nariances between standard costs
TRANSCRIPT
NARIANCES BETWEEN STANDARD COSTS
AND ACTUAL COSTS IN SELECTED
ELEMENTARY SCHOOL FOODSERVICE PROGRAMS/
by
Deborah Pritchard Wilsonß\\
Thesis submitted to the Graduate Faculty of the
Virginia Polytechnic Institute and State University
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
in
Human Nutrition and Foods
APPROVED:
M.D. OIsen, Chairman
: C;,' l§€J¢§v“«Q-/,,_,, bowen J , ¤eIIas
V
December, 1979
Blacksburg, Virginia
ii
TABLE OF CONTENTS
CONTENTS PAGE
List of Tables and Figures iv
Introduction 1
Review of Literature 3
MethodologySample Selection .
Major Considerations 1ODemographic Description of Sample 11Departmental Organization 13
Data CollectionSetting of Standard Food Costs 14Determination of Actual Food Costs 17
Analysis of DataIdentification of Deviations 21Description of Total Population 23Limits for Acceptable Deviations 23
ResultsPresentation of Deviations 26
DescriptionThree Sets of Observations
° 38
Total Population 38
Limits for Control 42MAD's 44RSFE's 48
Tracking Signals 49Flexibility in Setting “trip levels" 52
Discussion 54
Conclusion 63
Summary 67
Bibliography 68
AppendixA. Nutritional Requirements 7O
B. Sample Menus 71C. School Names 74
D. Standard Cost Per Serving 77
E. Daily Food Usage Form 85
Vita 86
iii
LIST OF TABLES AND FIGURES
ITEM TITLE PAGE
Exhibit I Characteristics of Participating 13Districts
Exhibit II School Lunch Participation 1977-78 14
Exhibit III Number of Obseryations 22
Exhibit IV Daily Food Cost Deviation 26
Table Ia Daily Food Cost Deviation: 27,28,29Chesterfield County Schools
Table Ib Daily Food Cost Deviation: 30,31,32,33Petersburg City Schools
Table Ic Daily Food Cost Deviation: 34,35,36,37Prince George County Schools
Table II Deviations from Standard Cost 38
by Districts
Figure Ia Frequency of Food Cost Deviations 39Chesterfield County Snhools ·
Figure Ib Frequency of Food Cost Deviations 40Petersburg City Schools
.Figure Ic Frequency of Food Cost Deviations 41Prince George County Schools
Figure II Frequency of Food Cost Deviations 43Total Population
Exhibit V Sample Calculation of MAD's 45
Table III Summary of Mean Absolute Deviations 47
Exhibit VI Sample Calculation of RSFE 48
Exhibit VII Sample Calculation of Tracking Signal 49
Table IV Summary of Tracking Signals 51
Exhibit VIII Falling Creek Elementary: Example 56
of Uses
iv
Introduction
Beginning with the 1978-79 school year, everv school
division participating in the National School Lunch Program
was required to conform to cost-based accounting guidelines
as set forth in FNS 796-1, Financial Management Cost-Based
Accountabi1ity.(1) Requirements and guidelines for imple-
mentation in Virginia are set forth in the Manual for Cost-
Based Accounting Virginia School Food Service Program.(2)
The objective in developing such a set of guidelines was to
build a Management Information System (MIS) for school food
service leading to the development of reports designed to
aid in accomplishing the management functions of planning,
execution, and control.(3,4)
Essential to management is decision making, and
essential to decision making is information.(S) "Informa-
tion derives its value from the effect it has on the
behavior of the organization."(6) Therefore, the value
of a MIS is dependent upon its use and resultant impact
on management and the organization.
Of significance to the development of a MIS is the
definition of what such a system is. Many authors have
tried to define MIS. The underlying theme of need
assessment for organizational information and resultant
cost of information collection prevails in manv of these
definitions.(5,7.8) Mason and Mitroff proposed a definition
‘ 1
2
of MIS for use in research:
"An information system consists of at least oneperson of a certain psvchological type who facesa problem within some organizational context forwhich he needs evidence to arrive at a solution(that is, to select some course of action) andthat the evidence is made available to him throughsome mode of presentation." (9)
Accepting this as a basic definition, and the premise
offered by Coleman and Riley (6) that the information
derived is of value only as it affects the organization,
a deeper look at the problems existing and possible use
of information collected within the Cost-Based Accounting
system developed for use in school foodservice was deter-
mined to be a goal of this research.
Specifically, a problem in school foodservice for
which a standard cost accounting system could enhance the
management decision making process is that of food cost
control. The purpose of this research is to make use of
information contained in the cost-based accounting informa-
tion system by comparing actual food costs and standard
food costs as they occur and to evaluate the differences
to aid management in controlling costs.
Review of Literature
The term used in current literature to describe the
comparison of standard and actual costs is "variance
analysis". Several models for this comparison appear
in use. (10,11) All suggest the measurement of variance
involving the identification of processes in various areas
of an organization, and the reporting of quantifiable
information. The mechanics of variance reporting deals
with decomposition of a particular variance into its price
and quantity sources.(11)
"Variance Analysis" involves the use of standard costs
and actual costs. Standard costs are the predetermined
costs of manufacturing a single unit or a number of units
during a specified period in the immediate future. Stan-
dards are set for a “relevant range" - a given time period
and a given level of operation.(12) Polimeni and Adelburg
(13) qualifv standard costs as the planned costs of a
product under current or the normal level of performance.
Once established, standards serve as yardsticks against
which actual performances are compared. Anv difference
between expected and actual costs is called a "variance"
or a deviation from standard.
with these two sets of information, standard costs
and actual costs, any deviation mav be identified and
quantified. Dopuch, Birnberg, and Demski (11) offer
three different models for identification of deviations,
5
4
all of which varv in sophistocation. The one-factor
model has been selected for use in this cost studv. It
compares actual performance to standards and is the simplest
approach. The one-factor model aggregates the entire devi-
ation to a single number regardless of the source of
deviation (price or quantity). This model can be
representedbv:Vi
= ACi - BH1
where Vi is the deviation for the ith item
AC1 is the actual cost of the ith item
BCi is the budgeted cost of the ith item (11)
Example: The pre-cost value or standard cost of raw food
used to serve menu-day 1 is $0.45. On the day of service,
actual food cost was calculated to be $0.50.”
V for menu-day 1 = $0.50 - 0.45
= + .05
Deviations from standard costs are an integral part
of a standard cost and control system. Among other things,
they provide the most important source of control informa-
tion for evaluating performances within the range of
relevancv. (14) As the illustration above shows, a
positive deviation results when actual costs exceed standard.
When actual costs are less than standard, the deviation is
denoted bv a minus sign. Koehler (15) explains this
distribution of deviations around the standard as a result
5
of random distribution or factors bevond the day—to-day
control of management. He states that the mean of the
sample deviations should be close to the standard. If
the sample mean is not verv close to the standard, the
presence of assignable causes or those factors felt to
be within management's control is likelv.
Other authors have found different wavs of describina
the nature of deviations. Oswald Nielsen (16) refers to
deviations as possessing quantitative and qualitative
characteristics. Quantitative characteristics refer to
the abilitv to assign numerical distinctions to deviations,
such as dollars or percentages. Qualitatively, he suggests
that deviations mav be ranked in a hierarchy and that the
ranking is based on the relative importance of factors
causing the deviation. Nielsen calls the discriminating
attitude towards the relative importance of some deviations
the "principle of exceptions". He implies that a small
deviation does not deserve managerial consideration and
suggests that small deviations might well be disposed of
bv direct charge to cost of goods sold. in this respect,
deviance is considered to be large or small in relation
to the standard cost from which it varies.
A further definition considered when investigating
deviation is whether the cause of the deviation is
controllable or uncontrollable. Dopuch, sirnberg, and
Demski (17) divide the possible causes of variance as:
6
Type I Deviation: The deviation resulted from therandom aspect of the Drocess being controlled.Assuming the deviation is not statistically signi-ficant, no response by management is necessary.
Type II Deviation: The deviation resulted from atemporary or permanent change in the process.Further investigation is required to determinewhether:
a. The deviation is temporary in the sense thatperformance levels can be adjusted in the nextperiod. This is the general definition of acontrollable deviation.
b. The deviation resulted from a permanent changein the process. If this is established, manage-ment must review the decision process in orderto assess the effect of the deviation on thedecisions it has adopted and/or the decisionmodel being implemented. The deviation isnoncontrollable but, nevertheless, a responsemay be required. (17)
Once the deviation is identified and the nature more
fullv understood, control limits must be imposed to indicate
if the deviation is controllable or uncontrollable and if
further investigation is warranted. Determination of
whether deviation is due to random fluctuation occurring
in the period or due to fluctuations of the observed level
of performance is a problem of statistical analysis.
Fierman, Pouraker, and Jaedicke illustrate the procedures
for use of probability distribution for the observed level
of performance. (18) An explicit prior distribution of
performance levels is then used to set control limits.
Uopuch, et. al., suggest that the simplest rule to
follow is to investigate a deviation if its magnitude is
greater than or equal to some fixed percentage of the —
7
standard. (11) This implies tnat it is likely that any
variance less than the percentage cut off is due to random
factors and may be considered uncontrollable in management's
day-to·day decisions.
To set limits for random deviation, Richard Duvall
refers to a "traditional test" using standard deviations.
This test is based on the distribution of non-controllable
deviations. He states that if the activity is being
properly performed, the possible deviations for a particular
time period will be normally distributed over many time
periods and therefore the sum should be approximately
zero. Measurement over time to find the distribution
gives a check on the standards which have been set. (19)
The assumption is made that the standard is the mean of
the distribution.
Robert Magee suggests that cost deviations from two
cost processes may be affected by common factors. He
presents a model, refered to as the investigation decision
model, which exploits these commonalities. His approach
yields an estimation of whether the cost process is
"in-control" or "out-of-control". Benefits depend upon
the size of cost reduction benefits. (20)
All of the aforementioned evaluation techniques
were cited in variance analysis literature describing
profit oriented business and industry. No literature
reporting any of these techniques for establishing control
8
limits for a non-profit, service oriented industry, such
as school foodservice, was found.
Cost variance analysis can be approached through
the use of calculations of Mean Absolute Deviations (MAD's)
and Running Sum of Forecast Error (RSFE). They help
to define the deviations in terms of relative distance
from the standard and in relative bias on the positive
and negative side. (21-23) Also, as the sum of deviations
around a standard is-expected to approach zero, the
absolute value of deviations becomes an important consid-
eration in learning to work with and use data concerning
reported deviations. Thé MAD uses absolute values.
For example, if a five consecutive day period reported
food cost deviations of +30.05, -30.04, +30.02, +30.10,
and -30.03, the MAD for this data would be 30.048 and the)
RSFE would be +30.127. The MAD indicates that the average
deviation was I 30.048, and the RSFE of +30.127 shows a
bias of more positive deviations than negative.
Using the MAD and RSEE, a tracking signal for watching
the course of deviations may be calculated. Tracking
signals are set with a control limit known as a "trip
level" to indicate the need for further investigation.
"Trip levels" in the range of 5.0 to 7.0 MAD, depending
upon the degree of control needed, are in use in other
industries. (21)
The use of MAD, RSFE, and Tracking Signals represents
' 9
a viable approach to the analysis of cost variances and
the establishing of performance standards for school
foodservice.
Methodologv
Sample Selection: Major Considerations
Samples for this research were not randomly chosen.
Three centralized school divisions were selected. All
are located in the central region of Virginia, participate
in the National School Lunch Program, and conform to the
nutritional requirements of the Type A lunch pattern.
(See Appendix A for a description of Tvpe A Lunch nutri-
tional requirements.)
Selection of participating school divisions
(Ghesterfield Countv, Petersburg City, and Prince George
County) was made because of geographic proximitv to the
investigator and because all three operate with centrally
planned menus. In each district, a limited cycle of
menus is used. The cycles vary from three to six weeks.
· (See Appendix B for sample menus.) It is believed that
supervised foodservice operations using basic tools of
management control, such as centrallv planned menus,
would add to the ease of data collection and facilitate
evaluation within the smaller population of a centralized
district as well as describe the larger population of
similar school foodservice operations.
Elementary schools with on-site food preparation and
service (self-contained schools) were selected from among
the schools in the three participating divisions. A
10
11
total of twentv-nine schools met the requirements of being
self-contained, however, onlv twenty-seven were used
because of planned personnel changes which coincided with
this study. A relativelv homogeneous sample of elementary
schools provided limited food offerings and simplicity in
menu format. Foodservice uncomplicated bv diverse food
offerings and satellite feeding should provide more
replicable measurements.
