Automotive Factory Research Articles

The effects of a transactional analysis training programme on team leadership factors in automotive industry

The aim of this study was to identify a modality to improve the leaders’ and managers’ qualities and abilities requested in team leadership. The study sample was represented by 30 managers and leaders from a local automotive factory, randomly selected. The 16PF Inventory was used as a data collection tool. The results highlighted significant differences regarding Emotional Stability and Social Boldness between the managers who attended a Transactional Analysis based Leadership Training Programme and those who did not attend. No statistically significant differences were found regarding Warmth.

Automatic Simulation Model Generation Based on PLC Codes and MES Stored Data

One of the most widespread techniques to evaluate various aspects of an existing manufacturing system is discrete-event simulation (DES). However, building a simulation model of a manufacturing system needs great resource expenditures. Automated data col-lection and model buildup can drastically reduce the time of the design phase as well as support model reusability. Since most of the manufacturing systems are controlled by low level controllers they store structure and control logic of the system to be modeled by a DES system. The paper introduces an ongoing research of PLC code processing method for automatic simulation model gen-eration of a conveyor system of a leading automotive factory.

Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

This work presents an electricity consumption-forecasting framework configured automatically and based on an Adaptative Neural Network Inference System (ANFIS). This framework is aimed to be implemented in industrial plants, such as automotive factories, with the objective of giving support to an Intelligent Energy Management System (IEMS). The forecasting purpose is to support the decision-making (i.e. scheduling workdays, on–off production lines, shift power loads to avoid load peaks, etc.) to optimize and improve economical, environmental and electrical key performance indicators. The base structure algorithm, the ANFIS algorithm, was configured by means of a Multi Objective Genetic Algorithm (MOGA), with the aim of getting an automatic-configuration system modelling. This system was implemented in an independent section of an automotive factory, which was selected for the high randomness of its main loads. The time resolution for forecasting was the quarter hour. Under these challenging conditions, the autonomous configuration, system learning and prognosis were tested with success.

A simulation study integrated with analytic hierarchy process (AHP) in an automotive manufacturing system

A variety of circumstances, such as developing a new product, changing the design of an existing product, changing the production volume, or changing the product mix, can drive the need for a manufacturing system redesign. Simulation technology has been widely used for evaluating manufacturing system design alternatives. Various performance measures including throughput, utilization of resources, lead-time, and work in process are obtained from simulation studies. However, the dimensions of performance measures are different, and it requires that the trade-offs between them should be considered. In addition, personal preferences in the selection of performance measures vary from person to person. Thus, the multi-criteria decision-making (MCDM) system is required to complement the simulation technology. This paper presents a case study that integrates a simulation study with analytic hierarchy process (AHP), applied to the design of a transmission case line in a Korean automotive factory. The simulation model is developed with QUEST®. Four performance measures (criteria) and seven design alternatives are considered utilizing AHP.

A new algorithm and multi-response Taguchi method to solve line balancing problem in an automotive industry

One of the most important issues in an assembly line balancing problem is to control a flow of production and manufacturing to provide continuous flow to balance the production line. For this purpose, a line balancing problem was considered for a special assembly line in an automotive factory. A new algorithm was required to deal with balancing an assembly line which consisted of the same job which must be performed by more than one worker at the same time. In this way, the new algorithm was expected to be effective in such a case that jobs were simultaneously completed in a parallel way. In order to measure effectiveness of the proposed algorithm, performance criteria were identified as total number of assembly station, total number of workers, and line productivity. As a result of the application of the proposed algorithm with taking into consideration factors such as cycle time, an allowable number of workers in an assembly station, and an allowable idle time of a worker, alternative solutions were determined in order to measure these criteria. However, these alternative solutions do not give any information about which of the solutions provide not only minimization of the number of assembly station and number of workers on the line but also maximize the line productivity at the same time. Hereby, a multi-response Taguchi method was applied in order to investigate levels of factors which directly affected system performance criteria.

