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Cellular Manufacturing: Group Technology & Cell Design

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END3069 Computer
Integrated Manufacturing
Group Technology and Cellular
Manufacturing
Cellular Manufacturing
What is Cellular Manufacturing?
• Cellular manufacturing is an application of
group technology in manufacturing in which all
or a portion of a firm’s manufacturing system
has been converted into cells.
• A manufacturing cell is a cluster of machines or
processes located in close proximity and
dedicated to the manufacture of a family of
parts.
• The parts are similar in their processing
requirements, such as operations, tolerances
and machine tool capacities.
What is Cellular Manufacturing?
• The primary objectives in implementing a
cellular system are to reduce setup times (by
using part family tooling and sequencing) and
flow times(by reducing setup and move times
and wait time for moves and using smaller
batch sizes) and therefore, reduce inventories
and market response times.
• In addition, cells represent sociological units
conductive to teamwork. This means that
motivation for process improvements ofen
arises naturally in manufacturing cells.
Design of Cellular
Manufacturing Systems
• Design of cellular manufacturing systems is a complex
exercise with broad implications for an organization.
The cell design process involves issues related to both
structure and system operation.
• Structural issues include:
1. Selection of part families and grouping of parts into
families
2. Selection of machine and process populations and
grouping of these into cells
3. Selection of tools, fixtures and pallets
4. Selection of material handling equipment
5. Choice of equipment layout
Design of Cellular
Manufacturing Systems
• Issues related to procedures include:
1. Detailed design of jobs
2. Organization of supervisory and support
personnel around the cellular structure
3. Formulation of maintanence and inspection
policies
4. Design of procedures for production planning,
scheduling, control and acquisition of related
software and hardware
5. Modification of cost control and reward systems
6. Outline of procedures for interfacing with the
remaining manufacturing system
Evaluation of Cell Design
Decisions
• The evaluation of design decisions can be
categorized as related to either the system structure
or system operation.
• Typical considerations related to system structure
include:
1. Equipment and tooling investment (low)
2. Equipment relocation cost (low)
3. Intercell and intracell material-handling costs (low)
4. Floor space requirements (low)
5. Extent to which parts are completed in a cell (high)
5. Flexibility (high)
Evaluation of Cell Design
Decisions
• Typical performance variables related to
system operation are:
1. Equipment utilization (high)
2. Work-in-process inventory (low)
3. Queue lengths at each workstation (short)
4. Job throughput time (short)
5. Job lateness (low)
Cell Formation Approaches
• A number of cell formation approaches have
been developed.
• Machine-Component Group Analysis
• Production Flow Analysis
• Rank Order Clustering Algorithm
• Similarity Coefficient-Based Approaches
• Single-Linkage Cluster Analysis (SLCA)
• SLCA Algorithm
Machine-Component Group
Analysis
• Machine-component group analysis (MCGA) is
based on production flow analysis (PFA).
• In MCGA-based methods, machine-component
groups are formed by permuting rows and
columns of the machine-component chart in
the form of a zero-one matrix.
Production Flow Analysis
• Stage 1: Machine classification. Machines are classified on the
basis of operations that can be performed on them. A machine
type number is assigned to machines cappable of performing
similar operations.
• Stage 2: Checking part list and production route information.
For each part, information on the operations to be undertaken and
the machines required to perform each of these operations is
checked throughly.
• Stage 3: Factory flow analysis. This involves a micro-level
examination of flow of components through machines. This, in
turn, allows the problem to be decomposed into a number of
machine-component groups.
• Stage 4: Machine-component group analysis. An intuitive
manual method is suggested to manipulate the matrix to form
cells. However, as the problem size becomes large, the manual
approach does not work. Therefore, there is a need to develop
analytical approaches to handle large problems systematically.
