Model‐robust choice experiments: discussion and case study

Choice experiments are an effective way of obtaining objective information regarding the voice of the customer. They can be used to obtain the relevant customer attributes and importance rankings used in the first step of quality function deployment. They are also used extensively in marketing research. Optimal designs for choice experiments have been discussed in the literature. However, optimal designs are only optimal for a particular model. In this article we borrow ideas from quality engineering and industrial experimentation to develop designs for choice experiments that are model‐robust (in the sense that they are efficient for fitting a model involving main effects plus a few interactions that need not be specified in advance). A case study is presented to illustrate the use of a model‐robust design for a choice experiment. Two unsuspected interactions were discovered in the case study, and this discovery led to added insight regarding customer preferences and importance rankings of product attributes. These insights would not have been possible if an optimal design for the main effects model had been used. Copyright © 2011 John Wiley & Sons, Ltd.     

Analysis of the Prediction Problem with Errors in the Variables

In engineering and other scientific works variables are frequently measured with error, resulting in so-called errors-in-variables situations. The problem of estimating unknown parameters in an errors-in-variables model (EVM)has been extensively discussed in the literature while relatively little has been concerned with the prediction problem in the EVM context. In this paper the integrated mean square, error of prediction (TMSE) is developed for a multiple functional relationship model as a measure of the effect of errors in the variables on the predicted values. The IMSE may be used for assessing the severeness of measurement errors as well as for discriminating competing estimators. Relative performances of various estimation methods for a simple functional relationship are compared in terms of the IMSB, Proposed methods are illustrated with two examples, one from business forecasting and the other from work measurement.     

Policy mechanisms for supply chain coordination

The problem is to determine a review period and stocking policy that are mutually beneficial to a producer and a retailer. In our model, the retailer uses a periodic review, base stock policy for ordering the item from the producer's Distribution Center (DC). Excess customer demand is assumed to be lost. A make-to-order production system supplies to the DC. We show that given a review period, unless the manufacturer agrees to share the cost of carrying a fraction of the safety stocks at the retailer, the two will not agree upon the level of stocks to be carried in the store. We prove that there is an equilibrium value for this fraction, such that the retailer and the manufacturer are always in agreement with regard to the stocking level. We then show that complete coordination on the stocking level as well as the review period can be achieved solely through carrying out negotiations on credit terms. These theoretical results are used to construct an algorithm for calculating the optimal policy parameters for a supply chain. As part of the analysis we suggest a modification of the base stock policy for the positive lag lost sales case of periodic review inventory models that consistently outperforms the base stock policy in our numerical studies.     

Optimal new product design using quality function deployment with empirical value functions

The house of quality (HOQ) has been widely proposed as a method for capturing the voice of the customer (VOC) when developing requirements for new products. The methodology has lacked a formal mechanism for trading off customer preferences with technical feasibility and economic reality. We propose a non‐linear mathematical program for determining the optimal engineering specifications during new product development as a function of elicited customer value functions, engineering development and production costs and development time constraints. The model is shown to be computationally feasible for realistic‐size problems and to outperform heuristic approaches. Copyright © 1999 John Wiley & Sons, Ltd.     

Dynamic Operational Policies in an Automated Warehouse

In this paper we consider the optimal relocation of pallets with a high expectancy of retrieval within each storage rack of an automated warehouse to meet the fluctuating, short-term throughput requirements imposed on the automated storage-retrieval machines. The prepositioning of these pallets closer to the input/output point of each rack during off-peak periods will reduce the expected travel time for the storage/retrieval machines during future peak periods of the planning horizon.

As the model has been abstracted from an actual operating environment, we first describe the environment in which the problem has been posed. We then exploit the special structure of the problem to develop conditions that an optimal relocation policy should satisfy. Based on these optimality conditions, we develop a very efficient optimal relocation algorithm. Finally, we present the performance of several relocation policies in the warehouse studied.     

