Lead Time Reduction Research Articles

Challenges and Opportunities in the Adoption of Flexible Manufacturing Systems in Indian Manufacturing Industries

The manufacturing sector is increasingly adopting automation technologies to enhance productivity and flexibility. Among these technologies, Flexible Manufacturing Systems (FMS) offer substantial benefits, such as reduced lead times, improved production flexibility, and lowered operational costs. Despite its advantages, FMS adoption in Indian manufacturing industries has been slow due to several barriers, including high capital costs, technological complexity, and inadequate government support. This study investigates the current status of FMS adoption in India, identifies the key barriers hindering its implementation, and proposes a decision-making model to facilitate its wider adoption. The research gap highlights the lack of a comprehensive framework to quantify the barriers and assess the performance of FMS adoption. To address this, a two-phase Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology was used. This approach allowed for the classification and prioritization of barriers, as well as the development of a model for FMS selection based on statistical analysis. A case study was conducted using this methodology to evaluate various FMS models and provide a systematic decision-making framework for manufacturers. The results revealed that the most significant barriers to FMS adoption in India include high upfront costs, insufficient infrastructure, and the challenges posed by rapidly advancing FMS technology. By applying the AHP-TOPSIS approach, this study offers insights into overcoming these barriers and presents actionable recommendations for manufacturers and policymakers. The findings emphasize the need for increased government support and industry collaboration to accelerate FMS adoption in India. This research contributes to the development of a structured decision-making framework, which could help manufacturers implement FMS more effectively, thereby enhancing competitiveness in an increasingly dynamic global market. Keywords— Flexible Manufacturing Systems, Barriers to FMS, Indian Manufacturing, AHP-TOPSIS, Decision-Making Model

Improving the last mile delivery of vaccines through an informed push model: Experiences, opportunities and costs based on an implementation study in a rural district in Uganda.

Developing countries face challenges in ensuring equitable, timely, and efficient vaccine availability at health facilities. In Uganda, the distribution of vaccines from district stores to the last-mile health facilities is hindered by an unpredictable and unreliable mixed push-pull delivery system. This system often results in poor vaccine management, stock-outs, and missed vaccination opportunities. This pilot study aimed to enhance the efficiency of last-mile vaccine delivery by implementing an informed push model. The specific goals were to improve vaccine lead time, standardize cold chain management during transportation, and evaluate the costs of implementing the informed push model. A mixed methods approach was used to evaluate the impact of the informed push model in Gomba district, Uganda. Both quantitative and qualitative data were collected at baseline and endline. Quantitative data included mode, frequency, lead-time, and costs of vaccine delivery, vaccine stock status, and cold chain maintenance during transportation, gathered through semi-structured interviews. Qualitative data on experiences and challenges were collected using a guide. Descriptive statistics were used for quantitative data analysis, while an ingredients approach was used for costing data. Thematic analysis was applied to qualitative data. The informed push system significantly improved vaccine delivery efficiency and quality in Gomba district. The average lead-time for vaccine delivery reduced from 14 days at baseline to 5 days at endline. Timely vaccine receipt at health facilities increased from 36.8% to 100%. Temperature monitoring during transit improved from 26.3% to 100%. The proportion of facilities experiencing stock-outs dropped from 79.0% to 36.8%. Monthly distribution costs decreased from $494.8 ($0.07 per child) to $445.9 ($0.06 per child). The informed push model is a cost-effective strategy for improving last-mile vaccine delivery by reducing lead times, enhancing cold chain management, and decreasing stock-outs. Integration into the national immunization program is recommended for broader adoption in Uganda.

Factors Affecting the Adoption of Electronic Procurement Systems in Public Institutions in Tanzania: Evidence from Tanzania Prisons in Morogoro Municipal

