Alberto Lombardo

A weighted logistic regression for conjoint analysis and Kansei engineering

Customer needs for emotional satisfaction are increasingly being considered by product and service designers. While several existing methods such as conjoint analysis (CA), Kano model and quality function deployment support the translation of customer requirements into technical specifications, researchers are now working to develop methods aimed at integrating affective aspects into product design. Kansei engineering (KE) is a design philosophy that considers customer perceptions and emotions by adopting a multi-disciplinary approach. CA is a useful tool within a KE project. This article presents a methodology for conducting a KE project in early development phases. This methodology is based on two new procedures. The first one is aimed at calculating attribute importance weights by using respondent choice time in controlled interviews. The second procedure allows the exploitation of such weights in an ordinal logistic regression model for analysing the results of CA experiments. By using the proposed methodology, it is possible to identify product/service attributes able to induce specific emotions and feelings in customers and consequently choose the right development strategy. An application of the method for the design of mobile phones is presented. Copyright

A predictive maintenance policy with imperfect monitoring

For many systems, failure is a very dangerous or costly event. To reduce the occurrence of this event, it is necessary to implement a preventive maintenance policy to replace the critical elements before failure. Since elements do not often exhibit incipient faults, they are replaced before a complete exploiting of their useful life. To conjugate the objective of exploiting elements for almost all their useful life with the objective to avoid failure, condition based and, more recently, predictive maintenance policies have been proposed. This paper deals with this topic and proposes a procedure for the computation of the maintenance time that minimizes the global maintenance cost. By adopting a stochastic model for the degradation process and by hypothesizing the use of an imperfect monitoring system, the procedure updates by a Bayesian approach, the a-priori information, using the data coming from the monitoring system. The convenience in adopting the proposed policy, with respect to the classical preventive one, is explored by simulation, showing how it depends on some parameters characterizing the problem.

Relation of fruit color, elongation, hardness, and volume to the infestation of olive cultivars by the olive fruit fly, Bactrocera oleae

The susceptibility of olive cultivars to the olive fruit fly, Bactrocera oleae (Rossi) (Diptera: Tephritidae), has seldom been studied. This article examines factors associated with olive fruit fly infestation of 16 commonly planted Sicilian olive cultivars. Total infestation data were simultaneously correlated with categorical and quantitative factors using ordinal logistic regression. When all factors were included in the analysis, year, sampling date, cultivar, and fruit color were highly significant, but the quantitative factors fruit volume, fruit elongation, and fruit hardness were not. When the analysis was repeated excluding cultivar, all quantitative factors were significant, and elongation and volume were highly significant. Spherical, large, and hard fruit seemed to be preferred by B. oleae over fruit that are elongate, small, and soft. Therefore, fruit color, elongation, volume, and hardness provide useful information regarding the susceptibility of cultivars. In both organic and conventional olive cultivation, information about olive cultivar susceptibility to olive fruit fly will help orchard managers to produce quality oil and table olives while reducing treatments for olive fruit fly control.

Service Quality Design through a Smark Use of Conjoint Analysis

In the traditional use of conjoint analysis, in order to evaluate the relative importance of several elements composing a service, interviewed customers are asked to express their judgement about different scenarios (specific combinations of elements). In order to reduce the number of possible scenarios, design of experiments methodology is usually exploited. Previous experiences show that, even a limited number of proposed scenarios cause difficulty in answering for the interviewed customer if the scenarios differ for elements of very low interest to him/her. Consequently, a high rate of abandon of the interview has been observed. In this study it is assumed that a service can be decomposed in several improvable elements and/or enriched with new “optionals”. In both cases, what under study is assumed to be a set of dichotomous attributes. For each of these attributes, its marginal contribution to customer satisfaction has to be modelled and estimated. To obtain the required information, an opportune questionnaire is proposed to a sample of interviewed customers. An interviewing procedure consisting in a customer driven design of scenarios is followed, starting from the full‐optional scenario and eliminating one by one the less satisfying elements. for each interviewed customer, a ranking of attributes is so obtained. Then, by asking the interviewed customer to evaluate on a metric scale the scenarios he previously selected, a rating of attributes can also be obtained. A case study conducted in collaboration with a public transportation company is presented. Contrarily to previous experiences, the abandon rate proved extremely reduced.

