Is it time for computer-assisted decision making to improve the quality of food and nutrition services?

Abstract

What would be the preferred plan for meeting the food and nutrition needs of specific customers/clients? What piece of equipment will meet production needs at the lowest possible cost? What would be the impact on serving time of adding two additional items to the cafeteria line? These are but a few of the many decisions that dietitians are faced with making each day whether they work or consult in child care, school, health care, business and industry, or university foodservice operations. Computer technology could help facilitate this decision-making process, but it will take the combined efforts of practitioners, educators, and researchers to help ensure that it does. Decision making is an important and time-consuming activity of dietitians who work in all areas of food and nutrition services. Decision making has been described as the essence of management because managers make decisions in almost every aspect of their jobs; managerial activities revolve around decision making (1.Spears MC Foodservice Organizations.3rd ed. Prentice-Hall, Inc, Englewood Cliffs, NJ1995Google Scholar, 2.Turban E Decision Support and Expert Systems. MacMillan Publishing Co, New York, NY1988Google Scholar). The literature includes many definitions for the term decision making. Generally, those definitions indicate that decision making is a process in which alternatives are evaluated and a course of action toward a goal is decided. Markland and Sweigart (3.Markland RE Sweigart JR Quantitative Methods: Applications to Managerial Decision Making. John Wiley and Sons, New York, NY1987Google Scholar) caution that as the complexity of the decision-making environment increases, it will become increasingly difficult to consider all the factors that affect the outcome of a decision. Thus, rigorous and scientific approaches will be needed to analyze complex decisions. Since the 1960s, some dietitians have used computer technology to assist with decision making (4.Hoover LW Computers in dietetics: State-of-the-art, 1976.J Am Diet Assoc. 1976; 68: 39-42Google Scholar, 5.Hoover LW Computers in Nutrition, Dietetics and Foodservice Management: A Bibliography.3rd ed. University of Missouri, Columbia, Mo1985Google Scholar, 6.Kaud FA Effective Computer Management in Food and Nutrition Services. Aspen Publishers, Rockville, Md1989Google Scholar, 7.Computers in Food and Nutrition Services: Promises and Prospects. Report of the Tenth Ross Roundtable on Medical Issues. Ross Laboratories, Columbus, Ohio1990Google Scholar, 8.Willard R Computers in dietetics.Diet Currents. 1982; 9: 7-14Google Scholar, 9.Youngwirth J The evolution of computers in dietetics: a review.J Am Diet Assoc. 1983; 82: 62-67Google Scholar). Many authors have emphasized the benefits of using a computer to assist with food- and nutrition-related decisions and stressed how critical the application of computer technology is for dietitians. Yet research results (10.Finley DH Kim IY Use of selected management science techniques in health care foodservice systems.J Foodserv Systems. 1986; 4: 1-16Google Scholar, 11.Hoover LW Leonard MS Automated hospital information system functions for dietetics.J Am Diet Assoc. 1982; 80: 312-316Google Scholar, 12.McCool AC Garand MM Computer technology in institutional foodservice.J Am Diet Assoc. 1986; 86: 48-56Google Scholar, 13.Repko CJ Miller JL Survey of foodservice production forecasting.J Am Diet Assoc. 1990; 90: 1067-1071Google Scholar, 14.Miller JL Shanklin CW Forecasting menu item demand in foodservice operations.J Am Diet Assoc. 