Reconc. Analyses

RECONCILIATION ANALYSIS Of the Forty-Niners Shops

Octavio Urista

Tony Molina

1

Table of Contents About the Forty-Niners Shops: ..................................................................................................................... 2

Project Expectations: .................................................................................................................................... 2

Data: .............................................................................................................................................................. 3

Analysis: ........................................................................................................................................................ 4

Bookstore: ................................................................................................................................................. 5

Bookstore Convenience Store:.................................................................................................................. 7

Beach Hut: ............................................................................................................................................... 10

General Recommendations: ....................................................................................................................... 14

Store-Specific Recommendations: .............................................................................................................. 15

Future Analyses: .......................................................................................................................................... 16

2

About the Forty-Niners Shops: The Forty-Niners Shops is a non-profit auxiliary of California State University, Long Beach (CSULB)

incorporated by the state of California and acts as the commercial arm of the university. The Forty-Niners

Shops include 25 stores both on- and off-campus – with most of the stores located on-campus.

Furthermore, each store has 1-3 shifts, and 1-39 registers.

The store names are as follows:

1 2nd Street Store

2 Art Store

3 Beach Hut

4 Beach Walk

5 Blair Field

6 Bookstore

7 Bookstore Convenience Store

8 CBA & Brotman Hall Carts

9 Chartroom

10 Events Center (Pyramid)

11 Fresco Grill

12 George’s Greek Cafe

13 Library Starbucks

14 Los Alamitos Coffee House

15 Nugget

16 Outpost

17 Outpost Convenience Store

18 Parkside

19 Parkside Coffee House

20 Residence Hall

21 Residence Learning Center

22 Student Union Convenience Store

23 Taco Bell

24 UDP Starbucks

25 Value Transfer Station

At the start of each day, cash registers are funded with a starting bank. At the end of each day, all of the

money in the register is placed in a bag and sent to a central location; at which point, the money is

reconciled.

Project Expectations: Identify trends in overages and shortages with respect to stores, cashiers, and shifts.

Identify inefficiencies with respect to stores.

To accomplish these goals we used box-plots, CUSUM charts, Pareto charts, time-series plots, general

linear models (when model assumptions are satisfied).

3

Data: For this project, we received a data dump of all the activity that is recorded after the reconciling process.

The original data set consisted of 107,723 observations.

The cleaned data set consisted of 99,498 observations.

o Observations with beginning cash = 0 and register reading < 0 were removed.

These were all due to refunds/voids made with no initial money in the drawer.

We nor the accountants can make sense of these.

o Observations with register reading = beginning cash were removed.

These observations contribute no additional information, but they do inflate

certain statistics. For instance, the number of “Okay Reconciliations.”

The data set with significant observations consisted of 6,699 observations.

o An observation should be considered significant by the reconciler if it has an overage (or

shortage) of >$5 (or <-$5).

The data we received was compiled by the person reconciling the deposit. The data contains the

following information:

Bag Closing Date

Bag Reconciliation Date

Starting Bank

Final Register Reading (at shift-end)

Money Made

Shift

Register Number

Additional Comments

Amount Over/Short (computed automatically)

The large volume of significant observations made most of the graphs redundant. For this reason, we

chose to group observations by months.

4

Analysis:

3 stores (Bookstore, Beach Hut, Bookstore Convenience Store, Blair Field) account for about 2/3

of both shortages and overages across all 25 stores.

Residence Learning Center, Fresco Grill, Taco Bell, Residence Hall, Parkside Coffee House, and Los

Alamitos Coffee House all lacked shortages and overages.

There was a total amount of $210,685 in shortages and a total amount of $169,989 in overages

for a total deficit of $40,696.

We narrowed the focus of the rest of our analysis on the Bookstore, Beach Hut, Bookstore Convenience

Store– these 3 stores contributed about 63% of both the shortages and overages.

5

Bookstore -

Shift A contributes the most to both overs and shorts.

This is expected, as the mornings are always the busiest at the Bookstore.

Cashier 11 contributes the most to both overs and shorts.

