Original Equipment Manufacturers Research Articles

The interaction of CM’s encroachment time and OEM’s outsourcing strategy when considering the cost-learning effect

Contract manufacturing has been widely studied in recent years. At the same time, existing research rarely considers when should the contract manufacturer (CM, she) launch a product of her own brand to encroach into the market. To fill this research gap, this study considers a two-period game consisting of one original equipment manufacturer (OEM, he) and one CM. The CM with a cost-learning ability could strategically decide whether and when to produce a final product of her own to encroach the market. The results show that when the OEM has a strong brand advantage, the CM may lose profit from encroachment into the market because her cost-learning capability contributes to grabbing the OEM’s market share and then the decrease in OEM’s orders hurts the CM herself in turn. On the contrary, facing a weak-brand-advantage OEM, the CM will encroach in both periods. Furthermore, the OEM can also derive advantages from the CM’s encroachment when the OEM possesses a strong brand advantage. However, when the OEM’s brand advantage is at a moderate level, refraining from encroachment will result in Pareto Optimality for both the OEM and CM.

Remanufacturing in global supply chains: Self-operating or licensing?

Nowadays, globalization and decarbonization have emerged as new normals for supply chains worldwide. In this paper, we investigate remanufacturing strategies within a global supply chain, aiming to provide managerial insights through the research to assist businesses in better integrating into the new normals. Within the global supply chain, an original equipment manufacturer (OEM) located in the home country sells its products to a foreign country through a local retailer. To establish a closed-loop supply chain, the OEM faces two strategic choices, i.e., self-operating remanufacturing in its home country (Strategy A) or licensing remanufacturing to a local remanufacturer (LR) in the foreign country (Strategy B). Utilizing game-theoretical models, our study first explores the operational decisions regarding quantities and prices made by the OEM, the retailer, and the LR. The analysis reveals that a higher cost disparity between the new and remanufactured products or lower cross-border recycling costs for used products can potentially stimulate firms’ incentives for recycling and remanufacturing. Second, upon comparing the equilibrium profits under the two strategies, we demonstrate that lower cross-border recycling costs consistently encourage the OEM to opt for self-operating remanufacturing. Interestingly, we find that the impact of the production cost disparity on the OEM’s strategic choice is non-monotonic; that is, either a low or high disparity of cost can incentivize the OEM to choose licensing remanufacturing, whereas a moderate cost disparity leads to self-operating remanufacturing. We also explored the impact of remanufacturing strategies on sustainability. Furthermore, we have examined governmental preferences regarding firms’ adoption of remanufacturing strategies. Our findings indicate that both countries can benefit from the OEM’s self-operating remanufacturing, providing an explanation as to why cross-border recycling and remanufacturing are not entirely prohibited by governments worldwide.

Software Update Methodologies for Feature-Based Product Lines: A Combined Design Approach

The automotive industry is experiencing a significant shift, transitioning from traditional hardware-centric systems to more advanced software-defined architectures. This change is enabling enhanced autonomy, connectivity, safety, and improved in-vehicle experiences. Service-oriented architecture is crucial for achieving software-defined vehicles and creating new business opportunities for original equipment manufacturers. A software update approach that is rich in variability and based on a Merkle tree approach is proposed for new vehicle architecture requirements. Given the complexity of software updates in vehicles, particularly when dealing with multiple distributed electronic control units, this software-centric approach can be optimized to handle various architectures and configurations, ensuring consistency across all platforms. In this paper, our software update approach is expanded to cover the solution space of the feature-based product line engineering, and we show how to combine our approach with product line engineering in creative and unique ways to form a software-defined vehicle modular architecture. Then, we offer insights into the design of the Merkle trees utilized in our approach, emphasizing the relationship among the software modules, with a focus on their impact on software update performance. This approach streamlines the software update process and ensures that the safety as well as the security of the vehicle are continuously maintained.

