Cost Of Ownership Research Articles

Life Cycle Economic and Environmental Assessment for Emerging Heavy-Duty Truck Powertrain Technologies in China: A Comparative Study of Battery Electric, Fuel Cell Electric, and Hydrogen Combustion Engine Trucks.

Amid ambitious net-zero goals and growing demands for freight logistics, addressing the climate challenges posed by the heavy-duty truck (HDT) sector is an urgent and pivotal task. This study develops an integrated HDT model by incorporating vehicle dynamic simulation and life cycle analysis to quantify energy consumption, greenhouse gas (GHG) emissions, and total cost of ownership associated with three emerging powertrain technologies in various truck use scenarios in China, including battery electric, fuel cell electric, and hydrogen combustion engine trucks. The results reveal varying levels of economic suitability for these powertrain alternatives depending on required driving ranges and duty cycles: the battery electric for regional-haul applications, the hydrogen fuel cell for longer-haul and low-load driving conditions, and the hydrogen combustion engine to meet high power requirements. The GHG mitigation potential of these technologies, on the other hand, critically hinges on energy sources. All three powertrains could achieve over 55% GHG emission reductions if the power sector and hydrogen supply are substantially decarbonized by 2035. The analysis underscores the necessity for a broad policy approach on a life cycle basis to incorporate all powertrain technologies and all energy sources in a complementary manner for facilitating a low-carbon road freight future.

Unveiling sectoral coupling for resilient electrification of the transportation sector

Electrifying the transportation sector is crucial for reducing greenhouse gas emissions and offers numerous benefits including increased energy efficiency, lower total ownership costs, enhanced national energy security, and improved air quality. Despite the availability of necessary technologies, fully integrating the transportation and electricity sectors presents challenges in understanding all benefits and risks. Previous studies have not highlighted the role of coupling between these sectors. To better understand this coupling, this work reviews the structure of the current fossil-fuel-based transportation sector (including its dependence on the electricity sector) and case studies of its vulnerabilities to key risks. By adopting a systemic perspective, we uncover the indispensable interplay between the transportation and electricity sectors, shedding light on previously neglected dynamics. Leveraging the principles of grid architecture (GA), we introduce a hierarchical approach to assess vulnerabilities within the prevailing fuel-based transportation system and elucidate pathways for enhancement through electrification.

Total cost of ownership of heat pumps and policy choice: the case of Great Britain

Total cost of ownership of heat pumps and policy choice: the case of Great Britain

An innovative decision-making method for choosing a bus fleet based on logistics and sustainability aspects

The widespread adoption of electric vehicles (EVs) has played a significant role due to their much smaller carbon footprint compared to their internal combustion engine counterparts. This trend also applies to public transportation in Hungary, where battery electric buses (BEBs) are gradually being incorporated into the fleets of major passenger transport operators. In assessing the total cost of ownership (TCO) of these vehicles, factors such as the expected daily mileage, the current price, capacity, lifecycle, and degradation of the integrated drive train batteries—typically lithium-ion based—play a significant role. This is also considered, if the batteries' second life and reuse can significantly improve the TCO value. Based on the examination of domestic and international literature, it can be established that the selection of the appropriate vehicle fleet exclusively considers the TCO value, which disregards neither the significant benefits arising from the batteries' secondary life cycle, nor considering various quality indicators. This deficiency in fleet selection could result incorrect decisions. In our opinion, the consideration of both logistical and sustainability aspects is indispensable in the decision-making process. To prove this, the paper presents an innovative decision-making method developed by us, which considers the effects of battery degradation related to the secondary life cycle and key quality indicators when selecting the ideal fleet meeting the expected mileage performance. To validate the theoretical background, a case study was also prepared, which is included in the paper. The article also contains considerations related to the topic by Volánbusz Zrt.

