TDOC Z TA245.7 B873 NO.1601 Scanner Data in Managerial Decision-Making: The Texas Agricultural Experiment Station o Neville P. Clarke, Director The Texas A&M University System o College Station, Texas [Blank Page in Original Bulletin] Scanner Data in Managerial Decision-Making: A Case Study for Supermarkets by Oral Capps, Jr. Associate Professor Texas Agricultural Experiment Station l) bx Y Department of Agricultural Economics Texas A&M University l F ",._,,“f( kl" some» bravest Jeffrey M. Thomas Grocery Field Specialist Kroger Company Roanoke, VA Don L. Long Professor Emeritus Virginia Tech Blacksburg, VA O p k Acknowledgments We wish to formally thank the United States Department 0f Agriculture, Agricultural Marketing Service, for funding the research project. Special thanks in this regard are in order for Harold Ricker and Clarence Harris. We would also like to thank John DeMoss and the Virginia Food Dealers Association as well as Al Evans and William Miller of the Mid-Atlantic Food Dealers Association for their help in contacting the firms that participated in this study. We also would like to thank the following firms for their participation in this research: Austin’s Warehouse of Groceries; Jeffersonville, Indiana Bon Foods; Dumfries, Virginia Farm Fresh; Norfolk, Virginia Food City; Abingdon Virginia George’s Thriftway; Sykesville, Maryland Giant Foods; Carlisle, Pennsylvania Giant Open Air; Norfolk Virginia IGA Foodliner; Stuarts Draft, Virginia Ken Lewis-Liquor Discount; Louisville Kentucky Kroger, Inc.; Roanoke, Virginia Malone and Hyde; Nicholasville, Kentucky Richfood, Inc.; Richmond Virginia Santoni’s Markets; Baltimore, Maryland Ukrops; Richmond Virginia Value Foods; Baltimore, Maryland Wades; Christiansburg, Virginia Wetterau Food Services; Bloomington, Indiana importantly, I acknowledge review comments from my colleagues at Texas A&M University, namely Dick Edwards, Tom Sporleder, and Bob Branson. Any remaining errors or omissions are the responsibility of authors. Finally, a very special thanks goes to Stacy Zemanek for her diligent efforts in typing this manuscript. D Chapter 1e Table of Contents Page Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Introduction Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 14 Methodology Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 15 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 15 Outline of Management Responsibilities Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 16 Generic Organizational Structure of a Retail Grocery Firm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Specific Responsibilities of the Levels of Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Responsibilities of the Chief Executive Officer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Responsibilities of the Merchandiser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Responsibilities of the Store Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Responsibilities of the Department Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Responsibilities of the Chief Information Officer and Scanning Coordinator . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Commentary on the Interview Sessions Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Responses from Chief Executive Officers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Responses from Merchandisers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Responses from Store Managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Responses from Department Managers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Responses from CIOS and Scanning Coordinators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Responses from Wholesalers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 A Management Information System Model Based on Scanner Data Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Background on Management Information Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Potential Use of Scanner Data in Managerial Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Design of a Generic Management Information System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 CEO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Merchandiser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Store Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4O Department Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 CIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Scanning Coordinator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Operational Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Conclusions and Implications Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4F Potential Implications to Food Retailers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45\* Implications for Further Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49* Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3 v Executive Summary Q Although the supermarket industry is in position to surge forward in the application of information technol- ogy, to date relatively few resources have been devoted to generating and/or organizing scanner data to be used in managerial decisions. This study addressed the lag in effective usage of scanner data in managerial decision- aking. The purpose of this research was to clarify the informational needs of the various levels of management in supermarkets and to develop a management informa- tion system to deliver such information. The four spe- cific objectives of this project were: 1) to identify the de- cision-making roles of the various levels of management in a firm (chief executive officer, merchandiser, store manager, department manager, chief information officer, and scanning coordinator); 2) to identify the present usage of scanner data in decision-making; 3) to identify specific scanner-derived information which could facili- tate the decision-making process; and 4) to develop a generic firm-wide management information system that would provide each management level with the informa- tion it needs, coordinate total firm operations, but not burden a particular level with large volumes of unneces- sary data. The search for meaningful information has been the focus of a recent symposium and executive task force of food retailing leaders. Because of the scanning technolo- gy, a great deal of data is available to food retailers, and translating these data into information for management decisions is a major concern. Changes in the managerial environment, the data, computers, human resources, and software dictate changes in managerial practices. In creating a management information system, the aim is to identify key performance areas (e.g., profit, sales, gross margins) and indicators (e.g., sales per cus- tomer, shrinkage-theft, damaged goods, spoilage, and price inaccuracy—as a percentage of sales, gross mar- gin in dollars) for various managerial positions. This identification allows for a management-by- objectives orientation. To accomplish the objectives of this study, open- ended interviews were conducted with various levels of management within 17 cooperating retail grocery firms. The interviews placed emphasis on the current usages of scanner data and on how to facilitate the use of scan- ner data in managerial decision-making. This research substantiated the hypothesis that little use had been made of scanner data for managerial deci- qion-making in supermarkets. Also, barriers to the effec- ve use of scanner data were documented. The specific informational needs of the various levels of manage- ment, as discovered through the discussions with mana- Q gers of the cooperating firms, were used as the basis for the design of the management information system (MlS) model. The model in this study was a hybrid of the ‘pyramid-shaped and bottom-up approaches. Addition- ally, the critical element of this model was the existence Q: a a central data bank from which key reports were gen- erated to various levels of management. importantly, the MIS model rests on a number of explicit assumptions: 1. Decision-making requires relevant, reliable, timely, and concise information; 2. Most managers have more information than they know how to use; 3. Information required at various levels within the organization can be determined from manage- ment personnel; 4. MIS reports are one of several sources that a man- ager uses to make decisions; 5. A MIS has three major functions: data collection, data processing, and information delivery; 6. Developing a MIS is primarily a matter of consoli- dation and presentation of available data in us- able formats for the various levels of manage- ment; 7. Retail food firms have enough common charac- teristics that a MIS model defining key perform- ance areas and indicators can be used; and 8. There exists an identifiable set of key perform- ance areas and key performance indicators which can be classified into an operational MlS. Once the managerial responsibilities and the informa- tion needs for each level of management were defined, the form, timing, and content of various reports gener- ated and distributed were discussed. In general, the in- formation system was designed to facilitate exception reporting, that is, to point out potential problem areas. To establish an effective MIS, the retail firm must ini- tially have a vision of where it is going in terms of market- ing, operations, and distributions. The MIS model in this study centered attention primarily on the key perform- ance areas of operations and merchandising-the life- blood of the retail business. To implement this MIS, man- agement must prioritize information-system target areas (key performance indicators). Further, managing this information system, presumably by the chief infor- mation officer and scanning coordinator(s), is of paramount importance. Moreover, training personnel in the use of the information system is essential. Finally, management must realize that the development and im- plementation of the MIS is not a one-time event but an ongoing process. Marketing decision support system software must be able to leverage all the latest data, models, and statisti- cal analysis procedures. The software must have the ca- pacity for data base management, analysis, graphics, flexible report generation, and modeling, all in a user- friendly environment. The data base should be or- ganized in ways that can be easily altered when