INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 179
The Role of Additive Manufacturing in Advancing Lean Production
System
Onukwuli Somto Kenneth,
*
Okpala Charles Chikwendu and Udu Chukwudi Emeka
Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka – Nigeria
*Correspondence Author
DOI : https://doi.org/10.51583/IJLTEMAS.2025.140300022
Received: 06 March 2025; Accepted: 18 March 2025; Published: 03 April 2025
Abstract: Additive Manufacturing (AM), often referred to as 3D printing, is transforming the contemporary manufacturing
environment through flexibility enhancement, waste identification and reduction, as well as rapid prototyping enablement. The
integration of AM into Lean Production System (LPS) has the potential to improve quality and production efficiency, enhance
throughput and profitability, and also minimize resource consumption. This paper systematically explores the synergy between
AM and LPS which leads to not just waste reduction, but increases customization and also fosters Just-In-Time (JIT)
Manufacturing. It also offers a detailed review of their impact on production processes, sustainability, and scalability.
Furthermore, the paper discussed the limitations of AM and provided future directions in adopting AM within lean frameworks,
and also emphasized on Industry 4.0 technologies.
Keywords: additive manufacturing, lean production system, sustainability, industry 4.0, waste reduction, just-in-time
I. Introduction
To make effective decisions, companies that are integrating Additive Manufacturing (AM) and Lean Manufacturing (LM)
strategies must understand their synergies. This integration can enhance productivity, customer satisfaction, operational
efficiency, and sustainability goals. (Lakshmanan et al., 2023). Lean Production System is a strategic production approach aimed
at minimizing waste, it helps industries to maximize service resources, optimize production, and enhance customer satisfaction
(Okpala et al., 2020; Ihueze and Okpala, 2014). This idea originated in Japan after World War II, when Japanese manufacturers
realized that they could not afford the enormous costs required to rebuild ravaged facilities (Okpala, 2013b). Similarly, the aim of
Lean Manufacturing (LM) is a production system that reduces wastes, optimize the creation of values for customers, and also
utilizes fewer resources to manufacture high quality products at less the time, thereby increasing throughput and profitability
(Okpala, 2014; Ihueze and Okpala, 2011).
Modern manufacturing demands enhanced flexibility and personalization of products, but this demand cannot be achieved
effectively without incurring large amounts of waste using the traditional manufacturing methods. These factors pose a significant
challenge to manufacturing industries which led them to search for new tools and techniques to address the demands of the 21st-
century manufacturing, without jeopardizing product quality and customers’ satisfaction at a low cost. The process of Lean
production is depicted in Figure 1.
Figure 1: Process of lean production. Source: Abhishek Dixit et al. (2015)
Additive Manufacturing, commonly referred to as 3D printing, is revolutionizing production systems. It enables greater
customization and the production of on-demand components, thereby optimizing lead times and reducing the need for large
inventories (Alabi, 2024). The 3D digital model is typically created with the application of Computer Aided Design (CAD), but
reverse engineering methods such as 3D laser scanning or MRI/CT techniques may be applied to produce existing part geometry
digitally. Here, the solid model will be converted into an acceptable format appropriate for AM processing (Strong et al., 2018).
ASTM International defines AM technology based on seven principles as shown in Table 1, these principles can be implemented
using several key technologies, materials, and applications (Rahito et al., 2019).
Table 1: Principles of Additive Manufacturing. Source: Rahito et al. (2019).
ASTM Category
Basic Principles
Example of AM Technology
Material Extrusion
(ME)
The precipitation of build material occurs when
droplets are released through a heated nozzle.
3D inkjet technology
Fused Deposition Modeling (FDM)
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 180
Binder Jetting (BJ)
Liquid printing binder is applied layer by layer
to specific coordinates, binding the material
fragments together to form a 3D object.
3D inkjet technology
Vat Photo
Polymerization (VP)
Light curing is applied to the liquid polymer in a
vat.
Digital Light Processing (DLP),
Stereo Lithography
Powder Bed Fusion
(PBF)
The use of focused thermal energy to fuse an
exact point in a small area of the build material's
powder bed.
Direct Metal Laser Sintering (DMLS),
Electron Beam Melting (EBM), Selective
Laser Sintering/Melting (SLS/SLM)
Direct Energy
Deposition (DED)
The application of powder material occurs
simultaneously with the application of focused
thermal energy, which melts the material at the
target location.
Electron Beam,Laser Engineered Net
Shaping (LENS), Plasma Arc Melting
Laser cladding (LC)
Sheet Lamination
(SE)
Attachment of sheets or foils of materials.
Ultrasound consolidation/ Ultrasound
Additive Manufacturing (UC/UAM),
Laminated Object Manufacturing (LOM)
Cold Spray
Adhesion drives the high-velocity propulsion of
injected powder to form material.