Production Flow Analysis
Example:
Production Flow Analysis
Example:
Rank Order Clustering Algorithm
Rank Order Clustering Algorithm
Example:
Rank Order Clustering Algorithm
Solution: Use the steps of the ROC Algorithm
Step 1: For each row of the machine-component matrix,
assign binary weights and calculate decimal equivalents as
given in the following matrix:
Rank Order Clustering Algorithm
Solution: Use the steps of the ROC Algorithm
Step 1: For each row of the machine-component matrix, assign binary
weights and calculate decimal equivalents as given in the following
matrix:
1 × 29 + 1 × 28 + 1 × 27 + 1 × 26 + 1 × 25 + 1 × 23 + 1 × 22 + 1 × 21 + 1 × 20 =1007
Rank Order Clustering Algorithm
Step 2: Arranging rows by sorting the decimal weights in
decreasing order results in the following matrix:
Rank Order Clustering Algorithm
Step 2: Arranging rows by sorting the decimal weights in decreasing
order results in the following matrix:
1 × 24 + 1 × 23 +1 × 22 =28
Rank Order Clustering Algorithm
Step 3: Repeating steps 1 and 2 for columns results in
following matrix:
Rank Order Clustering Algorithm
• Step 4: There is no change in the row and colums
positions with further iterations.
• From the block diagonal matrix given in step 3, there
are a few possible ways to identify the number of part
families and machine groups. One such solution
results in three cells. Other solutions may result in two
or more cells. The question is which cell configuration
is the best.
• Selection of the best configuration depends on the
users and a number of factors such as part types,
machine types, and intercell and intracell materialhandling effort. This issue will be discussed in following
sections.
Similarity Coefficient-Based
Approaches
• In similarty coefficient methods, the basis is to
define a measure of similarity between
machines, tools, design features, and so forth
and then use it to form part families and
machine groups.
Single-Linkage Cluster Analysis
(SLCA)
• A hierarchical machine grouping method known as
Single-Linkage Cluster Analysis (SLCA) using
similarity coefficients between machines.
• The procedure is to construct a tree called a
dendrogram. Similarity coefficients between
machines are used to construct the dendrogram.
• The similarity coefficient between two machines is
defined as the ratio of the number of parts visiting
both machines and the number of parts visiting
one of the two machines.
Single-Linkage Cluster Analysis
(SLCA)
• The similarity coefficient between two machines:
SLCA Algorithm
• This algorithm helps in constructing
dendrograms. A dendrogram is a pictorial
representation of bonds of similarity between
machines as measured by the similarity
coefficients. It is used to present the clustering
results.
• The branches represent machines in the
machine cells and the horizontal lines
connecting branches represent the threshold
values at which machine cells are formed.
SLCA Algorithm
• The steps of the SLCA algorithm:
• Step 1: Compute similarity coefficients for all
possible pairs of machines
• Step 2: Select the two most similar machines to
form the first machine cell.
• Step 3: Lower the smilarity level (threshold) and
form new machine cells by including all the
machines with similarity coefficients not less than
the threshold value.
• Step 4: Continue step 3 until all the machines are
grouped into a single cell.
SLCA Algorithm
Example: Consider the matrix of 5 machines and
10 components given in previous example.
Develop a dendrogram and discuss the resulting
cell structures.
SLCA Algorithm
Solution:
SLCA Algorithm
Solution:
SLCA Algorithm
Solution:
SLCA Algorithm
Solution:
0.80
SLCA Algorithm
Solution:
0.60
SLCA Algorithm
Solution:
0.40
Evaluation of Cell Designs
• In previous example, we notice from the
dendrogram that four, three, two and one cells
are formed at similarity coefficients of 0.83,
0.70, 0.67 and 0.55, respectively.
• Also, five cells will be formed if each machine
is treated as an independent cell resulting in a
similarity coefficient of 1.
Evaluation of Cell Designs
• These cell configurations are shown in Table 12.8. The
question now arises, which cell configuration is the
best and what are the factors that influence such a cell
design.
Evaluation of Cell Designs
• To choose a cell design from a set of
alternatives, a criterion of minimizing the total
material handling cost of intercell (between
cells) and intracell (within cell) movements of
parts is particularly relevant if parts have a
number of operations visiting a number of
machines.
Evaluation of Cell Designs
• The factors influence these costs of intercell
and intracell moves:
Evaluation of Cell Designs
Evaluation of Cell Designs
Evaluation of Cell Designs
• The total cost of intercell and intracell
movements for the ith configuration:
Evaluation of Cell Designs
Evaluation of Cell Designs
Evaluation of Cell Designs
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