Demographie Description of Sample
In addition to the basic eriteria that all schools
participating served the Tvpe A menu pattern with elementary
portions and that preparation and service of such menus be
within the same building, other factors were considered
relevant. A review of certain demographie features in
each district refleeted the economic similarities and
differences within the total sample selected. Factors
such as inereasing/decreasing size of school-age population
and income help to describe the participants. A brief
description of the three loealities participating follows.
Chesterfield County covers an area of 442 square miles
between Richmond and Petersburg. The United States
Bureau of the Census gave a total population of 103,240
as of July 1, 1975, per eapita income of £4991 in 1974, and
a school enrollment in 1975 of 26,483. (24) Por the
school vear 1978-79, the county operated 37 schools for
an expanded school population of approximatelv 32,000
12
pupils. 0f these 37 schools, meals were prepared and
served within the same building in twelve elementarv
schools. 0nlv ten of these twelve schools were selected
due to personnel changes p1anned.1(See Appendix C for a
list of school names.)
Petersburg. a citv of 45,245 total population as of
July 1, 1975, listed per capita income in 1974 of $4116,
while the school population totalled 8595 in 1975. (24)
The 1978-79 school vear showed a slight decline in school-
age population (approximately 8500). Of the twelve
schools operated bv the city in 1978, nine were self-contained
elementaries and one a self-contained middle school for
a total of ten schools satisfving minimum criteria for
‘this research.2 (See Appendix C for a list of school names.)
A rural county located immediatelv south-east of
Petersburg, Prince George Countv's population of 18,451
as of July 1, 1975, offered a school population of 5,301.
Per capita income during this period (1974) was $4253. (24)
The 1978-79 school vear found a school population of about
5,500, slightlv larger than in 1975. Ten schools served
the 5,500 students in Prince George; seven self-contained
elementarv schools and three secondary.3 (See Appendix C
for a list of school names.)
1Current information provided bv Foodservice DepartmentChesterfield Countv Schools.
2Current information provided bv Foodservice DepartmentPetersburg City Schools.
3Current information provided by Foodservice DepartmentPrince George County Schools.
13Departmental Organization
Comparison of characteristics within the departmental
organization of each district revealed similarities and
differences within the three school divisions. Studentlunch participation divided into categories of paid lunches,
reduced price lunches, and free lunches, as defined by
USDA, described the daily make-up of participants in the
school lunch program within these three districts.
Exhibits I and II review these facts.EXHIBIT I
Characteristics of Participating Districts
Departmental PrinceOrganization Chesterfield Petersburg George
Centralized Leadership °Number and Titles Director Supervisor Director
AssistantDirector
ProcurementSpecialist
Is bid purchasing used? Yes Yes Yes
How long has department 12 years 1O years 3 yearsbeen centralizingoperations?
Is warehousingavailable withindistrict?
Partial x XComplete X
Is cold storageavailable?
Commercial x XDistrict XOwned
14 '
· EXHIBIT II
School Lunch Participation 1977-78
PrinceParticipation Data Chesterfield Petersburg George
Totale: Meals served 17,000 5,900 3,000daily
% Paid Lunches 85.6% 26% 64%
% Reduced Price 3.1% 9% 9%Lunches
% Free Lunches 11.3% 65% 27%
Current information provided by Foodservice Departmentswithin each participating school district.
Data Collection: Setting of Standard Food Costs
Standard food costs per menu item were determined bv
each of the three participating school districts for their
own operations. This is a function of the Central Office
during phase one of Cost-Based Accounting implementation.(2)
Standards were based on standardized recipes in use within
the respective school division and the prices paid for
food items at the time standards were set. (Note: All
three school districts use bid purchasing with a one
semester time period specified.) CBA Form #100, Pecipe
Costsheet was the form used to precost recipes thus giving
the expected or standard cost for each food item used in
menu planning. A description of the use of CBA Form
#100 and a copv of the form follows:
15
cu vom #1005¢¤¤¤F-=._..i....— RZCT-P! C¤S‘rSH£E‘r
uczrz: man or poxrxous:
souncz: rcmou szzxs:
um: mau: wm: 19
(1) .(2) (3) (4) (5)Anus ot Annan: of Price | Toc:}.Ingredieete Iagredieuzs Per g Coe: ot
. Iugredieace in lecipe ae Pnrcheeed Uni: 1 Ingredieacum
‘
’Toc:}. cos: of Recice
_
c„. s.„„.
16
Purpose of CBA form #100: To precost a standardized
recipe by totalling the costs of each ingredient
specified in the recipe and then dividing this total
cost by the number of servings the recipe yields.
Person(s) responsible for completion of this form:
In the case of the three participating school districts,
this is a function of the central office, as all
schools serving the same menu with the same food
prices would be able to use the same standards for
food costs. This function could be performed bv the
individual school foodservice manager if the division
has no centrally planned menus.
Procedure for completion of CBA Form #100 (procedure
is taken from Manual for Cost-Based Accounting, pp. 9-10)
Step 1List in column 1 the major ingredients in therecipe.
Step 2Enter in column 2 the amount of each ingredientneeded to prepare the recipe for the number ofservings planned.
Step 3Enter in column 3 the amount of food to purchase(AP) to provide the amount of each ingrediententered in column 2.
Step 4Enter the price per unit in column 4.
Step 5Multiply the price per unit (column 4) bv the
17
Step 5 (cont.)amount of food to purchase (column 3) to deter-mine the cost for each ingredient. Enter theresult in column 5.
Step 6Add the costs for all ingredients in column 5to obtain the total cost of the recipe.
Step 7Divide the total cost by the number of servingsprovided by the recipe to determine the costper serving. ·
Standard per plate food costs for each menu were
calculated by the investigator based on standards set
during phase one of Cost-Based Accounting implementation,
October, 1978. (See Appendix D for a list of standard
food costs per menu item.) In setting a district standard
for menu food cost, the standard cost for each menu item
was listed. Average condiment cost figures (or standard
condiment cost figures) as estimated for phase one imple-
mentation were used for Chesterfield and Prince George.
These figures appear in Appendix D. Petersburg did not
use an average daily condiment figure in phase one and
therefore one was not used in computing standard food
costs per menu. To illustrate this calculation, an
example is given in Appendix D.
Data Collection: Determination of Actual Food Costs
Data collection to determine actual food costs began
November 1, 1978, and continued until the Christmas holidays
(approximately thirtv-two working days). The test period
covered two reporting months as defined bv federal
18
regulations for school foodservice.
A "Daily Food Usage" form was used to report actual
food costs. A description of the form and of its use
follows:
Purpose of this form: To collect and present data
for the calculation of actual food costs bv menu
item on a daily basis.
Person(s) responsible for the completion of this form:
The foodservice manager in each school completed the
Daily Food Usage form. Information used to complete
the form was supplied bv the foodservice assistants
assigned the responsibility for preparation of each
menu item.‘
Procedure for completion of Daily Food Usage form:
All foodservice managers were instructed to use the
following procedure for completion of Dailv Food
Usage forms.
Before ServiceStep 1Fill in name of school and date, name of all menuitems and their corresponding portion sizes inappropriate blanks.
Step 2Have each person involved with production recordevery ingredient and amount of ingredient that isused for the menu item. (Include spices and everyother ingredient.) Have that same person record a"P" or
“G“next to the ingredient to indicate
"Purchased" or "Government".
19
DAILV rum usnsz M 6 In u scI«¤¤I.Studentsnum, °‘"€...._...........—..Emoloyeesvom‘*"'
ma-v~:·mount Used iz! of Portion otal wet er
G/P Inrediente Pounde Cana Portion Yield Cost Servin
Nun of Food Itun
Nun! of Food Item
Nune of Food Item
· Mund of Food Item
I
I
I
Nee of Food Item
II'I I
I I
Milk
II I
G•G¤v•x·nm•nt ‘P•Pu1·cn•sed TOTAL PER PLATZ
20
Step 2 cont.Record amounts of ingredients in common units ofpurchase, such as #10 cans. lbs., etc...After ServiceStep 5Condiments used and amount must be recorded asmiscellaneous.
Step 4Portion vield for each menu item must be recordedunder the correct column. Count all servingsYielded from the ingredients used.
Step 5Actual service figures and total must be recordedin the top left corner., (Information to be takenfrom School Lunch Form #12)
Step 6Fill in cost per unit for each ingredient used.
Step 7Multiplv "amount used" times "cost per unit" for eachingredient and place answer in "Total Cost" column.
Step 8 .To compute "Cost for Serving". add "Total Cost"figures for each menu item and divide bv "portionyield".
Step 9Add each food item's "Cost for Serving" togetherto vield "Total per Plate".
Training in the use of this form was conducted bv the
investigator with the help of foodservice administrators
in each participating school division. This tvpe of form
was not in use in Chesterfield Countv or Prince George
Countv prior to the time of this study. A minimum of 2%
hours of classroom instruction in the use of the “Daily
Food Usage" form was offered to employees in these counties
with on-the—job follow-up sessions planned on an individual
basis.
21
Petersburg foodservice managers had used the "Dailv
Food Usage" form for several years. Training in the use
of this form was not required, however an orientation to
the purpose of this research project was conducted.
Individual school foodservice managers completed "Dailv
Food Usage" forms for each day of the test period. The
"Dailv Food Usage" forms were gathered on a weekly basis by
the foodservice office in each school district and forwarded
to the investigator. Extension of cost per ingredient,
cost per serving, and total per plate was completed by
the school managers in Petersburg, and bv the investigator
for Chesterfield County and Prince George County. Unit
prices for ingredients were supplied bv the foodservice
department of each participating district.
Completed "Dailv Food Usage" forms provided the menu
of the day and the actual cost per serving of all menu
items. The column headed "Cost per Serving" identified
the actual food cost incurred in serving each menu item.
The total of all figures in the "Cost per Serving" column
entered on the line entitled "Total per Plate" represented
the actual food costs bv school for each dav of the test
period. (See Appendix E for a completed sample "Daily
Food Usage" form.)
Analysis of Data: Identification of Deviations
As a first step in evaluating raw data, daily food
cost deviations for each school were identified. Dopuch,
22
Birnberg, and Demski's one·factor model identified food
cost deviations. The total per plate cost obtained from
the completed "Daily Food Usage" forms represented the
actual food cost (ACi) by school for each day of the test
period„ (See Appendix E for a sample completed form.)
Standard food costs (BCi) were determined by each of the
three participating school districts. Identification of‘
food cost deviations by school resulted from the comparison
of actual costs and standard costs or Vi = AC1 - BCi.Observation of daily food cost deviations for each
school were then grouped by districts. Combined, the
three sets of deviations equalled the total measured.
Exhibit III shows the total number of observations made.
EXHIBIT III
Number of ObservationsDistrict No. Schools No. Days in _ Total
Participatina Test Period ' ObservationsA x B
Chesterfield 10 29 290 (289*)
Petersburg 10 32 320
Prince George 7 33 231 (222*)
Total Population 27 841 (831*)
* Totals of columns A times B are not correct sinceinsufficient data caused rejection of some observations.
25
Analysis of Data: Description of Total Population
Daily food cost deviations were plotted onto frequency
distribution histograms for each of three districts and
for the total population. This was done to determine
whether or not the distributions were normal. The
chi-square criteria was used as a test for "goodness of
fit" to determine if a total of the entire population
distribution could be approximated with the normal
distribution.
Continuing the descriptive phase of evaluation, the
central tendency of the three districts and of the total
population is described bv the population mean. The
population mean,Y:%ä;„ described the mean value of measure-
ments expected from any subsequent random samples.
Standard deviations, as represented byÜ;ÄäE, were calculated
for the three districts and the total population. The
practical significance of using standard deviation as
a measure of variability involved application of the
Empirical Rule which states that given a "normal" distri-
bution of measurements, standard deviations on either
side of the mean represent intervals of known percentages
of measurements. (25)
Analysis of Data: Limits for Acceptable Deviations
Calculations of Mean Absolute Deviations and Running
Sum of Forecast Error were used to set control limits for
food cost deviations. MAD's is the term for the average
24
deviation of actual food cost from standard ignoring the
plus or minus sign which means that deviations of -.05
and +.05 would be considered as equals.(21) MAD's
is a running average. For this research, a ten consecutiye
day period was considered adequate for computation of MAD's
because this represented fifty percent or more of the menu
cycles used by each district and therefore would be repre-
sentative of their typical menu pattern and costs. For
example, Bellwood Elementary school in Chesterfield
reported actual food costs for 29 days. Food cost devia-
tions were calculated for each day. MAD's were calculated
for days 1-10, 2-11, 5-12,... 20-29, for a total of 20
MAD's calculated for Bellwood. As Bellwood was one of
ten Chesterfield schools participating, 200 MAD's were
calculated from the data reported in Chesterfield.