What Drives and Inhibits Consumers from Adopting Electric Two Wheelers: A Behavioural Reasoning Theory Perspective

This study contributes to the body of work on electric vehicle adoption by going beyond traditional frameworks like the Technology Acceptance Model, the Theory of Reasoned Action and the Theory of Planned Behaviour. It presents a comprehensive framework that highlights both the drivers and barriers specific to Tier 2 cities, a significant market for two wheelers in India. Although two wheelers are the preferred mode of transport in India, there has been limited research on the adoption of electric two-wheelers (E2Ws) and their role in achieving decarbonization goals. This study aims to assess the adoption of E2Ws in Tier 2 cities across India, which represents a significant market for two wheelers. To explore this, the study employs a qualitative approach using a theoretical framework to understand the reasons behind the adoption and non-adoption of E2Ws in Tier 2 cities. Eighty-nine interviews were conducted in different Tier 2 cities across four northern states. The results identified several key reasons for adoption, including monetary savings, non-financial incentives, driving pleasure, ease of use for women and senior citizens, suitability for local travel, and a pro-environmental approach. Conversely, the barriers to adoption included high ownership costs, negative motives, inadequate vehicle space, poor repair and service infrastructure, distrust, resale concerns, and negative review consensus. From a managerial perspective, this study provides strategic insights for marketers, outlining ways to promote the mass adoption of E2Ws by understanding and addressing key motivators and inhibitors.

Modelling the transformation of energy-intensive industries based on site-specific investment decisions

The transition towards climate-neutral industry is a challenge, particularly for heavy industries like steel and basic chemicals. Existing models for assessing industrial transformation often lack spatial resolution and fail to capture individual investment decisions. Consequently, the spatial interplay between industry transformation, energy availability, infrastructure availability, and the dynamics of discrete investments is inadequately addressed. Here we present a site-specific approach that considers individual industrial sites to simulate discrete investment decisions. The investment decision is modelled as a discrete choice among alternative technologies with their total cost of ownership as the main decision criterion. Process costs depend on the scenario-specific assumptions, such as energy carrier prices, policy instruments and local infrastructures. The age of production units and their reinvestment cycles are considered the main restrictions on the dynamics of the transition. The results provide high spatial resolution to capture the spatial and temporal dynamics of industry transition under varying process and policy assumptions. The presented model and its results can be coupled with energy system models to assess the implications of site-specific industry transition on energy system related research questions. We conduct an exemplary case study for a transformation pathway of the European primary steel production.

Study of the Total Ownership Cost of Electric Vehicles in Romania

Due to the significant increase in the number of EVs, this manuscript presents a study of the total ownership cost of electric vehicles in Romania. The total cost of ownership (TCO) includes the initial purchase price, maintenance costs, power prices, and government incentives or subsidies unique to the market in Romania. The TCO was calculated for battery electric vehicles (BEVs) and internal combustion vehicles (ICEs). Several vehicles were selected for the study, representing the models with the highest sales in Romania and a similar price range. The results show that EVs have a lower TCO compared with internal combustion vehicles if the battery replacement cost for EVs is not considered in the analysis. If this cost is considered, the TCO for the BEVs has a significant increase due to the high cost of the battery. Another analysis performed regards the CO2 emissions. These are higher for ICEs compared to BEVs, so the BEVs help reduce emissions.

Co-Design Optimization and Total Cost of Ownership Analysis of an Electric Bus Depot Microgrid with Photovoltaics and Energy Storage Systems

Due to the increasing share of battery electric buses (BEBs) in cities, depots need to be adapted to the increasing load demand. The integration of renewable energy sources (RESs) into a depot can increase the self-consumption, but optimal sizing is required for a cost-efficient and reliable operation. Accordingly, this paper introduces a co-design optimization framework for a depot microgrid, equipped with photovoltaics (PVs) and an energy storage system (ESS). Three European cities are considered to evaluate the effect of different environmental conditions and electricity prices on the optimal microgrid design. Accurate models of the different subsystems are created to estimate the load demand and the power generation. Different energy management strategies (EMSs), developed to properly control the power flow within the microgrid, are compared in terms of operational costs reduction, one of which was also experimentally validated using a hardware-in-the-loop (HiL) test setup. In addition, the total cost of ownership (TCO) of the depot microgrid is analyzed, showing that an optimally designed depot microgrid can reduce the charging-related expenses for the public transport operator (PTO) by 30% compared to a scenario in which only the distribution grid supplies the BEB depot.

