Florian M. Arnold, Gordon Lemme, M Hess, S Witt · 5 authors
Abstract The effort for checking and correcting the spatial movement accuracy of a processing machine with 5 axes is very high. Calibration and recalibration must be carried out directly on the machine by the machine supplier or a company specializing in this. This is associated with high personnel and long machine downtimes. These constraints can be improved by using modern methods. A consistent approach of an AR-supported measurement procedure for the preparation and execution of a required measurement run and the execution of the calibration itself as a digital service is presented. This enables rapid execution by the machine operator himself using a DoubleBallBar measurement system without the need of the presence of the machine manufacturer. The determination of the calibration parameters on the basis of the measurement data is then carried out by the machine manufacturer and provided as a service. The basis for such a secure and auditable service is a digital service platform. This serves as an intermediary between the user and the machine manufacturer and uses distributed ledger technology. The approach presented is the subject of current development work.
In this article, we discuss a valuation framework for Decentralized Autonomous Organizations (DAOs). As previous work on DAO valuation is limited, we attempt to introduce a formalized framework on the subject. Although we base ourselves on conceptual frameworks from corporate finance, we introduce DAO-native valuation concepts. We argue that DAO token valuation can be mainly done in two ways, namely, according to a fundamental valuation approach and a comparable analysis approach. In the fundamental valuation approach we attempt to value the DAO token according to fundamentals while in the comparables approach we attempt to compare DAO tokens based on various metrics. Finally, we discuss various token specific considerations to potentially take into account in the valuation exercise. A valuation framework would allow the community to gauge DAOs’ performance in value generation for token stakeholders and help introduce more accountability towards the development teams behind the DAO.
Johannes Fottner, Dana Clauer, Fabian Hormes, Michael Freitag · 13 authors
The paper at hand presents a definition of autonomous intralogistics systems and a classification of intralogistics systems with regard to their degree of autonomy. Intralogistics -; a complex interplay of different logistics functions - covers the organization, control, execution and optimization of internal material and information flows. Over the past two decades, numerous authors have observed and proclaimed an increase in complexity in manufacturing and supply chain operations. A key approach to face this challenge is a paradigm shift from centralized, hierarchical organization structures towards, networked and autonomous systems. Autonomous intralogistics systems enable self-contained, decentralized planning, execution, control, and optimization of internal material and information flows through cooperation and interaction with other systems and with humans.Based on the definition of autonomous intralogistics systems, the authors propose a two-dimensional classification framework covering different automation stages for different intralogistics task levels. The developed classification framework is applied to various industry use cases to evaluate and discuss the state of the art regarding the implementation of autonomous intralogistics systems. Finally, the paper provides an outlook on future research and poses key research questions.
Manufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.
Joachim Stanke, Martin Unterberg, Daniel Trauth, Thomas Bergs
Abstract Networking and digitization in manufacturing enable novel methods of data-driven analysis and optimization of processes through cross-process data availability. The creation of digital twins plays an important role in this. However, not all data relevant for a digital twin can be measured directly in the process. Therefore, methods are needed that enable the modelling of quantities that are difficult or impossible to measure directly in the process, such as the finite element method. In many companies, however, neither the know-how nor the necessary IT infrastructure for finite element simulations is available. External commissioning processes are also not suitable for achieving the goals of higher productivity and agility pursued with the digitization and networking of manufacturing processes. In this contribution, an architecture is presented that enables the fully automated use of finite element simulation as a service. The architecture is developed using the case study of fine blanking. First, the requirements of the architecture to be created are determined. Important characteristics of the architecture should be scalability as well as interfaces and means of payment suitable for machine communication. In addition, ensuring data integrity is an important requirement when creating the digital twin. Based on the identified requirements, an architecture is then presented that meets these requirements by using cloud computing and distributed ledger technologies and interfaces that can directly process measurement signals from the process and communicate with the architecture. Finally, the capability of the architecture is tested, possible applications and limitations are discussed, and future extensions are considered.
