Original Equipment Manufacturers Research Articles

Competition and organizational structure co-optimization of OEMs in a product-service supply chain

Smart Connected Products (SCPs) and Digital Services (DSs) play pivotal roles in facilitating the digital and intelligent transformation of the manufacturing industry. In the context of the service-oriented transformation of manufacturing, SCPs are sold by Original Equipment Manufacturers (OEMs) via a retailer, whereas DSs are either provided directly by OEMs or indirectly through the retailer, thereby forming a product-service supply chain. This paper constructs a three-stage dynamic game model and analyzes two DS strategies, i.e., integrated and separated DS strategies, for competing OEMs by integrating and separating manufacturing and DS departments. These strategies encompass four scenarios: both OEMs integrate them, both separate them, and one OEM integrates while the other separates them. We analyze the equilibrium DS levels, the prices of SCPs and DSs, demands, and profits in four scenarios, and explore the service and pricing decisions of supply chain members at equilibrium.The results indicate that OEMs can achieve higher profitability by separating manufacturing and DS departments as competition intensity increases within a certain range. Secondly, when both OEMs adopt the same strategy, integration of departments is more profitable if competition is low, whereas separation yields higher profits when competition is intense. Interestingly, two Nash equilibria emerge: an OEM achieves the highest profit by adopting an integrated DS strategy in an asymmetric scenario when competition is weak and digital R&D is challenging. Conversely, both OEMs can reach a Nash equilibrium by separating their departments when competition is intense and digital R&D is relatively simple. Thirdly, from the perspective of the retailer and the entire supply chain, integrating manufacturing-service at both OEMs provides the highest return when competition is low. However, under intense competition, the highest profits are achieved when OEMs adopt different DS strategies. Finally, in a highly competitive market without a retailer, an asymmetric scenario proves most profitable when digital R&D investment is low, whereas separating manufacturing-service yields the highest profits when digital R&D investment is high.

Condition-based maintenance for multi-component systems: A scalable optimization model with two thresholds

Many original equipment manufacturers (OEMs) provide customized after-sales service contracts tailored to their customers’ specific requirements. While these customized offerings may increase customer satisfaction and loyalty, they are likely more expensive, and that in times that OEMs are striving to reduce maintenance costs and ease the workload of their service engineers. To address this challenge, we introduce a quantitative methodology for shaping maintenance policies that minimize overall maintenance costs for systems comprising multiple heterogeneous components over a finite lifespan. Our proposed two-threshold condition-based maintenance policy incorporates scheduled visits and semi-urgent interventions, using component-level control thresholds to preventively trigger component replacements. Scheduled visits provide opportunities for grouping component replacements, capitalizing on positive economic dependencies.Our optimization model and solution methodology are tailored to systems consisting of numerous components. We assume a fixed time interval between consecutive scheduled visits, allowing us to employ a decomposition approach that ensures model scalability. The optimal policy for each component is determined by formulating a single-unit replacement model as a finite horizon Markov Decision Problem solved via dynamic programming. Simultaneously, at system level, we optimize the maintenance interval for the entire system using an iterative approach.The results of our case study highlight that complementing scheduled visits with semi-urgent interventions - both triggering preventive replacements - leads to a reduction in the volume of corrective maintenance. From a practical standpoint, our findings offer valuable insights for OEMs seeking to enhance their after-sales service contracts while concurrently reducing maintenance-related costs. Specifically, our model is particularly valuable for OEMs that service systems with high shutdown costs, rely heavily on customer satisfaction, and favor more attractive policies for their service engineers (i.e., reducing the number of emergency corrective interventions).

