Tire Company Research Articles

Exploring the technological leaders using tire industry patents: A topic modeling approach

Researchers have long been interested in the transportation industry, recognizing its significant impact on human societies. Specifically, the automobile industry and its components have garnered special attention due to the evolving nature of transportation technologies. With this in mind, this study aims to explore the current and future technologies within the tire industry as a technological-based component of the automotive industry. Additionally, the research examines the key topics, leaders of technology, and strategies of technology-owning companies in these topics. To carry out this investigation, we employed a combination of methods including patent analysis and topic modeling. The selection of leading technology companies was based not only on the number of patents but also on the impact and quality of their patents, which was carefully evaluated. By leveraging these methods and results, tire companies and other industries can gain a competitive edge in technology, acquire insights into the current and future market share of leading companies, and develop effective roadmaps for their own advancement.

End-to-end tire defect detection model based on transfer learning techniques

Visual inspection of defective tires post-production is vital for human safety, as faulty tires can lead to explosions, accidents, and loss of life. With the advancement of technology, transfer learning (TL) plays an influential role in many computer vision applications, including the tire defect detection problem. However, automatic tire defect detection is difficult for two reasons. The first is the presence of complex anisotropic multi-textured rubber layers. Second, there is no standard tire X-ray image dataset to use for defect detection. In this study, a TL-based tire defect detection model is proposed using a new dataset from a global tire company. First, we collected and labeled the dataset consisting of 3366 X-ray images of faulty tires and 20,000 images of qualified tires. Although the dataset covers 15 types of defects arising from different design patterns, our primary focus is on binary classification to detect the presence or absence of defects. This challenging dataset was split into 70, 15, and 15% for training, validation, and testing, respectively. Then, nine common pre-trained models were fine-tuned, trained, and tested on the proposed dataset. These models are Xception, InceptionV3, VGG16, VGG19, ResNet50, ResNet152V2, DenseNet121, InceptionResNetV2, and MobileNetV2. The results show that the fine-tuned VGG19, DenseNet21 and InceptionNet models achieve compatible results with the literature. Moreover, the Xception model outperformed the compared TL models and literature methods in terms of recall, precision, accuracy, and F1 score. Moreover, it achieved on the testing dataset 73.7, 88, 80.2, and 94.75% of recall, precision, F1 score, and accuracy, respectively, and on the validation dataset 73.3, 90.24, 80.9, and 95% of recall, precision, F1 score, and accuracy, respectively.

STUDY OF CHANNEL OPTIMIZATION OF G TYRE COMPANY IN THE NEW RETAIL FORMAT

G Tire Company, a global leader in tire manufacturing, rapidly expanded its offline retail network upon entering the Chinese market. However, traditional offline retail faces challenges with the emergence of “new retail,” which integrates the internet with physical stores. Rising “new retail” firms like TuHu have impacted G Tire's traditional channels, prompting a need for a “new retail” transformation.
 This paper reviews literature on “new retail” and the tire industry, analyzing the industry’s development and transformation. Based on research and distribution channel theory, a four-element model for “new retail” channel analysis is proposed. The surveys and analysis revealed issues such as e-commerce impact, dense urban channels, and a lack of brand diversity due to data shortages. Improvement strategies include channel coordination, product enhancement, traditional channel optimization, and management upgrading.

Symmetric laminates for enhanced tire durability: A numerical study on belt edge separation

Tire failure due to belt edge separation is a common issue caused by various factors such as adhesion loss, high heat generation, and interlaminar shear. This research paper focuses on studying the phenomenon of belt edge separation in tires by investigating the effect of belt lay arrangement on this failure mode. The study examines two types of belt arrangements, namely Symmetric and Asymmetric, in both four belt and three belt package configurations, using advanced finite element analysis (FEA) techniques. MSC-MARC commercial Finite Element software is employed to conduct FE analysis on an 11R22.5 tire model under steady-state rolling conditions. Through numerical simulations, the study reveals that the symmetric belt arrangement, irrespective of the belt package configuration (four belt or three belt), minimizes strain energy density, principal strains, and principal stresses in the belt edge region compared to the asymmetric arrangement. Interestingly, the asymmetric arrangement is commonly used by tire companies for Truck Bus Radial (TBR) tires. Based on the findings of numerical simulations, this research paper proposes a new three belt package configuration that effectively utilizes the symmetric belt lay arrangement as a potential solution to reduce belt edge separation in TBR tires.