situa- tions or services change without doing massive reprog- ramming. Additionally, information about shelf space, end-of-aisle displays, use of advertising, and use of coupons should be retained so that impacts on sales, item movement, and net contribution can be made. The software should have the capacity to allow many users to access the same integrated data base. Either the chief information officer and scanning coordinators (internal support) or part-time or full-time consultants (external support) must understand enough about data analysis, statistical analysis, and modeling to make sure that the appropriate checks have been made and the appropriate questions have been asked when recommendations based on computer analyses are made. These people should report directly to top and middle management as part of staff groups. Management of scanner data has traditionally been considered a mainframe application regulated by highly specialized technicians. However, supermarket firms may use personal computers to tame the scanner data monster, particularly to evaluate product performance and sales trends and to track certain items. Cost and benefits are the key components in the deci?" sion to continue, alter, or discontinue the MIS. Con- sequently, audits of benefits (hard and soft) received from the MIS are necessary. With regard to costs, accord- ing to Food Marketing institute (FMI), the top 20 percent of supermarket firms on average have allocated roughly‘ 0.50 percent of dollar sales to information systems. To quote Ross (2), “the value of any information system must ultimately be measured by the quality of manage- ment decisions. Anything less is inconclusive, anything more unnecessary.” CHAPTER 1 Introduction in Background Arguably, the development of Universal Product Bar Codes (UPCs), and concomitantly scanning checkout systems, may be the most important innovation in the ‘retail food industry. With the introduction of scanning Q checkout systems, tremendous possibilities exist for the generation of data and the use of such data at all levels of managerial decision-making (departmental level, store level, supervisory level, and senior management level). According to an executive task force set up by the Food Marketing Institute, the supermarket industry is in a position “to surge forward in the application of infor- mation technology” (1). Although the hardware and software are currently available, to date it appears that relatively few resources have been devoted to generat- ing and/or organizing scanner data to be used in manage- rial decisions. Consequently, it is very likely that scanner data are under utilized from a managerial point of view. importantly, data and information are not synony- mous. Information corresponds to data which have been “retrieved, processed, or otherwise used for inference purposes or as a basis for forecasting or decision-mak- ing” (2). Data transform to information only when col- lected, analyzed, and presented in a form resulting in the communication of intelligence (3). Simply put, data are just facts. Information is something upon which action is taken. Along this line, little thought has been given to data collection and presentation in terms of which staff members need the information, what needs the various staff members have, and in what form the staff members could best use the information. Different levels of man- agement are likely to have different needs for informa- tion relative to type, complexity, and time span. The search for meaningful information was the focus of a recent Supermarket News symposium of food retail- ing leaders from across the United States (4). In a ques- tionnaire developed by James Stevenson, Director of the Food Industry Management Program at the University of Southern California, and sent to retailers and manufac- turers, the information explosion rated very high in im- portance. As to rank order, in-store scanning was number one, personal computers in the store number two, computerized labor scheduling number three, shelf space management systems number four, direct product profitability number five, UCS/computer-to-computer number six, electronic mail number seven, and last, “warehouse automation. Because of the scanning tech- nology, a great deal of data is available to food retailers, and translating data into information for management decisions is a major concern. Changes in the managerial environment—the data, computers, human resources and software — dictate changes in managerial practices. Objectives .Q The purpose of this research was to clarify the infor- .national needs, specific to scanner data, of the various levels of management in retail grocery firms and to de- velop a generic management information system to de- liver the necessary data. In this light, this study has four specific objectives: 1. To identify the decision-making roles of the vari- ous levels of management in a firm; 2. To identify the present usage of scanner-derived information; 3. To identify information which could improve deci- sion- making (type of data, desired form of presen- tation, and desired timing); and 4. To develop a firm-wide information system which provides each management level with relevant in- formation and coordinates total firm operations, but does not burden a particular level with large volumes of unnecessary data. The aim of the management information system is to identify key performance areas and indicators for vari- ous managerial positions. Key performance areas refer to those “activities or functions vital to accomplishing firm objectives” (5). Such areas include inventory, profit, gross margins, expenses, and sales. Key performance in- dicators refer to quantitative measures used by manage- ment, either implicitly or explicitly, to make decisions re- quired by the various levels of management (5). Key per- formance indicators include inventory turns, shrinkage as a percentage of sales, gross margin dollars, customer counts, and sales per customer. Importantly, key per- formance areas and indicators change with position in the management hierarchy. The identification of key per- formance areas and indicators allows for a management- by-objectives orientation. Hypotheses It appears that changes in scanning systems have been so rapid and varied that techniques for effectively incorporating the technology into managerial decision- making systems are lacking. Even after more than a dec- ade, food retailers are still experiencing problems man- aging “the scan data monster” (6). This study addresses this lag in effective usage of scanner data in managerial decision-making. In this light, the following four hypoth- eses are put forward: 1. The implementation of applications of scanner data is difficult to achieve; 2. There has been relatively little use of scanner data by firms to capture the benefits available through applications designed to improve the decision- making process; 3. The industry lacks a management information system; and 4. The design of a management information system is feasible. Literature Review In the February 1986 issue, the grocery industry trade magazine, Supermarket Business, predicted that 1986 would be the year of the point-of- sale connection. That is, technological improvements would allow the scan- ning computer to be directly linked to the retail automa- tion computer and that the resulting improvements in in- formation management, both in store and at headquar- ters, would serve as a catalyst in resolving problems that have plagued the retail grocery industry (7). While such a prediction was quite optimistic, it was not one that is completely unattainable. Scanning, and the information it yields, already has led to broad changes in the retail food industry such as item non-pricing and evaluation of checker productivity. Scanning has experienced considerable growth since its inception in July 1972 by the Kroger Company in Cin- cinnati, Ohio. Originally, growth was slowed by reluc- tance of managers to adopt scanning. Among the reasons for this reluctance was the expressed resistance by consumers and some consumer groups to item non- pricing. However, by 1985, more than 11,000 stores had adopted scanning and more than one-third of all super- market purchases were checked by scanners (8). Table 1.1, reproduced from the September 1985 issue of Pro- gressive Grocer, gives A.C. Nielsen estimates of past and projected future growth of scanning. The survey proba- bly was taken in early 1985 and the figures for 1984 were preliminary estimates (Table 1.1). Table 1.1. Growth of Scanner Installations Total Percent Average Number Scanning Number Of Change Of New Sales As A Stores With Versus Scanning Stores Percent Of Year Scanning A Year Ago Per Month Total Sales 1979 1387 159 71 6 1980 2931 111 129 14 1981 4568 56 137 21 1982 6486 42 159 28 1983 8150 26 139 35 1984 9930 22 148 4O Future Projected Growth 1985 11550 16 135 45 1986 12990 12 120 5O 1987 14250 1O 105 54 1988 15390 8 95 57 Source: A. C. Nielsen Estimates From: Progressive Grocer, September 1985 The figures in Table 1.1 indicate the probable con- tinued growth of scanning installations through 1988. If these predictions are accurate, and they seem consis- tent with current trends, by the end of 1988 there should be more than 15,000 stores with scanning capabilities which will handle approximately 60 percent of all super- market sales. It should be noted that the growth of scan- ner installations is increasing at a decreasing rate and that scanning sales as a percent of total sales is mono- tonically increasing. The increasing number of scanning systems in the grocery industry is indicative of the acceptance of this technology by the industry. Benefits derived from adop- tion generally have been separated into two categorie “hard” or tangible benefits, and “soft” or intangible benv efits. Hard benefits refer to the savings accrued from scanning systems via the improved speed and accuracy in operations. Examples of “hard” benefits include (9): 1. Increased checkstand productivity; 2. Reduced shrinkage through improvements in- price accuracy, reductions in theft, and improve- ments in produce margins via more accurate weighing; 3. More efficient bookkeeping; and 4. Reductions in labor costs through reductions in price marking and price changes. In general, these hard benefits have provided the jus- tification for investment in scanning systems. While