The Production Logistic Theory as an Integral Part of a Theory of Production Technology

Today’s manufacturing companies operate in a turbulent environment. Globalisation, increasing market dynamism and ever shortening product life cycles are just some of the aspects that characterise the steady rise in competitive pressure (Roland Berger Strategy Consultants GmbH 2012; Abele and Reinhart 2011; Sirkin et al. 2004). Moreover, factors such as sustainability and the conservation of natural resources are playing an increasingly important role (BMU 2012; Deutsche Post AG 2010). In order to maintain sustainable production in a turbulent environment, it is necessary to be able to anticipate impending changes and to determine and assess available alternative courses of action. The determination of potential action strategies requires knowledge of how production facilities behave at all levels, including those of production networks, machines and processes

The Role of Blockchain in Enterprise Procurement

Disruptive technologies, such as artificial intelligence, machine learning, or the blockchain, have the potential to transform entire industries as previous publications have outlined. They especially offer new opportunities to improve existing processes in the dimensions of performance, cost, and quality. Despite a continuously growing number of contributions, research and practice lack an adequate understanding about how blockchain can be used to achieve these benefits. Against this backdrop, this paper presents the current state of blockchain research in the field of enterprise procurement and outlines several avenues for future research. Therefore, we define relevant processes and functions to build a research framework for structuring and categorizing the current body of literature. Our results suggest that previous work mostly focuses on blockchain application scenarios with a focus on communication and transaction, while widely neglecting numerous fields, such as data integrity and data access

Evaluating the Logistic Performance Capability of Regeneration Processes

For years now, it has been recognized that logistic performance capability contributes enormously to a production enterprise's competitiveness and as such is a critical control lever. In doing so, the orientation on customer wishes (e.g. delivery dates) represents a key parameter not only in the value-adding production but also in product regeneration. Since production and regeneration processes have different characteristics, production planning and control measures cannot be directly transferred to regeneration processes. As part of a special research project, the Institute of Production Systems and Logistics Hannover is focused on increasing the logistic performance capability of regeneration processes for complex capital goods. The aim is to ensure logistic targets are met by implementing a model specifically designed to align the capacities and load in regeneration processes

FLECSim-SoC: A Flexible End-to-End Co-Design Simulation Framework for System on Chips

Hardware accelerators for deep neural networks (DNNs) have established themselves over the past decade. Most developments have worked towards higher efficiency with an individual application in mind. This highlights the strong relationship between co-designing the accelerator together with the requirements of the application. Currently for a structured design flow, however, it lacks a tool to evaluate a DNN accelerator embedded in a System on Chip (SoC) platform.To address this gap in the state of the art, we introduce FLECSim, a tool framework that enables an end-to-end simulation of an SoC with dedicated accelerators, CPUs and memories. FLECSim offers flexible configuration of the system and straightforward integration of new accelerator models in both SystemC and RTL, which allows for early design verification. During the simulation, FLECSim provides metrics of the SoC, which can be used to explore the design space. Finally, we present the capabilities of FLECSim, perform an exemplary evaluation with a systolic array-based accelerator and explore the design parameters in terms of accelerator size, power and performance

Non-thermal processes in colliding-wind massive binaries: the contribution of Simbol-X to a multiwavelength investigation

Several colliding-wind massive binaries are known to be non-thermal emitters in the radio domain. This constitutes strong evidence for the fact that an efficient particle acceleration process is at work in these objects. The acceleration mechanism is most probably the Diffusive Shock Acceleration (DSA) process in the presence of strong hydrodynamic shocks due to the colliding-winds. In order to investigate the physics of this particle acceleration, we initiated a multiwavelength campaign covering a large part of the electromagnetic spectrum. In this context, the detailed study of the hard X-ray emission from these sources in the SIMBOL-X bandpass constitutes a crucial element in order to probe this still poorly known topic of astrophysics. It should be noted that colliding-wind massive binaries should be considered as very valuable targets for the investigation of particle acceleration in a similar way as supernova remnants, but in a different region of the parameter space.Comment: 4 pages, 2 figures, to appear in Proc. of the Second Internqtionql Simbol-X Symposium, held in Paris (France

An Analytical Model of Configurable Systolic Arrays to find the Best-Fitting Accelerator for a given DNN Workload

Since their breakthrough, complexity of Deep Neural Networks (DNNs) is rising steadily. As a result, accelerators for DNNs are now used in many domains. However, designing and configuring an accelerator that meets the requirements of a given application perfectly is a challenging task. In this paper, we therefore present our approach to support the accelerator design process. With an analytical model of a systolic array we can estimate performance, energy consumption and area for each design option. To determine these metrics, usually a cycle accurate simulation is performed, which is a time-consuming task. Hence, the design space has to be restricted heavily. Analytical modelling, however, allows for fast evaluation of a design using a mathematical abstraction of the accelerator. For DNNs, this works especially well since the dataflow and memory accesses have high regularity. To show the correctness of our model, we perform an exemplary realization with the state-of-the-art systolic array generator Gemmini and compare it with a cycle accurate simulation and state-of-the-art modelling tools, showing less than 1% deviation. We also conducted a design space exploration, showing the analytical model’s capabilities to support an accelerator design. In a case study on ResNet-34, we can demonstrate that our model and DSE tool reduces the time to find the best-fitting solution by four or two orders of magnitude compared to a cycle-accurate simulation or state-of-the-art modelling tools, respectively