Five Factors that Impact the Globalization of Innovation

Overview: Many multinational companies develop innovative new businesses in one geographic market that they would like to expand to another. This is often challenging, however, because market, economic, and cultural factors, as well as the regulatory environment, make direct transfer unlikely to succeed. Strong internal factors also can impede the transfer of an offering or business from one region to another. This article uses the experience of three businesses developed at Goodyear Tire & Rubber Company in the commercial trucking sector. All of these businesses started in one market and were later reshaped to enable their success in very different markets. We discuss the challenges these market expansions faced and the ways in which they were addressed. We propose a simple set of five factors (MERCI) for other companies seeking to grow through global expansion of innovative new businesses.

The Impact of Digital Transformation on New Product Development with the Mediating Role of Organizational Intelligence

Objective: This article aims to investigate the impact of digital transformation on new product development (NPD) with the mediating role of organizational intelligence. Methods and Materials: The research employs a descriptive-correlational design, utilizing questionnaires and field studies to collect data from 100 experts in the rubber manufacturing industry, specifically from Barez and Artavil Tire companies. Confirmatory factor analysis (CFA) was used to assess the validity of the questionnaire, with structural equation modeling (SEM) conducted using Smart PLS software to test the hypotheses. The reliability and validity of the constructs were evaluated through composite reliability (CR), average variance extracted (AVE), and discriminant validity measures. Findings: The study found that digital transformation significantly affects NPD performance (path coefficient = 0.586, t-value = 9.849), and organizational intelligence strongly mediates this relationship (path coefficient = 0.817, t-value = 12.838). Additionally, digital transformation directly enhances organizational intelligence (path coefficient = 0.296, t-value = 2.865). The R² value for NPD was 0.717, and for organizational intelligence, it was 0.688, indicating strong explanatory power. Conclusion: Digital transformation significantly impacts NPD success, particularly when supported by high levels of organizational intelligence. Companies that invest in digital infrastructure and knowledge management systems are better equipped to handle the complexities of NPD, leading to more innovative and competitive products. Organizational intelligence serves as a key enabler in leveraging digital tools for successful product development.

Tire Company’s Valuation in Using Free Cash Flow to Firm and Relative Valuation Method

Tire company shares are considered to be the most prospective in 2018. This is related to the increasing level of tire exports, especially to Europe. Goodyear Indonesia (GDYR), Gajah Tunggal (GJTL), and Multistrada Arah Sarana (MASA) are three listed companies listed on the Indonesia Stock Exchange in 2014-2018. This thesis analyzes the intrinsic price of shares in the Tire Sub-Sector using the Discounted Cash Flow - Free Cash Flow to Firm (FCFF) Method and the Relative Valuation (RV) approach, so that interested parties or investors can assess how the third issuer's position shares. The basis of the estimation is an analysis of the financial statements of the three companies in the period 2014 to 2018, to conduct a fair valuation of shares in 2018 and an extension of the estimated share price in 2019. With the DCF-FCFF approach, the results show that GDYR shares are overvalued in all scenarios. GJTL's share price is also overvalued in the Optimistic and Moderate scenarios, but the Pessimistic scenario is overvalued. Meanwhile, MASA's share price is overvalued in all scenarios. With the PER approach, the results show that GDYR & MASA's shares are in an overvalued position in all scenarios. However, GJTL shares are in an overvalued position in all scenarios. With the PBV approach, the results show that the share price of GDYR & MASA are in an overvalued position in all scenarios. However, GJTL's share price is overvalued in all scenarios.