it is generally believe these benefits have provided a good re- turn on investment, most food retailers and industry analysts believe that the soft or intangible benefits offer an even greater return. Soft benefits include savings and/ or increases in sales due to improved managerial and merchandising decisions made possible by the wealth of information provided by scanners. Examples of soft ben- efits include (10, p.27; 11, pp. 9-10): 1. Improvements in shelf space allocation: Compari- sons of sales, gross profit, direct product profit (DPP), etc. can be compared to facings or the amount of shelf space allocated and the location on the shelf; 2. Improved inventory shrink control: Shrinkage rates by item or category can be provided. Allows better monitoring of items on deal or allowance and in general allows for more price accuracy. If direct store delivery (DSD) is implemented, the combination of back door and front end informa- tion results in an extremely good inventory con- trol system; 3. Improvements in labor scheduling: Accurate sales data indicate sales in certain departments and total sales as well as customer counts at a specific time of day or day of week. The result is improve- ments in departmental and front-end scheduling; 4. Improvements in DSD goods identification: A clear identification of all DSD merchandise sold at the store improves management control; 5. Improvements in new item evaluation: Obtair‘ quick accurate assessment of new item perform- ance; 6. Improvements in out-of-stock position: Improved product inventory control procedures should help reduce out-of-stocks; 7. Improvements in advertising and promotion re- sults: Evaluate the impact of price specials and special displays immediately and more acct rately; ‘W 8. Improvements in pricing decisions: Impacts of price changes are readily available; 9. Improvements in product mix selection: Product movement data, dollar sales, and margins help de- termine the optimum assortment of merchandise needed; l0. Improvements in profitability analysis: A depart- ment's contribution to the store’s overhead or a store’s contribution to a division’s overhead can be readily calculated; 11. Improvements in customer relations: Descriptive receipt tape, increases in checkout accuracy, and increases in speed of checkout; 12. Improvements in store security: Ability to monitor checkers either on store terminals while processing transactions, or by statistical analysis of refunds granted, coupons accepted, overrings, etc. Item purchases can be compared to item sales to determine whether there is a noteworthy quantity of any item purchased but not sold. If there are large discrepancies, perhaps items brought into the store as inventory are not being sold but are disappearing through some form of theft or pilferage; 13. Design of fresh meat, poultry, seafood, and pro- duce systems: Use of variable weight UPC sym- bols provides detailed data which allows control over sales, spoilage, and margins; and 14. Other uses: Monitor bad check information, au- tomatic reordering, perpetual inventory, calcula- tion of store gross profits by department and com- modity class. Once item purchase (through direct store delivery) and sales data are available, per- petual inventories of items carried at the store level can be maintained. Automatic reorders are based on preparing orders from item sales move- ment. In general, these applications are placed into one of the following three categories based on the nature of the application (12): 1. Tracking: These reports monitor the activities of the business and serve as a means for the man- ager to spot potential problems and oppor- tunities; 2. Analysis: These reports involve the reorganiza- tion and integration of data and other information to answer questions; and 3. Experimentation: Searches for the cause and ef- fect relationships between merchandising actions and the change in sales or profit. It is different from analysis since it involves screening out fac- tors through preplanned controls. Monitoring of item movement is an example of a track- ing application. An analysis application differs from a tracking application in that it attempts to answer spe- cific merchandising questions rather than simply show- ifiing the results of certain actions. For example, various display forms may have been used in different stores to determine the most beneficial method of introducing a new product. An analysis application would not neces- sarily reflect a cause and effect relationship since other factors besides the display type would not be taken into account. An experimentation application, then, would involve the removal of other influential factors so that the results of the various displays and price levels on sales and prof- its could be analyzed. It might be necessary for the ex- periment to be conducted in a number of similar stores in areas with similar socio-economic groups. Also, fac- tors such as weather and competitors’ actions must be taken into account. The use of scanning data as a management and mer- chandising tool did not begin until the late 1970s or early 1980s (13). Even now only a few pioneering firms have begun to realize some of the intangible benefits of scan- ning. The following is a list of some applications, espe- cially soft benefit applications, in use in various super- markets around the country: 1. Giant Food, lnc., Landover Maryland, used scan- ner information to track sales of different cuts of meat to determine methods which increased sales and profits and reduced waste (14); 2. Ralph’s Grocery Co., Los Angeles, California, used scanners to determine the optimum price level for profit maximization of test items (14); 3. Wegman’s Food Markets, Rochester, New York, scan and print scannable coupons which reduces the cost of handling coupons and helps prevent the misuse of coupons (15); 4. Gromer’s Supermarket, Elgin, Illinois, developed CASS (Computer Assisted Supermarket System). The program allows for more precise shelf space allocation, and gives reports such as return on in- ventory investment. CASS will also give the aisle and shelf location of every item plus a numerical code of 1,2, or 3 which indicates whether the cus- tomer must reach up, straight ahead, or down to choose a product. Gromer’s was also the first store to scan DSD products at the back door and was one of the first stores to install a Toledo Meat Management system (16); 5. Lucky Stores, Dublin, California, and Ralph’s Supermarkets, are using the space allocation software Spaceman II. This software produces color schematics or planograms for straight, staggered, or sloped shelves and for pegboards and freezer coffins. The program indicates sales, gross profit, return on inventory investment, and direct product profit (Supermarkets Launch and direct product profit (17); and 6. Shaw’s Supermarket, Massachusetts, has created its own scanner driven shelf replenishment sys- tem. Shaw’s also has a shelf management system. The system sets an order point based on the in- ventory required to meet consumer demands and the amount of the product sold from order point to delivery. When the actual inventory gets to the order point, an order is automatically placed by the computer (18). A March 1985 publication by the Food Marketing Insti- tute (FMI) entitled Retailer Applications of Scanning Data provides additional insight into current applica- tions of scanner data in retail groceries. The report was prepared for FMI by Willard Bishop Consulting Economists, Ltd. The documentation of these applica- tions was the result of interviews with approximately sixty progressive companies to determine the type of ap- plications in which they were involved. In this survey, the current applications of scanner data were found to ad- dress problems in one of five general categories: 1) shelf management, 2) managing promotional inventories, 3) profit improvement, 4) evaluating merchandising alter- natives, and 5) setting buying guidelines. Seventy-five percent of the companies surveyed used one or more of five types of product movement report, with no single type of report clearly preferred. The three most popular reports, each being used in about 25 per- cent of the companies surveyed, were: 1) a direct store delivery report showing movement and price of direct store delivery items; 2) an advertised item report show- ing movement and price history for items advertised or displayed; and 3) a zero movement report which lists the items with no activity. The other two reports, used re- spectively by 10 and 15 percent of companies surveyed, were a retail price exception report which listed the items scanning at a price different from established headquarters prices and a profit report which matched item movement with gross profit (19). The survey also indicated several applications cur- rently being developed in a number of the surveyed com- panies. Shelf allocation applications were clearly the most popular area of development with 30 percent of the surveyed companies working in this area ( 19). The em- phasis on shelf allocation is readily visible in the number of computerized shelf management systems on the mar- ket such as COSMOS (Computer Optimization and Simu- lation Modeling for Operating Supermarkets), HOPE (Higher Operating Profits Through Efficiency), SLIM (Store Labor and Inventory Management), Accuspan, and Spaceman ll. Basically, all these systems determine space allocation and product assortment based on his- torical item movement. Other applications under devel- opment which involve the use of scanning data, as indi- cated by the survey, include a direct product profit re- port (15 percent of companies surveyed), automatic reorder systems (10 percent of companies surveyed), coupon scanning (10 percent of companies surveyed), and merchandise exception (10 percent of companies surveyed). The survey also indicated that 90 percent of the executives interviewed desired continued develop- ment of scanner applications in their companies (19). It is evident from the preceding examples that super- markets are capable of using scanner information as a managerial tool. If, however, a supermarket desires out- side help to achieve some of the benefits available through scanner data, there are several market research firms with expertise in this area. TRIM Inc. and Be- haviorscan are two notable examples. For example, the Los Angeles based TRIM Inc. was hired by a midwest re- tailer to determine the comparative advertising effec- tiveness of four competing newspapers (20). 