The impact of using different lean manufacturing tools on waste reduction

Kleszcz²,

Management Systems in Production Engineering

2021

The article presents the results of the research on the level of implementation of Lean instruments in the ceramics industry. The research was carried out in ceramic factories in Poland and Germany. Based on the results obtained, the most commonly used Lean instruments in the ceramics industry were defined. The dependence of the use of Lean instruments on the size of the enterprise was also determined. It was established that the Lean activities undertaken in the ceramics industry are focused solely on the implementation of individual methods and tools, without a pre-defined goal. This approach in the ceramics industry is ineffective, but unfortunately it is most often used in the implementation of the Lean philosophy, especially in small ceramics enterprises.

Section: Lean Manufacturingmentioning

confidence: 99%

Implementation of Lean Instruments in Ceramics Industries

Kleszcz²,

Management Systems in Production Engineering

2021

“…Terminology of Lean Manufacturing is a system efficiency approach by reducing waste (Leksic, Stefanic and Veza, 2020;Rifqi et al, 2021). A good production system needs to identify non-value added activities in the production chain.…”

Section: Lean Conceptmentioning

confidence: 99%

Integration of Waste Assessment Model and Lean Automation to Improve Process Cycle Efficiency in the Automotive Industry

Setiawan

Jaqin

et al. 2021

QIP Journal

Purpose: This study aims to improve Process Cycle Efficiency (PCE) by reducing waste in the assembly production line. This research is a case study conducted in a four-wheel vehicle manufacturing company to create a Lean production system. Methodology/Approach: This study uses the Waste Assessment Model (WAM) and Lean Automation integration methods carried out by 5 expert judgements in the selected four-wheeled vehicle industry. Findings: Based on the WAM method, this study found the biggest waste on the assembly line, namely transportation with a percentage of 20.44%. Improvements made with Lean Automation can increase the value of PCE from 56.76% to 63.62%. Research Limitation/Implication: This research is limited to one model, namely Multi Purpose Vehicle (MPV). This research provides benefits for companies related to waste reduction. For similar companies, this research can be input for manufacturing practitioners in improving manufacturing performance to compete in the global market. This Lean production can reduce manpower which has an impact on saving production costs. Originality/Value of paper: This paper provides added value related to the application of Lean terminology which aims to improve industrial automation-based manufacturing companies.

“…The elimination of waste is most often carried out by implementing lean tools such as 5S, standardized work, Kaizen, Poka-Yoke, and value stream mapping (VSM) [27,28]. They are most commonly applied to make production processes more effective and to reduce lead-time or cost of production, but they can also be applied for maintenance operations.…”

Section: Waste In Lean Manufacturingmentioning

confidence: 99%

The Use of Artificial Intelligence Methods to Assess the Effectiveness of Lean Maintenance Concept Implementation in Manufacturing Enterprises

2020

The increase in the performance and effectiveness of maintenance processes is a continuous aim of production enterprises. The elimination of unexpected failures, which generate excessive costs and production losses, is emphasized. The elements that influence the efficiency of maintenance are not only the choice of an appropriate conservation strategy but also the use of appropriate methods and tools to support the decision-making process in this area. The research problem, which was considered in the paper, is an insufficient means of assessing the degree of the implementation of lean maintenance. This problem results in not only the possibility of achieving high efficiency of the exploited machines, but, foremost, it influences a decision process and the formulation of maintenance policy of an enterprise. The purpose of this paper is to present the possibility of using intelligent systems to support decision-making processes in the implementation of the lean maintenance concept, which allows the increase in the operational efficiency of the company’s technical infrastructure. In particular, artificial intelligence methods were used to search for relationships between specific activities carried out under the implementation of lean maintenance and the results obtained. Decision trees and rough set theory were used for the analysis. The decision trees were made for the average value of the overall equipment effectiveness (OEE) indicator. The rough set theory was used to assess the degree of utilization of the lean maintenance strategy. Decision rules were generated based on the proposed algorithms, using RSES software, and their correctness was assessed.