The RSFE is the cumulatiye algebraic sum of deviations
which determines if deviations have any positive or
negative bias.(21) A random variation about the standard
cost would theoretically result in a RSFE = 0. FSFE's
were also calculated for every ten consecutiye day period.
Using these MAD's and RSFE's, a "tracking signal"
was computed for the same ten day periods bv dividing the
MAD into the RSFE. The tracking signal indicates the
relative degree of bias to the deviations; a large number
indicates a high degree of bias. When the tracking signal
"trips" or exceeds the limits set for desirable deviation
25
which are set bv management, the actual cause of this
deviation is investigated.
Review of MAD's and tracking signals as calculated
for the measured deviations was performed to establish
limits for "trip levels" that could be proposed for use
by school foodservice management. Goals for maximum
levels of acceptable deviations were set.
Results
Presentation of Deviations _
The data from all 831 observations were grouped by
districts. Daily standard food costs by districts and
daily actual food costs by school were compiled into the
format of Tables Ia, Ib, and Ic, Daily Food Cost Deviations.
Results are listed by school for each day of the test
period. These tables provided the basic information for
the identification of food cost deviations.
Food cost deviations were calculated by comparing
actual food cost and standard food cost via the Dopuch,
Birnberg, Demski model: Vi = AC1 - BCi. Exhibit IV
illustrates this comparison.
EXHIBIT IV
Daily Food Cost Deviation
Chesterfield CountyDay Actual Standard
1 School Name Food Cost - Food Cost = DeviationDellwood $0.544 $0.490 +.054Bon Air .483 .490 -.007Chalkley .492 .490 +.002Crestwood .518 .490 +.028Curtis .519 .490 +.029Davis .492 .490 +.002Ettrick No Data .490Falling Creek .479 .490 -.011harrowgate .500 .490 +.010Reams Road .550 .490 +.060
26
I27
QjU'!->\N|\J—*O\OG7~]O‘~~I1-I>\>l|\>-*O\O®~]O\U'1->vI|\>—¤**3U)
Q O¢+O cgmI I I I I I I I I I I I I I I I I I I I I I I I I Q;
ßxß->\N->->->4>\NU~|J'l«>«>->\>1«>\J1\N·—>\;4->\D·>->-|> BH1-|>OOG\-·¤\N®G)\OO\f\>\N®\OC!)->\NCD\NO\O\f\)->·-•kO C)[DI·—•·NO\~.¤-{>®\)|®f\)OJT«>~l|\)Ox:ü¤IIO\\OG)O|\)—•·—*®O 0äIO
E <+c+
O PU!I I I I I I I I I I I I I I I I I I I I I I I I
I-¢~->\>IUl·>J1->«>\2l\»l~¤->~>·>->··>J'|«>->U|4>JT->->U'\ dl-*OO\®§|\)\JJ—•O(D—*O\JTG)\O¤J'|~]C!\-·•O'\~]O\—->·*|\J·P~
SI-•©m<mw<Om©uO~wmw<Om—wmwwma äälI|+++||||+++++++++++|l++ OI I I I I I I I I I I I I I I I I I I I I I I I I
(DUI-4>—*OOO\.T|~lOJ\!\)-¤OOG\\:lI'\)|'\J|\)-•OO|\.)O»J'1 < Ovl->G)®\fI>®G*->f\>-ß-¥=~·~lJ1\O|\)«>\NUl\OO\®O\~]·> • C7(D
O ¥>¤d m r-—•I I I I I I I I I I I I I I I I I I I I I I I I I CQ- E·>¢«>vJ->JIJ>-ßvlul->-b->4>\N->->\N->\N·>->«>—>-> ¢+¤ (D ·<\)l(IJ\DG’\O-A-¤~l-I>J1~l-·>G)~lO\O\s.I’1G>~l(I>P(I>-•f\)<I) S V'!
I|+l|+||||I|+||+II++IIl+| V1 (DOI I I I I I I I I I I I I I I I I I I I I I I I I *-J
ÖOOOOOOOOOOOOOOOOOOOOOOOOO('D Q- !>O U3CDG\O\I\)~‘|\O—PO->Of\)\.N»I10-¤O\O«IIf\>\O~>O~l·>~] • Ä} O t"U} *31
O $>Q ii ·%I I I I I I I I I I I I I I I I I I I I I I I I I
¤ H
->->·>\.~l\>l->·>->->\>l\>I\>l->\J'I-F~~f\&f1·$>·>\:J->\I1«>->-> 1:+97 d· U Q}•*U'T-ßJ1JTO\\J7\O-•—>\O\O\DO®|\)J1f\)\NU\\O\N—*O\O Sil-* < L=JkO«>LO0\—>OOf\)~l«>\OO\kO->u'IJI~l\2¤·-•\DO\—*«>\>II\J <
I-IIl+II+|++II||+++++|+++|I+U2OOOOOOOOOO-¤OOO—>OOOOOOOOOO(D O I-4
O O\N|'\)-¤@#~]®O~]-*J‘\—*\N—*|\)O-•->~]\O·ßO~]xJ7f\) • l—* Z
CD
° äi?->·>U!\N«>«P~->#->«>U‘l·>\J1·>A~>»J7\»l->«>—>J>ä->JI c+(D->·>O\O·>O*·>~]\»lOOU'70&O~J1\O«>\O••-*~]Q)O~]-• SU!
+I++++I|++|+++++|+|++|I++ U3I I I I I I I I I I I I I I I I I I I I I I I I I
OOOOOOOOOOOOOOOOOOOOOOOOO (D9OU'|\C|\Jf\)\NI\)O#\Nf\)-*—*OO\->—*Of\)JT-¢\N\NU’T(\) <~l<I~l\O|\JI’\)f\)«>->~l|\)->\Ou'\\D®\14\„O„!'\O~J’1I'\)~lC!*G>
•
° ää-?·>u1\»IUl\1·lJ14>->«b·\N->·>«P~—>«>>n!1\»|ß«¤~->\J1->\Nx!1 1+*1vlO\OGJ~l|\>\N~lJ1\N&OUI~l(I)—*~l->J1~l\N\OO\f\kO-¤ Sd«PO·>·>|'\J—*®-*G\->~]-•|\)J7|\)|\)\OJ‘|O\O\—*xJ"|O\O\\O EJ:I|l+|+lI+II+||+++I+++|+I+wOOOOOOOOOOOOOOOOOOOOOOOOO CDOO—•f\)«ßCD\N·-*O\\N|\)—*OO|'\)l'\)—-•\>l|'\)~]|'\)—*—-•f\)f\) <
28
mmmw Uwmam P
<‘=JUJUOc+I·-•·O OIDUJ
¤·HOm>m 3mMO\·-MUM OHO
¤!¤-·‘«.+d
O >w•••• OQmv!->U1
4-•·•—*ouqß ¤M
NZI-*0
+I++ O
OOOO mO·u· <-¤1~1m •
O Pw••••OO IQ
mmum ¢¤ >wmwm P Uqwww P> U
MM U+|I+ H
OOOO 0 P
O >O 5•••• O¤‘g+
dü M5
mwmw PW ¤I—•I-•Cb
II+I 0 ¤·
OOOO 0ww—m <4>O¤—•¤l •
O >O•••• O!-1mv!-bw <+<bOmwm 5mwwwm Nö
MtIII+ O•••• UOOOOO m¤Omoc <¤lG>xO¤l •
O ¤>Q••••
OQU1\.¤4>m d-H—~->rx>4>
¤«-•-mmm® PMHm
+II+
OOOO Q-¤O-N <$Om->4> •
29
Ü‘<
*1:1UJU
O Q Od·9··*•I I I I I I I I I I I I I I I I I I I I I I I I I I
Ö\J’\\N->U'!«>\J’\->\N->-P~->-P\2I\NU'!->->->\>l->\J'\\>|«>\.»I->\J’\->->·> @91
O\—-•f\)l'\)|'\)G\U'\·>CD\N®|\)CU'\«ß~]|\)C\Jl\J”¥G\\D(DCD|'\)—*-*®C) OHOUI@c+¢+
G o :>¤I I I I I I I I I I I I I I I I I I I I I I I I I I I I
IU'l\>l\N-¤#->\N\N·>«>-P->\N\N-P-Ä-P-Ä-P--#>->«>\)l#«P~-ß-P\N-> ¢+<O-*&OkO\.•JOG\~l->\J‘IO~lI\J\O&OU'lI\)®(I)—1G\OkOI\)O\~lx»lkOxD
:29-•~
|—*
+IIIIII+++lII+l+lI++l+l+9I9|+I I I I I I I I I I I I I I I I I I I I I I I I I I I I Q ÖOOOOO-¤OOOOOOOOOOOO—>OOOOOOOOOO CD
<2—-*G*O®~lC)\O\)|<®I'\7~]\N\>|~l->\2lUT-«><G\—-*\)J\N|\>CDU'¥|'\)f\)
•
G :>:=1I I I I I I I I I I I I I I I I I I I I I I I I I I I I
9-3
f\70~]G)f\)sl'|·>~!7—*~l'9—•\J'9\OO'\U|G)®\2|\OnHO\N\N~]UT®O(DÖ
Q7}-*IIIIIl+++I+I+lII|I++I+++l+II2,+Fg.II I I I I I I I I I I I I I I I I I I I I I I I I I I
OOOOOOOOOOOOOOOOOAAOOOOOOOOON g •—•
O\J'\-·•\}’7—*\>|l\)OO~]—>\J1\„O-*~l|'\)->O—>!\J~]O\f\}O\|\)\>l-¤—-¤ 4 gp4>—~».¤->O—·>xO-·wC¤vJ~1x¤x¤-•kD->~10\O¤¤4>m~l~1—1—»O
•G
O O :>O 0I I I I I I I I I I I I I I I I I I I I I I I I I I I I g
Ulm-PCD·>U’T<lG)G*—-*O®\O~]«>—*—>OC}\D\O~]~]O—>O\N-¤4 gmp-4 p.O\N\OO·-*f\J—*f\}\Ol'\)~]O\@~]<O®O\Nf\)~l\NO\Nf\)\O~]—^xg g)p;·‘|—•- :3
9-* 5++++|+++|++++++I|++++++++|II|yqI
I I I I I I I I I I I I I I I I I I I I I I I I I I I g
OOOOOOOOOOOOJOOOOCAOOOOOOOOOQQ.
