• Nutritional Sciences
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• 제9권3호
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• pp.201-211
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• 2006

The purpose of this study was to provide dietitians with the guidance in forecasting meal counts for a university/college foodservice facility. The forecasting methods to be analyzed were the following: naive model 1, 2, and 3; moving average, double moving average, simple exponential smoothing, double exponential smoothing, Holt's, and Winters' methods, and simple linear regression. The accuracy of the forecasting methods was measured using mean squared error and Theil's U-statistic. This study showed how to project meal counts using 10 forecasting methods for dietitians. The results of this study showed that WES was the most accurate forecasting method, followed by $na\ddot{i}ve$ 2 and naive 3 models. However, naive model 2 and 3 were recommended for using by dietitians in university/college dining facilities because of the accuracy and ease of use. In addition, the 2000 spring semester data were better than the 2000 fall semester data to forecast 2001spring semester data.

• 동아시아식생활학회지
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• 제18권6호
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• pp.1089-1098
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• 2008

The principal objective of this study was to reveal the relationship between the menu management indicators and menu performance indicators in contract-foodservice operations. Menu indicators differed according to the type of business, type of contract, type of serving, and number of service lines. In accordance with the results of our correlation analysis, we noted significant correlations between menu performance indicators and menu management indicators. The first of these was the correlation between the food cost ration and meal counts, food loss, and the use of prepared vegetables. The second of these was the correlation between food cost per meal and forecasting error, food loss, and inventory turnover. The last of these correlations was the negative correlation between menu CSI(customer satisfaction index) and the use of prepared vegetables. According to the results of our canonical correlation analysis, 2 significant functions were identified. In the first function, we noted significant correlations between meal counts, use of prepared vegetables, food loss, and food cost ratio. Additionally, we noted significant correlations between forecasting error, inventory turnover, food loss, and food cost per meal in the second function. Menu management indicators had no influence on customer satisfaction.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2000년도 추계학술대회
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• pp.36-44
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• 2000

The purpose of this study is to introduce a more efficient forecasting technique, which could help result the reduction of cost in removing the waste of airline in-flight meals. We will use a neural network approach known to many researchers as the “Outstanding Forecasting Technique”. We employed a multi-layer perceptron neural network using a backpropagation algorithm. We also suggested using other related information to improve the forecasting performances of neural networks. We divided the data into three sets, which are training data set, cross validation data set, and test data set. Time lag variables are still employed in our model according to the general view of time series forecasting. We measured the accuracy of our model by “Mean Square Error”(MSE). The suggested model proved most excellent in serving economy class in-flight meals. Forecasting the exact amount of meals needed for each airline could reduce the waste of meals and therefore, lead to the reduction of cost. Better yet, it could enhance the cost competition of each airline, keep the schedules on time, and lead to better service.

• 동아시아식생활학회지
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• 제18권2호
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• pp.242-252
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• 2008

The objective of this study was to suggest a new efficiency measurement indicator for evaluating the menu management efficiency of decision making units(DMUs) in contract-foodservice operations and to determine the relationship between the DEA(data envelopment analysis) menu efficiency score and menu factors. The results of applying DEA revealed relatively efficient types of service and frequency of meals. The efficient service was shown as a self-service type that operates Monday to Saturday. The considered menu factors included meal price, food cost per meal, meal counts, number of menu items, use of favorite menu use, forecasting error, accuracy of ordering, ratio of inventory, ratio of food loss, use of processed foods and use of prepared vegetables are considered. There were significant correlations between the DEA score and meal price, meal counts, number of menu items, ratio of food loss, accuracy of ordering and use of processed foods respectively. According to the regression results, menu price had a positive influence on the DEA menu efficiency score, and food cost per meal and the use of prepared foods had negative influences respectively.

