Sławomir Kłos

Throughput Analysis of Automatic Production Lines Based on Simulation Methods

The effectiveness of manufacturing systems is a very important ratio that decides about the competitiveness of enterprises. There are a lot of factors which affect the efficiency of production processes such as: operation times, setup times, lot sizes, buffer capacities, etc. In this paper, a computer simulation method is used to model the throughput and provide a product life span analysis of a number of automatic production lines. The research was done for two topologies of manufacturing systems and different capacities of intermediate buffers. Due to the fact that an increase of intermediate buffers results in an increase of work in process, a flow index of production is proposed that includes a relationship between the throughput of a system and the average life span of products. The simulation model and experimental research was made using Tecnomatix Plant Simulation software.

A Model of an ERP-Based Knowledge Management System for Engineer-to-Order Enterprises

Today’s enterprises require effective systems of knowledge management (KM) that guarantee proper know-how recording and processing in order to create innovative products and technologies. Part of a company’s know-how is recorded in an enterprise resource planning (ERP) system database as data and information which could be transformed into knowledge. In this, paper, a model of a KM system for manufacturing companies that carry out engineer-to-order (ETO) production is proposed. The model is based on ERP systems and includes tools that enable data and information processing and its subsequent conversion into a form of knowledge. An analysis and understanding of the model may result in an improvement of innovation processes in high-tech manufacturing companies. The proposed model may additionally support the research and design activities of manufacturing enterprises. Illustrative examples are provided.

Implementation of the AHP Method in ERP-Based Decision Support Systems for a New Product Development

Effective management of R&D projects determines the competitiveness of manufacturing enterprises. The proper choice of product prototypes that might be manufactured and sold forms part of the critical, strategic decisions of each company. These decisions have a direct impact on the financial results of the enterprise, development possibilities and its competitive position. Depending on the size and characteristics of companies, an enterprise can make a variety of decisions annually, from a few to several dozen, regarding the introduction of new products into production. Therefore, the development of a decision support system for the selection of the best prototypes is very important especially for manufacturing enterprises that bring many new products onto the market. The decision support system should be based on an analysis of technical and business factors of new products and enable the improvement of effectiveness and reduce decision time. To automate the process of decision-making the data required for the decision support system should be acquired from the ERP (enterprise resource system). The proposed decision support system is based on the AHP method. An illustrative example is given.

The Topological Impact of Discrete Manufacturing Systems on the Effectiveness of Production Processes

The structure of a manufacturing system plays an important role in the effectiveness- and the work-in-progress- of production processes. In this paper, the impact on the throughput and average lifespan of products of different topologies, within a discrete manufacturing system, including production resources and buffers, is analysed. In order to analyse the behaviour of the system’s computer simulation software, Tecnomatix Plant Simulation is employed. The topology of the system is expanded from the simple production line to the rather more complex structure of two production lines with different, interconnection variants between buffers and production resources. For each topology of a given manufacturing system, a set of simulation experiments is prepared. The input values of the simulation experiments, along with the different allocation of buffer capacities, is taken into account. The output values are the manufacturing system’s throughput, along with the average lifespan of the products, which indicate the work-in-progress.

An Intelligent System for Core-Competence Identification for Industry 4.0 Based on Research Results from German and Polish Manufacturing Companies

This article aims to present the concept of an intelligent system for core-competence identification for Industry 4.0, and is based on a survey and data obtained from 62 Polish Manufacturing Enterprises in the Lubuskie region and from 23 German Manufacturing Enterprises from the Brandenburg region - so from 85 manufacturing enterprises in “the region: cross-border cooperation”. Special attention is placed on the determination of the weightings of the competencies of employees in the context of the adoption and use of technologies related to Industry 4.0. This is followed by a discussion of the supporting literature and of the results of the empirical studies. Finally, part of an intelligent system for core-competence identification for Industry 4.0 is presented.

