* Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

Toyota production system : beyond large-scale production

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.

Review: A state-of the-art survey of TOPSIS applications

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This paper proposes a fuzzy TOPSIS method based on alpha level sets and presents a nonlinear programming (NLP) solution procedure. The relationship between the fuzzy TOPSIS method and fuzzy weighted average (FWA) is also discussed. Three numerical examples including an application to bridge risk assessment are investigated using the proposed fuzzy TOPSIS method to illustrate its applications and the differences from the other procedures. It is shown that the proposed fuzzy TOPSIS method performs better than the other fuzzy versions of the TOPSIS method.

Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment

Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in systems, processes, designs or services and has been used in a wide range of industries. The conventional risk priority number (RPN) method has been criticized to have many deficiencies and various risk priority models have been proposed in the literature to enhance the performance of FMEA. However, there has been no literature review on this topic. In this study, we reviewed 75 FMEA papers published between 1992 and 2012 in the international journals and categorized them according to the approaches used to overcome the limitations of the conventional RPN method. The intention of this review is to address the following three questions: (i) Which shortcomings attract the most attention? (ii) Which approaches are the most popular? (iii) Is there any inadequacy of the approaches? The answers to these questions will give an indication of current trends in research and the best direction for future research in order to further address the known deficiencies associated with the traditional FMEA.

Review: Risk evaluation approaches in failure mode and effects analysis: A literature review

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The paper proposes a new workforce effectiveness metric that is a sophisticated evolution of a pre-existing overall labour effectiveness (OLE) indicator. The KPI, named revised OLE (ROLE), provides a structured methodology to measure in a holistic way the losses relating to labour, maintaining some formal similitude to the overall equipment effectiveness (OEE),A new structure of losses is proposed to overcome the drawbacks and the difficulties that usually affect the data collection stage, referring to directly measurable quantities or, when this is not the case, suggesting a viable method to quantify the loss. Besides, this approach facilitates the comprehension of labour-related issues, suggesting potential countermeasures. The novel ROLE indicator has been defined, based on this new structure, to evaluate the labour effectiveness in batch process industries. A real case study is provided, which explains the methodology and illustrates the capability of the corresponding KPI.,The present work analyses the labour performance indexes available in literature, with the aim of evidencing those aspects that can be properly observed and quantified and, at the same time, categorizing them to identify their possible drawbacks. A new structure of losses is derived, with respect to four different categories, which may help to measure the losses themselves more effectively,The paper investigates some important KPIs dealing with labour performance and individuates some significant drawbacks. Then it suggests a new, inclusive structure of losses and a modified KPI that not only measures effectiveness but also allows to identify viable countermeasures.

Revised overall labour effectiveness

The present paper is aimed at investigating, through simulation, the behaviour of the POLCA method when a make-to-order (MTO) production system is highly unbalanced, in terms of both routings and times. In particular, the study is addressed to verify: 1) the effective capability of the POLCA method to improve the uncontrolled system; 2) the impact of how orders are processed at each workstation, to show that POLCA performance can be further improved in such circumstances by adopting an appropriate selection rule. Germs and Riezebos (2010) proved that POLCA is very effective in reducing the total throughput time (TTT) with respect to the corresponding unconstrained production systems. Owing to this, it appears evident that POLCA represents a valuable and effective make-to-order (MTO) production control method. However, they only considered balanced systems, hence some observations must be carefully addressed. In fact, often, in real world applications, the systems to be controlled are designed to process units with very different routings, each with significantly different probability to occur.

A study on the importance of selection rules within unbalanced MTO POLCA-controlled production systems

This paper aims at presenting a revisited and extended version of the injury theory, originally developed by Ramalingam and Watson (1977) and Ramalingam (1977). This theory allows deriving tool-life distributions when either a single catastrophic event or a gradual and cumulative wear process terminates the tool useful life. Since these models cover great importance in tool-life modelling, this paper would contribute in this direction with further observations and results, giving special attention to the mathematical formulation. In particular, besides deriving known results following a different approach, we extend the theory to the case of multiple machines with both single- and multi-tool configurations. Moreover, as examples we derive different expressions of the tool-life distribution in the cases studied related to particular formulations of the injury rate.

Stochastic theory of tool life – theoretical developments on the injury theory

Lean manufacturing has become a paramount production paradigm in many industrial sectors and overall equipment effectiveness (OEE) is a widely accepted and adopted lean metric for any type of machine/equipment. However, the standard OEE formulation presents two important limits: 1) it deals with a single machine; 2) gives a deterministic measure of effectiveness. The objective of this paper is to overcome both of these two shortcomings by proposing a Fuzzy approach for measuring the OEE of a manufacturing line (OEEML). Hence, starting from a possible definition of OEEML, this paper provides a framework to measure its modal value and its variability on an entire manufacturing line. The approach exploits fuzzy triangular numbers instead of burdensome stochastic quantities to measure this variability. Furthermore, it integrates a method which enables us to avoid the annoying 'overestimation effect' of FTNs. The integrated approach has proved to be useful in consistently capturing the OEEML variations, also, both the modal value and the range obtained from its application are good estimators of both the OEEML and its variability. A full case study is finally provided to show the effectiveness of the proposed approach in diagnosing problems and addressing improvement actions.

Marco Frosolini

Papers

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Revised overall labour effectiveness

A study on the importance of selection rules within unbalanced MTO POLCA-controlled production systems

Stochastic theory of tool life – theoretical developments on the injury theory

Integrating considerations of uncertainty within the OEE of a manufacturing line