• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.

• Food Science of Animal Resources
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• v.27 no.3
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• pp.320-328
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• 2007

This study was conducted to determine the effect of pH adjustment and the addition of cryoprotectants on the quality characteristics of chicken breast surimi. We prepared surimi from Alaska pollack, as a the control, by two time washing times and the addition of cryoprotectants. Different preparations of surimi were manufactured by adjusting to pH 11.0 and the addition of different addition cryoprotectants during frozen storage (T1 : 5% sorbitol and 0.3% polyphosphate, T2: 4% sugar, 5% sorbitol and 0.3% polyphosphate, and T3: 2% salt, 4% sugar, 5% sorbitol and 0.3% polyphosphate). The moisture content was significantly lower in the control and T3 samples. The crude protein content was increased with storage times. The crude protein was higher in the control. The water-holding capacity, myofibrillar protein and shear force were significantly higher in T3 than other surimi samples. All gel characteristics were significantly higher in the control and T3 than other surimi samples. pH 11.0 adjusted chicken breast surimi had greater lightness than the control, and T3 samples had the highest lightness and whiteness. Sensory evaluations were significantly higher in the control and T3 than the other samples. The gel, and physical characteristics and sensory evaluation of T3 were similar to the control. T3 samples had superior color and pH than the control Alaska pollack surimi.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.5
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• pp.700-707
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• 1997

In order to express exactly the total resistance of bottom trawl nets subjected simultaneously to the water flow and the bottom friction, the influence of frictional force was added to the formular for the flow resistance of trawl nets obtained by previous papev and the experimental data obtained by other investigators were analyzed by the formula. The analyzation produced the total resistance R (kg) expressed as $$R=4.5(\frac{S_n}{S_m})^{1.2}S\;v^{-1.8}+20(Bv)^{1.1}$$ where $S(m^2)$ was the wall area of nets, $S_m\;(m^2)$ the cross-sectional area of net mouths, $S_n\;(m^2)$ the area of nets projected to the plane perpendicular to the water flow, B (m) the made-up circumference at the fore edge of bag parts, and v(m/sec) the dragging velocity. From the viewpoint that expressing R in the form of $R=kSv^2$ was a usual practice, however, the resistant coefficient $k(kg{\cdot}sec^2/m^4)$ was compared with the factors influencing it by reusing the experimental data. The comparison gave that the coefficient k might be expressed approximately as a function of BL only and so the resistance R (kg) as $$R=18{\alpha}B^{0.5}L\;v^{1.5}$$ where L (m) was the made-up total length of nets and $\alpha=S/BL$. But the values of a in the nets did not deviate largely from their mean, 0.48, for all the nets and so the general expression of R (kg) for all the bottom trawl nets could be written as $$R=9\;B^{0.5}\;L\;v^{1.5}$$.