10 The previous examples of practical usages of scanner data by various supermarkets and market research com- panies represent isolated cases of attempts to capture the benefits of scanning. The most comprehensive ant up-to-date published report relative to applications of‘ scanner data found in the literature is the ScanLab proj- ect. The ScanLab project was initiated in 1981 as a joint effort between the General Foods Corporation and Dick’s Supermarkets of Platteville, Wisconsin. The purpose of the project was to aid the retailer in achieving a more ef- fective use of scanner data. I‘ The ScanLab system was designed to deliver informa- tion in the form of three reports: the Store Topline Re- port, the Primary Summary Report, and the Trend Re- port. These reports can be used in a large number of ap- plications including analysis of product assortment, new item tracking, item movement, retail sales dollars, gross profit, return on inventory investment, and shelf al- location using ScanLab alone or in conjunction with a packaged shelf management system (21). These reports were designed to be a comprehensive and functional managerial and merchandising tool. The reports can handle multiple departments, categories, and sub- categories and can be generated on command or on a regular basis. The Store Topline Report (see Table 1.2) was designed to give management a tool to monitor department per- formance. The report gives performance by category or commodity class within a department. Also reported were the number of items tracked within each commod- ity class, the movement in absolute terms and as a per- centage of department totals, sales volume in dollars and as a percentage of department totals, and gross profit in dollars and as a percentage of the department totals. In addition, the report also gave an estimated shelf inventory allocation, a figure on gross profit per cubic foot based on the estimated allocation, and the re- turn on inventory invested (22). The Primary Summary Report (see Table 1.3) was de- signed to be a tool for analysis of the performance of all items in each category. The report gives a description of the item and indicates factors that could influence the sale or gross profit such as allowances, direct store deliv- ery items, and the occurrence of merchandising activi- ties. Also, the report gives several measures of weekly performance such as unit movement, retail sales dollars, gross profit dollars, gross profit per cubic foot, esti- mated shelf inventory, and the return on inventory in- vested. This report could be used for shelf allocation, new item tracking, and seasonal and holiday product analysis (22). The Trend Report (see Table 1.4) was designed to test‘ new merchandising concepts or strategies. The report is able to evaluate item movement for a period of 13 weeks. Therefore, the effects of a merchandising change on profits or sales can be tracked to determine the profita- bility of the change. The report is provided on command, but can be set up for generation on a regular basis. In ad- dition, the report gives retail price, retail sales dollars, gross profit dollars, gross profit dollars per cubic foot estimated shelf inventory, return on inventory invest-Y- ment, unit movement, and purchase incidence on a by é .0 6w»: 50580900 N000; 120000 _wEO.Iw.CwQ mIRINIvIEQENE Bk 20G 500w ..00»R:00w 005cm dm>mmmmm mRIOE 012 .ZO_._.<~_OmxOo N002 éNNzNo >m mm»: PIOIMQEOQ NIO RNI NRN.I 8.I mNO NR. IN NN NINRN NN III NNI 0008mm zmNomN NNR II NNN NmI IN I. NI.I I. NN.IO 0.0 N NN _z<0 N wjom Rmm>>w zNNQE INR RIO mNI NNm 3N NI NRRI NI NNNR N.I Rm NR wRwnmo 0z< mmE zmwom» NNR NN v0.0 NNm NNN N. NNNI N. NNNIO ION IN NR N520 zwwomm NIR NII NNN RNNI NRN 3O NNIOm N.N Rm.IIN 3O NNI NN 10300 Qfimm zwNoml NIR RN mNI NRN NIN IO. NR.m N. 8.8 mN ION NN >mmz zwwomm 8R NIO mI.I IONN NNN NI NNNN m.I NINII NN NN mNI wm=5m>0z zmwomz 8R NIO NNI NNN RN m. RN.R w. vm. 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O 0.0 0? <._.0n_ z_?_IO .0 2<00O 0300 00 000v?00 < 0.0 0.? v.? 0.? 00 00.0 0? 00. 00. 0?.0 v 00 O 0.0 0? 0.00 Z_0.<00 D< >0Z<._. 00 0000?00 < 0.0 0.? v.? 0.? 0v 00.0 0? 00. 00. 0?.0 v 00 ?0.0 0? 00|_._> 0205000 I0_IO\20O 0300 Om 00?0000 < 0.0 0.0 0.0 N.0 vN 00.0 0? 00.0 00.? 00.0 0 0v 0N.v 0? 00ON00¥O_I Om 0000?00 < 0.0 0.0 N.0 0.0 v0 00.v 0? 00.? 00.? 00.0 0 N0 N0 ?? 0? 000010.00 00004._02 XO0Z_ 00<._._00 Z0>Z_ 0.0 DO 0 0 P20>02 P20>02 0N_0 x0 Z0_._.n=0O000 20.: 0000 0 Z0>Z_ 00000 00.30 ._._ZD __00 __00 0|_0I0 0 0.000 00000 00|_<0 .023 |_<0.0._. 20._._ 0.00I0 00000 .=<.r00 0.00 00000 00000 .=<._.00 \ . . . . - - 00<._O 00 0N. 200.: - - - - -\ \ . . . . . . . . . . . . . . . - - v_00>> 00<00>< . . . . . . . . . . . . . . . - - \ 00-00 A0§00>> ._.E00220O ._0m0O00m v0 v0\00\?0 I 00020? HA0V0P<0 >00O000 ?0 H._.Z02._.0<000 ._.0v_0<2000D0 0.¥O_0 A0O00OP0 0.00000 >0<2230 >0<2_0n_ &m<._Z) Total Meat Prod Groc KPI-3 Gross Profit $ Total Meat Prod Groc Firm Zone 1 Store 1 Store 2 Zone 2 Store 1 Store 2 KPI-4 KPl-5 KPI-6 Inventory Turns Weekly Avg. Total Meat Prod Groc Customer Count Avg. $ Sales Per Customer Firm Zone 1 Store 1 Store 2 Zone 2 Store 1 Store 2 ‘This format should include other areas of interest such as frozen foods, the bakery, or the deli. Key Performance Indicators (KPI): (l) Dollar Sales (2) Gross Margins (Percentages) (3) Gross Profit Dollars (4) Inventory Turns Table 5.4 CEO Report for Evaluation of Advertising (5) Customer Counts (6) Sales Per Customer Key Performance Indicators [KPl): (1) Number of Specials (2) Dollar Sales of Specialized Items (3) Percentage of Dollar Sales of Specialized Items (4) Gross Margins (Percentages) (5) Gross Margins on Specials (Percentages) (6) Number of Coupons Redeemed (7) Customer Count (8) Sales Per Customer (9) Percentage of Customer Purchase (l0) Percent of Customers Purchasing 34 Advertising Report (Monthly) KPl-1 KPI-2 KPl-3 KPI-4 KPl-5 % Special # Specials $ Sales Specials Sales to Total GM (%) GM on Specials Total Groc Meat Prod Total Groc Meat Prod Groc Meat Prod Total Groc Meat Prod Total Groc Meat Prod Firm Zone 1 Store 1 Store 2 Zone 2 Store 1 Store 2 KPl-6 KPI-7 KPl-8 KPl-9 KPl-1O Avg. $ Sales o?» Customers % Customers # Coupons Redeemed Customer Per Customer Purchasing Specials Purchasing Only Specials Total Groc Meat Prod Count Total Groc Meat Prod Total Groc Meat Prod Total Groc Meat Prod Firm Zone 1 Store 1 Store 2 Zone 2 Store 1 Store 2 ‘This format should include other areas of interest such as frozen foods, the bakery, or the deli. A (2) Department Dollar Sales Merchandiser Although scanner data have little potential to aid the merchandiser in the management of facilities and per- sonnel, there exists considerable potential in the areas of inventory management and the evaluation of goals and strategies. The Department Evaluation Report in Table 5.5 provides the merchandiser with basic data to evaluate the performance of personnel with merchandis- ing duties in individual stores and zones. The report pro- vides information on sales and profitability as well as the percent of items scanned and the degree of price accu- racy for departments within stores and zones. Total sales, total department sales, and department sales as a percentage of total sales help determine if the depart- ment is achieving a “reasonable” sales volume. The fig- ures for departmental gross margin, price accuracy and the percent of items scanned are indicative of opera- tional effectiveness. In the area of capital management, the merchandiser has considerable responsibility in inventory manage- ment. Responsibilities in this area include shelf sets and product mix, display of merchandise, ordering, and shrink control. The Category Evaluation Report (Table 5.6) is the primary report to evaluate shelf sets, space al- location, and product mix. This report divides all the merchandise in a store into categories and supplies in- formation on the performance of a category. For each category, information is provided on: 1) the number of items in the category; 2) the units moved; 3) unit move- ment as a percentage of department movement; 4) dollar sales; 5) category sales as a percentage of department sales; 6) gross margin; 7) gross profit dollars earned by the category; 8) category gross profit dollars as a per- centage of department gross profit dollars; 9) the number of specialized items in the category; and 10) the dollar sales of specialized items as a percentage of cate- gory sales. From this report, categories are chosen, on the basis of performance, for reset or for consideration Table 5.5 Department Evaluation Report for the Merchandiser of price changes. The Category Evaluation Report also could be used to evaluate special displays or methods of packaging. To accomplish this task, the display or pac- kage type is set up as a category and tracked over weekly, instead of monthly, periods. When a particular category is chosen, the Reset Re- port or the Pricing Report (Table 5.7) are generated. These reports contain more specific information to be used to reset shelves or to change item prices. For exam- ple, the Reset Report gives a description of each item in the category and lists the size, the number of units per case of the product, and the price. The report also pro- vides weekly average figures (based on the previous period) as to: 1) unit movement; 2) unit movement as a percentage of category movement; 3) dollar sales; 4) dol- lar sales as a percentage of category sales; 5) gross mar- gin; 6) gross profit dollars; and 7) gross profit dollars per item as a percentage of category gross profit dollars. Other reports used to evaluate product mix and to man- age space allocation, once the category is selected, in- clude the Slow Movement Report and the New Item Movement Report (Table 5.7). The Slow Movement Re- port lists items by category that have experienced move- ment of less than 6 items over a 4-week period. The New Item Movement Report shows the weekly movement of new items over a series of consecutive weeks. These re- ports help to weed out slow moving items and to evaluate new items to determine if they should be con- tinued. To aid the merchandiser in ordering, the Warehouse Ordering Report (Table 5.8) was developed. This report compares total retail movement with movement from the warehouse. The Warehouse Ordering Report is