Multi-Metal Deposition
AM has expanded its applications into many fields, including medical, electronics, fashion, automotive, construction, and
research (Wimpenny et al., 2016). It evolved from a rapid prototyping tool to a manufacturing technology capable of producing
effective end-user products (Parupelli and Desai, 2019). Polymers, metals, ceramics, electronic materials, and biological materials
can all be additively processed (Gibson et al., 2021), as well as lightweight products (Oettmeier and Hofmann, 2017). However,
the most commonly used materials are thermoplastics, ceramic pastes, metal, and ceramic powder and metal. (Guo and Leu,
2013).
Additive Manufacturing: A Tool for Lean Production
Additive Manufacturing is transforming traditional production systems by aligning with the principles of Lean Manufacturing. As
depicted in figure 2, it complements lean principles by reducing waste, minimizing inventory, and improving production
flexibility.
Figure 2: Contribution of AM in achieving lean production system objectives. Source: Driouach et al. (2023).
Table 2 highlights real-world examples and comparative case studies on the role of additive manufacturing in advancing lean
production system.
Table 2: Case Studies
Company
Industry
Additive Manufacturing
Application
Impact on Lean Production
Key Takeaways
General
Electric (GE)
Aerospace
3D-printed fuel nozzles
for jet engines
Reduced part count from 20 to 1,
25% weight reduction, and lower
Improved efficiency, cost
savings, and sustainability
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 181
material waste
BMW
Automotive
3D-printed jigs, fixtures,
and spare parts
Faster prototyping, reduced lead
time by 80%
Increased flexibility, cost
reduction, and leaner workflows
Siemens
Energy
3D-printed gas turbine
blades
Increased efficiency, improved part
performance, and reduced
production time
Enhances lean manufacturing
through rapid iteration and
lower waste
Adidas
Consumer
Goods
3D-printed midsoles
(Futurecraft 4D)
Shortened supply chain, reduced
inventory needs
On-demand production,
customization, and waste
minimization
Boeing
Aerospace
Additive manufacturing
for lightweight parts
Weight reduction, lower fuel
consumption, reduced production
complexity
Supports lean by minimizing
overproduction and enhancing
efficiency
Ford
Automotive
3D-printed prototypes
and tools
90% cost reduction in tooling and
increased speed to market
Eliminates excess inventory and
accelerates development cycles
John Deere
Agriculture
On-demand 3D printing
of spare parts
Reduced downtime and minimized
excess stock
Leaner inventory management
and improved serviceability
NASA
Space
Technology
3D printing of spacecraft
components
Lightweight structures, cost
savings, and in-space
manufacturing
Enables lean and agile
production for space
exploration
Waste Reduction
The 21st-century product design cannot be achieved effectively without incurring large amounts of waste with the traditional
manufacturing methods, as displayed in Table 3. These trends and patterns are emerging as a result of technological, economic,
and social progress (Ferreira et al., 2020). Therefore, modified processes are required to create a clean environment.
Table 3: Material Efficiency in Subtractive vs. Additive Manufacturing. Source: Gibson et al. (2021)
Process
Material Usage (%)
Waste Generated (%)
Additive
90–95
5–10
Subtractive
50–60
40–50
Additive Manufacturing (AM) is an important technological enhancer necessary for the implementation of a circular economy.
This is because the circular economy enables the regeneration of material flows while also striking a balance between economic
development and resource sustainability. AM is expected to use less material than 3D printing, produce less waste, and be
recyclable (Huang et al., 2020). It also allows for complex geometries that are often unattainable with traditional techniques
because of its layer-by-layer approach (Gibson et al., 2015), and is a reuse method to recycle thermoplastic and resource
optimization (Kreiger et al., 2013; Santander et al., 2020).
Time and Cost Reduction
Traditional lean production system rely heavily on Just-In-Time (JIT) production to reduce wastes and inventory costs (Okpala,
2013a). The seven inherent wastes in manufacturing processes which lean manufacturing identifies, reduces, or possibly
eliminates are depicted in figure 3.
Figure 3: The seven inherent wastes in manufacturing processes. Source: Okpala (2014)
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 182
According to Sasson and Johnson, (2016), production costs can be divided into two categories: The first category contains well-
structured costs like materials, labor and machine costs. The second category contains unstructured costs, like those associated
with build breakdown, machine setup, and inventory. However, some of the most significant advantages and cost minimization in
additive manufacturing may be concealed in poorly structured costs. Because additive manufacturing can potentially create a
complete unit in one build, it reduces the need for some inventory and transportation costs, which has an impact throughout the
supply chain (Thomas, 2016). AM further enhances JIT by enabling on-demand production, where components are manufactured
when required, thereby eliminate the necessity of large inventories and also reduce lead times.
Enhanced Design Capabilities
AM introduces unparalleled design flexibility, which aligns closely with LPS principles of continuous improvement and
responsiveness to customer demands. It provides distinctive technical and economic advantages, like the ability to fabricate
intricate geometry, compatibility with generative design techniques, and the opportunity for low-cost fabrication of relatively few
parts (Gibson et al., 2021; Leary et al., 2021). This capability allows manufacturing companies to consolidate multiple parts into a
single design, thus reducing assembly time and improving product reliability, which is impossible or inefficient with traditional
methods.
Sustainability and Energy Efficiency
The sustainability benefits of AM align with lean production’s principle of resource optimization. AM systems often use
recyclable materials, and their energy consumption can be lower than traditional systems when producing small batches.