1988; 88: 443-449Google Scholar, 15.Miller JL Forecasting in foodservice surveys in three types of operations.NACUFS J. 1990–1991; 15: 13-16Google Scholar) suggest that few foodservice managers use any form of quantitative or computerized assistance in their decision making. The critical question is why? Waller (16.Waller A Introduction.in: Computers in Food and Nutrition Services: Promises and Prospects. Report of the Tenth Ross Roundtable on Medical Issues. Ross Laboratories, Columbus, Ohio1990Google Scholar) suggested possible reasons why dietitians fail to adopt and use computer technology effectively: unique aspects of nutrition services management, functions, or labor relations make systems difficult to use; dietitians are not prepared educationally to use systems; systems are oversold and may not be as beneficial as originally thought; and introduction of computer technology into food and nutrition service involves a notable change for many employees. Use of computers in decision making varies from a basic use of providing computations and information to the much more sophisticated development of expert systems to assist with decision making (Table). Dietitians need to move from a reliance on the basic uses of computers for their decision making to the more advanced uses. Computer simulation and expert systems provide the tools needed to advance decision making in all aspects of dietetics practice.TableCategorization of use of computers to assist with decision making in food and nutrition servicesCategory of UseDescriptionReported applicationsaNumbers in parentheses refer to references in which the application is discussed.BasicComputation, organization, and summarization of dataForecasting 17.Messersmith AM Moore AN Hoover LW A multi-echelon menu item forecasting system for hospitals.J Am Diet Assoc. 1978; 72: 509-515Google Scholar, 18.Miller JJ McCahon CS Miller JL Foodservice forecasting using simple, mathematical models.Hospitality Res J. 1991; 15: 43-58Google Scholar, 19.Miller JL McCahon CS Bloss BK Food production forecasting with simple time series models.Hospitality Res J. 1991; 14: 9-21Google Scholar, 20.Miller JJ McCahon CS Miller JL Foodservice forecasting: differences in selection of simple mathematical models based on short-term and long-term data sets.Hospitality Res J. 1993; 16: 95-102Google Scholar, 21.Tienor CA David BA A dietary department applies procedure for developing a demand forecasting system.J Am Diet Assoc. 1976; 68: 460-462Google Scholar, 22.Wood SD A model for statistical forecasting of menu item demand.J Am Diet Assoc. 1977; 70: 254-259Google ScholarMenu planning 23.Balintfy JL Nebel EC Experiments with computer-assisted menu planning.Hospitals. 1966; 40: 88-97Google Scholar, 24.Bowman JL Brennan EM Computer-assisted menu planning provides control of food service.Hospitals. 1969; 43: 107-113Google Scholar, 25.Gelpi MJG Findorff IK Computer plans modified diets.Hospitals. 1973; 47: 62-67Google Scholar, 26.Gue RL Mathematical basis for computer-planned nonselective menus.Hospitals. 1969; 43: 102-104Google ScholarProductivity/labor scheduling 27.Brown DM Hoover LW Total factor productivity modeling in hospital foodservice operations.J Am Diet Assoc. 1991; 91: 1088-1092Google Scholar, 28.Brown DM Hoover LW Productivity measurement in foodservice: past accomplishment—a future alternative.J Am Diet Assoc. 1990; 90: 973-981Google Scholar, 29.Leyshock PJ Tracy DL Using a computerized system to monitor clinical dietetic productivity.Topics Clin Nutr. 1992; 7: 69-77Google Scholar, 30.Lieux EM Manning CK Productivity in nutrition programs for the elderly that utilize an assembly-serve production system.J Am Diet Assoc. 1991; 91: 184-188Google Scholar, 31.McGary VE Donaldson B A model of a centralized tray assembly conveyor system for a hospital. I. Four strategic components.J Am Diet Assoc. 1969; 55: 366-371Google Scholar, 32.McGary VE Donaldson B A model of a centralized tray assembly conveyor system for a hospital. II. Station work content.J Am Diet Assoc. 1969; 55: 480-484Google Scholar, 33.Mayo CR Olsen MD Frary RB Variables that affect productivity in school foodservices.J Am Diet Assoc. 1984; 84: 187-193Google Scholar, 34.Ruf K Matthews ME Production time standards. Application of Master Standard Data is best way to determine food service production time standards.Hospitals. 1973; 47: 82-90Google Scholar, 35.Waldvogel CF Ostenso GL Quantity food production labor time.J Am Diet Assoc. 1977; 79: 172-177Google Scholar, 36.Waldvogel CF Ostenso GL Labor time per portion and volume in foodservice.J Am Diet Assoc. 1977; 70: 178-180Google Scholar, 37.Zolber KK Donaldson B Distribution of work functions in hospital food systems.J Am Diet Assoc. 