de- signed to be delivered weekly and contains the UPC, item description, and item size for each item in every cat- egory. Movement information for the entire firm is com- piled and presented to the merchandiser as total cases of product moved. The information provided includes: 1) total firm movement in cases for the previous week; Department Evaluation Report (Monthly) Department: KPI-1 KPI-2 KPI-3 Total Dept. Dept. Sales Sales Sales % of Total KPI-4 KPI-5 KPI-6 KPI-7 KPI-8 Dept. Inventory GM (%) GP $ Turns ACC Scan 0/0 PFICG CV0 Firm Zone 1 Store 1 Store 2 Zone 2 Store 1 Store 2 Key Performance Indicators (KPI): (1) Total Dollar Sales (3) Department Sales as a Percentage of Total Sales (4) Department Gross Margin (Percent) (5) Gross Profit Dollars (6) Inventory Turns (7) Price File Accuracy (8) Percent of Items Scanned Table 5.6. Category Evaluation Report for the Merchandiser Category Evaluation Report (Monthly) KPI-1 KPI-2 KPI-3 KPI-4 KPl-5 KPI-6 KPI-7 KPI-8 KPl-9 Item # Units % $ % % Special Specials % Category Description Items Moved Dept Sales Dept GM (%) GP ($) Dept items ofTotal aaa bbb ccc *This report is based on the ScanLab Store Topline Summary Report as printed in ScanLab: Scan for Merchandising Decisions, General Foods Corporation, 1984, p. 4. Key Performance indicators (KPI): (1) Number of Units Moved (2) Unit Movement as a Percentage of Department Movement (3) Dollar Sales (4) Category Sales as a Percentage of Department Sales (5) Gross Margin (Percentage) (6) Gross Profit Dollars Earned by the Category (7) Category Gross Profit Dollars as a Percentage of Department Gross Profit Dollars (8) Number of Specialized Items in the Category (9) Dollar Sales of Specialized Items as a Percentage of Category Sales Table 5.7 Sub-Category Reports for the Merchandiser to Evaluate Product Mix: Reset, Pricing, Slow Movement, and New item Movement Reports Reset Report (On Request) Store: Dept: Category: KPI-1 KPI-2 KPI-3 KPl-4 KPI-5 KPI-6 KPI-7 Item Units Per Unit % $ % % Description Case Price Movement CATM Sales CATS GM (%) GP$ CATGP This report is based on the ScanLab Primary Report as printed in ScanLab: Scan Data for Merchandising Decision. General Foods Corporation, 1984, p. 5. The Reset Report shows weekly average figures for the previous period. Store: Dept: w Key Performance Indicators (KPI): (1) Unit Movement (2) Unit Movement as a Percentage of Category Movement (3) Dollar Sales (4) Dollar Sales as a Percentage of Category Sales (5) Gross Margin (Percentage) (6) Gross Profit Dollars (7) Gross Profit Dollars as a Percentage of Category Gross Profit Dollars 36 Table 5.7 (Continued) Pricing Report (On Request) Store : Item UPC Description Dept.: KPI-1 Movement % Category Category: KPI-2 KPI-3 KPl-4 KPI-5 GP % Price GM (%) GP$ CAT Key Performance Indicators (KPl): (1) Movement as a Percentage of Category Movement (2) Price (3) Gross Margin (Percentage) (4) Gross Profit Dollars (5) Gross Profit Dollars as a Percentage 0f Category Gross Profit Dollars Slow Movement Report (Monthly) Penod: Store Firm or Zone: Category KPI-1 KPI-2 Item Description Price Movement Shows items in each category within a department that move less than 6 units a month Key Performance Indicators (KPI): (l) Price \ (2) Movement 37 Table 5.7 (Continued) New ltem Movement Report (Monthly) Store Zone or Total Firm: Period: KPI Movement (items or tonnage) Category UPC Item Description Wk1 Wk2 Wk3 Wk4 Wk5 Table 5.8 Sub-Category Reports to Aid the Merchandiser in Ordering: Warehouse Ordering, Specialized ltem, Holiday File, and Vendor Reports Warehouse Ordering Report (Weekly) Department: KPI-1 KPI-2 KPI-3 Item Warehouse Category UPC Description Total Firm Movement Warehouse Movement Inventory All indicated movement is case movement. Key Performance Indicators (KPI): (1) Total Firm Movement (2) Warehouse Movement (3) Warehouse Inventory 38 Table 5.8 (Continued) Specialized Item Report (Monthly: Place on File) Department: Week of: Week of: Gross Gross UPC Item Price Margin % Movement Price Margin % Movement Key Performance Indicators (KPI): (1) Price (2) Gross Margin (Percentage) (3) Movement Holiday File Report Vendor Re on . . p Department: Vendor: KP‘ KP] 2 Pl -1 - K -3 KPI-4 KP "1 lftzlf KPM _ Unit Gross Gross UPC Description Price Movement Margin % GP $ UPC Item Pme Movement Margm A’ Prom $ The Holiday File should be collected for items indicated by the merchandiser and for weekly periods prior to and after a holiday. The reports should be filed for later use. Key Performance Indicators (KPDI (1) Price (3) Gross Margin (Percentage) (2) Item Movement (4) Gross Profit Dollars 39 2) average weekly movement (cases) over the past 8 weeks; 3) warehouse movement for the previous week, 4) average weekly warehouse movement over the past 8 weeks; and 5) estimated warehouse inventory. The weekly movement figures, compared to the warehouse movement, should help the merchandiser estimate the total amount of store inventory. The estimated ware- house inventory figure provides the merchandiser with an indication of the amount of a product to order so that the inventory at the warehouse will be sufficient to meet the expected demand by the stores for the following week. Other reports to aid the merchandiser in ordering for specials and for holidays also were developed. The Specialized Item Report (Table 5.8) depicts items that have previously been specialized and gives price and movement information for the merchandiser to use as a basis to place future orders. The Holiday File (Table 5.8) gives similar information but is designed to show the performance of seasonal items or items of special in- terest at a particular holiday (e.g., cranberry sauce at Thanksgiving). The Holiday File is designed to collect in- formation several weeks prior to and after a holiday. By recommendation, a historical file of this report should be constructed as an aid in ordering for the holiday in future years. Finally, the Vendor Report (Table 5.8) was designed to compile information on all items rep- resented by a particular vendor. This report, which supplies information on movement in the previous month, gross margin, and gross profit dollars, should be used to facilitate dealings with the various vendors. Scanner data have considerable potential in decisions of the merchandiser in regard to goals and strategies of the firm. Specific areas where scanner data could prove beneficial to merchandisers include profitability analy- sis, evaluation of sales goals, and evaluation of merchan- dising strategies such as pricing and advertising. The Ad- vertising Report (Table 5.9) provides information on the Table 5.9 Advertising Report for the Merchandiser attractiveness of advertising efforts by giving figures on the sales of specialized items and the percent of custom- ers purchasing specialized items. The report also gives profitability figures to indicate whether or not the items on special are adversely affecting profitability. Store Manager Personnel management is a major responsibility of the store manager. Table 5.10 contains three reports pro- duced from scanner data to assist the store manager in this area. The Department Evaluation Report (Table 5.10) and the Cashier Evaluation Report (Table 5.10) provide the store manager with information to evaluate person- nel in the various departments of the store. The Depart- ment Evaluation Report gives weekly sales and profita- bility figures by department as well as figures indicating the operating discipline of the department (percent of items scanned and degree of price accuracy). The Cashier Evaluation Report provides weekly productivity figures (customers per hour, dollar sales per hour, and items checked per minute) as well as figures to deter- mine operating discipline (scan percent) to be used in evaluating cashiers. The Department Evaluation Report and the Cashier Evaluation Report can be used for mak- ing wage and bonus decisions and for developing the store operating budget. The Labor Scheduling Report (Table 5.10) gives total sales, customer counts, and sales by department to aid in labor scheduling at the front end and in various service departments such as the bakery or deli. Inventory management is an important part of the re- sponsibilities of the store manager. Shelf replenishment is perhaps the primary responsibility concerning inven- tory management. To assist the store manager, the Aver- age Movement Report (Table 5.11) was designed. This re- port enumerates characteristics of the distribution of movement of a particular product-average movement (mean), dispersion of movement (variance), minimum Advertising Report (Monthly) Department: KPl-l KPl-2 KPI-3 KPl-4 KPI-5 KPI-6 KPI-7 KPI-8 # Items $ Sales $ Special Dept. GM # Coupons Purchasing Purchasing Specialized Specials Sales to Total GM % Specials Redeemed Specials Only Specials Firm Zone 1 Store 1 Store 2 Zone 2 Store 1 Store 2 Key Performance Indicators (KPI): (1) Number of Items (2) Dollar Sales of Specialized Items (3) Percentage of Dollar Sales of Specialized Items (4) Gross Margins (Percentages) (5) Gross Margins on Specials (Percentage) (6) Number of Coupons Redeemed (7) Percentage of Customers Purchasing Specials (8) Percentage of Customers Purchasing Only Specials 4O Table 5.10 Personnel Evaluation Reports for the Store Manager Department Evaluation Report (Weekly) KPI-1 KPl-2 KPI-3 KPI-4 KPI-5 KPl-6 KPI-7 $ Sales % Gross Gross Inventory % Items % Price Sales of Total Margin % Profit $ Turns Scanned Accuracy Grocery Produce Meat Fish Deli Bakery FF Dairy Total Cashier Evaluation Report (Weekly) KPI-1 KPI-2 KPI-3 KPl-4 KPI-5 KPI-6 Customer $ Sales Items Scan Time in Hourly Cashier per Hour per Hour per Minute % Subtotal Wage Labor Scheduling Report (Weekly) KPI-1 KPI-2 KPI-3 KPI-4 Day Time Total Sales Customer Count Produce $ Sales Deli $ Sales 7:00 a.m.—7:30 a.m. 7:30 a.m.—8:O0 a.m. 8:00 a.m.—8:30 a.m. 8:30 a.m.