Lean Production Systems are enterprise-specific, methodical frameworks for the continuous orientation of all enterprise processes to the customer in order to achieve overall objectives. Due to an ...increasing complexity of the digital transformation, the design of Lean Production Systems 4.0 is a challenging task for industrial engineering practice. For this, academia and industrial practice were analysed in a combined approach of a systematic literature review and a field study. The systematic literature review of 62 out of 1600 scientific papers shows that especially the tools, processes, and methods in Lean Production Systems are subject to digital transformation. By involving industrial practice in a field study, quantitative and qualitative insights were used to check scientific hypotheses and identify practical requirements by industrial engineers. As a result, 10 guidelines for the design of future Lean Production Systems 4.0 are derived. The practical implications of this paper enhance the evolution of enterprise-specific Lean Production System 4.0 frameworks.
Abstract Paper aims considering that the fourth industrial revolution can be a primary engine for process innovation, this research proposes a 4.0 Production System applicable to the auto parts ...industry. Originality the study’s relevance is observed from the assimilation of practical issues with the scientific process for constructing a 4.0 Production System for the sector in question. Research method this research is structured from a real case study, proposing an artifact from Design Science Research. Main findings the results obtained provided a competitive advantage to the company, with a reduction of 23% in lead time, 16% in HM/Fuel Tank, 55% in WIP, and 38% in the total distance traveled by operators, in addition to an ROI of 9.22%. Implications for theory and practice the practical application of the artifact showed that its use is viable; however, to extract the maximum potential, it is suggested to insert it into the company's culture.
Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering ...design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.
The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology ...(ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.
This research was carried out using an R & D (Research and Development) approach or development research that produces a product. By using the ADDIE method (analysis, design, development, ...implementation and evaluation). This research aims to develop a production system and product innovation. The resulting development products are in the form of development models, 1) Standard Operating Procedures, and 2) Product Innovation. The model was developed with performance and needs analysis. In the initial development process, researchers consulted and validated with economic experts and IT experts to obtain input and suggestions about the model being developed and to determine the feasibility of the product development model. By using a questionnaire as a research instrument. The research results obtained from the expert validation test results show that the model score obtained from the economic expert validation test results is (89.28), while the IT expert validation test (85) is in the very good category and does not need revision. In product trials carried out on 15 production employees at CV. Mitra Jaya Company has results with the highest score of 88.25, meanwhile, product innovation was tested on 4 consumers and obtained results with the highest score of 100. Thus, the Standard Operational Procedure development model can be used as a guideline in the production process, while product innovation can be applied to improve old products and new products to increase sales.
Dairy small ruminants account for approximately 21% of all sheep and goats in the world, produce around 3.5% of the world's milk, and are mainly located in subtropical-temperate areas of Asia, ...Europe, and Africa. Dairy sheep are concentrated around the Mediterranean and Black Sea regions, where their dairy products are typical ingredients of the human diet. Dairy goats are concentrated in low-income, food-deficit countries of the Indian subcontinent, where their products are a key food source, but are also present in high-income, technologically developed countries. This review evaluates the status of the dairy sheep and goat sectors in the world, with special focus on the commercially and technically developed industries in France, Greece, Italy, and Spain (FGIS). Dairy small ruminants account for a minor part of the total agricultural output in France, Italy, and Spain (0.9 to 1.8%) and a larger part in Greece (8.8%). In FGIS, the dairy sheep industry is based on local breeds and crossbreeds raised under semi-intensive and intensive systems and is concentrated in a few regions in these countries. Average flock size varies from small to medium (140 to 333 ewes/farm), and milk yield from low to medium (85 to 216 L/ewe), showing substantial room for improvement. Most sheep milk is sold to industries and processed into traditional cheese types, many of which are Protected Denomination of Origin (PDO) cheeses for gourmet and export markets (e.g., Pecorino, Manchego, and Roquefort). By comparing break-even milk price among FGIS countries, we observed the following: (1) most Greek and French dairy sheep farms were unprofitable, with the exception of the intensive Chios farms of Greece; (2) milk price was aligned with cost of production in Italy; and (3) profitable farms coexisted with unprofitable farms in Spain. In FGIS, dairy goat production is based on local breeds raised under more extensive systems than sheep. Compared with sheep, average dairy goat herds are smaller (36 to 190 does/farm) but milk yield is greater (153 to 589 L/doe), showing room for improvement. Goat milk is mainly processed on-farm into dairy products for national markets, but some PDO goat milk cheeses (e.g., Murcia al Vino) are exported. Processed goat milk is sold for local human consumption or dehydrated for export. Mixed sheep-goat (e.g., Feta) and cow-sheep-goat milk cheeses are common in many countries. Strategies to improve the dairy sheep and goat sectors in these 4 countries are proposed and discussed.
