EXTRACT LOSS ANALYSIS IN THE BOTTLING LINE

IROSHA KARUNARAHTNE Monash University BEng (CHEM) Undergraduate

1

Table of contents

Introduction

Problem Statement

Process map

Approach

Past data review

Measurement phase

Analyze phase

Implementation phase

Conclusion

2

Extract loss reduction in in the filling area

Introduction Beverage manufacturing has been done for centuries. With the revolution of the commercialize

industries the global market has been a real competition. But with the market volatility, the

manufacturers tends to give a wide attention to their economic stability. Due to that the

companies lean towards to improve the processes and compete among others. Financial aspect

of a manufacturing company is always threatened by its product losses. Obtaining the

maximum possible yield for an industrial process is a big hurdle, and it always processed with a

greater challenge. Waste or product losses have been a major burden in production where it

reduces the efficiency and the productivity of the process. Therefore in the recent past waste

reduction have been a dominant force within manufacturing organizations with the growing

economy. Hence the industrial experts are constantly reviewing the performance of their

progressions.

In a breweries point of view extract loss or the beer loss has been the major concern. From the

malt milling to beer filling will include various losses throughout the course. Out of that extract

loss in a bottling area can be a huge financial cost. Since it is the final and the cleanest product

that the customer required. Therefore many beverage companies take an extra effort in cutting

down the losses in a filling line. It has been so clear that the bottle filling needs a distinct

improvement in its operations.

Problem Statement When it comes to beer bottling there can be several losses and defects. These are some of the

ways that losses can be caused,

Mechanical defects within the equipment.

Lack of technology where it necessary.

Due to procedural issues.

Lack of knowledge and experience within the operators.

Poor material usage and bottle handling.

The main problem that was focused in this context is the extract loss generated in the bottling

area. The impact from the product losses in the filling line have contributed negatively on the

final productivity. Therefore it has a significant effect on the total production output. The

bottling area has the process shown below.

3

-

Figure 1: Filling line process map

4

The extract loss points are shown by in the process map. Those are as following

1. Volume measurement in the Bright Beer Tank (BBT).

2. Filler and the crowner

3. 2nd sighter

4. Pasteurizer

5. Labeler machine

6. 3rd sighter

7. Packer

There were several behaviors that how loss occurs in the bottling line, such as breakages, under

fills, overfills, bottle rejects due to bad state, non-crowner bottles, etc. Meanwhile along with

above matters there are some inaccuracies in the volume measurements taken in the BBTs. So

with the limited time a small study was achieved to recognize the main issues that caused the

losses.

Approach Waste is a financial loss to a company and certainly reduces its profits. With all these in mind it

is vital to conduct a study regarding the losses to find a key solution. As mentioned in the

problem statement the main task was to distinguish between various forms of wastes that have

been generated in the past. Hence the time was utilized more into a cumulative analysis to see

the criticality of the main problem according to the past references. Therefore cost effective

methods are needed to be discovered and implement to eliminate the root causes. The study is

done at Asia Pacific Brewery Lanka in its bottling line. The study was conduct within 2 weeks of

short period.

Nevertheless a small study was performed with the limited time possessed. Therefore the

approach was done by focusing more on to a critical problem based on the historical data. As

the initial step the past data was taken in to account. A brief evaluation was done to foresee

the main problem that contributes to the extract loss. Due to the time constrain as mentioned

above the study was executed by targeting the critical issues.

Next the measuring and data collection phase was performed to collect the necessary data to

emphasis on the key problem. The data was collected and a cumulative analysis was prepared

in order to understand the correlation with the extract loss. Then the problem was reviewed to

see the possible reason and factors affecting it.

The volume measurement in the BBT area was also given a special concern since it also gives an

unknown extract loss to the final production. The main concern over there was due to the

inaccuracy of the volume readings that is taken by the operators. The possible reasons and

improvements have been suggested since not much time was left.

5

Past data review The past data was taken from the year 2014 January to December. The extract loss percentage

was compared with the KPI value of year 2014 which is 2%.

The above figure shows that the extract loss is above the target KPI value. The average extract

loss is about 2.6% which is well above the target value. So improvements are needed to be

done in order to cut down the loss. Therefore the main losses were analyzed through the data

obtained from the glass breakages and rejection report (Includes in the appendix). It mainly

consist of glass breakages and under fills/rejects categories. The main losses that are in the

report is

Filler crowner breakages.

2nd Sighter under fills/rejects.

Pasteurizer breakages.

Labeler breakages

3rd Sighter under fills/rejects.

