1. Submitted by
Chinmai Kethineni
Shiva Teja Sepuri
TO REDUCE THE FAILURE ORDERS IN
BACCI PIZZERIA
Under the guidance of
Dr . K.S. Krishnamoorthy

2. INTRODUCTION
Description of organization:
 Bacci Pizza Express is a small family-owned pizza store located in West
Bradley avenue Peoria IL.
 It has its main branch in Chicago IL
 Recently, they began a new line of business in catering for dinner parties.
 Their capacity for dine in and take out ranges from 25 to 30 people.
 Their operating hours are 10:30 am to 3 am through Mon to Friday and 11:00
am to 3:00 am on Saturday and Sunday.
 It has 12 employees working in the Pizza store in different shifts.

3. PROBLEM STATEMENT:
 Bacci Pizza Express is experiencing lot of orders getting wasted after being
prepared.
 As the number of rejections is high the store is losing its profits.
 Most importantly it is losing its reputation. Even though after constantly
trying to reduce the wastage the store is failing.
 This project mainly focuses analyzing the causes for wastage of orders after
preparing them and offer solutions for it.

4. DATA COLLECTION:
 The data is collected in kitchen between each and every order on order
waiting time, cooking time, waiting time for driver and delivery time.
 The data is also collected from drivers about delivery time and distance they
have to travel for each delivery.

5. ANALYSING THE TIME CONSUMED:
 The time for each and every task is found by using a stopwatch in kitchen
from receiving order to delivering the food. The recorded data is in minutes
and is as follows:

6. HISTOGRAM FOR TOTAL TIME CONSUMED:
0
1
2
3
4
5
6
7
1-10 11-20 21-30 31-40 41-50 51-60 61-70
Frequency
Frequency
Histogram for total time consumed
Wait bins Frequency
1-10 0
11-20 1
21-30 4
31-40 6
41-50 6
51-60 3
61-70 1
More 0

7. BUILDING CONTROL LIMITS:
 X-bar and S chart are used as sample size is >10
 Population parameters estimated for each zone are summarized in the table
below.
 The values of B3, B4 and A3 are found for n=4 as
Xdouble
bar= 9.784524
Std.Dev
bar= 6.855826
For n=4
B3= 0
B4= 2.266
A3= 1.628

8. CALCULATIONS:
 The upper control limit is given by UCLX. The lower control limit is given
by LCLX. A3 is a control chart constant that depends on the subgroup size.
 The upper control limit is given by UCLS. The lower control limit is given
by LCLS. B4 and B3 are control chart constants that depend on the subgroup
size.
 The values of U.C.L and L.C.L for X-bar and S charts are found as follows
C.L(S) U.C.L(S) L.C.L(S) C.L(X D.B)
U.C.L(X
D.B) L.C.L(X.D.B)
L.C.L(X
D.B)
6.855826 15.5353 0 9.784524 20.9458084 0 -1.376761

9. CONTROL CHARTS:
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7 8 9 101112131415161718192021
Std.dev
C.L(S)
U.C.L(S)
L.C.L(S)
0
5
10
15
20
25
1 3 5 7 9 11 13 15 17 19 21
Mean
C.L(X D.B)
U.C.L(X D.B)
L.C.L(X.D.B)
S-chart for the collected data X-bar chart for the data collected

10. REGRESSION ANALYSIS FOR DISTANCE AND
DELIVERY TIME:
 From the output of excel we
have the R-square value is
close to 1 (0.953) which
means it can explain the
maximum portion of
variables in y values.
 Which also indicates the
there is good relation of x
and y.
0
5
10
15
20
25
30
35
40
0 5 10 15 20
Series1
Distance
(miles)
Delivery
time(min)
2.8 10
3.2 15
9.4 25
3 10
4.2 15
6.8 20
5.6 16
14.3 35
3.2 12
3.4 14
5.7 18
4.3 15
2.5 12
4.6 15
7.2 20
3.9 11
4.4 15
3.6 12
4.1 14
8 20
2.5 10

11. ANALYSING THE REJECTION OF ORDERS:
 The data for number of orders failed by pickups, rejection of orders,
cancellation of orders, wrong delivery and miscellaneous are taken for 25
days.
 This data is used to find which causes more number of rejections in orders
and to suggest the recommendations to control the failures.

