1. James MacMartin
Supply Chain Game 1 Prafulla Kumar Shahi
Executive Summary Swaminathan Kandaswamy
Jacobs Industries contacted our team to manage their supply chain network for two years. In the
preceding two years, the company had faced a stable but seasonal demand of about 39.2
units/day. Our forecast suggested a peak average demand during summer of about 50 units/day
during summer and a low average demand of 13 units/day during the winter. Using our
forecasting and inventory management, we achieved a revenue of $44,156,850 and an ending
cash balance of $6,148,047. Our fill rate for the two years was 83.7% and our cycle service level
was 33%. In the first year, we lost 28.5% of the demand, but in the second year, due to better
inventory management policies, only 4.1% of the demand was lost.
The major challenge was to come up with a mathematical model to incorporate both the (Q,r)
model and the aggregate plan to choose between the chase and lever strategies. These choices
would decide the capacity, reorder point, size of batches and optimal safety inventory. We
started building inventory and capacity for the first peak season later than we should have and
lost a significant portion of that demand. We wanted to minimize the inventory cost and hence
decided to follow a chase strategy and built up a capacity of 40 units after the first 15 days which
came into effect 90 days later. We also decided to try to capture as much demand as possible in
order to maximize revenue. According to our initial calculations (refer Worksheet Production Plan
tinker (4)), a capacity of 40 would have led us to a negative cash position in the first quarter of
the following year and hence we decided to add an additional 6 units of capacity. We were able
to capture a portion of the peak demand in the first season. We chose the reorder point as the
primary driver for inventory management. Batch size was limited to either 200 or 400 depending
on the cash position and available inventory. Due to increased capacity, we held lesser inventory
and incurred reduced inventory costs. Initially, we had decided to produce and ship a final batch
of 200 by truck and then cover the final demand using smaller batches and ship via mail. But in
the last two months, we expected to face a demand of about 870 units and hence decided to
produce a final batch of 400 units. However, the realized demand was only 760, which resulted
in a final inventory of 147 units and obsolescence costs of $147,000.
We learnt that our strategy of inventory management may not necessarily be the best method
for this application. The capacity needed for meeting demand was chosen because of high
expected inventory costs, but the inventory costs did not play a very important role in this study.
The most critical decision points were where the reorder point had to be adjusted, and the
production cycle before day 1460, where all inventory turned obsolete. It was critical that no
extra inventory was in the pipeline or WH after day 1460. In this scenario, it would have been
more favorable to stock-out of a few days’ worth of demand rather than end up with excess
inventory. The last batch of 400 also proved to be unnecessary. A better decision would have
been to produce a batch of 200 and then decide whether to supply the final demand by producing
in small batches and transporting by mail. As a result of our strategy, we achieved a high fill rate,
but a lower profit due to high production cost.

2. James MacMartin
Supply Chain Game 1 Prafulla Kumar Shahi
Executive Summary Swaminathan Kandaswamy
This game taught us important principles in inventory management both on a strategic and
operational level. We learnt to consider all factors that go into running a production operation:
expected costs in production, available cash, inventory and projected demand. The most
important lesson learnt was that forecasts are never accurate, as the last period ended with a
severe drop in demand which left us with a large amount of inventory. Our strategy to try to
maintain a high service level caused us to keep a higher safety inventory which ended up unsold.
In a future game, a better inventory policy would be to maximize the revenues based on a simple
but accurate mathematical model. We should also expect the actual demand to fluctuate and
hence decide on a reasonable Fill rate for determining the inventory policy. A very high fill rate
could lead to excess inventory and cause losses.