Natalie M. Scala

Managing Nuclear Spare Parts Inventories: A Data Driven Methodology

This paper presents a methodology for developing a spare parts inventory management system with a focus on the nuclear power sector. Often, demand for spare parts is highly intermittent and cannot be accurately forecasted through traditional methods. Examples include nuclear power generation equipment, ground space systems, and aircraft engine parts. We take a data-driven engineering management approach and develop a four-step methodology for spare parts management in such environments. These steps comprise an influence diagram for identifying relevant factors, weighting of influences through the analytic hierarchy process, grouping parts according to inventory criticality indices, and the development of base stock inventory policies for each group. This approach allows the system to be actively managed within a continuous improvement framework through employee engagement and input, and mathematical assumptions are not made in the models. To our knowledge, no such integrated, comprehensive methodology for spare parts has been developed. The techniques employed in this research can be effectively used together to holistically manage the entire spare parts process, or they may be used separately to manage portions of the process. This paper provides an overview of the methodology, and the entire approach is illustrated via a test bed nuclear power generation facility.

A Value Model for Asset Tracking Technology to Support Naval Sea-Based Resupply

Abstract A value model is developed for military logistics that fulfill emergent requests for tailored resupply packages from the sea. Asset tracking technologies, including radio frequency identification, barcoding, internal positioning systems (IPS), and camera-aided technology, are considered as alternatives to a multi-objective decision model. Model measures include registration of inventory in the system, stowage factor enablement, storage location precision, retrieval identification accuracy, system compatibility, and security. The decision model is presented using insights from subject matter experts. Given the requirements of selective offloading in dense storage environments, IPS is the preferred asset tracking technology. Sensitivity analysis and recommendations for engineering managers are provided.

Eliminating the Weakest Link Approach to Army Unit Readiness

One of the most difficult measurements to obtain with some level of accuracy is military readiness. While a multitude of factors exist that affect the ability of a unit to achieve success in mission, an accurate assessment of readiness is crucial and drives federal funding, defense policy, and deployment decisions. The current readiness metric for the U.S. Army statically assesses units on personnel, equipment on hand, equipment readiness/serviceability, and unit training proficiency using a weakest-link approach. This leads to reporting challenges and the tendency for commanders to subjectively upgrade their units’ assessments. This research proposes a metric that evaluates units with greater precision, flexibility, and robustness. By taking a decision analysis approach and using desirability functions, we are able to measure readiness based on a set of priorities, adapting for type of mission and unit. We test our metric using notional case studies and discuss extensions to other branches of the U.S. mi...

Analyzing supplier quality management practices in the construction industry

ABSTRACT Supplier quality management (SQM) is an important function in the construction industry. Many construction organizations place high importance on using quantitative analyses to select effective SQM practices that ensure that materials, assemblies, and fabricated equipment for the construction project are within quality specifications. However, traditional quantitative analysis methods may be limited because the process of acquiring enough data to conduct the analyses is time consuming and costly. This article discusses the use of principal component analysis (PCA) to analyze a number of SQM practices from construction organizations known for their effective SQM. PCA is useful in this study because the data available for analysis are small in size and multivariate. SQM practices are discussed extensively and validated with subject matter experts (SMEs) using the analytical hierarchy process (AHP). We show that suppliers work observation, supplier performance rating, inspection effort tracking, and inspection and testing plans are important practices for SQM. We propose a quantitative methodology that can be used by quality engineers to analyze small sample size data. The research also describes how AHP, an analysis method based on expert judgment, can be used to validate and support the conclusions drawn from small sample size analyses. Identification of important SQM practices can benefit construction professionals with limited resources.