The study investigated the factors affecting the adoption of electronic procurement systems in public institutions in Tanzania, evidence from Tanzania Prisons in Morogoro Municipal. A survey research design and mixed research approach were utilized. The target population included procurement employees in Tanzania public institutions represented by Kingolwira Prison, Mkonowa Mara Prison, Kihonda Prison, and Remand Prison in Morogoro Municipal Municipality. The study involved 52 respondents. Purposive sampling technique wase used in selection of respondents where all respondents with knowledge on electronic procurement systems were used. Data was gathered through questionnaires with likert scale statements and analysed using descriptive statistics. In addition to that, data deduction techniques whereby conclusions were made by logically reasoning from general principles or theories to specific instances or outcomes based on the available data. User opinions on e- procurement adoption in Tanzanian public institutions highlight significant concerns about technological infrastructure, including hardware availability, software support, and internet reliability. While many acknowledge benefits such as reduced paperwork and enhanced efficiency, opinions vary on its effectiveness in reducing lead times and corruption. Security remains a critical issue, with mixed views on data accessibility and sharing safety. Overall, addressing infrastructure challenges and security concerns is crucial to ensuring successful implementation and broader user acceptance of e- procurement systems.

Development and design of a carbon fiber insole intended for individuals with partial foot amputation

Ankle and foot orthotics are suggested for gait rehabilitation therapy after a stroke. Clinical practice necessitates adjusting the ankle foot orthosis torque to accommodate each patient’s unique stride and body function. The objective of this paper is to develop a customized, cost-effective footplate orthosis from carbon fibers, manufactured using 3D printing, for individuals with partial foot amputations. The footplate is designed for use in developing nations and aimed to be made available with a reduced lead time. The experiment work carried out in this research is used to design, analyze, and validate a prosthetic footplate. A foot of size UK 7 with a 2 mm thick model of the human foot was modeled using Unigraphics-NX. The footplate’s design is being tested both conceptually and empirically. The footplate manufactured by applying load at toe and heel shows that the carbon fiber successfully restores leg length and reduces pressure on the feet’s delicate distal end.

Smart design and additive manufacturing of bending tools to improve production flexibility

For the automotive industry, especially on the part of Tier 1 and Tier 2 suppliers, the future will be about maintaining sovereignty in the form of technology openness and accelerating digitization. The product portfolio, which is generally passed on by OEMs to suppliers for production, often includes body parts that cannot always be manufactured economically with the prevailing production technology. The reason for this is a high diversity of model-variants, which requires smaller batches. To this end, highly flexible large-series production cells for body sheet components that can be scaled in all dimensions are being developed and tested. For the first time, they will make it possible to redesign the process planning in series production on a component-specific basis. The aim is to reduce production costs for new, geometrically different component variants. The basic components of the flexible manufacturing system are, firstly, new flexible forming technologies which have the potential to produce typical vehicle part geometries. Secondly, a process generator develops the corresponding production plan. A digital mapping of the manufacturing processes enables the selection of cost-, efficiency-, flexibility- and resilience-optimized production chains depending on the number of parts. Established manufacturing processes to produce car body components are supplemented in the cell by flexible processes such as 3D swivel bending. As a use case for flexible manufacturing, a concept for Rapid Tooling of 3D swivel bending tools is developed. In the flexible manufacturing system to be developed, a method of a standardized process sequence to produce forming tools within 24 h has been lacking to date. For this purpose, the concept of an automated design is being developed in which a reconfigurable tool body can be sliced into sheet metal stripes The active tool surface is additively manufactured after the tool has been packaged using LMD and adapted to individual requirements. The goal in the application of Rapid Tooling is to reduce lead times and development costs through a largely automated tool design and lead time-optimized manufacturing concept.

Hybrid manufacturing cost models: Additive friction stir deposition, measurement, and CNC machining

Based on its potential to reduce lead times, hybrid manufacturing, which often includes both additive manufacturing and machining processes, is receiving more attention from manufacturers as they seek to increase their supply chain resilience and efficiency. A new solid-state additive manufacturing, referred to as additive friction stir deposition (AFSD), has shown the potential to become an important process for hybrid manufacturing. To justify the selection of a hybrid manufacturing approach, the cost needs to be estimated for comparison to conventional approaches. Historically, hybrid manufacturing costs have been difficult to estimate due to the complexity and diversity of the manufacturing processes. This paper proposes cost models that include additive friction stir deposition, structured light scanning, milling, and turning, which can be combined in hybrid manufacturing process planning. These cost models are demonstrated in a case study and cost estimates are compared for hybrid and conventional (machining-only) manufacturing approaches. For the selected case, the hybrid manufacturing process cost was $1007.58, while the conventional milling process cost was $833.60. The results of the case study show that both labor and material costs must be considered to make an informed decision between hybrid and conventional manufacturing approaches.

