scholarly journals Statistical Quality Study of the Parts Produced in an Automobile Industry: A Daimler India Case Study

2021 ◽  
Vol 1206 (1) ◽  
pp. 012022
Author(s):  
Tanmay Pati ◽  
Shruti Kabra ◽  
Utkarsh Chadha
Keyword(s):  
Automobile Industry ◽  
Manufacturing Industry ◽  
Production Quality ◽  
Statistical Quality Control ◽  
Statistical Tools ◽  
Automobile Part ◽  
Statistical Quality ◽  
Quality Study ◽  
Cumulative Sum Charts

Abstract Every manufacturing industry considers defect-free production in this competitive era. Though it is practically impossible to improve the rate of defect minimization, statistical tools can be used. In this case study, we take data from Daimler India, an automobile part manufacturing industry specialized in manufacturing OEM tools like chassis, oil filter nipple, and so on, and study how many defects are made during the day. The defect-causing process was eliminated after using statistical quality control tools such as np charts and cumulative sum charts, resulting in higher production quality.

Implementation of green supplier development process model in Indian automobile industry

2018 ◽  
Vol 29 (5) ◽  
pp. 938-960 ◽  
Author(s):  
Shivangi Viral Thakker ◽  
Santosh B. Rane
Keyword(s):  
Automobile Industry ◽  
Process Model ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Supplier Development ◽  
Journal Articles ◽  
Content Type ◽  
Automobile Components ◽  
Green Suppliers

Purpose The purpose of this paper is to develop a green supplier development (GSD) process model and validate the model with a case study in Indian automobile industry. Design/methodology/approach A literature survey of peer-reviewed journal articles, survey reports and paradigmatic books with managerial impact is done for the research. The process of GSD is modeled using stage-gate approach and KPIV and KPOV of the process are determined. The process model is implemented in an Indian automobile components manufacturing industry for validation. Findings The industry implemented the model with ten suppliers and was able to successfully convert seven of them into Green suppliers. Remaining three suppliers were asked to repeat the process again or terminate the contracts. Model implementation took around three years starting from planning of resources and finances to actual development of suppliers. Research limitations/implications The model implementation was done with a small automobile industry and hence the validation and implications may be generalized by taking the case study further in different industries. It would be beneficial to test the model with case studies of large-scale industries. Practical implications The process model for implementing GSD activities will help managers in taking complex investment decisions. The stages and process inputs and outputs are clearly defined which helps the managers to successfully develop the suppliers. Originality/value This paper puts forward the process model that should be implemented for the successful development of green suppliers. It might represent new opportunities for rigorous and relevant research in the area of green supply chain.

scholarly journals Assessing the key enablers for Industry 4.0 adoption using MICMAC analysis: a case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Srijit Krishnan ◽  
Sumit Gupta ◽  
Mathiyazhagan Kaliyan ◽  
Vikas Kumar ◽  
Jose Arturo Garza-Reyes
Keyword(s):  
Automobile Industry ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Positive Outcome ◽  
Content Type ◽  
Strategic Integration ◽  
Micmac Analysis ◽  
New Business ◽  
Academic Professionals

PurposeThe aim of this research is to assess the key enablers of Industry 4.0 (I4.0) in the context of the Indian automobile industry. It is done to apprehend their comparative effect on executing I4.0 concepts and technology in manufacturing industries, in a developing country context. The progression to I4.0 grants the opportunity for manufacturers to harness the benefits of this industry generation.Design/methodology/approachThe literature related to I4.0 has been reviewed for the identification of key enablers of I4.0. The enablers were further verified by academic professionals. Additionally, key executive insights had been revealed by using interpretive structural modelling (ISM) model for the vital enablers unique to the Indian scenario. The authors have also applied MICMAC analysis to group the enablers of I4.0.FindingsThe analysis of this study’s data from respondents using ISM provided us with seven levels of enabler framework. This study adds to the existing literature on I4.0 enablers and findings highlight the specificities of the territories in India context. The results show that top management is the major enabler to I4.0 implementation. Infact, it occupies the 7th layer of the ISM framework. Subsequently, government policies enable substantial support to develop smart factories in India.Practical implicationsThe findings of this work provide implementers of I4.0 in the automobile industry in the form of a robust framework. This framework can be followed by the automobile sector in enhancing their competency in the competitive market and ultimately provide a positive outcome for the Indian economic development led by these businesses. Furthermore, this work will guide decision-makers in enabling strategic integration of I4.0, opening doors for the development of new business opportunities as well.Originality/valueThe study proposes a framework for Indian automobile industries. The automobile sector was chosen for this study as it covers a large percentage of the market share of the manufacturing industry in India. The existing literature does not address the broader picture of I4.0 and most papers do not provide validation of the data collected. This study thus addresses this research gap.

Data Quality Improvement in Clinical Databases Using Statistical Quality Control: Review and Case Study

2013 ◽  
Vol 47 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Hassan Assareh ◽  
Mary A. Waterhouse ◽  
Christina Moser ◽  
Russell D. Brighouse ◽  
Kelley A. Foster ◽  
...  
Keyword(s):  
Quality Control ◽  
Quality Improvement ◽  
Data Quality ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Clinical Databases

scholarly journals DEVELOPING A LEAN BENCHMARKING PROCESS TO MONITOR THE CARBON EFFICIENCY IN PRECAST CONCRETE FACTORIES—A CASE STUDY IN SINGAPORE

2013 ◽  
Vol 8 (2) ◽  
pp. 133-152 ◽  
Author(s):  
Peng Wu ◽  
Josua Pienaar ◽  
Darryl O'Brien
Keyword(s):  
Sustainable Development ◽  
Carbon Emissions ◽  
Automobile Industry ◽  
Manufacturing Industry ◽  
Lean Production ◽  
Precast Concrete ◽  
Good Practice ◽  
Concrete Column ◽  
Carbon Efficiency

The lean production philosophy was developed in the automobile industry and put into practice in the manufacturing industry to reduce waste, inventory and improve productivity. The lean concept has recently been introduced to the manufacturing industry to meet the challenge of sustainable development. However, it seems that the concept of sustainable development was defined too broadly for the manufacturers to guide their preferred decisions and behaviours. This research aims to narrow down the concept of sustainable development and focus on the carbon emissions that can be reduced by applying the lean concept in the precast concrete factories. The results demonstrate that the lean production philosophy can help precasters to reduce the level of carbon emissions, in terms of reducing waste and eliminating unnecessary energy consumption in multi-handling, multi-delivery, maintaining inventory and other non-value adding activities, in line with its concentration on eliminating non-value adding processes. An amount of 5.80% carbon emissions can be reduced in the production process for a specific type of precast concrete column. The lean benchmarking process provided in this study is helpful for the precasters to monitor the carbon efficiency in the precast concrete factories. In addition, the analysis provides good practice guidance when precasters are trying to reduce carbon emissions to meet guidelines from regulatory authorities.

Application of statistical quality control for limestone grade: a case study

2007 ◽  
Vol 116 (1) ◽  
pp. 25-31
Author(s):  
J. Bhattacharya ◽  
M. Islam ◽  
A. Kumar ◽  
G. Santosh
Keyword(s):  
Quality Control ◽  
Statistical Quality Control ◽  
Statistical Quality
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