A multi-dimensional classification of production systems for the design and selection of production planning and control systems

2000 ◽  
Vol 11 (5) ◽  
pp. 481-496 ◽  
Author(s):  
Bart L. Maccarthy ◽  
Flavio C. F. Fernandes
2020 ◽  
Vol 12 (9) ◽  
pp. 3791 ◽  
Author(s):  
Olumide Emmanuel Oluyisola ◽  
Fabio Sgarbossa ◽  
Jan Ola Strandhagen

Many companies are struggling to manage their production systems due to increasing market uncertainty. While emerging ‘smart’ technologies such as the internet of things, machine learning, and cloud computing have been touted as having the potential to transform production management, the realities of their adoption and use have been much more challenging than anticipated. In this paper, we explore these challenges and present a conceptual model, a use-case matrix and a product–process framework for a smart production planning and control (smart PPC) system and illustrate the use of these artefacts through four case companies. The presented model adopts an incremental approach that companies with limited resources could employ in improving their PPC process in the context of industry 4.0 and sustainability. The results reveal that while make-to-order companies are more likely to derive greater benefits from a smart product strategy, make-to-stock companies are more likely to derive the most benefit from pursuing a smart process strategy, and consequently a smart PPC solution.


2021 ◽  
pp. 165-185
Author(s):  
Manuel Woschank ◽  
Patrick Dallasega ◽  
Johannes A. Kapeller

AbstractThe integrated planning and control of logistics processes can be seen as one of the basic prerequisites for the successful implementation of smart production systems and smart and lean supply chains, as well. Therefore, modern Industry 4.0 approaches are mainly focusing on (1) the principles of decentralization and (2) the usage of real-time data to improve the overall logistics performance in terms of promised delivery dates, work in progress, capacity utilization, and lead-times. In this context, this chapter systematically evaluates the application of decentralized production planning and control strategies, e.g., KANBAN and CONWIP, in comparison with traditional approaches, like MRP. Moreover, the impact of real-time data usage in production planning and control systems on lead-times and work in progress is investigated using a discrete event simulation based on primary data from a make to order manufacturer. The results of this industrial case study research confirm the significant potential that lies in smart production systems and smart and lean supply chains and, therefore, in the introduction of Industry 4.0 technologies and technological concepts in production and logistics systems.


2018 ◽  
Vol 108 (03) ◽  
pp. 148-154
Author(s):  
G. Höllthaler ◽  
C. Richter ◽  
M. Hörmann ◽  
A. Zipfel ◽  
J. Fischer ◽  
...  

Mit zunehmender Komplexität in Produktionssystemen, die sich auch aus der steigenden Variantenvielfalt ergibt, sind Entscheider im Produktionsumfeld mit immer komplexeren Fragestellungen konfrontiert. Zur Entscheidungsunterstützung stellt dieser Beitrag ein simulationsbasiertes Assistenzsystem vor. Neben einer allgemeinen Beschreibung des Aufbaus des Assistenzsystems wird im Detail der integrierte Materialflussservice betrachtet, der zur Beantwortung von Fragen rund um die Produktionsplanung und -steuerung zum Einsatz kommt.   An increasing complexity in production systems resulting, for example, from a growing number of variants, gives rise to more complex issues which stakeholders in the production area have to cope with. To support these stakeholders, this paper introduces a novel simulation-based assistant system. In addition to the general description of the assistant system’s setup, a detailed presentation of the integrated material flow service is given. This service deals with issues of production planning and control.


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