Systems Design Project: Transport Optimization and Green Supply Chain

Prepared by:Brian Murphy

Senior in Systems Science and Engineering Washington University, St. Louis, MO

Phone: (805) 698-5295Email: bcm1@cec.wustl.edu

Supervised by: Mike Cannon

Logistic Functional ManagerLockheed Martin, O’Fallon, IL

Phone: (618) 334-7309Email: mike.cannon@lmco.com

Bob RipeshoffChief Architect of Savi Logistics Systems

Lockheed Martin, O’Fallon, ILPhone: (618) 806-0606

Email : robert.d.riepshoff@lmco.com

Background:

The US Government moves more supplies, people and materials by a tremendous

margin than any other entity or organization in the world. Consequently, their supply

chain and transport activities yield tremendous amounts of carbon emissions. The current

administration has made the reduction of carbon emissions a staple of its domestic policy.

In addition, Lockheed Martin, a key provider for the government’s technological and

logistics needs has made it a company-wide imperative to go green. Both the

government and Lockheed Martin can make a significant stride towards this goal by

reengineering their supply chain processes for the purpose of minimizing carbon

emissions.

Aside from reducing carbon emissions, there are a number of other incentives to

go green. A greener and more efficient supply chain will likely reduce the amount of

energy used over the course of the supply chain. This proves beneficial for any company

or supplier looking to reduce expenses as the cost of energy continues to increase. There

is also current and pending legislation that penalizes high energy consumption and carbon

emissions while rewarding a reduction in both. A company’s commitment to a greener

supply chain amounts to less government fees which in turn amounts to a stronger

financial standing. Finally, as consumers’ desire for less carbon intensive goods and

services continues to increase, a forward looking company with a sundry of green

initiatives becomes more appealing to the market.

My Contributions:

Ultimately, the goal of the project is to develop an optimal supply chain that will

reduce carbon emissions and, in turn, reduce total supply chain costs. The first step

towards that goal involves thorough research of a current supply chain. What needs to be

established are the resources and demands that drive the supply chain. The resources

generally include suppliers, manufacturing plants, storage facilities, distribution centers

and the methods of transportation in between. The demand simply includes what the

customer orders.

Once the aforementioned pieces of information are known, the supply chain can

be visually mapped out and both a phase-by-phase and holistic grasp of how the supply

chain functions can be achieved. From there, it is necessary to identify the costs

associated with each phase of the supply chain (i.e. storage costs, transportation costs,

manufacturing costs, energy costs etc.). In addition, once we know the methods of

transportation used across the supply chain, we need to calculate their respective carbon

emissions.

Once we have compiled all of this data, we can begin to mathematically model

the system and use linear programming to find an optimal utilization of the resources

within the supply chain that minimizes carbon emissions and total costs while satisfying

customer demand. The supply chain information gathered from the research will also be

utilized to formulate the associated constraints of this linear programming model.

Methods and Technology Employed:

A simple example of a simply chain can be visualized as follows:

When we seek to form a linear programming problem based on the above model, the

main issues are determining the choice of available facilities and designing the

transportation routing between parties to satisfy customer demand while reducing overall

supply chain costs (although, for the purpose of this project, the goal is to reduce

greenhouse gases and costs). This sort of problem is generally constrained by capacity

limitations for suppliers, manufacturers and distribution centers.

The objective function for this example will reflect a minimal cost (to reiterate,

the objective function for this project will reflect minimal costs and carbon emissions)

and is generally composed of five parts. The first three parts include the raw materials,

manufacturing, holding costs and the transportation between parties. The other two parts

represent the fixed operating costs of plants and warehouses. All these things considered,

we end up with the following variables:

And an optimization problem that looks like this:

Expected Outcomes:

Using the data acquired on methods of transport and their respective carbon

footprints along with the data acquired on the resources and demands that drive the

supply chain, we hope to formulate a modified version of the general model described

above that will lead to significant reductions in carbon emissions and, in turn, costs.

Represent capacity constraints of suppliers, plants, distribution centers

and customers respectively.

Ensures item is delivered to only operating plants and warehouses

respectively