|\7\NOJ10«>O*—*->~]\NOC-*f\)f\)\N—*|\)4>C\®\>l·F~\}7—*C)C)-• 4
O O >¤L‘I I I I I I I I I I I I I I I I I I I I I I I I I I I I
•J\\N->-«><>->·#\N\N\>l#·ß\J·¥\.NJ>-«>U1·>\N\¤->->-P•\N->->·>«>U1(-y-H
-•O\NG\\N®—-*~]®\Oä<JT®\O—-*O·~lC\I\)\J1·ß\N~]\NkO\!\|\)Q gp!OO*—>-*\D~l->J'|CD·>f\J\N®\N«P~\N~]<—•OUTf\)O\\O~]O\N\OQ ggg
+IIIII++||IIl+II+Il+I+|+II+++Fg?}
I I I I I I I I I I I I I I I I I I I I I I I I I I I I •
OOOOCDOOOOOOOOOOOOOOOOOOOOOOOQO->OO\O-•O·-¤\>I\>|f\)O\21—•U||\JI\)f\)—*®(I>\J‘|O-•f\)\>l-*—•..;9I
I I I I I I I I I I I I I I I I I I I I I I I I I
I—•I\J—•O-•—l\OOOI\>«>—lJI—-¤I\J\>I~1G7kO~l-•\O®-¤<I>OO\x:lU-1gg
Q mm
+lIIIII+|+IIIIlIII++l++++l|++PwI I I I I I I I I I I I I I I I I I I I I I I I I I I I •
OOOOOOOOOOOOOOOOOOAOOOOOOOOOO·-•f\JT\)|\)\N\:|OU~lCD\O|\)·-*\N\N\OOC)OO\„»1f\)O«>n>"I|\7-^~]-—>ON <¤„-•\O\.!'!|\)O·>~”|(D\O\;I\>|C)U'|J'|O\\)l«¤O\~]O->\>IO\\O—·*s„¥'\»J'|->Q
•
BO
hamQ Oc+O ONE
I I I I I I I I I I I I I I I I I I I I I I I I
IJ\·>->-P->·>\!'\->&I'1->\J"|—P#->J'\U7«>«>J1->—P->->d'|U'| S-vHQwv
Ed'
O >>I I I I I I I I I I I I I I I I I I I I I I I I I
¢¤
anE
I++I++l++I++|++++lIl+I+|l OI I I I I I I I I I I I I I I I I I I I I I I I
ICOdOOdOOOOéOOOOOOOOOOOOOO Q• U U
0 >O >U v H
I I I I I I I I I I I I I I I I I I I I I I I I I Hdg H ·<S E
H:++++I++++++++++++|+|+l+|+ O H D EIIIIIIIIIIIIIIIIIIIIIIIII@@@鮢@®®@¢®¢®@@HÖÖÖHÖÜÖÖ
m¤ UOwm<Om#©ßCO##MwOu¢memewmw < Q O jd
„ :+O >> H H H
I I I I I I I I I I I I I I I I I I I I I I I I I
ol«>->4>U'1#-P~U'\xf!·>·>U\:J7->->\H\¤\D«P~O*->->·>-PUTU7:+"d U} U~l\NU·l\O~]\D\>JI\)\D\»l~]&a|f\)\JJ\)l®\>|(I)->\N~l-•\D-•u'I
S• O bjgzH
|+++|+++I|+++++++++I+I+++ H O >I I I I I I I I I I I I I I I I I I I I I I I I I •a
ÖÖÖHÖÖÖÖÖÖHÖÖÖÜÖÜÖÖÖÖÖÖÖÖ QH E H
Z
O >¤I I I I I I I I I I I I I I I I I I I I I I I I I
:+0SFT
I|++|+++I|II+l+I+I+l+|+|| ÖHI I I I I I I I I I I I I I I I I I I I I I I I I
OOOéOOCOOOOOOOOOOOOOOOOOH QCh-¤\J100\OOO\D~]f\)~l\!100'\|\>Y\)«>l'\J«>|\)®\N~]—• <U'1~]O-·•\O®|\)U'!-•—•I\>\>lJ'||\)-•~]O\O\O\G)\O\:I-¤\N\O
•
O ää\J'I«P~->~'I\»|\!'IO\->\!'I\J'!\J'\->~!'|4>·d\U'\·P~->„J'I~!'|->\„~I~J1\I1-|>~
d•Q
S|\)CDU1I\}\O®~lO\!\\OO~l<0\&O->O\C7\\1|I\>G)U'1U'\U‘I\O E-J|+++|++++++++l+++I+++I+|UI I I I I I I I I I I I I I I I I I I I I I I I
IOOONOO¢OOOOOOOAOOOOOOOOOO QUI·-•«>\NCD~“|O|’\7éO\UlOG)Y\J->|\)f\)\I\|\)\>lI\)\D\OOf\> <U'\CDO|'\)®G)xJTC)|\>U'\—•-PCD·•O\\!\\Df\)O\xJ\·>|\)O\J7-^
•
51
uuwwmmm ¤I\)-•OkO(]J~]O\ zu
O Omtn
mßßammß ¤HOHGUIQ-6-6-
O >>• • • • • • •Qß
¤M>->«>U1-I>«J‘I 6-Q-5mWHI--*0
+| I l+I+ O
OOOOOO- 0x¤—-OOOv¤—- <~]—*O\|\)\NOU'| •
O >w• • • • • • • Q|—| -§dß55
td+I+++I+ O
0Q- c'—•I\>—-x¤—I~lG> <—-u10—•kOvlN • 37O >> 5• • • • • • • Q•
6-*d:1• 5JIGNOUT-¤~]\N m 5I-*¤:‘
0+I+I+++ I-·· Q-• • • • • • • U}-JOOOOOOO 0}-*>mmmmcm <vJ«l>OJI¤JkOG> •
O ¥>¤•• • • • • • • QQ)—>«>·>-I>U1->-4> 6-0NGONOOQ ßw
WmI—*O
II|I+I+ 5-OOOOOO 0weOwwN# <ua->-O—•OCD •
O ¥>!T‘• • • • • • •QQ
U1\.>1-I>-I>JI->-> 6-05\OvIvIOkD->CD+I+
+I+ UOOOOOOO 0OGBO -•c¤O
<~1~l\»l -•-I>·\:J •
32
"EIU)Od
O OwmI I I I I I I I I I I I I I I I I I I I I I I I I I I I
@*1ON
U3d
O >3I I I I I I I I I I I I I I I I I I I I I I I I I I I I
d@$@0
00IIII+l+l++lI++|++I++++|+|+|II I I I I I I I I I I I I I I I I I I I I I I I I I I I
0N
O $>U2I I I I I I I I I I I I I I I I I I I I I I I I I I I I
d$ P$97 W
d
I I I I I I I I I I I I I I I I I I I I I I I I I I I I
0 C"
O PP< ÖI I I I I I I I I I I I I I I I I I I I I I I I I I I I
:5
+I+|+|+|++|II+I+++++l+|+++|+ l·-*· @I I I I I I I I I I I I I I I I I I I I I I I I I I I I
0
O I>I£I I I I I I I I I I I I I I I I I I I I I I I I I I I I
giII I I I I I I I I I I I I I I I I I I I I I I I I I I
0
äädm$d
lI+++++|l++lI+III+++III+++lI wgI I I I I I I I I I I I I I I I I I I I I I I I I I I I
0
www
33
wwvxm td|\)—•O\O _m
·<*=.1UJU0:++-*-
O Oibu:
NOOO 0*10C¤¤»¢+¢+
O PKW• • • • ()|-•·(ß
U1«>->->· c+¤-·<+· -¤>~.oxo 5¤·a>
Nwm-> SDI-*+1I—*<b¤5
I+++ c'• • • • bj QOOOO 0 Huwcmm 4 IQOvlvl-> •
O PU)• • • • Oc} pg
mau-> :+5 >>xouamo 5m ¤¤«I>—>G\-P~ WH UI-‘¢+ P11+I I I
[-1
OOOO Cb¤‘
_ un-¢>->-> 4N¤\4>O\ • 33OPP<cuncwo
5:5m :5:1
»—-•<¤:5 mI I+I I-^· ¤·
QOOOO (Dwww-> 4¤>u—1u •
O PES
95 I3•-· 5I |+I ¢+
OOOO (DG3-•wl\> 4Ovl—~G>
•
O PS• • • • QQ¤\-&>-|>¤\ crmumqm 5:+m—•ON @4
I·-·*•-•-+|++ (D
OOO- tb~l—>O\~l 4O\O·>N •
34
OHO OgwI I I I I I I I I I I I I I I I I I I I I I I I I
¤HOW
H
I I I I I I I I I I I I I I I I I I I I I I I I I
HQ¢Ngg¤¥ P
I I I I I I I I I I I I I I I I I I I I I I I I I
Q Q
Ä)Ü
O P5 O OI I I I I I I I I I I I I I I I I I I I I I I I I
HH N U P¢O Q
ääO m
I I I I I I I I I I I I I I I I I I I I I I I I IQH 5 O<H H•QO>O m H
••••••••••••••••••••••••• Qm Q}HHCQ O Hgg
Q
O PII I I I I I I I I I I I I I I I I I I I I I I I I
HH¤Hgg
I I I I I I I I I I I I I I I I I I I I I I I I I
OOOOOOOOOHOONOOOOOOOOOOOO Q
35
wmwuwwmm U
Cd!-•~
Ogül• • • • • • • • Q, gp->«>\.!'\\»I«>-l>«>·«$=~ Q1k0—•~1U’1~lU‘1~l<D (Umb-··<I->—>->kD<4>N 0*10
C¤Qdc+
PE• • • • • • • • QQ«>¤¤\;4\„~l-hä-I>-¤· dwmw©mmm<0 ¤u\NO\-¤O\N-I>G\—> w}-*|—*¤I I I I |+++ *4• • • • • • • •
¤OO—>OOOOO CD H~lG)G70\J1000 4 P->(I)UI«P~O¤~l|\>|\) • CU
W t‘*P5 M• • • • • • • • Q)-Q
U'1\J'I->\N\NJ1—P~·> <+*'$ HMDO-*O\&O\>lG>|'\) 50 O\»l—•CDU'1CD«I>kO<J\ wi
I··‘CD G++I+|+++ 4 O• • • • • • • • Ö}-l-
¤OO—•OOOOO CDI-* dxO<I>u1-¤>~l—¤-• 4+-* I-*·O‘~]O\-*—•\]U'10\ • ¢‘¤
E:>O (D• • • • Z• • • QQ) Q,
¢mwwo¢mw ¢HmOmm #m© ¤m®®§~U®O® WO
m wäI + I +dI + I• • • • ß• • •
¤O-•rx>O OOO CDm~¤OO —-¤4>O 4kOI\>~l<I -•¤\-> •
PIII• • • • • • • •Qß
mmawwmma vwwmqmwwmo 5H
++I+|+++ O• • • • • • •
•O-OOOC>OO 0x»I—7kOt\J<I>—lxOO 4
56
Ö 0d!-*·O 0gMI O I O I I O I O O I I O O O I I I I O I I I O I I I I ¤;
@*1
M dd
O bzO O O O O O O ZI O O O O O O O O O O I O O I O O O I O pg
Cd CU
O O O O O O O WO O I O I O O O O O O O O O O O O O O
OOOO->OOOOOOOOOOOOOOOOOOOOOOO 0 Q
O bw I3O I O O O O O O O O O O O O I O ZI O O O O ZI ZI O O
di:\O GNUTO Cd $1
(D+IIII+IIIlIII|++dl|++ldld+++ @O I O O O O O O O O O O O I O O NO O O O O O
OOOOO-¤OOOOOOOf\>OOOOOOOO O OO-- CDOONO
biO I I I I I O I O I ZI ZI O ZI I I I O I I I I I I I
-P~ O\~l U4-¤~]\!!\N\N\>|O\D—*T\)\N Cd
++I I I|I+IIc++d+Id++I+I+I+I+I+O O O O O O O O O O ml NO O ml O O O O O O O I O O
OOOOOOOOOOOO OO OOOOOOO-¤I\)OOO (D
U'!
57
wuvzwm Uuw->O~o _P
4MC/JUOe-H-··O ogm• • • • • Q;
¤·H_ &O—•~lJI~l Gm~‘I«>«I>«>xO O‘1O
¢¤¤•e+¢+
O PZ• • • • • QQ IQ!>
c¤—1mOrx> C¢+ Uc'~.n4>C>vI $¤¤' U
H tdI + I + I |—I
OOOOO tb 0wcwm-I>m 4-•—•OO\G\ • 3}O !>U> I3• • 'Z'• • OO cI·mwowu e+C 1-··wm wm C«+ :3~1~¤U—-——l PII C
. Q H (DI I¢++I ¤•
-O OO CI!O’\~l uno 4OJI ~lIx> •
O !>£• • • • • og)J1-I'>\>Iv4vJ «+Hc>o~co¢>a> Ca-
NO
I I
OC>—•OO <¤4
Description: Three Sets of Observations
Food cost deviations within each of the three districts
were plotted onto frequency histograms to show the distri-
bution of the deviations as can be seen in Figures Ia,
Ib, and Ic. The appearance of the distribution in each
case was somewhat symmetrically balanced around zero.
Standard deviations for districts showed the Variabi-
lity among deviations reported by each of three districts.
Table II lists the standard deviations and number of
observations for the three districts.
TABLE II
Deviations from Standard Cost bv District
Mean DeviationDistrict from Standard _Standard Deviation N
Chesterfield -30.0034 30.0414 289
Petersburg +30.012 $0.0603 320
Prince George -30.004 30.0762 222
Description: Total Population
The deviations measured within the three participating
school districts were combined to reflect a broader, more
representative View of the population of elementary,
self-contained school foodservice operations. The entire
831 deviations reported by the sampled districts were
39
13%
/2f
/00..