Contextual anomaly detection for multivariate time series data

With the advancement of sensing technologies, sensor data collected over time have become more useful for detecting anomalies in underlying processes and systems. Sensor data are often affected by contextual variables, such as equipment settings, and can have different patterns, even in normal states depending on the contextual variables. Motivated by this problem, we propose a contextual anomaly detection method for multivariate time series data. We first build a prediction model using training data consisting of only normal observations, and then perform anomaly detection based on the prediction errors for future observations. The prediction model is based on a long short-term memory (LSTM) network that can flexibly model complex relationships between variables as well as temporal correlations between successive time points using the high expressive power of deep recurrent neural networks. In particular, to incorporate the contextual information while ensuring that it does not propagate over time but affects the response data only at specific target time points, we extend the standard LSTM by adding a layer for the contextual variables separately for each time step. The performance of the proposed method was verified with several open-source datasets and a real dataset from a global tire company.

The Road to Sustainable Tire Materials: Current State-of-the-Art and Future Prospectives.

The development of a 100% sustainable tire has emerged as a milestone for several tire companies across the globe. It has created new commercial opportunities for the biobased, renewable, and recycled polymer materials. However, there are concerns that the incorporation of such sustainable new materials may have an undesirable impact on the main performance properties of the tire. At the same time, with new capabilities and product innovations, it can help us meet society's need in a more sustainable fashion and protect the environment. This Feature first outlines the opportunities and need for sustainable tire materials. Next, it describes the main types of sustainable material attributes in tire material, elastomers, reinforcing agents, fibers, and plasticizers, among a few others. The challenges to achieving the performance properties are discussed with possible design guidelines. Recent approaches to the tire attributes are described in the form of a meticulous overview of the existing literature, with a critical analysis of some of them. This contribution attempts to highlight, in a comprehensive way, sustainable tire materials on the basis of recent research advancements, existing challenges, and prospective future scope in this field.

Comparison of production decision-making models under carbon tax and carbon rights trading

Tesla, the leader in the electric vehicle industry, as of fiscal 2021, the company's intangible revenue (carbon rights trading) was $1.46 billion, of which tires are an important part of electric vehicles. Reducing carbon emissions in the production process is a direct way to protect the environment. The tire industry is under increasing pressure to reduce carbon emissions and formulate sustainable development plans. It is even more necessary to trade carbon rights to offset emissions that cannot be eliminated in operations. This study considers carbon emission mechanisms and carbon offsets and uses green mathematical programming models combined with activity-based cost and constraint theory to develop three scenarios. The tire company recycles the defective tires in the production process, in addition to adding additional income, and can reduce the cost and expense of the process. The results of the study show that the recycling of defective tires by tire companies in the production process can reduce the cost and expense of the process in addition to adding additional revenue, carbon credits can be used as a deduction for production emission control, and have a significant impact on the company's sustainable competitiveness. In addition, this study also conducted a sensitivity analysis of environmentally sustainable carbon emission reduction targets, which can provide a reference for the company's internal carbon management decision-making.

Diagnostics in Tire Retreading Based on Classification with Fuzzy Inference System

Currently, there are tire retreading companies whose evaluations are not wholly accurate; due to various factors, sometimes customers are forced to agree to what is decided, and this means that the customer can sometimes pay for services that do not necessarily guarantee the correct operation of the tire or, failing that, shorten its life. This work aims to develop a tire diagnostic system that allows for evaluating a tire’s faults and can thus be more precise when determining if it needs retreading or a change process. The diagnostic system is focused on demonstrating that fuzzy logic can be applied in diagnosing the condition of tires. The methodology consisted of determining the variables to be considered in the evaluation of tires, such as blowing out, flange breakage, band failure, and patching, then applying fuzzy logic. Subsequently, the execution tests of the built diagnostic software were carried out for its validation in a case study of a tire retreading company. The result was a margin of error of 1.6% accuracy versus 5.6% from the operator experience. The conclusion was that fuzzy logic could be applied correctly in the field of tire retreading, providing substantial savings in time and resources for related companies, as well as giving customers confidence since, by using more accurate results, the diagnostic system will make the tire evaluation efficient.

