Search results

The purpose of this article is to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the analysis of variance (ANOVA) application after the preliminary test on the model specification.

A new approach is proposed to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the ANOVA application. First, we determine this relationship from the general perspective of Six Sigma methodology under the normality assumption. Then, the approach is extended to a balanced two-stage nested design with a random effects model in which a preliminary test is used to fix the main test statistic.

The features of mean-shifted and inflated (but centred) processes with the same specification limits from the perspective of Six Sigma are studied. The shift and inflation coefficients are derived for the two-stage balanced ANOVA model. We obtained good predictions for the process shift, given the inflation coefficient, which has been demonstrated using numerical results and applied to case studies. It is understood that the proposed method may be used as a tool to obtain an efficient variance estimator under mean shift.

In this work, as a new research approach, we studied the link between mean shift and inflation coefficients when the underlying null hypothesis is rejected in the ANOVA. Derivations for these coefficients are presented. The results when the null hypothesis is accepted are also studied. This needs the help of preliminary tests to decide on the model assumptions, and hence the researchers are expected to be familiar with the application of preliminary tests.

After studying the proposed approach with extensive numerical results, we have provided two practical examples that demonstrate the significance of the approach for real-time practitioners. The practitioners are expected to take additional care before deciding on the model assumptions by applying preliminary tests.

The proposed approach is original in the sense that there have been no similar approaches existing in the literature that combine Six Sigma and preliminary tests in ANOVA applications.

The purpose of this paper is to propose a new approach in which cost‐based process weights are used to determine a unique weighted‐defects per million opportunity (DPMO) and its corresponding overall sigma level in order to classify an organization as either “world‐class,” “industry average” or “non‐competitive.”

In order to achieve this objective, the proposed approach uses both internal and external performances of the products and processes in terms of costs involved to determine cost‐based process weights. These weights are then incorporated into the respective DPMOs for computing weighted‐DPMOs. Finally, a unique weighted‐DPMO and its corresponding sigma level are found.

The proposed method is a new one and it involves various costs for determining process weights. The findings reveal that the weight‐based overall sigma level is more realistic than the one that is calculated without weights. Further, the results of this study could provide interesting feedback to six‐sigma practitioners, as they are particular about DPMOs and return on investments in project implementations.

The results of this paper are based on the weights of respective processes and their products that are calculated using various cost aspects. Determining such weights by means of any other process and product factors incorporating the effects of various marketing activities, if any, could extend its generality and fulfil the gap.

The proposed method is simple to implement and the required data can be collected without any additional commitments. Also, it is more generic so that it can be adapted by organizations of any nature. This paper recommends change in the practice from simply using the DPMOs with equal importance to using the weight‐based DPMOs for evaluating overall sigma level (performance) of an organization.

The proposed approach would have a high value among six‐sigma quality practitioners and researchers as it provides a new and more realistic measure for overall performance of an organization during the evaluation process.

The purpose of this paper is to develop an innovative and quite new Six Sigma quality control (SSQC) chart for the benefit of Six Sigma practitioners. A step-by-step procedure for the construction of the chart is also given.

Under the assumption of normality, in this paper, the construction of SSQC chart is proposed in which the population mean and standard deviation are drawn from the process specification from the perspective of Six Sigma quality (SSQ). In this chart, the concept of target range is used to restrict the shift in the process within plus or minus 1.5 times of standard deviation. This control chart is useful in monitoring the process to ensure that the process is well maintained within the specification limits with minimum variation (shift).

A step-by-step procedure is given for the construction of the proposed SSQC chart. It can be easily understood and its application is also simple for Six Sigma practitioners. The proposed chart suggests for timely improvements in process mean and variation. The illustrative example shows the improved performance of the proposed new procedure.

The proposed approach assumes a normal population described by the known specification of the process/product characteristics though it may not be in all cases. This may call for a thorough study of the population before applying the chart.

The proposed SSQC chart is an innovative approach and is quite new for the practitioners. The paper assumes that the population standard deviation is known and is drawn from the specification of the process/product characteristics. The proposed chart helps in fine-tuning the process mean and bringing the process standard deviation to the satisfactory level from the perspective of SSQ.