Determining inventory levels in a CONWIP controlled job shop

We extend the concept of CONWIP control to a job shop setting, in which multiple products with distinct routings compete for the same set of resources. The problem is to determine the fixed overall WIP level and its allocation to product types (WIP mix) to meet a uniformly high customer service requirement for each product type. We formulate an optimization problem for an open queuing network model in which customer orders pull completed products from the system. Then, assuming heavy demand, we derive a throughput target for each product type in a closed queuing network and provide a simple heuristic to find a minimum total WJP and WIP mix that will achieve an operating throughput close to this target. In numerical examples, the WIP mix suggested by this approach achieves the customer service requirement with a relatively low total WIP     

A data-driven approach for the optimisation of product specifications

In order to develop the profit-maximising, market share-maximising or cost-minimising bundle of product engineering specifications with proper performance levels, an optimisation model driven by operating data is proposed. The operating data are input as the sources to conduct the optimisation and a data-based customer satisfaction function can be formed. Then, a customer choice model developed from the customer satisfaction is constructed to estimate the customer choice probability. The expected market share (EMS) then can be derived from the choice probability. After all, a multi-objective model is constructed to maximise the EMS and minimise the total engineering cost. The candidate Pareto-optimal solutions can be obtained by solving the optimisation model. Then a membership function is defined to select the optimal solution from the Pareto-optimal solutions. A case study for optimising the smartphone’s specifications is conducted to demonstrate the effectiveness of the newly developed approach. Compared with the commonly used Conjoint Analysis (CA) method in determining the most desired levels for product specifications, the proposed data-driven method can avoid the situation where the user’s preferences are irrational, making the proposed method be more practical in measuring customer preferences than the utility-based model.     

Parallel time scales and two-dimensional manufacturer and individual customer warranties

We deal with a system whose failures depend on several parallel effects, such as the time in use L and the mileage H. Manufacturer warranties are typically described by a two-dimensional region in the (L, H)-plane. A proper determination of the warranty limits must be based on a two-dimensional distribution of time to failure on this plane. The aim of this paper is to demonstrate the possibility of designing individual warranties for a “nontypical” customer who has a very low or very high usage rate b = H/L, and to show a simple way to calculate warranty limits by minimizing the lifetime coefficient of variation. The latter is carried out by introducing the “best” combined time scale in the form K = (1-ε)L+εH which provides the minimal lifetime coefficient of variation.     

A broader view of the economic design of the X-bar chart in the semiconductor industry

This paper starts from a notice made in the semiconductor industry: a process control system and especially control charts provide information that can be exploited for correlation analyses during process investigations. In this industry, key and costly investigations are made for improving yield and reducing scrap. Daily, engineering teams are working at manufacturing improvements. Without process data, their work could take much more time and lead to weak improvements. Nevertheless, design of process control systems and in particular control charts lacks from taking into account this remark as there is no sound application to infer an optimal control chart depending on business parameters like yield, scrap, customer audits, etc. Meetings between several engineering teams (process control, quality, process integration, industrial engineering and production) occur frequently to find an affordable quantity of controls for each operation. The literature point of view does not provide more recommendations to take into account the reuse of data into these costly investigations. The paper investigates this issue. For this first investigation, work has been focused on the design economics of control charts for a simplified process model. The paper translates this concept into the Lorenzen and Vance's (1986) model. It simulates the design economic of a control chart taking into account this new model and infers new optimal statistical process control (SPC) set points. An analysis of this new link is made in a context of yield improvement, providing reference for knowing optimal quantity and frequency of controls.     

Product portfolio planning with customer-engineering interaction

A critical decision that faces companies across all industrial sectors is the selection of an optimal mix of product attributes to offer in the marketplace, namely product portfolio planning. The product portfolio planning problem has to date generally been considered from a marketing perspective, with the focus being on customer concerns i.e., how alternative sets of product attributes and attribute-level options interact and compete among the target customer segments. From the engineering perspective, the operational implications of product portfolio decisions have been tackled with a primary concern about the cost and complexity of interactions among multiple products in a manufacturing environment with increasing variety. Consideration of the customer and engineering interaction in product portfolio planning is becoming increasingly important, manifested by the efforts observed in many industries to improve the coordination of marketing, design and manufacturing activities across product and process platforms. This paper examines the benefits of integrating customer concerns over product offerings with more engineering implications. To leverage both the customer and engineering concerns, a maximizing shared-surplus model that considers customer preferences, choice probabilities and platform-based product costing, is proposed to address the product portfolio planning problem. A heuristic genetic algorithm procedure is applied to solve the mixed-integer combinatorial optimization problem involved in product portfolio planning. Initial findings from a case study on notebook computer portfolio planning suggests the importance of the research problem, as well as the feasibility and potential of the proposed framework.     