Airborne soil-derived dust hazards in aviation

Abstract Airborne mineral dust poses a safety challenge for aviation. Several fatal accidents have happened in dust-laden air due to reduced visibility, strong gusty winds, and wind shear. Dust-induced icing also contributed at least to two fatal accidents. Furthermore, atmospheric dust has long- and short-term effects on aircraft operating condition due to corrosion and abrasion on the aircraft surfaces, and molten ingress deterioration of engine hot section components. The combined impact can increase operating and maintenance costs, and increase the overall cost of ownership. While the scientific community has started preparing and providing products based on atmospheric dust modeling and observation, there are still important data and information gaps in the fundamental science. These include (i) insufficient data which could be used to better understand the effects of dust on aircraft as well as on ground systems and operations (e.g., four-dimensional information of dust mineralogy, cost-benefit analysis of the impact of dust on aviation along flight routes), (ii) the identification of airborne dust monitoring and modeling products and services that could enable the flow of relevant information in commercial aviation and in decision-making workflows, and (iii) the underdeveloped, unclear, or absent role of dust hazards in regulations and operational procedures as well as in the training, skillset, and knowledge base of pilots. This review is aimed at both academic and aviation stakeholders, and presents the current state-of-the-art knowledge at the intersection of dust hazards, aviation safety, and impacts on flight operations and aircraft maintenance.

A Study on Electric Vehicle Footprint in South Africa

There has been a progressive global increase in the usage of electric vehicles in this dispensation. This is mostly due to the need to decarbonise the transport sector and mitigate the concerns of climate change and depleting oil reserves of which South Africa is not an exception. In fact, South Africa is the country with the highest CO2 emissions in Africa and can reduce its carbon footprint by embracing green mobility. Compared to the internal combustion engine (ICE) market, the electric vehicle (EV) market in South Africa is still in its early stages, with limited local production and usage since its introduction to the country’s automotive sector in 2013. Therefore, in this study, the usage of EVs in South Africa, along with adoption rates and challenges were carried out to make a stronger case that would offer a better pathway for increased EV adoption in the country. It has been discovered that the slow adoption rate of EVs is due to factors such as EV procurement, ownership costs, vehicle parts, safety issues, battery technology, tax and import duties, load shedding, and availability of charging stations. This paper also provides insights into government policies, funding, and other efforts that can support EV adoption in the country through the analyses of primary and secondary data. The proposed strategies include the introduction of tax rebates on imported EVs, local production of EVs and their vehicle parts, retrofitting ICE vehicles to EVs, and science-informed strategies to transition from ICE to electric vehicles. Furthermore, more renewable energy grid integration and renewable energy-powered EV charging stations would also provide support for the energy required to power EVs even during load shedding. Preliminary findings from the survey also suggest that the local production of EV components and government-sponsored training programmes on various EV skills are crucial for increasing the adoption rate of EVs in the country.

Comparison of Container Orchestration Engines

Container orchestration engines have become essential for managing containerized applications in modern cloud-native architectures. These tools automate the deployment, scaling, networking, and management of containers, enabling seamless application lifecycle management. With a growing number of orchestration solutions available, understanding their features, strengths, and limitations is crucial for selecting the right platform. This paper presents a comparative analysis of prominent container orchestration engines, highlighting their core functionalities, architectural design, and suitability for different use cases. Key areas of comparison include resource allocation, fault tolerance, scalability, and integration with DevOps workflows. The study explores how these platforms address challenges such as dynamic workload management, service discovery, and inter-container communication while maintaining high availability and system resilience. The analysis reveals that while some platforms excel in simplicity and ease of deployment, others provide advanced features tailored to complex, large-scale systems. Additionally, open-source orchestration tools are evaluated against proprietary solutions in terms of community support, customization capabilities, and total cost of ownership. This comparative study aims to assist organizations and developers in identifying the most suitable container orchestration engine based on their operational needs and technical constraints. By understanding the trade-offs and unique features of each platform, stakeholders can make informed decisions that optimize performance, reduce operational overhead, and support efficient application delivery in a rapidly evolving technology landscape. This abstract underscores the importance of aligning platform capabilities with organizational goals for successful containerized application management.

