Pedagogy and Teaching Objectives
Littlefield Technologies is appropriate for courses that
include material on utilization, queueing, scheduling, and inventory. Instructors
can tailor assignments to different levels of complexity and to focus on
different topics by setting simulation parameters (e.g., demand pattern)
and by selecting the set of parameters that the students will be able to
modify during the game. The different parameters that are potentially available
to students to modify, along with the topics they could illustrate are
listed as follows.
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purchasing and selling machines. Typically the purchase price
of machines is greater than the retirement price. Students can apply their
understanding of utilization and queueing to locate bottlenecks in the
factory based on historic utilization, historic queueing levels, and historic
demand levels. Students can also achieve an intuitive feel for the non-linear
relation between queueing and utilization, and can build their intuition
on timing irreversible capital investments in the presence of variability.
Finally, students can use demand trends to predict when capacity will be
insufficient, illustrating long-term capacity planning.
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queue sequencing. Littlefield Technologies' process flow
is re-entrant, consisting of four steps on three stations. Students can
experiment with queue sequencing rules at the station with two process
steps. Students can see that queue sequencing rules do not generally have
the same effect on lead times that capacity does in steady-state operations,
but can be important in transient states when, for example, new capacity
has been added and there queue in front of the last step.
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lot sizing. Students can gain a better understanding of the
effect of changing lot sizes (also called batch transfer sizes) in the
presence of set up times. In particular, splitting a job into multiple
lots increases time spent on setups but might also decrease the time to
perform an entire job by allowing portions of a job to be simultaneously
processed on several machines.
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inventory order quantity. This is the order quantity for
the factory's raw materials. Students can deepen their understanding of
the assumptions behind the economic order quantity model by extending the
model's principles to a scenario where demand is random and non-stationary.
Instructors can also create scenarios where cash is at least temporarily
short (to purchase desperately needed capacity for example), motivating
students to consider and possibly implement a just-in-time ordering system
to liquidate inventory and raise cash. Assignments can also be set up where
students are motivated to apply the Newsvendor model to an inventory setting.
See assignment teaching notes in the instructor's packet for more details.
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inventory re-order point. This is the inventory level that
triggers the next order for raw materials. Students can gain an intuitive
feel for how a re-order point / order quantity system is set up and the
resulting inventory profiles to expect. Students can deepen their understanding
of safety stocks by extending ideas in class to a re-order point system,
possibly in the presence of non-stationary demand. The safety stock is
the expected inventory remaining at the end of the four-day lead time for
raw materials orders. Assignments can also be set up to emphasize the relationship
between capacity and safety stocks in a make-to-order environment with
tight lead times (i.e., the effect of stock-outs on lead time becomes greater
when there is less excess capacity).
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contract choice. Students can select from a menu of contracts
under which new customer orders will be accepted. The more lucrative contracts
have tighter lead time requirements and greater penalties for not achieving
lead times. The menu of contracts can be used to motivate students to understand
how capacity, lot size, and inventory parameters interact to determine
lead times. Students also build their understanding of lead time as a distribution
rather than a single number.
Pedagogical Foundation
Littlefield Technologies is an example of a goal-based
scenario. (See for example, Shank, Fano, and Jona, "The design
of goal-based scenarios." The J. of Learning Sci. 3(4) 1994.) In
their research, Schank et al have described goal-based scenarios as comprising
a "clear, concrete goal to be achieved, a set of target skills to be learned
and practiced in the service of this goal, and a task environment in which
to work." Goal-based scenarios are especially appropriate for generating
an understanding of complex systems, which allows students to systematically
refine their understanding and intuition of system behavior through exploration
and iterative experimentation. In the context of Littlefield Technologies,
the system is a combined stock replenishment system and queueing system
that can illustrate many of the principles taught in an introductory production
and control course. By iteratively making decisions, observing the effects
of their decisions, and refining those decisions, students develop an intuition
for how inventory systems and production systems behave. At least as important,
students develop the set of skills that apply lecture concepts. For example,
a lecture may present utilization, queueing, and bottleneck analysis, and
Littlefield Technologies would then allow students to develop such skills
as identification of bottlenecks and capacity sizing and timing to meet
non-stationary, random demand. By providing a scenario that is somewhat
representative of a real situation and where course material can be applied,
goal-based scenarios build student enthusiasm for course material. Finally,
the constant availability of team standings in Littlefield Technologies
has also built enthusiasm and allowed students to continually assess their
performance relative to peers.