Integrating AHP with QFD for robot selection under requirement perspective

Selection of a robot is an important task, as improper selection may adversely affect a firm's production by reducing the quality of the product, thereby reducing productivity as well as profitability. To effectively select a robot for a specified job, several factors have to be considered. The objective of this paper is to explain how, using a combined AHP/QFD model, the authors are able to determine if the deployment of robots in industry helped in performance enhancement from requirement perspective. Incorporating a simple and novel cost factor measure in the proposed integrated AHP/QFD model aids justification of the implementation of a robotic system in a manufacturing firm from an economic point of view also. The proposed integrated approach also identifies technical requirements followed by customer requirements. In this paper, an integrated model combining AHP and QFD has been delineated for the industrial robot selection problem. Seven technical requirement factors have been considered for the case study.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

On optimal upgrade strategy for second-hand multi-component systems sold with warranty

Reliability improvement strategies such as upgrade, reconditioning and remanufacturing have been extensively adopted by dealers of second-hand systems to improve the system reliability and reduce the warranty servicing cost. However, most existing studies on this topic do not consider the multi-component structures of complex second-hand systems, and either treat them as black-box systems by ignoring their internal structures or simply deal with individual components. In this paper, a new upgrade model is developed for complex second-hand systems sold with a non-renewing free repair/replacement warranty, by explicitly considering their multi-component configurations. Two types of components, i.e. repairable and non-repairable components, are taken into account. During the upgrade process, non-repairable components can be upgraded only by replacement (if necessary), while repairable ones may be imperfectly upgraded with various degrees. The main objective of the dealer is to determine which components to upgrade and the corresponding upgrade degrees, to minimise the total expected servicing cost. In view of the problem structure, a marginal analysis based algorithm is presented. It is shown that the proposed upgrade strategy contains the ‘no upgrade’ strategy and the ‘component-level perfect upgrade/replacement’ strategy as special cases, and outperforms them. Finally, several extensions of the proposed upgrade model are discussed.     

Component choice for managing risk in engineered systems with generalized risk/cost functions

The constrained optimization of resource allocation to minimize the probability of failure of an engineered system relies on a probabilistic risk analysis of that system, and on ‘risk/cost functions’. These functions describe, for each possible improvement of the system's robustness, the corresponding gain of reliability given the considered component or management factor to be upgraded. These improvements can include, for example, the choice of components of different robustness levels (at different costs), addition of redundancies, or changes in operating and maintenance procedures. The optimization model is generally constrained by a maximum budget, a schedule deadline, or a maximum number of qualified personnel. A key question is thus the nature of the risk/cost function linking the costs involved and the corresponding failure-risk reduction. Most of the methods proposed in the past have relied on continuous, convex risk/cost functions reflecting decreasing marginal returns. In reality, the risk/cost functions can be simple step functions (e.g. a discrete choice among possible components), discontinuous functions characterized by continuous segments between points of discontinuity (e.g. a discrete choice among components that can be of continuously increasing levels of robustness), or continuous functions (e.g. exponentially decreasing failure risk with added resources).This paper describes a general method for the optimization of the robustness of a complex engineered system in which all three risk/cost function types may be relevant. We illustrate the method with a satellite design problem. We conclude with a discussion of the complexity of the resolution of this general type of optimization problem given the number and the types of variables involved.