Science and Business Developments

Science and Business Developments

Analisis pengaruh struktur aktiva, manajemen hutang, dan manajemen piutang terhadap profitabilitas perusahaan

The purpose of this study is to analyze the effect of asset structure, debt management, accounts receivable management on the profitability of tire companies listed on the BEI 2010-2020 and analyze the asset structure, debt management, and receivables management simultaneously on the profitability of tire companies listed on the BEI 2010 -2020. This study uses quantitative research methods because the data obtained in the form of numbers (numeric). The population used in this study are companies engaged in tire companies listed on the Indonesia Stock Exchange (IDX) since 2010-2020, totaling 4 companies. The technique for determining the sample in this research is using a purposive sampling technique. The results of this study are only receivable management (ARTO) which has a significant effect on the profitability of the tire company studied, while asset structure and debt management have no significant effect. Therefore, receivable management is the receivables turnover ratio which is the main variable in increasing the company's profitability.

Analysis of the Driving Factors of the Extended Producer Responsibility System in China’s Tire Industry

The effective treatment of wrap tires could mitigate black pollution, reduce carbon emission, and supply risks. The Extended Producer Responsibility (EPR) system is an effective way to deal with the problems aroused from wrap tires in some countries. In order to analyze the driving factors for the EPR system in the Chinese tire industry, this study first identifies the key driving factors based on literature analysis, expert interviews, and field research. And then, using the grey decision-making and trial evaluation laboratory (Grey-DEMATEL) method, the interrelationships among these factors were quantified analyzed. We find that the causal factor is to “strengthen the service function of dealers and service providers and improve the satisfaction of sales channels.” Finally, sensitivity analysis was performed to ensure the robustness of the results. Conclusions are summarized below. If the implementation of the EPR system for tire companies can promote the sales of new tires, achieving the economic and environmental benefits must be the most critical factor. The effect factor is to fulfill social responsibility and achieve the brand effect. This result is in line with the original intention of tire companies to fulfill social responsibility and realize resource recycling. The key factors are related to policy guidance and regulations at the national level. And managerial implications are suggested for promoting the EPR system in China tire industry.

A Study on the Analysis of Financial Performance with reference to PTL Enterprises Ltd.

Financial analysis is the method of reviewing companies, programs, expenditures, and other activities relevant to finance to assess their performance and appropriateness. Financial analysis is usually used to determine whether an enterprise is sufficiently stable, solvent, liquid, or competitive to warrant a monetary investment. Financial analysis is used to assess economic trends, set monetary policies, build long-term business activity plans, and identify investment projects or companies. This is achieved using the financial numbers and data synthesis. A financial analyst must scrutinize the financial statements of a company - the statement of income, the balance sheet, and the statement of cash flow. The study is carried out for the period of five financial years (2014-2015 to 2018-2019). Secondary data are based on the annual reports of 2014-2015 to 2018-2019 of PTL Enterprises Ltd. (Indian Tyre Company). The various tools used for the study are ratio analysis and t-Test. One of the most common methods of evaluating financial data is to derive ratios from the data in the financial statements to be compared with those of other firms or against the past output of the company itself.

Deep embedding kernel mixture networks for conditional anomaly detection in high-dimensional data

In various industrial problems, sensor data are often used to detect the abnormal state of manufacturing systems. Sensor data are sometimes influenced by contextual variables that are not related to the system health status and may exhibit different behaviours depending on their values, even if the system is in a normal condition. In this case, a conditional anomaly detection method should be used to consider the effects of contextual variables. In this study, we propose a conditional anomaly detection method, particularly for high-dimensional and complex data, using a deep embedding kernel mixture network. The proposed method comprises embedding and kernel mixture networks. The embedding network learns low-dimensional embeddings from high-dimensional data, and the kernel mixture network models the distribution of the learned embeddings conditional on contextual variables. The two networks enable a flexible estimation of conditional density using the high expressive power of deep neural networks. The two networks are trained simultaneously such that the high-dimensional data are embedded into a low-dimensional space, to assist conditional density estimation. The effectiveness of the proposed model is demonstrated using real data examples from the UCI repository and a case study from a tire company.