Breakages at the packer.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Jan Feb March April May June July Aug Sep Oct Nov Dec

Actual Extract Loss vs Target Extract Loss (KPI)

Actual loss Target loss

Figure 2: Comparison of the actual extract loss with the KPI value

6

From the above categories the main focus was to analyze the data at 2nd Sighter and the 3rd

Sighter. The main reason was that those losses consist a higher percentage from the total

losses. The following table shows that under fills and reject at the 2nd and 3rd Sighter consist of

1.71% average loss which is 82.6% of the total loss.

The above table shows the total loss through under fills and rejects from the total production.

And it shows that it comprise of 1.76% loss. So the data suggest that the most amount of loss is

generated through the 2nd Sighter and the 3rd Sighter. The total bottle production was obtained

from the monthly production report. The Pareto analysis shown below illustrates that 79% of

the losses are caused at the 2nd and the 3rd sighter. Therefore the measurement phase was

done on those two points to distinguish the impact between under fills and rejects.

Table 1: Bottle loss parentage based on the respective machine bottle count

Table 2: Bottle loss percentage based on the total production in terms of bottles

7

Measurement Phase The measurement phase includes data collection and the data analyzing. Initially the data

collection plan was prepared. Due to the presented time and the planned overhaul that started

on 18/02/2015 the data was collected on 4 days for 29 hours. The data was collected on 11th,

12th, 13th, and 16th of February 2015. According to the past data analyze measuring phase done

on two points. The two measuring points were as following

1. 2nd Sighter after the filler.

There were three types of bottles that was put out as waste at the 2nd sighter. They were under

fill low, under fill normal and rejects. The average volume of each type was found using a

measuring cylinder. For each type 20 bottles were used to measure the mean volume. But the

rejects were considered as a full bottle with a volume of 625ml.

Under fills low – 314ml

Under fills normal – 555ml

Rejects – 625ml

48.93

79.06

91.64

96.50100.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

2nd Sighter Loss % 3rd Sighter Loss % Pasteurizer Loss % Filler breakage Loss%

Packer Loss %

% lo

ss f

rom

th

e t

ota

l lo

ss

Loss Points

Pareto analysis on loss points in the filling line

Figure 3: Pareto analysis on loss points in the filling line

8

2. 3rd Sighter after the labeler.

At this point also there were three types of bottles were identified as waste. They were under

fills normal, rejects and non-crowner bottles. Same as at the 2nd sighter the mean volume was

measured for under fill normal. Over here rejects and non-crowner bottles were assumed to

have the full volume.

Under fills normal – 588ml

Rejects – 625ml

Non-crowner – 625ml

On the first three days the data was collected hourly and the waste was differentiate

accordingly as above at the two points. But on the fourth day the total waste was differentiate

from 6am to 5pm without an hourly count. That was mainly done to identify any odds

throughout the day. The data was measured based on the number of bottles that were put out

of the each sighter. Afterwards it was converted into the extract loss based on the volume by

using the mean values presented above for each type. The data collected on each day is

presented below with remarks for any special incidents.

Table 3: Data collected on 11/02/2015 and 12/02/2015

12/02/2015 from 10am to 3pm

Cause No of

Bottles Extract

loss percentage

loss

Under fills 180 72.45 47.92

Very low (314ml) 118 37.05 24.50

Normal (555ml) 32 17.76 11.75

Normal (588ml) 30 17.64 11.67

Rejects (625ml) 114 71.25 47.12

No crowners (625ml) 12 7.50 4.96

Total 306 151.20

11/02/2015 from 10am to 5pm

Cause No of

Bottles Extract

loss percentage

loss

Under fills 189 83.27 47.71

Very low (314ml)

97 30.46 17.45

Normal (555ml)

39 21.65 12.40

Normal (588ml)

53 31.16 17.86

Rejects (625ml)

123 76.88 44.05

No crowners (625ml)

23 14.38 8.24

Total 335 174.52

9

Table 4: Data collected on 13/02/2015 and 16/02/2015

On 16th there was a special incident regarding reject bottle count. The situation raised by

getting bad condition empty bottles in to the filling area. Resulting in an immediate high

reject count.

Observations made during the measuring phase.

During the hourly counting there were some unusual numbers on rejects at certain

hours.

Most of the time whenever the filler stop operating due to a stoppage the under fill

count increases drastically.