Demand Information Forecasting and Sharing in a Remanufacturing Closed‐Loop Supply Chain

ABSTRACTWe delve into the incorporation of demand information forecasting and the subsequent information sharing in a closed‐loop supply chain (CLSC). We consider the competition between an original equipment manufacturer (OEM) and an independent remanufacturer (IR). The OEM autonomously determines whether to forecast demand information and further considers whether to disseminate the realized demand signal to the IR. We find that the IR adopts either a partial‐ or a full‐remanufacturing strategy to compete with the OEM. The OEM shares negative demand signals with the IR, as such signals decrease the retail prices and increase the production quantity of all‐new products. Additionally, the sharing of negative demand signals helps alleviate the cannibalization effect caused by the introduction of remanufactured products. When examining the optimal sharing strategy in equilibrium, the OEM only invests in forecasting demand if the forecasting cost is relatively low. Furthermore, the numerical studies reveal that, in equilibrium, the OEM does not necessarily benefit from possessing more precise demand information. Additionally, we assess the influence of consumers' environmental awareness on the production decisions of both companies. The findings indicate that heightened environmental awareness prompts the IR to adopt a full‐remanufacturing strategy, and the information management strategies remain robust.

Cybersecurity Maintenance in the Automotive Industry Challenges and Solutions: A Technology Adoption Approach

Numerous attempts have been made to create a secure system that meets the criteria and requirements of the automotive vehicle development life cycle. However, a critical gap exists in the secure development lifecycle, particularly concerning the development and maintenance of software after the vehicle has been sold by the manufacturer. This step is often overlooked by original equipment manufacturers (OEMs), especially after the expiration of the vehicle warranty period, given the cost that it will require to update and test the software in their vehicles. This paper addresses the issues that affect current and future vehicle cybersecurity, during the maintenance of cybersecurity, and how the neglect of it could end up creating hazards for the vehicle owner or other road users. To accomplish this, we will employ the technology adoption model (TAM) as a theoretical framework, which is used to understand and predict how organizations adopt technology. Thus, through qualitative and quantitative research, including text mining, we identify the challenges in the adoption and diffusion of cybersecurity maintenance in the automotive sector and its supply chain. In addition, we propose possible solutions on how to maintain a level of security that will benefit road users, OEMs and regulators, covering the cybersecurity needs for the vehicle’s usable life, taking into account the vehicle’s heterogeneity of components and technology, connectivity, environmental impact and cost of production and maintenance of a vehicle.

Cellular Vehicle-to-Everything Automated Large-Scale Testing: A Software Architecture for Combined Scenarios

As the commercialisation of Intelligent Connected Vehicles (ICVs) accelerates, Vehicle-to-Everything (V2X)-based general testing and assessment systems have emerged at the forefront of the research. Current field testing schemes mostly follow the norms of traditional vehicle tests. In contrast, Original Equipment Manufacturers (OEMs) have increasingly focused on the potential influence of V2X communication performance on the application response characteristics. Our previous work resulted in a C-V2X (Cellular-V2X) large-scale testing system (LSTS) for communication performance testing. However, when addressing the need to combine application and communication, the system software faces confronts heightened technical challenges. This paper proposes a layered software architecture for the automated C-V2X LSTS, which is tailored to combined scenarios. This architecture integrates scenario encapsulation technology with a large-scale node array deployment strategy, enabling communication and application testing under diversified scenarios. The experimental results demonstrate the scalability of the system, and a case study of Forward Collision Warning (FCW) validates the effectiveness and reliability of the system.

Electric Vehicle Thermal System Concept Development for Multiple Variants Using Digital Prototype and AI

The automotive industry is experiencing a surge in system complexity driven by the ever-growing number of interacting components, subsystems, and control systems. This complexity is further amplified by the expanding range of component options available to original equipment manufacturers (OEMs). OEMs work in parallel on more than one vehicle model, with multiple vehicle variants for each vehicle model. With the increasing number of vehicle variants needed to cater to diverse regional needs, development complexity escalates. To address this challenge, modern techniques like Model-Based Systems Engineering (MBSE), digitalization, and Artificial Intelligence (AI) are becoming essential tools. These advancements can streamline concept development, optimize thermal and HVAC system design across variants, and accelerate the time-to-market for next-generation EVs. The development of battery electric vehicles (BEVs) needs a strong focus on thermal management systems (TMSs) and heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in maintaining optimal battery temperature, maximizing range and efficiency, and ensuring passenger comfort. This article proposes a digital prototype (DP) and AI-based methodology to specify BEV thermal system and HVAC system components in the concept phase. This methodology uses system and variant thinking in combination with digital prototype (DP) and AI to verify BEV thermal system architecture component specifications for future variants without extensive simulation. A BEV cabin cooling requirement of 22 °C to be achieved within 1800s at a high ambient temperature (45 °C) is required, and its verification is used to prove this methodology.

