OMGT2292 - Strategic Operations & SCM: Quantitative Problem Solving

This assignment presents a quantitative problem-solving scenario focused on optimizing a supply chain network. The student's solution determines the most cost-effective allocation of plastic pellets from multiple plants to various customer zones, minimizing transportation costs. The solution identifies the total demand, the most significant market, and the least significant market. The student highlights the importance of minimizing costs by prioritizing shipments from the lowest transportation cost. The solution emphasizes the use of supply and demand constraints to ensure balance and profit maximization. The student compares their solution to another student's, highlighting the impact of constraint inequalities on transportation costs. In conclusion, the student emphasizes the need for correct objective functions and constraints for a valid solution, and provides references to relevant literature.

Quantitative problem solving 1
QUANTITATIVE PROBLEM SOLVING
By (Name)
Course
Professor’s Name
Institution
Date

Quantitative problem solving 2
Quantitative Problem Solving
Summary and Iteration of Solution
From the solver model, the total demand for plastic pellets is 3190 tons, with the
customer zone in Terre Haute having the most significant market, and Spokane the least. The
solution minimizes the transportation cost by allocating the most significant quantity of supply to
the consumer destination, where the lowest transportation cost is incurred. Goods supplied from
Philadelphia are shipped to NYC at $45 because it suffers the most economical price as
compared to other destinations. The number of plastic pellets supplied to a particular destination
is less or equal to the total demand in that zone. Surplus from the different plants is used to meet
the deficit in the market at the consumer zones (Davis et al., 2019).
Since the objective function is aimed at minimizing transportation costs, the allocation of
supply to the customer zones starts from the lowest price possible to the higher values. The
constraints in the solution are based on supply and demand (Stanton & Melynyk, 2017). Quantity
should be less or equal to the maximum plant capacity, whereas the sum of the need for a
specific consumer zone should be equal to the total demand as provided from the data. The
minimum available solution is:
Minimize Z= $161620
The solution ensures that transportation cost is minimized while allocating supply to
demand (Innocent, 2014; Lapinskaite & Kuckailyte, 2014). This provides for profit-making as
well as consumer satisfaction, which is the main aim of production. Therefore, I think the
solution is justified in that it gives a balance to supply and demand in the supply chain, which

Quantitative problem solving 3
aids in inventory keeping (Lewis et al., 2013), and provides the company with a way to
maximize profit while lessening the cost of the product (Swanson et al., 2016).
When solving the quantization problem, I considered the fact that, since what is required
is optimization by reduction transportation cost, consumer zones and production points with the
least cost of transportation between them should receive the priority in the allocation of demand.
The most supply available should be allocated to satisfy demand at specific points before moving
on to the next. Another consideration was that supply to a certain consumer zone should not be
more than demand at that point.
Comparison
While comparing my work with that of another student, I found out that his solution gave
a higher transportation cost was higher. This deviation was coming from the inequality signs
used to come up with the constraints for the objective function. His inequality sign for demand
constraint was less than or equal to as opposed to mine, which was equal to. Demand at any time
should be exactly equal to the total demand given. I, therefore, disagree with his statement of
demand constraint.
Conclusion
The solution uses supply and demand factors, and transportation cost to specific
destinations to come up with the optimum cost of production. The objective function and
constraints should be correct to ensure that the solution obtained is valid for the quantization
problem.

Quantitative problem solving 4
References
Davis, A. & Huang, R. a. T. J. D., 2019. Managing Multilocation Demand Supply Chains: An
Experimental Investigation. History.
Indre', J. L. a. K., 2014. The Impact of Supply Chain Cost on the Price of the Final Product.
Business, Management, and Education, Volume 12.
Innocent, N. a. D. U. A. C., 2014. Supply chain management optimization problem. The
International Journal of Engineering And Sciences, 3(6), pp. 1-9.
Lewis, B. M., Erera, A. L. & Nowak, M. A. a. W. I. C. L., 2013. Managing Inventory in Global
Supply Chains Facing Port-of-Entry Disruption Risks. Transportation Science, 47(2), pp. 162-
180.
Stanton & Melnyk, S. a. D., 2017. The customer-centric supply chain. Supply chain management
review, 20(12), pp. 28-39.
Swanson, D. W. B. D. & Gu, J. a. W. M. A., 2016. Full Steam Ahead: Firms in the US Economy
Adjust Inventory for Changes in Transportation Costs But Not the Reverse. Transportation
Journal, 55(3), pp. 282-295.