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Article
Publication date: 14 May 2018

Claudia Vásquez Rojas, Eduardo Roldán Reyes, Fernando Aguirre y Hernández and Guillermo Cortés Robles

Strategic planning (SP) enables enterprises to plan management and operations activities efficiently in the medium and large term. During its implementation, many processes and…

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Abstract

Purpose

Strategic planning (SP) enables enterprises to plan management and operations activities efficiently in the medium and large term. During its implementation, many processes and methods are manually applied and may be time consuming. The purpose of this paper is to introduce an automatic method to define strategic plans by using text mining (TM) algorithms within a generic SP model especially suited for small- and medium-sized enterprises (SMEs).

Design/methodology/approach

Textual feedbacks were collected through a SWOT matrix during the implementation of a SP model in a company dedicated to the local distribution of food. A four-step TM process (performing acquisition, pre-processing, processing, and validation tasks) is applied via a framework developed under the cloud computer paradigm in order to determine the strategic plans.

Findings

The use of categorization and clustering algorithms show that unstructured textual information produced during the SP can be efficiently processed and capitalized. Collected evidence reveals the potential to enhance the strategic plans creation with less effort and time, improving the relevance, and producing new technological resources accessible to SMEs.

Originality/value

An innovative framework especially suited for the SMEs based on the synergy assumption of the coupling between TM and a generic SP model.

Details

Industrial Management & Data Systems, vol. 118 no. 4
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 2 February 2015

Claudia Foerster, Guillermo Figueroa and Eric Evers

A quantitative microbiological risk assessment (QMRA) was developed to estimate the probability of getting listeriosis as a consequence of chicken and beef consumption in Chile…

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Abstract

Purpose

A quantitative microbiological risk assessment (QMRA) was developed to estimate the probability of getting listeriosis as a consequence of chicken and beef consumption in Chile. The paper aims to discuss these issues.

Design/methodology/approach

As a first step a deterministic retail-to-home model was constructed for the Chilean susceptible population, including storage, cross-contamination and cooking. Next, two probabilistic models were developed, including variability and/or the uncertainty of some of the parameters. The probabilistic models were analyzed by Monte Carlo simulations with 100,000 iterations.

Findings

Of the total susceptible population used in the model (2.81 million people), the deterministic model estimated 11 and two listeriosis cases because of beef and poultry consumption, respectively and the variability model estimated a mean of 322 and 7,546 cases for beef and poultry consumption, respectively. The uncertainty analysis showed large ranges, with realistic estimates made with an initial concentration of Listeria monocytogenes of 0.04-1 CFU/g and a dose-response parameter r ranging from 10-14 to 10-10.

Research limitations/implications

The lack of information was the major limitation of the model, so the generation of it has to be a priority in Chile for developing less uncertain risk assessments in the future.

Practical implications

Raw animal products can be the cause of listeriosis cases if they are not stored, cooked and/or handled properly. Consumer education seems to be an essential factor for disease prevention.

Originality/value

This is the first QMRA made in Chile, and also the first study of listeriosis in non-processed meat.

Details

British Food Journal, vol. 117 no. 2
Type: Research Article
ISSN: 0007-070X

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