• 동아시아식생활학회지
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• 제20권5호
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• pp.823-835
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• 2010

The research aimed to measure the efficiency of using multi stores in a foodservice company using by DEA (data envelopment analysis) which is a new management science technique. The study also attempted to identify relevant variables affecting DEA efficiency in order to suggest methods for improving efficiency. The data were collected from 148 contract foodservice operations, which were operated in similar fashion in October 2009. The DEA efficiency was calculated as an output-oriented BCC Model. Sales, and CSI (customer satisfaction index) were used as output variables whereas food cost, labor cost, and management expense were used as input variables to calculate the DEA efficiency. Operation process variables of the unit consisted of the were consist of ratio of regular employee, ratio of housekeeper, meal counts, meal price, food cost per meal, contract period, number of menu items, forecasting accuracy, order accuracy, inventory turnover, use of processed food, deviation of food cost, number of new menus, and number of events. According to the BCC score and profitability, units were classified into four groups: High efficiency-high profitability (HEHP), High efficiency-low profitability (HELP), Low efficiency-high profitability (LEHP), and Low efficiency-low profitability (LELP). The HEHP group contained 54 units, which mostly contracted management fee type and had a high meal price. The units were also very large and, served three meals. Twenty of the units were operated with high labor cost: most of these were factories and hospitals. The LEHP group contained 20 units, that were mainly office stores of large scale and medium price. Fifty-four LELP group had a low meal price. A high performance group must have high efficiency, profitability, and satisfaction. The BCC score was over 0.969, the meal price was over 4,116 won, the food cost was over 2,077 won, and meal counts per month were over 10,212 meals.

• Journal of Nutrition and Health
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• 제20권6호
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• pp.442-455
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• 1987

The purpose of the study were to assist foodservice managers in complex decision making by utilizing computerized cost accounting system and to relieve managers from repetitive and routine tasks so that more adequate patient care and consultation can be provided. The scope of the computer-assisted cost accounting system consists of budget, menu planning, purchasing, inventory, cost control and financial reporting. The content of the computerized system are summarized as follows ; 1) For budgeting monthly income was estimated by calculating unit cost of each meal and forecasting serving numbers. The actual serving numbers for patients and employees were totaled everyday, and utilized as the basic data base for estimating income and planning menu. The monthly lists of meal sensus were generated. 2) for menu planning concersion factors were computed based on the standarized recipe for 50 servings. Daily menus for patients and employees which include total amounts of each ingredient and cost analyzed information were generated. 3) Daily and monthly purchasing report for each food item classified by patient and employee meals were generated. 4) Inventory transactions such as recipts and issues were totalized daily for each stocked item, and monthly inventory reports were generated. 5) Cost analysis reports for each menu item were generated into two ways based on the budget coat as well as the purchasing cost. 6) Editing new recipes and updating food costs change to the data base were carried out. 7) Financial reports were generated monthly, first-half and second-half of the year, and yearly basis.

• 한국정보시스템학회지:정보시스템연구
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• 제10권2호
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• pp.151-164
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• 2001

현재의 항공사 기내식 수요예측 시스템으로는 항공기 운항의 지연이나 초과 주문으로 인한 손실 문제를 해결하기 힘든 것으로 알려져 있다. 이러한 문제를 해결하기 위해 본 연구에서는 항공기 기내식 시계열 자료만을 입력변수로 사용한 단순인공신경망모형(simple neural network model), 단순인공신경망모형에 전통적인 시계열 기법(본 연구에서는 지수 평활법)의 예측 결과를 입력변수로 추가한 혼합인공신경망모형(hybrid neural network model), 그리고 혼합인공신경 망 모형에 상관관계가 높은 다른 시계열 자료(본 논문에서는 유사 노선의 다른 항공기 기내식 시계열 자료)를 인공신경망의 입력변수로 추가시킨 하이퍼혼합인공신경망모형(hyper hybrid neural network model)을 새로운 항공기 기내식 수요예측 기법으로 제안하고, 이들 모형의 예측력을 비교 분석하였다. 분석 결과 하이퍼혼합인공신경망 모형의 예측력이 가장 우수한 것으로 나타나, 인공신경 망을 기반으로 한 수요예측에 있어 상관관계가 높은 다른 시계열 자료를 입력변수로 추가함으로써 인공신경망모형의 예측력을 개선시킬 수 있음을 알 수 있었다

• Nutrition Research and Practice
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• 제4권2호
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• pp.163-172
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• 2010

