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A design methodology for configuration of manufacturing cells
~ Pergamon
A DESIGN
Computers ind. Engng Vol. 34, No. 1, pp. 63-75, 1998 © 1998 Published by Elsevier Science Ltd. All rights reserved PII: S0360-8352(97)00151-4 Printed in Great Britain 0360-8352/98 $19.00 + 0.00
METHODOLOGY FOR CONFIGURATION MANUFACTURING CELLS
OF
RICHARD E. BILLO Department of Industrial Engineering, 1037 Benedum Hall, University of Pittsburgh, Pittsburgh, PA 15 261, U.S.A. Abstract--This paper presents a general design methodology for manufacturing cells. The approach makes use of the observation that 85% of the production demand of a manufacturing facility can be attributed to 15% of the products manufactured in the facility. This logic was extended to manufacturing cell design. Specifically, within a part family, those parts that have a high steady demand should be placed in cells that are configured and operated similar to a flow line. Those part numbers within the family that have little demand should be assigned to cells designed to operate more as a job shop. In this way, a manufacturing cell that is designed to serve both the high and low demand components will not be impeded by imposed constraints resulting from demand or processing time considerations for individual parts within the family. The author presents a ten step approach for analysis and design of such cells after initial machine-component groupings have been formed. © 1998 Published by Elsevier Science Ltd. All rights reserved.
1. INTRODUCTION
Cellular Manufacturing is manufacturing based on groups of processes, people and machines to produce a specific family of products with similar manufacturing characteristics [1]. Manufacturing cells have shown benefits in reducing throughput times, making the manufacturing system more flexible, and improving quality [2]. A review of the literature in cellular manufacturing reveals that the majority of research focuses on the cell formation process. Array-based methods [3-5], cluster analysis methods [6, 7], graph theoretic methods [8,9], and mathematical programming methods [10, 11] have all been developed to group parts and machines into cellular configurations. Although much progress has been made in these cell formation techniques, they represent only the beginnings of the cell configuration process, and are not sufficient for the design of a robust cellular manufacturing system. Once initial machine-component groupings are identified, the design team is still faced with a myriad of other decisions including number of cells, number of machines in each cell, physical layout, material movement strategy, setup/changeover design, cell balancing, and scheduling. 2. PARETO'S LAW AND FACILITIES PLANNING
Many factors can influence the configuration of a manufacturing facility, and ultimately the efficiency of its operation. These may include product variety, operation times, production demand, process costs, number of moves, and so forth. For example, Tompkins and White [12] observed that Pareto's law often applies to the product mix of a facility. They found that 85% of the production demand can be attributed to 15% of the products manufactured in the facility. They then concluded that the facilities plan should consist of a mass production line for the 15% of high-volume items and a job shop arrangement for the remaining 85% of the product mix. These conclusions were based on the common knowledge that a flow line layout is most appropriate for parts with high demand and low variety, while a functional arrangement is best suited for a product mix characterized by high variety and low demand. This logic can be extended to manufacturing cell design. For example, Fig. 1 illustrates a comparison of different kinds of manufacturing systems based on their production rate and product variety [13]. From this figure, the reader may notice that the operational coverage of manufacturing cells overlaps portions of both job shop and flow line configurations. The high variety constraint associated with the job shop is eliminated through the usage of the manufacturing 63
64
Richard E. Billo
m~ volume 1000
100
Production rates
Parts per Hour
10
Low
volume
1
1
10
100
1000
10000
Variety (Number of different parts per system) Fig. 1. Comparison of different kinds of manufacturing systems with ceils [13].
cell with its similar process flow feature allowing operational efficiencies to take place. However, variation in production demand within a cell is typically not controlled through the application of manufacturing cells. Some parts within the family may have high, consistent demand, while others have low, sporadic demand requirements. Knowledge of this constraint at the outset of a manufacturing cell design project can positively impact many succeeding cellular design considerations including setup/changeover strategies, scheduling, cell replication, and so forth. 3. RESEARCH PURPOSE
The purpose of this research was to develop a general methodology for cell design. The methodology employs common manufacturing data such as production demand, production rates, and capacity, all of which are deemed critical for making further decisions on cellular configuration. Production demand refers to the volume of units required to meet the market's requirements over a given time period. Production rate is the number of parts per hour that can be completed at the bottleneck operation in a manufacturing cell. Capacity is the total production time available in a manufacturing cell as measured from the bottleneck operation in the cell. By formally and systematically utilizing such additional production information in the cell design process, designers can achieve greater efficiencies in cellular operation. Within a part family, better cell operation can occur when parts are further grouped and cells replicated according to similar production demand or processing time requirements. Demand for individual parts within a family can provide insight into the most appropriate layout and operation of cells. High-demand products may need a cellular layout operating like a flow line, while medium- and low-demand parts may need a layout that is also cellular in structure, but operate more like a job shop. Although the physical arrangement of the cells would remain in the classic " U " or "C" type serial configuration commonly associated with manufacturing cells, operation of the cells may be quite different depending on the product mix. By analyzing the production demand or processing time requirements for each part within the part family, cells can be designed to operate
Cell designmethodology
65
in a manner that would gain the greatest efficiencies. For example, manufacturing cells designed to operate as a flow line would tend to have a smaller product mix with high volumes. In this arrangement, fewer setups and changeovers would be required and production could be controlled through a pull manufacturing technique and a simple mixed model schedule. In contrast, those cells designed to operate as a job shop may have a lower throughput rate due to the need for more frequent setups. Product through these configurations may best be scheduled using a group scheduling heuristic [14]. 4. PREMISESOF A CELLDESIGN METHODOLOGY The methodology to be described in this research is based on three underlying assumptions: 1. More than one cell per part family may be needed to satisfy demand. We have termed this situation as cell replication. 2. Pareto analysis of demand versus product mix for a part family shows distinct cutoff points justifying different cell strategies. 3. There are operational advantages for configuring and operating cells as flow lines or job shops.