Open access
Manufacturing Process and Optimization
Digital Transformation in Industry
Additive Manufacturing and 3D Printing Technologies
The accelerated growth of technological advancements has triggered the expansion of customer demand leading to highly complex supply chain networks. One viable way original equipment manufacturers (OEMs) can respond to changing purchasing habits is to redesign their strategic and operational activities to build far-reaching information and resource avenues allied with effective marketing policies. These newly implemented policies need to comply with extended producer responsibility (EPR) guidelines that also well align with rising consumer awareness towards green consumption. To achieve this, manufacturers must create efficient end-of-life product (EOLP) return structures and ensure value creation through product recovery operations to dwindle the cascading waste of discarded products. From an environmental viewpoint, retrieving the value embedded in returned items through remanufacturing or recycling has been proven to be effective in reducing the amount of industrial solid waste. EOLP processing operations are heavily reliant on customers' participation in returning outdated devices making product collection a crucial step in point-to-point supply chains. To entice end-users, the OEMs need to design environmentally and economically benign product take-back strategies that would spark the volume of product returns. These constraints dictate two structural challenges: how manufacturers and consumers can become active participants of EOLP treatment activities, and how fast and efficiently OEMs can respond to the changing market and capital needs while preserving their sustainability levels. In terms of active participation, trade-in incentives can help stimulate additional revenue channels for OEMs through product remanufacturing while helping companies comply with the EPR legislations. Trade-in policies are set forth as part of long-term marketing strategies and include incentive programs that aim at enticing current and potential customers to trade-in their used products with newer generations at a discounted price or for instant credit. Within the context of purchasing behavior, trade-in programs positively impact customers' buying decisions by granting buyers the ability to claim the scrap value of their existing devices. Particularly in oversaturated industries such as electronics and automotive, take-back incentives are a pipeline for OEMs to generate significant residual value by reselling remanufactured products on secondary markets. Moreover, offering special discounts or credits in lieu of old devices fuels new product sales by creating an additional revenue stream. Still, in today’s fast-changing market dynamics, inept trade-in practices that fail to eliminate the ambiguity surrounding the prediction of the true quality of returned products bring functional and financial burdens to organizations. The conventional intransigent trade-in schemes fail to address this uncertainty leading to a number of unnecessary inspection, disassembly, and shipment steps resulting in increasing complexity and product recovery cost. Achieving an accurate trade-in scheme is a highly complex multi-dimensional problem requiring novel solutions that traditional manufacturing and supply chain technologies are incapable of offering by design. Such challenging task inevitably necessitates strategic initiatives that stem from the utilization of cutting-edge groundbreaking information technologies for rapid response to customer needs and reduced complexity across all operational layers. Despite the numerous methodologies investigating the potential value gain from remanufacturing and product acquisition pricing policies, there is no study in related literature that incorporates trade-in programs into an intelligent remanufacturing structure. A majority of previous studies propose preventive models with pre-determined and rule-based explicit model parameters hindering the practicability of the substantial volume of data generated by the increased use of technological tools. These models, inevitably, fall short in successfully incorporating long-term manufacturing goals into sustainable business strategies. With this motivation, the architectural framework this dissertation introduces addresses a predictive product recovery model for product returns to enable an autonomous, sensor-embedded, and decentralized disassembly and remanufacturing system. The main objective of this research is to investigate the feasibility of cost- and resource-effective end-of-life product management systems in a smart reverse logistics network where trade-in rebate decisions take place in an autonomous ecosystem. This research, while filling the emerging gap in the utilization of current digital technologies to determine quality-dependent acquisition strategies, also provides a novel quantitative analysis on the efficiency of trade-in policymaking. This model can be employed in manufacturing industries for precise assessment of value creation amid digital advancements in a future-oriented platform. Due to its highly saturated formation, the consumer electronics industry offers a more suitable platform for this study. Therefore, this study examines a trade-in model for a specific technological product, game console, with the help of a case study. First phase of the dissertation evaluates the performance degradation pattern of discarded electronics products in a ubiquitous manner through timestamp data enablers. To handle this highly complex large-volume data, a discrete-event simulation model is developed from the original equipment manufacturer viewpoint. The model aims to examine the behavior of returned devices as well as the expected overall cost of product recovery operations. Following this, a design of experiments study is utilized for the experimentation using Taguchi’s Orthogonal Arrays (OAs). Employing the findings obtained in the first phase, the second phase of the study deals with trade-in policymaking to determine an engaging quotation for varying quality of returned products from the perspectives of all parties involved in the transaction. To achieve this, an initial model for trade-in-to-upgrade incentives is established for discrete sets of quality standards in case where returned products are grouped into three quality classes based on their usage time. The model is then expanded to compare two product acquisition strategies, namely, trade-in-to-upgrade incentives and instant credits. To achieve a realistic strategy, two rebate models are constructed in a simulation-based game setting to mimic the customer behavior and to obtain the resulting payoffs for the OEM in a dynamic ecosystem. To handle the uncertainty in the customer's decision towards the incentive offer, logistic regression analysis is conducted to maximize the likelihood of the acceptance rate. Finally, trade-in policies are compared to obtain favorable strategies augment revenue streams.
