Roll No. Name B13003 Abhra Chaudhuri B13134 Anirban Chakraborty B13137 Archit Singh B13176 Supratim Gupta

LITTLEFIELD

INITIAL

ANALYSIS

Submitted By BM: PPC : Group 1

LITTLEFIELD SIMULATION

Introduction:

On preliminary investigation the Littlefield simulation presents itself as a simplified factory

environment where various operational decisions have to be made over 200 simulated days to

maximize the cash balance.

Though the virtual factory is simple enough with just 4 work stations, viz. stuffing, testing and

tuning and only four steps in the process flow, closer analysis reveals there are a number of

decisions hidden within the simulation in order to complete it successfully.

Before starting the game we have tried to pinpoint the areas which we think will play the

deciding role in determining which teams outperform the others. In this brief summary we have

tried to categorize those areas and pin down our gut feeling with regards to achieving success

on each of the parameters.

Major Decision Areas:

According to us the major decision areas in the simulation will be:

a) Reorder Point

b) Order Quantity

c) Machine procurement

d) Demand forecasting

e) Contract Selection

f) Lead time Control

g) Lot size at machines

We will try and present our take on these key issues briefly and how we plan to tackle them:

a) As mentioned in the introduction on the website the Reorder Point can be any multiple

of 60. We believe the key trade off in deciding the reorder point would be to find a

balance between the risks of running out of stock and subsequently be unable to service

customer demand and the increased holding costs associated with keeping a high

inventory level permanently. Striking the ideal balance here would result in optimizing

operational cash flows.

b) The Reorder Quantity of order also has to be a multiple of 60. The fixed cost associated

with each order is stated as $ 1000 and cost per item is $ 10. So it would seem logical to

maximize the order as much as possible in order to spread the fixed cost over a large

number of items. However, ordering a quantity that is too large would result in

inventory hoarding and exorbitant holding charges which may reduce cash flows

alarmingly. This situation will be further worsened if there is a dip in the demand

following a large order as the large amount of RM will keep lying in the store without

getting processed and sold.

c) Machine procurement will probably have very severe implications as the price of all the

machines is very high in the range of $ 90,000 to $ 100,000. In order to make decisions

with regards to machine procurement the available demand data of the first 50 days will

have to be utilized in order to generate a forecasted demand over the entire game

period. Using this demand machine procurement will have to be planned and timed in

such a way that sufficient numbers of machines are present to service the peak demand

when possible. However, the cost implication of leaving the newly procured machines

idle if the forecasts suggest dip in demand after the peaks will also have to be taken into

consideration before deciding final procurement strategy. A cost benefit analysis will

have to be carried out in order to find whether buying machines is more beneficial than

sacrificing some demand in the peak periods rather than having the newly procured

machines lie idle after the demand slumps.

d) The demand data of the first 50 days will have to be studied to discover any perceptible

pattern in the demand. After this initial inspection a couple of apt forecasting models

will have to be applied to come up with a demand forecast which will define all future

capacity and procurement decisions.

e) It is mentioned that the default contract selected in the game has a payoff of $ 1000 for

delivery of 60 kits and all kits need to be supplied within a lead time of 24 hours to

ensure full payment. The payoff decreases to 0 linearly from 24 hours to a maximum of

72 hours of lead time. This loss in revenue will be a major determinant in capacity

incrementing decisions. Further the more lucrative and challenging contracts where

there is a higher payoff with lower lead times will have to be explored to calculate if it is

possible to maximize revenue.

f) Controlling the lead time so that the loss in revenues does not become higher than

procuring a new machine for the bottleneck will be one of the key decision areas. The

level of lead time increase will have to be monitored on short intervals to ensure that

the increase does not cause the revenues to suffer too heavily.

g) Another area which maybe tweaked around with to maximize efficiency and in turn

responsiveness is the lot size at each machine. The set up times of each machine will

have to utilized along with the processing time per raw kit in order to determine

whether there is benefit which lies in decreasing or increasing lot sizes at the machines

Conclusion:

If we are able to manage all these parameters effectively and function as a team we believe

we will be able to maximize our cash flow, while at the same time ensuring that our

responsiveness remains high and customers have to wait the lowest time possible for our

products. Due to the short period of 250 days over which the simulation will be running, we

look forward to taking educated proactive decisions based on forecasts and time study

calculations to stay ahead of the competition.