professor of supply chain engineering, its, indonesia president, … · 2018-08-16 · professor of...
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![Page 1: Professor of Supply Chain Engineering, ITS, Indonesia President, … · 2018-08-16 · Professor of Supply Chain Engineering, ITS, Indonesia President, Indonesian Supply Chain & Logistics](https://reader033.vdocuments.mx/reader033/viewer/2022041912/5e67cde8ce9dcf23f4039712/html5/thumbnails/1.jpg)
Professor of Supply Chain Engineering, ITS, IndonesiaPresident, Indonesian Supply Chain & Logistics Institute (ISLI)
E-mail: [email protected]
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25 Top SC, 2016
Sumber: Supply Chain Management Review, 2016
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Top Supply Chain Players 2016 (Gartner)
156 – 13.2% – (4)% - 36,9 – 10.8% – 3.6% - 108,4 – 0.5% – 20.4% - 0
3,5 – 25,3% – 16,3% - 94,3 – 11,4% – 1,1% - 9
3,9 – 16,7% – 11,2% - 9
TOR – ROA – Revenue Growth - CSR
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Network Value
Characteristics of Leaders
Product
Demand
Supply
Enterprise outcomes
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Important in Managing a Supply Chain
You can forecast the demand accurately
You have a reliable supply
You have Innovative products, services, and business models
You have information visibility
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Low / very low information visibility
Silo-based, poor cross-functional team
Reactive, rather than strategic buying
Decision is not data-driven
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ANALYTICSEVERY DAY WE CREATE
2,500,000,000,000,000,000(2.5 QUINTILLION) BYTES OF DATA
Fill 10 million Blu-ray discs, the height of which stacked, would measure the height of 4 Eiffel Towerson top of one another
Data stored grows4X FASTER THAN THE WORLD ECONOMY
Increasing quantity of data allows forMORE QUALITATIVE APPROACH
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Collecting
Cleaning
Analyzing
Simulating
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Problems with Truck Productivity
Wait for assignment
Loading assignment
Loading process
Travelling forward
Travelling backward
Unloading process
Wait for unloading
Long cycle time due to:• Uncertain travelling time• Held in distributors• Queueing in Depo
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Uncertain delivery time
Short distance
Long distance
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The Problem of Long Waiting Time
• Destination varies substantially (could be as short as a few kilometers and as long as over 700 kilometers), but there was no attempt to segment the queue.
• Time window is not taken into account when departing trucks. Many trucks wait for the following day for unloading.
Queue time before loading, average around 4 hours
Queue time @ Plant travel Queue time @ destination Travel back 1 cycle
2,5 cycle
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00 06.00 18.0024.00
ONOFF OFF
00
Loading Travelling WaitingReady for
Unload
Maximize the probability of this event falling in the green area
N (7; 2)
0 wp. 0.5
U[0 – 8] wp. 0.5
24.00
00 06 18 30 42 54 66 78 90 102
OFF OFF OFF OFF OFFON ON ON ON
Departure U [ 03 – 10]
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Efficient Frontier Analysis
An Integrated Shipment Planning and Warehouse Capacity Decision: A Case Study of Bulk Item
00 0 00
33 3
33
2323 2323 23
393939 3939
150.00
160.00
170.00
180.00
190.00
200.00
210.00
220.00
230.00
240.00
85.0% 87.0% 89.0% 91.0% 93.0% 95.0% 97.0% 99.0%
Co
st P
er T
on
(T
ho
usa
nd
of
Ru
pia
h)
Service Level
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
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An Integrated Shipment Planning and Warehouse Capacity Decision: A Case Study of Bulk Item
Cost per ton Service level
Running 30 replications, the distribution of cost per ton and service level looks...
