Combining Drum-Buffer-Rope Algorithm and Kansei Engineering to Control

Combining Drum-Buffer-Rope Algorithm and Kansei Engineering to Control
Capacity Constrained Worker in a Bioproduction System
Mirwan Ushada*. Guntarti T. Mulyati*. Adi D. Guritno*. Haruhiko Murase**
*Universitas Gadjah Mada, Faculty of Agricultural Technology,
Department of Agroindustrial Technology Jl. Flora No.1, Bulaksumur ZIP 55281 Indonesia
(Tel/Fax: +62-274-551219; e-mail: [email protected]; [email protected]; [email protected]).
** Osaka Prefecture University, Graduate School of Engineering, Department of Mechanical Engineering
1-1 Gakuen-cho, Naka-ku, Sakai-shi, Osaka ZIP 599-8531, Japan (e-mail: [email protected])}
Abstract: Worker capacity is influenced by various factors of standard time, physical, mood and
environment. The Capacity Constrained Worker (CCW) can be described as a worker capacity is close or
equal with incoming material so that the fluctuation of incoming material flows possible to shift the
CCW into a bottleneck condition of process. Recognizing CCW and bottleneck is essential to maintain
the planned product flow in each work station. The objective of the research is to identify and control
CCW and bottleneck. The case study of this research is in half-fermented `Tempe` industry. CCW and
bottleneck were identified using 4 parameters of standard time, profile of mood status, heart rate and
environmental condition. The research results indicated that the CCW was identified on worker of
peeling station while bottleneck was identified on the worker of threshing station. The arrival rate of each
station inside the Bioproduction system was balanced using the lowest service rate. A buffer time is
derived by deviation between service rate of worker in CCW and bottleneck and balanced arrival rate
using Drum-Buffer-Rope algorithm. Buffer time was added before threshing and peeling station.
Keywords: Heart rate, Motion study, Profile of Mood States (POMS), Time study, Working environment.
1. INTRODUCTION
In Japan, Bioproduction system is identical with the
development of plant factory. It involves technologies such
as process control for the plant growth environment,
mechanization for material handling, system control for
production and computer applications. The advantages of a
plant factory include production stabilization, higher
production efficiency, and better quality management of
products through a shortened growing period, better
conditions, lower labor requirements, and easier application
of industrial concepts (Murase and Ushada, 2006).
the fundamental industry category which contributes to
Indonesian economy which involves bioproduction system.
There is more than 54.6 million UMKM who use the man
power almost 97.2%. The sustainable and productivity of
MSMA should be maintained based on the workers capacity
(Anonym, 2010). As shown in Fig. 1, the condition of work
station for the worker is complex and influenced by many
factors. Using the ideas and methods of Theory of
Constraints (TOC) (Goldratt, 2010), Bioproduction system is
potential to achieve a large reduction of work-in-process and
finished-goods inventories, significant improvement in
scheduling performance, and substantial earnings increase.
Recognizing Capacity Constrained Worker (CCW) and
bottleneck is essential to maintain the planned product flow
in each work station (Radovilsky, 2008). The CCW can be
described as a worker capacity is close or equal with
incoming material so that the fluctuation of incoming
material flows possible to shift the CCW into a bottleneck
condition of process. Production output depends directly on
how the system handles CCW and bottleneck. Any disruption
in a production flow caused by ineffective usage of a CCW
decreases production output. A time buffer is essential for
CCW. The main TOC technique for identifying and
controlling CCW and bottleneck is referred to as DrumBuffer-Rope (DBR) (Goldrat, 2010).
Fig.1 Work station condition in Indonesian MSMA
In Indonesia, Bioproduction system is identical with the
Micro-Small-Medium Agro-industry (MSMA). MSMA is
Kansei Engineering approach is applicable to model the
human sensibility factors using comparison between verbal
and non verbal responses (Nagamachi, 2002). Ushada et al.
(2012) has utilized diagram method of Kansei Engineering
flow from the zero-level concept to extract the Kansei words.
The others potential method to apply Kansei words is using
Profile of Mood States (POMS). It measures the affective
mood state fluctuation in a wide variety of populations. The
POMS identifies and assesses transient and fluctuating
affective mood states of human (McNair et al., 1992).
In this research we formulate the CCW as follow:
CCW = f {C, VR, NVR, WE}
C
= Work response capacity (kg/minute)
VR
= Verbal response (Mood)
NVR
= Non-Verbal response (Heart rate)
WR
= Working Environment
(1)
Nowadays, various control techniques was developed to
improve productivity in Bioproduction system. The physical
constraints associated with resources involved in the
bioproduction process, such as materials, workers, and
equipment. Ushada et al. (2006) has applied the grey level
co-occurences texture analysis to control the surface
temperature of biomaterial. Ushada and Murase (2009) has
developed an intelligent watchdog model to support the
equipment for the quality control system. In this research we
want to combine DBR algorithm and Kansei Engineering to
control CCW and bottleneck in Bioproduction system
The goal of the research is to improve the worker
productivity in bioproduction system. The specific objective
is: 1) To identify the CCW and bottleneck using the work
capacity, POMS, heart rate and working environment; 2) To
control the CCW and bottleneck by combining DBR
algorithm and Kansei Engineering.