7.f'.
fo
RA"
..I ..Ä:)
I [o +.av• #1a vw? #a4 #.10
8m Deviations (in cents)
FIGURE Ia
Frequency of Food Cost Deviations
Chesterfield County
40
/25-
{Oc
75
[O
ae "'a
3{
O5
_-
zu-.30 ·.-Z4 wr? -./,1 —.¤& 0 tag ~r.l& ·I·.l•' #.26* 1~3¤
Q2$4"* Deviations (in cents)
FIGURE Ib
Frequency of Food Cost Deviatious
Petersburg City
41
/21
/90
vf
ya
gf'
5;§
—K_111$
$*3** -.24 zu era eoe 0 1-.•6 +.:.1 nu +.4v 1..10 1.se
Ä Deviations (in cents)
FIGURE Ic
Frequency of Food Cost Deviatione
Prince George County
42
plotted onto a frequency distribution histogram, Figure II.
The expectation given the nature of the system was to
approach zero. The population mean Qq T) was +,0019,
which for practical purposes was considered to be zero.
With the central tendency of the total population
expressed as zero, the dispersion of deviations around
this mean was described by use of the standard deviation.
For the 831 observations in this research, one standard
deviation was I .0595. One standard deviation contained
almost 75% of the total population measured, and two
standard deviations represented about 95% of the population.
Chi square test for "fitness“ indicated that the
population could not be approximated by the Normal distri-
bution, however, the distribution is symmetrical about the
mean with more measurements falling closer to the mean
than expected in a Normal distribution.
Limits for Control
with a total population that may not be approximated
with the normal distribution, the use of standard deviations
to pinpoint limits for management control is inappropriate.
The percentage of food cost deviations found to be within
I 1ßx is not the expected 68% of all observations which
is representative of the normal distribution. Instead,
upon counting the food cost deviations falling closest to
the mean, 75% of all observations fall within I 1Xx .
,And although this 75% of observed deviations appears to be
430n+6‘¥+0'Z4~1-N1l
b3*"!+\:4vlE‘*2
uall *1* s:12 O
$ TS4- ·«46 ärx* ““ ä‘{
+-4 vl 4-*es ‘” H 2Fo O ä 5E *32 z:0- cb O 94
- 3 ·:* Q Hvs '° ”T”°¤•-4 GS
2 _, ¤ QO o+· >· E-§ ¤6 E,
« *° 54 H g\+-*2
I‘r·~O'}gp
PQaa_0Tic:-+47-x.z-+4vc:0ß·•··l
*'1+>*:6-+4S§:>+'¤>
+ ¤ * _ Q0 O Q bouaubaxg3 l 2 “*
44
symmetrically distributed about the mean of zero, the
ability to make probabilistic statements about individual
values from subsequent samples is limited.
Selection of MAD's made use of the absolute value
of deviations (+30.05 = -$0.05) and provides a current
look at deviations by using the ten day running average
of deviations. Even though the Chi square analysis indi-
cated that the population was not Normal, the use of MAD's
is still feasible since the distributicn was symmetrical
about the mean.
Criteria used to set limits for acceptable/unaccebtable
MAD's includes use of the 75% of all observations reported
within i 1] as nreviously reported in this study. As a
beginning step, the 75% of all MAD's found closest to the
standard of zero are considered acceptable or not worthy
of further investigation and the 25% of MAD's farthest
away from zero are considered worthv of further investigation.
MAD's
When standards for food costs are set, management
needs a measure of accuracv to help evaluate the method
of setting standards and to pinpoint problems for further
investigation. MAB is one such method of providing such
a measure of accuracy. In this research, MAD's are
calculated bv schools for ten dav periods. Exhibit V
shows the steps followed in calculating MAD's.
45
EXHIBIT V
Sample calculation of MAD's: Bellwood Elementary
MAD's = Daily Food Cost Deviations for N Days
MAD for days 1-10 = $0.22 = $0.022"RT
MAD for days 2-11 = §Q;Q4 = $0.024 .10
MAD for days 3-12 = $0.24 = $0.024’W
Note: No algebraic sign is used before MAD's since thisis an absolute deviation.
The example given shows three MAD's of $0.022, $0.024,
and $0.024. To management, this means that the food cost
deviations as reflected in a moving average of absolute
deviations (MAD's) are increasing slightly and that the
average deviation from standard reported during this
period of time is around i $0.023. In using an "averaging"
approach, deviations are looked at as part of a trend
rather than single and sometimes isolated problems.
In all, 598 MAD's were calculated from food cost
deviations reported in Tables Ia, Ib, and Ic. Of these,
200 were calculated for Chesterfield, 230 for Petersburg,
and 168 for Prince George. Results of calculation of
MAD's were grouped into even intervals. Table III
shows these groupings bv district and for the total
46
population. A maximum desired limit on MAD's of > $0.05
was set to contain over 29% of all reported results.
The level was increased from the goal of 25% to obtain
a convenient and easy to locate cut-off point and because
with personal experience in working with food costs in
school foodservice, the limit of 29% seemed realistic and
obtainable.
Looking at Table III, the maximum desired MAD level
for the total population set apart 29% of MAD's as warrent-
ing further investigation. However, this percentage was
different for each school district involved. Only 9%
of MAD's reported bv Chesterfield was > $0.05, while
Petersburg resulted in 54.8% MAD's >-$0.05. and Prince
George 49.4% MAD's 7 $0.05. Interpretation of the results
that Petersburg and Prince George reported many more MAD's
above the maximum level than did Chesterfield indicates
that deviations reported in Petersburg and Prince George
covered a wider numerical range than did the deviations
reported in Chesterfield. Or in other words, more
deviations in Chesterfield were closer to zero than in
the other districts. The implication to management is
that the situation reported in Chesterfield is "in-control"
in regards to deviations from standard cost, and that
Petersburg and Prince George are "out—of-control . The
MAD's are showing where to begin investigation. This
conclusion is consistent with the distribution of food
47
TARLF III
Summarv of Mean Absolute Deviations
MAD's Grouped into Intervals .
Range Number of MAD's which fell within each intervalTotal
(in cents) Chesterfield Petersburg Prince George Population
é~so.o; 59 20 V 19 98
.031-.055 41 22 11 74
.056-.040 48 46 14 108
.041-.045 26 41 14 81
.046-.050 15 21 27 61Maximum Desired MAD
.051-.055 5 19 12 56 .
.056-.060 4 14 11 29'
.061-.065 4 16 7 27
? .066 0 51 55 84Total MAD'S 200 250 168 598Calculated
Maximum level indicates cut-off point to trigger furtherinvestigation by management.
48
cost deviations as shown in Figures Ia, Ib, and Ic.
RSFE
Another calculation made using deviations reported
in Tables Ia, Ib, and Ic, was the Running Sum of Forecast
Errors. Unlike the MAD's, the RSFE uses the algebraic
signs of deviations and therefore reflects any accumulation
of positive or negative bias in deviations. A RSFE of
approximately zero shows that as much negative as positive
deviation exists. Exhibit VI shows the calculation of
RSFE using the same deviations as used in Exhibit V,
sample calculation for MAD's.
EXHIBIT VI
Sample Calculation of RSFE: Bellwood Elementary
RSFE = algebraic sum of food cost deviations for n days
RSFE for Days 1-10 = $0.054 + .007 - .026 - .008 + .006+ .019 + .025 + .025 + .024 + .052
= +50.154
RSFE for Days 2-11 = $0.007 - .026 - .008 + .006 + .019
+ .025 + .025 + .024 + .052 + .069
= +30.169
RSFE for Days 5-12 = -50.026 - .008 + .006 + .019 + .025
+ .025 + .024 + .052 + .069 + .005
= +50.167
Algebraic signs indicate direction of bias in deviations.
In these examples, more deviations were positive than
49
negative thus the positive RSFE. In other words, food
costs were higher than expected.
RSFE's calculated for the food cost deviations reported
in this study did show differing degrees of bias and both
positive and negative bias was found. For this reason,
calculations were continued and the technique using the
tracking signal was employed.
Tracking Signals
Calculation of tracking signals combines MAD's and
RSFE's to create an indicator of trends in deviations.
Exhibit VII shows a sample calculation using MAD's and
RSFE's from Exhibits V and VI.
EXHIBIT VII
Sample Calculation of Tracking Signal: Bellwood Elementary
ZIZIZZIIZZIZZIZIZZIZZZZZZZIZZIIZZIIIZIZZZZZIIIIZZZIZIIZIIZ
Tracking Signal = RSFEF17H>’
Tracking Signal for Davs 1-10 = +30.134 = +7.0t0.022
Tracking Signal for Davs 2-11 = +30.169 = +7.04"STFEZ
Tracking Signal for Ways 3-12 = +S0.167 = +6.96"WTUF/T
Tracking signal indicates when the deviation is exceeding
predetermined standards. When the tracking signal trips
50
or exceeds the limit set for control purposes, the manage-
ment information system, such as the Uost-Based Accounting
System, should be reviewed for possible reasons for the
existing "out-of-control" situation. In the example of
Bellwood, if the tracking signals of +7.0, +7.04, and
+6.96, all exceed a hypothetical limit of +6.5, the
indication is that the average food cost is exceeding
the permissable deviation limits.
A total of 598 tracking signals were calculated for
all deviations reported in Tables Ia, Ib, and Ic. Both
positive and negative tracking signals were found. How-
ever since deviations less than standard are considered
to be as serious as deviations greater than standard in
school foodservice, negative and positive bias were not
differentiated. These tracking signals were grouped into
even intervals without separation for direction of bias.
Table IV shows these groupings bv school district and
for the total population.
A maximum desired tracking signal "trip level" of
.>6.5 was set as it contained slightly more than 24% of
all tracking signals calculated and because it found a
convenient cut-off point. The trip level for tracking
signals reflects the presence of excessive food cost
deviations and the bias for positive or negative deviations.
Again, as with the MAD, trip levels of individual
districts varied. The trip level for the total population
51
TABLE IV
Summary of Tracking Signals
Tracking Signals Grouped Into Intervals
Range Number of Tracking Signals which fell within interval
Chesterfield Petersburg Prince George §g;äIation
é. 1 50 27 21 981.1-1.5 6 14 9 29
1.6-2.0 11 7 10 28
2.1-2.5 8 16 10 54
2.6-3.0 13 17 11 41
3.1-3.5 15 11 12 58
3.6-4.0 9 15 10 54
4.1-4.5 9 13 7 29
4.6-5.0 12 11 15 66
5.1-5.5 4 18 7 29
5.6-6.0 5 10 12 27
6.1-6.5 8 16 7 51
Trip Level6.6-7.0 17 8 11 36
7.1-7.5 -8 10 9 27
7.6-8.0 6 9 6 21
8.1-8.5 6 5 4 15
8.6-9.0 8 6 1 15
9.1-9.5 2 4 1 7
9.6-10 1 9 6 16
? 10 L ...‘L ...L. ..LTotal 200 230 168 598
Trip Level indicates cut—off point to trigger furtherinvestigation by management.
Note: Table IV includes positive and negative trackingsignals. Algebraic signs were not used.
52
set apart 24% of all tracking signals as worthy of further
investigation. District results varied from 25% in
Chesterfield, 23.9% in Petersburg, to 23.3% in Prince
George greater than the maximum desired trip of 6.5.
Flexibility in Setting "Trip Levels"
Consistent with both the MAD's and tracking signals,
the number of schools exceeding the trip levels fluctuated
bv districts. These fluctuations indicate different
degrees of variation in reported deviations. To manage-
ment within these districts, this information indicates
that different problem areas exist. For example, Chester-
field reported 9% of MAD's above the trip and 25% of track-
ing signals above the trip. As a first step towards
investigation of existing problems, Chesterfield would·
look at reasons for bias in deviations rather than the
largeness or smallness of deviations. To further illustrate
the MAD's and tracking signals shown in Exhibit VII areA
typical of the results found in Chesterfield. MAD's of
$0.022, $0.024, and $0.024 are well within the predeter-
mined level of $0.05. No reason to investigate further
exists. However, the tracking signals (+7.0, +7.04, and
+6.96) consistently trip showing that daily food cost
deviations were bias in the positive direction. This
gives reason to investigate the accuracv of standards
and possible problems that would cause deviations to be
greater than standard.
55
Because of these fluctuations experienced within the
three districts, use of a maximum desired goal for trip
limits was made. It is recommended that as this maximum
goal is reached, that management evaluate and re—establish
goals for individual use. For example, as 91% of all
MAD's reported by Chesterfield already satisfy the maximum
desired goal, establishment of tighter trip limits would
help to pinpoint problem areas within this system.
