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Title
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Advanced resource planning
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Author
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Abstract
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| Advanced Resources Planning hits the bottom of what we know as aggregate planning. This approach differs from other approaches in that it explicitly recognizes the stochastic nature of manufacturing systems. Therefore, it is an ideal high-level tuning and planning tool which can be used in various planning environments like MRP, ERP, JIT, Load-Oriented Planning, Theory of Constraints, Finite Scheduling, POLCA systems, and perhaps many more. The main purpose is to set aggregate planning parameters right before diving into any other operational planning decision. In this sense, we opt to offer realistic lead time estimations, lot sizes, utilization levels, customer service levels and quoted delivery times. The underlying approach is a waiting line network, which is heavily adapted in order to make it useful for planning purposes. The main feature is that both input parameters and output parameters are considered as stochastic variables. In this way it allows us to model manufacturing environments in a more realistic and intuitive way, including all kinds of uncertainty and variability. As a consequence, the output of the planning effort is also a stochastic variable: it has an average, a variance and the entire lead time distribution. The latter makes it possible to obtain high customer service levels or to establish realistic delivery times, which can be met with a high probability. This mathematical approach as such is not suited for people operating a manufacturing system. We illustrate the approach with software, named i-CLIPS, and we review some implementations and their results. (C) 2003 Elsevier Science Ltd. All rights reserved. |
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Language
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English
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Source (journal)
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Robotics and computer-integrated manufacturing. - New York
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Source (book)
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12th International Conference of Flexible Automation and Intelligent, Manufacturing (FAIM), 2002, DRESDEN UNIV TECHNOL, DRESDEN, GERMANY
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Publication
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New York
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2003
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ISSN
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0736-5845
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Volume/pages
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19
:1-2
(2003)
, p. 211-218
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ISI
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000181481700023
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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