—9:0O a.m. etc. The Labor Scheduling Report is delivered weekly but contains sales figures and customer counts averaged over the previous 4 weeks. The report gives figures for 30-minute intervals for each day. 41 Table 5.11 Inventory Management Report for the Store Manager movement, and maximum movement. The Average Movement Report should be calculated on a regular HOHdaY Ffle (BY Request) basis. Further, this report should list only those items Department. whose average movement fluctuates sharply, say in ex- cess of two or three standard deviations from the mean. Weeks 0f: Ordering for specials and holidays are special prob- Units KPH KPl-g lems for the store manager. Thus, the Specials Report Per and the Holiday File exhibited in Table 5.11 were de- UPC Description Case Price "em Movemem veloped. The Specials Report provides price and move- ment information on items that previously had been E- specialized. This information could be used as an aid in ordering items the next time they are featured. The Holi- for several weeks prior to and after holidays. This infor- mation would be saved and used by the store manager include items requested by the store manager or merchan- diser. The report is generated for a number of weeks prior to Department Manager and after a holiday. The reports are kept on file to aid with the next years ordering. Since the responsibilities of department managers are‘! so similar to those of the store manager, similar reports 42 Movement (Monthly) KPI-1 KPI-2 KPl-S KPI-4 ~ i Average Variance Minimum Maximum Dept. Item Movement of Movement Movement Movement lu Special Report (Monthly: Save in File) Department: Units KPI-1 KPI-2 KPl-t KPI-2 Per Week of: Week of: UPC Description Case Price Movement Price Movement day File would be used to track sales of specific items k would be useful to both levels 0f management. In fact, the Cashier Evaluation Report, the Labor Scheduling Re- port, and the Average Movement Report as well as the Specials Report and the Holiday file developed for the store manager should also be received by various de- partment managers. However, the Department Evalua- tion Report developed initially for the store manager may be modified for department managers. The mod- ified version is exhibited in Table 5.12. While the report for the store manager supplies information for depart- ments, the report for department managers supplies in- formation for categories within departments. Finally, for evaluation of displays or categories within a depart- ment, a Category Evaluation Report (Table 5.6) from the merchandiser could be requested. CIO The CIO has little use for actual scanner data other than to aid in monitoring the operating discipline of the firm concerning scanning systems and in checking the master price file. The Scanning Report exhibited in Table 5.2 received by the CEO should also be received by the CIO. This report enumerates scan percentages and de- gree of price accuracy by department. Consequently, this report provides the CIO with a means to monitor the operating discipline in the firm. The only other report for the CIO is the Category Price Range Check of Master Price File (Table 5.13). This weekly report divides the master price file into categories. For each category, a price range is set to in- clude all item prices in that category. The report is de- signed to list all items in a category that are outside a specified price range. Although this report cannot verify individual item prices, it is a way to quickly check the price file for errors. ltems with inaccurate prices that fall Table 5.12 Evaluation Report for the Department Manager inside the price range will have to be found and cor- rected by manually auditing the price file. Scanning Coordinator As with the CIO, actual scanner data are of little use to the scanning coordinator. However, scanner-derived information to monitor operating discipline would be useful to the scanning coordinator. To monitor store discipline concerning the operation of the scanning system, the scanning coordinator should receive, with some changes, the same reports as the CIO. The scanning coordinator should receive weekly, rather than monthly, the Scanning Report exhibited in Table 5.2. If a problem with the scan percent in a department arises, the scanning coordinator can request a Percent Scanned Report shown in Table 5.14. This table simply shows the scan percent for each category in a depart- ment to help pinpoint problems. The scanning coordinator also should receive a weekly report similar to the Category Price Range Check of Master Price File Report in Table 5.13. The report for the scanning coordinator should be set up similarly, but should only include items and categories from the price file of his/her particular store, which may differ from the master price file of the firm. ltem prices should be checked against a price range for a category to help find pricing errors. While this report cannot take the place of manual price audits of the store price file and shelf price tags, it should help the scanning coordinator catch some pricing errors. Operational Considerations To establish an effective MlS, the retail firm must ini- tially have a vision of where it is going in terms of market- ing, operations, and distribution. Integrating an informa- Category Evaluation Report (Weekly) CCC Store: Dept. KPl-t KPI-2 KPl-S KPI-4 KPI-5 KPl-6 KPI-7 KPI-8 $ Sales % GP $ % Items % Price Inventory Category Sales of Dept. GM % GP $ of Dept. Scanned Accuracy Turns aaa bbb Key Performance Indicators (KPI): (1) Dollar Sales (2) Sales as a Percentage of Department (3) Gross Margins (Percentages) (4) Gross Profit Dollar (5) Gross Profit Dollars as a Percentage of Department (6) Percentage of ltems Scanned (T) Price Accuracy (Percentage) (8) Inventory Turns 43 Table 5.13 Reports for the CIO Scanning Report (Weekly) *This Report is the same as the Scanning Report for the CEO in Table 5.2. Category Price Range Check of Master Price File (Weekly) Department: KPl-1 Category Price Range KPl-2 KPl-3 items Outside Price Range Price An exception report that checks for prices outside a given range for a category. Manual checks of the price file may also be necessary. Table 5.14 Percent Scanned Report for the Scanning Coor- dinator Store: Department: KPI Category: Scan % aaa bbb ccc tion-system plan into a total business plan can be dif- ficult in the supermarket industry clue to varying plan- ning requirements of different parts of the business. Mer- chandising and operations, the lifeblood 0f the retail business, have relatively short planning horizons. Human resource, store development, and finance func- tions 0f the retail firm have longer-term planning re- quirements than operations and merchandising. The differences in planning horizons must be recog- nized by management before beginning the process of developing a MlS. The MlS model in this study centers attention primarily on the key performance areas of op- erations and merchandising. To implement this MlS, it is necessary to identify key performance indicators (e.g. movement, dollar sales, gross margins, gross profit dol- 44 lars). In essence, then, management must prioritize in- formation- system target areas. To accomplish this task, several factors (not necessarily inclusive) warrant con- sideration: 1) resources available; 2) look at what the competition is doing; 3) cost/benefit evaluations, and 4) risk assessment. Secondly, this information system will need to be managed, presumably, by the chief informa- tion officer and scanning coordinator(s). Third, training personnel in the use of the information system is essen- tial. Finally, management must realize that the develop- ment and implementation of the MlS is not a one-time event, but an ongoing process. Management of scanner data has traditionally been considered a mainframe application regulated by highly specialized technicians. However, supermarket firms may use personal computers to manage scanner data (6), particularly to evaluate product performance (gross profit dollars, retail dollars, unit movement) and sales trends as well as to track certain items. No direct link be- tween personal computers and the mainframe is neces- sary. Although not the most efficient approach, data can be entered from a point-of-sale printout into any popular microcomputer spreadsheet program (e.g., LOTUS, SUPERCALC). Consequently, managing scanner data and hence information flows may be less difficult than before because of personal computers. Costs and benefits are the key components in the deci- sion to continue, alter, or discontinue the MIS. Con- sequently, audits of benefits (hard and soft) received from the MIS are necessary. With regard to costs, accord- ing to an FMl information system study from 1985 (1), supermarket firms spend an average of 0.26 percent of dollar sales on information systems. The top 20 percent allocate 0.48 percent, however. This set of figures does not include automation equipment and maintenance costs. By comparison, wholesale firms spend 0.43 per- cent of sales on information systems; the top 20 percent allocate 0.68 percent. To quote Ross, “the value of any information system must ultimately be measured by the quality of management decisions. Anything less is incon- clusive, anything more unnecessary.” Summary Scanners have been a profitable investment for super- markets. However, there still exists great potential for ad- ditional bottom line dollars. These potentials lie largely in “soft” benefit areas, additional and more accurate in- formation on which to base management decisions. This chapter makes a case for firm management to develop and implement an informational system to better cap- ture these benefits (dollars). Although the different as- pects of the chapter (Table 5.1, as well as Tables 5.2-5.14) l‘! are generic and probably not directly applicable to any specific firm, they do provide a structural framework which can be altered (deletions, additions, or other changes) to fit management informational needs of a particular firm. CHAPTER 6 Conclusions and Implications Introduction The focus of the research has been on the identifica- tion of the decision-making roles of the various levels of management in a supermarket, the identification of pre- sent and potential usage of scanner-derived informa- tion, and the development of a firm-wide management information system based on scanner data. The informa- tion was gleaned through discussions with managers of retail grocery firms in the five-state area of Virginia, Maryland, Pennsylvania, Kentucky, and Indiana. The firms were among the most progressive in this region. Conclusions are presented as well as the implications of these findings to the retail food industry. Finally, this chapter serves to document further research topics. Conclusions The findings of this research substantiated the hypothesis that there has been little use of scanner data by firms to aid in managerial