A heijunka study for the production of standard parts included in a customized finished product

Efficient order execution plays a crucial role in the activity of every company. In production planning it is important to find a balance between the fluctuations of orders and stability of production flow regarding the company. One of the methods of achieving this goal is heijunka (production leveling). This paper presents a study of choosing the best variant of the production planning and control system for the production of standard parts. Three variants are investigated regarding delays in order delivery. The analysis of variants was conducted using a simulation method. The method of choosing the best variant for the production system being investigated is also proposed. The results show that the best variant is a mix of production leveling and production "for stock".

Aligning the Production Planning and Control Process with Prognostics and Health Management

Production planning and control (PPC) is the heart of any manufacturing company and entails tasks such as resource planning, sequencing, or capacity control. While an increasing complexity within production makes it difficult to determine the best production plan, the advances in PHM and the emergence of predictive maintenance also offer new opportunities to optimize PPC. While there is much research on PHM and PPC, little has been done to align both disciplines. Through post-prognostics decision-making, different PPC decisions, such as continuing the production, shutting down a machine, or reducing its workload, can be elevated by a remaining useful life (RUL) estimation. However, it is unclear how exactly this prognostics information can be exploited and how processes, organization, and technology must be aligned to attain a more efficient and flexible production. Further, PHM has long been implemented beyond research, but it is unknown whether and how practitioners intertwine it with their PPC. This work aims to analyze how processual, organizational, and technological changes through PHM can lead to advanced PPC. This goal is attained by means of a multivocal literature review (MLR) in which scientific PPC and PHM literature and standards are analyzed, and an aligned PPC process proposed. The findings are juxtaposed with grey literature, revealing fits and gaps between research and practice, and a research agenda is presented.

Labor flexibility integration in workload control in Industry 4.0 era

The 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.

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

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.

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.