2. CASE STUDY AND METHODS
2.1 Case Study of `Tempe` Industry
The case study of the research is in Industry of `Tempe`
`Muchlar`, Bantul, Yogyakarta Special Province. `Tempe` is
made from cooked and slightly fermented soybeans and
formed into a patty, similar to a very firm veggie burger. This
industry produced half fermented `Tempe` as shown in Fig.2.
The industry supplies the product to other division in Sleman
region to be a final `Tempe` product.
2.2 Motion and Time Study
Motion and time study is an effective method to measure the
leanness of a production system (Meyers and Stewart, 2002).
It is applicable to measure the time buffer of CCW. When an
operator is observed for a period of time, the number of units
produced during this time, along with the performance rating,
gives Eq.2:
tN 
t0
 PR
N
(2)
= Time worked
= Number of units produced
= Performance rating
tN
t0
PR
A standard time is derived by adding to normal time for
personal needs (such as washroom and coffee breaks),
unavoidable work delays (such as equipment breakdown or
lack of materials), and worker fatigue (physical or mental).
The buffer is indicated in Eq.3:
ts  t N  ( AF  t N )
(3)
= Time buffer
= Allowances Factor
ts
AF
A buffer time is derived by deviation between µ service rate
of worker in CCW and bottleneck and b balanced arrival
rate using Drum-Buffer-Rope algorithm
ts 
1
  b
(4)
2.2 POMS
In this research, POMS are proposed to derive AF, we used
the POMS to measure six identifiable mood or affective
states. Questionnaire result is analyzed using T score or
known as Total Mood Disturbance (TMD) in Eq.5:
TMD = TA + D + AH + F + C – V- F
TA
D
AH
F
= Tension－Anxiety
V
= Depression－Dejection F
= Anger－Hostility
C
= Friendliness
(5)
= Vigor
= Fatigue
= Confusion.
We proposed the mood efficiency, Em in Eq.6 as follow
Em 
TMDa
TMDb
Fig. 2 Half fermented `Tempe`
TMDa  TMDb
TMDa
= TMD Score when starting the work
= TMD Score when finishing the work
(6)
2.3. Heart Rate
Heart Rate (HR) was selected as physiological criteria
because it is simple, reliable, and provides good accuracy on
manual measurement (Louhevaara and Kilbom, 2005). HR
was measured using finger pulseoxymeter during 6 days of
work. These 6 days represents the normal day of working in
the bioproduction system. In each day we measured the HR
every hours with the periods of 07.00, 08.00, 09.00, 10.00,
11.00 am, 12.00, 13.00 and 14.00 pm.
2.4. Working Environment
The treatment of temperature, dew point, wet bulb , mixing
ratio and relative humidity was measured using Thermo
Recorder (Extech RH520 Data Logger). Light intensity was
measured using Lightmeter (LX-101 A, Lutron). The noise
was measured.
60
Score
40
10
0
A-H
m
2
C
D-D
POMS
No
1
Bottleneck
CCW
Work Station
Threshing
Finished
Product
n
Elements
1
2
3
Buffer
(Time)
2
Washing
1
2
3
Figure 3 described the Drum-Buffer-Algorithm. A "drum" is
a control point in the production system associated with a
CCW. The name "drum" is derived from the fact that the
CCW will set the timing for the rest of the system. "Rope" is
the term used for the communication feedback to the workers
before the CCW so that each of them produces only the
amount of CCW can complete.
3. RESULTS AND DISCUSSION
3.1 Calibration of POMS
The Indonesian version of POMS questionnaire was
developed. Therefore the calibration of POMS was required
to test the questionnaire. The calibration was pursued in 2
(Two) groups of respondents: 1) 57 students, Department of
Agroindustrial Engineering, Faculty of Agricultural
Technology, Universitas Gadjah Mada as shown in Fig.4; 2)
8 workers MSMA, `GNP Snack` in Muntilan, Magelang,
Province of Central Java as shown in Fig. 5.