The purposes of this study were to assess hospital foodservice quality and to identify causes of quality problems and improvement strategies. Based on the review of literature, hospital foodservice quality was defined and the Hospital Foodservice Quality model was presented. The study was conducted in two steps. In Step 1, nutritional standards specified on diet manuals and nutrients of planned menus, served meals, and consumed meals for regular, diabetic, and low-sodium diets were assessed in three general hospitals. Quality problems were found in all three hospitals since patients consumed less than their nutritional requirements. Considering the effects of four gaps in the Hospital Foodservice Quality model, Gaps 3 and 4 were selected as critical control points (CCPs) for hospital foodservice quality management. In Step 2, the causes of the gaps and improvement strategies at CCPs were labeled as "quality hazards" and "corrective actions", respectively and were identified using a case study. At Gap 3, inaccurate forecasting and a lack of control during production were identified as quality hazards and corrective actions proposed were establishing an accurate forecasting system, improving standardized recipes, emphasizing the use of standardized recipes, and conducting employee training. At Gap 4, quality hazards were menus of low preferences, inconsistency of menu quality, a lack of menu variety, improper food temperatures, and patients' lack of understanding of their nutritional requirements. To reduce Gap 4, the dietary departments should conduct patient surveys on menu preferences on a regular basis, develop new menus, especially for therapeutic diets, maintain food temperatures during distribution, provide more choices, conduct meal rounds, and provide nutrition education and counseling. The Hospital Foodservice Quality Model was a useful tool for identifying causes of the foodservice quality problems and improvement strategies from a holistic point of view.

• 한국식품영양학회지
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• 제26권4호
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• pp.895-908
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• 2013

The purpose of this study is to examine female baby boomers' dietary habits and their attitudes together with their needs for future perspectives of dietary life. Our aim is to use these findings as a basic data when forecasting for food-related industries or policy making. A survey is being carried out for a total of 358 female baby boomers and analyzed by SPSS 12.0. The following is a summary of this study. The average age is 52.6 years old, most of them graduated from highschool (63.1%) and had a nuclear type of family (76.1%). Only 39.0% is composed of housewives, others had either full-time or part-time jobs. Self-assessment of stress is not so high and only 8.1% are dissatisfied with their lives. 38.2% are either overweight or obese in terms of BMI, and most of them are non-smokers (97.2%) or non-drinkers (63.0%). Their mean dietary habit scores are $70.6{\pm}11.8$, and the scores show significant relations with their education levels (p<0.01), monthly income (p<0.01), life satisfaction rates (p<0.001), stress levels (p<0.001), smoking habits (p<0.05), drinking habits (p<0.05), regular exercises (p<0.001) and regular health check-ups (p<0.05). The rate of skipping breakfast, lunch and dinner are 18.2%, 1.1%, 5.2% respectively. The main reason for skipping breakfast is the 'lack of time'. With regards to the frequency of grocery shopping, almost half of the subjects (55.7%) said '1~2 times per week' and bought mainly raw food sources such as vegetables, fruits, and meats. The majority of the subjects (91.3%) report that they cooked meals at homes, and took about 1 hour of time. The subjects also point out that cooking was a bothering task, and only 46.4% would prepare meals at home, while others would rather eat out or eat convenience foods. The main reasons for not wanting meal services in the elderly welfare facility are because they didn't want to live such places (48.4%) and the meals are tasteless (31.3%). As for delivery meal services, 60.1% are aware of it, and 39.9% would consider using it in the future. Factors to be considered when using the delivery meal service are sanitation (43.7%), nutrition (28.7%), taste (18.4%), price (6.3%), and brand name (2.9%). This study is expected to be used as useful information when developing food-related strategies for baby boomers in the future.

• 대한영양사협회학술지
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• 제25권1호
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• pp.44-58
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• 2019

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu quality deterioration, and preventing rising costs. Compared to other demand forecasts, the menu of dietary personnel includes diverse menus, and various dietary supplements include a range of side dishes. In addition to the menus, diverse subjects for prediction are very difficult problems. Therefore, the purpose of this study was to establish a method for predicting the number of meals including predictive modeling and considering various factors in addition to menus which are actually used in the field. For this purpose, 63 variables in eight categories such as the daily available number of people for the meals, the number of people in the time series, daily menu details, weekdays or seasons, days before or after holidays, weather and temperature, holidays or year-end, and events were identified as decision variables. An ensemble model using six prediction models was then constructed to predict the number of meals. As a result, the prediction error rate was reduced from 10%~11% to approximately 6~7%, which was expected to reduce the residual amount by approximately 40%.