Left: January and August were the heaviest months with respect to shorts.

This is expected as these are the months when the bookstore receives the most traffic.

As well as this, the bookstore has more seasonal workers around this time.

Right: Without shift D (a shift with <10 observations), all of the shifts have statistically equal

variances. This is what we wish (and expect) to see.

Z

D

CB

C

B

A

250200150100500

P-Value 0.028

P-Value 0.000

Multiple Comparisons

Levene’s Test

Sh

ift

Test for Equal Variances: Avg. Over/Short vs ShiftMultiple comparison intervals for the standard deviation, α = 0.05

If intervals do not overlap, the corresponding stdevs are significantly different.

6

There was significant shifts in January 2011, February 2011, April 2011, and May 2011.

Left: The un-adjusted Xbar/R-charts.

Right: The adjusted Xbar/R-charts.

*Boxplots were not provided due to the large number of observations.

**Test for Equal Variances: Avg. Over/Short vs Cashiers could not be graphically produced because some

cashiers had <3 observations.

*** Regression could not be run as data did not satisfy the normality assumption (with and without

Johnson Transformation).

Note: Box-Cox transformation cannot be performed on negative data.

Month**

Year**

April

Septe

mber

Febru

ary

July

Dec

ember

May

October

Mar

ch

Augus

t

Janu

ary

2015

2014

2014

2013

2012

2012

2011

2011

2010

2010

5000

2500

0

-2500

-5000

-7500

-10000

Cu

mu

lati

ve S

um

0

UCL=3577

LCL=-3577

CUSUM Chart of Bookstore

201520142013201220112010

2

0

-2

-4

Year

Sa

mp

le M

ea

n

__X=-0.535

UCL=2.869

LCL=-3.938

201520142013201220112010

30

20

10

0

Year

Sa

mp

le R

an

ge

_R=10.11

UCL=18.36

LCL=1.861

1

Tests are performed with unequal sample sizes.

Xbar-R Chart of Avg. Over/Short for Bookstore

2015201420122010

2

0

-2

Year

Sa

mp

le M

ea

n

__X=-0.585

UCL=1.681

LCL=-2.852

2015201420122010

12

8

4

0

Year

Sa

mp

le R

an

ge

_R=6.73

UCL=12.22

LCL=1.24

Results exclude specified rows: 13:24, 37:48


Xbar-R Chart of Avg. Over/Short for Bookstore

7

Bookstore Convenience Store:

It is strange that shift B contributes as much to shortages, despite its small contributions to the

amount overages.

Cashier 21 contributes most to both overages and shortages.

Left:

o There were many unexpected spikes.

Right:

o There was a lot of large shortages in September of 2013 and thereon after.

Mo.

Year

May

Dec

ember

July

Febru

ary

Septe

mbe

r

April

Novem

ber

June

Janu

ary

August

2015

2014

2014

2014

2013

2013

2012

2012

2012

2011

500

0

-500

-1000

-1500

-2000

-2500

-3000

Cu

mu

lati

ve S

um

0

UCL=519

LCL=-519

CUSUM Chart of Mean Over/Short

8

Left:

o We see that cashier 22 has much more variation than the other shifts.

o Cashiers 21-23 all have fairly large outliers, with cashier 22 being of special interest.

o The median is at about 0 for all 4 cashiers, as is desired.

Right:

o We see that all shifts have about the same variance.

o Shifts B and Z have fairly large outliers, with shift Z being of special interest.

o The median is at about 0 for all 3 shifts, as is desired.

Based on Levene’s Test, all of the shifts have statistically equal variances.

Based on Levene’s Test, all of the cashiers do not have statistically equal variances.

