Concerning current approaches to planning of manufacturing processes, the acquisition of a sufficient data basis of the relevant process information and subsequent development of feasible layout ...options requires 74% of the overall time-consumption. However, the application of fully automated techniques within planning processes is not yet common practice. Deficits are to be observed in the course of the use of a fully automated data acquisition of the underlying process data, a key element of Industry 4.0, as well as the evaluation and quantification and analysis of the gathered data. As the majority of the planning operations are conducted manually, the lack of any theoretical evaluation renders a benchmarking of the results difficult. Current planning processes analyze the manually achieved results with the aid of simulation. Evaluation and quantification of the planning procedure are limited by complexity that defies manual controllability. Research is therefore required with regard to automated data acquisition and selection, as the near real-time evaluation and analysis of a highly complex production systems relies on a real-time generated database. The paper presents practically feasible approaches to a multi-modal data acquisition approach, its requirements and limitations. The further concept of the Digital Twin for a production process enables a coupling of the production system with its digital equivalent as a base for an optimization with a minimized delay between the time of data acquisition and the creation of the Digital Twin. Therefore a digital data acquisition approach is necessary. As a consequence a cyber-physical production system can be generated, that opens up powerful applications. To ensure a maximum concordance of the cyber-physical process with its real-life model a multimodal data acquisition and evaluation has to be conducted. The paper therefore presents a concept for the composition of a database and proposes guidelines for the implementation of the Digital Twin in production systems in small and medium-sized enterprises.
Purpose The purpose of this paper is to explore activities, challenges, and suggest tactics for the design of industrial reconfigurable production systems that can easily adapt to changing market ...opportunities. Design/methodology/approach The paper synthesizes the empirical findings of seven case studies including 47 in-depth interviews at four manufacturing companies. Findings A conceptual production system design process and including activities that enables a long-term perspective considering reconfigurability is proposed. Additionally, critical challenges indicating that reconfigurable production system design is not a trivial issue but one that requires separate control and coordination are identified and tactics to overcome the challenges described. Research limitations/implications The authors propose a process for designing reconfigurable production systems that are better suited to adjust to future needs. The knowledge of reconfigurability from the reconfigurable manufacturing system literature is applied in the general production system literature field. This study contributes to a clearer picture of managerial challenges that need to be dealt with when designing a reconfigurable production system. Practical implications By clarifying key activities facilitating a long-term perspective in the design process and highlighting challenges and tactics for improvement, the findings are particularly relevant to production engineers and plant managers interested in increasing the ability to adapt to future changes through reconfigurability and improve the efficiency of their production system design process. Originality/value Although reconfigurable production systems are critical for the success of manufacturing companies, the process of designing such systems is not clear. This paper stretches this by giving a comprehensive picture of the production system design process and the activities that need to be considered to meet these challenges.
Future cyber-physical production systems (CPPS) constitute a complex and dynamic network of services and plant components such as actuators and sensors. Consequently, the manual technical ...specification and design of these systems are a complex and time-consuming task involving extensive expert knowledge. In scope of CPPS, current approaches to reduce the engineering effort focus on manufacturing technology. There are initial approaches in the domain of process engineering available. However, these neither consider the knowledge-supported definition of recipe-based operations nor the assignment of dynamic service networks to process modules. The objective of this contribution is the design of a concept and a systematic approach to automate the engineering of batch process plants respecting dynamic service networks and process modules using a knowledge-based assistance system. For this purpose, a declarative recipe description is combined with an ontological model. This enables an automatic inference of technical requirements. Based on this information, a multistage orchestration algorithm selects and combines process modules and networked services to find appropriate engineering solutions. Finally, a comprehensive case-study demonstrates that the proposed approach is able to automate the target-oriented selection and combination of process modules and service networks.