The Impact of an ERP System on the Technical Preparation of Production

The area of the technical preparation of production includes tasks related to the development of design, technology, tools and any equipment necessary to create new products. This functional area has key strategic importance for the development of almost every aspect of a production company. This is an integral area associated with the implementation of an ERP system, because data for materials, structures, devices, technologies, tools, etc., are created in the technical preparation of production. Based on an analysis of the functionality of ERP systems and associated case studies, a methodology can be proposed for assessing the impact of the functionality of an ERP system on the efficiency of the processes performed in the technical preparation of production area. The proposed methodology allows, already at the stage of the implementation of an ERP system, for the selection of features and an evaluation of the potential benefits in the area of the technical preparation of production. An analysis of the impact of the effectiveness of an ERP system on technical preparation of production activities is generally concerned with the processes involved in creating bills of materials, manufacturing technologies, cost calculation, variant operations and variant part lists, etc.

An Approach to Buffer Allocation, in Parallel-Serial Manufacturing Systems Using the Simulation Method

The buffer allocation problem (BAP) concerns real, discrete manufacturing systems that complete repetitive, multi-assortment productions. In the automotive industry, the allocation of the correct, buffer capacity is especially important in order to obtain an acceptable throughput and work-in-progress. The BAP is an NP-hard combinatorial optimisation problem. The methodology for the allocation of buffer capacity in a parallel-serial, manufacturing system is proposed, in the paper and deals with the compromise between high, system throughput and low-level work-in-progress. The methodology is based on the simulation method. In order to analyse the behaviour of the manufacturing system, Tecnomatix Plant Simulation Software is used. Simulation experiments are conducted for the different capacities of buffer allocation within a manufacturing system. To evaluate the results of the simulation, a system performance index is proposed.

An Analysis of the Efficiency of a Parallel-Serial Manufacturing System Using Simulation

The efficiency of discrete manufacturing systems and the level of work-in-progress are most important topics, especially for producers of automotive parts. In the present paper, an analysis of the throughput and average product lifespan of a parallel, serial manufacturing system, with varied buffer allocations and operating times, is presented. A model of the manufacturing system has been prepared using Tecnomatix Plant Simulation Software. This study was conducted using theoretical data sets and various statistical distributions of processing times. The main goal of the research was to analyse the impact of buffer allocation on the behaviour of a general, parallel manufacturing system. The methodology for preparing simulation experiments is here proposed.

Knowledge Acquisition Using Computer Simulation of a Manufacturing System for Preventive Maintenance.

Preventive maintenance is an important component of the ‘Industry 4.0 Concept’. Modern industry requires intelligent, autonomous and reliable manufacturing systems. Should a manufacturing system fail, it should be able to reorganise itself and put into effect a production plan, based on a scenario of selected activities. The problem is just how to predict possible failures and prepare scenarios for the behaviour of a system. With regard to the preventive maintenance system, potential failures can be modelled and then, by using computer simulation, based on simulation experiments, a database of maintenance knowledge can be created. In the paper, the methodology for the acquisition of maintenance knowledge, using the computer simulation method, is proposed. The example, put forward as an illustration, was prepared using Tecnomatix Plant Simulation software.

The Impact of Buffer Allocation in Assembly-Line Manufacturing Systems on the Effectiveness of Production Processes

The allocation of buffers in an assembly-line manufacturing system plays an important role in the effectiveness and the work-in-progress of production processes. In this paper, the impact of buffer allocation on the throughput and average product lifespan is analysed. The behaviour of the system is investigated using the computer-simulation method and the Tecnomatix Plant Simulation software tool. The different variants of an assembly-line manufacturing system are considered (varied allocation of buffers and operation times). For the assembly-line manufacturing system, simulation experiments are prepared (varied allocation of buffer capacities and processing times). The input values of the simulation experiments, along with the different allocations of buffer capacities, are taken into account. The output values are the manufacturing system’s throughput, along with the average lifespan of the products, which indicate the work-in-progress.