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Tasks to fulfil during production planning, production control and production monitoring [based on 8].
Source publication
Digitalization is changing industrial production and is offering huge potential for producing companies. One effect resulting from the increasing presence of information and communication technology in production is the increasing quantity and quality of production feedback data. However, only collecting large amounts of data of data does not lead...
Contexts in source publication
Context 1
... presenting the developed concept, it is important to discuss the topics of production planning, production control and production monitoring in general. Fig. 1 gives an overview on operational tasks that have to be fulfilled by producing companies. The structuring of the tasks and the following description are based on the Hanoverian Supply Chain Model -a framework for PPC and supply chain management (www.hasupmo.education) ...
Context 2
... aim of production control is to realize the previously prepared production plan as economically as possible despite unavoidable disruptions like the lack of staff or delayed deliveries [9]. As can be seen in Fig. 1 three tasks have to be fulfilled during production control: order release, sequencing at the workstations and capacity control [10]. The first task is the order release after which the orders are physically in production. At the workstations the orders are brought into a defined sequence for processing (e. g. FIFO, due date oriented or ...
Context 3
... presenting the developed concept, it is important to discuss the topics of production planning, production control and production monitoring in general. Fig. 1 gives an overview on operational tasks that have to be fulfilled by producing companies. The structuring of the tasks and the following description are based on the Hanoverian Supply Chain Model -a framework for PPC and supply chain management (www.hasupmo.education) ...
Context 4
... aim of production control is to realize the previously prepared production plan as economically as possible despite unavoidable disruptions like the lack of staff or delayed deliveries [9]. As can be seen in Fig. 1 three tasks have to be fulfilled during production control: order release, sequencing at the workstations and capacity control [10]. The first task is the order release after which the orders are physically in production. At the workstations the orders are brought into a defined sequence for processing (e. g. FIFO, due date oriented or ...
Citations
... The investigations into the use of production feedback data into PPC has increased over the past decade. Schäfers, Mütze [24] created an integrated concept for acquisition and utilization of production feedback data using radio frequency identification (RFID) technology to support PPC. This study mainly focused on applications for scheduling, capacity planning, and production control. ...
... This highlights the importance of analyzing the company's available historic production feedback data in order to identify which data to capture on the shopfloor. Companies can, for instance, differentiate between essential, important, and optional feedback points for data capture [24]. Further, companies must determine whether data should be monitored continuously to assess the accuracy of the static master data or if the data only needs to be verified or recalculated at regular intervals. ...
Planning quality depends on the use of correct, accurate, realistic, and reliable planning data. Industry 4.0 has facilitated large-scale data collection from a variety of sources, including production feedback data. The hierarchical nature of traditional production planning and control (PPC) limits the ability to use such data to improve planning quality. This paper explores how planning quality can be improved through the application of production feedback data into tactical production planning. The paper shows that while current tactical planning is mainly based on static master data, some of the master data for planning should instead be dynamically determined based on analysis of production feedback data. The paper develops a conceptual model for how production feedback data can be linked to tactical planning, illustrates how production feedback data can be applied in tactical planning, and proposes a method for how companies can integrate production feedback data into their tactical planning. Future work includes application and testing of the proposed concept in real-life cases and studies to better understand the specific relationship between the accuracy of master data and the performance of production plans.
... The application of data from a more diverse range of sources has made way for more integrated, dynamic and realtime PPC, aptly labelled smart PPC [1][2][3]. A consequence of the growing application of digital technologies in production is a large increase in both the quantity and quality of data captured on the shopfloor [4]. Examples include data about the current statuses of active production jobs, utilized resources, and set-up and processing durations for process steps [5]. ...
... There is now a growing body of research on the use of this type of production feedback data for PPC, see for instance Oluyisola [8] , Schuh, Thomas [5], Schuh, Reuter [9], Reuter and Brambring [6], and Schäfers, Mütze [4]. These studies have mainly investigated the use of production feedback data on a conceptual level or for control purposes, while literature on its usefulness for tactical planning and scheduling is still scarce. ...
