Delivery Lead Time Research Articles

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.

Digital Transformation In The LPG Industry: Leveraging IoT And Data Analytics For Operational Efficiency

This study presents a comprehensive systematic review exploring the transformative impact of Internet of Things (IoT), Supervisory Control and Data Acquisition (SCADA) systems, and automation technologies on the operational efficiency of the Liquefied Petroleum Gas (LPG) industry. Leveraging the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a rigorous review process was conducted to identify, screen, and synthesize existing research. A total of 90 high-quality articles were systematically reviewed to assess how digital technologies are revolutionizing key aspects of LPG operations, including inventory management, predictive maintenance, and fleet optimization. The findings reveal that integrating IoT and SCADA systems enhances real-time monitoring, significantly reduces manual errors, and improves accuracy in inventory tracking, leading to cost reductions of up to 30%. Moreover, predictive maintenance enabled by IoT data analytics was shown to decrease unplanned downtime by 40% and maintenance costs by 25%, further ensuring the reliability of equipment in this high-stakes industry. The review also highlights how automation and digitalization streamline logistics, optimize fuel usage, and improve customer satisfaction by reducing delivery lead times by 20%. Additionally, these technologies support sustainability goals by minimizing carbon emissions through optimized fleet management. By providing a detailed synthesis of current research, this review not only underscores the benefits of digital transformation in the LPG industry but also identifies gaps in the literature, suggesting avenues for future studies to enhance operational excellence, safety, and sustainability in this critical sector.

Analysis of Determining Order Quantity of Spare Parts in Oil and Gas Well Service Activities Using Monte Carlo Simulation

This research aims to explore the analysis of determining the order quantity of spare parts in well service activities in the oil and gas (Migas) industry using the Monte Carlo simulation approach. The Migas industry requires efficient inventory management to maintain operational smoothness, but this need must be balanced with efforts to minimize excessive storage costs. Therefore, it is important to determine the optimal order quantity to meet operational needs while avoiding unnecessary inventory accumulation. The Monte Carlo simulation method was chosen because of its ability to address uncertainty in inventory analysis, allowing for modeling variations in demand, delivery lead times, and other factors affecting inventory management. It is expected that this approach will provide a better understanding of how decisions regarding the order quantity can be made more effectively in the context of well service activities in the Migas industry. The main expectation of this research is the development of a model that can assist Migas companies in optimizing the management of their spare parts inventory. Thus, it is hoped that this research will make a significant contribution to improving operational efficiency, reducing storage costs, and increasing customer service levels in the Migas industry. It is also hoped that the results of this research will pave the way for the development of inventory management strategies that are more adaptive and responsive to dynamic business environments.

Synchronizing the Supply of Components for Automotive Assembly amid Chip Shortages and Other Supply Delays

The push for more battery-powered electric vehicles, the COVID-19 pandemic, and other global supply chain destabilizing events (e.g., the war in Ukraine) have made it necessary for manufacturers to consider innovative approaches to sourcing and materials management. This study assesses the value of sharing delivery due date updates, which are then used to convert spent time into a fraction of the expected total time for order fulfillment (i.e., progress updates), to synchronize the delivery of components for an automotive manufacturer who, amid long and variable lead times for semiconductor chips, maintains a strategy of waiting for the delivery of all needed parts before commencing production. Having progress updates in a fractional form puts numerical bounds (i.e., 0% to 100%) on the shared information, and this facilitates closed-form analytical comparisons. We develop analytical models to show decreases in expected assembly-related costs based on the progress update information shared between two suppliers—one supplier has a shorter and more reliable order fulfillment lead time than the other. When there are more than two suppliers, we use a simulation model to estimate the expected value of sharing progress updates. Our results show that increasing the frequency of shared progress updates, and having more suppliers share progress updates, results in increased cost-savings percentages. However, the opposite occurs with increasing uncertainty in suppliers’ delivery lead times. The computational study provides support for the existence of a critical mass level of suppliers needed to achieve a given percentage cost saving. This study can help automakers to make informed decisions on encouraging information sharing among suppliers, by first creating a critical mass of suppliers to share progress updates. Our investigations also help show the conditions in which to best leverage such information sharing strategies while waiting for the delivery of all needed components prior to production.

Shortening The Last-Mile: Impact of Zipline Medical Drone Delivery on The Operations of Hard-to-Reach Healthcare Facilities in Northern Ghana.

