Manufacturing capability measurement for cellular manufacturing systems

Abstract

An important element in the design and management of cellular manufacturing systems is the ability to determine capabilities. This paper describes a generic methodology of Capability Analysis (CA) which can be used for the integrated assessment of various capability factors. The main aspect of CA is the ability to compare di erent factors alongside one-another. This is done by determining priority con® dence scores (PCS values) which are capability scores (measures of performance) represented as proportions of required capability scores adjusted to take into account improvement potential, factor importance and ® nancial considerations. CA is used to form a recovery schedule of targets for improvement ranked in order of priority and is produced by ranking the PCS values for each capability level (cell, product, part, workstation and operation). Targets at a higher level can be broken down into more detailed targets at a lower level using the concept of transparency. The workings of CA are demonstrated using an example.

Organizational knowledge encapsulation and re-use in collaborative product development

“…The system decomposes the product design into features and intelligently explores all the possible production alternatives, ultimately allocating the production of specific parts to given production equipment within the supply chain. The CAPABLE system is designed to operate during the earliest phases of design where the quantitative analysis of design solutions is not always possible; it therefore uses the concept of 'capability analysis' to measure the likely performance of a plan (Baker and Maropoulos 1998). The essential idea of capability analysis is to model an expert's preference for the selection of a particular process or piece of equipment in the process plan, derived from one of the three types of enterprise knowledge.…”

“…Hence, capability factors can directly utilize the ontology as a source of knowledge. A capability analysis (CA) method (Baker and Maropoulos 1998) has been developed to prioritize knowledge values associated with product, process and resource entities. When applied to the process planning problem, CA makes it possible to identify potential design or implementation problems with the process plan and feed them back to designers to prompt further detailed analysis or re-design.…”

Design, Development & Implementation Of Ontological Knowledge Based System For Automotive Assembly Lines

“…Sensors and motorized stops are used all over the transportation system to track the pallets and express them down dissimilar conveyors as per the data and information kept in every individual pallet. Every fresh assembly arrangement distributed to the end-user, e.g., automotive assembly line, will be self-possessed of an exclusive mixture of such transportation, congregation and test interconnected component (Baker and Maropoulos, 1999). Whilst some components will unavoidably be exclusive to a new function, the greater part (typically >70%) will be stand on the reprocess of preceding perfunctory component.…”

Dynamic and distributed early planning assessment by a hybrid simulated annealing and greedy algorithm

“…product, process and resource. A capability analysis methodology [16] is then used to prioritize these knowledge statements according to their potential for improvement and calculates a priority con®dence score (pcs) ranging from 0, inclusive, which is awarded to the best or ®ttest item of knowledge, to 1, inclusive, which represents the worst value for an item of knowledge. The planning algorithm utilizes these PCS metrics to penalize poor knowledgeperforming entities of a plan, as shown by K Lˆp cs J C J …5 †…”