4.1. The need for cell replication Once initial part and machine groupings are formulated, the design team must determine whether there is a need to replicate the cells. Based upon the bottleneck operation, defined here as the operation within the cell with the lowest production rate (or its reciprocal--longest operation time), the capacity of the original cell must be determined and compared against the demand for the parts in the cell.
4.2. Pareto analysis for cells Once it is determined that the cell should be replicated to meet overall demand requirements of the part family, the design team must determine the expected distribution of production demand for the part family. It would be naive to believe that all demand distributions would result in a perfect match with a Pareto distribution. Even so, any analysis, whether Pareto distributions result or not, can provide additional insight into an effective means for assigning parts to replicated cells. A Pareto analysis is typically used as an "aid" in identifying groups and not as an inflexible set of rules. The Pareto distribution identifies the following categories according to the 80-20 rule: (a) the low-mix, high-volume parts, and (b) the medium-to-high-mix, low-volume parts. The first category represents 15-20% of the part numbers in the product family that constitute 80-85% of the demand of the family. The second category represents the remaining 80-85% of the part numbers that constitute only a very small portion of the demand for the product family. In the present study, distribution of demand for individual part numbers in a family was analyzed, although total processing time for a part family may be easily substituted. Demand for individual part numbers was first sorted in descending order, then graphed. The design team determines points on the graph where the distribution significantly fluctuates from a smooth distribution. Given these points and known capacity constraints of the cell, the design team may gain a better understanding of the number of cells needed, the most appropriate assignment of individual part numbers to cells and the operating strategy of the cell.
4.3. Operational advantages of different configurations The third premise for the cell design methodology is that the production demand for a part reveals operational advantages for the cell configuration problem. When a Pareto chart reveals significant differences among part numbers based on differences in their demand and/or their total processing time (demand × operation time), then further grouping of parts into replicated cells can result in an opportune production system.
66
R i c h a r d E. Billo
The high-volume parts constitute the commodity items for which there is a large steady demand. The market in which these products compete are generally established markets. Manufacturing cost is a n important performance metric for these products. The current production system may have hindered these products from reaching their lowest cost potential by not allowing them to have a continuous, uninterrupted flow. Grouping these parts into a replicated cell that operates as a flow line allows many improvements to be obtained. Consistent and repetitive production schedules can be delivered through simple mixed model scheduling techniques. In addition, more effective product movement techniques such as pull production can be employed, changeovers or setups need occur less frequently, inventories can be stabilized, throughput times can be shortened, and a low manufacturing cost per unit can be achieved. Finally, mechanization or automation is more feasible for these products. This is due to the fact that shorter throughput times, less direct labor, fewer product variations, and a relatively small number of changeovers provide an easier justification for automation and mechanization on these high-volume products. The low- and medium-volume parts tend to be special order items for which the demand is typically unsteady. The low- and medium-volume parts have generally resulted from a proliferation of a product line into niche markets. These parts usually result from the need to meet the needs of a large, varied customer base. Batch processing using a functional layout has the flexible processing characteristics to efficiently manufacture these products. Manufacturing operations or machines in a job shop have the capability to make a variety of products, and machines are less dependent on one another. However, in a job shop, operations are typically separated by large distances causing long throughput times and excess inventory. A cell that provides a continuous flow of material through machines co-located can help overcome the shortcomings of a functional layout by reducing move time, and simplifying scheduling of machines through usage of a group scheduling system. 5. C E L L D E S I G N
METHODOLOGY
In this section, the method for cell design is described along with definitions of terms used for capacity and cell requirement calculations. The methodology incorporates several common manufacturing attributes such as demand, production rate, operation time, utilization, and capacity.