Industry 4.0 is providing unprecedented opportunities for the capture and use of data into production planning and control (PPC). The accuracy of such data for PPC has been found to have a direct positive effect on operational performance. This study builds on a dynamic approach where production feedback data is used to improve the accuracy of master data used in tactical planning. The study applies a model-based approach using data from a real case. Two illustrative sensitivity analyses indicate that even small deviations in the accuracy of master data have an impact on the production schedule in terms of job sequence and makespan. The paper's main theoretical contribution is the development of six propositions on this relationship, where in short, the sequence appears to be sensitive to the accuracy of both changeover time and processing time. The paper illustrates how sensitivity analysis can be used in investment decisions about which production feedback data to capture and use for PPC purposes. Further research should test the propositions in more real cases and other production environments and carry out sensitivity analyses with more types of master data, variables, and combinations.
... Data collection is the fundamental process in data-driven studies [29]. A solid and clean dataset can also facilitate the achievement of the targeted results in the study [30]. ...
Energy efficiency and operational safety practices on ships have gained more importance due to the rules set by the International Maritime Organization in recent years. While approximately 70% of the fuel consumed on a commercial ship is utilized for the propulsion load, a significant portion of the remaining fuel is consumed by the auxiliary generators responsible for the ship's onboard load. It is crucial to comprehend the impact of the electrical load on the ship's generators, as it significantly assists maritime operators in strategic energy planning to minimize the chance of unexpected electrical breakdowns during operation. However, an appropriate handling mechanism is required when there are massive datasets and varied input data involved. Thus, this study implements data-driven approaches to estimate the load of a chemical tanker ship's generator using a 1000-day real dataset. Two case studies were performed, namely, single load prediction for each generator and total load prediction for all generators. The prediction results show that for the single generator load prediction of DG1, DG2, and DG3, the decision tree model encountered the least errors for MAE (0.2364, 0.1306, and 0.1532), RMSE (0.2455, 0.2069, and 0.2182), and MAPE (17.493, 5.1139, and 7.7481). In contrast, the deep neural network outperforms all other prediction models in the case of total generation prediction, with values of 1.0866, 2.6049, and 14.728 for MAE, RMSE, and MAPE, respectively.
... In order to support decision-making and the coordination of processes within the internal supply chain, control loops and KPIs as well as visual instruments are generally used in production controlling [3,6]. However, cross-departmental controlling rarely works in practice due to target systems as isolated sources of information not being defined end-to-end. ...
In order to increase the effectiveness and efficiency of (digital) production controlling, information systems are useful tools to provide and display required information. Regarding condensing information related to production logistics, logistic models and frameworks, like the Supply Chain Operations Reference (SCOR) model, provide a collection of Key Performance Indicators (KPIs). The transfer of KPIs from logistic models to effective information systems faces the challenge of providing user-specific and appropriate information through individual key figures on strategic, tactical and operational levels. Additionally, a missing structural and procedural organization of (digital) production controlling prevents effective information management and consequently homogenous and targeted decisions within production controlling. To exploit the potential of data-driven production based on the transformation of raw data into useful information with the aim of effective information management, an approach for a production controlling governance is presented below. This approach systematizes corporate strategies, organizational structures, the production controlling process, KPIs resulting from logistic models, and information needs to ensure homogenous information systems and targeted decision-making processes in the context of production controlling.
... K. H. Lee et al., 2014); 43. (Schäfers et al., 2019); Figure 3 describes the informational flow between physical and cyber elements that compose an ecosystem 4.0 for Shop Floor Control. In this framework, the RFID, responsible for data-gathering, collects and uploads information for Big Data. ...
The advent of Information and Communication Technology brought some advances in the field of Operations Management. RFID, when used as Ordering Coordination System, provides benefits not only for Production Control but also for some organisational functions that support the manufacturing sector. To identify these benefits, this work used a Systematic Literature Review. The results presented 15 benefits, classified into 4 areas that impact the Shop Floor Control and a framework containing the information flow necessary to operationalise the SFC 4.0. The discussion presented some insights relating the benefits with the maturity level of the entire SFC ecosystem when adopting RFID technology as an Ordering Coordination System and a brief agenda for future studies in this field of knowledge. It was possible to conclude that although the use of RFID is not new, its use as a premise for SFC operationalisation is yet in its initial stage of maturity, which could be reinforced by the fact that the most cited benefits were related to the initial stage of informational flow presented in the framework.