Synthesis of activity-based costing and deep learning to support cost management: A case study in the automotive industry

Activity based costing (ABC) models for cost management and big data analytics are deeply rooted in academia and practice to efficiently manage operational costs. Inspired by cost management and deep learning (DL) theories we develop a multilayer perceptron (MLP) using a nested particle swarm optimized (PSO) neural network algorithm for self-controlled architecture and weight optimization. This innovative approach enables to mimic ABC while increasing prediction accuracy. In a real-world case study we use wheel manufacturing data provided by a large original equipment manufacturer (OEM) and follow a full factorial experimental design. Furthermore, we benchmark our novel DL approach with results derived from a traditional ABC analysis. We demonstrate that this intelligent cost estimation model can mimic ABC using few cost drivers contributing to efficient and transparent interorganizational cost management. Major quantitative findings within a conducted field experiment demonstrate a high forecast accuracy with low absolute cost percentage error (CPE) deviation of the novel cost estimation approach in industry. The results extend the scope of PSO for the simultaneous optimization of the architecture and weights of neural networks. Additionally, we prove that the PSO algorithm is a suitable alternative to traditional optimization methods in DL.

Analyzing the Suitability of Vehicle Telematics Data as a Surrogate Safety Measure for Short-Term Crashes

Traffic safety analysis relies heavily on the comprehensiveness of traffic crash data. However, crash occurrences are rare events. Researchers have sought alternative measures closely linked to crash occurrences, known as surrogate safety measures, to address this challenge. The advent of vehicle telematics has introduced a valuable source of data, consisting of time-stamped positional information of vehicles, facilitated through telecommunication networks. Wejo Data Services Inc. collects vehicle telematics data through collaboration with various automotive original equipment manufacturers (OEMs) to furnish connected vehicle data along with driver event variables, such as braking and acceleration. This study seeks to investigate the relationship between hard-braking events and the incidence of total crashes and peak period crashes across different facility types using analysis of variance and negative binomial models. The results show a strong correlation between hard braking and crash frequency. The ratio of hard-braking events to crashes exhibits notable variations across peak hours, off-peak periods, and nighttime. Further, the coefficient of hard braking is significantly higher on freeways compared with non-freeways. The model performance of different short period models improved better than the extent of improvement in total crash model after hard braking was introduced as an explanatory variable. The marginal effects of hard braking on crash frequency on freeways are consistently higher than those in non-freeway segments.

A multinational firm’s global manufacturing location and outsourcing strategies

Due to rising labor costs in China and trade friction, many multinational firms (MNFs) consider moving manufacturing outside China to Southeast Asian Countries. Based on observations in real world, we build a three-tier supply chain model consisting of one component supplier, two contract manufacturers (CMs), an MNF, and a local original equipment manufacturer (OEM). The MNF monopolizes the market in country 1 (e.g., U.S.), but competes in country 2 (e.g., China) with the local OEM. The component supplier and one CM are located in country 2, and the other CM is located in country 3 (e.g., a country in Southeast Asia). We investigate the MNF’s global manufacturing location and procurement outsourcing strategies with considerations of tariffs and market environments. Currently, the MNF outsources its manufacturing to the CM in country 2, and decides whether to continue the cooperation with the current CM or develop a new CM in country 3. The MNF also considers whether to outsource its component procurement to the corresponding CM. Our study reveals that the MNF’s optimal strategy depends on the relative market sizes, the fixed cost of developing a new CM, as well as the manufacturing cost and tariff disadvantages in country 2. Interestingly, we find that the tariff disadvantage in country 2 does not always motivate the MNF to cooperate with the CM in country 3. Instead, an appropriate tariff may be beneficial to the MNF that manufactures in country 2. In addition, the MNF’s preferred procurement strategy is related to its manufacturing location. When cooperating with the CM in country 2, the MNF would switch from direct procurement to outsourced procurement as the market size of country 1 increases.

