Towards a Pay-Per-X Maturity Model for Equipment Manufacturing Companies

In the paper, we analyze the recovery pattern of Japanese electrical equipment manufacturing companies after the President Trump remark in August 2019. The President’s remark made the companies’ stock prices decreased severely. The research consists of two parts. In the first part, we conducted Random Matrix Theory to extract representative decline/recovery patterns. Then we tagged A/B/C/D to the companies’ recovery types. The class A means a strong recover power. Then as the second part, we conducted machine learning tree-based classification using the tags A/B/C. The predictors are eight variables like ROA, ROE, and VAR. The resultant Decision Tree model provided us with the two different approaches to the class A group. The recovery and repulsion power will be higher in the company with high ROA and in the company that manufactured the product with high VAR. In addition, another class A company group is made and the feature is the high inventory turnover ratio.

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Assessment of Intellectual Capital Efficiency in China Transportation Equipment Manufacturing Companies: Using DEA

Advanced Technology in Teaching - Advances in Intelligent and Soft Computing ◽

10.1007/978-3-642-29458-7_59 ◽

2012 ◽

pp. 405-412

Author(s):

Yanjie Hu ◽

Xiuli Wang ◽

Lanxia Yang

Keyword(s):

Intellectual Capital ◽

Manufacturing Companies ◽

Transportation Equipment ◽

Equipment Manufacturing ◽

Capital Efficiency

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An Industry 4.0 maturity model proposal

Journal of Manufacturing Technology Management ◽

10.1108/jmtm-09-2018-0284 ◽

2019 ◽

Vol 31 (5) ◽

pp. 1023-1043 ◽

Cited By ~ 7

Author(s):

Reginaldo Carreiro Santos ◽

José Luís Martinho

Keyword(s):

Literature Review ◽

Industry 4.0 ◽

Industrial Revolution ◽

Maturity Model ◽

Manufacturing Companies ◽

Content Type ◽

Five Dimensions ◽

Industrial Companies ◽

Smart Products ◽

Proposed Model

Purpose In recent years, the development and application of innovative and disruptive technologies in manufacturing environments is shaping the fourth industrial revolution, also known as Industry 4.0. The purpose of this paper is to describe a tool to assess the maturity level in implementing Industry 4.0 concepts and technologies in manufacturing companies. Design/methodology/approach Using a framework to develop maturity models found in literature, three main steps were taken: the model design from the literature review on industry 4.0 and the comparative analysis of existing models; interviews with engineers and managers of relevant industries; and pilot tests in two relevant industrial companies. Findings The proposed maturity model has 41 variables considering five dimensions (organizational strategy, structure and culture; workforce; smart factories; smart processes; smart products and services). The studied companies showed different levels of Industry 4.0 implementation. According to respondents, the model is useful in making an initial diagnosis and establishes a roadmap to proceed the implementation. Practical implications Empirical evidence supports the relevance of the proposed model and its practical usefulness. It can be used to measure the current state (initial diagnostic and monitoring assessments), and to plan the future desired state (goal), identifying which transformational capabilities should be developed. Originality/value The literature review did not return an enough complete maturity model to guide a self-administered assessment. Therefore, the proposed model is a valuable tool for companies and researchers to understand the I4.0 phenomenon, plan and monitor the transformation actions.

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The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model

Journal of Information Systems Engineering and Business Intelligence ◽

10.20473/jisebi.6.1.70-78 ◽

2020 ◽

Vol 6 (1) ◽

pp. 70

Author(s):

Fitri Retrialisca ◽

Umi Chotijah

Keyword(s):

Big Data ◽

Qualitative Methods ◽

Data Retrieval ◽

High Volume ◽

Maturity Model ◽

Manufacturing Companies ◽

Maturity Level ◽

Is Implementation ◽

Organizational Ability ◽

Big Data Technology

Background: Big data technology has been used in several sectors in Indonesia. Adoption of big technology provides great potential for research, especially achievement in the implementation of big data in manufacturing companies. The Data Warehousing Institute (TDWI) Maturity Model is a tool that can be used to measure the state of "As-is" implementation of big data using 5 main dimensions. Maturity level shows the level of organizational ability to adjust big data technology currently.Objective: This study aims to measure the level of maturity in the implementation of big data technology in manufacturing companies PT. XYZ. This measurement is considered very important because it can know the process of managing data that is structured and has a high volume of data and provides more transparent reporting. This can help the company in making decisions that provide good information, so the company can increase the trust of stakeholders.Methods: This study uses qualitative methods to analyze research data using TWDI Maturity Model tools. Interview technique is used to retrieve respondent data where interview preparation guidelines are made by paying attention to 5 dimensions and 50 indicators in TDWI.Results: The research showed that the implementation of big data technology in the company as a whole has reached the level of corporate adoption. Infrastructure, data management, and analytics dimensions have reached the corporate adoption level while the organizational and governance dimensions are still at an early adoption level.Conclusion: To measure the maturity level of adoption of big data technology in manufacturing companies can use qualitative methods with TDWI Maturity model tools, interview guides for data retrieval by considering the 5 dimensions and 50 indicators that exist in TDWI.

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The Role of Safety Training in Original Equipment Manufacturing Companies on Employee Perception of Knowledge, Behavior Towards Safety and Safe Work Environment

International Journal of Safety and Security Engineering ◽

10.18280/ijsse.100514 ◽

2020 ◽

Vol 10 (5) ◽

pp. 689-698

Author(s):

Ravi Sharma ◽

Dharmesh K. Mishra

Keyword(s):

Work Environment ◽

Learning Process ◽

Work Experience ◽

Development Program ◽

Safety Training ◽

Hierarchical Regression ◽

Manufacturing Companies ◽

Safe Work ◽

Equipment Manufacturing ◽

Original Equipment Manufacturing

Training is important for the development of skills and knowledge. The purpose of this study was to explore the influence of post- safety training on the supervisors learning process, behaviour towards safety and development of safe work environment in the automotive original equipment manufacturing (OEM) companies. In the present study, a total of 129 supervisor level employees from different Indian automotive OEMs units, who have undergone a minimum of one-day safety training in the past three years, were a part of the survey. The survey was administered with the aid of a pre-validated designed questionnaire (developed in consultation with industry experts) to collect responses from the supervisor’s level employees during the period of January- August 2019. The 63 different manufacturing OEM automotive units from the Delhi- NCR and Pune- Nashik - Kolhapur from Western region agreed to participate in the survey. The results obtained were tested using multiple hierarchical regression analysis in a stepwise method, along with the correlation coefficient analysis. The results indicated that knowledge acquisition, learning process, and employee involvement regarding risks and hazards identification were positively related to the perceived effectiveness of post- safety training by the supervisors. It was further found that the post- safety training has no significant contribution towards the perceived self- behavior change and development of safe work environment. The effectiveness of safety training and development of safe environment along with the change in behavior towards the safety is related to factors such as related education in safety and health, working experience in the field of safety or EHS domain and knowledge of the supervisors gained through their career which play a significant role. A safety culture can be created by the organization by harnessing the safety-related work experience of the supervisors and periodically conducting the skill development program.

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