1970; 56: 39-45Google ScholarProduction scheduling/equipment utilization 38.Gottlieb R Couch MA Using the cross chart in planning kitchen layouts.J Am Diet Assoc. 1960; 36: 585-592Google Scholar, 39.Lambert CU Beach BL Computerized scheduling for cook/freeze food production plans.J Am Diet Assoc. 1980; 77: 174-178Google Scholar, 40.Stinson JP Guley HM Use of a branch and bound algorithm to schedule food production in a semi-conventional food service system.J Am Diet Assoc. 1982; 81: 562-567Google ScholarNutrient analysis 41.Brisbane HM Computing menu nutrients by data processing.J Am Diet Assoc. 1964; 44: 453-455Google Scholar, 42.Hertzler AA Hoover LW Development of food tables and use with computers: review of nutrition data base.J Am Diet Assoc. 1977; 70: 20-31Google Scholar, 43.Hjortland MC Duddleson WG Porter C French AB Using the computer to calculate nutrients in metabolic diets.J Am Diet Assoc. 1966; 49: 316-318Google ScholarNutritional assessment 44.Hoffman CJ A survey of pocket computer use for nutrition services.J Am Diet Assoc. 1991; 91: 225-226Google Scholar, 45.Plummer PF The effective use of the computer for nutrition assessment.Topics Clin Nutr. 1987; 2: 47-52Google Scholar, 46.Underbakke G Computer applications in clinical nutrition.in: Kaud FA Effective Computer Management in Food and Nutrition Services. Aspen Publishers, Rockville, Md1989: 78-103Google ScholarIntermediateSimulation of alternativesEntree serving time 50.Beach BL Ostenso GL Entree serving times.J Am Diet Assoc. 1969; 54: 290-296Google ScholarMenu planning 51.Eckstein EF Menu planning by computer: the random approach.J Am Diet Assoc. 1967; 51: 529-533Google ScholarProduction scheduling 52.Guley HM Stinson JP Computer simulation for production scheduling in a ready foods system.J Am Diet Assoc. 1980; 76: 482-487Google Scholar, 55.Matthews EM David BD Effect of varying the number of entree selections in the hospital menu.J Am Diet Assoc. 1971; 59: 575-581Google ScholarCustomer flow 54.Lopez-Soriano EM Matthews ME Norback JP Improving the flow of customers in a hospital cafeteria.J Am Diet Assoc. 1981; 79: 683-688Google ScholarDining room seating 53.Knickrehm ME Digital computer simulation in determining dining room seating capacity.J Am Diet Assoc. 1966; 48: 199-203Google ScholarAdvancedInteraction with decision maker in helping make a decision; expert systemsForecasting 63.Davis CL Miller JL Pearson J Brooks B Expert systems technology utilized in foodservice production forecasting.J Am Diet Assoc. 1992; 92 (suppl): A-21Google Scholara Numbers in parentheses refer to references in which the application is discussed. Open table in a new tab The basic use of computers in decision making includes tasks such as computation, summarization, and organization of information (Table). Practitioners in food and nutrition services have applied these basic uses to assist with decision making in schools, hospitals, and universities. Published examples include forecasting (17.Messersmith AM Moore AN Hoover LW A multi-echelon menu item forecasting system for hospitals.J Am Diet Assoc. 1978; 72: 509-515Google Scholar, 18.Miller JJ McCahon CS Miller JL Foodservice forecasting using simple, mathematical models.Hospitality Res J. 1991; 15: 43-58Google Scholar, 19.Miller JL McCahon CS Bloss BK Food production forecasting with simple time series models.Hospitality Res J. 1991; 14: 9-21Google Scholar, 20.Miller JJ McCahon CS Miller JL Foodservice forecasting: differences in selection of simple mathematical models based on short-term and long-term data sets.Hospitality Res J. 1993; 16: 95-102Google Scholar, 21.Tienor CA David BA A dietary department applies procedure for developing a demand forecasting system.J Am Diet Assoc. 1976; 68: 460-462Google Scholar, 22.Wood SD A model for statistical forecasting of menu item demand.J Am Diet Assoc. 1977; 70: 254-259Google Scholar), menu planning (23.Balintfy JL Nebel EC Experiments with computer-assisted menu planning.Hospitals. 1966; 40: 88-97Google Scholar, 24.Bowman JL Brennan EM Computer-assisted menu planning provides control of food service.Hospitals. 1969; 43: 107-113Google Scholar, 25.Gelpi MJG Findorff IK Computer plans modified diets.Hospitals. 1973; 47: 62-67Google Scholar, 26.Gue RL Mathematical basis for computer-planned nonselective menus.Hospitals. 