Understanding Time Loss in Manufacturing Operations

2015 ◽  
Vol 761 ◽  
pp. 619-623 ◽  
Author(s):  
Zuhriah Ebrahim ◽  
Amir Hamzah Abdul Rasib ◽  
Mohd Razali Muhamad
Keyword(s):  
Automotive Industry ◽  
Product Variety ◽  
Performance Measure ◽  
Manufacturing Companies ◽  
Literature Study ◽  
Time Loss ◽  
Machine Method ◽  
Critical Elements ◽  
Manufacturing Operations ◽  
Manufacturing Performance

Nowadays, manufacturing high product variety is essential for manufacturing companies in order to be sustainable in a volatile market. However, maintaining a shorter lead time in manufacturing operations is also crucial as the speed delivery becomes one of the manufacturing competitive priorities. Motivated by this issue, this study aims to develop a model of Time Loss (TL) for sub-assembly processes in automotive industry. In relation to this, critical elements of TL will be clearly justified through a thorough analysis of literature study in the aspects of Man, Machine, Method, and Material (4M). In this study, the critical elements of TL is defined as an unnecessary activity that needs to be eliminated or minimized. The relationships between the critical elements of TL are clarified through the flow of activities involved in the concept of manufacturing input-output. Finally, the critical elements of TL are compared to the existing non-financial manufacturing performance measures presented in isolated models (e.g. leanness, agility, responsiveness, etc.). Results of the analysis show that the critical elements of TL can be represented as a holistic performance measure of manufacturing operations that includes leanness, agility, fitness, responsiveness, flexibility, and sustainability.

scholarly journals Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies

2021 ◽  
Vol 11 (15) ◽  
pp. 6787
Author(s):  
Jože M. Rožanec ◽  
Blaž Kažič ◽  
Maja Škrjanc ◽  
Blaž Fortuna ◽  
Dunja Mladenić
Keyword(s):  
Machine Learning ◽  
Automotive Industry ◽  
Demand Management ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Forecast Errors ◽  
Manufacturing Companies ◽  
Case Scenario ◽  
Crucial Component

Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance.

Differences in the impact of AMO bundles of line managers and frontline workers on the performance of manufacturing firms

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alberto Bayo-Moriones ◽  
Alejandro Bello-Pindado
Keyword(s):  
Manufacturing Industry ◽  
Manufacturing Firms ◽  
Manufacturing Companies ◽  
Linear Regression Models ◽  
Line Managers ◽  
Content Type ◽  
Frontline Workers ◽  
Hrm Practices ◽  
Manufacturing Performance ◽  
The Impact

PurposeThe purpose of this paper is to analyse the impact on manufacturing performance of human resource management (HRM) practices across two job levels within manufacturing firms in Argentina and Uruguay: that of line managers and frontline workers. HRM practices are categorised into three bundles defined by the AMO theoretical framework: ability, motivation and opportunity.Design/methodology/approachThe article uses data from a survey to 301 manufacturing plants in Uruguay and Argentina. Given the characteristics of the dependent variable, linear regression models have been estimated in order to test the hypotheses.FindingsThe results show that the ability and opportunity bundles for line managers are positively associated with manufacturing performance. However, only the motivation bundle affects manufacturing performance for frontline workers.Research limitations/implicationsThe main limitations are the use of cross-sectional data, the focus on two specific countries and the analysis of two employee categories that are not completely homogenous. The paper extends the contingency perspective in HRM by examining the relevance of job level as a contingent factor in the HRM-performance relationship in the manufacturing industry.Practical implicationsThe results suggest that manufacturing companies should target HR investments more towards line managers than to frontline employees. More specifically, they should concentrate efforts on the ability and opportunity bundles.Originality/valueThe article contributes to the very limited empirical evidence on the impact of HRM differentiation on firm performance by analysing sub-dimensions in a context not previously analysed.