Furthermore, localized production enabled by AM reduces transportation needs, which tends to reduce carbon emissions.
(Baumers et al., 2011). AM also supports sustainable manufacturing by reducing material usage during the design iteration
process, and ensures that only the required material is used, thus minimizing waste even during experimentation with multiple
design iterations.
Agile Design Iterations
In traditional manufacturing, altering a product’s design can require extensive retooling, resulting in increased costs and
production delays. The integration of AM into LPS eliminates these barriers by allowing for rapid design iteration through digital
modeling. Nike company applies AM to produce customized shoe soles that are tailored to individual athletes' foot structures and
performance needs. The rapid prototyping enabled by AM reduced development time by over 30%, thus enabling Nike to respond
to customer feedback and market trends effectively (Hou, 2023). Adidas also uses AM in its Futurecraft line to create shoes with
mid-soles customized to an individual’s running style and biomechanics. Manufacturers can quickly prototype, test and refine
components without disrupting the production line. This innovation has resulted in increased customer engagement and higher
product demand.
Adaptation to Market Changes and Customization
AM facilitates a lean and responsive manufacturing approach by enabling rapid adjustments in production in response to
customer feedback or market demands. It also empowers manufacturers to deliver mass-customized products that meet specific
customer requirements, without the added complexity of traditional methods. The medical device industry has used AM to create
custom implants, hearing aids, and prosthetics. For example, 98% of global hearing devices are now produced using AM,
allowing a perfect fit for patients while minimizing lead times and costs (Wohlers, 2021).
Improved Quality and Reliability
Improved quality and reliability are central tenets of lean manufacturing. Modern AM systems include integrated sensors and
monitoring tools that provide real-time feedback on production quality. The process of designing with reliability in mind involves
understanding and incorporating the fundamental variations introduced in a process (Leemis, 2009). Statistical design methods
allow for the inclusion of statistical distributions for prevalent design values to inform engineering decisions based on variability
in mechanical, geometric, and operating parameters. (Tuegel and Penmetsa, 2006).
The precision, consistency, and customization offered by AM ensure that components meet strict quality standards, especially in
high-stakes industries like aerospace and medical devices. Boeing for instance, uses AM to manufacture titanium parts for its 787
Dreamliner, thus enabling them to achieve dimensional accuracy within micrometers, which is essential for aerodynamics and
safety standards (Boeing, 2020). In the automotive industry, AM is used to create precision parts for engines and other critical
systems. Ford (2018), observed that the reduction in defects has improved reliability and longevity, with manufacturers reporting
up to a 25% decrease in warranty claims due to improved part quality.
II. Challenges in Implementation
Implementing additive manufacturing systems within lean manufacturing presents several challenges, despite the potential
benefits of enhanced efficiency and reduced waste. The integration of these technologies requires careful consideration of various
barriers that can hinder successful adoption. Table 4 highlights the benefits and challenges of applying Additive Manufacturing
(AM) in advancing Lean Production System.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 183
Table 4: Benefits and challenges of AM application on LPS
Aspect
Benefits of Additive Manufacturing (AM) in Lean
Production
Challenges of Additive Manufacturing (AM) in
Lean Production
Waste Reduction
Minimizes material waste by using only the required
material.
High initial material costs and material availability
issues.
Customization
Enables on-demand, customer-specific production.
Longer design-to-production time for complex
parts.
Inventory
Management
Reduces the need for large inventories through on-
demand manufacturing.
Requires robust digital infrastructure for efficient
operation.
Lead Time
Reduction
Speeds up prototyping and production, reducing cycle
time.
Slower printing speeds for large-scale production.
Complexity in
Design
Facilitates complex and lightweight designs without
extra costs.
Requires skilled workforce for design optimization.
Supply Chain
Efficiency
Shortens supply chains by enabling localized
production.
Dependence on consistent raw material supply and
standards.
Energy
Efficiency
Uses less energy compared to traditional subtractive
methods.
Some AM processes have high energy consumption
per unit.
Tooling Cost
Reduction
Eliminates the need for expensive molds and tools.
Limited material options for end-use functional
parts.
Sustainability
Supports sustainability goals by using recyclable
materials.
Some AM materials are not biodegradable or
widely recyclable.
Scalability
Ideal for low to medium-volume production with
flexibility.
Less competitive for mass production compared to
traditional methods.
Industries are leveraging hybrid approaches, AI-driven optimizations, and automation to address AM challenges. These strategies
enhance cost-efficiency, speed, material performance, and sustainability, making AM a key enabler of Lean Production Systems.
Table 5 depicts how industries are overcoming the challenges of Additive Manufacturing (AM) in Advancing Lean
Production Systems with specific strategies.
