Improvement Design of Packaged Juice Production Process using Business Process Reengineering
Published at : 24 Dec 2024
Volume : IJtech
Vol 15, No 6 (2024)
DOI : https://doi.org/10.14716/ijtech.v15i6.5630
Pratama, N.R., Agustin, P.A. 2022. Improvement Design of Packaged Juice Production Process using Business Process Reengineering. International Journal of Technology. Volume 15(6), pp. 1851-1861
| Novandra Rhezza Pratama | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, 16424, West Java, Indonesia |
| Putri Amaliyah Agustin | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, 16424, West Java, Indonesia |
Process efficiency is required in
packaged juice production to meet the greater demand for its product. The
manual production process causes time loss, poses a bottleneck, and results in
waste due to the time-consuming process. This study aims to design an
improvement in the production process of packaged juice by eliminating the
waste that occurs to increase the productivity and efficiency of processing.
Process Activity Mapping and Waste Assessment are used to map the activities
and identify waste. The improvement design is executed using a Business Process
Reengineering (BPR) through implementing a combination of BPR best practices
and simulation using iGrafx software. Process improvement involves the entire
production process that consists of three stages, namely the puree-making
stage, production stage, and packaging stage. This research resulted in designs
of improvement solutions. The models of the improvement solutions are then
simulated and produce an improved production process time. This solution resulted
in a 34.9% reduction in distance traveled and a 50.54% reduction in total
processing time.
Business process reengineering; Packaged juice production; Process activity mapping; Waste; Waste assessment
In 2020, Indonesia continued the trend of
increasing fruit production by recording an increase of 10.46% from production
in 2019 (BPS, 2020). However, the
abundance of fruit production in Indonesia is not in line with the level of
fruit consumption of the Indonesian population. Until 2020, the reality of the
average consumption of fruits and vegetables in Indonesia was only
89gr/Cap/Day, far below the WHO's recommended RDA of 150gr/Cap/Day (BPS,
2021). Currently, packaged juice as one of the
processed products derived from the fruit is often an alternative and a good
approach to increase fruit consumption (Mushtaq,
2018). The continuous increase in consumer demand for packaged
fruit juice presents opportunities for players in the Indonesian food and
beverage industry to meet consumer needs, improve food security, and achieve
Sustainable Development Goals (SDGs) (Berawi, 2019).
PT API is a food and beverage industry company specializing in packaged fruit juice drinks. One of PT API's production lines, namely packaged juice with the "Signature" label, has continued to increase in demand every month since the beginning of 2021. Given this projection, the current daily production capacity for "Signature" products may not be sufficient due to several constraints. These include the manual nature of the production process, which is time-consuming, lacks accuracy, and requires a significant number of workers. Due to these production problems, corrective steps needed to be taken by the company in the "Signature" production process to improve the business process and assess the waste problems.
Business processes can be stated as the
formality of operations and process behavior of activities in an organization (Saragih, Dachyar, and
Zagloel, 2021). The fundamental rethinking and radical redesign of business processes to
achieve dramatic improvements in critical, contemporary measures of
performance, such as cost, quality, service, and speed (Kyfyak and Lopatynskyi, 2018;
Dumas et al., 2018; Gunasekaran, and Kobu, 2002).
Business Process Reengineering (BPR) is a philosophy of improvement (Towill, 2001) that aims
to improve process performance (Mansar, Reijers, and Ounnar, 2009) by
redesigning the processes an organization operates (Al-Shammari, M., 2009),
maximizing value-added activities, and minimizing other activities (Martonová, 2013). There
are 5 BPR steps (Mudiraj,
2014; Guimaraes and Paranjape, 2013): Prepare for BPR, Map &
Analyze As-Is Process, Design To-Be Processes, Implement Reengineered Process,
and Improve continuously. There are 10 best practices that can be applied in
BPR (Kumar
and Bhatia 2012; Mansar and Reijers, 2007), that is
task elimination, task composition, integral technology, empower, order
assignment, resequencing, specialist generalist, integration, parallelism, dan
numerical involvement.