The interaction of location and inventory in designing distribution systems

Many companies face the strategic decision of deciding on the number of Distribution Centers (DCs), their location, and which customers they serve. One objective for a company facing this decision is to maintain acceptable service while minimizing the fixed costs of operating the DCs, inventory holding costs at the DCs, and transportation costs between plants and DCs, and DCs and customers. For insight into this problem, we develop an analytical model for a stylized version of it. However, since the general version of the problem is NP-Hard, we also develop heuristic procedures. We solve a variety of example problems to test the performance of these heuristics relative to optimal solutions and a lower bound based on a relaxation of the original problem. Managerial insight based on our computational studies is provided. We also present a small case-study example motivated by our interaction with Frito-Lay, Inc     

Economic design of the specification region for multiple quality characteristics

Economic specification limits have typically been developed on the basis of a single quality characteristic. From the viewpoint of the customer, products are often evaluated based on multiple quality characteristics. The specification region for multiple quality characteristics must be determined on an economic basis where we minimize the total loss to both the producer and the customer and thus to the whole society. In this paper a multivariate normal distribution is considered for the quality characteristics. The specification region is given by truncating the multivariate normal distribution. We present the optimization model to develop the specification region for multiple quality characteristics based on the framework of multivariate quality loss function.     

Balancing user preferences for aircraft schedule recovery during irregular operations

In this paper, the irregular operations problem is approached for the first time in a way that allows an airline to provide for schedule recovery with minimal deviations from the original aircraft routings. A network model with side constraints is presented in which delays and cancellations are used to deal with aircraft shortages in a way that ensures a significant portion of the original aircraft routings remain intact. The model is flexible, allowing user preferences in the objective, and thereby reflecting the immediate concerns of the decision-maker in the recovery schedule. The model can be tailored by airline operations personnel to emphasize differing solution characteristics. Fleet data provided by Continental Airlines are used to demonstrate the approach. Extensive testing indicates that optimal or near-optimal solutions are routinely obtained from the LP relaxation of the network formulation. When integrality is not achieved, a rounding heuristic is provided that finds feasible solutions within a small fraction of the optimum.     

用单一表进行响应建模的方法在感性设计中的应用

顾客之间不一致的个体特征可能导致不同顾客对同一产品的感性评分之间存在显著的变差,从而很可能存在部分顾客对产品的感性很不满意。针对该问题,在感性设计过程中考虑顾客群体的异质性,引入质量工程中的一种稳健性设计方法来设计感性产品,不仅使该产品大体上符合顾客群体的感性意象,而且使不同顾客对同一产品的感性反应的波动降到最小乃至消除。具体步骤为:首先,提出"感性距离"的概念,用以量化产品的感性质量;然后,将产品的各项感性参数作为控制因子,将顾客之间不一致的个体特征视为噪声因子,将感性距离作为响应变量,应用单一表进行响应建模的方法,求得产品感性参数的稳健设置。以手机的感性设计为例,对上述方法进行了实际应用。     

Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle

Quality function deployment (QFD) is a product planning management instrument which has been used in a broad range of industries. However, the traditional QFD method has been criticised much for its deficiencies in acquiring experts’ opinions, weighting customer requirements (CRs) and ranking engineering characteristics (ECs). To overcome the limitations, an integrated analytical model is presented in this study for obtaining the importance ratings of ECs in QFD by integrating decision-making trial and evaluation laboratory (DEMATEL) technique and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method under hesitant fuzzy environment. In particular, the hesitant fuzzy DEMATEL is used to analyse the interrelationships among CRs and determine their weights, and the hesitant fuzzy VIKOR is utilised to prioritise ECs. Finally, the feasibility and practicality of the proposed method are verified by an example regarding the product development of electric vehicle.     

