Transcript Slide 1
Lunch Talk
(ICET 2013 and FINS)
Dr. Kriangkrai Waiyagan
Dr. Kanya Auckaraaree
University of Novi Sad
INSTITUTE OF FOOD TECHNOLOGY
26 June 2013
Faculty of Agro-Industry
Prince of Songkla University
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Introduction to University of Novi Sad
The University of Novi Sad is a university
located in Novi Sad, the second largest city
in Serbia. The University was founded on
28 June 1960. Today it comprises 14
faculties.
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IMPROVEMENT OF STORAGE SYSTEM
IN CANNED SEAFOOD WAREHOUSE
Dr. Kanya Auckaraaree, Dr. Kitti Cherdrungsri, and
Miss Prapaporn Soonton
Department of Agro-Industrial Technology
Faculty of Agro-Industry, Prince of Songkla University, Thailand
www.psu.ac.th
Warehouse : A case study
Raw material and
Packaging material
Work in process
(Unlabelled canned
seafood)
Finished products
Warehouse : A case study
Operation area
= 68.73%
Storage area
= 31.27%
Warehouse : A case study
Door
= type of work in process
Work in process
Warehouse wall
Warehouse wall
or
= Type of work in process
Labeling area
Door
One direction
or
or
Storage area
Storage area
Warehouse : A case study
Before Improvement
1. Searching time for storage area
(minutes/pallet)
2.05
2. Time taken for transport products from
storage to labeling department
(minutes/pallet)
4.30
3. Defective products
3.1 Amount of defective products
(cans/month)
3.2 Cost of defective product (Bath/year)
239
43,020
4. Fuel usage
4.1 Fuel usage (liter/month)
4.2 Cost of fuel usage (Bath/year)
1,305
559,548
Storage System Improvement
1. Product classification and determination of storage area
High amount
Local sale
product
Low amount
(20 Items)
Work in process
(92 Items)
(24 Items)
Export
product
High amount
(10 Items)
Low amount
(38 Items)
Storage System Improvement
375 m2
Average of total amount of WIP
= 2,176 pallets
Raw
material
300 m2
Capacity =
2,038 pallets
775 m2
Storage System Improvement
2. Redesign of storage layout
Factor :
- Sharp corner
- Positions of wall and pillars
Design :
- Forklift truck aisle, storage
aisle, personal aisle
- Two directions
288 pallets
90 pallets
312 pallets
Raw material
288 pallets
312 pallets
216 pallets
Storage System Improvement
3. Item groups analysis
ABC Analysis
number of pallet picked (pallets/year)
• Class A : 70% pallets/year
• Class B : 20% pallets/year
• Class C : 10% pallets/year
4. Storage location assignment
Similarity index analysis (Bindi et.al., 2009)
Sij
a
min(TurnValue i;TurnValue j ) (1)
1
a (b c) max(Turnvalue i;TurnValue j )
4
Storage System Improvement
Similarity index of class A products in local-high production
Items
A1
A2
A3
A4
A1
N
A2
0.83
N
A3
1.06
1.28
N
A4
1.51
1.24
1.59
N
Before: A1, A2, A3, A4
{A3, A4} and {A1}
{A3, A4} and {A2}
After: A3, A4, A1, A2
{A3, A1}= 1.06
{A4, A1} = 1.51
{A3, A2} = 1.28
{A4, A2} = 1.24
1.51 1.51
1.28
New Storage layout
Raw material
Raw material
Raw material
Raw material
Conclusion
Conclusion
Before
1. Searching time for storage area
(minutes/pallet)
2.05
2. Time taken for transport products from
storage to labeling department
(minutes/pallet)
4.30
3.2 Cost of defective product (Bath/year)
83.41%
0.34
1.28
70.23%
3. Defective products
3.1 Amount of defective products
(cans/month)
After
239
41.01%
43,020
141
25,380
4. Fuel usage
4.1 Fuel usage (liter/month)
4.2 Cost of fuel usage (Bath/year)
1,305 38.97%
559,548
797
443,268
STUDY ON IMAGE PROCESSING FOR
COLOR GRADING OF COOKED WHITE SHRIMP
Kriangkrai Waiyagan1*,
Niyada Masunee2,Nootcharee Thammachot2,Supapan Chaiprapat2
1Prince of Songkla University, Faculty of Agro-Industry, Thailand
2Prince of Songkla University, Faculty of Engineering, Thailand
Introduction
Cooked shrimp
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Objective and Scope
Objective
To presents an application of image processing techniques for
measuring color of cooked white shrimp
Scope
Matching algorithm between average RGB colors of shrimp
converted to CIE and SalmoFan scales is not included in this study
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Introduction
Shrimp color grading
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Introduction
Shrimp color grading
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The Experimental Design and
Results
Measure the cooked shrimp’s color
2
3
A colorimeter Color Flexand CIE L*a*b*color space
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The Experimental Design and
Results
One-way ANOVA:
abdominal segment number
The assumptions:
1. Normality test: All data groups were normally distributed.
2. Equal variances:There were no differences in variances
between two or more sample groups.
3. Hypothesis testing
(1)
(2)
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Case Study
Color measurement: abdominal segment number 2
2
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Case Study
Color measurement: scale 29 of SolmoFan
2
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Conclusions
This paper presents the application of image processing techniques for measuring
color of cooked white shrimp compared with SalmoFan.
Cooked white shrimps are graded by color of their abdominal segments.
The results revealed that the abdominal segment number 2 has darker red-orange
color than other segments.
There is a need research to optimize matching between average RGB colors of
shrimp converted to CIE and SalmoFan scales by using decision making tools and
artificial intelligence techniques such as multi-criteria decision analysis,
expert system, fuzzy logic and artificial neural network.
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University of Novi Sad
INSTITUTE OF FOOD TECHNOLOGY
Services
Knowledge
transfer
Research
Independent scientific institution from 1st January 2007.
Founded on the basis of resources and 50 years of tradition
of departments of Faculty of Technology in Novi Sad
Background
Over 250 successfully completed national and international scientific projects
Over 750 projects of food industry plants
Over 1000 expert reports, studies, elaborates, analysis
Over 10.000 samples of food and feed products tested each year
Thousands of published scientific papers and presentation at scientific meetings …
Hundreds of organized courses, seminars, conferences…
Dozens of published books and journals