Lean Manufacturing (LM) is a method
that helps companies identify and eliminate waste (Gupta and Jain, 2013; Wahab, Mukhtar,
and Sulaiman, 2013) to increase value (Driouach, Zarbane, and Beidouri, 2019), improve
quality (Anvari,
Ismail, and Hojjati, 2011), increase production output (Singh, Singh, and Singh,
2018), and reduce lead time and production costs (Ahmad et al., 2019; Satao,
2012). There are eight types of manufacturing waste (Liker, 2004):
Overproduction, Excessive Inventory, Defect, Unnecessary Motion,
Transportation, Overprocessing, Waiting, and Non-Utilized Resources. Waste can
be divided into two categories (Gaspersz, 2007), namely non-value-added activities (NVA) and necessary but non-value-added
activities (NNVA).
To describe the production process in
detail from each activity, Process Activity Mapping (PAM) can be used (Amrina and Andryan, 2019; Zuting
et al., 2014; Pude, Naik, and Naik, 2012). PAM
involves simple steps, first, analyzing the production process which is carried
out sequentially from the beginning, followed by a detailed recording of all
items needed in each process and conducting waste assessment (Mikkelsen, Lydekaityte, and
Tambo, 2021; de Bucourt et al., 2012; Teichgräber and De Bucourt, 2012). The
waste assessment is used to identify critical waste that occurs throughout the
manufacturing process (Singh, Ramakrishna, and Gupta, 2017; Rybicka et al., 2015; Moinuddin,
Collins, and Bansal, 2007) by distributing questionnaires to expert
practitioners (Ekanayake
and Ofori, 2004). The questionnaire was conditioned based on the Borda Count Method (BCM) (Emerson, 2013). BCM is
one of the methods used to determine an alternative with the highest preference
from several alternatives to be selected (Orouskhani, Teshnehlab, and
Nekoui, 2019; Albanna and Karningsih, 2018; Van Erp and Schomaker, 2000).
This study discusses how we overcame technical problems,
such as manual processing, bottlenecks, and long production times, as well as
waste problems, including identifying the waste generated and how we overcame
it. We improved our company efficiency and productivity by using the methods
mentioned above. Through this research, we were able to determine the required
production process tools, identify and eliminate waste, and design process
improvements. We aspired that this research would contribute to enhancing the
science of BPR and LM application in manufacturing processes.
Figure 1 Methodology of this Research using combination of BPR and PAM
3.1. Overview of
“Signature” Production Process
Figure 2 As-is Production Floor Layout
3.2. Current Process
Activity Mapping
The identified activities
are mapped into PAM with the following Table 1 and Table 2. Overall we can see
that the production process consists of 76 processes, a total distance traveled
of 195 m, and a total number of workers of 19 workers, with a total production
time of 24 hours 51 minutes 5 seconds.
Table 1 Number of processes, the distance of movement,
and the number of workers in the current PAM
|
Process |
Puree Making |
Production |
Packaging |
Total Process |
|
Operation |
9 |
18 |
18 |
45 |
|
Transportation |
3 |
11 |
4 |
18 |
|
Inspection |
0 |
4 |
2 |
6 |
|
Delay |
1 |
4 |
1 |
6 |
|
Storage |
1 |
0 |
0 |
1 |
|
Total Process |
14 |
37 |
25 |
76 |
|
Distance Traveled (m) |
45 |
96.6 |
54.14 |
195 |
|
Total Number of Workers |
5 |
6 |
8 |
19 |
3.3. Critical Waste
Identification
Questionnaires were distributed to
experts to rank the types of waste that occur in the production process.
Experts were asked to rate the types of waste with a rank of 1 to 8, then
processed using BCM. Table 3 shows the
result of the waste score.