Table 5: Surmounting the challenges of AM in advancing LPS
Challenge
Industry Example
Strategy Used
Impact on Lean Production
High Initial Investment
& Material Costs
GE Aviation
Hybrid Manufacturing (combining
AM with traditional machining)
Reduces costs, improves efficiency, and
maintains precision
Slow Production Speed
& Scalability Issues
Siemens
AI-Based Process Optimization
(AI-driven print path optimization)
Enhances speed, minimizes waste, and
improves scalability
Material Limitations &
Quality Control
NASA & Boeing
Advanced Material Development &
In-Situ Monitoring
Ensures consistent product quality and
expands material choices
Post-Processing
Complexity
BMW
Automated Post-Processing &
Surface Finishing
Reduces lead time, improves efficiency,
and eliminates manual inefficiencies
Integration into Existing
Supply Chains
John Deere &
Ford
Distributed & On-Demand
Manufacturing
Lowers inventory costs and supports
Just-in-Time (JIT) manufacturing
Skilled Workforce Gap
Siemens & HP
AI & Automation for Simplified
Operations
Reduces the need for expert operators
and improves workforce efficiency
Environmental Concerns
(Energy Usage & Waste
Management)
Adidas
Futurecraft 4D
Sustainable Manufacturing &
Circular Economy
Reduces waste, promotes sustainability,
and aligns with lean principles
High Initial Investment
One of the most significant challenges to the wide spread use of additive manufacturing lies in its high initial investment costs. To
successfully integrate AM into lean manufacturing requires substantial capital for the acquisition of equipment and training for
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 184
specialized knowledge. Sharma et al. (2024), explained that smaller manufacturers or organizations with limited budgets often
find it difficult to adopt AM. It is advised that smaller organizations should source for collaborative financing, outsource AM
services, or lease. AM implementayion in an SME differs from that of a large global organization, this is because investing in new
manufacturing technologies is frequently associated with the market structure and organizational size. The size of a
manufacturing facility is a crucial factor in implementing new technology successfully, thus before adopting a new manufacturing
technology, an organization may need to redesign its systems and procedures (Saberi et al., 2010).
Material Limitations
One of the primary limitations of additive manufacturing is the limited number of materials that can be used concurrently. This
limitation can impact the performance, durability, and quality of the final products. According to Zuo et al. (2024), traditional AM
techniques often rely on single-material processes, thereby limiting the functionality and application of printed parts, particularly
in large-scale applications, but Tang et al. (2023), suggested that advances in Scalable Multiple-Material Additive Manufacturing
(SMAM) can address these limitations by integrating various materials and enhancing process control.
The range of printable materials in AM is restricted, particularly for high-temperature applications. Also, insufficient data on the
mechanical properties of available materials further complicates material selection, thus affecting the overall effectiveness of
designs (Marzola et al., 2020). Consequently, this restriction on choice of material can limit the broader adoption of additive
manufacturing for certain applications, which is common where diverse material properties are essential for the product’s
functionality and lifespan. The variety and properties of materials available for AM applications are still evolving but are not as
extensive as those available through conventional methods. As a result, companies looking to adopt AM must carefully assess the
specific material requirements of their production needs and determine whether AM can meet those needs effectively.
Scalability for High-Volume Products
Several factors affect the scalability of additive manufacturing processes for high-volume production in lean manufacturing,
including: technological innovation, operational management practices, and process stability. AM is particularly well suited for
low to medium production volumes, rapid prototyping, and custom manufacturing. Innovations in AM technologies can
significantly improve process parameters such as time, cost, and dependability, this enables AM to compete with traditional
manufacturing methods (Huang et al., 2021). Scalable multiple-material additive manufacturing systems enhance production
capabilities by integrating features like in-line quality inspection and error correction, which are essential for maintaining high
standards in mass production (Qu et al., 2022). To effectively improve the volume of mass production, there should be a
significant increase in the printers used. This approach allows for the parallel production of multiple parts or the same part across
different machines. However, Mellor et al. (2014), posited that the synchronization and continuous monitoring of print jobs across
machines can be managed with software.
The Future of AM in Lean Production System
The future of Additive Manufacturing within Lean Production System is set to undergo significant evolution, driven by the
incorporation of digital technologies and the principles of Industry 4.0. Table 6 outlines the future prospects of Additive
Manufacturing (AM) in Lean Production Systems.
Table 6: Future prospects and potential impact of AM on LPS
Future Prospect
Potential Impact on Lean Production System
Advanced Materials
Development of stronger, lightweight, and sustainable materials will enhance production
efficiency and product performance.
Faster Printing Speeds
Improved AM technologies will significantly reduce production time, making lean
manufacturing more agile.
Mass Customization
AM will enable large-scale personalization of products without increasing production costs.
Integrated AI & Automation
AI-driven design optimization and automated AM processes will enhance precision and reduce
human intervention.
On-Demand Manufacturing
Distributed manufacturing models will minimize supply chain dependencies and reduce
inventory waste.
Hybrid Manufacturing
Combining AM with traditional methods (e.g., CNC machining) will optimize production for
complex and high-strength components.
Sustainability Innovations
Use of biodegradable and recycled materials will align AM with eco-friendly lean practices.
Scalability Improvements
Advances in multi-material and high-speed 3D printing will make AM more viable for mass
production.
Enhanced Digital Twin
Digital replicas of manufacturing processes will improve real-time monitoring and predictive
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 185
Technology
maintenance.
