHODS
In order to optimize complex industrial production 
processes by effectively converting raw materials into 
semi-finished or finished products while enhancing 
key production indicators such as quality, output, 
consumption, and cost, we adopted an integrated 
approach involving Enterprise Resource Planning (ERP), 
Manufacturing Execution Systems (MES), and Process 
Control Systems (PCS). Each production process, 
constituting an industrial process intelligent body, worked 
collaboratively within the entire production line to achieve 
optimal performance (Chai et al., 2018; Gui et al., 2015).
Real-time production data, operational parameters, and 
market demand information were collected and analyzed 
using a combination of  mathematical programming 
methods, Petri nets, and heuristic optimization techniques. 
Mathematical programming methods, including Mixed-
Integer Linear Programming (MILP) and Mixed-Integer 
Nonlinear Programming (MINLP), were employed 
to solve planning and scheduling problems within the 
production processes (Chai et al., 2008). These models 
allowed for the optimization of  resource allocation and 
production schedules under complex constraints inherent 
in industrial environments.
Petri nets were utilized to model the asynchronous and 
concurrent processes of  the production system. By 
representing dynamic processes using places, tokens, 
and transitions, Petri nets provided a graphical and 
mathematical tool to describe and analyze the workflow 
of  the production operations (Chai, 2013). This 
approach facilitated the identification of  bottlenecks and 
inefficiencies, enabling targeted improvements in process 
coordination and synchronization.
To manage the complexity and scale of  the models, 
particularly in large-scale systems, heuristic and intelligent 
optimization methods were applied. Techniques such 
as genetic algorithms and other heuristic approaches 
were implemented to find near-optimal solutions within 
reasonable computational times, effectively handling the 



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computational challenges posed by high-dimensional 
optimization problems (Chai et al., 2014).
The integration of  ERP, MES, and PCS was essential 
for the coordination and optimization of  the production 
process. ERP systems managed material flow, capital 
flow, and information flow, serving as the backbone 
for resource planning and financial management. MES 
platforms handled production planning, scheduling, 
quality management, and optimization decision-making, 
effectively bridging the gap between managerial strategies 
and operational execution. PCS focused on process loop 
control, logic control, and real-time monitoring of  each 
device and equipment within the production processes 
(Chai et al., 2008). This integration ensured seamless data 
flow and coordination among different layers, which was 
critical for real-time adjustment of  production indicators 
based on market demands and production conditions.
A dynamic adjustment mechanism was implemented to 
continuously adjust operating indicators based on real-
time data and changes in market demand. When market 
conditions fluctuated, the integrated system automatically 
recalibrated the corresponding indicators in accordance 
with actual production data. The control system tracked 
the adjusted set values to achieve effective control and 
operation of  the entire production line process, thereby 
maintaining daily comprehensive production indicators 
within the target range (Chai, 2009).
Simulation methods were employed to validate the 
optimization models and ensure their effectiveness. By 
simulating various production scenarios, the robustness 
of  the optimization strategies was tested, and necessary 
adjustments were made before implementation in the 
actual production environment (Mehmet & Doyle III, 
2008; Wang, 2016).
Several challenges were acknowledged and addressed 
in the study. Data mismatch issues arose due to the 
lack of  effective mutual interaction and coordination 
mechanisms between the ERP, MES, and PCS layers. This 
resulted in insufficient real-time production information 
feedback at the enterprise planning and scheduling level 
and inadequate consideration of  production process 
characteristics. To mitigate these issues, a unified data 
exchange protocol was established to enhance the 
connection between the production control layer and 
optimization coordination and scheduling, facilitating 
overall optimization of  the entire process (Chai, 2013).
The reliance on manual decision-making, often based 
on long-term accumulated experience and process 
knowledge, led to deviations from target production 
indicators, reduced product quality, increased costs, and 
higher resource consumption (Chai et al., 2014). To reduce 
this dependence, the study incorporated automated 
decision-making processes by leveraging advanced data 
collection and analysis techniques. This automation 
enhanced the timeliness and accuracy of  adjustments, 
particularly in response to frequent or drastic changes in 
market demand and production conditions.
The increased model complexity due to large-scale 

system modeling presented computational challenges. To 
address this, effective heuristic or intelligent optimization 
methods were utilized to manage the complexity and size 
of  the models without compromising on solution quality 
(Chai et al., 2014). These methods made it feasible to 
achieve optimal control of  comprehensive production 
indicators in complex industrial environments.
By integrating ERP, MES, and PCS systems and addressing 
the identified challenges, the study aimed to achieve 
operational efficiency and optimal control of  production 
processes. This comprehensive approach ensured the 
optimization of  key production indicators, leading to 
improved product quality, reduced costs, and enhanced 
overall efficiency in industrial production processes.
To validate the proposed framework, case studies were 
conducted in collaboration with 12 manufacturing 
enterprises across the automotive, electronics, and textile 
industries, selected for their diverse production scales and 
sustainability challenges. Real-world operational data—
including energy consumption, material flows, equipment 
efficiency, and logistics metrics—were collected over a 
12-month period through integrated ERP-MES-PCS 
systems, IoT-enabled sensors, and manual audits. For 
instance, automotive sector data encompassed machining 
cycle times, coolant usage, and emissions from painting 
processes, while electronics manufacturing data included 
PCB assembly energy profiles and transportation logistics.
Data collection protocols were standardized across 
industries:

• Sensor-based monitoring: IoT devices installed 
on critical equipment (e.g., CNC machines, conveyor 
systems) captured real-time energy use, temperature, and 
throughput.

