Don G. Wardell

Run-Length Distributions of Special-Cause Control Charts for Correlated Processes

We derive run-length distributions of the special-cause control chart proposed by Alwan and Roberts for correlated observations, given that the assignable cause to be detected is a shift in the process mean. Both recursive and closed-form solutions are derived for the run-length distribution, average run length (ARL), and standard deviation of the run length (SRL) for any AR(p) process, and approximate solutions are derived for the more general ARMA(p,q) processes. The expressions derived do not depend on the type of shift in the process mean. Numerical results are illustrated for the ARL and SRL of the ARMA(l,l) model, given that the shift in the mean is a step shift. These results show that the ARL and SRL of the specialcause control chart are relatively smaller when the process is negatively rather than positively autocorrelated. Regardless of the sign of the autocorrelation, the shape of the probability mass function of the run length reveals that the probability of detecting shifts very early is subs...

A Comparison of Quality Function Deployment and Conjoint Analysis in New Product Design

Abstract In this work, we compare two product design approaches, quality function deployment (QFD) and conjoint analysis, by applying each to the design of a new all-purpose climbing harness for the beginning/intermediate ability climber that would complement a leading manufacturer’s existing product line. While many of the optimal design features were the same under both approaches, the differences allow us to highlight the strengths of each approach. With conjoint analysis, it was easier to compare the most preferred features (i.e., ones that maximized sales) to profit maximizing features and also to develop designs that optimize product line sales or profits. On the other hand, QFD was able to highlight the fact that certain engineering characteristics or design features had both positive and negative aspects. This tradeoff could point the way to “out of the box” solutions. QFD also highlighted the importance of starting explicitly with customer needs, regardless of which method is used. Rather than competing, we view them as complementary approaches that should be conducted simultaneously; each providing feedback to the other. When the two approaches differed on the optimal level or importance of a feature, it appeared that conjoint analysis better captured customers’ current preferences for product features while QFD captured what product developers thought would best satisfy customer needs. Looking at the problem through these different lenses provides a useful dialogue that should not be missed. QFD’s ability to generate creative or novel solutions should be combined with conjoint analysis’ ability to forecast market reaction to design changes.

The Impact of Service System Design and Flow Experience on Customer Satisfaction in Online Financial Services

Prior research examines customer satisfaction in retailing and e-commerce settings, yet online financial services have received little research attention. To understand customer satisfaction with this fast-growing service, this study investigates the role of flow experience, a sensation that occurs as a result of significant cognitive involvement. The study examines how service system characteristics affect the cognitive states of the flow experience, which determines customer satisfaction. The flow construct and total experience design suggest a structural model that is empirically tested using responses from a large sample of online investors. In support of the model and most of the hypotheses it suggests, the empirical results clarify the important antecedents and consequence of flow experience in online financial services and suggest the viability of using a dual-layer experience construct to investigate customer satisfaction.These findings can help researchers and service providers understand when, where, and how flow experience is formulated in online financial services.

Run Length Distributions of Residual Control Charts for Autocorrelated Processes

A FORTRAN program is given to calculate the run length distribution (RLD), the average run length (ARL), and the standard deviation of the run length (SDRL) for residual control charts used to monitor autocorrelated process output. RLD, ARL, and SDRL values are calculated for processes that can be modeled by pure autoregressive models of order p (AR(p)), pure moving-average models of order 1 (MA(1)), and mixed autoregressive moving-average models of orders p and 1 (ARMA(p,1)), given that the assignable cause to be detected is a step shift in the process mean.

Can Customers Detect Script Usage in Service Encounters? An Experimental Video Analysis

Service scripts are predetermined guides for employees to follow when delivering service to customers. Some services require employees to strictly follow a script, whereas others use scripts more flexibly, if at all. Extant research regarding service scripts in the domain of service operations has mainly addressed the topic from more of a process view as a control mechanism for the encounter but minimal research has examined customer perceptions of scripted service. The authors examine a pivotal first question, which is if customers can detect different approaches to script use. To answer the question, the authors conducted a video experiment of face-to-face service encounters in the hospitality industry. The results indicate that customers can detect degrees of script use across both standardized and customized encounter types. This work serves as initial empirical evidence that customers are indeed capable of detecting subtleties in scripting approaches in different service situations and supports that script level is an important service design construct for research. Furthermore, the authors highlight the use of a video experiment as an innovative methodology for assessing customer perceptions of intangible aspects to services in a realistic setting. One implication of this study is that managers need to assess the impact that different script levels have on customer perceptions of various service performance measures. Managers should also consider the effect script detection has on customer perceptions of the service experience and service brand to assure their script approach aligns with the organization’s service strategy.

Small-Sample Interval Estimation of Bernoulli and Poisson Parameters

Abstract To find an interval estimate for the parameter of the Bernoulli or the Poisson distribution usually requires the sample size to be large so that the normal approximation may be used. Small-sample intervals have been proposed earlier, but the procedures have required tables and are inexact. In this note we give a simple procedure for finding a small-sample confidence interval with minimum interval width. We also give a geometric interpretation to the minimization problem.

Statistical Process Monitoring of Customer Satisfaction Survey Data

Understanding and monitoring customer needs and satisfaction are crucial requirements of quality management. Many organizations collect frequent customer satisfaction data over time using customer surveys. A logical yet infrequently used means of monito..

An Interactive Excel VBA Example for Teaching Statistics Concepts

It is often challenging for business students to learn abstract statistical concepts and apply these concepts to their work. Three concepts in particular that we have found difficult to communicate effectively are the Central Limit Theorem, interval estimation and hypothesis testing. To improve the effectiveness of teaching these fundamental statistical concepts, we developed a Visual Basic for Applications VBA driven Excel spreadsheet that is built around one simple business scenario. The scenario involves setting the filling speed in a cereal filling plant. The faster the filling speed, the larger the variation in cereal box weights and the higher the chance of having an out-of-control filling process. On the other hand, the lower the filling speed, the less efficient the plant is at utilizing capacity. Through interactively finding the optimal filling speed, students are exposed to these key statistics concepts as well as random sampling techniques. Hence, we integrate the illustration of three important statistical concepts in one simple yet practical business scenario. Moreover, the Excel VBAdriven example demonstrates several Excel statistical formulae that are useful to business students. We conducted an in-class open-book quiz to two sections of professional MBA students to assess teaching effectiveness of this interactive example. The results showed that the scores of those using the interactive VBA demo were superior to those exposed to more traditional techniques at 10% significance level. A follow-up on-line feedback survey further supported the usage of the Excel VBA-driven example in enhancing student learning.