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Dealing with Uncertainty in Transportation and Distribution
Presented as a keynote Speech
Nyoman Pujawan, Ph.D, CSCP, Professor Head of Graduate Programs & Director of Logistics and Supply Chain
Management Laboratory Department of Industrial Engineering
Sepuluh Nopember Institute of Technology (ITS) E-mail: [email protected]
Typology of Uncertainty in Supply Chain Operations
Industry Example
Supply side Manufacturing operations
Transportation & Distribution
Support services
Automotive
Beverage / mineral water
Fertilizer
Cement
Oil & gas
Aerospace
Strategies for Dealing with Uncertainty
Buffering:
• Inventory buffer
• Lead time buffer
• Capacity buffer
REACTIVE/ RESPONDING
PROACTIVE/ REDESIGNING
SC Risk Management:
• Information Visibility
• Collaboration (internal & external)
• Reducing complexity
• Postponement
• Reducing lead time
• Disintermediation
Strategy References
Information sharing Lee et al (1997), Tang (2006)
Cross-functional coordination Pujawan & Graldin (2009); Pujawan & Smart (2011);
CPFR Tang (2006)
VMI Dysney & Towill (2003); Tang (2006), Wong et al (2009)
Consolidation Thomas & Griffin (1996)
Disintermediation Lee et al 91997)
Stable pricing policy Lee et al (1997)
Reducing lead time Hayya et al (2011); Ryu & Lee (2003); Ouyang et al (2007); Glock (2011)
Reducing setup time Diaby (1995); Samaddar (2001); Coates et al 91996)
Reducing order cost Zhang et al (2007);
Safety stock, Augmentation Quantity Pujawan (2004), Pujawan & Silver (2008), Osman & Demirli (2012)
Safety lead time Molinder (1997); Chang (1985)
Freezing schedule Blackburn et al., 1985; Kadipasaoglu and Sridharan, 1995; Zhao et al., 1995;
Xie et al., 2003; Robinson et al, 2008
Increasing component commonality / Part standardization
Meixell (2005); Mohebbi & Choobineh (2005); Baud-Lavigne et al (2012)
Adding backup supplier Tang (2006)
Supplier development Shin et al (2000)
Risk pooling / inventory centralization Weng (1999); Park et al (2010); Gaur & ravindran (2006); Snyder et al
(2007)
Extra production capacity Das (2011)
Outsourcing / subcontracting Stevenson & Spring (2007)
Capacity flexibility Pujawan (2004)
Postponement Lee & Tang (1997), Tang (2006), Yang et al (2004), Gunasekaran & Ngai
(2005), Ernst & kamrad (2000); Lin & Wang (2011)
Responsiveness
Cost
Safety Stock
Stock Centralization VMI
CPFR
Cross-functional coordination
Reducing lead time
Freezing schedule
Part standardization
Supplier development
Extra capacity
Safety lead time
Increasing capacity flexibility
Disintermediation
Outsourcing
Information sharing
Postponement
Uncertainty in Transportation and Distribution
• A problem of high importance in practice, but received limited attention by the academics
– Cost related to transportation is substantial
– Complex, closed-loop system
– A great challange for countries with poor transportation infrastructure and key people work with high risks & low wages (and low morale)
Transportation is a complex issue in Indonesia [over 17.000 islands, 3 time zones, population +230 millions, West-East imbalance]
Uncertainty Related to Transportation & Distribution
Loading
Shipment
Unloading
Going back
waiting
waiting
• Too many trucks/ships coming • Arrive not during working hours • No items to be loaded
• Trucks / ships are in queue • Arrive not during working hours • Full storage
• Uncertain road/sea condition • Uncertain weather condition • Uncertain truck /ship condition • Drivers make more stops than normally
• Uncertain road /sea condition • Uncertain weather condition • Uncertain vehicle/ships condition • Drivers make more stops than normally
• Uncertain unloading speed • Breakdown of equipments
• Uncertain loading speed • Breakdown of equipments
Process Analysis in Transportation: Much opportunities for improvements
Productive Time: loading, sailing, unloading
Non-Productive Time: Waiting / Queueing
40% - 60% Productive Time: loading, travelling, unloading
Non-Productive Time: Waiting / Queueing
30% - 70%
Problems
• 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
Proposed Solutions
• Solution 1: Queue segmentation (priority is given to short destination)
• Solution 2: Consider time window (trucks departing 24 hours, but most destinations follow normal working hours / day time only)
Express Loading for Short Destinations and Take into Account Time Windows
Normal path
Express path
Zone I between 03.00 – 10.00 Zone III for the rest of time
Zone II for night delivery Zone III for the rest of time
OBJECTIVE: minimize truck arriving between 5 pm to 6 am + minimize Q time for short distances.
Case 1: Simulation for Short Distance, Low Uncertainty
• Travel time N[7; 2] hours
• There is a probability that a truck arrives at destination within time window, but has to queue/wait until the following day. This probability is about 50% and in case of waiting, the waiting time is uniformly distributed between 0 – 8 hours.
Planning for time windows is critical for short distance
00 06.00 18.00 24.00
ON OFF OFF
00
Loading Travelling Waiting Ready 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 OFF ON ON ON ON
Departure U [ 03 – 10]
Results: Relatively Short Distance, Travel Time about 7 hours
Range of Departure Time
P[Arrive= Time Window]
P[Unload = Same Day]
[03.00 - 08.00] 99% 97%
[03.00 - 09.00] 97% 95%
[03.00 - 10.00] 95% 92%
[03.00 - 11.00] 93% 89%
[03.00 - 12.00] 84% 80%
[03.00 - 13.00] 77% 73%
About 27% probability that a truck has to
wait for the following day for unloading
The results on Monte Carlo simulation with sample size of 1295.