Regulatory & Standardization
Developments
Improved industry standards and regulations will increase reliability and wider adoption in
lean production.
Hybrid Systems in Lean Manufacturing.
The hybrid of the lean management system is a combination of some elements from lean technology to be integrated into two or
three elements, which offer the opportunity to leverage the strengths of both technologies. The integration of simulation tools with
lean value stream mapping enables manufacturers to visualize and optimize production scenarios dynamically (Abdel-Jaber et al.,
2022). This system will help to organize a better workplace for efficiency and reduce waste (Ahmad et al., 2017). Successful
implementation of these processes can improve communication between processes, reducing waiting times from 14% to 11% and
achieve optimal line balancing (Ani et al., 2022). Also it can effectively reduce non-value-added activities, leading to improved
overall equipment effectiveness Pimpalkar and Madgule, (2024). Bevilacqua et al., (2015), narrated that the incorporation of lean
practices with AM results to lead time reduction, reduction of batch change and transition intervals by up to 50%, while
increasing the overall efficiency of equipment by 25%. These systems enable manufacturers to meet production demands more
effectively while adhering to lean principles. Hybridizing additive manufacturing with lean manufacturing methods also provides
a way to overcome additive manufacturing's limitations in large-scale manufacturing conditions.
Integration with Industry 4.0
The combination of additive manufacturing and Industry 4.0 represents an unprecedented change in manufacturing processes,
increasing efficiency, customization, and sustainability. This synergy uses advanced technologies like Artificial Intelligence (AI),
the Internet of Things (IoT), and Digital Twin Technology to optimize supply chain management and production. Industry 4.0
systems apply AI for the analysis of data for the prediction and management of process errors, (MELTER et al., 2024) and
supports real-time monitoring and data analytics, which allows for immediate adjustments in AM processes (Sousa et al., 2024).
AM plays a crucial role in smart factories, where interconnected systems enhance operational efficiency and reduce downtime
(Jafar et al., 2024).
The use of IoT devices and data analytics improves operational efficiency, allowing manufacturers to respond quickly to market
demands and customer preferences with the application of sensors (Khorasani et al., 2022; Igbokwe et al., 2024a). These sensors
provide real-time tracking of the printing process, achieving over 98% accuracy in spatial localization, which is crucial for defect
identification (Akhavan et al., 2024). Manufacturing companies can optimize production schedules and reduce time to market by
simulating manufacturing processes in real time with the application of digital twin technology.
Some of the challenges with the integration of industry 4.0 include initial investment and the necessary infrastructure (Nwankwo
et al., 2024; Igbokwe et al.,2024b), the lack of compatible technologies, such as advanced robotics and AI (Arteaga and Chan,
2021; Okpala and Okpala, 2024), and a lack of skilled personnel. Addressing these issues is critical for full realization of the
potential of AM in a competitive manufacturing environment.
III. Conclusion
Additive manufacturing has demonstrated considerable potential as a valuable tool in Lean Production System through the
provision of solutions to key lean principles. One of its most compelling features is the ability to minimize material waste with
the usage of the exact amount of material required for each component, unlike traditional subtractive manufacturing methods. It
also enhances on-demand production, and also reduces overproduction and excess inventory wastes. Successful implementation
of these processes leads to lower energy consumption and reduced carbon footprints, thus aligning with sustainable
manufacturing goals. This contributes not only to cost savings but also supports sustainability initiatives within production
systems.
However, despite its obvious benefits, AM faces a number of challenges that may impede its widespread adoption. While there
are challenges that could be addressed, the potential of additive manufacturing to drive continuous improvement in production
processes is undeniable. As the technology matures and is integrated with other advanced manufacturing techniques, AM will
play a more significant role in the future of lean manufacturing. As it will enable manufacturers to meet the growing demands for
customization, sustainability, and efficiency, ultimately empowering companies to deliver greater value to their customers.
References
1. Abdel-Jaber, O., Itani, A., and Al-Hussein, M. (2022). Hybrid Lean Decision-Making Framework Integrating
Value Stream Mapping and Simulation: A Manufacturing Case Study. 153–163.
https://doi.org/10.24928/2022/0118
2. Abhishek Dixit, Vikas Dave, and Alakshendra Pratap Singh. (2015). Lean Manufacturing: An Approach for Waste
Elimination. International Journal of Engineering Research And, V4(04). https://doi.org/10.17577/ijertv4is040817
3. Ahmad, A. N. A., Lee, T. C., Ramlan, R., Ahmad, Md. F., Husin, N., and Abdul Rahim, M. (2017). The Hybrid
Lean System to Improve Manufacturing Environment. MATEC Web of Conferences, 135, 00050.
