scholarly journals 264 Harvest season, carcass weight, and fat measurement effects on lamb carcass characteristics and economic comparison of moderate and heavy weight lamb carcasses in the Western lamb processing industry

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 190-191
Author(s):  
Jaelyn Whaley ◽  
Warrie Means ◽  
John Ritten ◽  
Tom Murphy ◽  
Cody Gifford ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Carcass Characteristics ◽  
Simulation Models ◽  
Carcass Weight ◽  
Labor Turnover ◽  
Minor Effect ◽  
Intermountain West ◽  
Turnover Rates ◽  
Ideal Weight

Abstract Carcass characteristics and economic impact estimates of over-finished lambs on the processing sector were evaluated in two commercial Intermountain West abattoirs. Lamb carcasses were surveyed throughout the year using digital images and imaging software (n = 9,532). Estimations of abattoir costs and returns included loading labor, downtime cost, price of fat, live and carcass trucking costs from the two largest lamb processors in the Intermountain West. Profitability comparisons were made using Monte Carlo simulation models replicating live and carcass prices for distributions based on historical pricing data to assess overall profitability of a carcass in an ideal weight range (29.5 - 39.0 kg) and a carcass that exceeds ideal weight (> 39.0 kg). Overall means show that the average lamb carcass exceeded packer preferred hot carcass weight (40.76 ± 9.29 kg) and industry acceptable 12th rib fat thickness (8.17 ± 3.79 mm). There were seasonal differences in hot carcass weight and fat measurements with carcasses being lighter weight (P = 0.05) and trimmer (P = 0.05) in the summer months. Monte Carlo simulation found that the additional yield from heavier carcasses offset costs of harvesting them. However, factors such as machine wear and increased labor turnover rates should be considered, although difficult to quantify. Collectively, the current study shows that U.S. lamb carcasses are too heavy and excessively fat but have minor effect on processor profitability.

Experimental Error Measurement in Monte Carlo Simulation

2010 ◽  
pp. 92-142 ◽  
Author(s):  
Lucia Cassettari ◽  
Roberto Mosca ◽  
Roberto Revetria
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Experimental Error ◽  
System Simulation ◽  
Simulation Models ◽  
Error Measurement ◽  
Mean Square ◽  
Optimal Functioning ◽  
Set Up ◽  
And Control

This chapter describes the set up step series, developed by the Genoa Research Group on Production System Simulation at the beginning of the ’80s, as a sequence, through which it is possible at first statistically validate the simulator, then estimate the variables which effectively affect the different target functions, then obtain, through the regression meta-models, the relations linking the independent variables to the dependent ones (target functions) and, finally, proceed to the detection of the optimal functioning conditions. The authors pay great attention to the treatment, the evaluation and control of the Experimental Error, under the form of Mean Square Pure Error (MSPE), a measurement which is always culpably neglected in the traditional experimentation on the simulation models but, that potentially can consistently invalidate with its magnitude the value of the results obtained from the model.

The Application of Quantitative Risk Assessment to Microbial Food Safety

1998 ◽  
Vol 61 (5) ◽  
pp. 640-648 ◽  
Author(s):  
DAVID JOHN VOSE
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Food Safety ◽  
Probability Distributions ◽  
Simulation Models ◽  
Practical Method ◽  
Assessment Model ◽  
Quantitative Risk Assessment ◽  
Suitable Model

Quantitative risk assessment (QRA) is rapidly accumulating recognition as the most practical method for assessing the risks associated with microbial contamination of foodstuffs. These risk analyses are most commonly developed in commercial Computer spreadsheet applications, combined with Monte Carlo simulation add-ins that enable probability distributions to be inserted into a spreadsheet. If a suitable model structure can be defined and all of the variables within that model reasonably quantified, a QRA will demonstrate the sensitivity of the severity of the risk to each stage in the risk-assessment model. It can therefore provide guidance for the selection of appropriate risk-reduction measures and a quantitative assessment of the benefits and costs of these proposed measures. However, very few reports explaining QRA models have been submitted for publication in this area. There is, therefore, little guidance available to those who intend to embark on a full microbial QRA. This paper looks at a number of modeling techniques that can help produce more realistic and accurate Monte Carlo simulation models. The use and limitations of several distributions important to microbial risk assessment are explained. Some simple techniques specific to Monte Carlo simulation modelling of microbial risks using spreadsheets are also offered which will help the analyst more realistically reflect the uncertain nature of the scenarios being modeled. simulation, food safety

QUANTIFYING UNCERTAINTY IN SIMULATION MODELING

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jace Thibault ◽  
Simaan AbouRizk
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Simulation Modeling ◽  
Simulation Models ◽  
Light Rail ◽  
Rail Transit ◽  
Engineering Systems ◽  
Quantifying Uncertainty ◽  
Monte Carlo Simulation Model

Uncertainty can be defined as a state of either incomplete or otherwise bounded knowledge. Simulation models, and the engineering systems that they represent, often contain various types of uncertainty. Different approaches and theories can be applied to model these various types of uncertainty with a range of degrees in difficulty and accuracy. The objective of this paper is to explain the various types of uncertainty found in simulation models and to examine where uncertainty can be better represented or potentially reduced. To achieve this objective, a Monte Carlo Simulation model called the As-Planned Model is developed to estimate both cost and schedule using a risk-based approach for a simplified, Light Rail Transit construction project. After the project is complete, the As-Planned model is then compared to the project’s actual results. The resulting conclusions about various types of uncertainty are derived through both output comparison as well as uncertainty analysis.

scholarly journals Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models and Monte Carlo Simulation

Safety ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 57 ◽  
Author(s):  
Fatemeh Davoudi Kakhki ◽  
Steven Freeman ◽  
Gretchen Mosher
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Generalized Linear Models ◽  
Cost Estimation ◽  
Financial Risk ◽  
Linear Models ◽  
Simulation Models ◽  
Workers Compensation ◽  
The Usa ◽  
Compensation Claims

Insurance practitioners rely on statistical models to predict future claims in order to provide financial protection. Proper predictive statistical modeling is more challenging when analyzing claims with lower frequency, but high costs. The paper investigated the use of predictive generalized linear models (GLMs) to address this challenge. Workers’ compensation claims with costs equal to or more than US$100,000 were analyzed in agribusiness industries in the Midwest of the USA from 2008 to 2016. Predictive GLMs were built with gamma, Weibull, and lognormal distributions using the lasso penalization method. Monte Carlo simulation models were developed to check the performance of predictive models in cost estimation. The results show that the GLM with gamma distribution has the highest predictivity power (R2 = 0.79). Injury characteristics and worker’s occupation were predictive of large claims’ occurrence and costs. The conclusions of this study are useful in modifying and estimating insurance pricing within high-risk agribusiness industries. The approach of this study can be used as a framework to forecast workers’ compensation claims amounts with rare, high-cost events in other industries. This work is useful for insurance practitioners concerned with statistical and predictive modeling in financial risk analysis.

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