PurposeThe purpose of this paper is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations.Design/methodology/approachThis study ...reviews the current understanding of decision structuredness for determining a decision-making approach and conducts a case study based on an interactive research approach at a global manufacturer.FindingsThe findings show the correspondence of intuitive, normative and combined intuitive and normative decision-making approaches in relation to varying degrees of equivocality and analyzability. Accordingly, the conditions for determining a decision-making choice when implementing process innovations are revealed.Research limitations/implicationsThis study contributes to increased understanding of the combined use of intuitive and normative decision making in production system design.Practical implicationsEmpirical data are drawn from two projects in the heavy-vehicle industry. The study describes decisions, from start to finish, and the corresponding decision-making approaches when implementing process innovations. These findings are of value to staff responsible for the design of production systems.Originality/valueUnlike prior conceptual studies, this study considers normative, intuitive and combined intuitive and normative decision making. In addition, this study extends the current understanding of decision structuredness and discloses the correspondence of decision-making approaches to varying degrees of equivocality and analyzability.
•The SUSBTOR-potato model was improved to simulate the effects of elevated atmospheric CO2 concentrations and high temperatures on crop growth.•Potato yields were simulated with region-specific ...soils, cultivars and crop management across the globe with current and future scenarios.•Projected potato yields will decline by the end of the century due to climate change, but impacts and impact uncertainties will vary across regions.
Potato is the most important non-grain crop in the world. Therefore, understanding the potential impacts of climate change on potato production is critical for future global food security. The SUBSTOR-Potato model was recently evaluated across a wide range of growing conditions, and improvements were made to better simulate atmospheric CO2 and high temperature responses. Comparisons of the improved model with field experiments, including elevated atmospheric CO2 concentrations and high temperature environments, showed a RRMSE of 26% for tuber dry matter. When using the improved model across 0.5 × 0.5° grid cells over all potato-growing regions in the world, the simulated aggregated country tuber dry yields reproduced nationally-reported potato yields with a RRMSE of 56%. Applying future climate change scenarios to current potato cropping systems indicated small global tuber yield reductions by 2055 (−2% to −6%), but larger declines by 2085 (−2% to −26%), depending on the Representative Concentration Pathway (RCP). The largest negative impacts on global tuber yields were projected for RCP 8.5 toward the end of the century. The simulated impacts varied depending on the region, with high tuber reductions in the high latitudes (e.g., Eastern Europe and northern America) and the lowlands of Africa, but less so in the mid-latitudes and tropical highland. Uncertainty due to different climate models was similar to seasonal variability by mid-century, but became larger than year-to-year variability by the end of the century for RCP 8.5.
Manufacturers are pursuing energy-efficient production in response to the fluctuating energy price, growing global competition, rigorous international laws, and severe environmental crisis. This ...article proposes to boost the energy efficiency of production systems by controlling the production. It extends the existing energy-saving control research by presenting integrated modeling, analyzing, and controlling approaches. The work starts from the modeling of the production systems and establishes an analytical model to systematically quantify the production loss resulted from energy-saving control and the various disruptions. A dynamic control algorithm is proposed to reduce energy consumption and maintain desirable productivity. Simulation studies are utilized to demonstrate the application of the proposed method and verify its effectiveness. Note to Practitioners-The previous research indicates that it is possible to strategically turn off stations during production for energy saving. However, production systems are complex dynamic systems consisting of interconnected stations and supporting subsystems. Similar to station random failures, turning off stations for energy saving can severely jeopardize the production, and deviate the production from the desired target. Therefore, a quantitative method is established to calculate the production loss resulted from the energy-saving control and disruptions. The method is important to understand the real control cost. Based on the analysis, a dynamic control algorithm is formulated to balance the achieved control benefit and the production loss. It provides plant managers a useful tool to make energy-saving control decisions with a thorough understanding of production system dynamics. The presented research is established for serial batch production systems. The examples of batch stations include the refrigerator foaming equipment in refrigerator assembly lines and the vacuum oven in battery assembly lines. The model cannot be directly applied in serial-parallel production systems.