Meta-inventory management decisions: A theoretical model

The manufacturing industry leverages the traceability and visibility of Industrial 4.0 to integrate digital and physical twins for solving the intricacy of a production system. Physical entities can be converted into digital twins with smart IoT (Internet of Things) devices and computational techniques. Research efforts have generally focused on the development of digital twin models for basic practical applications. Advanced applications of digital twins have not been widely reported. An innovative use of digital twins for inventory management has only been analysed theoretically and reported as meta-inventory management for the first time by Wang and Huang (2023). This paper extends the novel concept of meta-inventory by using a theoretical Newsvendor model for original equipment manufacturing (OEM) and own brand manufacturing (OBM) factories. The impacts of meta-inventory on both supply chain members, including the factory and its downstream retailer, are investigated through two kinds of response models (e.g., hybrid and separate response models). Analytical and numerical results show that a factory achieves better performance by using a separate model since it clarifies the responsibility of digital twins. The hybrid response model holds a higher proportion of digital inventory, but its final profits are less than that in the separate model due to fewer orders and higher prices and costs of uncertainty. OBM can better leverage the advantage of digital twins than OEM. Also, both supply chain members benefit from the implementation of meta-inventory out of profit increase, price reduction, and risk hedging. This research provides guidance for manufacturing sites to implement digital twins and reduce their concerns on investing in meta-inventory.

Financing decisions of third‐party remanufacturers with different co‐opetition modes: The impact of blockchain adoption

AbstractThe third‐party remanufacturer (TPR) can cooperate with the original equipment manufacturer (OEM) and remanufactured products are sold by the OEM (i.e., the cooperation mode). The TPR can also sell remanufactured products individually to compete with the OEM's new products (i.e., the competition mode). Considering the issue of false information in remanufacturing supply chains and the TPR's limited capital, we build game models with blockchain to explore the TPR's optimal financing strategies under different co‐opetition mode selections. We find that a high blockchain usage prompts the TPR to finance from the OEM in the competition mode, while a low blockchain usage motivates the TPR to choose the cooperation mode and opt for a bank credit. Besides, we also find that the difference between the TPR's optimal profits of cooperation and competition modes increases with the interest rate.

Multifaceted multi-criteria decision making framework to prioritise the electric two-wheelers based on standard and regional driving cycles

The primary objective of the carried-out research work is to utilise the multi-criteria decision-making (MCDM) techniques to rank the top-selling electric two-wheeler (E2W) models based on the different performance features stated by original equipment manufacturers and estimated through standard and regional driving cycle (DC) analysis. First and foremost, the vehicle structural, powertrain, performance features, and cost component-related data of eight top-selling E2W models (M1–M8) are gathered from the official websites of OEMs and local dealers. Then, performance features like energy consumption per km (EC/km), driving range (DR), well-to-wheel (WTW) CO2 emission, total cost of ownership (TCO), and total cost of ownership per km (TCO/km) are estimated under standard and regional DCs through the developed energy consumption and TCO models. Further, the ten different MCDM tools are utilised for prioritising the E2W models under each DC with the attributes’ weights estimated from the entropy methods. To enhance the trustworthiness of ranking results, sensitivity analysis is done using six distinct combinations of attribute weights. Moreover, the M7 E2W is identified as the best E2W by utilising Borda Count to integrate all the ranking results. Overall, this study provides an in-depth understanding for users about how to select E2W with minimal effort.

Constitutive model of die-cast light-alloy thin-walled parts considering geometric imperfection

Replacing steel with aluminum and shifting from forging to casting have become important means of automotive lightweight. Large integrated die-cast parts are gaining popularity among original equipment manufacturers. Engineers often ignore the influence of geometric imperfection on mechanical properties when simulating strength of thin-walled parts, therefore causing a bias. To investigate the relation between geometric imperfections and mechanical properties of die-cast light alloy, quasi-static tensile tests are conducted on the specimens of JDA1b aluminum alloy and JDM1 magnesium alloy. The specimens exhibit varying geometric imperfection factors (from 0 to 90 %) achieved by introducing circular holes with different diameters. The results show that the strength and elongation of JDA1b and JDM1 alloys decrease significantly as the geometric defect factor increases. Even small holes can significantly affect tensile strength. A constitutive model that incorporates the stress limit value and geometric imperfection factors is proposed, which has higher accuracy than the J–C model. Experiments and simulations on a die-cast thin-walled part validated the idea and proposed constitutive model. These findings provide essential insights into the influence of structural holes on the mechanical properties of die-cast light-alloy materials. The proposed constitutive model offers high-precision computational support for simulating the mechanical performance of parts.