https://doi.org/10.1051/matecconf/201713500050
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 186
4. Akhavan, J., Xu, K., Vallabh, C. K., and Manoochehri, S. (2024, June 17). Real-Time Print Tracking in Metal
Additive Manufacturing Using Acoustic Emission Sensors and Vision Transformer Algorithms. Volume 2:
Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso
Manufacturing; Quality and Reliability. https://doi.org/10.1115/MSEC2024-125391
5. Ani, M. N. C., Kamaruddin, S., and Azid, I. A. (2022). Enhancement of the efficiency of internal supply chain
production system through process-to-process interaction. International Journal of Productivity and Quality
Management, 35(3), 332. https://doi.org/10.1504/IJPQM.2022.122294
6. Arteaga Irene, Y. J., and Chan, W. K. V. (2021). Additive Manufacturing Global Challenges in the Industry 4.0 Era
(pp. 316–336). https://doi.org/10.1007/978-3-030-90275-9_26
7. Baumers, M., Tuck, C., Wildman, R., Ashcroft, I., and Hague, R. (2011, August). Energy Inputs to Additive
Manufacturing: Does Capacity Utilization Matter? University of Texas at Austin.
https://doi.org/10.26153/tsw/15275
8. Bevilacqua, M., Ciarapica, F. E., De Sanctis, I., Mazzuto, G., and Paciarotti, C. (2015). A Changeover Time
Reduction through an integration of lean practices: a case study from pharmaceutical sector. Assembly
Automation, 35(1), 22–34. https://doi.org/10.1108/AA-05-2014-035
9. Bhamu, J., and Singh Sangwan, K. (2014). Lean manufacturing: literature review and research issues. International
Journal of Operations and Production Management, 34(7), 876–940. https://doi.org/10.1108/IJOPM-08-2012-0315
10. Boeing. (2020). THE BOEING 2020 ANNUAL REPORT. Boeing, 1–188.
https://www.annualreports.com/HostedData/AnnualReportArchive/b/NYSE_BA_2020.pdf
11. Driouach, L., Zarbane, K., and Beidouri, Z. (2023). The impacts of additive manufacturing technology on lean
manufacturing. Journal of Achievements in Materials and Manufacturing Engineering, 120(1), 22–32.
https://doi.org/10.5604/01.3001.0053.9641
12. Ferreira, I. A., Godina, R., and Carvalho, H. (2020). Waste Valorization through Additive Manufacturing in an
Industrial Symbiosis Setting. Sustainability, 13(1), 234. https://doi.org/10.3390/su13010234
13. Ford Motors. (2018). FORD 2018 ANNUAL REPORT. Ford, 1–198.
https://www.annualreports.com/HostedData/AnnualReportArchive/f/NYSE_F_2020.pdf
14. Gibson, I., Rosen, D., and Stucker, B. (2015). Additive Manufacturing Technologies. Springer New York.
https://doi.org/10.1007/978-1-4939-2113-3
15. Gibson, I., Rosen, D., Stucker, B., and Khorasani, M. (2021a). Additive Manufacturing Technologies.
https://doi.org/10.1007/978-3-030-56127-7
16. Gibson, I., Rosen, D., Stucker, B., and Khorasani, M. (2021b). Additive Manufacturing Technologies. Springer
International Publishing. https://doi.org/10.1007/978-3-030-56127-7
17. Gibson, I., Rosen, D., Stucker, B., and Khorasani, M. (2021c). Additive Manufacturing Technologiese (Latest Edi).
Springer Nature. https://link.springer.com/book/10.1007/978-3-030-56127-7#keywords
18. Guo, N., and Leu, M. C. (2013). Additive manufacturing: technology, applications and research needs. Frontiers of
Mechanical Engineering, 8(3), 215–243. https://doi.org/10.1007/s11465-013-0248-8
19. Hou, Q. (2023). Analysis Of Nike Brand Operation and Marketing Strategy in Different Business Periods Based
On 4P Marketing Theory. Highlights in Business, Economics and Management, 23, 636–641.
https://doi.org/10.54097/zfqbj268
20. Huang, R., Yao, Q., and Liu, Y. (2020). Waste recycling 3D printing and silk-making system. Journal of Physics:
Conference Series, 1550(3), 032153. https://doi.org/10.1088/1742-6596/1550/3/032153
21. Huang, Y., Eyers, D. R., Stevenson, M., and Thürer, M. (2021). Breaking the mould: achieving high-volume
production output with additive manufacturing. International Journal of Operations and Production Management,
41(12), 1844–1851. https://doi.org/10.1108/IJOPM-05-2021-0350
22. Igbokwe, N. C., Okpala, C. C. and Nwamekwe, C. O. (2024a). The Implementation of Internet of Things in the
Manufacturing Industry: An Appraisal. International Journal of Engineering Research and Development, vol. 20,
