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

Durchblick im Variantendschungel : visuelle Assistenzsysteme als Flexibilitätshebel auf dem Shop Floor

Die steigende Personalisierbarkeit von Produkten fuhrt zu einem wachsenden Variantenspektrum in der Fertigung. Nicht zuletzt aufgrund der damit einhergehenden Produktionskomplexität und den hohen Wandlungsanforderungen an die Montage werden viele komplexe Stückgüter weiterhin überwiegend manuell montiert. Visuelle Assistenzsysteme geben den Mitarbeitern die nötige Handlungsunterstützung, wenn kein Produkt dem anderen gleicht und damit das Fehlerpotenzial steigt

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

METHODOLOGY FOR THE DEVELOPMENT OF RFID VALUE ADDED SERVICES TO IMPROVE SUPPLY CHAIN OPERATIONS

Despite the potential of RFID technologies to improve supply chain operations and control, RFID projects – especially in production heavy sectors – are often restricted to basic recognition functionalities, leaving much of the vast potential to improve supply chain operations and control unharnessed. This paper aims at providing a procedure to identify advanced value added services based on the RFID technology, tailor made to companies’ specific requirements. Considering all relevant processes the developed approach focuses on potential benefits from the RFID deployment throughout the supply chain. As a key element a case study for SMEs in the electronic component industry is presented

Target System Based Design of Quality Control Strategies in Global Production Networks

Increasing globalization drives companies to produce in global networks, where each site acts autonomously according to its individual target system, influenced by specific location factors or its defined specialization. Despite distributed value creation processes, the overall production quality must be ensured. Hence, a simulation-based approach is presented, which allows for designing an optimal across-site quality control strategy by evaluating different quality measures depending on individual target systems of different sites. At first, a categorization of quality measures and an applicable target system model are presented. Secondly, a simulation approach is described to evaluate implemented measures according to defined performance indicators

UTILIZING A PROCESS MANAGEMENT APPROACH TO STANDARDISE THE APPLICATION OF VALUE STREAM MAPPING

The systematic improvement of processes is practically realised in the industry and economy by operating a Process Management (PcM) System. Many authors also recommend a systematic repetition of Value Stream Mapping (VSM); but the global experiences in applying VSM do more or less indicate a single or punctual application of VSM to improve processes or value streams. The findings discussed in this paper describe the systematic embedding of Value Stream Mapping into the life cycle of a Process Management System using the principles of “innovation and continuous improvement”. Thus the Process Management System enables a systematic, regularly repeating application of VSM by extending its 4-Step-procedure. One suitable way to standardise the application of VSM is specified in this paper on a theoretical base to make this approach international applicable

DISPO 4.0 – simulation-based optimization of purchase order lot sizes

This paper presents the development and evaluation of a digital planning method for the operational purchasing that enables an automated and product-specific optimized material requirements planning. With materials planning today facing a volatile and complex market environment, the trend towards automated processes in procurement is the key to competitiveness. Although simulation-based optimization for one of the key tasks in material requirements planning, purchase order lot size planning, offers great potential, these digital tools are not yet readily available and thus not widely used in the industry. Thus, this paper develops a simulation-based optimization of purchase orders and evaluates the method in two case-studies in the capital good industry, using 15 purchase order lot sizing algorithms. The results point to a significant cost savings potential and provide insights into the comparative benefits of the algorithms