Systems Engineering roles to handle emergent properties and behaviors in complex technical systems

AbstractEngineering projects for complex technical systems such as automobiles demand extensive requirement specifications and corresponding hierarchy levels in system architectures. Especially when considering emergent phenomena, such as total weight or aerodynamics, a closely networked collaboration of discipline‐specific and cross‐disciplinary roles is required. Further, in large organizations with a group structure, resulting functional and non‐functional contents need to be managed by distinct Systems Engineering roles. For example, the role “property manager” takes care of achieving overarching product properties, such as weight or aerodynamics, which cannot be directly fixed, but result from many specifications. This paper proposes new Systems Engineering roles and their application in a German Original Equipment Manufacturer (OEM). For validation, the roles have already been applied in everyday engineering projects at the OEM. The concept proved to be indispensable and transferable to other Systems Engineering projects.

Flying in the Strike Zone: Urban Air Mobility and Wildlife Strike Prevention

The number and rate of wildlife strikes continue to increase worldwide. More than 30 years of wildlife strike data indicate that approximately 92% of wildlife strikes for commercial aircraft occur at or below 3500 ft above ground level. Since urban air mobility (UAM) operations are envisioned to carry cargo and passengers up to an altitude of 4000 ft above ground level, UAM operations will face increased exposure to wildlife strike risks throughout their operations. Moreover, the comparatively small size of UAM aircraft, proposed cruising speeds of up to 170 kt, and quieter electric engines are expected to reduce opportunities for threat perception and evasive maneuvers by aircraft operators and wildlife. This paper presents how well-established wildlife strike prevention measures can be adapted to meet the needs of UAM operations and how, with new technology, original equipment manufacturers (OEMs), regulatory agencies, aircraft operators, communities, airspace managers, and vertiport operators can work together to enhance safety.

Analysis of manufacturers' dual‐source and dual‐channel strategies under the trend of brand weakening

AbstractIn the context of the world economic downturn, consumers are showing a trend of brand weakening. In order to explore the product advantages of the source manufacturer, that is, the competitive supplier, we establish a game theory model to study the strategy choice of original equipment manufacturers (OEM), competitive suppliers (CS), and noncompetitive suppliers (NS) based on consumer heterogeneity. By dividing consumers into quality consumers and ordinary consumers and considering horizontal competition (between suppliers) and vertical competition (between OEM and suppliers), it is found that the products of the source manufacturers are becoming more and more popular with consumers. Contrary to the traditional view, we find that under the trend of brand weakening, the products of the source manufacturers should be positioned above the price range acceptable to consumers, and the OEM products of the brand owners should be appropriately reduced. The interesting question is whether CS will discontinue the supply of OEM components once it has its own branded products. Further, we analyze that CS terminating the component sales business is an untrustworthy threat to OEM. Finally, we find that there will be a “win–win” zone between OEM and competing suppliers, and the “win–win” region will decrease as the manufacturing cost of the competing supplier increases and increase as the manufacturing cost of the noncompeting supplier increases.

Reverse logistics platform decisions integrating crowdsourced contracting: A three-level interactive optimization approach for product design considering remanufacturing

Currently most original equipment manufacturers are reluctant to remanufacture their end-of-life products due to lack of adequate infrastructure for remanufacturing profitable products. To solve the problem, we propose a three-level interactive decision-making framework for product design considering remanufacturing by establishing reverse logistics platform integrating crowdsourced contracting. The reverse logistics platform can incorporate a large population of manufacturer and consumer crowds in an extended manufacturing network. The crowdsourced manufacturing paradigm can maximize the utilization of external resources, thereby enhancing overall remanufacturing efficiency. We propose a three-level mixed 0–1 programming model based on above framework with a Stackelberg game to handle the complex decision-making process. A combination of analytical solutions and a nested genetic algorithm is developed to implement the model. A laptop case study is presented. To verify the effectiveness of our algorithm, we compare the results of the three algorithms in solving the model proposed in this paper. Furthermore, applying our proposed model to three additional cases of varying levels of complexity further validates the applicability of the model for our problem over traditional integrated optimization models and two-stage optimization models. Sensitivity analyses are conducted on three key parameters of the model, providing valuable insights for both industry and academia.