Table
2 Overall Production
Process based on Current PAM
|
Value
Category |
Total |
Percentage |
Time (hh:mm: ss) |
Time Contribution |
|
VA |
34 |
44.7% |
14:10:23 |
57.00% |
|
NVA |
7 |
9.20% |
01:02:33 |
4.20% |
|
NNVA |
35 |
46.10% |
09:38:09 |
38.80% |
|
Total |
76 |
100.00% |
100.00% |
Table
3 Result of Waste
Score with Borda Count Method (BCM)
|
Waste |
Total |
Percentage |
Rank |
|
Waiting |
37 |
25.34% |
1 |
|
Transportation |
27 |
18.49% |
2 |
|
Unnecessary Motion |
22 |
15.07% |
3 |
|
Defect |
19 |
13.01% |
4 |
|
Overprocessing |
13 |
8.90% |
5 |
|
Excessive Inventory |
10 |
6.85% |
6 |
|
Non-Utilized Resource |
9 |
6.16% |
7 |
|
Overproduction |
9 |
6.16% |
8 |
From this score, it can be concluded that along the
production line, the three largest contributing wastes are: waiting,
transportation, and unnecessary motion. Waiting time between processes is
mostly caused by machine set-up processes and separate processes between
workstations. According to PAM, transportation is the second highest number of
processes after an operation. This was deemed too frequent and a cause for
concern with current production floor layout arrangements, requiring workers to
carry out more transporting activities. Unnecessary Motion occurs due to the
unnecessary movement of workers during the production process, which can be
caused by transportation and over-processing. The authors focused on solving
these problems in the next steps, as these already covered more than half of
the total score.
3.4. Determination of
Process Improvement Solutions
Based on as-is process analysis, we proposed
solutions based on BPR Best Practices. The Business Process Improvement Plan is
shown in Table 4. A solution was designed based on the proposed
implementation of two technologies, namely an automatic labeling machine and 3
in 1 integrated filling machine, to assist the production and packaging stages.
The implementation of this machine is proposed because, based on the As-Is
model, the packaging stage is the process that takes the longest time because
the labeling on the product packaging is done manually. With the implementation
of this machine, more precise labeling can be achieved in less time. The
automatic labeling machine can reduce the possibility of defects caused by
damaged labels due to worker errors. This, in turn, ensures that the use of
resources in the form of sticker labels is in line with the production plan and
does not require additional goods requests. Figure 3 shows the proposed To-be
production floor layout.
With the addition of a 3 in 1 machine,
the process of bottle washing, filling, and bottle closing can be carried out
in one machine, thus speeding up processing time and shortening process stages.
This machine is needed to balance the speed of the automatic labeling machine
so that starving does not occur. With the implementation of the 3 in 1
Integrated Filling Machine, there is no need for a bottle-washing workstation
because the bottle-washing phase is carried out directly on the proposed 3 in 1
machine. Using these solutions, we can reduce the total processes in the future
PAM (Tables 5 and 6). Comparing Table 5 with Table 1, we can see that the total
process is reduced from 76 to 48, the total distance traveled is reduced from
195 m to 127 m, and the total number of workers is reduced from 19 workers to 8
workers. Comparing Table 6 with Table 2, we can also see that total production
time is reduced from 24 hours 51 minutes 5 seconds to 12 hours 17 minutes 29
seconds, amounting 50.54% decrease in total time.
Table
4 Business Process
Improvement Plan.
Figure 3 Production Floor Layout for To-be Process. Yellow marked are layout
changes, and green marked are additional areas
The To-be model is mapped with BPMN based on the As-is
model. Figure 4 shows the To-be model of Puree Making stage, Figure 5 shows the
Production stage, and Figure 6 shows the Packaging stage of the “Signature”
Production Process. Processes marked in red are eliminated, yellow indicates a
change in processing time, green indicates a change in the sequence of
processes, and purple represents new processes that were added. There were no
changes in the Puree-making stage.