1969; 43: 102-104Google Scholar), productivity assessment and employee scheduling (27.Brown DM Hoover LW Total factor productivity modeling in hospital foodservice operations.J Am Diet Assoc. 1991; 91: 1088-1092Google Scholar, 28.Brown DM Hoover LW Productivity measurement in foodservice: past accomplishment—a future alternative.J Am Diet Assoc. 1990; 90: 973-981Google Scholar, 29.Leyshock PJ Tracy DL Using a computerized system to monitor clinical dietetic productivity.Topics Clin Nutr. 1992; 7: 69-77Google Scholar, 30.Lieux EM Manning CK Productivity in nutrition programs for the elderly that utilize an assembly-serve production system.J Am Diet Assoc. 1991; 91: 184-188Google Scholar, 31.McGary VE Donaldson B A model of a centralized tray assembly conveyor system for a hospital. I. Four strategic components.J Am Diet Assoc. 1969; 55: 366-371Google Scholar, 32.McGary VE Donaldson B A model of a centralized tray assembly conveyor system for a hospital. II. Station work content.J Am Diet Assoc. 1969; 55: 480-484Google Scholar, 33.Mayo CR Olsen MD Frary RB Variables that affect productivity in school foodservices.J Am Diet Assoc. 1984; 84: 187-193Google Scholar, 34.Ruf K Matthews ME Production time standards. Application of Master Standard Data is best way to determine food service production time standards.Hospitals. 1973; 47: 82-90Google Scholar, 35.Waldvogel CF Ostenso GL Quantity food production labor time.J Am Diet Assoc. 1977; 79: 172-177Google Scholar, 36.Waldvogel CF Ostenso GL Labor time per portion and volume in foodservice.J Am Diet Assoc. 1977; 70: 178-180Google Scholar, 37.Zolber KK Donaldson B Distribution of work functions in hospital food systems.J Am Diet Assoc. 1970; 56: 39-45Google Scholar), production scheduling and equipment use (38.Gottlieb R Couch MA Using the cross chart in planning kitchen layouts.J Am Diet Assoc. 1960; 36: 585-592Google Scholar, 39.Lambert CU Beach BL Computerized scheduling for cook/freeze food production plans.J Am Diet Assoc. 1980; 77: 174-178Google Scholar, 40.Stinson JP Guley HM Use of a branch and bound algorithm to schedule food production in a semi-conventional food service system.J Am Diet Assoc. 1982; 81: 562-567Google Scholar), nutrient analysis (41.Brisbane HM Computing menu nutrients by data processing.J Am Diet Assoc. 1964; 44: 453-455Google Scholar, 42.Hertzler AA Hoover LW Development of food tables and use with computers: review of nutrition data base.J Am Diet Assoc. 1977; 70: 20-31Google Scholar, 43.Hjortland MC Duddleson WG Porter C French AB Using the computer to calculate nutrients in metabolic diets.J Am Diet Assoc. 1966; 49: 316-318Google Scholar), and nutrition assessment (44.Hoffman CJ A survey of pocket computer use for nutrition services.J Am Diet Assoc. 1991; 91: 225-226Google Scholar, 45.Plummer PF The effective use of the computer for nutrition assessment.Topics Clin Nutr. 1987; 2: 47-52Google Scholar, 46.Underbakke G Computer applications in clinical nutrition.in: Kaud FA Effective Computer Management in Food and Nutrition Services. Aspen Publishers, Rockville, Md1989: 78-103Google Scholar). Comprehensive computer systems for food and nutrition services have enabled dietitians to make decisions related to purchasing, production, financial management, productivity, and nutrient analysis based on objective, scientific information rather than relying on intuition, judgment, and experience (6.Kaud FA Effective Computer Management in Food and Nutrition Services. Aspen Publishers, Rockville, Md1989Google Scholar, 47.Moore AN Tuthill BH Computer Assisted Food Management Systems. University of Missouri, Columbia, Mo1971Google Scholar). All of the aforementioned authors (17.Messersmith AM Moore AN Hoover LW A multi-echelon menu item forecasting system for hospitals.J Am Diet Assoc. 1978; 72: 509-515Google Scholar, 18.Miller JJ McCahon CS Miller JL Foodservice forecasting using simple, mathematical models.Hospitality Res J. 1991; 15: 43-58Google Scholar, 19.Miller JL McCahon CS Bloss BK Food production forecasting with simple time series models.Hospitality Res J. 1991; 14: 9-21Google Scholar, 20.Miller JJ McCahon CS Miller JL Foodservice forecasting: differences in selection of simple mathematical models based on short-term and long-term data sets.Hospitality Res J. 1993; 16: 95-102Google Scholar, 21.Tienor CA David BA A dietary department applies procedure for developing a demand forecasting system.J Am Diet Assoc. 