decision-making. Firms have tended to focus on the tangible benefits realized through the implementation of a scanning system. At- tempts to utilize scanner data for decision-making have been thwarted by inappropriate forms of scanner infor- mation delivered to managers and by the lack of training on the usage of the data. This research also substantiated the hypothesis that to design an effective management information system, it is essential that managerial responsibilities be defined and stratified. importantly, management must define what information is needed at present as well as in the future. Once done, analysis of the potential for scanner data in decision-making as well as the design of the form, content, and timeliness for delivery of these data for each level of management of a retail food distribution firm may be determined. In this research, a generic man- agement information system (MIS) was designed to pro- vide each management level with the information it needs without burdening a particular level with large volumes of unnecessary data. The success of the MIS will depend largely on the communication and data infras- tructure, the base for all information required by the or- ganization. In general, reports were developed primarily to facilitate management by exception. The monthly or weekly reports to managers were tailored to point out potential problem areas. When these problem areas were identified, more specific reports could be re- quested to aid a manager in correcting problems. In short, the goals of this MIS were twofold: l) to generate useful reports but simultaneously minimize review time by management personnel; and 2) to direct attention to critical areas (key performance indicators). Potential Implications for Food Retailers This research offers a firm the basic framework to use in analyzing its specific decision-making process and for designing a MIS tailored to the structure of the firm. Be- cause scanning is a condition of doing business, man- 45 agement of information will likely be a decisive factor in determining which firms are best prepared to meet in- tense competition. On the basis of the search of literature and the discus- sions with managers, firms that have implemented scan- ning systems have improved profits even though the benefits realized have been limited to tangible benefits. The implementation of a MIS outlined in Chapter 5 should result in additional increased profits. The intangi- ble benefits that can be realized through improvements in managerial decision-making resulting from such a system take the form of both increased revenue and de- creased costs. Increases in revenue should accrue from improvements in inventory management, shelf and space allocation, and from improvements in pricing and advertising. Decreased variable costs should result from improved labor scheduling and improved loss (shrin- kage) control. The realization of these intangible bene- fits could result in some additional labor costs since ad- ditional staff members may be needed for the compila- tion of reports. These costs, however, should be minimal when compared to the original costs of implementing scanning systems. Thus, the realization of the intangible benefits could result in greater profits than those realized to date through tangible benefits. Another potential application concerns the analysis and adaptation of the reports outlined in the generic MIS. Reports may be analyzed in terms of needs-content, form, timeliness—for specific management levels and/ or responsibilities. In agreement with Lodish and Reibs- tein, marketing decision support software must be able to leverage all the latest data, models, and statistical analysis procedures. The software must have the capac- ity for data base management, analysis, graphics, flexi- ble report generation, and modeling—all in a user- friendly environment. The data base should be or- ganized in ways that can be easily altered when situa- tions or services change. For example, without doing massive reprogramming, a firm must be able to incorpo- rate new products or changes in sales districts into the data base. In addition, information about shelf space, end-of-aisle displays, use of advertising, and use of coupons also should be retained so that impacts on sales, item movement, and net contribution can be made. The software should have the capacity to allow many users to access the same integrated data base. The sys- tem needs a wide variety of output capabilities, ranging from simple tables to presentation - quality graphics and reports. To be able to divide and aggregate the data simultaneously into such categories as product, region, salesperson, and time period is of paramount impor- tance. Either the chief information officer and scanning coor- dinators (internal support) or part-time or full-time con- sultants (external support) must understand enough about data analysis, statistical analysis, and modeling to make sure that the appropriate checks have been made and the appropriate questions have been asked when recommendations based on computer analyses are made. These people should report directly to top and middle management as part of staff groups. Implications for Further Research Work on this project brought to light several pos- sibilities for future significant research. These areas in- clude: 1) the documentation of costs and benefits result- ing from the implementation of the MIS; 2) the develop- ment of a training program for managers on the use of the reports in the MIS; 3) the potential benefits of con- necting front-end (point-of-sale) scanning systems with direct store delivery systems to achieve a comprehen- sive inventory management system; 4) the general use of scanner data for consumer demand analysis; 5) the specific use of scanner data for the estimation of short- run, own-price, and cross-price elasticities for various commodities; and 6) the use of scanner data to achieve the optimum use of limited resources of a firm through analysis of linear programming models. The logical next step in the development of a MIS would be implementation into a retail environment. Ini- tially, however, it is of merit to conduct research in re- gard to the documentation of costs and benefits from the implementation of a MIS. Such a feasibility analysis would be useful to managers considering a shift to an in- formation system for management. Another area of potential fruitful research might deal with the development of an effective, efficient training program. The training program should utilize specific examples and case studies. Additionally, this program might concentrate on optimizing usage of scanner-de- rived information by managers. Further, additional work on the design of management decision-making information distribution systems is de- sirable. One particular aspect might involve the most ef- ficient way to incorporate the scanner management in- formation system into the total information distribution system of the firm. A specific study might concern the integration of a scanner point-of-sale information sys- tem with a direct store delivery (DSD) system to form a single information system. Such a system would allow managers to track merchandise movement from the 46 back door to the front-end. This system would aid the manager in determining shrink and would help set up pa- rameters for automatic reordering. Scanner data have tremendous potential for use in the analysis of consumer demand for specific products or commodity classes. Scanner data possess obvious ad- vantages over aggregate annual, quarterly, or monthly time-series data of prices and consumer purchases, trad- itional sources of data for empirical analyses. The time- series data are too general for product specific decision- ‘ making and may not reflect current market conditions. For more detailed data for specific products, researchers typically rely on consumer panels and consumer sur- veys. However, such traditional cross-sectional data are expensive in terms of collection, and generally, the col- lection of such data occur only periodically. Scanner data, on the other hand, provide researchers with a read- ily available, relatively inexpensive source of product- specific information of actual customer purchases at given prices. Thus, scanner data may prove to be the most detailed and definitive source of retail food indus- try statistics available to researchers. This detailed and timely source of information should lead to more reli- able demand analysis for disaggregate food and nonfood commodities. The use of item-specific movement data permits the estimation of short-run, own-price, and cross-price elas- ticities of demand for various commodities. The estima- tion of demand elasticities for individual items has ramifications in pricing and ordering decisions. The knowledge of the respective elasticity measures could lead to more effective marketing strategies by aiding managers in predicting the effects of price changes for specific products. Additionally, scanning of uniform product codes provides feedback on optimal pricing of grocery items and other products. The allocation of limited resources of a firm is a con- tinual problem. For example, the allocation of limited shelf space to maximize profit is a constant concern of food retailers. Scanner data can provide item-specific in- formation that could be used in analyses of linear prog- ramming models to determine the optimal allocation of shelf space. Optimization of product mix as well as ad- vertising and pricing strategies could also be achieved through linear programming models. 10. 12. 13. 