... An adaptable and technology-supported infrastructure is implemented in the learning factory to realize different operation scenarios of a production environment (e.g., flow-shops or job-shops) and the associated main thematic issues. Implemented technologies such as RFID, electronic shelf labels (ESL), and RTLS enable high data availability and are thus modularly connectable so that participants in training courses can design the production system freely [8]. This is also a reason why each technology in the learning factory should be flexible and compatible with a maximum number of scenarios. ...
Production in industrial companies is exposed to more and more frequent cycles of change, while at the same time, the market demand in terms of flexibility, high logistical performance and low costs are constantly increasing. Production systems must thus be designed to be capable of change. In order to increase the transformability of production systems and thus improve the market competitiveness of companies, it is, therefore, necessary to meet changes in the product portfolio, demand quantities and other drivers of change efficiently. An aspect to be considered is internal transport, which is increasingly performed by automated guided vehicles (AGVs), which, however, are often relatively inflexible concerning the routing and thus require complex reprogramming in case of changes.
This is why researchers in the Learning Factory of the Institute of Production Systems and Logistics (IFA) investigated how in-plant transportation by AGVs can be realized more effectively through the use of real-time locating systems (RTLS), even in rapidly changing production environments. The following contribution describes the challenges of designing and planning internal transport using AGVs and how new routes can be automatically created based on information about the current factory layout through RTLS. In particular, the paper provides insights into the central problem that arises in the adaptive planning of routes, the solution adopted and shows what advantages arise from the combination of AGVs and RTLS.
... material handling and assembly. To provide a suitable database for planning and decision making in such production systems the implementation of technologies for collecting real-time data is crucial (Schäfers et al. 2019, Mundt et al. 2019 Abstract: Production companies often operate in a dynamic and volatile environment. This leads to an increasing demand for continuous changes in their production systems and processes. ...
... material handling and assembly. To provide a suitable database for planning and decision making in such production systems the implementation of technologies for collecting real-time data is crucial (Schäfers et al. 2019, Mundt et al. 2019. The recent technological improvements in the sector of real-time locating systems (RTLS) offer new possibilities to gather and leverage such data fostering transparent manufacturing system design and control. ...
Production companies often operate in a dynamic and volatile environment. This leads to an increasing demand for continuous changes in their production systems and processes. Furthermore, decreasing product life cycle times and rising market demand for product variety and individualized products is bringing about the necessity for the monitoring and coordination of processes in the operations phase. As a result of these developments production management is facing new challenges in decision making for the optimal settings of the production system design and the related coordination of production. All of this demands enormous efforts to maintain a consistent and reliable database for the ongoing configuration and coordination of the production system. It is thus a remarkable challenge for industrial companies. Real time locating systems (RTLS) with their ability to continuously monitor the current position and parameters (speed, direction, etc.) of process resources (operators, equipment, products, etc.) offer several potential benefits for the manufacturing industry. The potential areas of application can be identified in different layers of production management. They range from data acquisition on shop floor level through reconfiguration of production systems to providing real time feedback for the blue collars. Furthermore, it allows dynamic coordination of production orders for industrial plants via appropriate digital twin (DT) technologies. This paper proposes an original framework for RTLS in industrial environments and presents a case study for framework application at the TU Graz Learning Factory.
... In order to support decision-making and the coordination of processes within the internal supply chain, control loops and KPIs as well as visual instruments are generally used in production controlling [3,6]. However, cross-departmental controlling rarely works in practice due to target systems as isolated sources of information not being defined end-to-end. ...
In order to increase the effectiveness and efficiency of (digital) production controlling, information systems are useful tools to provide and display required information. Regarding condensing information related to production logistics, logistic models and frameworks, like the Supply Chain Operations Reference (SCOR) model, provide a collection of Key Performance Indicators (KPIs). The transfer of KPIs from logistic models to effective information systems faces the challenge of providing user-specific and appropriate information through individual key figures on strategic, tactical and operational levels. Additionally, a missing structural and procedural organization of (digital) production controlling prevents effective information management and consequently homogenous and targeted decisions within production controlling. To exploit the potential of data-driven production based on the transformation of raw data into useful information with the aim of effective information management, an approach for a production controlling governance is presented below. This approach systematizes corporate strategies, organizational structures, the production controlling process, KPIs resulting from logistic models, and information needs to ensure homogenous information systems and targeted decision-making processes in the context of production controlling.