(a)
(b)
(c)
0
2
4
6
8
10
0,85
5
0,86
0
0,86
5
0,87
0
0,87
5
0,88
0
0,88
5
0,89
0
0,89
5
0,90
0
0,90
5
0,91
0
0,91
5
0,92
0
0,92
5
0,93
0
0,93
5
0,94
0
0,94
5
0,95
0
0,95
5
0,96
0
0,96
5
0,97
0
0,97
5
0,98
0
0,98
5
0,99
0
0,99
5
1,00
0
Mor
e
Freq
uen
cy
Scenario 0
0
2
4
6
8
10
0,85
5
0,86
0
0,86
5
0,87
0
0,87
5
0,88
0
0,88
5
0,89
0
0,89
5
0,90
0
0,90
5
0,91
0
0,91
5
0,92
0
0,92
5
0,93
0
0,93
5
0,94
0
0,94
5
0,95
0
0,95
5
0,96
0
0,96
5
0,97
0
0,97
5
0,98
0
0,98
5
0,99
0
0,99
5
1,00
0
Mor
e
Freq
uen
cy
Scenario 3
0
2
4
6
8
10
0,85
5
0,86
0
0,86
5
0,87
0
0,87
5
0,88
0
0,88
5
0,89
0
0,89
5
0,90
0
0,90
5
0,91
0
0,91
5
0,92
0
0,92
5
0,93
0
0,93
5
0,94
0
0,94
5
0,95
0
0,95
5
0,96
0
0,96
5
0,97
0
0,97
5
0,98
0
0,98
5
0,99
0
0,99
5
1,00
0
Mor
e
Freq
uen
cy
Scenario 23
0
2
4
6
8
10
0,85
5
0,86
0
0,86
5
0,87
0
0,87
5
0,88
0
0,88
5
0,89
0
0,89
5
0,90
0
0,90
5
0,91
0
0,91
5
0,92
0
0,92
5
0,93
0
0,93
5
0,94
0
0,94
5
0,95
0
0,95
5
0,96
0
0,96
5
0,97
0
0,97
5
0,98
0
0,98
5
0,99
0
0,99
5
1,00
0
Mor
e
Freq
uen
cy
Scenario 39
(a)
(b)
(c)
0
2
4
6
8
10
16
6
16
8
17
0
17
2
17
4
17
6
17
8
18
0
18
2
18
4
186
188
19
0
19
2
19
4
19
6
19
8
20
0
20
2
20
4
20
6
20
8
21
0
21
2
21
4
21
6
21
8
22
0
22
2
224
More
Fre
qu
ency
Scenario 0
0
2
4
6
8
10
16
6
168
17
0
17
2
17
4
17
6
17
8
18
0
18
2
18
4
186
18
8
19
0
19
2
19
4
19
6
19
8
20
0
20
2
204
20
6
20
8
21
0
21
2
21
4
21
6
21
8
22
0
222
22
4
Mo
re
Fre
qu
ency
Scenario 3
0
2
4
6
8
10
16
6
16
8
17
0
17
2
17
4
17
6
17
8
180
18
2
18
4
18
6
18
8
19
0
19
2
19
4
19
6
19
8
20
0
202
20
4
20
6
20
8
21
0
212
21
4
21
6
21
8
22
0
22
2
224
Mo
re
Fre
qu
ency
Scenario 23
0
2
4
6
8
10
16
6
16
8
17
0
17
2
17
4
17
6
17
8
18
0
182
18
4
18
6
18
8
19
0
19
2
19
4
19
6
19
8
200
20
2
20
4
20
6
20
8
21
0
21
2
21
4
21
6
218
22
0
22
2
22
4
Mo
re
Fre
qu
ency
Scenario 39
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On Demand Ride Services Demand-Supply Matching Research
Rider/DemandAnywhere and Anytime
(uncertain)
Driver/Supply
Anywhere and Anytime (uncertain)
Driver is not an employee
• Demand & Supply dalam jumlah besar
• Demand & Supply Matching in large area
• Memuaskan kebutuhan stakeholder (Driver, Rider, Provider)
EconomyGrowth
Population Growth
City SizeGrowth
The need of Transportation
Increase
❖ Low Idle time❖ Low Traveling time &
distance❖ High income &
bonus
➢ High Order fulfillment rate
➢ High Customer satisfaction
➢ Low Customer cancelation rate
Supply/Driver
Provider
o Good Serviceo Low Pick-up timeo Fast Order waiting
timeo Cheap
Demand/Rider
Stakeholder Needs
Enabler Factors:✓ Sharing Economy Phenomena ✓ Mobile Device Technology (cangggih, murah) ✓ Internet Technology (4G, 5G, murah, cepat)✓ Digital Economy (transaksi digital)
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The Study of Contract and Spot Carrier in Logistics and Transportation
Lala Ayu Kantari and Prof. I Nyoman Pujawan
Industrial Engineering Department
In general, a shipper is partnering with a long-term transportation contract
On the spot carrier can handle one-time shipment without a prior contract agreement
Research Methodology
Simulation and Experiment
α Contract & (1-α) Spot carrierOnline matching scenario
Supply chain configuration
+ Low risk+ Sustain delivery- Not flexible
Dedicated no. of truck
Unmet demand
+ Flexible+ Real-time agreement
- Higher price
- Higher risk- Low availability
Additional truckDemand
RetailersDemandLocation Time windows
ShipperRules and PolicyLocationCapacity Carriers
Mileage CostAvailabilityLocation
Result and Analysis
Product Fill RateReliability
Transportation Cost
Shipper
Contract carrier
On the spot carrier
Retailers
Supply Chain System
Online matchingscenario
Uncertain demand
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Inflexible push model
Demand pull, but costly
Adaptive and synchronized
• Flexible configuration• High visibility, continuous scanning and signaling• Intelligent capability (optimization, simulation)
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Statistics Decision Analysis
Finance Marketing HRM
Information Technology
Technology & Innovation
Project Management
Operations Planning and
Control
Transportation & warehouse
SC StrategyPurchasing &
SupplyRetail & Digital
SC
Thesis Proposal Thesis
Tools
Functional Management
Specific Management
SC Core
Research
https://intip.in/mmtitsjakarta