3
Soaking I
1
4
Boiling I
1
5
Crushing
1
2
6
Peeling
1
2
3
7
Soaking II
1
8
Boiling II
1
9
Fermentation
1
10
Packaging
2
1
2
60
Before treatment
After Treatment
50
Score
40
30
20
10
0
C
T-A
V
FR
3.2 Motion Study
Motion study was pursued to differentiate the work element
for the time study. The work elements for each work station
can be described as shown in:
Fig. 3 Drum-Buffer-Rope algorithm
A-H
F
Fig. 5 Calibration of POMS in workers of MSMA
Drum
Information
Feedback (Rope)
T-A
Table 1. Work elements for `Tempe` industry
Raw Material
Dispatching Point
1
30
20
2.3 Drum-Buffer-Rope Algorithm
Raw
Material
Before lunch time
After lunch time
50
D-D
POMS
F
V
F-R
Fig. 4 Calibration of POMS in respondents of student
Description
Preparing soybean from
the bag
Handling soybean to the
threshing machine
Handling
threshed
soybean to the washing
bath
Put the soybean in to the
bath
Cleaning the soybean
Put back the soybean in
to the bath
Handling soybean to the
tube and move to boiling
Preparing the boiled
soybean to the crushing
Handling of the soybean
Adding the water to the
crusher
Handling the soybean to
the container
Peeling
Separate the skin and
handling to soaking II
Handling the soaked
soybean to the container
and transfer to boiling II
Put to the container and
handling
to
the
fermentation
Handling the soybean to
the plate
Fermentation
Packaging to the bag
Weighing the bag
H R (Pu lse / m in u te )
3.3 Profile of heart rate
Profile of heart rate can be described in Fig. 6
90
85
80
75
70
65
WS 1 WS 2 WS 3 WS 4 WS 5 WS 6 WS 7 WS 8 WS 9 WS 10
Worker of each work station
Fig. 6. Profile of Heart Rate (HR) for workers in each work station
3.4 Measurement of Constraints
Table 1 indicated the measurement results for identifying the CCW and bottleneck.
Table 2. Identification of constraint in `Tempe` industry
No
Work Station
Kansei
Mood
Efficiency
Heart Rate
(Pulse/min)
Standard
Time
(Kg/min)
Environment
Temperatur
e (0C)
Dew
Point(0C)
Wet
Blub
(0C)
27.4
7±0.
47
Mixing
Ratio
(g/kg)
19.80±
0.60
1
Thresing
0.78±0.22
79.69±5.40
0.0085
29.54±1.66
24.70±0.51
2
Washing
0.87±0.08
80.42±5.06
0.068
29.54±1.66
24.70±0.51
27.4
7±0.
47
19.80±
0.60
3
Soaking I
0.79±0.12
79.92±4.55
0.021
34.37±1.12
26.40±0.66
28.9
1±0.
71
22.11±
1.02
4
Boiling I
0.94±0.05
80.79±4.83
0.027
34.37±1.12
26.40±0.66
28.9
1±0.
71
22.11±
1.02
5
Crushing
0.89±0.08
82.25±3.91
0.059
34.37±1.12
26.40±0.66
28.9
1±0.
71
22.11±
1.02
6
Peeling
0.91±0.07
83.56±3.70
0.132
34.37±1.12
26.40±0.66
28.9
1±0.
71
22.11±
1.02
7
Soaking II
0.88±0.10
84.98±2.83
0.032
34.37±1.12
26.40±0.66
28.9
1±0.
71
22.11±
1.02
8
Boiling II
0.92±0.05
83.94±3.92
0.018
34.37±1.12
26.40±0.66
28.9
1±0.
71
22.11±
1.02
9
Fermentation
0.90±0.11
84.00±2.24
0.10
32.36±0.55
26.23±0.60
29.0
4±0.
69
21.90±
0.82
10
Packaging
0.84±0.22
83.15±3.90
0.06
32.36±0.55
26.23±0.60
29.0
4±0.
69
21.90±
0.82
RH
(%)
Noise
(db)
78.
42
±4.
77
78.
42
±4.
77
64.
77
±3.
55
64.
77
±3.
55
64.
77
±3.
55
64.
77
±3.
55
64.
77
±3.
55
64.
77
±3.
55
72.
81
±1.
62
72.
81
±1.