Prevalence of hearing loss and accuracy of self-report among factory workers

Noise represents one of the most common occupational health hazards. A Healthy People 2020 objective aims to reduce hearing loss in the noise-exposed public. The purpose of this study was to describe and compare perceived and measured hearing, and to determine the prevalence of hearing loss among a group of factory workers. Data collected as part of an intervention study promoting hearing protector use among workers at an automotive factory in the Midwest were used. Plant employees (n=2691) provided information regarding their perceived hearing ability, work role, and other demographics. The relationships among audiograms, a single-item measure of perceived hearing ability, and demographic data were explored using chi-square, McNemar's test, Mann-Whitney U-test, sensitivity, and specificity. The prevalence of hearing loss among noise-exposed factory workers was 42% (where hearing loss was defined as >25 dB loss at the OSHA-recommended frequencies of 2, 3, and 4 kHz in either ear). However, 76% of workers reported their hearing ability as excellent or good. The difference in perceived hearing ability was significant at each tested frequency between those with and without measured hearing loss. Self-reported hearing ability was poorly related to results of audiometry. Although this group of workers was employed in a regulated environment and served by a hearing conservation program, hearing loss was highly prevalent. These findings, together with national prevalence estimates, support the need for evaluation of hearing conservation programs and increased attention to the national goal of reducing adult hearing loss.

Supplier selection and performance evaluation in just-in-time production environments

The purpose of this paper is to aid just-in-time (JIT) manufacturers in selecting the most appropriate suppliers and in evaluating supplier performance. Many manufacturers employ the JIT philosophy in order to be more competitive in today’s global market. The success of JIT on the production floor has led many firms to expand the JIT philosophy to the entire supply chain. The procurement of parts and materials is a very important issue in the successful and effective implementation of JIT; thus, supplier selection and performance evaluation in long-term relationships have became more critical in JIT production environments. The proposed systems can assist manufacturers in handling these issues. In this research, neural network based supplier selection and supplier performance evaluation systems are presented. The proposed approach is not limited to JIT supply. It can assist manufacturers in selecting the most appropriate suppliers and in evaluating supplier performance. The proposed neural network based systems are tested with data taken from an automotive factory, and the results show that the proposed systems can be used effectively.

A combined methodology for supplier selection and performance evaluation

Today, organizations that wish to carry on the sustainable growing need a robust strategic performance measurement and evaluation system because of changing demands of consumers, reduced product life cycle, competitive and globalised markets. In this study, a new methodology is introduced and proposed for increasing the supplier selection and evaluation quality. The new approach considers both qualitative and quantitative variables in evaluating performance for selection of suppliers based on efficiency and effectiveness in one of the biggest car manufacturing factory in Turkey. This new methodology is realized in two steps. In the first stage, qualitative performance evaluation is performed by using fuzzy AHP (Analytical Hierarchical Process) in finding criteria weights and then fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is utilized in finding the ranking of suppliers. So, qualitative variables are transformed into a quantitative variable for using in DEA (Data Envelopment Analysis) methodology as an output called quality management system audit. In the second stage, DEA is performed with one dummy input and four output variables, namely, quality management system audit, warranty cost ratio, defect ratio, quality management. As a result, comparing with the present system applied by the car factory, the new method seems to be some advantages and superiorities for making the decision in buying the quality car luggage side part (panel) by selecting the suitable supplier(s) in an automotive factory of Turkey.