16/02/2015 from 6am to 5pm extract loss

Cause No of

Bottles Extract

loss percentage loss

Under fills 528 225.61 52.70

Very low (314ml) 288 90.43 21.12

Normal (555ml) 180 99.90 23.34

Normal (588ml) 60 35.28 8.24

Rejects (625ml) 310 193.75 45.26

No crowners (625ml) 14 8.75 2.04

Total 852 428.11

13/02/2015 from 10am to 4pm

Causes No of

Bottles Extract

loss percentage loss

Under fills 143 62.88 50.91

Very low (314ml)

74 23.24 18.81

Normal (555ml)

28 15.54 12.58

Normal (588ml)

41 24.11 19.52

Rejects (625ml)

89 55.63 45.04

No crowners (625ml)

8 5.00 4.05

Total 240 123.51

Figure 4: 2nd sighter after the bottle filling

10

Table 5: Difference between the volume loss and the extract loss

Date Volume

used (HL) Production

Volume (HL) Volume loss (HL)

Volume loss %

Total extract loss

(L)

Total extract loss (HL)

Loss % Diff

11/2/2015 150.94 148.88 2.06 1.36 174.52 1.75 1.16 0.21

12/2/2015 107.59 104.16 3.43 3.19 151.20 1.51 1.41 1.78

13/2/2015 110.96 108.39 2.57 2.32 123.51 1.24 1.11 1.20

16/2/2015 224.78 217.85 6.93 3.08 428.11 4.28 1.90 1.18

The above data for volume used and production volume was taken as an average for the

stipulated time from the daily production report. The difference between the actual volume

loss and the total extract from the two loss points are shown. Further analyzing is done under

the analyze phase.

Analyze Phase By using the data that was collected at the measurement phase the following analyze was

prepared. A Pareto analysis was prepared to understand the data that was gathered in the four

days. Two graphs were prepared as below.

1. Analysis based on the percentage extract loss.

45.31

75.29

95.94100.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

Rejects Underfills Normal Underfills Low No crowner

Pareto analysis based on the percentage extract loss

Percentage loss Cumulative lossFigure 5: Pareto analysis based on the percentage extract loss

11

From the above graph it clearly shows that rejects and under fills have an equal impact on

the waste that is generated at that loss point in terms of the extract. There is 45.31% of

rejects and 50.63% of total under fills in this category based on the total extract loss. So it

suggest that an outside factor is present with the extract loss in the bottling area due to the

rejects. The extract loss from rejects at this point is a major worry that has been neglected

completely.

2. Analysis based on the percentage frequency.

The above analysis was based on the frequency which is the amount of bottles put out from

the two sighters. From there also it proves that rejects bottles have a huge influence on the

under fill/ reject category. From this figure it suggest that 53.58% of the bottle includes

under fills and 42.6% are rejects.

So from the both Pareto analysis it suggest that rejects have a dominating effect on this

waste category. The data clearly interprets that an external issue was causing extract losses

in the filling line which is a major worry. The two main problems that are present at the

moment are under fills and the rejects.

42.60

81.25

96.18100.00

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

Rejects Underfills low Underfills Normal No crowner

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Pareto analysis based on the percentage frequency

Frequency % Cumulative %

Figure 6: Pareto analysis based on the percentage frequency in term of number of bottles

12

The under fills can occur due to technological and mechanical concerns. These issues are

hard to analyze during a production time and with a short period of time in hand. When it

comes to technology it’s always difficult to predict its faults. But definitely it can have

certain mechanical complications which is need to be found through a deep analysis.

According to the machine manual it suggest some of the following reasons for causing

under fills.

Filler bowl level too low.

Liquid cone defectives.

Vacuum and snifting valves have got stuck or leaking.

Contact element of the bottle lifting element is too low

Operating butterfly not correctly adjusted.

Air tube bent.

Filling valve not operating properly

Contact pressure of bottle lifting element is too low.

Air tube deflector defectives.

During the data collection phase under fills were caused due to the low filler bowl level. It

mainly occurs during filler stoppages. Once the filler stops due to a line stoppage or for any

other reason and once it starts it tends to cause under fills. The main reason is during a

minor stoppage if the filler bowl level is low it won’t fill automatically. So once the

production starts it will cause under fills. And another way it happened was due to excessive

foaming. Some of the bottles that were put out of the sighters contained heavy foam which

had little product in it. But completely to reach for a full and final solution the filler needs to

be investigate thoroughly for long period of time.

Meanwhile more than focusing on a mechanical fault it was much simpler to look at the

rejects which are caused externally. In the other hand reject bottles plays a serious threat to

the amount of waste produced. Bottles are put out as rejects depending on its condition

and state. The following are some of the reasons for bottle rejects that were found at waste

points.

Damaged bottles.

Small pieces of dirt inside the bottles

Marks inside the bottle wall.

Labels are stuck inside the bottles.

Even from the results it suggest the criticality of the problem raised by rejects. A reject bottle is

considered as a full bottle which is a huge loss. Therefore in under fills also it can consist of

rejects which is neglected in this study. But the main point that is interpreted by the result is

amount of loss that cause due to rejects. These are some of the reasons why reject bottles are

found as waste with product in it.

