Freight Volatility Kurn Ma Manish Kumar Agenda Project Motivation Over 5000 trucking companies 400000 trucks went out of business in 2012 There are about 8000 fewer trucks available nationwide on any given day ID: 555659
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Slide1
Quantifying the Impact of Deployment Practices on Interplant Freight Volatility
Kurn
Ma
Manish KumarSlide2
AgendaSlide3
Project Motivation
Over 5,000 trucking companies (~400,000 trucks) went out of business in 2012.
There are
about 8,000 fewer trucks available nationwide on any given day. (
ATA
)Lack of replacement of the retiring driversSlide4
Sponsor Company
Logistics
Raw
Materials
Manufacturing
60%
12%
Cost Drivers
Typical
Day in the Supply Chain
Description
(‘000) per day
Orders
1
Shipments
2
Tenders
3
Cases picked
325
Cases moved in warehouse
6,000
Potential Lane Combinations
23,000
Pallet-Miles
30,000Slide5
Sponsor Company
Plants
&
Near Plant Warehouses
(Full Pallet and
Picked Pallets)
Distribution Centers
(Full Pallet and
Picked Pallets)
Customer
Warehouse
Customer Store
(Consumer)
DC Shipments
Direct Plant
Fulfillment
Direct to
Consumer
DistributorsSlide6
Thesis Problem
Identify levers that impact this volatility: endogenous & exogenous
How can we mitigate this volatility through internal decisions?
Recommend deployment practices to reduce this volatility
Monthly shipments from Plant to DC- # of palletsSlide7
Methodology
Data Analysis
Forecasted Demand
Actual Demand
Production Data
Simulation ModelDiscrete Event SimulationPlatform: Visual Basic in MS ExcelSlide8
Project Scope
One year time horizon
Single plant to single DC
15 product groups analyzed (44% of overall freight volume)
Truckload volume analyzed at weekly levelSlide9
Assumptions
Entirely pull-based deployment from plant
All products have same MAPE (variable across scenarios)
All products have same reorder and target levels
7% inventory holding costSlide10
Formulation
*Slide11
FrameworkSlide12
Results: Unmanaged scenario
Model outputs consistently show that bi-weekly deployment generates lowest volatility
It provides 100% stock service level at the lowest average inventory at DC
Changes in forecast accuracy do not impact the volatility (only size of shipments)
The randomness in production output is very low to have any impact
# of weekly truckloads for each deployment frequency
Daily
Bi-weekly
WeeklySlide13
Results: Unmanaged scenario
It provides 100% stock service level at the lowest average inventory at DC
Changes in forecast accuracy do not impact the volatility (only impacts the size of shipments)
The randomness in production output is very low to have any impact
Inventory at DC for each deployment frequency
Daily
Bi-weekly
WeeklySlide14
Results: Managed scenario
Eliminates the need for spot market trucks
Loads are delayed and evenly distributed the following weekSlide15
Conclusion
Bi-weekly deployment schedule performs better both with respect to shipment volatility and inventory holding
Management of shipments by delaying them and forcing them to be exactly as per forecasted loads provides desired service level
Change in demand accuracy does not impact the volatility
Further
Research