1976; 68: 460-462Google Scholar, 22.Wood SD A model for statistical forecasting of menu item demand.J Am Diet Assoc. 1977; 70: 254-259Google Scholar, 23.Balintfy JL Nebel EC Experiments with computer-assisted menu planning.Hospitals. 1966; 40: 88-97Google Scholar, 24.Bowman JL Brennan EM Computer-assisted menu planning provides control of food service.Hospitals. 1969; 43: 107-113Google Scholar, 25.Gelpi MJG Findorff IK Computer plans modified diets.Hospitals. 1973; 47: 62-67Google Scholar, 26.Gue RL Mathematical basis for computer-planned nonselective menus.Hospitals. 1969; 43: 102-104Google Scholar, 27.Brown DM Hoover LW Total factor productivity modeling in hospital foodservice operations.J Am Diet Assoc. 1991; 91: 1088-1092Google Scholar, 28.Brown DM Hoover LW Productivity measurement in foodservice: past accomplishment—a future alternative.J Am Diet Assoc. 1990; 90: 973-981Google Scholar, 29.Leyshock PJ Tracy DL Using a computerized system to monitor clinical dietetic productivity.Topics Clin Nutr. 1992; 7: 69-77Google Scholar, 30.Lieux EM Manning CK Productivity in nutrition programs for the elderly that utilize an assembly-serve production system.J Am Diet Assoc. 1991; 91: 184-188Google Scholar, 31.McGary VE Donaldson B A model of a centralized tray assembly conveyor system for a hospital. I. Four strategic components.J Am Diet Assoc. 1969; 55: 366-371Google Scholar, 32.McGary VE Donaldson B A model of a centralized tray assembly conveyor system for a hospital. II. Station work content.J Am Diet Assoc. 1969; 55: 480-484Google Scholar, 33.Mayo CR Olsen MD Frary RB Variables that affect productivity in school foodservices.J Am Diet Assoc. 1984; 84: 187-193Google Scholar, 34.Ruf K Matthews ME Production time standards. Application of Master Standard Data is best way to determine food service production time standards.Hospitals. 1973; 47: 82-90Google Scholar, 35.Waldvogel CF Ostenso GL Quantity food production labor time.J Am Diet Assoc. 1977; 79: 172-177Google Scholar, 36.Waldvogel CF Ostenso GL Labor time per portion and volume in foodservice.J Am Diet Assoc. 1977; 70: 178-180Google Scholar, 37.Zolber KK Donaldson B Distribution of work functions in hospital food systems.J Am Diet Assoc. 1970; 56: 39-45Google Scholar, 38.Gottlieb R Couch MA Using the cross chart in planning kitchen layouts.J Am Diet Assoc. 1960; 36: 585-592Google Scholar, 39.Lambert CU Beach BL Computerized scheduling for cook/freeze food production plans.J Am Diet Assoc. 1980; 77: 174-178Google Scholar, 40.Stinson JP Guley HM Use of a branch and bound algorithm to schedule food production in a semi-conventional food service system.J Am Diet Assoc. 1982; 81: 562-567Google Scholar, 41.Brisbane HM Computing menu nutrients by data processing.J Am Diet Assoc. 1964; 44: 453-455Google Scholar, 42.Hertzler AA Hoover LW Development of food tables and use with computers: review of nutrition data base.J Am Diet Assoc. 1977; 70: 20-31Google Scholar, 43.Hjortland MC Duddleson WG Porter C French AB Using the computer to calculate nutrients in metabolic diets.J Am Diet Assoc. 1966; 49: 316-318Google Scholar, 44.Hoffman CJ A survey of pocket computer use for nutrition services.J Am Diet Assoc. 1991; 91: 225-226Google Scholar, 45.Plummer PF The effective use of the computer for nutrition assessment.Topics Clin Nutr. 1987; 2: 47-52Google Scholar, 46.Underbakke G Computer applications in clinical nutrition.in: Kaud FA Effective Computer Management in Food and Nutrition Services. Aspen Publishers, Rockville, Md1989: 78-103Google Scholar, 47.Moore AN Tuthill BH Computer Assisted Food Management Systems. University of Missouri, Columbia, Mo1971Google Scholar) have suggested that use of computer-generated information has improved decisions, thereby resulting in increased efficiency and effectiveness of food and nutrition services. Savings in food and labor costs have also been a positive benefit from computer use, yet according to Waller, “effective