iss. 7, https://www.ijerd.com/paper/vol20-issue7/2007510516.pdf
23. Igbokwe, N. C., Okpala, C. C. and Nwankwo, C. O. (2024b). Industry 4.0 Implementation: A Paradigm Shift in
Manufacturing. Journal of Inventive Engineering and Technology, vol. 6, iss. 1,
https://jiengtech.com/index.php/INDEX/article/view/113/135
24. Ihueze C. C. and Okpala C. C. (2014). The Tools and Techniques of Lean Production System of Manufacturing.
International Journal of Advanced Engineering Technology, vol.5, iss. 4
http://technicaljournalsonline.com/ijeat/VOL%20V/IJAET%20VOL%20V%20ISSUE%20IV%20%20OCTBER%
20DECEMBER%202014/Vol%20V%20Issue%20IV%20Article%205.pdf
25. Ihueze C. C. And Okpala C. C. (2011). A Survey of Optimum Manufacturing Strategy as a Tool for Enhanced
Industrial Revenue. Australian Journal of Basic and Applied Sciences
http://ajbasweb.com/old/ajbas/2011/December-2011/1321-1329.pdf
26. Jafar, M. R., Tripathi, N. M., Yadav, M., and Nasato, D. S. (2024). Additive Manufacturing in the Age of Industry
4.0 and Beyond. In Advances in Pre- and Post-Additive Manufacturing Processes (pp. 213–230). CRC Press.
https://doi.org/10.1201/9781003428862-11
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 187
27. Khorasani, M., Loy, J., Ghasemi, A. H., Sharabian, E., Leary, M., Mirafzal, H., Cochrane, P., Rolfe, B., and
Gibson, I. (2022). A review of Industry 4.0 and additive manufacturing synergy. Rapid Prototyping Journal, 28(8),
1462–1475. https://doi.org/10.1108/RPJ-08-2021-0194
28. Kreiger, M., Anzalone, G. C., Mulder, M. L., Glover, A., and Pearce, J. M. (2013). Distributed Recycling of Post-
Consumer Plastic Waste in Rural Areas. MRS Proceedings, 1492, 91–96. https://doi.org/10.1557/opl.2013.258
29. Lakshmanan, R., Nyamekye, P., Virolainen, V.-M., and Piili, H. (2023). The convergence of lean management and
additive manufacturing: Case of manufacturing industries. Cleaner Engineering and Technology, 13, 100620.
https://doi.org/10.1016/j.clet.2023.100620
30. Leary, M., Downing, D., Lozanovski, B., and Harris, J. (2021). Design principles. In Fundamentals of Laser
Powder Bed Fusion of Metals (pp. 119–154). Elsevier. https://doi.org/10.1016/B978-0-12-824090-8.00013-5
31. Leemis, L. (2009). Reliability( Probabilistic Models and Statistical Methods). AscendedIdeas (January 31, 2009),
Secon Edit.
32. Marzola, A., Mussi, E., and Uccheddu, F. (2020). 3D Printed Materials for High Temperature Applications (pp.
936–947). https://doi.org/10.1007/978-3-030-31154-4_80
33. Mellor, S., Hao, L., and Zhang, D. (2014). Additive manufacturing: A framework for implementation. International
Journal of Production Economics, 149, 194–201. https://doi.org/10.1016/j.ijpe.2013.07.008
34. Melter, O., Citek, D., Hvizdal, A., Gabriel, M., Kolisko, J., and Kotes, P. (2024). Development of a Robotic
Fabrication System for Cementitious Materials. Mm Science Journal, 2024(3).
Https://Doi.Org/10.17973/Mmsj.2024_06_2024041
35. Nwankwo, C. O., Okpala, C. C. and Igbokwe, N. C. (2024). Enhancing Smart Manufacturing Supply Chains
Through Cybersecurity Measures. International Journal of Engineering Inventions, vol. 13, iss. 12,
https://www.ijeijournal.com/papers/Vol13-Issue12/13120106.pdf
36. Oettmeier, K., and Hofmann, E. (2017). Additive manufacturing technology adoption: an empirical analysis of
general and supply chain-related determinants. Journal of Business Economics, 87(1), 97–124.
https://doi.org/10.1007/s11573-016-0806-8
37. Okpala, S. C. and Okpala, C. C. (2024). The Application of Artificial Intelligence to Digital Healthcare in the
Nigerian Tertiary Hospitals: Mitigating the Challenges. Journal of Engineering Research and Development, vol.
20, iss. 4, http://ijerd.com/paper/vol20-issue4/20047681.pdf
38. Okpala C. C., Nwankwo C. O., and Onu C. E. (2020), “Lean Production System Implementation in an Original
Equipment Manufacturing Company: Benefits, Challenges, and Critical Success Factors” International Journal of
Engineering Research and Technology Vol. 9, iss. 7 https://www.ijert.org/volume-09-issue-07-july-2020
39. Okpala C. C. (2014). “Tackling Muda – The Inherent Wastes in Manufacturing Processes” International Journal of
Advanced Engineering Technology vol. 5, iss. 4, http://technicaljournalsonline.com/ijeat/VOL%20V/IJAET%20
VOL%20V%20ISSUE%20IV%20%20OCTBER%20DECEMBER%202014/Vol%20V%20Issue%20IV%20Articl
e%202.pdf
40. Okpala C. C. (2013a), “The World’s Best Practice in Manufacturing” International Journal of Engineering
Research and Technology, vol. 2, iss. 10, http://www.ijert.org/view-pdf/5760/the-worlds-best-practice-in-
manufacturing
41. Okpala C. C. (2013b), “The Status of Lean Manufacturing Initiatives in The UK Small and Medium Sized
Enterprises – A Survey” International Journal of Engineering Research and Technology vol. 2, iss. 10,
http://www.ijert.org/view-pdf/5761/the-status-of-lean-manufacturing-initiatives-in-the-uk-small-and-medium-
sized-enterprises--a-survey
42. Parupelli, S. K., and Desai, S. (2019). A Comprehensive Review of Additive Manufacturing (3D Printing):
Processes, Applications and Future Potential. American Journal of Applied Sciences, 16(8), 244–272.