Purpose: This research study investigated the impact of Zipline Medical Drone Delivery on the operations of last-mile healthcare facilities in northern Ghana. Design/Methodology/Approach: The research study adopted an exploratory research design to determine the impact of Zipline’s medical drone delivery on the operations of last-mile healthcare facilities in Northern Ghana. An online survey questionnaire was deployed to collect quantitative data from 159 trained employees of last-mile health facilities who were selected through a purposive sampling technique from last-mile drone-served healthcare facilities. Findings: The study established that drone delivery speed, logistics efficiency and availability of drone delivery resources significantly impacted zipline delivery of medical commodities to last-mile public healthcare facilities in Northern Ghana, reducing commodity delivery lead-time and improving access to essential healthcare supplies for efficient service delivery. Research Limitation/Implications: This research focused mainly on the impact of drone delivery of medical commodities on the operations of last-mile public healthcare facilities in Northern Ghana. Practical Implications: The results of this study show that by enhancing drone delivery speed, logistics efficiency and drone resource availability, the operations of public healthcare facilities can be improved by ensuring that essential medicines are made available, patient waiting time is significantly reduced and outpatient services are improved. Social Implication: The study recommends the establishment of a Zipline-Administrators partnership which will eventually result in a reduction of medical commodity lead-time, improved accessibility of patients to essential health commodity supplies and general cost savings to the community. Originality/Value/Novelty: Although drone technology has been the subject of numerous studies in Ghana, none has investigated the impact of medical commodity drone delivery services on the operations of last-mile healthcare facilities in Northern Ghana. This study therefore explored the impact of drone delivery services from the last-mile healthcare facilities’ perspective.

Waste Analysis In Delivery Process Using The Lean Distribution Method

In the current era of globalization, the existence of goods delivery service companies supports almost every area of human life. In the process of delivering goods, PT. X always strives to provide fast and precise service. However, the problem faced by the company is the long lead time for delivery of goods. This study employs a lean distribution approach, applying Value Stream Mapping and identifying waste using the 8 waste framework. The research findings revealed waiting recapitulation of 20%, excess processing of 18%, overproduction of 17%, defects of 13%, non-utilized talent of 10%, inventory of 9%, transportation of 7%, and motion of 6%. With proposed improvements, the delivery process time can be reduced from 8243 minutes to 5929 minutes.

Hybrid simulation-optimization approach for planning relief-aid distribution with a real-world case study

PurposeAuthorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.Design/methodology/approachA decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.FindingsThe comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.Originality/valueThe efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

Research on B2C Cross-Border Electronic Commerce Return Logistics Model Selection Based on Estimated Return Rate

This paper investigates the estimated return rate and optimal order quantity under three cross-border e-commerce return logistics modes: direct mail (from predecessor), in situ destruction (new), and insurance (new). The estimated return rate under each model was analyzed and it was found that different modes have different thresholds in delivery lead time (the time retailers need to deliver goods to customers), and within which the estimated return rate increases as the delivery lead time increases. And a size comparison of the estimated return rates for the three models was conducted. A profit model was constructed based on the estimated return rate model, the optimal order quantity was calculated, and the effects of different factors (tax, postage, and delivery lead time etc.) on it were analyzed. For the insurance model, the effect of bearing the insurance ratio between retailers and consumers on the optimal order quantity was examined. The goal of this paper was to construct a model of the estimated return rate for the two new modes and to compare the estimated return rate of the three modes, which provides a reference for retailers to choose among the diversified return logistics modes and then make the best ordering strategy according to the influence of different factors on the optimal order quantity.

Leveraging computer vision towards high-efficiency autonomous industrial facilities

AbstractManufacturers face two opposing challenges: the escalating demand for customized products and the pressure to reduce delivery lead times. To address these expectations, manufacturers must refine their processes, to achieve highly efficient and autonomous operations. Current manufacturing equipment deployed in several facilities, while reliable and produces quality products, often lacks the ability to utilize advancements from newer technologies. Since replacing legacy equipment may be financially infeasible for many manufacturers, implementing digital transformation practices and technologies can overcome the stated deficiencies and offer cost-affordable initiatives to improve operations, increase productivity, and reduce costs. This paper explores the implementation of computer vision, as a cutting-edge, cost-effective, open-source digital transformation technology in manufacturing facilities. As a rapidly advancing technology, computer vision has the potential to transform manufacturing operations in general, and quality control in particular. The study integrates a digital twin application at the endpoint of an assembly line, effectively performing the role of a quality officer by utilizing state-of-the-art computer vision algorithms to validate end-product assembly orientation. The proposed digital twin, featuring a novel object recognition approach, efficiently classifies objects, identifies and segments errors in assembly, and schedules the paths through the data pipeline to the corresponding robot for autonomous correction. This minimizes the need for human interaction and reduces disruptions to manufacturing operations.