... Especially due to shortened product life cycles, the ability to steadily adapt to changing conditions within factory planning and operation is becoming increasingly important [1,2]. For this reason, it is essential to improve transparency regarding the processes within the factory, to implement useful technologies for collecting (real-time) data and to use the provided information effectively and in an appropriate way [3,4]. This way, previous barriers in achieving production logistics objectives, such as a high schedule reliability and low delivery times towards the customer, can be overcome. ...
... For this, depending on the training, either the components are manufactured in a job shop production or the manufactured components are assembled in a finishing assembly line. [3,7,12,13] In line with developments in the field of production system design, the IFA Learning Factory is continuously evolving in terms of both content and technology. In recent years, technologies such as RFID, electronic shelf labels (ESL), smart glasses and the RTLS, which is in the focus of this article, have been implemented. ...
... The throughput element thereby follows the logic that the completion of an operation, in Figure 3 marked by feedback point E, is equal to the entry Z to the following operation. Therefore, it is necessary to actively record 5 feedback points per operation in order to differentiate all shown time segments [3].When using a classic PDA, there is a high effort for all needed bookings of the feedback points. Often this also leads to the fact that bookings are postponed by the employees and are collectively carried out to avoid e.g long walks between the workplace and the terminal. ...
Production companies operate in an increasingly dynamic and unpredictable market environment. The ever shorter innovation cycles and the associated decreasing product life cycles require production systems and processes to change continuously. Furthermore, rising complexity in market enfactories through product variety leads to an increased coordination effort in factory operations. To stay competitive, decision making in factory planning as well as production planning and control (PPC) must be done target-oriented and in the shortest time possible. A fundamental basis in this respect is a reliable and consistent database, needed for the overall and holistic improvement of the logistics performance of a company’s internal supply chain. However, the collection and preparation of data is associated with a high expenditure of time and thus represents a great challenge. Additionally, required data in practice is often inhomogeneous or inconsistent, which makes it difficult to track ongoing processes precisely. Due to the continuous availability of exact positions at any time, Real-Time Locating Systems (RTLS) offer the potential to provide movement data of essential elements of the production system such as material supply, resources and workforce with comparatively little effort. To demonstrate the resulting potentials for factory planning and PPC, use cases for RTLS have been identified and implemented in the Learning Factory of the Institute for Production Systems and Logistics (IFA). After a short introduction of the IFA Learning Factory and the principle of Real-Time Locating Systems, the use cases in factory planning, factory operation and production monitoring are described. Having summarized the achieved benefits provided for the training participants in the IFA Learning Factory a summarizing conclusion is given, underlining the great possibilities of real-time localization in production environments and providing an outlook on further research activities.
... Industrial application empowered by the 5G and AI adoption allows businesses to use data-driven strategies to optimize their performance by gathering and analyzing data through the whole product lifecycle [5,6]. Consequently, it is even more crucial for manufacturing enterprises to apply 5G and AI technologies to support production planning, enhance their competitiveness and add their commercial value [7]. ...
5G and AI (Artificial Intelligence) are changing industrial production and offer great potential for manufacturing enterprises. One of the effects resulting from the increasing quantity of production data is the increasing demands of transmission of large amounts of data, fast transmission speed, and rapid data analysis. However, merely relying on traditional communication technology and manual data processing does not lead to high transmission performance and low analysis time. It is essential to integrate 5G and AI technology to flexibly transmit large amounts of data and real-time data. To demonstrate the feasibility and potential of these two technologies, a concept was developed at the Advanced Manufacturing Technology Center (AMTC) at the Tongji University (Shanghai, China) and further implemented in the AMTC learning factory in cooperation with wbk of Karlsruhe Institute of Technology (Karlsruhe, Germany) and Ruhr-University Bochum (Bochum, Germany). This paper presents the learning factory design in detail, describing the concept design, training environment and training phases in the AMTC learning factory. It is followed by a case study consisting of specific examples of 5G and AI, implemented in the AMTC learning factory. The importance of integrated 5G and AI applications is pointed out and discussed.
