Developing Talent from a Supply-Demand Perspective: An Optimization Model for Managers

While executives emphasize that human resources (HR) are a firm's biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem

A Multi-Phase Approach for Product Hierarchy Forecasting in Supply Chain Management: Application to MonarchFx Inc

Hierarchical time series demands exist in many industries and are often associated with the product, time frame, or geographic aggregations. Traditionally, these hierarchies have been forecasted using top-down, bottom-up, or middle-out approaches. The question we aim to answer is how to utilize child-level forecasts to improve parent-level forecasts in a hierarchical supply chain. Improved forecasts can be used to considerably reduce logistics costs, especially in e-commerce. We propose a novel multi-phase hierarchical (MPH) approach. Our method involves forecasting each series in the hierarchy independently using machine learning models, then combining all forecasts to allow a second phase model estimation at the parent level. Sales data from MonarchFx Inc. (a logistics solutions provider) is used to evaluate our approach and compare it to bottom-up and top-down methods. Our results demonstrate an 82-90% improvement in forecast accuracy using the proposed approach. Using the proposed method, supply chain planners can derive more accurate forecasting models to exploit the benefit of multivariate data.Comment: 25 pages, 2 figures, 8 table

Supply Chain Management: Supplier Performance and Firm Performance

This research examines the relationship between supply chain management (SCM) practices, supplier performance, and company performance. The results provide empirical evidence that selected purchasing practices and customer relation practices are strongly ssociated with the perceived financial and market success of firms responding to the survey

A new acquisition model for the next disaster: overcoming disaster federalism issues through effective utilization of the Strategic National Stockpile

Using primary data collected from interviews with federal and state government officials and secondary data related to PPE distribution and state healthcare statistics, we discovered evidence that the use of the Strategic National Stockpile (SNS) to distribute personal protective equipment to state and local agencies in need during the height of COVID-19 was indeed poorly designed to cope with the COVID-19 emergency, leaving many states with shortages of badly needed medical supplies. As a result, many states struggled to organize an uncoordinated procurement response – which we suggest is due to federalism issues. To overcome federalism challenges and increase future disaster preparedness, we recommend four necessary reforms to the SNS that include 1) the incorporation of uncompensated industry experts into SNS administration, 2) the provision of an emergency production board for times of crisis, 3) elevated political leadership for the SNS, 4) improvement of federal-state supply chain governance

Tools and Techniques of Quality Management: An Empirical Investigation of Their Impact on Performance

An investigation of quality management at an operational rather than a strategic level is described. Using a survey of senior quality personnel, data were collected on four aspects of quality: management; quality tools; documentation; and the dimensions of quality that companies measure. Regression analysis confirms suggestions in the literature that company performance is positively affected by a culture in which quality is ingrained. Moreover, it identifies positive relationships between several widely used operational practices and company performance

Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making

A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). According to the experimental results, our expert model can detect 96.4% of failures within 50% of the sample. This suggests that our method is effective at diagnosing failures in railcars fleet.Comment: 21 pages, 7 figures, 3 table

The Impact of Opioid Prescribing Limits on Drug Usage in South Carolina: A Novel Geospatial and Time Series Data Analysis

Background: To curb the opioid epidemic, legislation regulating the amount of opioid prescriptions covered by Medicaid (Title XIX of the Social Security Act Medical Assistance Program) came into effect in May 2018 in South Carolina. Methods: We employ a classification system based on distance and disparity between dispensers, prescribers, and patients and conduct an ARIMA analysis on each class and without any class to examine the effect of the legislation on opioid prescriptions, considering secular trends and autocorrelation. The study also compares trends in benzodiazepine prescriptions as a control. Results: The proposed classification system clusters each transaction into 16 groups based on the location of the stakeholders. These categories were found to have different prescription volume levels, with the highest group averaging 96.50 in daily MME (95% CI [63.43, 99.57]) and the lowest 37.78 (95% CI [37.38,38.18]). The ARIMA models show a decrease in overall prescription volume from 53.68 (95% CI [53.33,54.02]) to 51.09 (95% CI [50.74,51.44]) and varying impact across the different classes. Conclusion: Policy was effective in reducing opioid prescription volume overall. However, the volume of prescriptions filled where the prescribing doctor is located more than 1000 miles away from the patient went up, hinting at the possibility of doctor shopping.Comment: 15 pages, 4 figures, 4 table

Assessing adoption factors for additive manufacturing: insights from case studies

Background: Research on Additive Manufacturing [AM] provides few guidelines for successful adoption of the technology in different market environments. This paper seeks to address this gap by developing a framework that suggests market attributes for which the technology will successfully meet a need. We rely on classical technology adoption theory to evaluate the challenges and opportunities proffered by AM. Methods: We apply a framework of technology adoption and assess these parameters using seven case studies of businesses that have successfully adopted AM technology. Results: We find that successful business adoption is highly associated with the relative advantage of AM to rapidly deliver customized products targeted to niche market opportunities. Conclusions: Our findings provide a decision framework for AM equipment manufacturers to employ when evaluating AM technology across various market environments. All five adoption characteristics were found to be important however, the primary decision criterion is based on the relative advantage of AM over other, traditional, technologies. From a practitioner perspective, our research highlights the importance of AM in attaining a competitive advantage through responsive, customized production which can address the needs of niche markets