Table 5 Number of processes, the distance of movement,
and the number of workers of future PAM.
|
Waste |
Problem |
Solution |
BPR
Best Practices |
|
Process
Efficiency and Accuracy |
The
production process is done manually, and there are no accurate measuring
tools in the process, so the process takes a long time and produces a lot of
waste |
Automate
processes by implementing automated machines to reduce process work time,
increase the accuracy of resource use, and eliminate waste that occurs along
the production line |
Integral
Technology, Task Elimination, Parallelism |
|
Integration |
The
implementation of each phase in a process often changes places so that the
flow between processes is stagnant and intermittent |
Re-layout
on the production floor, which also requires process resequencing |
Resequencing,
Task Elimination |
|
Process |
Puree Making |
Production |
Packaging |
Total Process |
|
Operation |
9 |
14 |
6 |
29 |
|
Transportation |
3 |
6 |
1 |
10 |
|
Inspection |
0 |
4 |
1 |
5 |
|
Delay |
1 |
2 |
0 |
3 |
|
Storage |
1 |
0 |
0 |
1 |
|
Total Process |
14 |
26 |
8 |
48 |
|
Distance Traveled (m) |
44.71 |
69.54 |
13.24 |
127 |
|
Total Number of Workers |
4 |
2 |
2 |
8 |
Table
6 Overall Production
Process of Future PAM.
|
Value Category |
Total |
Percentage |
Time (hh:mm:ss) |
Time Contribution |
|
VA |
37 |
77.10% |
10:36:56 |
86.40% |
|
NVA |
0 |
0.00% |
0:00:00 |
0.00% |
|
NNVA |
11 |
22.90% |
1:40:33 |
13.60% |
|
Total |
48 |
100.00% |
12:17:29 |
100.00% |
Figure 4 To-be model of Puree Making Stage, include Process No. 1-14
3.5. Discussions
The implementation time of this solution takes around 3-5 days. Comparing Tables 1-2 and Tables 5-6, this solution is able to solve the NVA problem, reducing the total time from around 24 hours to just around 12 hours, reducing labor use from 19 workers to 8 workers, reducing the distance traveled from 195 m to 127 m amounting to 34.9% decrease of distance traveled, and reduce total production time from 24 hours 51 minutes 5 seconds to 12 hours 17 minutes 29 seconds amounting 50.54% decrease of total time. The automatic labeling machine has approximately 2.8 × 1.6 × 1.5 meters, and the 3 in 1 integrated filling machine has dimensions of 2.1 × 1.7 × 2.35 meters (Zhang and Li, 2015). The cost of procuring these machines may vary, depending on the capacity. In this case, with the production speed of 2,000 bottles per hour, the additional cost will be around $4,000 for the automatic labeling machine and $3,500 for 3 in 1 integrated filling machine.
Figure 5 To-be model of Production Stage, include Process No. 15-39
Figure 6 To-be model of Packaging Stage, include Process No. 40-63
In this research, we improved the business
process of the "Signature" production process and assess the waste
problems. Based on the discussion of the analysis that has been carried out
previously, the proposed process improvement results in the following
conclusions: Improvement solutions to improve process efficiency are designed
by combining the implementation of BPR best practices, namely integral
technology, task elimination, parallelism, and resequencing. To implement the
solutions, we changed the production floor layout, eliminated, parallelized,
and resequenced the production process, automated the bottle labeling process
by installing an automatic labeling machine, and combined and automated the
bottle washing, filling, and capping processes by installing a 3-in-1
integrated filling machine; These solutions resulted in a 34.9% reduction in
distance traveled and a 50.54% reduction in total processing time. This
research only provides the potential solutions for the improvement that needs
to be tested and selected. Further research can be done by selecting a solution
through a feasibility analysis. Research on problems and improvements in other
production lines also needs to be done, considering that PT API is not a
company that produces a single product but a multi-product. Future research can
also combines more detailed aspects of facilities planning, such as facilities
location and systematic layout planning.
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