1. Quantifying the Impact of Deployment Practices on Interplant Freight Volatility
Kurn Ma
Manish Kumar

2. Agenda Project Motivation Research Problem Methodology Results
Conclusion & Future Considerations

3. 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 drivers

4. Sponsor Company 60% Typical Day in the Supply Chain Cost Drivers
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
30,000
Logistics
Raw Materials
Manufacturing
60%
12%

5. Sponsor Company Direct Plant Fulfillment Plants Customer & Warehouse
Near Plant Warehouses
(Full Pallet and
Picked Pallets)
Customer Store
(Consumer)
Direct Plant Fulfillment
Distribution Centers
(Full Pallet and
Picked Pallets)
DC Shipments
Distributors
Direct to Consumer

6. Monthly shipments from Plant to DC- # of pallets
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 pallets

7. Methodology Data Analysis Simulation Model Forecasted Demand
Actual Demand
Production Data
Simulation Model
Discrete Event Simulation
Platform: Visual Basic in MS Excel

8. 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 level

9. 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 cost

10. * Production Schedule Demand Simulation Production Variability
Formulation
Production Schedule
2 week inventory position
Based on system DOS target
Demand Simulation
Random Distribution based on MAPE
Back Calculation of daily forecast error *
Production Variability
Distribution fit
Production Factor *

11. Framework

12. 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
Daily
Bi-weekly
Weekly
# of weekly truckloads for each deployment frequency

13. 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
Bi-weekly
Weekly
Daily
Inventory at DC for each deployment frequency

14. Results: Managed scenario
Eliminates the need for spot market trucks
Loads are delayed and evenly distributed the following week

15. Conclusion Further Research
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