scholarly journals A Framework of Production Planning and Control with Carbon Tax under Industry 4.0

2018 ◽  
Vol 10 (9) ◽  
pp. 3221 ◽  
Author(s):  
Wen-Hsien Tsai ◽  
Yin-Hwa Lu
Keyword(s):  
Production Planning ◽  
Industry 4.0 ◽  
Programming Model ◽  
Carbon Tax ◽  
Optimal Solution ◽  
Production Planning And Control ◽  
Mathematical Programming Model ◽  
Tire Industry ◽  
Planning And Control ◽  
And Control

In recent years, the international community has placed great emphasis on environmental protection issues. The United Nations has also successively enacted relevant laws and regulations to restrain international greenhouse gas emissions and some countries implemented carbon tax levies to reduce air pollution. The tire industry is a manufacturing industry with high pollution and high carbon emissions; therefore, the purpose of this paper is to propose a framework of production planning and control with carbon tax under Industry 4.0 and use the tire industry as the illustrative example. In this framework, the mathematical programming model, with Activity-Based Costing (ABC) and Theory of Constraints (TOC) for production planning, is used to achieve the optimal solution under various production and sale constraints in order to find the optimal product-mix maximizing the profit. On the other hand, Industry 4.0 utilizes new technologies such as 3D printing, robot and automated guided vehicle (AGV) and links all the components in the manufacturing systems by using various sensor systems, Cyber-Physical Systems (CPS) and Internet of Things (IoT) to collect and monitor the activity data of all the components in real-time, to give intelligent responses to various problems that may arise in the factory by the real-time analysis results of cloud computing and big data and to attain the various benefits of Industry 4.0 implementation. The parameters of the mathematical programming model will be updated periodically from the new big data set. In this paper, an illustrative example is used is used to demonstrate the application of the model. From the optimal solution and sensitivity analyses on increasing the raw material’s prices and carbon taxes will affect the profits. This framework can provide a general approach to help companies execute production management in the way of more efficiency, less cost, lower carbon emission and higher quality across the value chain for the tire industry and other industries.

scholarly journals The concept of operation and production control

2021 ◽  
Vol 27 (2) ◽  
pp. 100-107
Author(s):  
Radosław Wolniak
Keyword(s):  
Artificial Intelligence ◽  
Production Planning ◽  
Industry 4.0 ◽  
Production Management ◽  
Production Control ◽  
Main Function ◽  
Production Planning And Control ◽  
Factors Affecting ◽  
Planning And Control ◽  
And Control

Abstract The theoretical aim of the paper is to analyses the main function and concept of production control in operation management. The empirical aim of the paper is to investigate polish production firm opinion about factors affecting production planning and control and also functions of production planning and control. Production control is very important in every factory, and every aspect of operation and production management especially in times of Industry 4.0 conditions. In the paper we presented all classical seven task of production management control. Also there is in the paper an analysis of main factors affecting production control in industrial organization. In the paper we analysed the problems connected with production control. Nowadays in the conditions of Industry 4.0 this is very important concept because the increasing level of digitalization of all industrial processes leads to possibility of detailed analysis of all processes and better level of control. Operation managers should have good level of knowledge about production control and especially quality control. They can use in this many new information tools like statistical methods and artificial intelligence. Especially we think that in the future many function of production control would be assisted by artificial intelligence. We also in the paper give results of research conducted on example of 30 polish production organizations located in Silesia region.

scholarly journals Green Production Planning and Control for the Textile Industry by Using Mathematical Programming and Industry 4.0 Techniques

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2072 ◽  
Author(s):  
Wen-Hsien Tsai
Keyword(s):  
Mathematical Programming ◽  
Textile Industry ◽  
Industry 4.0 ◽  
Programming Model ◽  
Environmental Issues ◽  
Mathematical Programming Model ◽  
Waste Reuse ◽  
Planning And Control ◽  
Green Production ◽  
And Control

The textile industry is one of the world’s major sources of industrial pollution, and related environmental issues are becoming an ever greater concern. This paper considers the environmental issues of carbon emissions, energy recycling, and waste reuse, and uses a mathematical programming model with Activity-Based Costing (ABC) and the Theory of Constraints (TOC) to achieve profit maximization. This paper discusses the combination of mathematical programming and Industry 4.0 techniques to achieve the purpose of green production planning and control for the textile industry in the new era. The mathematical programming model is used to determine the optimal product mix under various production constraints, while Industry 4.0 techniques are used to control the production progress to achieve the planning targets. With the help of an Industry 4.0 real-time sensor and detection system, it can achieve the purposes of recycling waste, reducing carbon emission, saving energy and cost, and finally achieving a maximization of profit. The main contributions of this research are using mathematical programming approach to formulate the decision model with ABC cost data and TOC constraints for the textile companies and clarifying the relation between mathematical programming models and Industry 4.0 techniques. Managers in the textile companies can apply this decision model to achieve the optimal product-mix under various constraints and to evaluate the effect on profit of carbon emissions, energy recycling, waste reuse, and material quantity discount.

Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0

2020 ◽  
Vol 31 (6) ◽  
pp. 1531-1558 ◽  
Author(s):  
Juan Pablo Usuga Cadavid ◽  
Samir Lamouri ◽  
Bernard Grabot ◽  
Robert Pellerin ◽  
Arnaud Fortin
Keyword(s):  
Machine Learning ◽  
Production Planning ◽  
Industry 4.0 ◽  
State Of The Art ◽  
Production Planning And Control ◽  
Planning And Control ◽  
And Control

scholarly journals Labor flexibility integration in workload control in Industry 4.0 era

2021 ◽  
Author(s):  
Federica Costa ◽  
Alberto Portioli-Staudacher
Keyword(s):  
Production Planning ◽  
Industry 4.0 ◽  
Shop Floor ◽  
Production Planning And Control ◽  
Labor Flexibility ◽  
Planning And Control ◽  
Workload Control ◽  
Low Volume ◽  
And Control ◽  
The Impact

AbstractThe paradigm shift toward Industry 4.0 is facilitating human capability, and at the center of the research are the workers—Operator 4.0—and their knowledge. For example, new advances in augmented reality and human–machine interfaces have facilitated the transfer of knowledge, creating an increasing need for labor flexibility. Such flexibility represents a managerial tool for achieving volume and mix flexibility and a strategic means of facing the uncertainty of markets and growing global competition. To cope with these phenomena, which are even more challenging in high-variety, low-volume contexts, production planning and control help companies set reliable due dates and shorten lead times. However, integrating labor flexibility into the most consolidated production planning and control mechanism for a high-variety, low-volume context—workload control—has been quite overlooked, even though the benefits have been largely demonstrated. This paper presents a mathematical model of workload control that integrates labor flexibility into the order review and release phase and simulates the impact on performance. The main results show that worker transfers occur when they are most needed and are minimized compared to when labor flexibility is at a lower level of control—shop-floor level—thus reducing lead time.

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