A fuzzy logic approach to an integrated maintenance/production scheduling algorithm

Scheduling algorithms play an important role in manufacturing systems as a means of meeting customer demands. On the other hand, fuzzy logic, which has been successfully implemented in many engineering applications, including the recent work of Vanegas and Labib (2001a,b), has an ability to produce a more gradual transition. This paper presents an algorithm for transforming maintenance data to shop floor information. These shop floor data are then used via a fuzzy-logic based scheduling algorithm to determine optimal production systems control policies. The frequency of breakdowns and the mean number of parts required are used as inputs to the fuzzy logic controller. These inputs are transformed to the mean part arrival rate. The output is then fed to the scheduling algorithm. Finally, the optimal batch size is calculated. The algorithm is demonstrated with simulation.     

Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.     

Performance management using a value-based customer-centered model

Performance management (PM) and customer management were the subjects of researchers’ attentions for several decades. This research tries to align these two approaches to provide strategic decision-making. The proposed approach suggests an integration of customer relationship management system incorporating PM system considering maximisation of customer lifetime value (CLV) metric. For this, a model is being developed to align PM and customer management. For customer management, we cluster customers based on four dimensions which are product combination, activity level, retention rate and CLV. This combination of attributes has not been used before and can bring more useful information. For example, it can reveal which product combination can pinpoint profitable, loyal and active customers. This helps for the prevention of customer migration. However, the main novelty of this research is proposing the application of CLV as a financial metric in strategic PM in an integrative approach. The research model has been applied in an Iranian commercial retail bank. In the implementation, various techniques and mathematical models including genetic K-means, analytic hierarchical processes and data envelopment analysis have been used.     

Automatic segmentation of digitized data for reverse engineering applications

Reverse engineering is the process of developing a Computer Aided Design (CAD)model and a manufacturing database for an existing part. This process is used in CAD modeling of part prototypes, in designing molds, and in automated inspection of parts with complex surfaces. The work reported in this paper is on the automatic segmentation of 3-Dimensional (3-D) digitized data captured by a laser scanner or a Coordinate Measuring Machine (CMM) for reverse engineering applications. Automatic surface segmentation of digitized data is achieved using a combination of region and edge based approaches. It is assumed that the part surface contains planar as well as curved surfaces that are embedded in a base surface. The part surface should be visible to a single scanning probe (21/2D object). Neural network algorithms are developed for surface segmentation and edge detection. A back propagation network is used to segment part surfaces into surface primitives which are homogenous in their intrinsic differential geometric properties. The method is based on the computation of Gaussian and mean curvatures of the surface. They are obtained by locally approximating the object surface using quadratic polynomials. The Gaussian and mean curvatures are used as input to the neural network which outputs an initial region-based segmentation in the form of a curvature sign map. An edge based segmentation is also performed using the partial derivatives of depth values. Here, the output of the Laplacian operator and the unit surface normal are computed and used as input to a Self-Organized Mapping (SOM) network. This network is used to find the edge points on the digitized data. The combination of the region based and the edge based approaches, segment the data into primitive surface regions. The uniqueness of our approach is in automatic calculation of the threshold level for segmentation, and on the adaptability of the method to various noise levels in the digitized data. The developed algorithms and sample results are described in the paper     

Designing the optimal process target levels for multiple quality characteristics

The selection of the optimal process target has become an important research area in which the focus is to increase productivity and improve product quality. Although the quality engineering literature related to this issue contains a vast collection of work, some questions still remain unanswered. First, most previous studies have viewed this issue from a manufacturer's perspective. When designing the optimal process target in the early stage, the customer's perception of product quality needs to be incorporated. Secondly, many researchers have carried out their studies based on a single quality characteristic. From the customer's viewpoint, however, products are often judged based on more than one characteristic. To address these questions, this paper first studies a multivariate quality loss function to capture customer dissatisfaction with product quality, and then proposes an optimization scheme to determine the most economical process target levels for multiple quality characteristics. The optimization procedures are demonstrated in a numerical example, and the effects of process parameters are examined by conducting a sensitivity analysis.