Cooperation mode selection strategies in platform ecosystems: analyzing brand value, cross-selling, and platform empowerment

Purpose This paper aims to investigate the vertical cooperative relationship between the core enterprise and the manufacturer within the platform ecosystem, specifically analyzing the optimal decision-making processes of both parties under the original equipment manufacturer (OEM) and original brand manufacturer (OBM) modes. Design/methodology/approach This paper uses game theory to analyze the problem, considering factors such as brand value difference, cross-selling and platform empowerment. It constructs the game models for both OEM and OBM modes and discusses the selection strategies for the cooperation mode. Findings The results indicate that the choice of cooperation mode by the manufacturer and the core enterprise depends on the relative size of their brand values. In cases of inconsistent choices, cooperation can be improved by designing a transfer payment contract. When the brand value is constant, the product price is comprehensively affected by cross-selling revenue, price elasticity coefficient, cost coefficient of sales effort and cost coefficient of platform empowerment. The enterprise reduces the price only when the potential revenue brought by increasing product sales exceeds the marginal profit brought by increasing product pricing; otherwise, it raises the sales price. Originality/value The platform ecosystem is emerging as a future direction for business mode development. However, there is a paucity of research on the cooperation modes between manufacturers and core enterprises within the platform ecosystem.

Unravelling supply chain complexity in maintenance operations of battery production

Europe’s emerging lithium-ion battery production sector faces immense challenges with Supply Chain Complexity (SCC). This article explores sources of and responses to SCC within maintenance operations of battery production. Using an engaged scholarship approach within automotive original equipment manufacturers (OEMs) and battery cell manufacturers in Sweden, qualitative data were collected and analysed using SCC theory. Our core findings reveal 37 sources of SCC classified by origin and type and 40 responses across four practice clusters. This study offers in-depth insights into the potential impact of SCC on maintenance operations in battery production and provides actionable guidance for managing complexity. It also identifies five future research avenues: investigating complexity interactions, matching sources and responses, exploring complexity-inducing responses, identifying internal-external interfaces and examining social aspects of SCC. In effect, the study sets the agenda for research on battery production maintenance and positions SCC as a versatile theoretical lens for understanding the emerging battery sector.

Maintenance plan adaptation based on health ratings of servitised machines through a fleet-wide machine clustering method

The increased requests for value-added services to integrate product performance push manufacturing companies to extend their service offerings to meet customers’ needs. In this context, maintenance planning can leverage new possibilities offered by digital technologies for data analytics services. The present research then proposes an approach for maintenance plan adaptation based on a data-driven method applied over a fleet of machines installed in different production sites. The method relies on collaborative prognostics to develop a clustering of machines’ behaviour aimed at providing the health ratings of the machines and the subsequent maintenance plan adaptation due to the deviation from the expected behaviour. The method is adopted from the perspective of an Original Equipment Manufacturer, as part of a transformation path towards an advanced provision of digitalization for maintenance service offerings. The method is validated in the context of two lines at selected customer’s premises. This demonstrates the viability and effectiveness of adapting the maintenance plans thanks to the data analytics in light of the current behaviour of the machines within the lines.