Modular multilevel converter-based hybrid energy storage system for electric vehicles: Design, simulation, and performance evaluation

ABSTRACT Electric vehicles (EVs) are critical to reducing greenhouse gas emissions and advancing sustainable transportation. This study develops a Modular Multilevel Converter-based Hybrid Energy Storage System (HESS) integrating lithium-ion batteries (BT) and supercapacitors (SC) to enhance energy management and EV performance. A control strategy equalizes voltage across SC modules, achieving deviations within 1% of reference values, while optimizing energy transfer for peak demands. Simulations using PSIM software demonstrate a 12% improvement in energy efficiency and a 15% enhancement in dynamic response compared to battery-only systems. The optimized energy flow extends battery life by 20%, reducing overall ownership costs. Evaluations under the New European Driving Cycle (NEDC) validate the system’s performance, with recorded energy efficiencies of 87% for urban driving, 89% for suburban conditions, and 92% for highways. Supercapacitor modules respond to peak demands within 0.8 seconds, delivering a maximum current of 170 A during acceleration. This innovative approach ensures balanced energy distribution, reduces voltage fluctuations, and increases overall system robustness. Future work will focus on experimental validation under real-world conditions and integrating advanced SC materials to enhance performance. This work bridges a critical gap in energy storage systems for EVs, contributing to cleaner transportation solutions and aligning with global sustainability goals.

A novel simulation and supervised machine learning-based prediction framework to predict the total transportation and energy costs for single-family households

This paper focuses on predicting the total transportation and energy costs (TTEC) for single-family households. A system boundary consisting of grid-powered electricity (GE) and solar-powered electricity (SE) as energy inputs and transportation vehicles that include Gasoline Vehicles (GV) and Electric Vehicles (EV) as transportation methods for energy outputs is studied. A novel three-stage evaluation framework is proposed to predict the TTEC under varying single-family household parameters. In the first stage, an energy balance simulation model is proposed to estimate the TTEC for an individual household. In the second stage, the simulation model is run several times under varying parameters to develop synthetic data that is used as input for the third stage supervised machine learning (SML) models. In the third stage, numerous SML models are trained and tested to determine the best SML model that enables us to predict the TTEC with high accuracy. This best SML model can be used as a substitute for simulation model, thereby reducing the computation burden of running the simulation model for each new single-family household. A case study of single-family households in Central Texas in the US is used as an application of the framework. The results indicate that regression SML models are best in predicting the total costs with an adjusted R-squared of 99.13% and 98.88% on training and testing datasets, respectively. In addition, the parameter analysis of regression SML models suggests that the house size, number of GVs, number of EVs, EV and GV ownership costs, and solar implementation at households are the most important parameters to predict TTEC for single-family households. Counterintuitively, number of residents, GV and EV mileage, solar system size, battery capacity and peak solar hours are not significant parameters that contribute to TTEC prediction.

(Invited) Stable Catalysts, Functionalized Supports and Fluorocarbon Additives for Fuel Cell Electrodes

Proton Exchange Membrane (PEM) fuel cell operating on hydrogen is an alternative for the diesel-powered internal combustion engines for medium- and heavy-duty vehicle applications due to their higher efficiency and zero-local greenhouse gases emission. To achieve this prospect, PEM fuel cells need to demonstrate improved durability over a much longer operational lifetime of ~30,000 hours (or an equivalent of a million miles of driving distance) and lower total cost of ownership (sum of capital and operational expenses) compared to the diesel-based incumbent. In this regard, the United States Department of Energy (DOE) has set an end-of-test platinum specific power density target of 2.5 kW/gPGM at a rated voltage of 0.7 V after running a 25,000-hour equivalent accelerated stress test (AST). Japanese New Energy & Industrial Development Organization (NEDO) has set an even more aggressive 2030 ultimate target of 5.3 kW/gPGM (Figure 1).Fuel cell cathode electrodes are composed of high surface area carbon black supported platinum or platinum-alloy nanoparticles for electrocatalytic Oxygen Reduction Reaction (ORR) dispersed along with perfluorosulfonic acid (PFSA) ionomer to serve both as proton conducting and binding agent. The continuous loss in power delivered by a fuel cell could be attributed to the i) loss in electrochemical surface area of Pt, ii) loss in kinetic activity of Pt/PtCo catalyst and iii) contamination effects of dissolved cobalt alloying element in the ionomer phase. Of this, the loss in electrochemical surface area is the single largest contributor to the fuel cell voltage degradation which is due to the coarsening of platinum nanoparticles either via migration-coalescence on the carbon support and/or via dissolution-reprecipitation (electrochemical Ostwald ripening).In this context, recent developments in fuel cell cathode materials from General Motors focusing on improving durability by modifying the platinum/carbon support interface and re-designing platinum/ionomer-water interface will be presented. These include the development of i) durable Pt catalysts via surface protection with ZrO2-x nanoclusters,[1] ii) sulfate functionalized carbon supports for alternate electrode structures,[2] and iii) small molecule fluorocarbon modified catalysts interfaces for improved durability. Acknowledgements: This work was partially supported by U.S Department of Energy (DOE), Hydrogen and Fuel Cell Technologies Office (HFTO) under grants DE-EE0008821 and DE-EE0010749.