adoption and use of computer technology has been slow and far from universal in our industry” (16.Waller A Introduction.in: Computers in Food and Nutrition Services: Promises and Prospects. Report of the Tenth Ross Roundtable on Medical Issues. Ross Laboratories, Columbus, Ohio1990Google Scholar, p 1). In the intermediate stage, computers are used to simulate alternatives to provide information for decision making (Table). Computer simulation is a process that uses a computer to design a model of a real system and conduct experiments with this model for the purposes of understanding the behavior of the system and/or evaluating various strategies for operation of the system (48.Pegden CD Shannon RE Sadowski RP Introduction to Simulation Using SIMAN. McGraw-Hill, New York, NY1990Google Scholar). As early as 1976, Hoover (4.Hoover LW Computers in dietetics: State-of-the-art, 1976.J Am Diet Assoc. 1976; 68: 39-42Google Scholar) encouraged foodservice directors to use computer simulation to examine variations of foodservice-related problems. Hoover indicated that use of computer simulation would allow foodservice managers to explore alternative solutions to problems in a less costly and time-consuming manner. Simulation could also be used in educational programs to help develop the decision-making skills of future practitioners. The use of simulation offers several advantages to practitioners and educators: the impact of changes in procedures can be examined without disrupting ongoing operations; time to complete specific tasks can be modified to examine the interrelation of system parts; bottlenecks in product flow can be identified; and variables that are most important to performance and their interaction can be determined (48.Pegden CD Shannon RE Sadowski RP Introduction to Simulation Using SIMAN. McGraw-Hill, New York, NY1990Google Scholar, 49.Aggarwal AK Simulation as a DSS modelling technique.Info Manage. 1990; 19: 295-305Google Scholar). Most of the work on applying simulation techniques to decision making in food and nutrition services occurred in hospitals and universities between the late 1960s and early 1980s. To our knowledge, use of simulation applications was not reported in school or business and industry foodservice. Simulation research focused on foodservice management issues such as menu planning, production scheduling, customer flow through serving lines, and dining room seating (50.Beach BL Ostenso GL Entree serving times.J Am Diet Assoc. 1969; 54: 290-296Google Scholar, 51.Eckstein EF Menu planning by computer: the random approach.J Am Diet Assoc. 1967; 51: 529-533Google Scholar, 52.Guley HM Stinson JP Computer simulation for production scheduling in a ready foods system.J Am Diet Assoc. 1980; 76: 482-487Google Scholar, 53.Knickrehm ME Digital computer simulation in determining dining room seating capacity.J Am Diet Assoc. 1966; 48: 199-203Google Scholar, 54.Lopez-Soriano EM Matthews ME Norback JP Improving the flow of customers in a hospital cafeteria.J Am Diet Assoc. 1981; 79: 683-688Google Scholar, 55.Matthews EM David BD Effect of varying the number of entree selections in the hospital menu.J Am Diet Assoc. 1971; 59: 575-581Google Scholar). Recent discussions on the use of computer simulation have focused on restaurant and hotel decision making related to reservation and seating policies (56.Hott DD Kilgore RA Animated simulation, a quantitative model for nonquantitative managers.Hospitality Educ Res J. 1987; 11: 33-40Google Scholar, 57.Lambert CU Kilgore RA An analysis of seating policies on dining room productivity.The Consultant. 