Hierarchical Decomposition of a Manufacturing Work Cell to Promote Monitoring, Diagnostics, and Prognostics

2017 ◽  
Author(s):  
Brian A. Weiss ◽  
Guixiu Qiao
Keyword(s):  
Machine Tools ◽  
Health Management ◽  
Control Strategies ◽  
Industrial Robot ◽  
Work Cell ◽  
Critical Elements ◽  
Functional Relationships ◽  
Manufacturing Operations ◽  
Robot Systems ◽  
And Control

Manufacturing work cell operations are typically complex, especially when considering machine tools or industrial robot systems. The execution of these manufacturing operations require the integration of layers of hardware and software. The integration of monitoring, diagnostic, and prognostic technologies (collectively known as prognostics and health management (PHM)) can aid manufacturers in maintaining the performance of machine tools and robot systems by providing intelligence to enhance maintenance and control strategies. PHM can improve asset availability, product quality, and overall productivity. It is unlikely that a manufacturer has the capability to implement PHM in every element of their system. This limitation makes it imperative that the manufacturer understand the complexity of their system. For example, a typical robot systems include a robot, end-effector(s), and any equipment, devices, or sensors required for the robot to perform its task. Each of these elements is bound, both physically and functionally, to one another and thereby holds a measure of influence. This paper focuses on research to decompose a work cell into a hierarchical structure to understand the physical and functional relationships among the system’s critical elements. These relationships will be leveraged to identify areas of risk, which would drive a manufacturer to implement PHM within specific areas.

scholarly journals Managing Manufacturing Complexity Drivers on Performance – An Initial Study

2018 ◽  
Vol 2 (1) ◽  
pp. 45-50
Author(s):  
Keyword(s):  
Strong Correlation ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Initial Study ◽  
Efficient Production ◽  
Product Management ◽  
Nonlinear Behaviour ◽  
Manufacturing Companies ◽  
Manufacturing Performance ◽  
Manufacturing Productivity

Manufacturing systems, in pursuit of cost, time and flexibility optimisation are becoming more and more complex, exhibiting a dynamic and nonlinear behaviour. Unpredictability is a distinct characteristic of such behaviour and effects production planning significantly. Complexity continues to be a challenge in manufacturing systems, resulting in ever-inflating costs, operational issues and increased lead times to product realisation. This challenge must be met with appropriate decision-making by manufacturing companies to secure competitive advantage without compromising sustainability. Assessing complexity realises the reduction and management of complexity sources which contribute to lowering associated engineering costs and time, improves productivity and increases profitability. Therefore, this study was undertaken to investigate the priority level and current achievement of manufacturing performance in Malaysia’s manufacturing industry and the complexity drivers on manufacturing productivity performance. The results showed that Malaysia’s manufacturing industry prioritised product quality and they managed achieved a good on-time delivery performance. However, for other manufacturing performance, there was a difference where the current achievement of manufacturing performances in Malaysia’s manufacturing industry is slightly lower than the priority given to them. The strong correlation of significant value for priority status was observed between efficient production levelling (finished goods) and finish product management while the strong correlation of significant value for current achievement was minimised the number of workstation and factory transportation system. This indicates that complexity drivers have an impact towards manufacturing performance. Consequently, it is necessary to identify complexity drivers to achieve well manufacturing performance.

scholarly journals Pengaruh Ukuran Komite Audit, Kepemilikan Manajerial dan Kepemilikan Institusional Terhadap Return On Assets (ROA)

2021 ◽  
Vol 4 (2) ◽  
pp. 645-655
Author(s):  
Celine Eriskha ◽  
Nanu Hasanuh
Keyword(s):  
Audit Committee ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Institutional Ownership ◽  
Multiple Linear Regression Analysis ◽  
Managerial Ownership ◽  
Manufacturing Companies ◽  
Literature Study ◽  
Return On Assets ◽  
Food And Beverage