A study on Optimizing supply chain management to ensure efficient delivery of salon products and equipment to all locations in Groom India salon and spa private limited

This study aims to optimize the supply chain management of Groom India Salon and Spa Private Limited to ensure efficient delivery of salon products and equipment to all its locations. The research will begin by evaluating the current end-to-end supply chain processes, identifying bottlenecks, and exploring digital solutions for real-time tracking and inventory management. By implementing software tools to enhance visibility and coordination, the study seeks to reduce delivery lead times, achieve cost savings through optimized inventory levels, and improve customer satisfaction and loyalty. The methodology will involve a combination of qualitative and quantitative analysis, including interviews with key stakeholders, data collection on current processes and performance metrics, and simulation modelling to test various optimization scenarios. The study aims to provide actionable insights and recommendations for enhancing the supply chain management of Groom India Salon and Spa Private Limited, ultimately contributing to its overall operational efficiency and customer service excellence.

Optimal ordering based on shortage scenarios under presence of a low-quality substitute and supply uncertainty

Global trade has shifted from short-distance supply chains within countries to long-distance supply chains between countries. As a result, delivery lead time has become a key operational factor, and uncertainty causes stock-outs, resulting in massive losses. An important solution for shortages is product substitutes, but they can affect the quality of the end products. Moreover, even when demand is expected and planned for, unexpected last-minute demand makes operational conditions challenging. This paper considers a single-period problem of replenishing a high-quality product under both lead time and demand-related uncertainty. Stock-outs during and at the end of the replenishment period can be handled only by providing a low-quality substitute product that requires repeated replacements, therefore affecting pending demand. We provide detailed modeling of possible stock-out scenarios and show that, in presence of these two types of uncertainty, the optimal order quantity of a high-quality product can be determined using an upper and lower threshold and a base stock level equal to anticipated (planned) demand over the replenishment period. In particular, we derive a closed-form solution for the optimal order quantity under general probability distributions of lead time and demand and explicit solutions for uniform distributions. We also show the conditions under which optimal order quantities can be insensitive to the cost of the high-quality product.

A modified variable neighborhood search algorithm for dynamic lot-sizing with supplier selection under varying delivery time quotation

This paper investigates a dynamic lot-sizing problem with supplier selection, where the manufacturer develops an aggregate plan to meet product delivery demand with the objective of minimizing the cost. Specifically, a novel supplier selection scenario with varying delivery time quotation is considered, i.e., the manufacturer places raw material replenishment orders with varying delivery time quotation and dynamically selects suppliers, and the suppliers guarantee delivery within the quoted delivery lead time. In the dynamic lot-sizing problem with supplier selection, a joint decision on the production plan, replenishment order, and supplier selection needs to be made. To tackle this complex problem, a modified Variable Neighborhood Search algorithm with nested two parallel neighborhoods (PN-VNS) is first developed for the decision on the production plan and replenishment order. Then, the decision on supplier selection needs to be made for the given replenishment order, and a pre-pruning optimization rule is proposed to obtain the optimal supplier scheme. Computational experiments are conducted to evaluate the performance of the proposed algorithm, and the experimental results demonstrate that our algorithm performs better both in terms of solution quality and running time.

Inventory Control for Omnichannel Retailing Between One Warehouse and Multiple Stores

In this article, we research a multiperiod two-echelon inventory control problem for omnichannel retailing [i.e., online channel, offline channel, and buy-online-and-pick-up-in-store (BOPS) channel]. Especially, we jointly consider the limited storage capacity of the stores and the delivery lead time of the warehouse. For the out of stock, the unsatisfied demands for multiple purchasing channels have different processes (i.e., lost sales or backlog), based on the different characteristics of multiple channels. First, we divide the multiperiod inventory control problem for omnichannel retailing into two subproblems (the inventory control problem for the stores and the inventory control problem for the warehouse). Then, to reduce the scale of the first subproblem and the computation effort, we propose a method based on Lagrangian relaxation and value iteration to solve the inventory control problem of stores. Further, based on the results of the inventory control problem of stores, we construct a delay-aware Markov decision process (DAMDP) for the inventory control problem of the warehouse and employ the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning technique based on the constructed DAMDP to solve the second subproblem. Besides, we also extend our analysis to the case of the low-level backlog in buying-online channels (i.e., online and BOPS channels). Finally, some numerical experiments are conducted to illustrate the performance of the inventory control policy for omnichannel retailing and reveal some managerial insights.