14. Literature Cited . Supermarket News, “Information-Technology Explo- sion Seen Near,” (January 1987):34. . Ross, Joel E., Modern Management Information Sys- tems, Reston: Reston Publishing Co., Inc. 1976. . Symonds, Curtis W. A Design for Business Intelli- gence, New York: American Management Associa- tion, 1971. . Supermarket News, “The Impact of lnformation,” (December 8, 1986):1-10. . Vastine, William J., and Ed Watkins, “Key Perform- ance Areas and Key Performance Indicators and How to Use Them,” Working Paper, Texas A&M University, 1974. . Competitive Edge, “Managing Scan Data with Per- sonal Computers,” 8, 12(August 1987): 1-4. . Shulman, Richard, “The Year ofthe POS Connection,” Supermarket Business, 41 (February 1986) : 13-14. . Johnson, Mary, “UPC Update: Shaping up the Sym- bol,” Progressive Grocer, 64(March 1985):93-96. . Capps, Oral Jr., “The Revolutionary and Evolutionary Product Code: The Intangible Benefits,” Journal of Food Distribution Research, (February l986):2l-28. Ricker, Harold S., “Status of Checkout Technology,” Journal of Food Distribution Research, 4(September 1973): 21-28. . National Grocers Association, The Benefits of Scan- ning: A Study of Scanning in the Retail Grocery Indus- try, ( 1984). Progressive Grocer Executive Report, “A Framework For Scanning Applications,” 3(May 1985):56-59, 62- 63. Chain Store Age Supermarkets, “Scanning the Data Revolution,” (June 1982):45-46,59. Knox, Andrea, “More Than One Way to Scan the Costs”, Philadelphia Inquirer, (June 25, 1978):I-8. . Kaplan, Rachel, and Elliot Zwiebach, “Two Chains Testing Coupon Scanning,” Supermarket News, 35(June 24, 1985):1. 47 16. O’Neill, Robert E., ” More Sales, More Profits, Less 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. Space,” Progressive Grocer, 64(October 1985):61- 64,68. Chain Store Age Executive, “Supermarkets Launch Test of Spaceman Two,” 61 (June 1985):34-39. Dumas, Lynne S., “Scan Data Becomes Cornerstone,” Non Foods Merchandising, (March 1985):20-22. Competitive Edge, 6(November 1985):l-4. Chain Store Age Supermarket, “Using Scanners to Maximize Return on Advertising Dollars Invested,” (June 1983):14-l5. Chain Store Age Executive, “Testing Merchandising Concepts,” 61 (May 1985):5-6,8, 11. General Foods Corporation, ScanLabsA Study of the Use of Scanning Data in Merchandising Decisions, (1982). Partch, Ken, and Doug Harris, “Instore Automation: How Can We Pull It All Together,” Supermarket Busi- ness, 39(April l984):21. Fletcher, Stanley M., S.E. Trieb, and Dick Edwards, “Economic Evaluation of Scanning,” Journal of Food Distribution Research, I5(February 1984):64-69. Moede, Eric A., “A Management Information System Model for Convenience Stores,” unpublished M.S. Thesis, Texas A&M University, December 1978. Fleckinger, Joseph M., Materials prepared for the 1977 National Association of Convenience Stores Controller’s Clinic., April 25-27, 1977. Dearden, John, “Can Management Information be Au- tomated?” Harvard Business Review, 42(1964):I28- 135. Massy, William F. “Issues in the Development of Infor- mation Systems,” Marketing Management and Ad- ministrative Action, ed. Stewart Henderson Britt and Harper W Boyd. New York: McGraw-Hill Book Co., 1973. Mintzberg, Hensey, “Making Management Informa- tion Useful,” ManagementRevieiv, 64,5(I975):34-38. a T u ' »_ H. ' H,‘ ~ . i - . V _-. - \ < \ t , ' - - ’ .' ’ I - _ . < ‘ ._. ~ _ k A , ‘ . I v§__ ~ _ ' V ' _ “y, ' _:;»~ , _ .~ _ ,_-, ‘ w '_ t .\ ' _. f - ‘ ' ~' " ,_ "~ .. 4| . *. ' l :' . J * ' v ~ ' .‘ “.1: j, s; .. ,. , _ “ . ._ f‘ I " __._-\ v q: W’ \ >. 4, _ I r‘ , . ' ‘ ' ~ ' ‘ ' > ~ ‘ Appendix A Set of Questions Used in the Personal Interview Sessions I 1. General Information Store Location Characteristics (Organization, Type, Square Footage, Sales Volume/ Week, Number of Items in Store) Managerial Levels 2. Parameters of Authority for Decision-Making 991*.“ (i) Labor Scheduling (ii) Pricing Decisions (iii) Decide Specials and/or Merchandising Schemes (iv) Ordering Decisions (v) Markdown Decisions (vi) Other What computerized reports do you presently get in these areas? . Give specific examples of how you use each. Why don’t you make more use of these reports? For the operating responsibilities you outlined above, what kind of fast, accurate information would you like to help you better manage your store? 7. Technical Information (i) How much influence in the operation? (ii) How are reports developed? (iii) Standard software? (iv) Form? (v) Do you write own software? (vi) Why don’t you think your reports are more widely read or used? (vii) Additional things that may be used? 8. Scanner information used for personal evaluation? Technical Information I. General Systems Information A. Description of Computer Equipment: Manufacturer Model Installation Date Core Storage (e.g., 24K, 36K) Disc Capability (# of megabytes) B. What computer programming language do you use? Cobol RPG Basic Other C. Are you using the telecommunication capability of the computer? Yes No D. Current Computer Applications: Please check each of the applications currently operating on your computer. Accounts Payable Labor Scheduling General Ledger Personnel Administration Payroll Director Store Delivery Operating Statements Scan Support Labor Analysis Other Sales & Gross Profit Analysis 49 II. Application Software III. A. Application Package(s): Package Name(s) Vendor Person Operating Memory Requirements B. Assessment of Purchased Application Packages: Package Easy Easy to Well Some Many Name(s) to Use Learn Documented Problems Problems l. 2. 3. 4. 5. 6. C. Self Developed Package(s): Name/Type Computer Operating Source of Package Vendor System Language 1. 2. 3. 4. 5. 6. Yes No 7. Are you willing to Trade? Sell? Give? Scanning/Micro Application Software Questionnaire A. What type of scanning equipment do you currently operate in your store(s)? Yes No Model Number NCR IBM Datachecker DTS Sweda TEC B. Who performs your host support? Yes No QWPWE“? 1. Wholesaler 2. Yourself 3. N0 Host Support C. lf yourself, what equipment do you use? Vendor Model Number Software Package Name l. 2. 3. 4. 5. D. Does your host support DSD items? Yes No E. Does your host support custom price files? Yes No 50 Comments Memory Required 6 F. What reports are you using from the host? Report Name(s): FPFHPSPNH G. D0 you use reports t0 assist your decision-making and in what areas? Yes No Where? Merchandising New ltem Orders Theft Prevention Vendor Profitability Scheduling Price Discrepancy Shelf Price Auidits Checker Productivity Other (list) H. Are you using any data from your scanning system directly in an application program? If so, what types of data? Data Tape(s) i.e.: ltem Sales, please list. 1. FDFDFRGWPFPNH 2. 3. l. Do you plan to attach your small business computer directly into your scanning system(s)? Yes No J. Are you currently sell your movement information to SAMI, A.C. Nielsen, etc.? Yes No Appendix B (1) Austin’s Warehouse of Groceries; Jeffersonville, Indiana: A four-store retail operation. Store sizes ranged from 25,000 to 33,700 square feet. (2) Bon Foods; Dumfries, Virginia: A five-store operation with two stores scanning and plans to implement scanning in a third. Host services were provided by Richfood, the supplier of this firm. The store visited was approximately 25,000 square feet and was currently using a DTS-545 scanning system. (3) Farm Fresh; Norfolk, Virginia: A 40-store, multiple zone operation with all stores scanning. All stores were free ~ standing (no host). Several stores were equipped with direct store delivery (DSD) systems. The scanning system used was the NCR-1255 series. (4) Food City; Abingdon, Virginia: A 30-store, one-warehouse operation with 20 stores scanning. Three scanning systems were used: (1) DTS, (2) SWEDA, and (3) Datachecker. Also, plans for the installation of DSD systems in several locations were in the offing. (5) Georges Thriftway; Sykesville, Maryland: A one-store operation with an area of 25,000 square feet. Their supplier offered host services but the firm had in-house service. The scanning system used was the NCR 8258- 1255 series. T‘ (6) Giant Foods; Carlisle, Pennsylvania: A 39-store operation with 26 stores operating National Semiconductor scanning systems. The company provided the host system. The store visited was 34,000 square feet with 17,000 square feet in selling space. 51 (7) Giant Open Air; Norfolk, Virginia: A 23-store operation with 6 stores scanning. The firm also owned 50 Tiny Giant convenience stores. In addition, the firm had 16 DSD sites. Richfood was providing scanning host services. The scanning system in operation was a DTS unit. (8) IGA Foodliner; Stuarts Draft, Virginia: A one-store operation with 12,000 square feet. The scanning system w the DTS-500D series. (9) Ken Lewis - Liquor Discount, Louisville, Kentucky: A one-store (5,000-item) operation with plans to add an additional store. The firm had scanner and DSD capabilities. The CEO planned to tie all systems to a central computer. (10) Kroger; Roanoke, Virginia: A 108-store division with 61 stores scanning and plans to install scanning systems‘ in 20 additional sites. The scanning vendors were NCR and IBM. The division also had operational DSD sites; supplying independent stores. The company provided host services to members. (1 1) Malone and Hyde; Nicholasville, Kentucky: A cooperative wholesaler supplying independent stores. The company provided host services to members. (12) Richfood, lnc.; Richmond, Virginia: A cooperative wholesaler providing host services to 50 member stores. The basic services included price changes and product movement reports. (13) Santoni’s Markets; Baltimore, Maryland: This operation included six supermarkets (two with scanning systems) and two convenience stores. The supermarket visited encompassed an area of 17,000 square feet. The scanning system used was the NCR-1255 series. (14) Ukrops; Richmond, Virginia: A 17-store operation with 15 stores scanning. The host services of the firm provided by Richfood, lnc., their supplier. The store visited encompassed an area of 33,000 square feet. The firm used the IBM-3663 and IBM-3683 scanning systems. (15) Value Foods; Baltimore, Maryland: The operation included 10 stores and a warehouse. The firm had no host computer at the time of the interview but had plans to obtain one. The store visited encompassed an area of 31,000 square feet. TEC-TS80 scanning systems were used. (16) Wades; Christiansburg, Virginia: A six-store independent operation with four stores scanning. The firm was supplied by Richfood but did not use the host services. The scanning systems in operation included the NCR-2126 and DTS-540 systems. (17) Wetterau Food Services; Bloomington, Indiana: A wholesaler providing scanning host services. 52 J [Blank Page in Ofiginal Bulletin] Mention of a trademark or a proprietary product does not constitute a guarantee or a warranty of the product by The Texas Agricultural Experiment Station and does not imply its approval to the exclusion of other products that also may be suitable. All programs and information of The Texas Agricultural Experiment Station are available to everyone without regard to race, color, religion, sex, age, handicap, or national origin. 2.5M-—10-88