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