Analysis of User Needs for the Redesign of a Postural Seat System

The identification and translation of customer needs early in the design process is a major challenge for product design researchers. Some needs are explicit and customers can state them very clearly. Other needs are implicit, so customers cannot express them, e.g., those pertaining to the affective and emotional sphere. In this work, we describe the methods most commonly used to capture explicit and emotional customer needs, and the traditional ways in which they are used. Moreover, an integration of QFD and Kansei engineering, a simplification of Kano methodology, and a new attribute weighing methodology based on the “choice time” are discussed for the design of an innovative postural seat system for patients affected by mental retardation.

Balanced Asymmetrical Nearly Orthogonal Designs for first and second order effect estimation

Abstract A method for constructing asymmetrical (mixed-level) designs, satisfying the balancing and interaction estimability requirements with a number of runs as small as possible, is proposed in this paper. The method, based on a heuristic procedure, uses a new optimality criterion formulated here. The proposed method demonstrates efficiency in terms of searching time and optimality of the attained designs. A complete collection of such asymmetrical designs with two- and three-level factors is available. A technological application is also presented.

Tool replacement with adaptive control in a non-stationary non-periodic stochastic process

Abstract The problem of optimum tool replacement is studied in the case in which tool performance is characterized by progressive decay over time following stochastic laws. A control system is assumed which detects, continuously or at fixed intervals, the service state of the tool. Assuming that the service state of the tool affects the marginal cost of production, the latter is used in order to minimize the unit production cost for an unlimited production horizon. The replacement policy proposed is able to update itself in process by means of an iterative procedure which converges to a conditioned optimum. The effectiveness of such a policy is demonstrated analytically, and illustrative examples obtained by simulation are shown.

Six Sigma in small– and medium–sized enterprises: a Black Belt project in the Swedish steel industry

Many big companies around the world have solid Six Sigma infrastructures and it is easy to find in literature successful case studies. Conversely, small and medium–sized enterprises (SMEs) generally suffer lower attention in the literature. This happens because SMEs have only recently approached the methodology; they have weaker connection with the academia; or they do not rigorously pursue the frameworks. This paper is based on a Six Sigma project in a Swedish medium–sized company that produces steel tubes mainly for hydraulic applications. The project focused on the improvement of warehouse activities related to cutting processes. This Black Belt project was part of a Six Sigma education at the Chalmers University of Technology in Gothenburg. The case study offers the opportunity to discuss general and specific issues that a SME faces in the implementation of the Six Sigma methodology and it contributes to connect academic debate to practical experience in industry.

A heuristic method for estimating attribute importance by measuring choice time in a ranking task

The evaluation of a product or service in terms of its attributes has been broadly studied in marketing, management and decision sciences. However, methods for finding important attributes have theoretical and practical limitations. The former are related to the selection of the most appropriate model; the latter are due to large number of variables that affect the specific experimental context. This study aims to present a new methodology that captures attribute preferences from a respondent and in particular, by using the choice time in a ranking task, it allows to indirectly obtain the importance weights for several tested attributes through a simple, fast and inexpensive procedure. Moreover, the method makes it possible to overcome problems associated with context, survey and cognitive variables so allowing to achieve more reliable conclusions. An application of the method concerning a cellular phone validates the proposed method.

Prioritisation of alternatives with analytical hierarchy process plus response latency and web surveys

This paper introduces a new method that combines the well-known analytical hierarchy process (AHP) with a response latency metric. The response latency is the time taken by respondents to make choices over pairwise comparisons. The analytical calculation of relative importance weights of the alternatives is made by using a response latency model previously validated in several case studies. This combination aims to overcome some drawbacks of the traditional AHP related to the use of a rating scale (the so-called Saaty scale), and it is a natural way to involve response latency in established decision-making methods. This new method can be profitably adopted in web surveys where it is easy to measure and record response latencies. An application to a new service development was used to test the proposed method. Findings show that the respondent effort, fatigue, and time consumption are drastically reduced, making the survey much simpler and faster. The obtained results seem to be very reliable in terms of judgement consistency.