https://doi.org/10.3844/ajassp.2019.244.272
43. Pimpalkar, R., and Madgule, M. (2024). Integration of lean manufacturing system with novel intuitive fuzzy
syncretic lean frame work to improve the overall equipment effectiveness. Concurrent Engineering, 32(1–4), 46–
57. https://doi.org/10.1177/1063293X241296710
44. Qu, M., Guo, Q., Escano, L. I., Nabaa, A., Hojjatzadeh, S. M. H., Young, Z. A., and Chen, L. (2022). Controlling
process instability for defect lean metal additive manufacturing. Nature Communications, 13(1), 1079.
https://doi.org/10.1038/s41467-022-28649-2
45. Rahito, Wahab, D., and Azman, A. (2019). Additive Manufacturing for Repair and Restoration in
Remanufacturing: An Overview from Object Design and Systems Perspectives. Processes, 7(11), 802.
https://doi.org/10.3390/pr7110802
46. Saberi, S., Mohd. Yusu, R., Zulkifli, N., and Megat Ahma, M. M. H. (2010). Effective Factors on Advanced
Manufacturing Technology Implementation Performance: A Review. Journal of Applied Sciences, 10(13), 1229–
1242. https://doi.org/10.3923/jas.2010.1229.1242
47. Santander, P., Cruz Sanchez, F. A., Boudaoud, H., and Camargo, M. (2020). Closed loop supply chain network for
local and distributed plastic recycling for 3D printing: a MILP-based optimization approach. Resources,
Conservation and Recycling, 154, 104531. https://doi.org/10.1016/j.resconrec.2019.104531
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
www.ijltemas.in Page 188
48. Sasson, A., and Johnson, J. C. (2016). The 3D printing order: variability, supercenters and supply chain
reconfigurations. International Journal of Physical Distribution and Logistics Management, 46(1), 82–94.
https://doi.org/10.1108/IJPDLM-10-2015-0257
49. Sharma, V., Bhanti, P., and Paliwal, M. K. (2024). Development of a fuzzy analytic hierarchy process model,
sensitivity analysis, and addressing barriers to additive manufacturing implementation in small- and medium-sized
enterprises (SMEs). Journal of Micromanufacturing, 7(2), 190–204. https://doi.org/10.1177/25165984241280839
50. Sousa, M. R. A. de, Alencar, D. B. de, Leite, J. C., and Santos Júnior, H. A. dos. (2024). Integration of Industry 4.0
technologies in industrial manufacturing processes. Revista de Gestão e Secretariado, 15(7), e3844.
https://doi.org/10.7769/gesec.v15i7.3844
51. Strong, D., Kay, M., Conner, B., Wakefield, T., and Manogharan, G. (2018). Hybrid manufacturing – integrating
traditional manufacturers with additive manufacturing (AM) supply chain. Additive Manufacturing, 21, 159–173.
https://doi.org/10.1016/j.addma.2018.03.010
52. Tang, T., Ahire, B., and Li, X. (2023). Scalable Multi-Material Additive Manufacturing of Bioinspired Polymeric
Material With Metallic Structures Via Electrically Assisted Stereolithography. Journal of Manufacturing Science
and Engineering, 145(1). https://doi.org/10.1115/1.4055793
53. Thomas, D. (2016). Costs, benefits, and adoption of additive manufacturing: a supply chain perspective. The
International Journal of Advanced Manufacturing Technology, 85(5–8), 1857–1876.
https://doi.org/10.1007/s00170-015-7973-6
54. Tuegel, E., and Penmetsa, R. (2006, May). Risk-Based Design and Certification of Aircraft: A Systems
Engineering Approach. 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials
Conference 14th AIAA/ASME/AHS Adaptive Structures Conference 7th. https://doi.org/10.2514/6.2006-2147
55. Wimpenny, D. I., Pandey, P. M., and Jyothish Kumar, L. (2016). Advances in 3D Printing and additive
manufacturing technologies. In Advances in 3D Printing and Additive Manufacturing Technologies.
https://doi.org/10.1007/978-981-10-0812-2
56. Wohlers. (2021). Wohlers Report 2021. Wohlers Associates. https://wohlersassociates.com/press-releases/new-
wohlers-report-2021-finds-7-5-growth-in-additive/
57. Zuo, Z., De Corte, W., Huang, Y., Chen, X., Zhang, Y., Li, J., Zhang, L., Xiao, J., Yuan, Y., Zhang, K., Zhang, L.,
and Mechtcherine, V. (2024). Strategies towards large-scale 3D printing without size constraints. Virtual and
Physical Prototyping, 19(1). https://doi.org/10.1080/17452759.2024.2346821