A novel two-stage dynamic pricing model for logistics planning using an exploration–exploitation framework: A multi-armed bandit problem

Dynamic pricing is utilized as a tool for determining pricing strategies in different sectors, such as the food industries, airline companies, and transportation services. Dynamic pricing aims to maximize revenue based on learning the price-demand relationship. It is imperative to perform price change testing within a short time frame since rapid changes can negatively impact the market. Therefore, there are two conflicting objectives: maximizing revenue and learning the demand function, which is called the exploration–exploitation concept. In this study, we propose an exploration–exploitation framework, focused on the domestic delivery service of Tipax Express Pars Company (TEPC), to determine the optimal selling price. In the exploration process, the initial demand function is proposed based on previous sales data using weighted linear regression. Then, the selling price and delivery lead-time parameters are incorporated into the demand function hypotheses. After that, the value of the optimal demand function parameters is determined based on the Z-Price Changes Algorithm (ZPCA). In the exploitation process, price optimization is designed as a Multi-armed Bandit (MAB) problem and solved using (i) Upper Confidence Bound Method (UCBM) (i.e., deterministic approach), and (ii) Thompson Sampling Method (TSM) (i.e., stochastic approach). The proposed exploration–exploitation framework is evaluated in 361 days, of which the exploration and exploitation durations are 31 and 330 days, respectively. In this study, 10,000 demand functions are investigated, which leads to the complexity of the developed model. The cross-validation approach, based on Mean Squared Error (MSE), is developed to decrease the complexity of the proposed model. A simulation study of 500 simulations demonstrates that TSM is more useful than UCBM because its solution is more robust. Furthermore, the results demonstrate that TEPC can gain maximum total expected revenue, maximum total number of selections during the exploitation stage, and maximum actual demand using a gaining market share strategy.

Effect of logistics integration and demand and supply uncertainties on manufacturing performance: A mediating role of product characteristics

This study examines the influence of distribution convergence, delivery lead times, manufacturing difficulties, material management, and procurement on company performance. Primary and secondary data from small and large-scale manufacturing enterprises will be gathered and analyzed using structured and explorative methods, with different analyses applied for causal and cross-comparison outcomes. It will be used as a mathematical model for the following investigation. It assessed the variables' correlation and function as a mediator between them. Cronbach's alpha, a measure of internal consistency, was used to assess overall variable agreement. The study shows the role of interdependence in logistics convergence, pricing fluctuation, and demand and supply uncertainty. Demand and supply uncertainty is one of lead time and substantially impacts outcomes. In this research, procurement lead time significantly impacts the relationship between overall market volatility and supply and demand characteristics. Efficacious Manufacturing requires well-planned procurement, as well as extensive logistical integration.

Simple Multi-Attribute Rating Technique for Warehouse Location Selection (Case Study: PT. PPT)

Indonesia’s Manufacturing Industry, encompassing processing and non-oil and gas processing, grew in Q3 of 2022, contributing 17.88% and 16.10% to GDP. Transportation and storage played a significant part, with a 15.93% year-on-year growth in Q1 2023, highlighting the sector’s importance. PT. PPT is a sub-holding of one of Indonesia’s companies. The company manages to trade PP/PE significantly from more sources (import, local producer, or local trader). This research will determine and analyze the criteria for the selection of warehouse locations for PT. PPT analyzes the weight of each criterion at each location in the warehouse location selection process for PT. PPT and obtain the priority location for PT. PPT to build a new warehouse. This research aims to guide and help the decision makers to choose and find a suitable location based on the Centre of Gravity (CoG) analysis conducted by a third-party consultant. The five locations resulting from the Centre of Gravity (CoG) as alternatives will be analysed with Kepner-Tregoe Analysis in the decision-making process, using Situation Appraisal defined in Business Issues. The author then employs a Rich Picture and Fishbone Diagram to assess the scenario or the process of select the new warehouse. The decision analysis was carried out in eight stages using the one of Multi-Criteria Decision Making (MCDM) method, which is the Simple Multi-Attribute Rating Technique (SMART). Lastly, using the preceding analysis, undertake a Potential Problem Analysis. The major criterion is the distance to the customer, which aligns with the company’s emphasis on customer satisfaction. Demand growth affects warehouse capacity, with more growth necessitating a larger facility. Distance from suppliers reduces delivery lead time, and the size of the land must accommodate demand growth and product capacity for PT. PPT.