Robot path planning for metrology in hybrid manufacturing

The objective of the wire arc additive manufacturing (WAAM) hybrid cell is to significantly reduce lead time associated with large scale parts. The WAAM process utilizes a 6 degree-of-freedom (DOF) robot manipulator in addition to a 2 DOF part positioner. After printing, the part is translated into position for scanning to inform the subtractive manufacturing process. Scanning in a manual setting can be a long and arduous process to generate scans of sufficient quality for the basis of informed machining. Effective path planning for scanning with the intent to reduce scanning time, increase quality of scans, and move toward a full automation of the hybrid manufacturing cell is investigated in this paper. Simulation software is used to create and verify path plans prior to importing and implementing on a 6 DOF robotic manipulator to which a GOM ATOS Q 3D scanner is mounted. The quality, amount of time necessary to produce, and number of scans required to produce a sufficient representation for machining are compared using three methods. The first method is a manual scanning configuration, the second is using a generalized path plan, and the third is using a geometry-based path plan.

Impacts of industry 4.0 on supply chain management of logistics providers: A methodical review

The arrival of Industry 4.0 transformed and reshaped manufacturing, and the logistics industry is not excluded. As such, there is a need to explore the influence of this latest technological innovation on industries with a focus on logistics providers; this research examines the impact of Industry 4.0 on logistics providers' supply chain management. Using qualitative analysis of secondary sources, this research identifies the key relevance of Industry 4.0 technologies to logistics providers while assessing their adoption and exploring their impact on supply chain performance. Through the series of examination of secondary sources, it was revealed that Industry 4.0 components such as IoT enable real-time tracking, reducing lead times and improving customer service. AI, on the other hand, helps optimize routing and scheduling while also enhancing efficiency. Nevertheless, the industry 4.0 implementation into the logistics provider supply chain is not without challenges; the challenges evidenced through the research include data security, interoperability, and workforce skills. This study recommends investments in technology and skills, data security measures, collaborative organizational structures, agility, change management, and benchmarking to navigate the transformative landscape of Industry 4.0 in logistics supply chain management.

Rapid investment casting: a techno-economic analysis for evaluating VAT photopolymerisation processes

Adopting additive manufacturing in investment casting, known as rapid investment casting, can significantly reduce lead time and cost. Despite many publications on these technologies, no quantitative techno-economic analyses refer to rapid investment casting. The paper provides helpful insights and a decision-making approach for replacing conventional production technologies (i.e. wax injection) with additive manufacturing technologies. The study is based on a techno-economic analysis framework. It allows assessing key performance indicators (e.g. crossover points and payback periods) for stakeholders to decide the suitable VAT photopolymerisation processes (i.e. stereolithography, digital light processing, and material jetting) according to specific production scenarios (i.e. load factors). Analytical cost models were developed in the frame of this work to assess the time and cost of the overall manufacturing process of the resin model for investment casting, considering the post-processing stages for each of the technologies investigated. The cost models were used to assess the benefits introduced by additive manufacturing in rapid investment casting. A sensitivity analysis evaluated the impact of energy, material, and labour costs for both key performance indicators (crossover points and payback periods). The techno-economic analysis yielded the following results: (i) crossover points in terms of production costs vary, ranging from thousands of units for small components in the jewellery or fashion industries to a few dozen units for larger parts in the mechanical industry, and (ii) crossover points in terms of time are lower than those related to cost. Additionally, digital light processing is regarded as the most promising technology, offering the best crossover points and payback periods across various scenarios, mainly when 3D printers are used at a low utilisation rate.

Emerging trends in large format additive manufacturing processes and hybrid techniques

AbstractLarge format additive manufacturing (LFAM) technologies are rapidly growing with significant potential for application in multiple technological sectors like aerospace, tooling, automotive, marine, construction, and energy. LFAM processes offer significant advantages including reduced lead time, cost, and material waste, which are further amplified due to the increased volume of the components. This review paper focuses on LFAM technologies with the highest technology readiness level, i.e., metal Directed Energy Deposition (DED), polymer extrusion, and solid-state deposition (i.e. cold spray additive manufacturing (CSAM)). Common system setups, the maximum deposition rate, and the range of processable materials, along with the achievable mechanical properties and geometrical characteristics, are outlined for each technology, both in individual and hybrid manufacturing formats. The main technological challenges are gathered and discussed to highlight the areas that require further development. Finally, the current industrial applications for LFAM technologies and the expected future developments are outlined. This review provides an overview of LFAM technologies’ current status and discusses their potential in improving the manufacturing of complex and large geometries, with a significant reduction in material and energy consumption, while ensuring high-quality and high-performance components.