Optimizing Humidity Level and Illuminance to Enhance Worker Performance in an Automotive Industry

The objective of this study is to determine the optimum values of environmental factors such as relative humidity (%) and illuminance (lux), on the operators’ productivity at Malaysian automotive industry. Production of automotive parts is among the largest contributor to economic earnings in Malaysia. The dominant work involve in producing automotive part were manual assembly process. Where it is definitely used a manpower capability. Thus, the quality of the product heavily depends on worker’s comfort in the working condition. Humidity and illuminance level can give significant effect on the worker performance. Humidity level, illuminance level and productivity rate were observed in automotive factory. An automotive manufacturing firm was chosen to observe the relative humidity level, illuminance level and worker’s productivity rate. The data were analyzed using Artificial Neural Network's (ANN) analysis. Artificial Neural Network's (ANN) analysis technique is usual analysis method used to form the best linear relationship from the collected data. It is apparent from the linear relationship, that is the optimum value of production (value?1) is attained when relative humidity is 54.86% RH and lighting value is 146.386 lux. Optimum value production rate (value?1) for one manual production line in that particular company is successfully achieved. Through ANN's system, optimum environmental factor manage to be predicted.

Optimization of Temperature Level to Enhance Worker Performance in Automotive Industry

Problem statement: Production of automotive parts is among the largest contributor to economic earnings in Malaysia. The dominant work involve in producing automotive part were manual assembly process. Where it is definitely used a manpower capability. Thus the quality of the product heavily depends on worker's comfort in the working condition. Temperature is one of the environmental factors that give significant effect on the worker performance. Approach: Temperature level and productivity rate were observed in automotive factory. An automotive manufacturing firm was chosen to observe the temperature level and worker's productivity rate. The data were analyzed using Artificial Neural Network's analysis (ANN). ANN analysis technique is usual analysis method used to form the best linear relationship from the collected data. Results: It is apparent from the linear relationship, that the optimum value of production (value≈1) attained when temperature value (WBGT) is 24.5°C. Conclusion: Optimum value production rate (value≈1) for one manual production line in that particular company is successfully achieved. Through ANN method, the optimum temperature level for the optimum manual workers' performance manage to be predicted.

Alternative Representations of Statistical Measures in Computer Tools to Promote Communication between Employees in Automotive Manufacturing

In manufacturing industry, many employees need to interpret and communicate statistical information to monitor and improve production processes. Often the information is reduced to the form of numerical measures, on the logic that numbers are a convenient and understandable type of information to pass among the diverse groups of employees that make up a manufacturing operation. We investigated by means of interviews and observation how several numerical measures, ‘process capability indices’, were used in an automotive factory and how employees were trained to use them. We found that the typical introduction to the measures deployed statistical and algebraic symbolism as well as laborious manual calculations that did not appear to support employees’ understanding of the underlying mathematical relationships. These measures therefore failed to be ‘boundary objects’ – artifacts that inhabit different social worlds and satisfy the informational requirements of each. The goal of our subsequent design-based research was to design a representation of the process capability indices that would be easier to engage with than the existing formal symbolism used in shop floor calculations and in training. We did this by re-presenting relevant mathematical relationships in computer tools – technology-enhanced boundary objects (TEBOs) – developed in collaboration with company trainers. To evaluate our interaction with three trainers and 37 trainees in three courses in two factories, and the impact of the computer tools on practice, we followed the computer tools’ trajectory from the stage of co-design with the original car factory through to the stage at which the computer tools were used by factories beyond this research project. The evaluation points to the importance of aligning statistical and workplace norms and meanings, and gives illustrations of how the tools facilitated communication between employees.

Using ENN-1 for fault recognition of automotive engine

In the automotive factory, various types of engines are assembled and dispatched. An engine fault not only damages the engine itself but also causes a break in the automobile system. Engine fault diagnosis can produce significant cost saving by scheduling preventive maintenance and preventing extensive downtime periods caused by extensive failure. Therefore, this paper presents a novel diagnosis method based on the extension neural network type-1(ENN-1) and applies it in the fault diagnosis of engine malfunction. The proposed ENN-1 has a very simple structure and permits fast adaptive processes for new training data. Moreover, the learning speed of the proposed ENN-1 is shown to be faster than the previous approaches. The proposed method has been tested on practical diagnostic records and compared with the multilayer neural networks (MNN) and k-means classification methods. The test results show that the proposed method is suitable for detecting vibration fault of automotive engine, and it is efficient in dealing with noise in the data.