Exploring the Economic Significance of Arctic Routes in the Supply Chain Field by Contrasting with Traditional Route

With global climate warming, the reduction of Arctic ice and the opening of shipping routes during the summer season, the Arctic route has gradually become an important choice in the field of supply chains as it connects Asia, Europe, and North America. It offers the advantages of reducing shipping time, lowering transportation costs, expanding trade opportunities, and diversifying supply chain routes. This article collected data from websites such as the Clarksons and the Shanghai Shipping Exchange to investigate the current number of voyages along the Arctic route and various factors that impact the cost of navigation. By comparing the navigation costs between the Arctic route and the traditional Suez Canal route, it was found that the Arctic region has higher voyage transportation costs compared to the Suez route, indicating that the Suez route still holds an economic advantage. However, choosing the Arctic route can shorten the delivery lead time and reduce certain supply chain costs.

A continuous approximation location-inventory model with exact inventory costs and nonlinear delivery lead time penalties

Most existing location-inventory models use exact locations (coordinates) for warehouses and customers but use simple approximations for inventory costs. A different approach is presented in this paper. While the locations are loosely modeled using a continuous approximation approach, a reorder point, order quantity inventory model with exact costs is used. Exact expressions for the transport time distribution and backorder time distribution (for Poisson demand) are combined with an exogenously given warehouse fulfillment time and form the delivery lead time. Customers perceive a long delivery lead time negatively. This creates a dynamic between the number of stocking locations used (which affects the transport time and cost) and the inventory policy (which affects the backorder time and inventory costs) because both influence the delivery lead time. Furthermore, we are able to model a nonlinear customer perception of the delivery lead time. Based on the presented model, the general characteristics and mechanisms of such a location-inventory problem are investigated. Among other things, we show for many probability distributions that the average backorder time per backorder is convex and that our model is quasi-convex, which allows for a wide range of optimization methods.

Environmental strategies for a healthcare system with green technology investment and pandemic effects

Today the healthcare industries struggle to cope with the ecological and social challenges for their customer services even while we are entering into a post-pandemic economy. Hospitals are quickly running out of essential medical supplies and firms are fraught to keep up with the rise in pent-up demand due to ongoing supply chain issues caused by the troubles from the pandemic. Therefore, we must design a sustainable inventory strategy for managing the healthcare supply chain (HSC) to ensure customer service and net-zero economy. This study explores sustainable inventory policies in a pandemic situation while considering the transport cost as proportional to COVID-19 intensity. We assume that the pharmaceuticals items have a predictable shelf-life and demand rate directly relates to the effectiveness of the investment in service facilities. The model incorporates the carbon emissions during the activities of transport, production, and storage, which are intended to be cut by green investment. The proposed scenario is modeled as an optimization problem and a solution approach has been designed for finding the sustainable inventory strategies such as order lot-size, delivery lead time, amount of green and service investment with the objective of reducing CO2 and overall cost. Key managerial insights into the influence of pandemic are extracted through the study of numerical and sensitivity. The suggested model is validated in the healthcare sector. The novelty of this research is examining the effect of green investment and COVID-19 pandemic on HSC. This work also reveals how green technology can play a vital role in managing the HSC issues caused by the pandemic in the post-COVID-19 environment.

Quality Comparison of Three Metal Additive Manufacturing Technologies on an Automotive Sealant Nozzle

Additive Manufacturing (AM) [1] is playing every day a bigger role in the automotive industry because of its cost competitiveness, short delivery lead times and potential for design flexibility and optimization. Plastics and polymers are the most common materials used to produce AM parts in this sector, however metal AM is increasing its importance as there are specific applications that require mechanical characteristics that can only be achieved with metals such as stainless steel, titanium, hard steel, copper, aluminum, and others. There is an increasing number of metal AM technologies and Original Equipment Manufacturers (OEMs) competing in the industry with a very widespread list of advantages and disadvantages of each of them. We are at a point where automotive manufacturers need to make a complex decision on which metal AM equipment to purchase. This paper describes the main metal AM technologies and highlight the advantages and disadvantages of each of them. Additionally, three of the most competitive Metal AM technologies are compared: Powder Bed Fusion (PBF), Metal Filament Deposition Modeling (MFDM) and Bound Metal Deposition (BMD) on a specific experimental sample. For this study, a very common and representative automotive part has been chosen that is well suited to be printed in metal and can be manufactured in the three chosen technologies. A nozzle from the automotive body plant used to distribute accuratey a sealant bead onto a body panel before the final assembly operation was selected. These sample parts have been trialed for function and evaluated in general terms from a quality point of view. The conclusions included in this paper will help the automotive industry players understand which technology to use for this specific part and other parts with similar characteristics. Additional work will focus on specific quality characteristics such as material composition, mechanical properties, dimensional accuracy, and specific defects found to compare these technologies in detail. Furthermore, a selection of other automotive parts and technologies will be necessary to enlarge the knowledge on the application of metal AM on this field.