Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry

Author(s):  
Nitin S. Solke ◽  
Pritesh Shah ◽  
Ravi Sekhar ◽  
T. P. Singh

Productivity Improvement has become an important goal for today’s automotive industry, as the customer demand is increasing every day. Organisation takes this as an opportunity to improve the business potential. Implementation of lean manufacturing tool results in productivity improvement and cost reduction in several companies. There is a strong correlation between productivity and labour productivity. Labour productivity improvement has a direct impact on business growth. This article presents a case study on application of lean manufacturing tool “Eliminate Combine Rearrange Simplify (ECRS)” in the manufacturing industry. This work is carried out in a medium scale automotive parts manufacturing company where all kinds of machining operation is taken place and the output of the product is supplied to vehicle manufacturer. This case study is illustrating increase in labour productivity in an automobile component machining line by applying the ECRS technique. Vertical Machining centre, Combination of tool, man and machine concept is used for implementing ECRS technique in the machining line. After implementation there is significant increase in labour productivity and reduction in throughput time and saving of space on the shop floor.


2022 ◽  
Vol 32 (1) ◽  
pp. 339-359
Author(s):  
M. Rajalakshmi ◽  
V. Saravanan ◽  
V. Arunprasad ◽  
C. A. T. Romero ◽  
O. I. Khalaf ◽  
...  

Author(s):  
D. P. Solomatine

Traditionally, management and control of water resources is based on behavior-driven or physically based models based on equations describing the behavior of water bodies. Since recently models built on the basis of large amounts of collected data are gaining popularity. This modeling approach we will call data-driven modeling; it borrows methods from various areas related to computational intelligence—machine learning, data mining, soft computing, etc. The chapter gives an overview of successful applications of several data-driven techniques in the problems of water resources management and control. The list of such applications includes: using decision trees in classifying flood conditions and water levels in the coastal zone depending on the hydrometeorological data, using artificial neural networks (ANN) and fuzzy rule-based systems for building controllers for real-time control of water resources, using ANNs and M5 model trees in flood control, using chaos theory in predicting water levels for ship guidance, etc. Conclusions are drawn on the applicability of the mentioned methods and the future role of computational intelligence in modeling and control of water resources.


Author(s):  
Robert W.D. Zondo

The majority of South Africans expect greater prosperity that can be accomplished through greater employment, high productivity and wage increases. Increased productivity can finance higher wages without burdening the customer with higher selling prices. Consequently, there should be strong co-operation between management and labour to improve productivity, thereby ensuring the survival of South African companies. To achieve this objective, organisations find themselves turning to their employees for creative suggestions and ideas on better ways of doing things. This sentiment underpins the concept of gainsharing. Gainsharing is a formula-based company-wide programme that offers employees a share in the financial gains of a company as a result of its improved performance. This motivation boosts a company’s productivity and radically reduces the cost of waste, spoilage, rejects and rework. This study examined the impact of a gainsharing programme on the improvement of labour productivity in the automotive parts manufacturing sector. The study investigated the production and related experience of two automotive parts manufacturing companies (referred to as A and B in this study) that have adopted a gainsharing strategy. The two companies operate in the eThekwini District Municipality in KwaZulu-Natal. It assessed if gainsharing is responsible for company labour productivity improvements. The investigation was achieved by collecting pre- and post-gainsharing quarterly data for spoilage, absenteeism, capital investment and labour productivity. Gainsharing improves labour productivity and reduces spoilage and absenteeism rates. In order to maximise performance, a comprehensive performance policy must be developed, which aligns pay (and other incentives) to performance. The study uncovered the strengths and weaknesses of gainsharing for labour productivity improvement in South Africa.


Sign in / Sign up

Export Citation Format

Share Document