5.1. Definition of terms C = capacity of original cell (parts/yr); Cnc = capacity of flow line cell (parts/yr); Cisc = capacity of job shop cell (parts/yr); P = production rate of original cell (parts/hr); Ot = operation time of original cell (parts/hr); Pi= production rate for part i (parts/hr); A = available hours (hours/shift); S = number of shifts (shifts/day); W = number of available workdays; U = utilization factor due to manufacturing inefficiencies; Unc = utilization in flow line cell; Uj~ = utilization in job shop cell; D = d e m a n d for all parts in the part family (parts/yr); Dnc = demand for flow line cell (parts/yr); Disc = demand for job shop cell (parts/yr); di= demand for parts i (parts/yr); Nnc = number of flow line cells; Nj~c= number of job shop cells.
5.2. Description of methodology Table 1 lists the major steps of the cell design methodology. It is initiated after the part family and the machine grouping have been identified. A brief description of the major steps of the method follows with a short explanation. The methodology is iterative in nature. As Step 10 (Refine the Cell) is carried out, capacity checks of each proposed cellular arrangement should be repeated. This is especially important during the cell balancing task as proposed changes to the work methods can impact production rates and operation times.
67
Cell design m e t h o d o l o g y Table 1. Cell design method I. 2. 3. 4. 5. 6. 7.
9.
10.
Determine the production demand for the product family and the cell's capacity If necessary, replicate the cell. Sort the product family's demand in descending order. Identify breakpoints on a Pareto chart of the demand distribution. Identify the "High Volume Point" on the graph. Identify a Flow Line Cutoff (FLC). Review the parts above and around the FLC label. Determine the capacity of a cell that has characteristics of a flow line and allocate highvolume part numbers. Determine the capacity of a manufacturing cell that has characteristics of a job shop and allocate low-volume part numbers. Refine the layouts - Address setup/changeover requirements Balance operations - Determine scheduling, material handling, batch size Detail final layout -
-
The equations and presentation graphics discussed in the methodology were implemented in an electronic spreadsheet operating on a 486DX personal computer. This application environment was chosen because production data, equations, and presentation graphics--all necessary components of the proposed cell design methodology--can easily be implemented and modified for different cell design requirements. The cell design methodology is explained and validated through a case study of a local manufacturer of gas valves. The company was attempting to design a series of cells to assemble, test, and package their products. Table 2 depicts the process with relevant manufacturing data. The three departments were originally designed in a functional layout with each department and its operations physically separated from the others. Within each department, independent workstations were located in close proximity. The material flow proceeded in batches of parts and the batches did not follow a clearly defined path. Schedules were generated through a Manufacturing Resources Planning (MRP II) System. As a result of this configuration, work-inprocess (WlP) levels were excessive at each operation within each department. Paperwork to track material was redundant and unnecessary, being tracked after each operation and between departments. The process flow for the part family used for illustration in this work was similar for most parts in the family with the only deviations being the omission of one of the assembly operations for several part numbers in the family. What follows is a description of how the cell design methodology was applied in the analysis, replication and subsequent detailed design of the cellular layout for this family of products. 5.2.l. Step 1: determine the production demand for the family and the cell's capacity. This first task is a gross assessment of capacity requirements for the cell. Its only purpose is to allow the designer to initially determine whether the cell must be replicated or not. The demand may be based on a one- or two-year historical period or could be based on future projections. The capacity is based on a manufacturing cell layout assuming only one machine for each operation. The capacity calculation considers production rate or its reciprocal--operation time--of the bottleneck operation, available working hours, and a utilization factor that included allowances for setup time, move time, and other inefficiencies resulting from the functional layout. Equation (1) depicts this initial estimate of capacity.