Thermal Comfort Assessment: A Case Study at Malaysian Automotive Industry

Problem statement: Thermal comfort has a great influence on the productivity and satisfaction of indoor building occupants. The exposure to excessive heat during work may cause discomfort and contributed to low productivity among workers. Malaysia known with its hot and humid weather where in most of the survey study published indicated that workers in Malaysia automotive industries had exposed to excessive temperature while working. The study investigated the thermal comfort level experienced by workers at Malaysian automotive industry. Approach: The study had been conducted at one automotive parts assembly factory in Malaysia. The human subjects for the study constitute operators at tire receiving section of the factory. The environment examined was the relative humidity (%), WBGT, air temperature and radiant temperature (°C) of the surrounding workstation area. The environmental factors were measured using Babuc apparatus, which is capable to measure simultaneously those mentioned environmental factors. The time series data of fluctuating level of factors were plotted to identify the significant changes of factors. Then thermal comfort of the workers was assessed by using ASHRAE thermal sensation scale by using Predicted Mean Vote (PMV). Further Predicted Percentage Dissatisfied (PPD) was used to estimate the thermal comfort satisfaction of the occupant. Finally the PPD versus PMV were plotted to present the thermal comfort scenario of workers involved in related workstation. Results: The trend of relative humidity curve from the graph also indicated the increasing level of discomfort. The radiant temperature observed seems consistent during the study while there was decreasing of WBGT start from afternoon due to the rain. The study revealed that the PPD value of 54% of the workers population at the workstation are likely to be satisfied with thermal comfort at this station while the PMV index from ASHRAE indicated the value 1.07-1.41. Conclusion: The empirical study from the PPD and PMV index indicated that workers working at this were influenced by the heat. The less of PPD value from 80% of the population satisfied with the thermal comfort showed the environment factors that were not good for worker while the PMV index showed the area of work is slightly warm.

The Institutionalization of Knowledge in an Automotive Factory: Templates, Inscriptions, and the Problem of Durability

This paper focuses on the process of institutionalization of technical and organizational knowledge in the work setting. Drawing on a rich qualitative study, we apply an inscription—delegation model to the analysis of knowledge dynamics in a greenfield automotive plant. In particular, we depict knowledge institutionalization as a recursive process that involves the progressive writing, enactment and reproduction of a generative template or code within a stable medium. We first reconstruct the chain of transformations by which agency and knowledge are embedded into a variety of technical and organizational artifacts, leading to the creation of a cognitive and institutional order. We then focus on the frictions and tensions that can emerge in the transformations, and we discuss problems of vulnerability and durability that may arise in the maintenance and reproduction of such order.

Is low-level laser therapy effective in the management of lateral epicondylitis?

The purpose of “Evidence in Practice” is to illustrate how evidence is gathered and used to guide clinical decision making. This article is not a case report. The examination, evaluation, and intervention sections are purposely abbreviated. A 55-year-old woman, who was right-hand dominant, was referred to my physical therapy clinic with a 6-month history of pain in her left lateral elbow. The patient was an assembly-line worker in an automotive factory. She worked 40 to 50 hours per week in a rotating 9-job “loop” manufacturing diesel engines. Each day she would perform a different job in the loop for the duration of her shift, until she had performed all 9 job functions, at which …

Identity, Institutions, and New Work Roles

In this article, the authors draw on institutional theories of organizations to account for the dynamics that shape workers’ agency and identity during the construction and the operation of a green field automotive factory. Based on a detailed field study, they try to understand the mechanisms that first led to the rise and institutionalization of a specific model of workers’ identity and then caused its subsequent, largely unexpected, collapse. In addressing the link between identity and institutions, the case emphasizes the regulative and constitutive influences of institutions, the role of sense making and cognitive processes in shaping workers’ agency and identity, and the factors leading to the erosion of established institutional practices.