The paper is the first of its kind. It is innovative and quite new to the Six Sigma practitioners who will find its application interesting.

While Six Sigma metrics have been studied by researchers in detail for normal distribution-based data, in this paper, we have attempted to study the Six Sigma metrics for two-parameter Weibull distribution that is useful in many life test data analyses.

In the theory of Six Sigma, most of the processes are assumed normal and Six Sigma metrics are determined for such a process of interest. In reliability studies non-normal distributions are more appropriate for life tests. In this paper, a theoretical procedure is developed for determining Six Sigma metrics when the underlying process follows two-parameter Weibull distribution. Numerical evaluations are also considered to study the proposed method.

In this paper, by matching the probabilities under different normal process-based sigma quality levels (SQLs), we first determined the Six Sigma specification limits (Lower and Upper Six Sigma Limits- LSSL and USSL) for the two-parameter Weibull distribution by setting different values for the shape parameter and the scaling parameter. Then, the lower SQL (LSQL) and upper SQL (USQL) values are obtained for the Weibull distribution with centered and shifted cases. We presented numerical results for Six Sigma metrics of Weibull distribution with different parameter settings. We also simulated a set of 1,000 values from this Weibull distribution for both centered and shifted cases to evaluate the Six Sigma performance metrics. It is found that the SQLs under two-parameter Weibull distribution are slightly lesser than those when the process is assumed normal.

The theoretical approach proposed for determining Six Sigma metrics for Weibull distribution is new to the Six Sigma Quality practitioners who commonly deal with normal process or normal approximation to non-normal processes. The procedure developed here is, in fact, used to first determine LSSL and USSL followed by which LSQL and USQL are obtained. This in turn has helped to compute the Six Sigma metrics such as defects per million opportunities (DPMOs) and the parts that are extremely good per million opportunities (EGPMOs) under two-parameter Weibull distribution for lower-the-better (LTB) and higher-the-better (HTB) quality characteristics. We believe that this approach is quite new to the practitioners, and it is not only useful to the practitioners but will also serve to motivate the researchers to do more work in this field of research.

The purpose of this paper is to propose an approach for studying the Six Sigma metrics when the underlying distribution is lognormal.

The Six Sigma metrics are commonly available for normal processes that are run in the long run. However, there are situations in reliability studies where non-normal distributions are more appropriate for life tests. In this paper, Six Sigma metrics are obtained for lognormal distribution.

In this paper, unlike the normal process, for lognormal distribution, there are unequal tail probabilities. Hence, the sigma levels are not the same for left-tail and right-tail defects per million opportunities (DPMO). Also, in life tests, while left-tail probability is related to DPMO, the right tail is considered as extremely good PMO. This aspect is introduced and based on which the sigma levels are determined for different parameter settings and left- and right-tail probability combinations. Examples are given to illustrate the proposed approach.

Though Six Sigma metrics have been developed based on a normality assumption, there have been no studies for determining the Six Sigma metrics for non-normal processes, particularly for life test distributions in reliability studies. The Six Sigma metrics developed here for lognormal distribution is new to the practitioners, and this will motivate the researchers to do more work in this field of research.

The purpose of this paper is to propose a chi‐square‐based heuristic statistical procedure that considers the most recent bid prices of self and competitors in proposing an optimum winning bid price. The use of simple regression method to predict the adjusted optimum bid price is also considered.

In order to achieve this objective, the proposed approach uses past bid prices of the case company and its competitors. A heuristic chi‐square statistical procedure is then developed to obtain the expected bid prices. The absolute difference between the prices is obtained and the same is adjusted to get the optimal bid price for the forthcoming bid.

It is demonstrated that the proposed heuristic chi‐square‐based approach is superior to that of the regression method, which is otherwise in practice in the case company, since the latter still gets support from the former. Further, the paper throws light on how a familiar statistical method could be conveniently but effectively applied as a tactic of marketing management.