However, Petersburg, with only 65% of reported MAD's
satisfying the maximum desired goal, needs investigation
into existing problems causing these fluctuations in
daily food cost deviations before setting tighter limits.
Discussion
The need to compare actual food costs to standard
food costs for school foodservice operations has been
well established. What is important is how this approach
can be employed in the school foodservice environment.
This research has contributed to the development of a
methodology to accomplish this along with suggestions for-
its use in the evaluation of managerial performance in
school foodservice.
The description of the collected measurements taken
in this research shows what was happening during the data
collection period. Food cost deviations used in this
description provided information for calculation of NAD's
and tracking signals. The locating of “trip levels“ for h
- MAD's and tracking signals was made as a beginning step
in suggesting the use of these tools for management
control.
The value of gathering detailed information describing
actual food costs is limited bv the uses made of those
efforts. The methods used in this research to evaluate
actual food costs provide one way to use such information.
By locating a maximum limit for acceptable deviations as
described by MAD's and tracking signals, management provides
itself with a beginning step for further investigation into
causes of existing problems. As MAD°s exceed a prescribed
limit or, tracking signals trip, the need for investigation
54
55
is triggered. In this research, limits for control were
set using the data collected on food cost deyiations.
Using actual data from each district enables management
to determine realistic limits for control. Only with
a bank of data representing actual food costs and standard
food costs as they exist in one's own situation, can
realistic standards for performance be set. The setting
of maximum desired limits for MAD's and tracking signals,
in this research, was a combination of observing the
distribution of the calculations (MAD's and tracking
signals) made from data gathered and personal experience
in working with the individuals to be charged with imple-
menting such standards for performance. Suggestions for
setting individual control limits for MAD's and tracking
signals include use of a predetermined percentage of
observations to be considered controllable, and flexibility
in finding an easy to use cut—off point. This predetermined
percentage should reflect the degree of control desired
by management, and a realistic assessment of ability to
work with such standards.
To explore the daily application of methodology
used in this study, Exhibit VIII shows examples of MAD'S,
RSFE's, and tracking signals from data gathered in this
research.
56
EYHIBIT VIII
Falling Creek Elementary
Days MAD's Limit? RSFE Signal Trip?
6-15 »0.048 No +$0.56 +7.5 Yes
7-16 .048 No + .55 +7.4 Yes
8-17 .052 Yes + .59 +7.6 Yes
9-18 .044 No + .318 +7.2 Yes
As can be seen in Exhibit VIII, in days 8-17, the MAD's
were 50.052. If the maximum MAD was ¢0.05, then manage-
ment would investigate this condition. Days 8-17 appear
to be the onlv period when such investigation is necessary. _
Now considering the tracking signals, in each instance
they exceed the trip level of 6.5 indicating that further
investigation is necessary.
To management, the combination of these two techniques
directs attention to the bias of deviations. Food costs
in this school are consistently higher than the standard.
Possible causes include portion control problems, failure
to follow planned menus, failure to use standardized
recipes, and failure to reflect changes in purchase prices
in current standards for food costs. If tracking signals
continued to trip in all schools within a district, the
validity of the standards for food costs would be suspect.
57
It is worth mentioning that, in this project, negative
tracking signals were considered equal in value to positive
ones. Investigation would proceed without regard to
direction of bias. The reasons for negative tracking
signals are the same as for positive: failure to follow
menus and standardized recipes, incorrect prices used in
calculating standards, incorrect standards for food costs,
and most important of all, portion control problems. A
negative bias of food cost deviations could be caused by
underportioning or failure to meet the nutritional require·
ments of the school lunch program. Consequences include
denial of Federal and State reimbursements which make up
a significant portion of the lunch budget.
It is also recommended that the calculation of standard
food costs be reviewed at planned intervals. Intervals
consistent with the purchasing or bid system used within
an operation should be adequate for most food items. For
example, if prices are guarranteed for a bid period of
one semester, standard food costs based on purchase prices
within this one semester period will remain constant.
Occassionally, cost for a certain food item will vary
significantly within the bid period such as the daily
fluctuations in produce prices experienced several years
ago. When this occurs, standard food costs and actual
food costs should be calculated to reflect the changes
experienced.
58
Problems in Data Collection
The general attitude towards what was referred to as
"paper work" may be cited as a real area for concern. The
informal feeling of school foodservice managers about
working with paper and pencil was more negative than neutral.
Most participants expressed an opinion that working with
people and with physical resources (food) were more ful-
filling for them. This attitude towards "paper work"
was reflected by a negative response to an increase in
data collection forms. In neither Chesterfield nor
Prince George has a Daily Food Usage form been used in the
past. Comprehension by managers of what was actually
required in completing this form was questionable and
completeness and accuracy of actual food cost collection
lacking at times. More training in the use of the Daily
Food Usage form could possibly help to solve this problem.
However, even in Petersburg where this type of form had
been used on a daily basis for several years, the accuracy
of reporting was occassionally dubious.
Perhaps of even greater importance, and certainly
related to actual food cost collection, was the problem of
time management. Poor use of time and the general feeling
of a lack of time often caused priorities to be re-evaluated.
With limited time, postponement of performance reports that
could be completed later occurred. This delay in complet-
ing a form such as the Dailv Food Usage form led to a
$9
decline in accuracy as well as incomplete information that
could have been offered on the day of production and ser-
vice of that menu. Inaccuracy in reporting actual vields
of food items (especially noted in pre-portioned items),
use of leftovers, vagueness about ingredients used within
recipes (cake served without ingredients listed for frosting)
were all typical errors made in recording actual food used.
Other notable problems observed in this research
included the use/misuse of some of the basic management
tools and the substitution of USDA Donated foods for
purchased foods. Firstly, the degree to which generally
accepted management tools were used became apparent in
review of Daily Food Usage forms. One of the most certain
causes of deviation was the change of a menu item. Failure
to follow centrallv planned menus on which the standard
food costs were based alwavs showed a deviation. When
recipes were misused, embellished, ingredients subsitituted,
etc..., food cost deviations were also a certainty.
Forecasting, as a management tool, has not been accepted
as long as some of the other management tools previously
mentioned. As a result, poor planning as shown by over-
production and subseouent questionable use of leftovers
was also a source of deviation. Increased awareness of
these basic tools (menus, recipes, forecasting ) and
training in their use would help to eliminate some of
these causes for deviation.
60
Use of donated foods in place of purchased foods and
vice versa became a contributor to food cost deviations
when the price of a substitutable item varied. An example
of this could be a standard cost per serving for Chicken
Pot Pie using purchased canned poultry at $1.00 per pound
verses an actual cost per serving of the same recipe but
using USDA canned poultry at $0.90 per pound. All other
factors being equal, a food cost deviation would be noted
because of the change in ingredient cost. Often this
problem is uncontrollable as USDA foods are delivered
after menus are planned and recipes are pre-costed with
purchased food prices. (Note: the reverse occurs as well
when recipes are pre-costed using USDA prices when purchased
foods will actually be used on the day of service.)
As previously discussed, some of the most notable
problems in conducting this research were the attitude of
foodservice managers towards completion of additional
forms and time management. These specific problems
contributed to inaccuracy in reported actual food costs
bv misrepresentation of foods used in production and bv
insufficient records of the use of leftovers. To aid in
eliminating this inaccurate reporting of food usage, some
adjustments in daily collection of data are suggested.
The lack of time to complete required Daily Food
Usage forms was noted frequentlv among participants.
Especially if this type of form were to be continued on
61
a daily basis would an abhreviated measurement of foods
used be desirable? Some categories of menu items exhi-
bited a more consistent and larger deviation. For example,
the meat/meat alternate component of the menu pattern
consistently showed large deviations. The reasons for
this are twofold: 1) this component is usuallv the most
expensive one of the menu, and 2) recipes for meat/meat
alternate are more susceptable to production problems.
For these same reasons, the reverse is true for the bread
component of the menu. Raw food cost for bread was
almost alwavs the lowest of anv menu item and as recorded
on Daily Food Usage forms, recipes were more uniformly
followed.
Another trend noted within this research was constant
food costs from schools within a district when pre-portioned
items were used. One school division used purchased
bread items for sandwiches and all three used pre-portioned
fish, hot dogs, hamburger patties, and some sliced sandwich
meats. One notable exception of this observation was
that Prince George managers frequentlv reported deviations
in food costs when using pre-portioned meat items. Dis-
cussion of this point with their foodservice Director
revealed the probability that the cause of reported devia-
tions was most likelv due to inaccuracy in reporting
actual portion vields rather than lack of portion-control.
If food cost deviation predictably appears with certain
62
menu components, concentration on these items to the
exclusion of some others would save managerial time.
Reporting of actual food cost data for meat/meat alter-
nate and vegetable/fruit components while bread and fluid
milk components are ignored could be accomplished with a
marked decrease in time used. It is suggested that other
menu offerings (such as desserts and condiments), which
do not necessarily help to fulfill requirements, be
reported as well as meat and vegetable/fruit components.
These items are subject tc greater deviation because of
the lack of porticn-control and lack of regularity of
appearance within a menu cycle. Note: If this suggestion
for selective data collection to compare actual and
_ standard food costs is accepted, "trip" levels for MAD's
and tracking signals should also be reviewed to ensure that
the trip signals the level of acceptable deviation.
Conclusion
In conclusion, the methodologv employed in this
research not only satisfies the goals of the project,
hut also supplies simple and feasible tools with which
school foodservice management may continue to compare
actual and standard food costs. It is the belief of the
investigator that use of MAD and Tracking Signals coupled
with individualized limits for qualitv control will lead
to long term benefits in improved financial management.
Conducting this research effort, an observation was
made that there mav be a possible correlation between the
organizational structure within each of the three parti-
cipating school foodservice departments and the relative
degree of food cost control as shown bv the results of
this case study. As an assessment of relative food cost
control in the three districts surveved, results of this
research reveal a consistent trend. Evaluation of food
cost deviations indicates the possibilitv of stronger
control within the Chesterfield schools. The strength
of food cost control, as shown bv the measures of central
tendencies, calculations of MAD's, and Tracking Signals,
weakens somewhat in Petersburg a.nd even more in Prince
George. Evaluation results as shown in Table III, and
Table IV, describe the relative strength of food cost
control as evidenced bv the measurement of actual food
costs during the data collection period of this research.
63
64”
The closer the observations to zero, the stronger the
control shown over food resources.
In proposing a reason for the consistent trend of
relative control over food costs, a review of the make-up
of each district was conducted. The strength of organiza-
tional structure as shown by leadership, experience of
leadership, and centralized policy, and the exposure of
foodservice managers to continuing education were noted
as being present at differing levels in the three partici-
pating school districts.
Correlating organizational structure and relative
degree of resource control would involve a multifacted
look at school foodservice. Attempting to define some of
the factors relating to school foodservice departments,
organizational strength was approached from the point of
view that time and experience as well as expertise effect
positive change. Time as represented bv number of years
in which policy changes towards implementation of centralized
departmental structure have occurred ranges from about
twelve in Chesterfield, to ten in Petersburg, and only
three in Prince George. Centralization is a continuing
process that requires years to accomplish. A well planned
implementation period could be expected to begin vielding
improvements within organization over the period of vears
needed to accomplish the transition.
Facts regarding the leadership within the three districts
65
were noted in Exhibit I. Chesterfield operates with a
veteran of over thirty years experience in school food
service as its Director as well as with an Assistant
Director and a Procurement Specialist. Petersbur¤'s
supervisor has twenty years of experience with that school
district. Prince George has had their present Director
of Foodservice for only two years.
Facts regarding the continuing education of foodservice
managers were not collected within the framework of this
research. An assessment of the educational achievements
of these foodservice managers might include participation
in the Associate of Arts degree programs available in
foodservice within their communities, planned in·service
training, and exposure to professional organizations
and meetings. All of these criteria for continuing
education were observed to be present in the three districts
surveved, even though a qualitative measurement of these
characteristics was not made.
It is with these observations in mind that an untested
hvpothesis is proposed. As organizational structure grows
and communications from within are refined (use of a MIS),
the efficiencv of management exercised within the organi-
zation also grows. The result of such a positive correla-
tion is an increase in the efficiency of the organization
as indicated by improved performance measurements. This
hypothesis presents the challenge to continue investigation
into Management Information Systems to assess the relative
66
state of the art, so to speak, within school foodservice.
It is onlv when we fully understand the industry that the
knowledge and experience needed to make performance
consistent with goals will be available. Economics
demands that the future be directed towards such an end
to ensure the survival of a child nutrition policy.