Paradigm shift in mechanical system design: toward automated and collaborative design with digital twin web

AbstractAnalyzing multi-vendor mechanical system designs requires a significant amount of manual work, resulting in a design paradigm where analysis is conducted only after the design is locked and components are selected. This leads to a suboptimal design with compatibility issues, over-dimensioned components, inferior performance, poor energy efficiency, and a lack of collaboration between OEMs (original equipment manufacturers) and system integrators. To overcome these issues, this paper proposes Co-Des (collaborative design) framework for automated and collaborative multi-vendor system design. The framework relies on standardized digital twin documents (DTD) of system designs, components, and analyses. The discoverability and distribution of these DTDs are enabled with digital twin web (DTW). Co-Des framework allows for finding suitable components for the design task by automatically running selected analyses employing component digital twins. In addition, OEMs can provide customized components for system integrators using the initial system design defined in the system design DTD. The use of the Co-Des framework was demonstrated with a windmill powertrain design use case, and the applicability of the automated assembly analysis for component selection was verified with performance measurements. The adoption of the proposed framework will lead to a paradigm shift from manual and siloed work relying on the exchange of PDFs to a more automated and collaborative design of mechanical systems. The adoption rate is defined by the willingness of system integrators to publish their initial system designs and OEMs their components as public digital twins.

Understanding after-market service channel selection for complex product-services supply chains: A game-theoretical approach

Today, cooperation between an OEM (Original Equipment Manufacturer) of Complex Products and Systems (CoPS) and her multiple Key Component Suppliers (KCSs) is characterized by a strategic partnership where long-term benefits are sought for both parties. Since CoPS after-market service can be provided by the KCS, the OEM, or an MRO (Maintenance, Repair, and Overhaul) party, different after-market service channels can be established. In this paper, we model each of these channels using game theory, compare their main features, and discuss the selection of the optimal after-market service channel. We show that the duration of the after-market service is not influenced by the service channel if the service cost remains constant. If the after-market service costs are equal for all parties, each party would prefer the other to perform the service. In the case of different costs, both the cost of the service and potential market size have the same effect on the OEM’s preference for the after-market service channel as on the CoPS ordered quantities. Furthermore, OEM and KCS after-market service channel preferences depend on market size, and “win-win” situations are discussed for the different channels, along with their impact on the long-term stability of the strategic partnership between KCS and OEM. Finally, a sensitivity analysis is carried out to examine the impact of several key parameters. The results presented in the paper serve to deepen the understanding of complex product-service systems and offer a number of managerial insights to select an appropriate after-market service channel for KCSs.

A dynamic resilience management framework for deep-tier supply networks

The unprecedented supply chain disruptions caused by COVID-19 has had severe operational and financial consequences to firms across industries and continents. While tactical reactionary strategies can help, firms are in need of proactive management approaches to design more resilient supply chain networks in the first place. Firms are looking for an effective framework to design and monitor supply networks, mitigate disruption consequences, and manage resilience under different scenarios. We propose a framework to manage the resilience of deep-tier automotive supply networks by integrating a simulation-based resilience assessment scheme for effectiveness with an efficient optimization-based framework to find optimal strategies for handling regular disruption events. The framework promotes network analysis techniques combined with discrete-event simulation informed by secondary data sources and global supply risk databases for improving resilience management. We validate the effectiveness of the proposed framework using a real-world global automotive original equipment manufacturer case study. Our results demonstrate that the proposed dynamic framework relying on deep-tier visibility can optimize resilience strategies through all key performance indicators. The results show an average of 35% and 40% reductions in back-ordered cost and shipment delays, respectively, with a marginal growth in holding cost when the proposed framework is implemented with deep-tier visibility.

Mitigating data inaccuracy and supply chain challenges in Western Romania's automotive industry

The purpose of this paper was to emphasize the importance of data accuracy within internal logistics systems and their extended influence on supply chains in automotive industry through 6-month multiple case studies conducted on 3 first tier original equipment manufacturers (OEM) based in Western Romania. The study investigates the most common causes of data inaccuracies among automotive suppliers and their approaches to reduce consequential supply chain issues and be more agile. Data collection and analysis revealed that main issues arise due to ordering quantities mismatching actual customer demand, a wide range of order lot sizes, lead times and delivery reliability concerns and the reluctance to shift away from mainstream cost-effectiveness and towards strategic added value thinking. These issues sourced significant other related operational challenges such as excessive inventory, short-term stockouts and subsequent express shipping services or product-related inconveniences (quality and capacity levels, contracted volumes and dedicated lines). The paper sources different logistics and supply chain strategies used by the 3 OEMs, their features and operational performance, as well as their overall effectiveness, which can be applied by other automotive industry suppliers to improve own results. Introducing more reliable real-time data collection tools and performance metrics has started hauling more focus towards solving these prevalent issues with some ongoing improvement projects showing up to 25% better results. For one of the 3 OEMs introducing a new warehouse management system has already sourced an overall quality increase (5 percentage points) due to a 60% higher utilization of its production equipment.

