IE 4303: Production & Inventory Control
Fall 2020

Mini-Project #1
Due Thursday, October 1, by 2:00 p.m. CDT

For this assignment, you will develop an Excel-based simulation tool using VBA macros to explore the
effect of demand variability on system performance. There are two components to this project: 1) the
simulation tool and 2) a report that describes experimentation and results. This project will be worth
16% of your overall course grade, with the simulation tool and the report each contributing 8%,

Part 1: Simulation Tool

This tool will simulate daily demand, demand fulfillment, and inventory replenishment of a single item
for 365 simulated days using the EOQ method of inventory control. Assume the following parameter
values as inputs to your tool:

Daily demand 10 units/day
Order cost $5/order
Holding cost $0.05/unit/day
Vendor lead time 4 days
Backorder cost $10/unit/day
Purchase cost $0.05/unit

The economic order quantity (EOQ) method of inventory control assumes that demand occurs
continuously. For this assignment, you will approximate continuous demand by assuming a daily time-
step, in which the following events occur in each simulated day:

• Start of the day: Any replenishment orders that are due from the vendor will arrive at the start of
the day and will be added to available inventory. This inventory is assumed to be available to fill
orders on the same day that it arrives. If there are backorders from the previous day (i.e., negative
inventory), those orders will be filled immediately.

• End of the day: Available inventory will be drawn down by the number of units demanded during
the day. You will then check to see if a replenishment order needs to be placed; if so, the order is
placed with the vendor, and the vendor’s lead time clock starts running (i.e., at the start of the next
day, the lead time will be reduced by 1 day).

The tool will be used to simulate three different scenarios, each of which must be run for 10 replications
of 365 simulated days. For all three scenarios, you will capture (at a minimum):

• One state variable – current inventory level – at the start and end of each simulated day
• Two system performance metrics – cost and service level – at the end of each 365-day simulation



The three scenarios are as follows:

1. Start by assuming that there is no demand variability (i.e., the system is deterministic). Determine
the optimal order quantity (Q*) and reorder point (r*) for this system, and use these values to
control inventory replenishment. Assuming that you start at time 0 with Q* units in inventory, run
the simulation, and collect relevant output data.

2. Now, assume that daily demand exhibits randomness (i.e., the system is stochastic) and follows a
normal distribution. Again, use Q* and r* to control inventory replenishment. Assuming that you
start at time 0 with Q* units in inventory, r

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