A Competitive Newsvendor Problem with Product Substitution under the Carbon Cap-and-Trade System

This study investigates the competitive issues of the newsvendor problem with product substitution under the carbon cap-and-trade system. Building on existing research, this paper introduces the carbon cap-and-trade system under uncertain market demand and considers that the original equipment manufacturer (OEM) can choose to procure raw materials from the contract manufacturer (CM), with both final products being substitutable. Furthermore, we explore the different substitution relationships between OEM and CM products under both pure competitive and co-opetitive modes. For this problem, decision models are developed for various scenarios, and optimal solutions satisfying given conditions are provided. We find that in one-way substitution, under pure competition, an increase in the OEM’s (or CM’s) green investment and substitution rate only leads to an increase in OEM’s (or CM’s) yields, while an increase in the OEM’s (or CM’s) green investment does not necessarily reduce CM’s (or OEM’s) yields. In the co-opetitive mode, an increase in the substitution rate and green investments of both manufacturers may lead to an increase in the yields of both manufacturers. Furthermore, an increase in carbon trading prices does not necessarily inhibit the manufacturer’s yields. Moreover, we find that under the same competition mode, under certain conditions, two-way substitution between OEM and CM can bring better profits to both manufacturers and the entire supply chain. When the two modes are in the same substitution scenario, and the CM cannot substitute for the OEM, the optimal decisions and total supply chain profits of the two modes are equal. Finally, through numerical analysis, we find that neither mode is necessarily optimal when CM can substitute for OEM. Additionally, it is observed that when the same mode is in different substitution scenarios, total supply chain profits may be enhanced in the presence of product substitution.

Optimal maintenance policy design considering learning effects under successive performance-based contracts

Successive short-term performance-based contracts (PBCs) would not only drive the original equipment manufacturer (OEM) to improve maintenance service quality, but also decrease the financial burdens of the equipment owner. This paper presents the optimal maintenance policy incorporating both preventive maintenance (PM) and upgrade actions for continuously monitored degrading equipment under successive short-term PBCs. The improvement factor model is adopted to characterize the impact of the PM and upgrade actions. We also consider that the OEM’s learning effects will affect the cost and duration of the PM, as well as the upgrade cost. The learning effect increases as the period of the contract accumulates. To characterize the varied equipment production and degradation rates across different PBC periods, a Wiener process with covariates is developed. Meanwhile, we assume that the reward and penalty parameters of the performance-based revenue function varied each contract period, thus motivating the OEM to enhance the equipment availability. Based on that, the optimal maintenance plan for each contract period is determined to maximize the OEM’s expected net profit. Numerical examples show the superiority and examine the sensitivity of the proposed successive short-term PBCs. Managerial insights are also given to guide the implementation of the proposed maintenance policy.

The Effects of Global Market Changes on Automotive Manufacturing and Embedded Software

The procedures used to create modern cars require extensive thought in various relevant scientific domains. Arguably, the most challenging obstacle facing the automobile sector is the management of production facilities by integrating software production lines, continuous integration, and continuous delivery/continuous deployment (CI/CD). All this is determined by market demands, the engine of a vehicle, and the complexity of assembling the entire car and installing its corresponding embedded software. As a result, concerns about various types of global change have grown, as has the lack of the ability to use fossil fuels, creating a substantial impact on the purchase and sale of modern automobiles. The research foundation is reflected in covering strategies for the deployment and administration of software, as well as opportunities for business improvement in particular production processes. This article strives to provide a summary of a scientific investigation of original equipment manufacturers, market segmentation, and the effects of global market changes on automotive manufacturing by examining the correlation between certain changes in the purchase of a specific brand and the powertrain of a vehicle. The research examines numerous datasets from the United States of America and Washington State, based on which we estimate possible future changes in the automotive industry’s sales.

