scholarly journals Teaching-Learning Strategies to Production Planning and Control Concepts: Application of Scenarios to Sequencing Production with Virtual Reality Support

2021 ◽  
Vol 20 (8) ◽  
pp. 108-125
Author(s):  
Fernando Elemar Vicente dos Anjos ◽  
Luiz Alberto Oliveira Rocha ◽  
Rodrigo Pacheco ◽  
Débora Oliveira da Silva
Keyword(s):  
Virtual Reality ◽  
Production Planning ◽  
Learning Strategies ◽  
Production Planning And Control ◽  
Production Engineering ◽  
Planning And Control ◽  
Process Manufacturing ◽  
Teaching Learning ◽  
And Control ◽  
The Impact

This paper aims to present scenarios to be applied in higher education to the theme of production planning and control, addressing factors of the production system and indicators arising from this process and the application of virtual reality to support the process. The applied method combines the development of six scenarios for virtual reality application and the discussion about the impacts in indicators from the production planning and control, for example, inventory in the process, manufacturing lead-time, use of equipment, and punctual delivery attendance. Findings revealed that the teaching-learning process of production planning and control, when applied through scenarios, generates opportunities for students to learn the impact in the indicators. The virtual reality in this environment supports creating differentiated teaching-learning environments to generate the most significant knowledge for students which positively impacts the future in the world of work. In addition, it allows people involved in the teaching-learning processes of production engineering to apply the concepts presented in the sequencing process, lean about the impacts of decisions on production sequencing indicators and appreciate the support of virtual reality to generate an environment more cognitive for students.

scholarly journals The Influence of Cognitive Biases in Production Planning and Control: Considering the Human Factor for the Design of Decision Support Systems

2021 ◽  
Author(s):  
Julia Bendul ◽  
Melanie Zahner
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Production Planning ◽  
Cognitive Biases ◽  
Production Planning And Control ◽  
Due Date ◽  
Planning And Control ◽  
Human Decision ◽  
And Control ◽  
The Impact

Production planning and control (PPC) requires human decision-making in several process steps like production program planning, production data management, and performance measurement. Thereby, human decisions are often biased leading to an aggravation of logistic performance. Exemplary, the lead time syndrome (LTS) shows this connection. While production planners aim to improve due date reliability by updating planned lead times, the result is actually a decreasing due date reliability. In current research in the field of production logistics, the impact of cognitive biases on the decision-making process in production planning and control remains at a silent place. We aim to close this research gap by combining a systematic literature review on behavioral operation management and cognitive biases with a case study from the steel industry to show the influence of cognitive biases on human decision-making in production planning and the impact on logistic performance. The result is the definition of guidelines considering human behavior for the design of decision support systems to improve logistic performance.

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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