Production system simulation is a powerful tool to achieve efficient operations in complicated production systems such as high-mix and low-volume production. However, it takes significant efforts and ...expertise to construct accurate simulation models. In this article, a novel modeling approach called as data-driven and multi-scale modeling is proposed. The proposed approach combines various modeling methods to maximize the simulation accuracy. In order to verify the usefulness of the proposed approach, computational experiments for simple production systems to compare modeling methods are conducted. The experimental results show that the superiority of modeling methods depends on the background knowledge and available information about target production system and the proper use of modeling methods is important to achieve high accuracy.
Production scheduling and machine maintenance are two inseparable operational issues in multistage production systems. Previous studies attempted to deal with this issue by simplifying this problem ...due to the degradation uncertainties of the machines, ignoring the substantial interactions between these two tasks and leading to less efficiency of the entire production system. In this study, we fill the gap and formulate the joint optimization problem with more emphasis on the interaction between job scheduling and maintenance for a series-parallel multistage production system. Specifically, a mixed-effect degradation model is proposed to leverage the underlying interaction between job scheduling and machine maintenance. To efficiently solve this joint problem, several properties from this formulation have been derived. A two-phase method considering condition-based information, with a proactive algorithm for local intensification and a condition-based workload reallocation strategy & maintenance strategy, is then developed to address the uncertainties from the machine degradation status. A numerical study is finally borrowed to demonstrate the higher production efficiency achieved by applying the proposed method, compared with other benchmarks. Note to Practitioners-This study is motivated by a practical scenario where both job allocation and maintenance need to be determined simultaneously in the multistage production system by the operators to achieve time and cost efficiency. We focus on developing a new scheme that job scheduling and machine maintenance are able to be conducted simultaneously. Two issues are noteworthy to better implement this scheme. First, for characterizing the interaction between scheduling and maintenance, the data collected in real-time can provide a sufficient basis for the degradation path, and the production parameters can be acquired from real practice. Second, this scheme can be offered to help decision-making by a two-phase solution framework given the condition-based information during the production process. Specifically, an appropriate job allocation planning can be obtained offline in the first phase of the proposed two-phase solution framework under a limited computing resource. Meanwhile, a condition-based adjustment strategy in the second phase can update the solution based on the in-situ condition information collected from the data platform to achieve higher production efficiency.
•Six Functionalities of a Human-Centric CPPS are illustrated.•Three levels of analysis of CPPS are defined.•The CyFL Matrix aims to identify and organise performances in Human-centric CPPS.•CyFL ...Matrix is a tool for companies interested in Human-centric CPPS’ performances.
In a near future where manufacturing companies are faced with the rapid technological developments of Cyber-Physical Systems (CPS) and Industry 4.0, a need arises to consider how this will affect human operators remaining as a vital and important resource in modern production systems. What will the implications of these orchestrated and ubiquitous technologies in production – a concept we call Cyber-Physical Production Systems (CPPS) – be on the health, learning and operative performance of human workers? This paper makes three main contributions to address the question. First, it synthesizes the diverse literature regarding CPS and social sustainability in production systems. Second, it conceptualizes a holistic framework, the CyFL Matrix, and outlines a guideline to analyze how the functionalities of a CPPS relate to operational and social sustainability-related performance impacts at different levels of analysis. Finally, it presents an industrial use case, which the CyFL Matrix and the related guidelines are applied to. In doing so, the study offers first support to researchers and managers of manufacturing companies willing to define suitable operational and social sustainability-related performances for Human-centric Cyber-Physical Production Systems of the future.
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