62
65.81
±3.40
Light
Intensity
(lux)
231.55±6
.05
65.81
±3.40
231.55±6
.05
85.84
±1.59
339.65±5
.19
85.84
±1.59
339.65±5
.19
85.84
±1.59
339.65±5
.19
85.84
±1.59
339.65±5
.19
85.84
±1.59
339.65±5
.19
85.84
±1.59
339.65±5
.19
73.16
±1.14
189.38±4
.24
73.16
±1.14
189.38±4
.24
Buffer Time =
26.6 kg/minute
0.053 minute/kg
Threshing
Raw
Soybean
7.6 kg/minute
Half
Fermented
Soybean
16.9 kg/minute
Washing
7.6 kg/minute
7.6 kg/minute
28.4 kg/minute
25.97 kg/minute
Packaging
Fermentation
7.6 kg/minute
7.6 kg/minute
29.9 kg/minute
Boiling I
30 kg/minute
Soaking I
7.6 kg/minute
18.2 kg/minute
Boiling II
7.6 kg/minute
29.2 kg/minute
Crushing
7.6 kg/minute
39.3 kg/minute
20.4
kg/minute
Peeling
Soaking II
7.6 kg/minute
Buffer
Time =
0.078
minute/kg
7.6 kg/minute
Fig. 7 Proposed Drum-Buffer-Rope algorithm
3.5 Identification of CCW and Bottleneck
3.6 Proposed Drum-Buffer-Rope Algorithm
CCW can be described as a worker capacity is close or equal
with incoming material so that the fluctuation of incoming
material flows possible to shift the CCW into a bottleneck
condition of process. Therefore, CCW and bottleneck were
identified based on work proportion of each station as shown
in Table 3. Based on the comparison of utility and mood
efficiency in Table 4, it can be concluded that the bottle neck
of `Tempe` industry is work station 1 or threshing station.
Worker in threshing station indicated the lowest mood
efficiency and the lowest utility. The CCW can be identified
in work station of peeling which have the lowest of service
rate and the highest utility. This CCW could be potential to
be a bottleneck.
Table 3. Work proportion of each station
Based on identification of CCW and bottleneck, we proposed
the Drum-Bufer Rope Algorithm in Fig. 7. The lower service
rate of peeling 7.6 kg/minute was set as the arrival rate of the
system. Buffer time was added before threshing and peeling
station.
Buffer time for threshing station was 0.053
minute/kg and 0.078 for minute/kg
Work Station
Threshing
Washing
Soaking I
Boiling I
Crushing
Peeling
Soaking II
Boiling II
Fermentation
Packaging
% Worker
100
100
10
100
100
100
10
50
100
100
% Machine
0
0
90
0
0
0
90
50
0
0
3.7 Analysis of Kansei Engineering
Figures 8 and 9 indicated the worker involvement in the work
station of threshing and peeling.
Fig. 8 Worker involvement in threshing the soybean
Table 4. Identification of CCW and bottleneck
WS
ʎ (K
g/min
)
µ
(Kg/mi
n)
p, ʎ /µ
1
2
3
4
5
6
7
8
9
10
26.6
16.9
30
29.9
29.2
20.4
39.3
18.2
26
28.4
117.6
14.7
47.6
37
16.9
7.6
31.2
55.5
10
16.6
0.23
1.15
0.63
0.81
1.72
2.70
1.25
0.33
2.60
1,71
Mood
Category
0.78±0.22
0.87±0.08
0.79±0.12
0.94±0.05
0.89±0.08
0.91±0.07
0.88±0.10
0.92±0.05
0.90±0.11
0.84±0.22
Bottleneck
Normal
Normal
Normal
Normal
CCW
Normal
Normal
Normal
Normal
Fig.9 Crushing and peeling station
Figures 10 and 11 indicated profile of heart rate in threshing
and peeling station. The research results validated the
difference of HR between work start and finish.
90
Work Start
Work Finish
85
HR (Pulse/Minute)
which have the lowest of service rate and the highest utility.
This CCW could be potential as bottle neck. Identification of
CCW and bottleneck was confirmed to total of mood
disturbance and heart rate of each worker.
80
75
70
65
1
2
3
4
Days of measurement
5
6
Fig.10 Profile of heart rate in threshing station
Based on identification of CCW and bottleneck, we proposed
the Drum-Bufer Rope Algorithm. Buffer time was added
before threshing and peeling. The lower service rate of
peeling 7.6 kg/minute was set as the arrival rate of the system.
Buffer time was added before threshing and peeling. Buffer
time for threshing station was 0.053 minute/kg and 0.078 for
minute/kg
REFERENCES
90
HR (Pluse/Minute)
85
80
75
70
Work Start
Work Finish
65
1
2
3
4
Days of measurement
5
6
Fig.11 Profile of heart rate in peeling station
Figures 12 and 13 indicated the profile of TMD between
work start and finish in threshing and peeling station. The
results indicated that worker of peeling station has more
consistent patterns between work start and finish than the
threshing station. These results confirmed the worker in
peeling station as CCW and the worker in threshing as the
bottleneck.
TMD
12
8
4
Work Start
Work Finish
0
1
2
3
4
Days of measurement
5
6
Fig.12 Profile of TMD in threshing station
TMD
12
8
4
Work Start
Work Finish
0
1
2
3
4
Days of measurement
5
6
Fig.13 Profile of TMD in peeling station
4. CONCLUSSION
CCW and bottleneck were identified based on work
proportion of each station. Based on the comparison of utility
and mood efficiency, it can be concluded that the bottle neck
of `Tempe` industry is threshing station. Worker in threshing
station indicated the lowest mood efficiency and the lowest
utility. The CCW can be identified in work station of peeling
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