Toyota Production System

The Toyota Production System (TPS) is an integrated socio-technical system, developed by Toyota, that comprises its management philosophy and practices. The TPS is a management system that organizes manufacturing and logistics for the automobile manufacturer, including interaction with suppliers and customers. The system is a major precursor of the more generic "lean manufacturing". Taiichi Ohno and Eiji Toyoda, Japanese industrial engineers, developed the system between 1948 and 1975.Strategos-International. [ Toyota Production System and Lean Manufacturing]. Originally called "just-in-time production", it builds on the approach created by the founder of Toyota, Sakichi Toyoda, his son Kiichiro Toyoda, and the engineer Taiichi Ohno. The principles underlying the TPS are embodied in The Toyota Way. Goals The main objectives of the TPS are to design out overburden (muri) and inconsistency (mura), and

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Modeling and simulation of a production system using a JAVA developed simulator

Juan Cervero Capdevila

2004

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

Dimitrios Kyritsis, Foivos Psarommatis Giannakopoulos, Xiaochen Zheng

Nowadays, the manufacturing industry is constantly changing. Production systems must operate in a highly dynamic environment where unexpected events could occur and create disruption, making rescheduling inevitable for manufacturing companies. Rescheduling models are fundamental to the robustness of production processes. This paper proposes a model to address rescheduling caused by unexpected events, aiming to achieve the zero-defect manufacturing (ZDM) concept. The goal of the model is to incorporate traditional and ZDM–oriented events into one methodology to calculate when the next rescheduling will be performed to effectively react to unexpected events. The methodology relies on the definition of two key time parameters for each event type: event response time (RT) and event delay response time (DRT). Based on these parameters, an event management algorithm is designed to identify the optimal rescheduling solution. The DRT parameter is calculated based on a multi-parametric dynamic formula to capture the dynamics of production. Moreover, ANOM, and ANOVA methods are used to analyse the behaviour of the developed method and to assess the level of robustness of the proposed approach. Finally, a case study based on real production scenarios is conducted, a series of simulation experiments are performed, and comparisons with other rescheduling policies are presented. The results demonstrate the effectiveness of the proposed event management algorithm for managing rescheduling.

2021

Predictive Maintenance Platform Based on Integrated Strategies for Increased Operating Life of Factories

Sangje Cho, Gökan May

Process output and profitability of the operations are mainly determined by how the equipment is being used. The production planning, operations and machine maintenance influence the overall equipment effectiveness (OEE) of the machinery, resulting in more 'good parts' at the end of the day. The target of the predictive maintenance approaches in this respect is to increase efficiency and effectiveness by optimizing the way machines are being used and to decrease the costs of unplanned interventions for the customer. To this end, development of ad-hoc strategies and their seamless integration into predictive maintenance systems is envisaged to bring substantial advantages in terms of productivity and competitiveness enhancement for manufacturing systems, representing a leap towards the real implementation of the Industry 4.0 vision. Inspired by this challenge, the study provides an approach to develop a novel predictive maintenance platform capable of preventing unexpected-breakdowns based on integrated strategies for extending the operating life span of production systems. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled Z-Bre4k, i.e. Strategies and predictive maintenance models wrapped around physical systems for zero-unexpected-breakdowns and increased operating life of factories.

SPRINGER-VERLAG BERLIN2018

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This course will cover all the aspects of product design and system engineering from learning relevant methods to the actual implementation in a hands-on practice of product development.

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Quality control (QC) is a process by which entities review the quality of all factors involved in production. ISO 9000 defines quality control as "a part of quality management focused on fulfilling