Assessing the viability of dynamic wireless power transfer in long-haul freight transport: A techno-economic analysis from fleet operators’ standpoint

This study presents a techno-economic assessment of dynamic wireless power transfer for long-haul freight transport, focusing on the fleet operator’s perspective. In particular, we compared three different powertrain technologies: a conventional powertrain and a battery-electric with or without a dynamic charger installed. For all three technologies, we developed a cost model to assess the total cost of ownership for a fleet operator using different scenarios.Notably, dedicated cost models were devised to estimate energy carrier costs and costs related to time loss incurred by fleet operators due to extended delivery times of electric trucks compared to conventional ones. The novelties in the cost model are twofold. First, dedicated cost models have been devised to estimate the costs related to the energy carriers (including the cost of infrastructure) and to the time loss incurred by fleet operators due to the extended delivery times of electric trucks compared to conventional ones. Second, the energy consumption by source and travel time were derived from an ad-hoc developed simulation approach models longitudinal dynamics of the case-study as well as the powertrain’s performance on the basis of experimentally derived look-up tables provided by manufacturers as well as by previous research projects. The simulation results provided by this model are instrumental to our enhanced cost model as it provides the required inputs and it allowed us to tailor the results to a specific delivery mission.Our results provide valuable insights for fleet operators considering the adoption of zero-emission trucks and to policy-makers and other infrastructure stakeholders regarding the conditions required for the cost-effectiveness of electric road systems.

A relevant method for evaluating information management systems in manufacturing

In the context of growing digitalization and the transition to domestic information management systems in manufacturing, it becomes critically important to develop adequate methods for evaluating their effectiveness. The relevance of this study is due to the need to create effective tools for the analysis and selection of information management systems that meet the specifics of the Russian market and the conditions of import substitution. The purpose of the study is to develop and justify a method for evaluating management information systems that take into account both quantitative and qualitative indicators. Methodologically, the study includes an analysis of existing valuation methods such as economic value added and total cost of ownership, identifying their shortcomings and developing a new method based on a weighted sum of criteria. The application of the proposed method has shown that it provides a more accurate and comprehensive assessment, taking into account technical characteristics and user satisfaction, which is critical for effective intra-company and strategic planning. The results confirm the hypothesis that the integration of quantitative and quantitative indicators, taking into account the importance coefficients, increases the reliability of the assessment of the information management system. Limitations include high resource requirements and the need to involve internal and external experts, which may limit practical applications. In the future, automation of the criteria normalization process and adaptation of the method to other industries are recommended.

A real-time energy and cost efficient vehicle route assignment neural recommender system

This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e. using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-k vehicles star ranking system, and (2) engage in more general assignment problems where n vehicles need to be deployed over m(m≤n) trips. This new assignment system has been deployed and integrated into the POLARIS.11POLARIS is an Argonne-based high-performance, open-source agent-based modeling framework for simulating large-scale transportation systems. Transportation System Simulation Tool for use in research conducted by the Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium (SMART, 2024).