Development of a Novel Transonic Fan Casing Making Use of Rapid Prototyping and Additive Manufacturing

Additive manufacturing (AM) presents significant cost savings and lead time reductions because of features inherent to the manufacturing process. The technology lends itself to rapid prototyping due to the streamlined workflow of quickly implementing design changes. Compared to traditional machining, AM techniques are simpler in execution for design engineers because they do not require detailed engineering drawings and they typically make use of the nominal geometry in computer models. A novel transonic fan casing assembly has been developed that makes use of AM inserts surrounding the rotor to provide an opportunity to cost-effectively change the corresponding flowpath. The rapid prototyping design philosophy developed from this work will allow for numerous experimental studies into the effects that different design parameters of casing geometries have on fan aerodynamic performance. A fan stage representative of a small turbofan engine was successfully tested with smooth-walled, additively manufactured inserts as a baseline case for future configurations. Before installing the 3D printed casing assembly, computational thermal stress analysis was performed to reduce the risk in implementation due to the demanding environment associated with the rotor. AM components and materials typically have nonlinear mechanical properties, adding to the complexity of the structural analysis. As part of the research, steady aerodynamic performance was measured over the entire relevant operating range of the fan.

Optimizing Supply Chain Management: Strategies for Enhancing Efficiency and Reducing Costs in Manufacturing Industries

The optimization of supply chain management (SCM) is crucial for enhancing efficiency and reducing costs in manufacturing industries. This study employs a qualitative research approach to explore various strategies for SCM optimization. Data were collected through semi-structured interviews with SCM experts, focus groups with key stakeholders, and an extensive review of secondary sources. The analysis reveals that the integration of information technology (IT), including ERP systems, supply chain management software, cloud computing, and the Internet of Things (IoT), significantly enhances supply chain visibility and coordination, leading to improved decision-making and reduced lead times. Additionally, lean manufacturing and Just-In-Time (JIT) practices are found to be effective in minimizing waste, optimizing inventory levels, and aligning production schedules with market demand, thereby reducing costs and increasing operational efficiency. Strategic supplier partnerships and collaborations play a vital role in achieving synchronization across the supply chain, improving quality, and managing risks. The adoption of sustainability and green supply chain management (GSCM) practices is also highlighted as a key driver for cost reduction and efficiency improvement. These practices not only enhance environmental performance but also drive innovation and provide a competitive advantage. However, the study acknowledges the challenges associated with implementing these strategies, including high initial costs, the need for skilled personnel, and the complexity of managing multiple supplier relationships. Despite these challenges, the findings suggest that a holistic approach to SCM optimization, integrating traditional methods with cutting-edge technologies, can significantly enhance supply chain performance. This research provides valuable insights for both academia and industry practitioners, offering practical guidelines for implementing effective SCM strategies. By adopting these strategies, manufacturers can build resilient and adaptive supply chains capable of meeting the evolving demands of the global market, ensuring sustainable growth and competitiveness.

From waste to worth: a mediation analysis of lean manufacturing and operational performance in developing country

PurposeLean Manufacturing Systems (LMS) gained popularity among manufacturers globally. However, their efficacy in developing and least-developed countries remained noticeably understudied. Motivated by this research gap, the researchers of this study designed a quantitative study with a structured survey technique to investigate its context-specific impact on the apparel industry of a developing country. Hence, this study aimed to examine the relationship between LMS and elimination of waste (EOW) and operational performance (OP) and comprehend how the EOW mediates the relationship between an LMS and OP within the apparel industry of a developing economy.Design/methodology/approachThe researchers collected data from 227 garment companies in Bangladesh. These organization-level data were then analyzed using the structural equation modeling approach with AMOS 20.0 software to examine the direct and indirect effects among EOW, LMS and OP.FindingsThe findings of this study suggest that EOW has a direct and significant effect on OP. This research also revealed that EOW has a partial mediating effect on the relationship between LMS and OP.Research limitations/implicationsThis research focused on a single industry administering self-reported data and cross-sectional design, limiting generalizability and causal inference.Practical implicationsLMS and directing efforts towards EOW can significantly improve the operational performance of apparel companies by reducing lead times and costs, improving quality and increasing productivity.Originality/valueThese findings can provide useful insight to managers, practitioners and future researchers to understand the relationship between EOW, LMS and OP to optimize their production processes and improve OP in the apparel industry.

