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The purpose of this paper is to explore the mediating effect of supply chain risk management integration (RMI) on the relationship between supply chain logistics performance (LP) and supply chain cost performance (CP), as well as on the relationship between LP and supply chain service performance (SP). The impact of CP and SP on overall firm performance (FP) is also explored. ISO 9001-certified firms and non-certified firms are assessed to determine whether superior risk-based thinking, as required in the latest ISO 9001 standard, has a positive impact on the different relationships.

A theoretical model is developed and tested based on the participation of 140 supply chain managers. The proposed structural equation model positively relates LP, RMI, CP and SP. RMI is positively linked to CP and SP, while CP and SP are positively related to overall FP. Two subsamples (a group of 63 ISO 9001-certified firms and a group of 77 non-certified firms) are used to evaluate the model.

For certified and non-certified firms, LP is positively related to RMI, CP and SP, and SP and CP are positively related to FP. However, for certified firms, RMI partially mediates the relationship of LP with both CP and SP, while for non-certified firms, RMI does not mediate these relationships. The findings suggest that ISO 9001-certified firms are able to leverage RMI efforts to impact positively on supply chain performance, whereas non-certified firms are not.

The study findings are based on the perceptions of managers. Even though the majority of the 63 certified firms included in this study were ISO 9001:2015 certified, the model results do not differentiate between companies certified to the 2008 version of the standard and the 2015 version (which specifically requires demonstration of risk-based thinking).

This study suggests that ISO 9001 provides a framework for risk management processes and collaboration with supply chain partners to positively impact the relationship of LP with cost and SP.

This is one of the first studies to characterize the benefits of using a structured approach for risk-based thinking that is associated with ISO 9001.

Industrial vending solutions are unique in that they represent a very specific form of vendor-managed inventory (VMI). The purpose of this paper is to investigate performance outcomes associated with industrial vending implementation, a topic that has been largely ignored by the academic community.

A survey instrument was developed from earlier work on VMI success. Structural equation modeling is used to identify relationships between three enablers (information exchange, information quality, and relationship quality), perceived vending system implementation success, and three outcomes (cost benefits, customer service benefits, and inventory benefits).

Statistical outcomes demonstrate support for the benefits arising from successful vending system implementation. This study demonstrates that industrial vending implementation success is strongly tied to the amount and quality of the information shared between the relationship partners.

Successful industrial vending implementation results in improved inventory control, increased levels of customer service, and tighter cost control. This study provides supply chain managers with current findings, which should aid them in evaluating their current and proposed vending solutions.

Although VMI has been studied in the past, little work has been conducted on industrial vending as a specific form of VMI. This is the first study to explore industrial vending from the viewpoint of VMI implementation and performance. Empirically tested study results that are grounded in transaction cost theory confirm a series of performance outcomes of industrial vending from a buyer’s perspective as well as a number of enablers for successful industrial vending implementation.

This study develops and tests a model exploring the relationship between supply chain (SC) counterfeit risk management and performance in the healthcare supply chain (HCSC).

In the proposed theoretical model, HCSC counterfeit risk management is characterized by HCSC counterfeit risk orientation (HCRO), HCSC counterfeit risk mitigation (HCRM) and HCSC risk management integration (HRMI), while performance is represented by healthcare logistics performance (HLP) and healthcare organization overall performance (HOP). Partial least squares structural equation modeling (PLS-SEM) and survey data from 55 HCSC managers are used to test the research hypotheses.

HCRO has a significant positive effect on HCRM, while HCRM has a positive impact on HRMI. With respect to HLP, HCRM has a nonsignificant effect, while HRMI has a significant impact, thus confirming the important mediating role of HRMI. Finally, HLP has a significant positive effect on the overall performance of healthcare organizations.

All study participants were from the United States, limiting the generalizability of the study findings to different countries or regions. The sample size employed in the study did not allow the authors to distinguish among the different types of healthcare organizations.

This study delineates between a healthcare organization's philosophy toward counterfeiting risks vs actions taken to eliminate or reduce the impact of counterfeiting on the HCSC. By offering firm-level guidance for managers, this study informs healthcare organizations about addressing the challenge of counterfeiting in the HCSC.

The purpose of this paper is to adopt a contingency theory from a quality perspective to develop a model for assessing the impact of counterfeit prevention efforts on supply chain (SC) performance.

Based on the participation of 140 managers across ten industry sectors, a theoretical model is proposed and structural equation modeling is used to examine the relationships among SC risk management integration, SC counterfeit risk orientation (CRO), SC counterfeit risk mitigation (CRM), SC metric consistency (MC) and SC performance (service and cost benefits).

Findings suggest that firms with greater SC risk management integration have a stronger orientation toward counterfeit risk, greater maturity in CRM, more consistent SC metrics and better SC performance outcomes. CRO alone was not found to significantly improve SC MC.

Results are based on managerial perceptions of SC counterfeit risk and performance metrics. Survey respondents were predominantly from the same country (the USA).

The paper represents a potential quality management framework for SC risk management, in the context of counterfeiting that includes a contingency perspective.

The study advances knowledge of how firms may address the challenging issue of counterfeiting in the SC. Empirical findings offer a firm-level quality management framework for managerial decision making in the context of counterfeiting.

The purpose of this paper is to explore the nature of ISO 9001 certification and diffusion in the USA in relation to organizational supply chain position, quality maturity, and supplier quality maturity.

Data for comparison in this study comes from an e-mail survey questionnaire of supply chain professionals across the USA. A pilot survey was initially e-mailed to 100 supply chain professionals and preliminary data from 27 respondents were used to revise the questionnaire. Respondents to the revised questionnaire returned 565 e-mailed questionnaires out of 2,924 active e-mail addresses.