A SIMULATION STUDY OF THE CRANKSHAFT LINE IN AN AUTOMOTIVE FACTORY

The major components of an engine are the cylinder block, cylinder head, crankshaft, connecting rod, and camshaft, which are more popularly known as the 5 C's. Thus, the engine shop usually consists of six sub-lines, including five machining lines and one assembly line. The flow line is the typical concept of the layout when the engineer designs the engine shop. This paper introduces a simulation study regarding the new crankshaft machining line in a Korean automotive factory. The major factors for designing the machining line are considered, and their effects on the system performance are evaluated with a three-dimensional (3D) simulation model that is developed with QUEST®. The initial layout is analyzed using the simulation model, and we suggest some ideas for improvement.

A Dynamic Algorithm for the Control of Automotive Painted Body Storage

This article introduces a simulation study relating to the dynamic operating algorithm for the painted body storage (PBS) in an automotive factory. The manufacturing process in the trim shop of an automotive factory is a typical example of the mixed-model assembly production. In the paint shop, which comes before the trim shop, same-colored bodies are grouped together to reduce the changeover cost. Therefore, the PBS is located between the paint shop and the trim shop to allow for the rescheduling of the input sequence of cars to the assembly line. In this article, a case study on PBS in an automotive factory is presented. The storage/retrieval algorithms are suggested, and the proposed system is verified using simulation models. A sensitivity analysis for operating factors is also carried out.

ALKYD PAINT WASTE CHARACTERIZATION AND DISTILLATION

ABSTRACT In the present study the characterization and distillation of alkyd paint waste have been carried out in order to establish the technical viability of organic solvents recovery and to decide upon the best environmental management for the waste stream from a paint spray-booth application in an automotive component factory. The paint is a black primer based on an alkyd resin with toluene and xylene as solvents, black carbon as pigment, fillers, and other minor components. The paint, paint wastes, and distillation wastes have been characterized by determination of the physicochemical properties of the solid (solvents content and flash point) and determination of ecotoxicity (EC50), total organic carbon (TOC), and metals concentration of the leachates. The highly volatile matter (HVM) content, determined by the weight loss at 200°C, has been used as a parameter to relate with the hazardous wastes regulations based on the characterization parameters EC50, TOC, and flash point. Distillation experiments have been performed in a simple batch mode with mixtures of paint waste, water, and additives in order to obtain high efficiencies in the recovery of volatile organic compounds and to obtain a final solid with nonhazardous behavior and good manageability qualities. A fractional factorial design (23–1) of experiments was carried out in order to study the influence of the distillation variables: water/paint waste ratio (2/1–3/1 (g/g)), temperature (110°–145°C), alkaline additives (Na(OH),CaO, Ca(OH)2), and amount of other additives (wt.% of bentonite, cement, and Ca(OH)2) on the solvent recovery, [% VOCs]Recovery, on the residual volatile organic compounds, [% VOCs]Distillation Waste, and on the total organic content (TOC) of the waste leachates. The present work shows the quantitative results of the alkyd paint waste distillation process under optimum conditions.

A Simulation Study for Implementing Color Rescheduling Storage in an Automotive Factory

This article outlines a simulation study relating to the design and the implementation of color rescheduling storage (CRS) in an automotive factory. In a painting shop, the colors of vehicles are changed frequently according to the assembly schedule. When the color of a vehicle is changed, the cleaning process of the painting gun is necessary, which generates a cost. The major objective of CRS is to reduce the changeover cost in the painting process by grouping vehicles with the same color. In this article, a case study of CRS in an automotive factory is presented. The procedure for designing CRS is explained, and the storage/retrieval algorithms are suggested. The proposed system is verified using simulation models, and comparisons among alternatives are examined to determine the best design. The authors also investigate the effect of color distribution on the system performance measures.