Effect of Grinding Media Grading on Liner Wear and Load Behavior in a Ball Mill by Using Rocky DEM

The liner is a wear-prone component in ball mills, subject to continuous impacts, squeezing, and abrasion from the grinding media during operation. Its service performance directly affects the working efficiency of the ball mill. The service life of mining ball mill liners is about 8 months, and frequent downtimes occur due to liner wear and loss of effectiveness, with liner replacement accounting for about 6% of the total cost, resulting in huge economic losses. This paper focuses on a Φ305 mm × 150 mm experimental ball mill, using the discrete element software Rocky Discrete Element Method (DEM) (software version number Rocky 2022 R2) for simulation modeling analysis. With Φ10 mm and Φ20 mm mono-size particle simulations serving as reference groups, this study investigates the motion states and liner abrasion patterns under different liner heights for both sizes of grinding media in Equilibrium Quality Manufacturer (EQM) and Original Equipment Manufacturer (OEM) gradations. The results indicate that the impact of liner height on the wear of the ball mill liners is related to the size and gradation of the grinding media. The degree of liner wear from highest to lowest is as follows: EQM > Φ20 mm > OEM > Φ10 mm. Due to the effect of the cylinder end cap, the wear at the axial center of the ball mill liner is more severe than at both ends, and the wear on the facing side of the liner is more severe than on the backside. A thorough study of the influence of ball mill grinding media gradation on the wear pattern of liners is of great theoretical significance for optimizing liner structures, improving grinding efficiency, and promoting energy saving and cost reduction in ball mills. This study provides theoretical guidance for understanding the mechanisms behind liner wear in ball mills and predicting the liner lifespan.

Modeling and control system optimization for electrified vehicles: A data-driven approach

Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs). This paper begins by establishing a PHEV model based on physical and data-driven models, focusing on the high-fidelity training environment. It then proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability, and improves the flawed method of equivalent factor evaluation based on instantaneous driving cycle and powertrain states found in existing research. Finally, comprehensive simulation and hardware-in-the-loop validation are carried out which demonstrates the advantages of the proposed control framework in fuel economy over adaptive-ECMS and rule-based strategies. Compared to conventional RL architectures that directly control powertrain components, the proposed control method not only achieves similar optimality but also significantly enhances the disturbance resistance of the energy management system, providing an effective control framework for RL-based energy management strategies aimed at real-vehicle applications by OEMs.

A hybrid framework for latency compensation in remote testing of automotive electronic control units

As the number of electronic control units (ECUs) in vehicles continues to grow, exchanging physical components between original equipment manufacturers (OEMs) and ECU suppliers presents logistical challenges, impeding the pace of development and leading to accumulation of costs. In this work, we introduce a conceptual framework that enables remote testing of geographically dispersed ECUs over the Internet. Pertinent to the Internet of Things (IoT) sphere, where interconnection of distributed components is hampered by the inherent challenges of latency, we propose a hybrid synchronous methodology that combines asynchronous test management with time synchronization mechanisms to mitigate the delay impact within the distributed environment. Additionally, we discuss challenges and prospects associated with this approach.

