Transcript Carrier Tape Cost Reduction Transition Plan Summary

Flexible Planning Model for a High
Tech Company with High Volume–
High Mixture
Eddy M. Delgado Arana 1,2, Larysa Burtseva 2, Brenda Flores-Rios 2, Roberto Ibarra 1,2, Frank Werner 3
1
Semiconductors reseach center, Skyworks Solutions Inc., Mexicali, Mexico
2
Universidad Autonoma de Baja California, Mexicali, Mexico
3
Otto-von-Guericke University, Magdeburg, Germany
Agenda
I. Introduction
II. Grouping the product into families
III. Batch sequence modelling
IV. Pilot test implementation
V. Conclusions
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Introduction
 The planning process leads to an NP-hard problem and includes
a large number of variables, which affect the decision making.
 A setup is a machine idle time due to the change of tools and
the adjustment to run other products (part numbers).
 There is a lack of visibility to take advantage of small changes
(recipe or tool) compared to a big one (product dimension).
 The grouping of products into families according to their
geometry or similarity of certain characteristics and batching of
lots belonging to the same family reduces drastically the setup
times and production costs.
 Currently the planning is realized at the product level rather
than at the family level.
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Introduction
Objective:
Develop a flexible planning model for the electric test area of the
plant to reach:
a) Makespan cycle reduction,
b) Manufacturing cost reduction,
c) Delivery on time.
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Grouping the products into families
 Product classification according to the A-B-C categories of the
inventories by the American Production and Inventory Control
Society (APICS)
Category
Characteristics
Quantity
Part Numbers
Geometries
A
B
High volume,
Middle volume,
High Frequency Middle frequency
65.90%
35
8
24.75%
114
12
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C
Low volume,
Low frequency
9.35%
407
50
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Grouping the products into families
 Policy of job sequencing:
a) First, assign resources to process the family that
contains the products of priority type A (confirmed
orders by the customers).
b) Then, assign resources to process the family that
contains the products of priority type B (to buffer
demand peaks).
c) Finally, assign the rest of the capacity to process the
family that contains the product of priority type C (to
forecast the future demands).
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Batch sequence modelling
 Classification of setup types:
– A production lot setup is performed when the next lot is of
the same part number.
– A recipe setup is performed when the next lot has different
part number and the current test tool is compatible with
next tool.
– A tool setup is performed when the next lot has a different
part number, the geometry is the same but the current tool
is not compatible with the next tool.
– A family setup is performed when the next lot has a
different geometry.
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Batch sequence modelling
 Classification of setup times
Geometric ranges
M1 1 to 3.9
M2 1.6 to 2.8
M1 4 to 6.9
M2 2.9 to 4.5
M1 7 to 11
M2 4.6 to 5.5
Setup type
Lot setup
Recipe setup
Tool setup
Family setup
Lot setup
Recipe setup
Tool setup
Family setup
Lot setup
Recipe setup
Tool setup
Family setup
M1
(minutes)
10 +/- 2.5
30 +/- 5.5
90 +/- 13.2
290 +/- 62.3
10 +/- 2.5
30 +/- 4.8
90 +/- 7.8
210 +/- 42.1
10 +/- 2.5
30 +/- 3.2
90 +/- 6.2
170 +/- 33.5
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M2
(minutes)
8 +/- 2
45 +/- 12.3
135 +/- 51.4
430 +/- 93.2
8 +/- 2
45 +/- 7.9
98.2 +/- 35.4
340 +/- 38.4
8 +/- 2
45 +/- 5.4
89 +/- 22.1
260 +/- 25.1
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Batch sequence modelling
Setup matrix morphology for products embedded
within the same family
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Batch sequence modelling
Single matrix morphology containing 4 families
Currently, in the plant catalog there are 83 product families
included into the matrix.
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Batch sequence modelling
General sequencing model
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Pilot Test Implementation
 Testing the model under simulated conditions.
 Testing the model under controlled conditions or in a pilot test.
 Development and implementation of the planning algorithm.
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Pilot Test Implementation
Family Selection
 A family type A, corresponding to the geometry 8.155.6 and
three part numbers, is selected. This family requires only a
single tool change.
Part No
X-1
X-2
W-1
X-1
10
30
30
Adjustments (minutes)
X-2
30
10
30
W-1
30
30
10
 This family of products has a volume that fills a capacity
equivalent to 19.5% of the installed machinery.
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Pilot Test Implementation
Lot processing time analysis
 Taxonomy data to calculate the test processing time of the
production lots
Lot
Part
Number
Stage
Trackin
Qty
Start Process Time
1
X-2
TEST
7113
2016-01-01 00:13:18
2
X-2
TEST
5486
2016-01-01 14:39:24
3
X-2
TEST
6043
2016-01-02 02:09:06
4
X-2
TEST
6769
2016-01-02 11:40:51
End Process Time
2016-01-01
14:28:24
2016-02-01
01:59:17
2016-02-01
11:31:02
2016-03-01
23:32:21
Equip
Number
Trackout
Qty
PF01
6836
PF01
4891
PF01
5971
PF01
6463
 Lot processing standard times, in seconds
Product No
Pkg Size
Test Time
Index Time
Threshold
Cycle time
X1
X2
W1
8.15x5.6
8.15x5.6
8.15x5.6
1.10
1.40
1.15
0.00
0.14
0.14
0.14
0.15
0.15
1.24
1.54
1.29
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Pilot Test Implementation
 Makespan calculation
where:
Q – Quantity of pieces,
St – Standard processing time,
f – Family,
Pri – Priority,
m – Assigned machine,
Prod – Product or Part Number.
Boolean variables: {
Same PartNo; Different ContactMask;
Same ContactMask; Different Geometry}
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Pilot Test Implementation
Scenarios to run:
1) Best Case - Only machines dedicated to the part
number processing were considered; the idle time caused
by the lot change was only taken.
2) Worst Case – The machines were shared among the
families; every processed lot was taken with the family
change idle time.
3) Proposed case - Minimal changes are considered to
process the volumes ordered by the customers according
to the family, priority and the part number.
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Pilot Test Implementation
 Time analysis per scenarios:
– The required time to process each production lot by
each scenario was calculated.
– A matrix of the results was created.
Scenario
Start
Finish
CT (days)
Best
2016-01-01 00:00:00
2016-01-23 02:07:00
22.09
Worst
2016-01-01 00:00:00
2016-01-31 06:47:00
30.28
Proposed
2016-01-01 00:00:00
2016-01-23 12:07:00
22.50
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Pilot Test Implementation
 The comparison of the results for the scenarios are
expressed in Delta days:
– The difference to move from the proposed to the best
scenario is 0.42 days.
Scenario
Best
Worst
Proposed
Best
0.00
-8.19
-0.42
Worst
8.19
0.00
-7.78
Proposed
0.42
7.78
0.00
– If moving from the proposed to the worst scenario, the
difference is 7.78 days per month.
– It represents additional 25.93% of the electrical test
installed capacity.
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Conclusions
 This model brings the basement and structure to
create a detailed short-term plan on the family level.
 It gives the flexibility to make on site quickly changes
whenever a part number does not arrive as planned.
 As it was demonstrated, a gain of up to 25.93 % in the
additional capacity was saved.
 The proposed model is exportable to any discrete
manufacturing process, which has a sequence of
production orders.
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Thank you!
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