13

The operator at the empty bottle sorting sighter misses the bad state bottles.

Washer faults or defects.

The operator at the 1st sighter cannot identify clearly regarding the bottle condition.

The operator at the 2nd sighter cannot identify clearly regarding the filled bottle

condition.

Accepting bottles that are in real bad condition.

Another problem that was identified during the study was that the extract loss obtained from

the loss points has a difference with the actual extract loss in terms of volume. One of the main

reason for this problem is caused due to inaccuracy in the volume measurement. From the

table 5 it suggest that there is a significant difference between the two values. The extract loss

only includes the under fills and reject category. But according to past data it suggest that

breakages only has a small effect on the total loss. As presented in the past data review the

breakages only includes 0.45% of the total bottle production in terms of number of bottles

produced. And it’s only 21.7% of the total loss according to the Pareto analysis as shown in the

Figure 3

The problem with the volume measurement is due to the inaccuracy in the volume

measurement through the volume indicator shown on the bright beer tank. With its shape it

does not have a constant volume horizontally. The indicator is shown below.

Figure 7: Volume indicator on the BBT (Tank 704)

Figure 8: Volume indicator on the BBT (Tank 701 to 703)

14

The volume is measured through the indicator by an operator. Since these tanks very tall the

volume measurement at the top level is hard. So the measurement that are obtained at that

level has certain errors. So with the human error and the indicator error will definitely give an

inaccurate final volume. A volume measurement between two values or markers shown in the

indicator is rounded to the nearest value. And the value is converted from the volume table

provided from the tank manufacturing company.

As an example according to explained above, if the

magnet indicates a value between 1150 and 1100 it will

be rounded to the nearest value depending on the

magnetic indicator. Generally in tank 701 to 703 includes

1.5HL between two markers (values). In tank 704 it

includes 3HL approximately. But that value is only an

average value due to the tank shape. So in that way it

contributes to the extract loss percentage at the end.

Even though it’s seems to be a small thing through this,

the data from table 5 shows a considerable difference

between the extract loss and the total volume loss.

Figure 9: Close look on the volume markers on the volume indicator

15

Implementation Phase Due to the limited amount of time it was unable to implement ideas or solutions for the above

mentioned problems and for its causes. But however it was able to go through some of the

current implementations.

In order to reduce and prevent the amount of reject bottles that are coming in to the filling

area following One Point Learning (OPL) board is displayed. Therefore the operator can clearly

identify the type of bottles that are needed to be reject.

Figure 10: One Point Learning (OPL) displayed in the bottling line

16

Suggestions

Operators needed to be more aware of the fact that reject bottles causes a huge extract

loss. Therefore the necessary knowledge regarding rejects must be given to the

operators.

Operators can inform the supervisors regarding any unusual amount of waste that is

been put out of the sighters. Therefore the supervisors can looked into that

immediately.

Currently displayed KPI boards can be filled properly on a daily basis review the

performance. Though the boards are been displayed nothing mentioned in the boards.

Hence the operator supervisors and the respective management can review into any

unusual data.

Figure 11: KPI Board in the bottling area

17

Figure 12: Bottling Performance board in the bottling area

During a shift change over the specific performance to the shift can be discussed briefly

among the supervisors and the line leaders. If there is any unusual amount of loss in the

shift it can be displayed on the respective boards daily. Therefore the performance can

be evaluated weekly and discuss it in order to improve it.

Standard operating procedures can be given to the operators. And put more OPL’s

around the bottling line regarding the small things that can be very useful.

For the exact volume measurement the flow meter can be replaced before the filler.

The figure below shows the current flow meter in place which is not working.

18

Flow meters are generally very accurate. Therefore the volume that is been used for production

can be accurately measured. So it will eliminate the extract loss that is been caused due to error

in the volume reading.

Conclusion

The bottling process of beer has faced many obstacles which could be alleviated through a

focused process improvement. A Pareto analysis can be used as a very effective tool to identify

key areas in the process that contributes heavily to cause product losses. Therefore it could be

benefit from a focus improvement initiative, thereby benefiting the overall company. An extra

effort needed to be put to eliminate the waste problem such as rejects. This study gives a

certain insight regarding the waste influenced by under fills and rejects. It gives the highest

amount of waste in the bottling line. For under fills the root cause may be due to its

technological and mechanical defects. But certainly rejects are mostly caused due to an outside

factor.

With the short time that was available it was unable to implement any recommended solutions

for the area that were identified to have a negative influence on the production process. It is

crucial to always ensure that the workers comply with the standard operating procedures in

order for waste to be reduced.