Least-cost light-duty vehicle fleet decarbonization and the electric vehicle conundrum

This paper evaluates the greenhouse gas (GHG) emissions and financial performance of the U.S. light-duty vehicle (LDV) fleet. It develops dynamic models for conventional (ICEV), hybrid (HEV), plugin hybrid (PHEV) and battery electric (BEV) LDVs in four vehicle classes (compact, sedan, SUV and pickup truck), estimating their component sizes, purchase prices, embodied emissions, fuel consumption, life cycle GHG emissions and total cost of ownership (TCO) as a function of vehicle miles travelled (VMT). These dynamic models are applied to the US LDV fleet with its wide range of VMT per vehicle, revealing that ICEVs have the lowest TCO at lower VMT, but HEVs and BEVs become more cost-effective as VMT increases. Financially optimal powertrain choice can then reduce fleet-wide GHG emissions by one-third with current technologies, without any subsidies or increased costs. Technological advances, particularly in battery costs and electricity emissions, are then powerful drivers of further fleet decarbonization.

Techno-economic analysis of diesel, natural gas, electric and hydrogen buses

Abstract Many regions and cities are implementing Electric (BEB – Battery Electric Bus) and hydrogen (FCB – Fuel Cell Bus) buses instead of the diesel (Diesel) and natural gas (CNG – Compressed Natural Gas) traditional ones. Many papers and reports compare the different Total Cost of Ownership of these buses but not always clarify mission, powertrain and context data. This study, starting from literature analysis, referring to a specific typical urban bus mission (17 km/h average speed per 12 h daily service), quotes techno-economic-environmental buses (purchase, maintenance, energy consumption and CO2 emissions costs), powertrains (combustion engine, battery, fuel cells, hydrogen storage) and context (diesel, CNG, electricity, hydrogen costs) data. Furthermore, a comparative analysis is carried out considering different operational scenarios based on high or low consumptions, high or low electricity costs, three hydrogen production ways and current (current, batteries and hydrogen technologies costs) and future (15% increase of diesel and CNG costs and decrease of batteries and hydrogen technologies costs) so evaluating 24 scenarios. The results shows that TCO is mainly constituted by every year costs (i.e. maintenance plus emissions and energy consumption,) respect to the one-time cost (so divided by the lifetime, i.e. purchase costs) except in the more competitive hydrogen scenarios where these 3 costs correspond equally to a 30% of the overall TCO. Thus, the bus choice, have to be made on the specific technologies and energies context costs instead on the international buses purchase cost. In fact, it is not possible to define a best technology for any scenario, especially for the future scenarios (so with a variation of diesel, CNG, battery, fuel cells, electricity and hydrogen costs). The best technology will depend on the specific context, and so specific analysis have to be made before to choose the technology to be applied. Indeed, CNG in the current scenarios is always the best technology followed by diesel, but CNG and diesel have emission costs and fossil fuel cost are expected to increase. BEB in the current and future scenarios are never the best technologies but it can be the best technology in scenarios with lower autonomy. FCB in the future scenarios are the best technology for 10 of the 12 scenarios analysed, that it is possible to say that FCB, taking into account the great possibilities of hydrogen production and the flexibility in power and energy respect the electric, even with the lower powertrain efficiencies respect to electric, is a technology that can be applied in many different contexts.

Practice makes perfect: Using the total cost of ownership to teach global locational decision making

In this paper we introduce a three-stage Total Cost of Ownership (TCO) experiential learning exercise. Using data collected from 199 students across eight different semesters, we offer hypotheses grounded in the eclectic theory of international production and in behavioral decision making (System 1 and System 2 thinking) to make recommendations for educators and managers. In part one of the exercise, using System 1 thinking, we asked students to select far-shoring (e.g., China) or near-shoring (e.g., Mexico) as a manufacturing location and provide rationale to support their decision. We found that students selecting far-shoring used resource- and efficiency-seeking criteria while students selecting near-shoring used strategic- and market-based criteria. In addition, we found that students were prone to possible availability bias. We identified demographic factors impacting the decision-making process, and highlighted important variables not utilized by students enabling us to recommend changes in college curriculums. In the second and third parts of the exercise, we asked students to reanalyze their decisions using Total Cost of Ownership (System 2 thinking) and then reflect on their pre and post TCO decisions. Interestingly, though unexpectedly, it was encouraging to see a considerable number of students either willing to change their perspective or provide citations or scholarly support as part of the reflection. Such an experiential learning intervention including reflection and documentation is an important exercise to build current and future supply chain managers who can adapt to a dynamic, complex, and global environment. Our study adds to the literature by demonstrating the role of bias in global sourcing decision making and ways to mitigate for it.