2322211

1000

500

0

-500

Cashier

Avg

. O

ver/

Sh

ort

Boxplot of Avg. Over/Short

ZBA

1000

500

0

-500

Shift

Avg

. O

ver/

Sh

ort


23

22

21

1

7006005004003002001000

P-Value 0.000

P-Value 0.042


Levene’s Test

Cash

ier

Test for Equal Variances: Avg. Over/Short vs CashierMultiple comparison intervals for the standard deviation, α = 0.05


Z

B

A

6005004003002001000

P-Value 0.048

P-Value 0.109


Levene’s Test

Sh

ift

Test for Equal Variances: Avg. Over/Short vs ShiftMultiple comparison intervals for the standard deviation, α = 0.05


20152014201320122011

20

0

-20

-40

Year

Sa

mp

le M

ea

n

__X=-10.64

UCL=14.45

LCL=-35.73

20152014201320122011

150

100

50

0

Year

Sa

mp

le R

an

ge

_

R=59.8

UCL=115.2

LCL=4.5

1

1

Xbar-R Chart of Months in Year


201520142011

20

0

-20

-40

Year

Sa

mp

le M

ea

n

__X=-9.58

UCL=13.74

LCL=-32.91

201520142011

120

80

40

0

Year

Sa

mp

le R

an

ge

_R=55.6

UCL=107.0

LCL=4.2

Xbar-R Chart of Months in Year

Results exclude specified rows: 6:29


9



The R-Sq (adj) is very low. That is, the variables in our model account for 0% of the variation in the

data. It is best to ignore this model.

Note: This is the data after having performed the Johnson Transformation

10

Beach Hut:

Shift Z contributed the largest amount of shortages, despite having among the least overages.

Cashier 3 contributed the most to both overages and shortages.

Left:

o There were many unexpected spikes. It is quite surprising to see such a downward trend

in the latter months.

Right:

o The CUSUM chart indicates that Beach Hut is doing better than the previous two years.

It is still doing quite poorly, however.

Month

Year

April

October

April

October

April

October

Febru

ary

Augus

t

Febru

ary

July

Janu

ary

2015

2014

2014

2013

2013

2012

2012

2011

2011

2010

2010

100

0

-100

-200

-300

Cu

mu

lati

ve S

um

0

UCL=52.1

LCL=-52.1

CUSUM Chart of Avg. Over/Short

11

Left:

o Shift D and Z appear to be particularly worrisome, however, our Pareto charts reveals

small contributions from both D and Z. We need not worry.

o The rest of the shifts appear to have a proper median.

o There is a couple of worrisome outliers.

Right:

o Cashier 4 appears to have a large variance and low median, however, our Pareto charts

reveal small contributions from this cashier. We need not worry.

Based on Levene’s Test, all of the cashiers do not have statistically equal variances.

ZDCB - Change BagCBA

0

-200

-400

-600

-800

Shift

Avg

. O

ver/

Sh

ort


1654321

0

-200

-400

-600

-800

Cashier

Avg

. O

ver/

Sh

ort


4

3

2

1

2000150010005000

P-Value 0.401

P-Value 0.010


Levene’s Test

Cash

ier

Test for Equal Variances: Avg. Over/Short vs CashierMultiple comparison intervals for the standard deviation, α = 0.05


201520142013201220112010

10

0

-10

-20

-30

Year

Sa

mp

le M

ea

n

__X=-9.54

UCL=9.91

LCL=-29.00

201520142013201220112010

120

90

60

30

0

Year

Sa

mp

le R

an

ge

_R=52.2

UCL=97.3

LCL=7.1

1

1


Xbar-R Chart of Avg. Over/Short for Beach Hut

2015201420122010

10

0

-10

-20

Year

Sa

mp

le M

ea

n

__X=-6.88

UCL=11.27

LCL=-25.02

2015201420122010

100

50

0

Year

Sa

mp

le R

an

ge

_R=48.7

UCL=90.8

LCL=6.6

1

Results exclude specified rows: 12:23, 34:45


Xbar-R Chart of Avg. Over/Short for Beach Hut

12



Note: This model contains Shift D (which has only 2 observations, both of which are outliers).

Further note: Shift D has a very large VIF.

Removing Shift D is recommended and will likely make other variables significant and improve the

model’s significance.

13

27.13% is a decent R-squared for economic data.

We note that Shifts add statistically significant variation to the model – both shifts A and B are

statistically significant at the 0.05 alpha level. Interestingly, they both have positive coefficients.