Summarv
A case studv approach was used to compare actual food
costs and standard food costs in selected school foodservice
operations in the State of Virginia. This research was
planned to follow the implementation of a Cost-Based
Accounting svstem in school foodservice. Three centralized
school districts in·the capitol area of the state volunteered
to help with data collection. Within these three districts,
a relatively homogeneous sample of twentv-seven elementarv
schools was selected. Standard food costs were determined
bv pre-costing standardized recipes in each of the three
districts. Central purchasing within each participating
school division made possible the use of standard unit
prices. Data was collected to represent actual food
costs in each building for a test period of approximatelv
two months (two financial operating periods for school
foodservice).
Results were presented as a description of the total
population as measured. Evaluation of results included
presentation of Mean Absolute Deviations (MAD's) and
Tracking Signals as tools for management's use. Maximum
desired levels were proposed for MAD's and Tracking
Signals. Results showed a consistent trend of relative
food cost control between the three participating school
districts.
67
68
1 FNS 796-1 Financial Management Cost-Based Accountabilitv,United States Department of Agriculture, Food and NutritionService, Washington, D.C. 20250.
2 Olsen, Michael, and Virginia State Department of EducationSchool Foodservice Division: Manual for Cost-Based AccountingVirginia School Food Service ivision, 1978.
3 Konvalinka, J.W., and Trentin, H.C.: "Management Informa-tion Svstems", Management Services, 2:27-39, 1965.
4 Ein-Dor, Phillip: "Parallel Strategv for MIS", Journalof Svstems Management, 26:30-35, 1975.
5 Coleman, Ravmond J., and Riley, M.J.: "The OrganizationalImpact of MIS", Journal of Svstems Management, 23:13-19, 1972.
6 Coleman, Ravmond J., and Riley, M.J., MIS: ManagementDimensions, San Francisco: Heyden-Day, Inc., 1972.
7 West, Glenn M.: "MIS in Small", Journal of SystemsManagement, 26:10-13, 1975.
8 Conroy, Christopher A., and Arthur Young & Co.: “Imp1e-menting the Standard Cost System", CPA Journal, 38:83-86,1976.
9 Mason, Richard O., and Mitroff, Ian I.: "A Program forResearch on Management Information Svstems", ManagementScience, 19:475-487, 1975.
10 Miller, Robert D., and Robinson, Terry L.: "PerformanceReports Based on Direct Costing: A Case Study", ManagementAccounting, 52:45-47, 1970.
11 Dopuch, Nicholas, Birnberg, Jacob G., and Demski, Jnel,Cost Accounting: Accounting Data for Mana ement Decision,New York: Harcourt Brace Jovanovich, Inc., 1974,
12 Horngren, Charles T., Introduction to Management Accounting4th ed., New Jersey: Pren ice-“a 1 Inc., 9 .
13 Polimeni, Ralph S., and Adelberg, Arthur: "Effects ofInflation Variance Analysis", CPA Journal, 46:64-65, 1976.
14 Parsons, Vincent A., and MacDonald, George A.: "StandardCost and Control Svstem", Management Accounting, 52:19-21,24,1970.
69
15 Koehler, Robert W.: "Statistical Variance ControlThrough Performance Reports and on-the-spot Observations",Management Accounting, 51:45-51, 1969.
16 Nielsen, 0swa1d:"The Nature and Importance of Variancesfrom Standard Cost of Production", Management Accounting,50:16-20, 1969.
17 Dopuch, Nicholas, Birnberg, Jacob G., and Demski, Joel:"An Extension of Standard Cost Variance Analysis", TheAccounting Review, 42:526—536, 1967.
18 Bierman, H., Fouraker, J.L.E.: "A Use of Probabilityand Statistics in Performance Evaluation", The AccountingReview, 36:416-417, 1961.
19 Uuvall, Richard M.: "Rules for Investigating CostVariances", Management Science, 13:631-640, 1967.
20 Magee, Robert PQ: "The Usefulness of CommonalitvInformation in Cost Control Decisions", The AccountingReview, 52(4):869—880, 1977.
21 Wight, oliver W., Production and Inventogy Managementin the Computer Age, Boston: Cahners Books International, Inc.
22 Makridakis, Spvros, Wheelwright, Steven C., InteractiveForecasting Univariate and Multivariate Methods, 2nd ed.,San Francisco: Holden-Day, Inc., 1978.
23 Makridakis, Spvros, and Wheelwright, Steven C., ForecastingMethods and Applications, Santa Barbara: John wiley and Sons,
24 U.S. Bureau of the Census, Countv and Citv Data Book 1977,ga statistical abstract supplementi, U.S. Government PrintingOffice, Washington, D.C., 20402, 1978.
25 Mendenhall, William, and Reinmuth, James E., Statisticsfor Mana ement and Economics, 3rd ed., North Seituate,Massachusetts: Duxßury Press, 1978.
70
Appendix A: Nutritional Requirements for School LunchProgram
To meet the requirements of the National School Lunch
Program, the lunch must contain as a minimum:
2 ounces Meat or Meat Alternate
5/4 cup combined Fruits and Vegetables
1 slice of whole—grain or enriched bread
1/2 pt. fluid milk
Requirements taken from: Food Ruvina Guide for Tvne A
school lunches, prepared bv Nutrition and Technical q
Services Staff, Food and Nutrition Service, U.S. Dept.
of Agriculture.
71
Apoendix B: Sample Menue
Chesterfield County
Day 1 Steak & Cheese SubButtered Corn, 5 cupPineapple Tidbits, 5 cupChocolate Pudding, 5 cupä pt. Milk
Day 2 Meatloaf w/ Grayy,_2 oz.Whipped Potatoes, 5 cupGreen Peas, 5 cupHomemade Rolls, 1Yellow Cake w/ Chocolate Frosting5 pr. Milk
Day 5 Mile High Ham Sandwich, 2 oz.Tator Tots, 5_cupGreen Beans, 5 cupFrosted Brownies5 pt. Milk
Day 4 Oven Fried ChickenMashed Potatoes, 5 cupSeaeoned Greene, 5 cupAngel Biscuite, 1Orange wedge, 5 orangeQatmeal Cookie, 15 pt. Milk
Day 5 Corn Dog _Green Peas, 5_cupCorn on Cob, 5 cupGingerbread Wedge5 pt. Milk
Day 6 Pizza _Chilled Appleeauce, 5 cupCrisp Cole Slaw, 5 cupCocoberry Cake5 pt. Milk
Day 7 Cheeeeburger on Bun CButtered Sliced Potatoes, 5 cupChopped Broccoli, 5 cupwedding Cookies, 25 pt. Milk
These menus are samples of Chesterfield's four week cycleof Elementary menus.
72
Appendix B: Sample Menus (cont.)
Petersburg City
Dav 1 Roast Beef Chunks and Gravv, 2 oz.Rice, Q cup _Peas & Carrots, Q cupHot Roll, 1Temtation w/ Pineapple, Q cup2 pt. Milk
Day 2 Spaghetti w/ Meat & Cheese, 2/5 cupTossed Salad, Q cup _Buttered French Bread, 1 sl.QPPlesauce, Q cup2 ot. Milk
Day 5 Fish 'n Batter w/ Tartar SauceMacaroni & Cheese, Q cupButtered Cabbage, Q cupCornbread, 1 squareFruited Cherry Gelatin w/ Topping, Q cupQ pi. Milk
Day 4 Hamburger on BunLettuce & Tomato, Q cupTater Tots, Q cupFruit Cup, Q cup
‘
Q pi. Milk
Day 5 Chicken Pot Pie. 5/4 cupGreen Beans, Q cupHot Roll, 1Peanuts, Q cupepple Criso, Q cupQ pt. Milk
Day 6 Raviolini w/ Meat & Cheese, 3/4 cupTossed Salad, Q cupHot Roll, 1Peaches, 2 cupQ pi. Milk
Day 7 Baked Ham Slice, 2 oz.Potato Salad, 2 cu¤Spinach, Q cupHot Biscuit, 1Bread Pudding, Q cupQ pi. Milk
These menus are samples of Petersburg's six week cycleof Elementary menus.
73
Appendix B: Sample Menus (cont.)
Prince George County
Day 1 Miniature Sub SandwichLettuce & Tomato, Q cupButtered Green Beans, Q cupBaked DessertQ pt. Miik
Day 2 Homemade PizzaburzerButtered Peas, 5 cupSpicy Applesauce, Q cup5 pt. Milk
Dav 3 Fish 'n Fries, 1 portion & Q cupCheese Wed¤e, Q oz.Cole Slaw, Q cupCornbread, 1 squareQ pt. Milk
Dav 4 Vegetable Soup, 6 oz.Grilled Cheese SandwichPeach Cobbler. Q cup5 pt. Miik
Dav 5 Hamburger on BunButtered Corn, Q cupPear Half, 1Peanut Butter Cookie, 1E pt. Milk
Dav 6 School Made PigzaTossed Salad. 5 cupChilled Peaches, 5 cupQ pt. Miik
Day 7 Turkey Dressine Supreme, 2 oz.Mashed Potatoes & Gravv, Q cupFruit Juice, 4 oz.Mixed Vezetables, Q cupHomemade Roll, 15 pt. Miik
These menus are samples of Prince George‘s three weekcvcle of Elementarv menus.
74
Appendix C: School Names
Chesterfield County
WorkingSchool Name Manager's Name Hours
Bellwood Elementary Christine Dettmer 7
Bon Air Elementary Margaret Kinney 7
Chalkley Elementary Janie Sapp 7
Crestwood Elementary Audrey Waters 7
Curtis Elementary Pauline Whitten 7
Davis Elementary Barbara Oulette 7
Ettrick Elementary Rose Cummins 7
Falling Creek Elementary Ana Belle Reed 7
Harrowgate Elementary Carol Stillwell 7
Reams Road Elementary Katherine Whitley 7
75
Appendix C: School Names (cont.)
Petersburg City
WorkingSchool Names Manager's Name Hours
Anderson Elementary Mae Moodly 7
Blandford Elementary Ruby Pernell 7
A.P. Hill Elementary Margaret Guthrie 7
Jackson Elementary Alma McCants 7
R.E. Lee Elementary Mary Artis 7
Petersburg Middle Yvonne Gittman 7
Stuart Elementary Ellen Allen 7
Virginia Avenue Percilla Anderson 7Elementary
Walnut Hill Elementary Jackie Wells 7
Westyiew Elementary Mildred Vaughan 7
76
Aopendix C: School Names (cont.)
Prince George County
WorkingSchool Names Manager's Name Hours
Beazley Elementary Catherine Bendall 6‘
Burrowsyille Primary Leola Hollowav 5%
Carson Primary Lillian Antis 5%
Harrison Elementary Francis Belshan 6
North Elementary Joyce Nelson 6
South Elementary Gladys Turner 6
Walton Middle Rosa Lee Goodwvn 6
77
APPENDIX D Continued
Chestertield County
Green Peas .040 .080Greene .038 .076Lettuce & Tomate „ .030 .060Potatoee, French Fried .061 .091 1 cupFotatoee, Mashed .016 .032Fotatoes, Slices .013 .026Potatoes, Tator Tote .057 .076 1 cupToeeed Salad w/ dressing .022 .044
BreadsItem Per Roll
Angel Biscuits, Butter .058Dinner Roll, Quick recipe .013uinner Roll, USDA B-13 .011Hamburger Bun, quick recipe.O23Hamburger Bun, USDA B-13 .019Hot Dog Bun, Quick recipe .027Hot uog Bun, USDA 3-13 .022
DessertsCakes
Cocoßerry Cake w/ Buttercream Frosting .043Pudge Cake w/ Fluffy Frosting, County C-30 .040Gingerbread w/ Powdered Sugar, USDA C-14 .023Ginger Spice Bar, county . .042Oatmeal Cake, County .051reenut Butter Cake w/ Feanut Butter .044
Frosting, USDA C-20Prune Spice Cake w/ Caramel Frosting .054
USDA 0-22 & County 0-29Yellow Cake w/ Chocolate Frosting, County .045Yellow Cake w/ Chocolate Frosting, USDA .048
Cookies and BarsBrownies w/ Frosting, County .062Graham Crackers, 2 .025Oatmeel Cookies, SES Journal, 1 ea. .024reanut Butter Cookies, County, 2 ea. .035nedding Cookies, County, 2 ea. .056
Fuddin s and miscellaneousApple Crisp, County. 2 cup .092Chocolate Pudding, County, ; cup .025Chocolate Pudding, Purchaeed, : cup .038Gelatin Dessert, z cup .015
OtherPotato Chips, 32 servings/lb. .026Condiment usage Figure .005
78
APFENDIX D: STANDARD COST FER SERVINGS
Chesterfield County
Meat/Meat AlternateItem _ Ele. Sec.