The results suggest that companies adopting ISO 9001 are likely doing so as part of an overall quality management program more so than from direct market pressure. It appears that ISO 9001 certification rates differ by organizational position in the supply chain. ISO 9001 certified organizations are more likely to have quality programs of greater maturity and suppliers with greater quality maturity than organizations that are not ISO 9001 certified. Certified organizations are also more likely to use suppliers that are ISO 9001 capable or certified than non-ISO 9001 organizations.

Not all industry groups or supply chain positions were well-represented due to some small sub-group sizes.

The study of ISO 9001 certification in US organizations has been lacking in the literature. Given the weak adoption rate of ISO 9001 in the USA, this study helps characterize ISO 9001 certification from a supply chain perspective and explores possible reasons for low certification rates.

This research extends the knowledge of quality management in the supply chain by studying ISO 9001 certification in relation to supply chain position and differences in quality maturity between certified and non-certified organizations in the USA.

Demand seasonality in the U.S. Imported Beer industry is common. The financial cycles of the past decade brought some extreme fluctuations to industry demand, which was trending upward. This research extends previous work in this area by comparing seasonal forecasting models for two time periods: 1999–2007 and 1999–2012. The previous study (Kros & Keller, 2010) examined the 1999–2007 time frame while this study extends their model using the new data. Models are developed within Excel and include a simple yearly model, a semi-annual model, a quarterly model, and a monthly model. The results of the models are compared and a discussion of each model’s efficacy is provided. While, the models did do a good job forecasting U.S. Import Beer sales from 1999 to 2007 the economic downturn starting in 2007 was deleterious to some models continued efficacy. When the data from the downturn is accounted for it is concluded that the seasonal models presented are doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

The relationship between electricity demand and weather in the United States has been studied as of late due to increased demand, de-regulation, and new pricing models. The influence of weather or seasonality in energy consumption, particularly electricity demand, has been widely researched. A significant scientific interest in the seasonality of energy consumption has led to an important number of papers exploring the role of weather variability and change on energy consumption. Most of these papers model demand as a function of seasonal climate factors.

The goal of this research is a broad examination of monthly residential electricity demand for a region of the mid-Atlantic using Excel and step-wise regression. This is achieved by using a sequence of models built in Excel in which different patterns are gradually introduced in the estimations. Data over a seven-year period is utilized. A backward elimination step-wise regression analysis is employed to determine which independent variables best model the data. Initial independent variables included high monthly temperature, low monthly temperature, time, year, month, seasonal quarter, and introduction of a “green” tax credit for solar and wind energy.

Models for forecasting the electricity demand and the predictive power of these models is assessed. The work is organized as follows: Data description and the methodology, trend and the seasonality of electricity usage in the mid-Atlantic region, the predictive power and seasonality of the models, and main conclusions drawn from the study.

This chapter presents an Excel-based regression analysis to forecast seasonal demand for U.S. Imported Beer sales data. The following seasonal regression models are presented and interpreted including a simple yearly model, a quarterly model, a semi-annual model, and a monthly model. The results of the models are compared and a discussion of each model's efficacy is provided. The yearly model does the best at forecasting U.S. Import Beer sales. However, the yearly does not provide a window into shorter-term (i.e., monthly) forecasting periods and subsequent peaks and valleys in demand. Although the monthly seasonal regression model does not explain as much variance in the data as the yearly model it fits the actual data very well. The monthly model is considered a good forecasting model based on the significance of the regression statistics and low mean absolute percentage error. Therefore, it can be concluded that the monthly seasonal model presented is doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

The purpose of this paper is to examine differences among industry classes and supply chain positions in order to gain insight into quality management program maturity across industries and within supply chains.

Data for comparison in this study comes from an e-mail survey of professionals across the USA, employed primarily in sourcing or logistics (i.e. Institute for Supply Management (ISM) and Council for Supply Chain Management Professionals (CSCMP)).

This study found that quality maturity varies by industry class. While prior studies have found differences by industry class, they have been limited to at most three classes, while this study examined 17 classes. This study also examines quality maturity by supply chain position, with the finding that quality maturity differed by supply chain position depending on how position is defined. Questions are raised regarding the proper characterization of supply chain position.

The sample group represents members in only two professional groups, ISM and CSCMP. Not all industry groups or supply chain positions were well-represented due to some small sub-group sizes.

Quality program maturity is generally not uniform and there are potentially many opportunities for substantial improvement across various sectors by specific industry. Partnering with suppliers is a recommended approach for sectors lagging in quality maturity.

This research extends the examination of quality management practice in the supply chain by studying a large number of industry classes and supply chain positions and assesses differences in quality maturity across these classes and positions.

This chapter addresses quality management (QM) content on the process quality management (PQM) level in the high-technology industry of semiconductor manufacturing. Identifying critical components of a manufacturing or service process and improving them to ensure superior quality at economic costs is the overall goal of PQM. Deming was a prominent proponent of PQM as a means to optimize the performance of a product or process. In optimizing the performance of a product or process, good design practices require the evaluation of designs from a process perspective. Advanced design techniques, namely design of experiments (DOEs), are cornerstone to the optimization process, to design management, and in turn to PQM. This chapter investigates the use of DOEs in the manufacture of semiconductors. Specifically, two underlying assumptions impact operations managers using DOEs: solution differences/similarities in underlying DOE optimization methods and marginal rates of substitution. Perhaps unknown to the user, DOE optimization techniques carry strong assumptions regarding these characteristics. This chapter investigates two commonly used DOE optimization approaches applied to the operational control of semiconductor wafer production, and demonstrates that each method contains assumptions about these characteristics, which are not intuitively evident to a user.