When observing the major financial problems that were revealed, the public questioned the performance of the big companies involved in this scandal, which contradicts the principles of Good Corporate Governance regarding accountability, equity, integrity, transparency and responsibility. This study aims to determine, test and explain the effect of the audit committee, managerial ownership, institutional ownership, on Return On Assets both partially and jointly in the food and beverage sub-sector manufacturing companies listed on the Indonesia Stock Exchange for the period 2014 to 2019. The sample was determined by purposive sampling. Data collection techniques using literature study and observation. The method used is multiple linear regression analysis. Based on the results of multiple linear analysis, it is found that Managerial ownership has a partial effect on ROA then Audit Committee Size and Institutional ownership partially have no effect on ROA, and simultaneously Audit Committee Size, Managerial Ownership and Institutional Ownership together have an effect on Return On Assets ( ROA). Keywords: Audit Committee, Managerial Ownership, Institutional and ROA

Pervasive Computing, Firm Characteristics, and Environmental Factors Conducive to the Adoption of Activity-Based Costing

2010 ◽  
pp. 184-203
Author(s):  
Sayel Ramadhan
Keyword(s):  
Small Firms ◽  
Relevant Literature ◽  
Product Variety ◽  
Business Environment ◽  
Computer Usage ◽  
Manufacturing Companies ◽  
Activity Based Costing ◽  
Structured Interviews ◽  
Study Approach ◽  
Size Criterion

The main purpose of this study is to provide evidence on the contextual featutres of firms adopting Activity-Based Costing (ABC) compared to those not adopting ABC. The study examines certain organisational and business environment variables which appear to have influenced the adoption of ABC. Based on a review of the relevant literature, it is hypothesised that firm size, the amount of overhead costs, the level of product variety, production complexity, the degree of competition, and the degree of computer usage are factors which encourage firms to adopt ABC. A list of manufacturing companies operating in Bahrain (332 firms) was obtained from the Ministry of Industry. Firms with (50) workers or more were selected for the study. The reason for limiting the study to firms with this number of workers is that small firms are less likely to be able to afford the cost of adopting and implementing an ABC system and its required changes. Total of (111) firms met this size criterion and a questionnaire was developed and distributed to the entire sample. Fifty seven questionnaires were returned completed; a response rate of (51.4%). The results of the study show that a small percentage of Bahraini manufacturing companies are adopting or planning to adopt ABC systems (26.3%). There were significant relationships between the adoption of ABC and the variables selected for the study except production complexity and the degree of computer usage. The results are consistent with previous research. However, further research using a case study approach with semi-structured interviews could be conducted in those firms which claim to have adopted ABC. This approach might be fruitful and would provide more insight in identifying the characteristics of ABC companies in the Bahraini context.

scholarly journals IDENTIFICATION OF TECHNOLOGY INTEGRATION CHALLENGES AT TWO GLOBAL AUTOMOTIVE OEMS

2020 ◽  
Vol 1 ◽  
pp. 2245-2254
Author(s):  
I. Alonso Fernández ◽  
M. Panarotto ◽  
O. Isaksson
Keyword(s):  
Technology Integration ◽  
Automotive Industry ◽  
New Technologies ◽  
Emerging Technologies ◽  
Cost Effective ◽  
Product Variety ◽  
Technological Innovations ◽  
Platform Design ◽  
Product Platforms

AbstractPlatform design has been firmly established in the automotive industry as a strategy to provide wider product variety while maintaining cost effective production. But this strategy can struggle to keep up with the pace and nature of emerging technologies. This paper reviews the existing approaches to modelling product platforms, and showcases the challenges at OEMs introducing new technological innovations in their platforms. A gap is identified in the methods to assess the ability of existing platforms to integrate new technologies whenever they become available.

Product Variety and Manufacturing Performance: Evidence from the International Automotive Assembly Plant Study

1996 ◽  
Vol 42 (3) ◽  
pp. 350-369 ◽  
Author(s):  
John Paul MacDuffie ◽  
Kannan Sethuraman ◽  
Marshall L. Fisher
Keyword(s):  
Product Variety ◽  
Assembly Plant ◽  
Manufacturing Performance ◽  
Automotive Assembly
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