Seasonal preparedness pilot – Sub-seasonal and seasonal prediction service for tyre companies

Seasonal preparedness pilot – Sub-seasonal and seasonal prediction service for tyre companies

Modelling and analysis of intelligent tyre alternatives for better performance

For more than ten decades people have been using vehicles rolling on pneumatic tyres. The essential factor for safety of automobile is tyre. Tyre conditional plays vital role in the safety of a passenger. In olden days wheels were made up of wood, later it became evolved into tube and tubeless tyre. A traditional tire is made up of reinforced rubber packed by using compressed air. Conventional tyre over period had been dominating market sector because of its riding comfort and strength. But it has its own disadvantages like problems of puncture, inflation. Under or over inflation of tyre cancause tire blowouts, tread separation. In recent years a few tyre companies and researchers are experimenting with non-pneumatic tyre performance including Michelin, Bridgestone, and goodyear but none of them has started its mass production. Non pneumatic tyres are the tyres which no longer need air pressure. In this project we replace traditional tyre by flexible tyre structure and finds out other intelligent alternatives to conventional tyres. We have investigated spoke’s structure designed for airless tyre and applied uniaxial load. We designed the structure in ANSYS design modeler and ANSYS static structural has been used to analyze whether the designed structure is valid or not. Three kinds of intelligent tyre alternatives have been identified and stressed their use, working and importance for future mobility.

Contextual anomaly detection for high-dimensional data using Dirichlet process variational autoencoder

Due to recent advances in sensing technologies, response measurements of various sensors are frequently used for system monitoring purposes. However, response data are often affected by some contextual variables, such as equipment settings and time, resulting in different patterns, even when the system is in the normal state. In this case, anomaly detection methods that do not consider contextual variables may be unable to distinguish between abnormal and normal patterns of the response data affected by the contextual variables. Motivated by this problem, we propose a method for contextual anomaly detection, particularly in the case where the response and contextual variables are both high-dimensional and complex. The proposed method is based on Variational AutoEncoders (VAEs), which are neural-network-based generative models suitable for modeling high-dimensional and complex data. The proposed method combines two VAEs: one for response variables and the other for contextual variables. Specifically, in the latent space of the VAE for contextual variables, we model the latent variables using a Dirichlet process Gaussian mixture model. Consequently, the effects of the contextual variables can be modeled using several clusters, each representing a different contextual environment. The latent contextual variables are then used as additional inputs to the other VAE’s decoder for reconstructing response data from their latent representations. We then detect the anomalies based on the negative reconstruction loss of a new response observation. The effectiveness of the proposed method is demonstrated using several benchmark datasets and a case study based on a global tire company.

Assessing advantage cluster: the case of Pithampur auto industry

Learning outcomes The learning outcomes of this paper will help students in understanding the dynamics of the formation of industry clusters and the benefits associated with industry clusters. The case will give stimulus towards the cluster competition. Case overview/synopsis The case describes the dilemma of a potential investor of a tyre company that wants to diversify its product line and is searching for a new strategic location. The investor is thoughtful about the Pithampur auto industry cluster for its upcoming investment. The case demonstrates how Pithampur has transformed into an “industry cluster” and the benefits it provides to firms in it. However, Pithampur is not the only auto industry cluster in India, clusters like Chakan-Pune is in competition with Pithampur for attracting investments. This is a cause of worry for the cluster’s stakeholders. The case projects amalgamation of concerns of the stakeholders of the clusters and those of potential investors in evaluating and benchmarking it with other clusters for a competitive future. Complexity academic level Suitable for both undergraduate and post-graduate students (MBA students). Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS: 11: Strategy.