Improving Lead Time through Lean Manufacturing: A Case Study

Lean manufacturing is a well-established methodology aimed at optimizing production by eliminating waste, enabling industries to thrive in a globally competitive environment. This paper presents a case study of a well-known automotive manufacturing industry on the axle process. This article aims the Value Stream Mapping (VSM) methodology was used in the axle process to reduce lead time by producing quality products with a reduction in non-value-added activities. The current state of the axle process was created using industry data from the past six months. Significant improvements were realized following the successful implementation of VSM, including a reduction in the lead time from 89.50 hours to 50.55 hours. The new state map was also created after implementing the improvements. The results illustrated that the VSM increased the effectiveness by 56.48% in the axle process.

Automated machine learning for fabric quality prediction: a comparative analysis.

The enhancement of fabric quality prediction in the textile manufacturing sector is achieved by utilizing information derived from sensors within the Internet of Things (IoT) and Enterprise Resource Planning (ERP) systems linked to sensors embedded in textile machinery. The integration of Industry 4.0 concepts is instrumental in harnessing IoT sensor data, which, in turn, leads to improvements in productivity and reduced lead times in textile manufacturing processes. This study addresses the issue of imbalanced data pertaining to fabric quality within the textile manufacturing industry. It encompasses an evaluation of seven open-source automated machine learning (AutoML) technologies, namely FLAML (Fast Lightweight AutoML), AutoViML (Automatically Build Variant Interpretable ML models), EvalML (Evaluation Machine Learning), AutoGluon, H2OAutoML, PyCaret, and TPOT (Tree-based Pipeline Optimization Tool). The most suitable solutions are chosen for certain circumstances by employing an innovative approach that finds a compromise among computational efficiency and forecast accuracy. The results reveal that EvalML emerges as the top-performing AutoML model for a predetermined objective function, particularly excelling in terms of mean absolute error (MAE). On the other hand, even with longer inference periods, AutoGluon performs better than other methods in measures like mean absolute percentage error (MAPE), root mean squared error (RMSE), and r-squared. Additionally, the study explores the feature importance rankings provided by each AutoML model, shedding light on the attributes that significantly influence predictive outcomes. Notably, sin/cos encoding is found to be particularly effective in characterizing categorical variables with a large number of unique values. This study includes useful information about the application of AutoML in the textile industry and provides a roadmap for employing Industry 4.0 technologies to enhance fabric quality prediction. The research highlights the importance of striking a balance between predictive accuracy and computational efficiency, emphasizes the significance of feature importance for model interpretability, and lays the groundwork for future investigations in this field.

Production Floor Digitalization

The digitalization of production floors is becoming increasingly important in the context of e-commerce manufacturers' interactions with customers. This paper investigates the nuances of production floor digitalization in the context of e-commerce manufacturing, examining the implications for both manufactures and buyers. E-commerce manufacturers, faced with the unique challenges of online retail, are leveraging digital technologies to optimize production processes and improve customer experiences. Data-driven demand forecasting, agile manufacturing practices, and seamless integration with e-commerce platforms are all important aspects of production floor digitalization in this context.Digitalization of the production floor provides opportunities for manufacturers to improve efficiency, reduce costs, and respond more quickly to market demand. Manufacturers can improve customer satisfaction and loyalty by implementing real-time data analytics and automation to streamline production workflows, reduce lead times, and ensure timely order fulfillment.On the other hand, production floor digitalization provides significant benefits to e-commerce buyers. Buyers can make more informed purchasing decisions and enjoy greater convenience and reliability in their online transactions thanks to improved product visibility, accurate delivery estimates, and customized purchasing procedures.