Batter DIpped Fish + 1 oz. cheeee $0.194 SOTTUZBarbecue -
County 0-56 .1218
.182Barbecue • Purchaeed .200Cheeseburger .192 .192Club Sandwich (Turkey, Cheese, Ham; .204Corn Dog .187 .187Frankfurter .090 .090Fried Chicken, Prepared .258 .258Fried Chicken, USDA .261 .344Hamburger .162 .162Humpty uumpty(Egg, Cheese, Mam) .220hasagna USDA D-31 .214 .286Meatloaf County D-59 .190 .254Mile High Ham, 2 oz. .161 .161Mile High Roast Beef, 2 oz. .209 .209Mile High Turkey, 2 oz.
V.120 .150 (21 oz.)
Mini Ravioli, County 0-60 .155 .232Pizza, USDA D·43 .136 (30) .163 (24) .204 (20)Salisbury Steak, County D-61 .178 .237
_ Spaghetti w/ Meat Sauce, County D-62 .188 .250Steak•Um (2_oz. w/ 1 oz. cheese} .212Steak-Um (21 oz.; .228Tacos, County D-63 .174 .280Toetados, County u•63 .174 .280 _
Che£'s Salad Bowl, County 3-21 .491 .491Turkey w/ gravy, 2 oz. .245 .245uressing .020 .020
Vegetable/FruitItem g cu: 1 cu:?¥EIts
Applesauce .036 .073Peaches, sliced .043 .086Pineapple Tidbits .062 .125Orange Juice .070Orange Wedges .053Congealed Salad .030 .ÜbO
vegetableeBroccoli .052 .104Carrot/Celery Sticks .040 .080Cole Slaw, USDA k VCM recipes .017 .034Corn, whole Kernel .028 .057Corn, on Cob .100 ea.Green Beans .030 .06C
79
APPBNDIK D Continued
Petersburg City
Meat/Meat AlternateItem V Ble. Sec.
Piziza.w/ cneeee $072656 507514Bologna Burger .1200 .1811hoast Beef Chunks w/ Gravy, Pur. .2567 .3851Reast Beef Chunks w/ Gravy, USDA .2095 .3142Spaghetti w/ Meat & Cheese .2962 .3949rish & Batter .1731Macareni & Cheese, USDA .0425 .0849Macareni B Cheese, zur. .0451 .0902H¤¤b¤I8¤! .1336- .2384 '
Chicken Pot Pie, Pur. .2603 .3470 -Chicken Pot Pie, USMA .2111 .2815Raviolini w/ Meat & Cheese, Pur. .2755 .3673Ravielini w/ Meat 8 Cheese, USDA .2609 .3478Baked Ham Slice .2440 .3660Barbecue .2124 .3186Brunswick Stew, Pur. .2249 .2999Brunswick Stew, USDA .2137 .2849Steak k Cheese Sandwich .2503Turkey & ßravv .2451 .3675Baked Beans. USDA .0292 .0583Baked Beans, Pur. .0312 .0623Peanuts .0375Crackers w/ Peanut Butter .0500Lasazna ' .2500 .2500Turkey Salad .1500 .1500
- Fried Chicken, USDA .2630 .2630Taces w/ Cheese .1500 .1830Heat Leaf .1700 .2450Vezetable/Fruit _ _
Item 1 cue cue + cueSpinacn, frozen .0895° 50478Spinach, canned .0900 .0450Cern, Pur. .0611 .0306Cern, USDA .0605 .0303Lettuce & Temate .0495 .0248Tri·Taters .0478Peas (USDA)& Carrots .0772 .0386Peas (Pur.) & Carrets .0786 .0303Teseed Salad .0600 .0300
w/ ureseing .0215 .0215Appleeauce, USDA .0645 .0323Applesauce, Pur. .0661 .0331Buttered Cabbage .0200 .0100Potato Reunds, USDA .0806 .0403Tater Tets, Pur. .1036 .0518
80
APPENDIX D Continued' Petersburg City
Green Beans .0894 .0447Peaches, USDA .0672 .0376Peaches, Pur. .0752 .0376Potato Salad .0774 .0387Cole Slaw .0500 .0250
· French Fries, USDA .0797' .0598French Fries, Fur. .0992 .0744 .
Pineapple .1200 .0600
Pear Salad w/ Grapes .0909 .0455Mashed Potatoes, Pur. .0296 .0148
Mashed Potatoes, USDA .0258 .0129Parsley Buttered Fotatoes .0300
Limes & Corn ~ .0700 .0350
DessertsGinzerbread w/ Con£ectioner's Sugar .0404 _ ·Peach Cobbler, USDA .1028, 3 cup
Peach Cobbler, Pur. .1108, 3 cup
Temtation, w/ Pineapple .1241, 3 cup
Fruited Gelatin .0552, 3 cup
Fruit Cup .1169, 3 cup
Apple Crisp .1240, 3 cup
Bread Pudding .0562Chocolate äacaroon Cake .0313 T _
Gelatin Dessert .0312, 3 cup .0156, 3 cup
Ice Cream .0760Spice Cake w/ Caramel Icinz .0365 _
Apple Cohbler .1041, 3 cup
Chocolate Pudding .0376, é cup
Strawberry Shortcake .0794
Van 0 Dip Cookie, 1 ea. .0545
Coconut Cake Souare .0600 _ '
Chocolate Cobbler .0480, : cup
Oatmeal Cookie, 1 ea. .0370
Other 3 cup 3 cuä
Rice, USDA . .
Rice, Fur. .0283 .0142
Pickle Chips .0132 ea.
Cranberry Sauce, USDA .0300
BreadDinner Roll w/ Butter, ea. .0288
Hamburger Bun, ea. .0234
French Bread, 1 slice .0285
Cornbread .059*Biscuit, 1 ea. .0345
81
APPENDIK D Continued
Prince George County
Meat/AlternateItem I
Ele. Sec.
Chicken Salad, #8 $0.1388 $0.1388
Fried Chicken, 2 oz. .2417 .2417
_Mini Meat Leaf#12 — .1795#10 .1915
Heast Turkey w/ Gravy, 2 oz. .1871 .1871
Pizza .1325 .1325Pizzaburger .1847Sllced Turkey w/ Gravy, 2 oz. .1266
Sleppy Joe .2125 .2125
Spaghetti w/ Meat Sauce .2182 .2182
Turkey Dressing Supreme .1958 .1958
Cheese Wedge, 1 ez. .0356 .0356
Barbecue -°
#16 .2279#12 _ .2422
Chili Dos. 2 ez chili.1466
Club Sandwich.1370
Cern Dog .1938 .1938
Egg Mcäoyal .2C75
A Fish, Batter uipped, 2 oz. .1100
Fish, Batter uipped, 3 cz. .1378 .1378
Hamburger, 2.4 oz. .1815 .1815
het Dog, 2 oz. .1200 .1200
Italian Sub .1847
Mlniature Italian Sub .1847
Steak Sandwich .2129
Vegetable Soup, 1 cup .1993
Grilled Cheese Sandwich .1450‘
Tomate Soup, 1 cup .0530' .
Chili Cen Carne, 1 cup .2094
Cheeseburger .2165
Vegetables/Fruits _ _Item cup cue
Apple nalf . 5:
Carret Sticks .0105
Carret & Celery Sticks .0154
Beans, Fork and .0355 .0710
Beans, Green .0399 .0798
Brecceli.0694 .1388
Cabbage, seasoned .0239 .0477
Cole Slaw .0233 .0465
Cern.0368 .0736
Corn on Ceb, ea..0938
Greens, Mixed .0354 .0708
82
APPENDII D Centinued
Prince George Uounty .
Lettuce & Tomate .0308 .0616Peas, Green .0425 .0850Potatees, French Fried, 0ven-Redi .0414 .0827Potatoes, French Fried, Fat Fried .0405 .0809Potatoes, Parslied .0343 .0686 IPotatoes, Mashed .0123 .0246Tater Tote .0210 .0420Toseed Salad .0256 .0512Vegetablee, Mixed .0448 .0896Tomate salad .0390 .0720Applesauce .0337 .0673Baked Apples .0673 .1273orange Juice, 4 oz. .0680Fruit Cocktail .0561 .1122Fruit Popsicle, ea .0650Fruited Jello .0567Qrange Quarters, 2 .0550Peaches .0414 .0827Pears, Halves
1 half . .06672 halves .1334
Tropical Apples .0674 .1348Peach Cobbler .1170 ~Fear 'n Cheese Salad .0780Cranberry Crunch .0310 .0620Cranberry Sauce .0318
BreadsItem Ble. Sec.
AngeI Eiscuit, ea. .0421 .0ZZ1Cornbread w/ Butter .0372 .0372Dinner Roll w/ Butter .0237 .0276French Bread w/ Butter, ea. slice .0472 .0472 ·Hamburger .0458 .0458Hot Dog Bun .0458 .0458Junior Hamburger Bun .0433Submarine Bun .0750Sandwich Bread, 1 slice .0200 .0200
DessertsCinnamon Crispie, 1 cookie .0233Ice Milk Bar . .0558Oatneal Cookie, 1 cookie .0299Peanut Butter Cookie, 1 cookie .0168Yellow Cake w/ Vanilla Frosting .0316Yellow Cake w/ Chocolate Frosting .0395Wedding Cookie, 1 cookie .0250
83
Other _
Potatp chips '3 oz. .02661 oz. .0532
Coudimeut Usage Figure .0050
84
APPENDIX D continued
Sample Calculation: Dailv Standard Food Cost
Cheeterfisld Countv
Serving Cost perDav Menu Item Size Servinz
1 Steak & Cheese SubSteak-Wm 2 oz. $0.178”heeee :_oz. .054Sub Roll 2: oz. .019
Buttered Cora g cup .057Pineapple Tidbits ; cup .062 ~Chocolate Puddinz ; cup .028Fluid Mllk t pt. .107Condiments Ave. Cost .005
‘
äÖ.I§U Total
_ 0
85
APPENDIX E¤¤1I.v rum: uam: M 6
~u am-I¤¤I. B°ll"°°d
Studlhtl Steak & Cheese Sub DATE 1 .AdgltsButtaredEmpldvees
8 Pineapple Tidbits _
TUTRL Checolate Pudding2
p1;_M11; 1978-79
ßmount Used ost Per lze df Partien Tdtal Cast erG/P Inredients PGUIWGS Cans Unit Pdrtiun Yield Cast Servin
Nu! of Fddd Item Sub Rclls2;.
Oz.
· watercuus
5I.lK—§l!§__ze„. E—Z}1§!§_—§Z¥l
I.- s ortenln l'§!K—ZfiI.!EI——_HEH.1är Ii!!EHj1!1LI.!H_—1«Bl»
=Neue of Fond Item Steak 5: Cheese
ceese ä.,
- Steak- m Il'ä1H_l=HßlI!3—§l§EI-IHREM . I:
Neme cf Fond Item Buttered Com
: com ... cs KIliEl! ·
Nune of Fond Item — ‘· ·-—~
· »· I=¤ " ¤I°OO84II
IINum er Feed ttm Chdcolate Pudding
··I '.
G .¤ I. Fl.ll—ZH•1!EI—— ., 7
-K§§?EI—§lP§§l—_ 7•J I
=¤ l!1•1@ I,. I2 · IF v¤ W·~# J
I
"‘·G¤v•x·nment-'
I;-¤u;~¤¤««¤ rum. san nur:$°·>"*‘
VARIANCES BETWEEN STANDARD COSTS
AND ACTUAL COSTS IN SELECTED
ELEMENTARY SCHOOL FOODSERVICE PROGRAMS
by
Deborah Pritchard Wilson
(ABsmRAcT)
The objective of this research was to compare actual
food costs with standard food costs in twenty-seven
elementary schools in Virginia using information derived
from the Cost-Based Accounting system.
Comparison of actual and standard food costs were made
using techniques adapted from industrial settings. These
techniques include MAD. RSFE, and tracking signals. These
techniques were then used to establish control limits for
cost variances between actual and standard costs.
As a result of the research effort, the use of MAD,
RSFE, and tracking signals proved to be a realistic approach
to cost variance analysis in school foodservice.