12. PARETO ANALYSIS:
 The data is summarised and arranged in descending order and cumulative frequency is
calculated to do Pareto analysis.
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
0
5
10
15
20
25
30
35
Frequency
C.percentage
Pareto analysis for failure causes
Frequency
cumulative
Frequency C.percentage
Rejectedorders 29 29 38.67%
PickupsFail 15 44 58.67%
cancellation 13 57 76.00%
miscellaneous 10 67 89.33%
wrongdelivery 8 75 100.00%
overall
rejections 75

13. P-CHART:
 As the sample size is
greater than 20and it’s
continuously varying
and not standard, P-
chart with varying with
sample sizes is used.
 The values U.C.L and
L.C.L are calculated by
using
Days Pickups fail
Rejected
orders Cancellation
wrong
delivery miscellaneous
1 1 0 0 0 0
2 0 0 1 0 0
3 0 0 0 0 1
4 1 0 0 0 0
5 2 1 1 1 0
6 2 6 2 0 2
7 0 0 0 1 2
8 1 0 0 0 0
9 0 0 0 0 0
10 0 0 1 0 0
11 0 0 0 0 0
12 0 0 0 1 0
13 1 8 1 2 1
14 1 2 2 0 1
15 1 1 1 0 0
16 0 0 0 0 0
17 0 0 0 0 0
18 1 0 0 1 0
19 0 1 1 0 1
20 1 6 2 1 1
21 1 4 0 1 0
22 1 0 0 0 0
23 0 0 1 0 1
24 1 0 0 0 0
25 0 0 0 0 0
Total 15 29 13 8 10

14.  Here the n values are calculated as the average of sample sizes and p-bar values are
calculated by averaging the overall proportions.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
1 2 3 4 5 6 7 8 9 10111213141516171819202122232425
proportion
U.C.L
C.L
L.C.L
P-chart with varying sample sizes for rejection of orders.
n-bar= 30.64
U.C.L= 0.109021
L.C.L= -0.05947

15. CONCLUSION:
 Reduction in time consumption:
The time consumed by each and every task is noted and found that most of the
deliveries are in control limits except one which consumed most time form
ordering to delivery.
This data was taken in weekday for 21 samples but in weekends there will be
more orders which are out of control limit.
 Recommendations:
 Delay is mostly observed in fryers than in oven. This can be eliminated by
increasing number of fryers and hiring an additional worker for weekends.
 Waiting time for drivers can be reduced by hiring more drivers according to
demand.
 Delivery time can be reduced by restricting the delivery zone not more than
10 miles.

16.  Reducing the Rejections:
Among all the failures rejection of orders by customers is found to be more but
there are also failures due to pick up fails, order cancellation, miscellaneous,
wrong delivery. These can be reduced by following recommendations.
 Recommendations:
 Rejection of orders is mostly because of late service. The late service can be
reduced by executing all the recommendations of reduction in time
consumption. Rejection of orders is also seen due to lack of quality of food.
It’s also observed that more waiting time reduces the quality of fries by
making them wither. This can also be avoided by reducing the overall time.
 Pickup fails is due to lack of interest of customers to get the order. This can
be avoided by asking the customer to pay while taking the order.
 Order cancellation is done due to the higher prices which are out of range of
some customers. This can be reduced by making deals with customer like
offering discounts giving extra fries for free etc.
 Miscellaneous failures are because of lack of training and poor awareness of
workers. This can be reduced by training workers perfectly and giving them
frequent breaks so that they can work with awareness
 Wrong delivery is seen in kitchen mostly observed in weekends when the
orders are high. This is done by giving the order to wrong person this can be
reduced by hiring an employee to deal with customers.