operation time and operation sequences. Though these methodologies are inadequate in achieving the neds of other extents of manufacturing. For example, parts with homogeneous shape, size, dimension or other design characteristics are believed to be clustered in a single family for design justification and elimination of part varieties, however parts which are clustered on the fact of homogeneous routing and the tooling needs are convenient to resolve the process planning issues.

Therefore the scope of this domain of research is believed to be expanded and examined to a wider span of part similarities. Part similarities are said to be identified sooner than the formation of part families. Part attributes such as shape, length/diameter ratio, material type, part function, dimensions, tolerances, surface finishing, process, operations, machine tool, operation sequence, annual production quantity, fixtures needed, lot sizes have been considered as the basis for similarity utilization as specified by Groover and Zimmers, (1984). Moon (1992) has stated that the complexity remains in acquiring an appropriate technique which provides an identifying competence of human being, such as identifying patterns in groups, and forming part families with the aid of intelligence.

Two different approaches are traced in past literature in order to form part families, first is production flow analysis (PFA) which deals with processing requirements of parts, operational sequences and operational time of the parts on the machines (Burbidge, 1996). Second approach is the Part Coding Analysis (PCA) which utilizes predefined coding schemes to facilitate the process using several attributes of parts such as geometrical shapes, materials, design features and functional requirements etc.

(Mitrofanov, 1966).

PCA is exposed in this study as an essential and effective tool for successful implementation of GT
concept. The part coding could be numbers (numerical) or alphabets (alphabetical) or a hybrid form of numbers and alphabets (alphanumerical) which are allotted to the parts to process the information (Ham et al., 1985). Parts are categorized based on significant attributes such as dimensions, type of material used, tolerance, operations required, basic shapes, surface finishing etc. In this approach, each part is assigned a code which is a string of numerical digits that stores information about the part. Singh and Rajamani (1996) demonstrated coding systems in their book and stated that the coding systems depict either hierarchical structure (monocode), or chain structure (polycode) or hybrid mode structure mixed with monocode and polycode.

Several part coding systems have been developed, e.g. Opitz (Opitz, 1970), MICLASS (TNO, 1975),
DCLASS (Gallagher and Knight, 1985) and FORCOD (Jung and Ahluwalia, 1992) etc. Han and Ham (1986) have claimed that part families could be established more realistically by practicing the PCA due to the advantage of using the manufacturing and design attributes concurrently. Offodile reported a similarity metric based on the numeric codes for any pair of parts which could be utilized with an appropriate clustering method such as agglomerative clustering algorithm to form efficient part families
(Offodile, 1992). Clustering analysis is practiced in Cellular Manufacturing System (CMS) as a competent methodology to facilitate the machine/part grouping problems.

Various machine/part grouping techniques are developed to solve manufacturing cell formation problems since last few decades, these include similarity coefficient methods, clustering analysis, array based techniques, graph partitioning methods etc. The similarity coefficient approach was first suggested by McAuley (1972). The basis of similarity coefficient methods is to calculate the similarity between each pair of machines and then to group the machines into cells based on their similarity measurements. Few studies have proposed to measure dissimilarity coefficients instead of similarity coefficient for machinepart grouping problems (Prabhakaran et al., 2002). Most of the similarity coefficient methods utilized machine–part mapping chart. Few of them are Single linkage clustering algorithm (McAuley, 1972),
Average linkage clustering algorithm (Seifoddini and Wolfe, 1986).

Array based methods consider the rows and columns of the machine-part incidence matrix as the