1989; 22: 40-42Google Scholar, 58.Lambert CU Lambert JM Setting reservation policies: a microcomputer-based simulation.Hospitality Educ Res J. 1988; 12: 403-409Google Scholar). The advanced use of computers involves systems designed to interact with the decision maker in the decision process. These “intelligent” computer systems are being developed by artificial intelligence researchers who study human thought processes and develop machines such as computers or robots to emulate these processes (2.Turban E Decision Support and Expert Systems. MacMillan Publishing Co, New York, NY1988Google Scholar). In essence, the goal of artificial intelligence research is to make machines do things that would require specialized knowledge if done by human beings (59.Firebaugh MW Artificial Intelligence. Boyd & Fraser Publishing Co, Boston, Mass1988Google Scholar). Expert systems, the branch of artificial intelligence research that uses specialized knowledge to emulate the decision-making ability of a human expert, have the potential of greatly enhancing the abilities of the food and nutrition services decision maker. Expert systems differ from other computerized systems in their ability to reason and explain (2.Turban E Decision Support and Expert Systems. MacMillan Publishing Co, New York, NY1988Google Scholar). The user of an expert system supplies facts or other information to the system. The expert system, which consists internally of a knowledge base and an inference engine, processes the information it is given and, based on a set of rules, draws conclusions and gives advice on the decision to be made (2.Turban E Decision Support and Expert Systems. MacMillan Publishing Co, New York, NY1988Google Scholar, 60.Giarratano J Riley G Expert Systems. PWS-Kent Publishing Co, Boston, Mass1989Google Scholar, 61.Graham A Lambert CU The educational application of expert systems in decision-making.Hospitality Tourism Educator. 1992; 4: 60-69Google Scholar). Bowen and Clinton (62.Bowen JT Clinton DN Expert systems: advisor on a disk.Cornell Hotel Rest Admin Q. 1988; 29: 62-67Google Scholar) suggested that whereas today use of expert system technology can give organizations a competitive edge, in the future, use of expert systems may be a requirement for survival in a competitive environment. Expert systems are needed to assist with decisions related to nutrition assessment and screening, equipment purchase, employee scheduling, production forecasting, and facility layout and design. Limited work has been done to develop expert systems for food and nutrition services. Davis et al (63.Davis CL Miller JL Pearson J Brooks B Expert systems technology utilized in foodservice production forecasting.J Am Diet Assoc. 1992; 92 (suppl): A-21Google Scholar) reported on the development of an expert system to assist with forecasting production needs in a university residence hall. The authors indicated that the expert system was used effectively by a production supervisor and a student dietitian to forecast production in the absence of the foodservice manager. Dietetics practitioners make decisions daily that affect the quality of the operations in which they work. Dietitians will need to be effective in using computers to assist with their decision making if they are going to compete successfully in the business environment of the future. The knowledge and performance requirements for entry-level dietitians, published by The American Dietetic Association (64.2nd ed. Accreditation Approval Manual for Dietetic Education Programs. American Dietetic Association, Chicago, Ill1991Google Scholar), recognize the importance of knowledge and application of computer technology in dietetics practice. Results of research conducted by the National Food Service Management Institute (65.Gregoire MB Sneed J Competencies for district school nutrition directors/supervisors.School Food Serv Res Rev. 1994; 18 (In press)Google Scholar) indicate the importance of competence in computer technology for district school nutrition directors/supervisors. Brown and Hoover (66.Brown DM Hoover LW Quantitative management techniques in dietetics: Improving practice through technology transfer.J Am Diet Assoc. 1988; 88: 1567-1575Google Scholar) stressed that facilitating the transfer of technology requires the combined efforts of educators, researchers, and practitioners. This combined effort can also help enhance the decision-making process in food and nutrition services. Rebovich et al (67.Rebovich EJ Wodarski LA Hurley RS Rasor-Greenhalgh S Stombaugh I A university-community model for the integration of nutrition research, practice, and education.J Am Diet Assoc. 