Open access
Manufacturing Process and Optimization
Product Development and Customization
Additive Manufacturing and 3D Printing Technologies
This paper presents an application of Decentralised Autonomous Organisation (DAO) in the field of design and AEC industry. The model is applied in the realm of shape grammar proposing the possibility of allowing multiple grammarists to collaborate in the definition of a new grammar within a Blockchain environment that acts as a distributed ledger. DAOs systems and Blockchain are introduced as well as shape grammar and its fundamental rules. The collaborative nature of a DAO with the inner logic of shape grammar, which bases its principle and rules in multiple variations and combinations of simple initial shapes, brings to the problem of recording and validating changes and improvements in the design chain. For this reason, a voting system to govern the process is introduced, based on both quantitative values, i.e. number of votes, and qualitative power, i.e. the reputation of who votes, applying a factor that scales the vote according to the expertise of the voter. An example is provided showing a possible scenario in a design environment along with validation criteria, and predicting future stages applied in an always more BIM-oriented practice.
In order to increase the level of global competitiveness and improve the performance of production system, a large number of manufacturing companies have implemented world class manufacturing (WCM) approach, which has developed based on the third industrial revolution and the need for mass production. The evolution in production equipment and communication technologies, and the demand of markets for personalized mass production, have forced manufacturing companies to transform their production systems and prepare for a revolution. This revolution, known as Industry 4.0 (I4.0), or the digital transformation, has been introduced as a new type of organization of manufacturing systems that is more flexible and agile, and is based on using large amounts of information and data in the decision-making process. One of the main characteristics of this concept is decentralization, which allows different subsystems to make decisions autonomously in order to have self-organization systems. There are some important differences between the principles of WCM and I4.0. World class manufacturing is mainly based on continuous improvement and cost reduction, without a global vision for profit optimization. Industry 4.0 is mainly based on using all accessible information and data of systems and making decentralized decisions, but it also involves a global vision and a systemic approach to global profit optimization. However, achieving these objectives takes a very long time, and the challenges are numerous. As with all projects, for a transformation project to succeed, it is very important to define the transition phase and the way to change and introduce these new principles. This paper presents part of our research project, in collaboration with the Fiat Powertrain Technologies company, concerning the transformation of their production system toward the factory of the future. We highlight the design principles of I4.0 and the potential of the WCM system for transformation and achieving development of the characteristics of I4.0. We focus on five of the principal technical pillars of WCM and the steps in their development, and present some modifications in adoption of the design principles of I4.0. An example of change in the professional maintenance pillar of WCM is also presented.
Globalization, unpredictable markets, increased products customization and frequent changes in products, production technologies and machining systems have become a complexity in today’s manufacturing environment. One key strategy for coping with the evolution of this situation is to develop or apply an enable technology such as intelligent manufacturing. Intelligent manufacturing system (IMS) is characterized by decentralized, distributed, networked compositions of heterogeneous and autonomous systems. The model of IMS is inherited from the organization of the living systems in biology and nature so that the manufacturing system has the advanced characteristics inspired from biology such as self-adaptation, self-diagnosis, and selfhealing. To prove this concept, an innovative system with applying the advanced information and communication technology such as internet of things, cognitive agent are proposed to integrate, organize and allocate the machining resources. Innovative system is essential for modern machining system to flexibly and quickly adapt to new challenges of manufacturing environment.
Smart manufacturing considered as a new trend of modern manufacturing helps to satisfy objectives associated with the productivity, quality, cost and competiveness. The smart manufacturing system is characterized by decentralized, distributed, networked compositions of autonomous systems. The model of smart manufacturing is inherited from the organization of the living systems in biology and nature such as ant colony, school of fish, bee's foraging behaviors, and so on. In which, the resources of the manufacturing system are considered as biological organisms, which are autonomous entities so that the manufacturing system has the advanced characteristics inspired from biology such as self-adaptation, self-diagnosis, and self-healing. In this paper, a cloud based smart manufacturing system for machining transmission cases is considered as research object in which the advanced information and communication technology such as cognitive agent, swarm intelligence, and cloud computing are used to integrate, organize and allocate the machining resources.
Smart manufacturing (SM) considered as a new trend of modern manufacturing helps to meet objectives associated with the productivity, quality, cost and competiveness. It is characterized by decentralized, distributed, networked compositions of autonomous systems. The model of SM is inherited from the organization of the living systems in biology and nature such as ant colony, school of fish, bee’s foraging behaviors, and so on. In which, the resources of the manufacturing system are considered as biological organisms, which are autonomous entities so that the manufacturing system has the advanced characteristics inspired from biology such as selfadaptation, self-diagnosis, and self-healing. To prove this concept, a cloud machining system is considered as research object in which internet of things and cloud computing are used to integrate, organize and allocate the machining resources. Artificial life tools are used for cooperation among autonomous elements in the cloud machining system.