The method is developed based on the experience of the case company in bid participation. Therefore, the study on the performance of the application of the proposed method from the perspective of different types of companies can be taken up as a future work. Also, if appropriate, the time and other factors may be considered as other independent factors along with the tender quantity in predicting the tender price.

The proposed heuristic chi‐square procedure is simple to implement as the required data on bid prices are usually available once the bids are open. The case study reveals how the method can be successfully implemented to win bids in a competitive market place. Also, the procedure is more generic so that it can be adapted as it is or with slight modification as desired by companies of any nature participating in bids.

The proposed approach would have a high value among manufacturing firms and marketing managers who participate in tenders with an aim to increase sales turnover which in turn will increase profit. The paper is unique of its kind and will help researchers to think of either extending or proposing such new approaches as they need.

The case presents a customer's experience during the purchase of a pair of shoes in an upmarket retail outlet of Bharat Footwear Limited (BFL), in Ahmedabad, wherein he was offered a discount coupon meant for shareholders, at a shared consideration of 60 (buyer) and 40 (agent). The customer needs to decide on the acceptance or otherwise of the deal. Adequate data is provided to discuss this central issue in a business context along with an estimate of the secondary market of discount coupons. The analysis of the case leads to a debate on whether the discount policy should be continued or otherwise. While several sharing arrangements for the discount amount are considered, the key to the situation is not such arrangements but a robust system in dispensing these coupons.

The main purpose of this paper is to propose a new approach to determine the project activities weight factors using data envelopment analysis. Afterward, the model is applied in Mobarkeh Steel Company as a case study. Accordingly, the project schedule and plans can be written on the basis of the gained weight factors.

This study proposed an approach to determine the weights of activities using Data Envelopment Analysis. This approach consists of four phases. In the first phase, project activities are extracted based on the work breakdown structure. In the second phase, the parameters affecting the importance of activities are determined through a review of the related literature and based on the experts' opinions. In the third phase, the proper data envelopment analysis model is chosen and the inputs and outputs are signified. Then, the activities' weights are determined based on the efficiency numbers. Finally, the model is solved for the case of Isfahan Mobarakeh Steel Company.

The proposed method aimed to calculate the project activities weight factor. Thus, influential parameters on project activities importance include activity duration, activity cost, activity importance which includes successors and predecessors, activity difficulty which includes skill related (education and experience), safety, communication rate, intellectual effort, physical effort, unfavorable work conditions and work related hazards, have been recognized. Then, Projects' data were extracted from the organizational expert's opinions and recorded data in documents. Thereupon, applying DEA, the activities weight factor were calculated based on the efficiency numbers. The results show that the model is applicable and has promising benefits in real-world problems.

Planning is one the most fundamental steps of project management. The ever-growing business environment demands for more complex projects with larger number of activities wants more efficient project managers. Organizational resources are limited; therefore, activities planning is a critical from the perspectives of both managers and researchers. Knowing the importance of the activities can help to manage activities more efficient and to allocate time, budget, cost and other resources more accurate. Different elements such as cost, time, complexity, and difficulty can affect the activity weight factor. In this study, the proposed approach aims to determine the weights of activities using Data Envelopment Analysis.

In a recent study, Liano, Liano and Manakyan (1999) conclude that the pattern of day‐of‐the‐week effects in stock indices differs between Democratic administrations and Republican administrations. Specifically, the weekend effect is more pronounced during Republican administrations. This paper re‐examines this issue. It incorporates into the analysis the implications of Connolly's (1989) findings that the weekend effect has disappeared since 1975. We confirm Connolly's results. However, contrary to Liano et al. (1999), we conclude that day‐of‐the‐week effects are not significantly moderated by the political administration.

Recent research has focused on the influence of the political party of the US President on stock indices. This current paper extends this area of research by including the influence of the political party that holds the majority in Congress into the research design. The results do not support the hypothesis that there is a statistically significant relationship between the political party of the president and the return on the stock index. However, the political party with the majority in Congress is significantly related to the return on the index. The returns during Republican controlled Congresses are higher than returns during Democrat controlled Congresses.