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Most research on the process control charts concentrates on the economic design of its parameters. Reduction of variance as a control decision has not been researched. Presents a model for optimal decision on variance reduction and includes the rejection losses of the non‐conforming units increasing due to increased variance, Taguchi loss of the conforming units, and the cost of reducing variance. Optimal policies are derived analytically for uniform distribution and numerically for normal distribution. Applications of the model to the area of machine replacement and global manufacturing are suggested.

Means, medians and SD for available socio‐economic status (SES) black‐white differences are here substituted for those of IQ in a between‐groups model published by the author over a decade ago. The goodness of fit of the SES variables used is compared with that for the earlier IQ data. Even when SES variables are relatively successful this can be viewed as additional evidence of the importance of IQ differences to black‐white differences in delinquency.

The purpose of this paper is to examine the impact of supply chain lead‐time averages and variability on an organization's financial performance.

The “executive” list for manufacturers, consisting of 1,264 individuals of the Institute of Supply Management provided the study's sampling frame, with surveys sent to 402 firms and responses obtained from 210 firms. The empirical model is tested using LISREL.

The results show that as variance in supply chain lead‐times increases, the financial performance of the organization decreases. Of equal significance, the results show that average supply chain lead‐times have no direct impact on financial performance. The results also indicate that demand uncertainty associates with greater supply chain lead‐time variance and that production technology routineness associates with lower supply chain lead‐time variance. Product complexity and organizational size have no impact on supply chain lead‐time variance or supply chain lead‐time average.

The research is an initial effort to understand variance in supply chain systems. An ongoing challenge in this area is operationalization of measures and data collection techniques that go beyond a single firm and examine a network of organizations cooperating in a value‐added supply chain.

The results suggest that managing the variance in a supply chain system may be more important to an organization's financial performance than managing averages.

This is particularly significant since organizations often act contrary to these findings, focusing scarce resources on reducing average lead‐times rather than on reducing variability in supply chain lead‐times.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

Pilot upgrade training is critical to aircraft and passenger safety. This study aims to identify variances in the US Air Force C-130J pilot upgrade training based on geographic location and provide a model to enhance policy that will impact future pilot training efforts that lower cost and increase operator quality and proficiency.

This research employed a mixed-method approach. First, the authors collected data and analyzed 90 C-130J pilots' aviation records and then contextualized this analysis with interviews of experts. Finally, the authors present a modified version of Six Sigma's define–measure–analyze–improve–control (DMAIC) that identifies and reduces the variances in C-130J pilot training, translating into higher quality outcomes.

The results indicate significant statistical variances across geographically separated C-130J pilot training organizations. This leads some organizations to have higher proficiency levels in specific tasks and others with comparative deficiencies. Additionally, the data analysis in this study enabled a recommended number of flight hours in several distinct categories that should be obtained before upgrading a pilot to aircraft commander to enhance standards.

This research was limited to C-130J pilot upgrades, but these results can be implemented within any field that utilizes hours as a measure of experience. Implications from this research can be employed to scope policy that will influence pilot training requirements across all airframes in civilian and military aviation.

This research proposes a process improvement methodology that could be immediately implemented within the C-130J community and, more importantly, in any upgrade training where humans advance into higher echelons of a profession.

Recently, there has been a lot of interest in the economics of quality control. Many researchers have considered the problem of determining the optimal target mean for a process, but almost all of them have assumed that the process variance is fixed and known in advance. The problem of simultaneously determining the optimal target mean and target variance for a process is considered. This might result in a reduction in variability and in the total cost of the production process. A reduction in variability upholds the modern concept of Taguchi’s loss function, which states that any deviation from the target value incurs economic loss, even when the quality characteristic lies within the specification limits. Taguchi’s loss function is incorporated to extend this study further to jointly determine the optimal target mean and variance.

The purpose of this paper is to present a new optimisation approach for product variance from the purchasing perspective.

The research is based on a case study of a collaboration with a global acting automotive Tier 1 supplier, who produces steering systems for cars and commercial vehicles. A total of 116 different variants of three components of a car automotive steering system were analysed and evaluated. The data were gathered from 13 sub-suppliers for three different types of a steering system.

Unnecessary time, money, quality and technology can be saved through a greater understanding of such product variances. The results of the case study lead to a general method to optimise existing product variance and present cost improvements and a new key performance indicator to manage product variance out of the purchasing department.

The research is based on a purchasing case study at a Tier 1 supplier of the automotive branch. The approach can be used for other company departments, e.g. logistics and for different industries than automotive.