Lean manufacturing for reducing lead time in foundry processes: a design science approach

Purpose The lean manufacturing (LM) approach is a highly effective method that can be implemented in any industry to streamline production processes, meet customer demand and eliminate any unnecessary waste. This paper aims to propose and evaluate a generic project-based framework grounded on the LM approach for reducing lead time in foundry processes. Design/methodology/approach Using design science research (DSR), we developed a generic LM project-based framework for reducing lead time in foundry processes. Findings The developed framework provides an alternative method to implement LM projects to reduce lead time and nonvalue activities in foundry factories. Practical implications The findings of this research can guide better lean practitioners for lead time reduction in foundry processes. Originality/value This paper contributes to the operational excellence literature when discussing the impact of the LM approach on foundry processes. In addition, the paper provides a roadmap for reducing lead time in a foundry company.

Impact of Digital Technologies on Enhancing Supplier Collaboration and Performance in Sweden

Purpose: The aim of the study was to examine the Impact of Digital Technologies on Enhancing Supplier Collaboration and Performance in Sweden. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study that impact of digital technologies on enhancing supplier collaboration and performance in Sweden has been transformative, driving significant improvements in efficiency, transparency, and strategic alignment. The adoption of digital tools such as cloud-based platforms, blockchain, and advanced data analytics has enabled Swedish businesses to foster stronger, more collaborative relationships with their suppliers. Digital technologies have streamlined communication and information sharing, allowing for real-time data exchange and more effective coordination across the supply chain. This enhanced transparency has reduced the risk of errors, improved order accuracy, and facilitated more agile and responsive supply chain operations. Consequently, businesses have been able to reduce lead times, optimize inventory levels, and enhance overall supply chain resilience. Unique Contribution to Theory, Practice and Policy: Resource Dependence Theory, Transaction Cost Economics & Social Exchange Theory may be used to anchor future studies on Impact of Digital Technologies on Enhancing Supplier Collaboration and Performance in Sweden. Organizations should prioritize investment in robust digital platforms and tools that facilitate real-time communication, transparency, and data-driven decision-making across supply chains. This includes adopting cloud-based collaboration tools, implementing AI-driven analytics for predictive insights, and integrating IoT for enhanced visibility. Implementing structured SRM strategies that leverage digital technologies can improve supplier engagement, trust, and performance. This involves establishing clear communication channels, setting performance benchmarks based on data analytics, and fostering collaborative partnerships through shared platforms. Policymakers should collaborate with industry stakeholders to develop regulatory frameworks that support digital transformation in supply chains. Clear guidelines and standards can promote adoption while ensuring data security, interoperability, and ethical use of emerging technologies like blockchain and big data analytics.

Scheduling for additive manufacturing with two-dimensional packing and incompatible items

Additive manufacturing technology can enable low-cost, efficient production of low-demand, highly-complex customized items with reduced lead time if production is judiciously planned. This paper addresses the assignment of requested items into batches and the scheduling of the batches onto 3D printers. The objective is to minimize the manufacturing makespan while satisfying items’ compatibility, two-dimensional no-overlap and containment packing constraints within a batch, and machine’s capability to manufacture a batch. The problem is modeled as a mixed integer linear program (MIP) that solves instances up to 100 items. For hard and large instances, this paper proposes a matheuristic that fathoms packings using a step-wise check procedure. Computational results reveal that the proposed heuristic improves the makespan of MIP solutions of hard instances by 12% on average, with improvements reaching up to 72% for instances with 150 items. They further show that the proposed heuristic finds the best makespan for 88% of all cases. Finally, we provide useful managerial insights for production flexibility and scheduling policies.

Evaluating the Impact of ERP Implementation on Supply Chain Performance: A Case Study of Youngs Food in Pakistan

This study investigated the impact of Enterprise Resource Planning (ERP) implementation on supply chain performance at Youngs Food, a leading FMCG company in Pakistan. Utilizing a quantitative research methodology, the study aimed to assess how ERP systems influence various aspects of supply chain management, including process automation, lead time reduction, cost reduction, and delivery improvement. Data were collected from employees involved in the ERP transformation process through a structured questionnaire. The findings revealed that ERP implementation significantly enhances supply chain performance. Specifically, process automation emerged as the most influential factor, followed by cost reduction, improved deliveries, and lead time reduction. The study confirmed that ERP systems streamline operations, reduce operational costs, and improve efficiency and customer satisfaction. The comprehensive analysis underscores the critical role of ERP systems in modern supply chain management, advocating for their strategic adoption to achieve competitive advantage and operational excellence.