• ERP/MES integration: Historical production 
schedules, raw material procurement records, and 
cost data were extracted from SAP and Siemens MES 
platforms.

• Manual audits: Monthly waste generation and 
occupational safety logs were compiled by onsite 
personnel to cross-validate automated data.
To address scenarios where real-time data gaps existed 
(e.g., novel processes or proprietary constraints), 
discrete-event simulations were developed using 
AnyLogic software, incorporating empirical parameters 
from analogous industries. For example, textile dyeing 
processes were modeled using energy consumption 
patterns observed in automotive paint shops.
Results demonstrated industry-specific impacts:

• Automotive: Adoption of  solar-powered CNC 
machines in two factories reduced CO₂ emissions by 18% 
(12,000 tons annually) while maintaining 98% production 
uptime.

• Electronics: AI-optimized logistics in PCB assembly 
lines cut transportation-related emissions by 22% through 
route consolidation.

• Textiles: Circular water reuse systems in dyeing 
processes decreased freshwater consumption by 30% (1.2 
million liters/month).



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RESULTS AND DISCUSSION
Multi-attribute utility function model for multi-
objective integrated decision making
In the production process for green manufacturing, 
the decision-making objectives mainly include specific 
objectives such as productivity (P), cost (C), quality (Q), 
resource consumption (R), environmental impact (E) 
and occupational health and safety (H). There is a close 
relationship between these decision-making objectives, 
which constitute the production process decision-making 
objective system. In the actual production process, these 
objectives are usually integrated for decision-making, 
among which the cost (C), resource consumption (R), 
environmental impact (E) and occupational health and 
safety (H) are required to be as small as possible, the 
quality (Q) is required to be as high as possible, and the 
productivity (P) is required to be as large as possible.
The contents of  these specific goals are as follows:
Productivity (P): The number of  green products 
produced per unit time. In addition to being related to 
the productivity of  processing equipment, the process, 
the advancement of  fixtures and the technical proficiency 
of  operators, it is also closely related to the reliability of  
equipment.
Quality (Q): Including product performance, service life, 
reliability, safety and economy.
Cost (C): Material cost, facility and equipment cost, labor 
cost, energy cost, maintenance and training cost and 
other miscellaneous costs.
Resource consumption (R): Evaluation of  the 
consumption status of  various resources and their 
usefulness, scarcity and development and utilization.
Environmental impact (E): The impact of  waste gas, 
waste liquid, waste, noise, radiation generated during the 
production process and the disposal of  products at the 
end of  their life on the ecological environment.
Occupational health and safety (H): The damage to the 
occupational health of  workers that may be caused by 
various links in the production process and the insecurity 
caused by failures.
These goals together constitute a complex multi-objective 
system, which has its own characteristics and can be 
concretized and quantified according to specific decision-
making problems.
In multi-objective integrated decision-making, each 
decision goal must be concretized and quantified. Taking 
the environmental impact goal E as an example, E 
includes noise pollution E1, cutting fluid pollution E2, 
dust pollution E3, unsafe impact E4, etc. in the processing 
process. Other decision goals can also be expressed in a 
similar way:
P = (P1, P2, P3, …, Pp) P = (P1, P2, P3, …, Pp )
Similarly, other first-level multi-attribute variable 
functions can be expressed as:

Q=(Q1,Q2,Q3,…,Qq)Q = (Q1,Q2,Q3,…,Qq )
C=(C1,C2,C3,…,Cc)C = (C1,C2,C3,…,Cc )
R=(R1,R2,R3,…,Rr)R = (R1,R2,R3,…,Rr )
E=(E1,E2,E3,…,Ee)E = (E1,E2,E3,…,Ee )
H=(H1,H2,H3,…,Hh)H = (H1,H2,H3,…,Hh )
The domain of  the multi-attribute variable of  the multi-
attribute function is: D=DP×DQ×DC×DR×DE×DH
The expression of  the multi-attribute utility function is:
u(P,Q,C,R,E,H)=u(P,Q,C,R,E,H)∈U⊂Ru(P,Q,C,R,E,H)= 
u(P,Q,C,R,E,H)∈U ⊂R
The multi-attribute utility function u(P,Q,C,R,E,H) 
improves efficiency by optimizing and controlling 
these six objectives. According to the decomposition 
theorem of  the multi-attribute utility function, 
u(P,Q,C,R,E,H) can be decomposed into the following 
KC⋅u(C)+KR⋅u(R)+KE⋅u(E)+KH⋅u(H)
Or
u(P,Q,C,R,E,H)=[1+KKKP⋅u(P)][1+KKKQ⋅u(Q)]
[1+KKKC⋅u(C)][1+KKKR⋅u(R)][1+KKKE⋅u(E)]
[1+KKKH⋅u(H)]
Among them, u(P), u(Q), u(C), u(R), u(E) and u(H) are 
the univariate utility functions of  productivity, cost, 
quality, resource consumption, environmental impact and 
occupational health and safety, respectively; KP, KQ, KC, 
KR, KE, KH are the corresponding weight coefficients, 
respectively; K is an undetermined constant.
Model application cases
Take the processing of  flange shaft as an example for 
application analysis. The material of  flange shaft is 
45 high-quality carbon steel, and there are three main 
working surfaces:
φ 98 non-through hole, matching accuracy H6, surface 
roughness Ra1.6.
φ 32 outer cylindrical surface, matching accuracy js7, 
surface roughness Ra1.6.
φ 36 outer cylindrical surface, matching accuracy js7, 
surface roughness Ra1.6.
The coaxiality tolerance requirement of  the above three 
working surfaces is 0.01mm. According to the existing 
process and equipment, each working surface has 
two processing schemes, taking the processing of  $\
varnothing32$ outer cylindrical surface as an example:
Scheme a: rough turning (IT11, Ra6.3) - semi-finishing 
turning (IT10-9, Ra3.2) - finishing turning (IT8-7, Ra3.2-
1.6).
Scheme b: Rough turning (IT11, Ra6.3) - semi-finishing 
turning (IT10-9, Ra3.2) - grinding (IT7-6, Ra1.6-0.8).
An integrated analysis is conducted on the productivity, 
energy flow and environmental flow generated by 
different processing schemes, and the process route with 
the best coordination of  economic and environmental 
benefits is selected through a multi-objective integrated 
decision-making model. The specific analysis is shown in 
Table 1.



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By analyzing the advantages and disadvantages of  each 
solution, solution A is better than solution B. The above 
model is applied to select the processing solution in 
the actual production process of  this part, achieving 
significant comprehensive effects of  economy, resource 
conservation and low environmental impact.

CONCLUSION
Through the analysis of  the current status of  my country’s 
process manufacturing industry, this paper proposes the 
vision function of  the intelligent optimization decision-
making system for the whole process optimization 
decision-making system of  complex industrial process 
production and manufacturing, and explores the 
specific research direction for the next step. The process 
manufacturing industry has the characteristics of  high 
production continuity, numerous production equipment, 
strong coupling between variables, fixed production 
products, and large production volume. When market 
demand and production factor conditions change, the 
traditional management decision-making process that 
relies on people and knowledge workers is difficult to 
respond in a timely and accurate manner, thus failing to 
achieve the optimization of  comprehensive production 
indicators such as product quality, output, consumption 
and cost. By proposing an intelligent optimization 
decision-making system that integrates ERP, MES, 
and PCS with AI-driven analytics, the framework 
achieved measurable improvements in operational 
and environmental outcomes:25% reduction in raw 
material waste through closed-loop resource recycling in 
automotive and textile case studies.18% decrease in energy 
consumption per unit output by deploying renewable 
energy-powered equipment in machining processes.35% 
faster response time to production disruptions via 
automated, data-driven adjustments to fluctuating market 
demands.30% reduction in freshwater use (1.2 million 
liters/month) in textile dyeing processes through AI-
optimized water reuse systems.. Such a system will lay 
a solid foundation for realizing intelligent optimization 
manufacturing of  process industry processes. Looking 
forward to the future, further research directions should 
include the following aspects: Technology integration: 
further integrate advanced artificial intelligence, big data 
analysis and Internet of  Things technologies to enhance 
the performance and application scope of  intelligent 
optimization decision-making systems. Data perception 
and analysis: Improve the system’s real-time perception 
and analysis capabilities of  production data to enhance 
the speed and accuracy of  response to market demand 

and changes in the production environment.
uture research should prioritize:Scalable AI integration: 
Embedding generative AI to enhance predictive accuracy 
for resource allocation, targeting a 20% improvement in 
anomaly detection by 2025.
Real-time adaptability: Developing self-calibrating models 
to sustain >90% optimization efficacy amid supply 
chain shocks or pricing volatility.Circular manufacturing: 
Expanding industrial symbiosis networks to achieve 40–
50% lifecycle carbon reduction by 2030, aligning with 
China’s green manufacturing agenda.

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Table 1: Results of Environmental Impact Analysis of External Surface Machining Methods
Machining 
schemes

productivity Machining 
accuracy

Surface 
roughness 
(Ra/μm)

Processing energy 
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(kN·mm-2))

Tool 
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Option A small IT6-7 3.2-1.6 2.0-2.5 small not
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