A company can be successful and competitive when it meets the customer needs with a maximum on satisfaction without generating of waste. Unnecessary existing product variance is a kind of waste. The insights of this paper support the operative user and the strategic company management to reduce and improve unnecessary variance in different sections.

The structured analysis of product variance from the purchasing perspective and the key performance indicator “variance share” are new to company management. The research focuses on the management of existing variance out of the purchasing department which is a segment that has received limited academic attention in research to date.

The purpose of this paper is to determine and contrast the risk mitigating effectiveness from optimal multiproduct time-varying hedge ratios, applied to the margin of a cattle feedlot operation, over single commodity time-varying and naive hedge ratios.

A parsimonious regime-switching dynamic correlations (RSDC) model is estimated in two-stages, where the dynamic correlations among prices of numerous commodities vary proportionally between two different regimes/levels. This property simplifies estimation methods for a large number of parameters involved.

There is significant evidence that resulting simultaneous correlations among the prices (spot and futures) for each commodity attain different levels along the time-series. Second, for in and out-of-sample data there is a substantial reduction in the operation's margin variance provided from both multiproduct and single time-varying optimal hedge ratios over naive hedge ratios. Lastly, risk mitigation is attained at a lower cost given that average optimal multiproduct and single time-varying hedge ratios obtained for corn, feeder cattle and live cattle are significantly below the naive full hedge ratio.

The application studied is limited in that once a hedge position has been set at a particular period, it is not possible to modify or update at a subsequent period.

Agricultural producers, specifically cattle feeders, may profit from a tool using improved techniques to determine hedge ratios by considering a larger amount of up-to-date information. Moreover, these agents may apply hedge ratios significantly lower than one and thus mitigate risk at lower costs.

Feedlot operators will benefit from the potential implementation of this parsimonious RSDC model for their hedging operations, as it provides average optimal hedge ratios significantly lower than one and sizeable advantages in margin risk mitigation.

Midwest Independent Transmission System Operator, Inc. (MISO) is a nonprofit regional transmission organization (RTO) that oversees electricity production and transmission across 13 states and 1 Canadian province. MISO also operates an electronic exchange for buying and selling electricity for each of its five regional hubs.

MISO oversees two types of markets. The forward market, which is referred to as the day-ahead (DA) market, allows market participants to place demand bids and supply offers on electricity to be delivered at a specified hour the following day. The equilibrium price, known as the locational marginal price (LMP), is determined by MISO after receiving sale offers and purchase bids from market participants. MISO also coordinates a spot market, which is known as the real-time (RT) market. Traders in the RT market must submit bids and offers by 30minutes prior to the hour for which the trade will be executed. After receiving purchase and sale offers for a given hour in the RT market, MISO then determines the LMP for that particular hour.

The existence of the DA and RT markets allows producers and retailers to hedge against the large fluctuations that are common in electricity prices. Hedge ratios on the MISO exchange are estimated using various techniques. No hedge ratio technique examined consistently outperforms the unhedged portfolio in terms of variance reduction. Consequently, none of the hedge ratio methods in this study meet the general interpretation of FASB guidelines for a highly effective hedge.

The purpose of this paper is to examine the potential gains in hedge ratio calculation for agricultural commodities by incorporating market linkages and prices of related commodities into the hedge ratio estimation process.

A vector autoregressive multivariate generalized autoregressive conditional heteroskedasticity (VAR‐MGARCH) model is used to construct a time‐varying correlation matrix for commodity prices across linked markets and across linked commodities. The MGARCH model is estimated using a two‐step approach, which allows for a large system of related prices to be estimated.

In‐sample and out‐of‐sample portfolio variance comparison among no hedge, bivariate GARCH, and MGARCH models indicates that hedge ratios estimated using the MGARCH approach reduce agricultural producers' and commercial consumers' risks in futures market participation.

The application is limited to an examination of Montana wheat markets.

Agricultural producers who use futures markets to reduce market risk will have a better method for determining hedging positions, because MGARCH estimated hedge ratios incorporate more information than hedge ratios estimated using existing practices.

Portfolio variance reduction is analogous to utility improvement for agricultural producers. More efficient hedging strategies can lead to better implementation of futures markets and increased social welfare.

This research substantially extends current literature on agricultural hedge strategies by illustrating the advantages of using an hedge ratio estimation approach that incorporates important information about prices at linked markets and prices of other commodities. Providing evidence that market portfolio variance can be lowered using the multivariate estimation approach, the research offers commercial agricultural producers and consumers a practical tool for improving futures market strategies.