1994; 94: 179-182Google Scholar) suggested a university-community model for the integration of nutrition research, practice, and education. The Figure presents a model depicting the interactions among education, research, and practice for the enhancement of computer-assisted decision making in food and nutrition services. Educators can provide educational preparation for future dietitians and continuing education for current practitioners on ways to enhance decision making through the intermediate and advanced levels of computer use. Dietitians need to understand and be confident about using techniques such as simulation and expert systems to enhance their decision-making ability. Educational programs need to emphasize the benefits gained by using simulation and expert systems to assist with decision making and include hands-on applications of these techniques. Many courses taught in dietetics programs include sections on decision making related to nutrition care and food production management; these courses could be enhanced by allowing students to use simulation or expert systems to assist with such decision making. Results of research by Miller (68.Miller JL Survey of computer technology in foodservice management education.J Am Diet Assoc. 1989; 89: 1279-1281Google Scholar) and Perkin and Kauwell (69.Perkin JE Kauwell GP A survey of computer education in coordinated undergraduate programs.J Am Diet Assoc. 1989; 89: 1500-1502Google Scholar) suggest that dietetics programs are including instruction on computer use. The applications being presented, however, do not appear to include use of computer simulation or expert systems. Unfortunately, few simulation and expert systems models currently exist to help dietitians with decision making. Several authors (70.Canter DD Management practices in dietetics.in: The Research Agenda for Dietetics Conference Proceedings. American Dietetic Association, Chicago, Ill1992Google Scholar, 71.Lafferty LJ Dowling RA Effectiveness of foodservice systems.in: The Research Agenda for Dietetics Conference Proceedings. American Dietetic Association, Chicago, Ill1992Google Scholar, 72.Monsen ER Forces for research.J Am Diet Assoc. 1993; 93: 981-985Google Scholar) have indicated the importance of expanding research efforts in the areas of simulation and expert systems. Researchers need to recognize that research in computer simulation and expert systems offers an excellent opportunity to work with dietetics practitioners in developing these systems. Results of simulation and expert systems research will provide valuable assistance in decision making to dietitians working in child nutrition programs, health care, universities, and private practice. Simulation research could explore customer service issues in schools, hospitals, or universities such as the impact on serving time of changing the number of products served or the number of employees serving these items or production issues such as efficient use of equipment for preparing meals. Simulation research could also examine the effect of different scheduling options. Expert systems research is needed to develop programs to assist dietitians with decisions related to equipment purchase, employee scheduling, nutrition screening and assessment, and employee or client training. Rose indicated that research is “the fabric of effectiveness in operational management” (73.Rose JC Research or practice?.J Am Diet Assoc. 1985; 85: 797-798Google Scholar, p 798) and Monsen (74.Monsen ER New practices and research in dietetics: the 1988.Journal J Am Diet Assoc. 1988; 88: 15Google Scholar) stressed that “practice is enhanced by research and research is driven by practice”; dietetics is strengthened as stronger links are developed between research and practice. Dietetics practitioners can provide the data and operational expertise needed for simulation and expert systems research. Researchers can use this information to develop simulation models and expert systems that will enhance the practitioner's decision-making ability. The decision-making process has a major impact on the quality and cost-effectiveness of operations. Educators, researchers, and practitioners need to form strategic alliances and combine their expertise to enhance the use of computer-assisted decision making in food and nutrition services, which will result in continuous improvement of services offered.