Assessment of overall remaining useful life of lubricants by integrating oil quality and performance

Effective lubricant health monitoring programs are essential for extending the lifespan of both the lubricant and machinery. An accurate and reliable remaining useful life (RUL) prediction is necessary for maintenance decision support. The degradation of used lubricating oil information trends evaluated using used oil analysis results is necessary. This study addresses the need for a comprehensive tool to consolidate oil analysis parameters and determine the remaining useful life (RUL) of used lubricating oil (ULO). A Performance Rating Index (PRI) was developed by integrating various oil degradation testing parameters, including membrane patch colorimetry for varnish and sludge potential, along with viscosity, foaming stability, water separability, air release properties, oxidation stability, rust prevention, and copper strip corrosion. The integration of routine and performance test results of a turbine oil at 25,480 operating hours exhibited a PRI of 48.5. Correlating with the ASTM degradation alarm limits of 50 %, the PRI results provide clear maintenance actions before the original equipment manufacturer's recommended overhaul. The PRI approach supports proactive maintenance by facilitating early oil replenishment, extending RUL, and reducing waste oil.

EXPRESS: Monitoring Technologies in Industrial Systems

Monitoring technologies, which are at the heart of the industrial Internet of Things (IOT) ecosystems, promise significant transactional efficiencies by making it easier to track product performance and contract compliance. These efficiencies are particularly compelling in industrial multi-vendor, multi-component systems, where complex component interdependencies often cause disputes around liability in the case of product failures. Drawing on transaction cost theory, fieldwork, and a national survey of industrial original equipment manufacturers we estimate how product performance contracts are specified in these contexts, and how these monitoring technologies can impact ex-post exchanges between OEMs and their suppliers. We find that systems architecture associated with the multi-component systems, as well as the presumed efficiencies of monitoring technologies, drive the contract designs through their potential impact on the disputes, monitoring, and contract writing costs faced by the OEM. However, we also find there are clear limits to the benefits offered by these monitoring technologies. The greater monitoring facilitated by these technologies appears to exacerbate disputes. This counterintuitive finding comports with the view that, when unforeseen interdependent failures occur, detailed data from monitoring may trigger a spate disputes over new and unexpected information, as well as over the appropriateness of existing protocols for failure metrics and remedies.

Only the Friendly Face? The Consequences of Consumer Education for Green Consumerism in Remanufacturing

Empirical studies suggest that investing in consumer education on green consumption not only naturally induces environmental sustainability but also yields various economic benefits for the original equipment manufacturers (OEMs). However, as far as we know, these studies overlook the potential cannibalization of new product sales. By developing a theoretical model that involves consumer education on green consumption when choosing between in-house or outsourcing remanufacturing, we find that if allowing the flexibility of remanufacturing outsourcing, consumer education on green consumption introduces opportunities for opportunistic behaviors that can compromise both profitability and environmental objectives. Specifically, when OEMs engage in remanufacturing, either in-house or through outsourcing, we observe that the incentive to invest in consumer education on green consumption is greater for in-house remanufacturing compared to outsourcing. This heightened incentive for consumer education enables OEMs to maintain higher profits under remanufacturing in-house, which results in a threat to environmental sustainability.

Meeting the challenge of mitigating Li-ion battery fires for aviation

The aviation industry faces a pressing challenge in reducing its environmental footprint, especially with the projected growth in global passenger traffic. Electrification and more-electric aircraft, particularly through Lithium-ion Battery (LiB) systems, offers a promising pathway to reduce emissions but introduces significant safety concerns, particularly regarding thermal runaway (TR) events. This prospective paper examines the unique challenges posed by aviation environments for developing safe LiB systems. It discusses multifaceted mitigation approaches, integrating fire containment structures with advanced thermal management and fire protection technologies. Various cooling technologies and fire suppression agents are explored for their effectiveness in extinguishing LiB fires and mitigating thermal runaway propagation. Integrated LiB suppression systems are proposed to combine fire containment, suppression, and thermal management functionalities for achieving the demanding specific energy density levels that will be required. Scaling safe LiB pack solutions for commercial aviation requires coordinated efforts among regulators, original equipment manufacturers (OEMs), engineers, and researchers to establish standardized design criteria, develop validated modeling tools, and establish rigorous certification testing requirements. In conclusion, addressing the safety concerns of large LiB packs in aircraft applications requires a holistic, integrated approach. This paper provides insights into current research, identifies key challenges, and outlines future directions for advancing LiB safety in aviation.