Case 2: Simulation for Long Distance, high Uncertainty
• Travel time N[85; 25] hours
• There is a probability that a truck arrives at destination within time window, but has to queue/wait until the following day. This probability is about 50% and in case of waiting, the waiting time is uniformly distributed between 0 – 8 hours.
An example of trip time distribution to a destination of about 730 km
0
5
10
15
20
25
30
35
400
10
20
30
40
50
60
70
80
90
10
0
11
0
12
0
13
0
14
0
15
0
16
0
17
0
18
0
19
0
20
0
21
0
22
0
23
0
24
0
36
5,0
7
Fre
qu
en
cy
Time interval
When travelling time is highly uncertain, planning for time windows may have no impact
00 06.00 18.00 24.00
ON OFF OFF
00
Loading Travelling Waiting Ready for
Unload
Maximize the probability of this event falling in green zone
N (85, 25)
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 OFF ON ON ON ON
Departure U [ 0 – 24]
Range of Total Time
Within Time Windows?
Arrival Time
Time Ready for Unload
[0-6] OFF 0 0
[6-18] ON 3 3
[18-30] OFF 4 1
[30-42] ON 11 12
[42-54] OFF 40 31
[54-66] ON 79 68
[66-78] OFF 151 149
[78-90] ON 199 189
[90-102] OFF 230 226
[102-114] ON 218 232
[114-126] OFF 168 166
[126-138] ON 117 127
[138-150] OFF 51 65
[150-162] ON 15 16
[162-174] OFF 9 9
Percentage OFF 50,4 50
Percentage ON 49,6 50
The results on Monte Carlo simulation with sample size of 1295.
Results: departure time interval has no effect on the probability of arriving within the time window
Range of Departure Time
Short Distance Long Distance
P[Arrive= Time Window]
P[Unload = Same Day]
P[Arrive= Time Window]
P[Unload = Same Day]
[03.00 - 08.00] 99% 97% 52% 51%
[03.00 - 09.00] 97% 95% 48% 48%
[03.00 - 10.00] 95% 92% 49% 50%
[03.00 - 11.00] 93% 89% 48% 48%
[03.00 - 12.00] 84% 80% 49% 49%
[03.00 - 13.00] 77% 73% 49% 50%
[00.00 - 24.00] 50% 50% 50% 50%
Shifting Paradigm: Reacting to Demand instead of Reacting to Order
• Orders from distributors do not reflect sales pattern (due to forecast error or speculative motives)
• This is worsen with the company’s policy to require one month frozen orders
If distributors overestimated demand, too many trucks
shipped, warehouse full, long queue, transportation cost is high
If distributors underestimated demand, shortages happened
Proposed Solution
• There is an obvious need for better information visibility (not only demand and inventory, but also truck in transit and the associated quantity, warehouse capacity, and unloading rate).
• Shifting paradigm FROM responding to order TO responding to sales / demand.
• Challenges: information accuracy and timeliness, stakeholders involvement.
Illustrative Example
Demand/day (tons)
Lead time (days)
On-hand (tons)
in-transit (tons)
# truck in transit
Unloading speed (truk
/ hari) WHS
capacity SDR TUR SCR
D1 50 1 100 32 1 2 200 2,6 0,50 0,41
D2 20 4 80 64 2 2 200 1,8 0,25 0,32
D3 100 2 250 32 1 3 500 1,4 0,17 0,16
D4 15 0,5 60 0 0 1 100 8,0 0,00 0,53
D5 120 1 30 32 1 3 400 0,5 0,33 0,00
D6 30 0,4 40 150 1 2 100 15,8 1,25 1,78
D7 20 2 50 0 0 1 100 1,3 0,00 0,10
• High priority is given to destinations with low SDR, TUR, and SCR. • Shipment should not be made to destinations with TUR or SCR values of 1 or
higher.
Algorithm
• Step 1: Retrieve data {SO, on hand, in transit, demand, lead time, SDR Target}
For all distributors:
• Step 2: Calculate SDR, TUR, and SCR
• Step 3: Sort according to SDR in ascending order. Do NOT ship today if both TUR and SCR values are 1 or more.
• Step 4: Calculate amount of day shipment
SQ = max[0, [(SDR Target-SDR)*daily demand*lead time]
• Step 5: Calculate number of trucks needed: #Trucks = [SQ/truck capacity]
• Step 6: Aggregate # trucks needed for the day [TN]
• Step 7: Find out number of trucks available [TA]. If TA < TN, do rationalization (Each is reduced by TA/TN).
Responsiveness
Cost
Safety Stock
Stock Centralization VMI
CPFR
Cross-functional coordination
Reducing lead time
Freezing schedule
Part standardization
Supplier development
Extra capacity
Safety lead time
Increasing capacity flexibility
Disintermediation
Outsourcing
Information sharing
Postponement
Uncertainty and Supply Chain Game
Plant
TSP
DIST
End Customers
Hold trucks
High Costs
Long frozen orders
Truck drivers
Research Implications
• Transportation scheduling to improve truck utilization considering stochastic travel times and waiting time at the destination point.
• Supply chain power structure in the context of transportation different rules of the game.
• The impacts of information visibility in improving transportation and distribution decisions.
The 6th Operations and Supply Chain Management (OSCM) Conference, Bali,
10 – 12 December 2014
www.oscm-forum.org
www.oscm2014.org