Proactive framework for obsolescence management of electrical equipment in oil and gas industry

PurposeEnergy consumption has increased significantly since the 1970s, which has increased demand for sufficient infrastructure in the oil and gas industry. Many top-notch oil and gas companies invested in and equipped their facilities with high-capacity electrical equipment to meet high demand and benefit from high revenues. This is becoming a challenge nowadays for old facilities in the oil and gas industry, as most of the electrical equipment installed has reached or even exceeded its lifetime. Moreover, many of the original equipment manufacturers (OEMs) for electrical equipment from the 1980s are no longer in market today. Therefore, the aim of this study is to develop a proactive, cost-effective obsolescence management framework for electrical equipment in the oil and gas industry, considering the aging factor of the equipment.Design/methodology/approachFirstly, the study begins with gathering available information and identifying criteria. Secondly, the data collection is evaluated by subject-matter-experts (SMEs) in asset management field to ensure compliance with updated international standards and relevant regulatory requirements. Thirdly, a multi-criteria decision-making process is used to rank criteria. Finally, a scoring system is developed to measure the electrical equipment obsoleteness.FindingsThe developed framework will assist decision-makers in making informed decisions about maintenance, replacement or upgrades, using knowledge from previous studies and experts’ input. The result finding indicates that considering aging correction factors when measuring equipment obsoleteness leads to accurately and correctly predicting the electrical equipment obsoleteness score.Originality/valuePrevious studies have addressed obsolescence management without taking equipment age into account, regardless of how the equipment is performing. Thus, the lack of a comprehensive obsolescence management framework that accounts for both cost-effectiveness and the aging factor in the oil and gas industry poses a critical challenge.

Market segmentation using predictive technology and shared-service contracts

‘Shared-service contracts’ provided by Original Equipment Manufacturers (OEMs) are a standard feature of capital-intensive, long-life products. In such contracts, OEMs assume a predetermined portion of the products’ long-term failure opportunity costs by conducting routine and breakdown maintenance tasks for their customers. Recently, OEMs have been utilizing predictive technology to improve service productivity. It enables OEMs to reduce failure opportunity costs mentioned while using the data generated to develop products at lower costs. We extend the adverse selection framework to model the market segmentation of products offered with shared-service contracts and predictive technology. Our model includes: (i) the cost of product development and operation, (ii) the failure opportunity cost driven by long-term product failures, (iii) the cost of designing product reliability, and (iv) the cost of incorporating predictive technology, which reduces the first two cost components. Here, the OEM targets a basic and a premium offering at their respective customer types. We find that using predictive technology improves the quality and price of OEM products, thereby increasing their profitability. However, predictive technology enables the OEM to design a product line with lower reliability. In addition, the OEM is more likely to choose a ‘premium-only’ strategy when predictive technology is present.

Blockchain adoption and mode selection strategies for remanufacturing supply chain under cap-and-trade policy

Environmental conservation and economic advantages have propelled the swift expansion of the remanufacturing industry. Many original equipment manufacturers (OEMs) delegate remanufacturing operations to third-party remanufacturers (TPRs) through outsourcing and authorization. Furthermore, blockchain technology enhances consumers’ intention to purchase remanufactured products by disclosing more product information. To investigate the impacts of blockchain on remanufacturing mode selection, this paper focuses on a competitive remanufacturing supply chain comprising an OEM and a TPR under a cap-and-trade policy. Further, four game-theoretic models are derived from whether to adopt blockchain technology and which remanufacturing mode to select. Three major findings are obtained by solving and comparing the four models: (1) Adopting blockchain does not consistently result in advantages for both the OEM and TPR, and they should adopt blockchain for information disclosure in instances where the unit blockchain adoption cost is low, and the disclosure degree of remanufactured product information is also low. (2) Compared with not adopting blockchain technology, if the unit blockchain adoption cost is low, blockchain technology will increase the remanufactured product sales, thereby fostering the advancement of remanufacturing and contributing to reducing the environmental impact. (3) Whether blockchain is adopted or not, the OEM always tends to be in the outsourcing remanufacturing mode. The TPR only opts for the outsourcing remanufacturing mode when consumers’ preference for remanufactured products is high. More importantly, adopting blockchain will likely enhance consumers’ preference for remanufactured products, potentially shifting the TPR’s inclination for remanufacturing mode from authorization to outsourcing in some instances.