We further note that Cashiers also add statistically significant variation to the model – Cashiers 1,

2, and 3 are statistically significant at the 0.10 alpha level. Again, they both have positive

coefficients.

Lastly, we note that neither January nor August were statistically significant.

Note: This is the data after having performed the Johnson Transformation

14

General Recommendations: Do not have cashiers reconcile their own drawers.

o As we saw in the Pareto charts, many stores do not contribute any shortages or overages.

There should be some natural variance in overages and/or shortages.

Tom (an accountant for CSULB’s Forty-Niner shops) attributes this to the fact that

these locations reconcile their own drawers.

Having experience as a cashier with and without a reconciliation system, it is

noticeable that there are less recorded “overages” and “shortages” in a system

where cashiers reconcile their own drawers.

Have managers train employees how to properly void orders and handle checks.

o Consider these entries for the Bookstore (note: there’s many more):

“check was counted twice $3073.91 Entered by mcajucom on 1/21/2015 5:35:59

PM I think what happen is they ran the check twice $3073.91 and also void

transaction of $359.00. Steve was showing me something about the void

transaction. 1-21-15 Entered by mcajucom on 1/21/2015 3:40:48 PM”

“missing 3 checks Entered by mcajucom on 1/14/2011 1:09:03 PM”

“missing 3 checks for 845.61 Entered by mclaussen on 6/25/2010 10:26:35 AM”

“check for $752.10 rang up twice Entered by ccurd on 1/18/2014 11:25:38 AM”

Require clearer comments from reconcilers.

o “not $533.00 its $553.00 Entered by mcajucom on 9/21/2010 10:54:51 AM bag 2A was

over $533.00 Entered by mcajucom on 9/21/2010 10:53:42 AM”

On multiple occasions reconcilers simply state that the amount entered is

incorrect. This is quite worrisome.

o “Nebraska problem Entered by mcajucom on 12/21/2010 3:42:23 PM hand written

charges $127.72”

We, or anyone looking back, will be unable to make any sense of this.

Do not allow any cashiers to use each other’s registers under any circumstances.

o It was often the case that one cashier would balance out another cashier in a different

shift. Consider these entries below (note: there’s many more):

“balances with 7B Entered by ccurd on 8/27/2013 9:51:35 AM”




Lessen the number of unnecessary cashiers and shifts recorded.

o Consider the Bookstore:

There are not 39 registers in the bookstore. How are there 39 cashiers?

Some of the cashiers and shifts had <10 observations

(Cashiers: 13, 14, 36-38, 99 & Shift: D).

Is there a reason why these had to be categorized distinctly?

Eliminate the possibility of creating incomplete forms.

o We deleted many observations with missing values.

This hurts future statistical analysis.

15

Store-Specific Recommendations: Bookstore:

o Specific cashiers to look at:

Bookstore Cashiers 11, 17 and 12 (based on Pareto Charts)

o Specific shifts to look at:

A (based on Pareto charts)

o This store performed poorly in January and December.

We understand this is likely due to the large volume of seasonal workers during

this time period. Consider longer training periods or better training methods.

Bookstore Convenience Store:


21 (based on Pareto charts),22 (based on box-plots)


B (based on Pareto charts), Z (based on box-plots)

o This store performed poorly in almost each month.

Beach Hut:


3 (based on Pareto charts and box-plots)


A, Z (based on Pareto charts and box-plots)

o This store performed poorly in the latter half of the year.

16

Future Analyses: Only Octavio will be returning next semester so he will be the person performing these analyses in the

future.

Use data mining techniques to filter out transactions that contained refunds and voids (which

caused them to appear as “short”).

o We did not provide a list of outliers due to the fact that we could not distinguish

efficiently whether an outlier was due to cashier error, void, or refund. This can be done

efficiently with data mining techniques.

Use regression analysis techniques as opposed to general linear model techniques so that

predictions can be made.

Use time-series techniques to analyze the data.

Use nonparametric techniques that may be more robust for data of this type.

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