This work studies the impact of Wireless Sensor Networks (WSNs) for oil spill detection and localization in Subsea Production Systems. The case study is the Goliat FPSO, with a realistic assumption ...about the presence of a WSN built upon the existing passive acoustic sensors installed on each subsea template to monitor the manifold. The sensors take local binary decisions regarding the presence/absence of a spill by performing an energy test. A Fusion Center (FC) collects such local decisions and provides a more reliable global binary decision. The Counting Rule (CR) and a modified Chair-Varshney Rule (MCVR) are compared. An objective function based on the Receiver Operating Characteristic (ROC) is used for threshold design. The FC, in case of a spill detection, provides an estimated position of the leak source. Four localization algorithms are explored: Maximum A-Posteriori (MAP) estimation, Minimum Mean Square Error (MMSE) estimation, and two heuristic centroid-based algorithms. Detection and localization performances are assessed in comparison to the (position) Clairvoyant Chair-Varshney Rule (CVR) and to the Cramér-Rao Lower Bound (CRLB), respectively. The considered framework requires the prior knowledge of the involved subsea production system in terms of components that in case of failure would cause a leakage and their corresponding failure rates.
Global pork production has increased fourfold over the last 50 years and is expected to continue growing during the next three decades. This may have considerable implications for feed use, land ...requirements, and nitrogen emissions. To analyze the development of the pig production sector at the scale of world regions, we developed the IMAGE-Pig model to describe changes in feed demand, feed conversion ratios (FCRs), nitrogen use efficiency (NUE) and nitrogen excretion for backyard, intermediate and intensive systems during the past few decades as a basis to explore future scenarios. For each region and production system, total production, productive characteristics and dietary compositions were defined for the 1970–2005 period. The results show that due to the growing pork production total feed demand has increased by a factor of two (from 229 to 471Tg DM). This is despite the improvement of FCRs during the 1970–2005 period, which has reduced the feed use per kg of product. The increase of nitrogen use efficiency was slower than the improvement of FCRs due to increasing protein content in the feed rations. As a result, total N excretion increased by more than a factor of two in the 1970–2005 period (from 4.6 to 11.1 Tg N/year). For the period up to 2050, the Shared Socio-economic Pathways (SSPs) provide information on levels of human consumption, technical development and environmental awareness. The sustainability of pig production systems for the coming decades will be based not only on the expected efficiency improvements at the level of animal breeds, but also on four additional pillars: (i) use of alternative feed sources not competing with human food, (ii) reduction of the crude protein content in rations, (iii) the proper use of slurries as fertilizers through coupling of crop and livestock production and (iv) moderation of the human pork consumption.
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•IMAGE-Pig model describes the functioning of pig production systems in 26 regions.•Feed demand, feed efficiencies, and excretion are estimated (1970–2050).•Total feed use increased, as past efficiency improvements grew slower than demand.•SSP1 shows a more sustainable path, with reduced meat demand and efficiency gains.•By 2050 feed use and environmental impacts can be strongly reduced or increased.
Matrix production systems are modular, cycle time-independent, and flow-oriented production systems. They combine flexibility plus productivity and consist of flexibly linked and freely accessible ...process modules. The derivation and design of these process modules in flexible structures is still very time-consuming due to many degrees of freedom and limiting constraints. This paper presents a method for deriving flexible process modules, taking into account the creation of increased automation potentials, flexible order flows, and specialization in processes. The method consists of seven steps to derive harmonized process modules for multiple products. It is suitable for all manufacturing industries to reduce the planning effort. The defined process modules can be further used for layout planning.
In the social production system, image data are rapidly generated from almost all fields such as factories, hospitals, and transportation, promoting higher requirements for image anomaly detection ...technologies, including low consumption, higher adaptability, and accuracy. However, existing anomaly detection methods are fragile to heterogeneous image data generated by complex social production systems and tend to require strong computing power and resource support. To address the above problems, a knowledge-driven anomaly detection framework is proposed, in which a local feature enhancement method is designed to strengthen the knowledge representation of the initial features extracted from images. The attention mechanism in deep learning is introduced to adjust the feature attention dynamically according to prior knowledge, which solves the problem of feature loss in the cascade training. To verify the effectiveness of the proposed framework, extensive experiments on social production datasets are conducted. The results demonstrate that our framework outperforms the selected methods on image datasets with different complexity and sample distributions.
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