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…
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
Keywords
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…
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.