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Research Areas for Group 3
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In
any production operation, the performance of the
operation depends on a set of controllable
factors: e.g., types and amounts of production
resources; choice of operating tactics; and parameterization of control policies. For
instance, some typical factors include the
targeted utilization level for key production
level for key production resources; the choice of
batch or lot sizes; and the policies for release
of work to the shop. To achieve the best overall
performance requires understanding the interplay
among these factors and their trade offs. The
intent of this research project is to develop
decision aids that can illuminate the boundary
trade offs and guide a production manager in
making decisions on resource acquisition,
operating tactics, and production control
policies.
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Whereas
we seek generic problems, we also expect that the
relevant factors and trade offs will vary greatly
across industries and production contexts. Hence,
we anticipate that the research will start by
being context specific, and thus will entail a
variety of company-based projects. Presumably,
priority will go to the projects that are more
likely to be broadly applicable. From these
projects, we expect to develop decision support
tools for the examination and analysis of the
relevant trade offs, as well as new insights or
principles for setting the factors to obtain the
optimal trade offs. We also will attempt to
develop more general frameworks for viewing the
factors and their interplay. There is
a great amount of academic research that has
examined boundary trade offs, as defined here. We
will attempt to draw upon this work wherever possible. Indeed, we expect that in many cases
the research will be to determine how to adapt
"off the shelf" models and results to
the real world of manufacturing. In this respect,
these opportunities may be ideal for LFM interns.
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The
primary short-term deliverables will be
documentation and learning's from the study and
modeling of trade offs in various partner manufacturing contexts; we also will develop a
tool kit for the analysis and optimization of
these trade offs.
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Balakrishnan;
Gershwin; Graves; Magnanti; Nguyen; Rosenfield;
Wein; in collaboration with MIT graduate
students; LFM interns; and colleagues from
industry labs.
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Expected
customer: All management levels
Expected use time frame: Medium and short-term
Expected benefits: High |
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Manufacturing
plants are becoming more customer service
conscious. They want to improve their service and
responsiveness to customers by quoting more
accurate lead times during order negotiation,
reducing delays between order entry and scheduling, providing timely order status
information, and accommodating customer
contingencies. They are also expanding their
roles to include services such as
"turnkey" supply- procuring
complementary items from other sources and
providing consolidated shipments to customers -
and management of customer inventories. With the
emergence of electronic commerce, manufacturers
have greater and (almost) instantaneous access to
detailed data from both suppliers and customers
(e.g., real-time information from point-of-sale
or point-of-use). The challenge is to eliminate
the barriers between customers and the shop floor
and exploit the available data with a view
towards providing better service at lower cost.
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Streamlining
the manufacturing-customer interface requires
both improving the connectivity between
interfunctional (e.g. sales and manufacturing)
and interorganizational (e.g., suppliers' and
customers') information systems, and building
relevant customer-driven models to support
various production and logistics decisions. This
project will focus on modeling rather than
information issues. Models are requried to
determine accurate due dates for prospective
orders, construct fine capacity schedules
on-line, develop cost-effective distribution
plans coordinated with procurement and
production, dynamically reschedule production and
delivery to respond to contingencies, and
forecast product mix and volume based upon
detailed end-use data.
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The
project will first identify, via case studies and
internship projects, some specific new research
and modelling opportunities relating to
manufacturing-customer integration. Potential
areas to investigate include due date quotation,
contingency planning, capacity planning for quick
response, and customer-operations interaction
mechanisms. This exercise can then lead to
context-specific or generic modelling projects.
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Balakrishnan;
Gershwin; Graves; Magnanti; Nguyen; Rosenfield;
Wein; in collaboration with MIT graduate
students; LFM interns; and colleagues from
industry labs.
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Expected
customer: Middle and senior management
Expected use time frame: Medium and short-term
Expected benefits: High |
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1997, Leaders for Manufacturing, Massachusetts Institute of
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Last updated: June 16, 2000.
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