Teaching operations management using a 'pseudo'-scientific approach

The purpose of this paper is to explore a theory led approach to teaching operations and supply chain management that has emerged from the analysis of seminal operations management developments and case research. This research identified common operations construct relationships encompassing variation, uncertainty, buffering mechanisms and trade-offs which are used to provide a common basis for explaining these developments, linking established theory with current professional practice. The construct relationships are further shown to comprise three distinct but coordinated strategies that provide a useful framework for case evaluatio

The Applicability of Transaction Costs Economics to Vertical Integration Decision: Evidences from a Brazilian Beef Processor

This article aims to explore the vertical integration decision of a beef processor from an integrated approach operations strategy and transaction costs economic theory. In this research, vertical integration as a structural decision of operations strategy determined by the occurrence of transaction costs. The paper presents one case study carried out in one beef Processor Company which illustrates the main theoretical assumptions. The results suggest that transaction costs economics helps to identify key points of major strategic decisions on vertical integration due to its behavioural perspective, reducing the effect of uncertainty and asset specificity of this decision. At the end of the paper, future research is suggested.vertical integration, operations strategy, transaction costs economics, Agribusiness, Agricultural Finance, Industrial Organization,

An optimal-control based integrated model of supply chain

Problems of supply chain scheduling are challenged by high complexity, combination of continuous and discrete processes, integrated production and transportation operations as well as dynamics and resulting requirements for adaptability and stability analysis. A possibility to address the above-named issues opens modern control theory and optimal program control in particular. Based on a combination of fundamental results of modern optimal program control theory and operations research, an original approach to supply chain scheduling is developed in order to answer the challenges of complexity, dynamics, uncertainty, and adaptivity. Supply chain schedule generation is represented as an optimal program control problem in combination with mathematical programming and interpreted as a dynamic process of operations control within an adaptive framework. The calculation procedure is based on applying Pontryagin’s maximum principle and the resulting essential reduction of problem dimensionality that is under solution at each instant of time. With the developed model, important categories of supply chain analysis such as stability and adaptability can be taken into consideration. Besides, the dimensionality of operations research-based problems can be relieved with the help of distributing model elements between an operations research (static aspects) and a control (dynamic aspects) model. In addition, operations control and flow control models are integrated and applicable for both discrete and continuous processes.supply chain, model of supply chain scheduling, optimal program control theory, Pontryagin’s maximum principle, operations research model,

FINANCIAL RISK IN COTTON PRODUCTION

Risk analysis continues to emphasize price and yield variability as the principal components of the decision-maker's risk environment. This research demonstrates the relative importance of financial risk for a representative cotton farm in Arizona. For highly leveraged operations, financial risk may account for 70 percent of the total risk faced by the producer. Implications for future risk analysis are discussed in light of these findings.Crop Production/Industries, Risk and Uncertainty,

Quantile Hedging in a Semi-Static Market with Model Uncertainty

With model uncertainty characterized by a convex, possibly non-dominated set of probability measures, the agent minimizes the cost of hedging a path dependent contingent claim with given expected success ratio, in a discrete-time, semi-static market of stocks and options. Based on duality results which link quantile hedging to a randomized composite hypothesis test, an arbitrage-free discretization of the market is proposed as an approximation. The discretized market has a dominating measure, which guarantees the existence of the optimal hedging strategy and helps numerical calculation of the quantile hedging price. As the discretization becomes finer, the approximate quantile hedging price converges and the hedging strategy is asymptotically optimal in the original market.Comment: Final version. To appear in the Mathematical Methods of Operations Research. Keywords: Quantile hedging, expected success ratio, model uncertainty, semi-static hedging, Neyman-Pearson Lemm

Modeling Coordinate Measuring Machine Scanning Operations

International standards require that dimensional inspection operations include an assessment of measurement uncertainty. Scanning coordinate measuring machines (CMMs) are frequently used to measure part surfaces and features, and there is a continuing need to improve their performance for high precision measurement applications. This research provides a mechanism for minimizing the uncertainty of measurements made with a CMM in scanning mode by developing a model of CMM scanning that allows selection of optimum scanning parameters. The method for selecting scanning parameters is based on models developed from measurements of a ring with a constant five micrometer amplitude swept sine wave machined on the inner and outer diameters. The inputs to the model are the scanning force, scanning speed, low-pass filter cut-off frequency, rotary table action, probe tip diameter, and ring orientation. The methods used in this work are based on techniques developed for point-to-point probing. The first phase of research develops a calibration method for the ring artifact and determines the calibration measurement uncertainty. The second phase develops models of CMM scanning operations based on measurements of the wavy ring. The final phase generates a measurement protocol to select scanning parameters based on these models. The primary significance of this research is that it provides a method to develop and validate a model of probe/workpiece interaction for a scanning CMM. Additionally, a method is provided to select the scanning parameters such as probe tip diameter, filters, scanning speed, and probing force to minimize measurement uncertainty. Finally, this work establishes a framework for future modeling of precision scanning operations. The methodology used is applicable to other precision metrology applications. This work will reduce uncertainty in scanning measurements and will minimize the number of measurement operations required to measure part features as well as surface texture. Therefore, this research has extended the capabilities of CMMs

RISK PERCEPTIONS AND MANAGEMENT RESPONSES: PRODUCER-GENERATED HYPOTHESES FOR RISK MODELING

Farm level risk analyses have used price and yield variability almost exclusively to represent risk. Results from a survey of 149 agricultural producers in 12 states indicate that producers consider a broader range of sources of variability in their operations. Significant differences exist among categories with respect to the importance of the sources of variability in crop and livestock production. Producers also used a variety of management responses to variability. There were significant difference among categories in the importance given to particular responses and their use of them. These results have implications for research, extension, and policy programs.Risk and Uncertainty,

A review of operations research methods applicable to wildfire management

Across the globe, wildfire-related destruction appears to be worsening despite increased fire suppression expenditure. At the same time, wildfire management is becoming increasingly complicated owing to factors such as an expanding wildland-urban interface, interagency resource sharing and the recognition of the beneficial effects of fire on ecosystems. Operations research is the use of analytical techniques such as mathematical modelling to analyse interactions between people, resources and the environment to aid decision-making in complex systems. Fire managers operate in a highly challenging decision environment characterised by complexity, multiple conflicting objectives and uncertainty. We assert that some of these difficulties can be resolved with the use of operations research methods. We present a range of operations research methods and discuss their applicability to wildfire management with illustrative examples drawn from the wildfire and disaster operations research literature