Improving Productivity by Deriving and Defining Target Conditions in the Value Stream of Packing

In the entire value stream of original spare part supply, packing is one of the main issues in the distribution system and its productivity is mainly affected by a particularly high proportion of manual work. This paper presents an approach to support practical work improvements in value streams generally, and from the theoretical and the practical point of view it shows how performance enhancing and learning enhancing target conditions or standards – e.g. for the working method – can be derived and defined from the ideal state and its characteristics in order to increase productivity. The implementation of a target condition is carried out by continuous and discontinuous improvements to the value stream of packing original spare parts

A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises

AbstractManufacturing enterprises are currently facing substantial challenges with regard to disruptive concepts such as the Internet of Things, Cyber Physical Systems or Cloud-based Manufacturing – also referred to as Industry 4.0. Subsequently, increasing complexity on all firm levels creates uncertainty about respective organizational and technological capabilities and adequate strategies to develop them. In this paper we propose an empirically grounded novel model and its implementation to assess the Industry 4.0 maturity of industrial enterprises in the domain of discrete manufacturing. Our main goal was to extend the dominating technology focus of recently developed models by including organizational aspects. Overall we defined 9 dimensions and assigned 62 items to them for assessing Industry 4.0 maturity. The dimensions “Products”, “Customers”, “Operations” and “Technology” have been created to assess the basic enablers. Additionally, the dimensions “Strategy”, “Leadership”, Governance, “Culture” and “People” allow for including organizational aspects into the assessment. Afterwards, the model has been transformed into a practical tool and tested in several companies whereby one case is presented in the paper. First validations of the model's structure and content show that the model is transparent and easy to use and proved its applicability in real production environments

DISPO 4.0 | Simulation-Based Optimization of Stochastic Demand Calculation in Consumption-Based Material Planning in the Capital Goods Industry

This paper presents a digital material planning approach, utilizing simulation-based optimization to select and parametrize article specific demand forecasting methods. Demand forecasts are the basis of material requirements planning in consumption-based material planning, and are an essential lever for efficient inventory and order calculation. Despite their acknowledged potential, digital tools for optimized demand calculation are still lacking in practice. Thus, the goal of the presented approach to provide an application-oriented method to optimally select and parametrize state-of-the-art forecasting methods, based on product-specific demand data. In this approach, a rule-based selection heuristic is combined with static simulation of demand time-series and a metaheuristics-based optimization of forecasting parameters, to provide automatically optimized article-specific demand forecasts. Case studies for two companies in the capital goods industry evaluate and quantify the application potential. The results point to significantly improved, item-specific demand planning

(Pro)renin receptor and V-ATPase: from Drosophila to humans

A decade ago, the (P)RR [(pro)renin receptor] was discovered and depicted as a potential activator of the tissue renin-angiotensin system. For this reason, the role of the (P)RR in cardiovascular diseases and diabetes has been particularly studied. However, the discovery of embryonic lethality after (P)RR gene deletion in mouse and zebrafish paved the way for additional roles of (P)RR in cell homoeostasis. Indeed, the (P)RR has been shown to associate with vacuolar H+-ATPase, hence its other name ATP6ap2. Developmental studies in Xenopus and Drosophila have revealed an essential role of this association to promote the canonical and non-canonical Wnt signalling pathways, whereas studies with tissue-specific gene deletion have pointed out a role in autophagy. The present review aims to summarize recent findings on the cellular functions of (P)RR emerging from various mutated and transgenic animal models

Spatio-temporally efficient coding: A computational principle of biological neural networks

Department of Biomedical Engineering (Human Factors Engineering)One of the major goals of neuroscience is to understand how the external world is represented in the brain. This is a neural coding problem: the coding from the external world to its neural representations. There are two different kinds of problems with neural coding. One is to study the types of neuronal activity that represent the external world. Representative examples here are rate coding and temporal coding. In this study, we will present the spike distance method that reads temporal coding-related information from neural data. Another is to study what principles make such neural representations possible. This is an approach to the computational principle and the main topic of the present study. The brain sensory system has hierarchical structures. It is important to find the principles assigning functions to the hierarchical structures. On the one hand, the hierarchical structures of the brain sensory system contain both bottom-up and top-down pathways. In this bidirectional hierarchical structure, two types of neuronal noise are generated. One of them is noise generated as neural information fluctuates across the hierarchy according to the initial condition of the neural response, even if the external sensory input is static. Another is noise, precisely error, caused by coding different information in each hierarchy because of the transmission delay of information when external sensory input is dynamic. Despite these noise problems, it seems that sensory information processing is performed without any major problems in the sensory system of the real brain. Therefore, a neural coding principle that can overcome these noise problems is neededHow can the brain overcome these noise problems? Efficient coding is one of representative neural coding principles, however, existing efficient coding does not take into account these noise problems. To treat these noise problems, as one of efficient coding principles, we devised spatio-temporal efficient coding, which was inspired by the efficient use of given space and time resources, to optimize bidirectional information transmission on the hierarchical structures. This optimization is to learn smooth neural responses on time domain. In simulations, we showed spatio-temporal efficient coding was able to solve above two noise problems. We expect that spatio-temporal efficient coding helps us to understand how the brain computes.ope

DISPO 4.0 | Digitalization of Inventory Calculation in Consumption-Based Material Requirements Planning in the Capital Goods Industry

This paper presents a material requirements planning method that determines optimal safety stock levels using a heuristic optimization, based on a deterministic simulation of stock levels. Material requirements planning is a key competitiveness factor in a volatile, global market environment and is becoming increasingly complex due to the availability of more products, product variants and fluctuating demand. Digitalization offers significant potential benefits for this planning domain, however, tools ready for use in industry applications are still lacking, leading to untapped potential in companies. The approach presented herein investigates available safety stock calculation algorithms, develops a heuristic-based optimization method that determines the best fitting algorithm for each product and optimally parameterizes the algorithm. The method utilizes a deterministic simulation as an evaluation function. A case study for a company in the capital good industry is implemented to evaluate the application potential. The results reflect significantly improved service levels with a minor increase in cost