Table 2. Case study process data Operaton S G I H T P
Operation description
Operation time (hr/valve)
Assemble safety nut, disc & washer Gage & assemble safety nut to valve body Assemble lower seat, stem, packing & packing nut Assemble gasket, handwheel, spring, ferrule & screw Test Package
0.0021 0.0035 0.0052 0.0034 0.0009 0.0016
Production rate (valves/hr) 484.9 280.2 191.1 291.5 1122.5 622.5
68
Richard E. Billo c = PaSWU
(1)
If operation time (Ot) is used instead of production rate, then Equation (1.1) can be substituted: C = (1/Ot)ASWU (1.1) The production demand for the family of cylinder valves was determined by reviewing a oneyear historical time period. The previous calendar year production demand for the family of 370 different valves was 844 687. Initially, it had to be determined if only one cell could meet the demand for the entire product family, or if the cell must be replicated. Application of (1) for the present case provided the following capacity for the cell: C = (191.1 parts/hr) x (7.75 hr/shift) x (2shifts/day) x (245 days/yr)(0.8) = 580562 parts/yr Utilization (U) was determined through historical records of operation utilization using the original configuration of equipment. Utilization was a metric that reflected the time available for production usage of operations. In the original configuration, due to manufacturing inefficiencies such as equipment downtime, frequent setups and changeovers, unbalanced material flow, etc., most operations did not always operate at full capacity. The assumption was made that if a cell had to process the entire part family, then U would not change significantly from the original manufacturing system. 5.2.2. Step 2: if necessary, replicate the cell. If any of the following statements are true, then replicate the cell and continue with Step 3: 1. The demand for the products in the cell is greater than the cell's capacity. 2. Even though the capacity is greater than the demand, there is an anticipated growth in the customer demand for the parts in the cell. 3. The operational advantages of grouping parts by their demand provide benefits that outweigh the lower equipment utilization that could possibly occur (i.e., shorter throughput times, better processing flexibility, higher production rates, shorter changeovers, etc.). Given an annual demand of 844 687 and a cell capacity of 580 562, the demand for the parts in the cell would have been greater than its capacity. Therefore, the cell must be replicated. 5.2.3. Step 3: sort the family's demand in descending order. This task marks the beginning of the Pareto analysis of the family. Using a commercial spreadsheet application, the cell design team can sort the product either by demand (D) or required processing time (D x Ot). Figure 2 illustrates the Pareto chart of the demand for the 370 parts comprising the family. The demand 140,000
..=
120,000 A D 100,000 ne n m 80,000 u a an I d
60,0oo 40,000 2o,ooo 0
J'"
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Parts within Cell
Fig. 2. Pareto chart of demand distribution.
,
Cell designmethodology
69
140,000 120,000 A D lOO,OOO n e n m 80,000 u a a n I d
6o,o0o
40,000 20,000
Parts within Cell
Fig. 3. Pareto chart of demand distributionfor first 27 parts. distribution varied from 125 441 parts per year to 2 parts per year. From the chart, it is obvious that the demand distribution for the parts closely resembles a Pareto distribution. 5.2.4. Step 4: identify breakpoints. Breakpoints (BP) are marked on the chart. These will be points in which two consecutive parts in the distribution can be readily distinguished from one another based upon the differences in their demands. Figure 3 illustrates a Pareto chart for the first 27 parts of the part family which caused the largest slope in the distribution. Judgments were made by the design team as to where significant breakpoints in demand occur for various products. All breakpoints in Fig. 3 occurred at the upper end of the distribution representing high-volume products. Low- and medium-volume parts tended to show a consistently smooth and shallow slope with no real breakpoints among successive parts. 5.2.5. Step 5: identify the "high volume point". Given the total demand for the part family, identify the "High Volume Point" on the graph where 80% of the demand is associated with the high volume parts. This is accomplished by summing the demand beginning at the highest volume part and ending at the part that results in over 80% of the production demand being accounted for in the grouping. Figure 3 displays the High Volume Point on the graph. 5.2.6. Step 6: identify a flow line cutoff (FLC). Identify the breakpoint that is nearest to the High Volume Point and label it "FLC". This step provides a rule to help the design team quickly identify the most appropriate breakpoint for those parts to be assigned to a flow line cell. If a distribution does not follow the 80-20 rule exactly, then the FLC serves to identify the breakpoint nearest to 80% of the volume. Figure 3 displays the FLC label for the current distribution of data. This corresponds to the point labeled BP-7. 5.2.7. Step 7: review the parts above and around the FLC label. This task often requires knowledge of the product line and the manufacturing process. Address the following questions: Do all the parts above the label have large stable demands? Should the FLC label be moved up or down in the demand distribution? Should parts below the FLC label be grouped into the cell resembling the flow line layout for a layout alternative? Thirteen of the 370 parts in the part family, approximately 3.5% of the parts, comprised the grouping above the FLC label. These 13 parts accounted for 564 847 parts per year. This was 67% of the total 844 687 parts per year for the entire part family. All of the parts above the FLC label showed a steady demand over the year. In this application, the FLC label fell on the SV-7 point. This point was over 90% higher than the next part in the distribution. Therefore, this appeared to be a good cutoff point to separate those parts which would be assigned to a flow line cell and a job shop cell. Moving the FLC label to one part lower in the production demand level would have included an odd size valve inlet which would have resulted in special material handling for the flow line. If the FLC label had been moved three parts lower in the distribution, a part that is custom-manufactured rather than built-to-stock is found which could be reflective of an unstable customer demand.
70
Richard E. Billo
5.2.8. Step 8." determine the capacity of a cell that has characteristics of a flow line and allocate high volume part numbers. This is a capacity determination for a cell that will resemble a flow line in its configuration and operation. The equation depicted in (2) represents a weighted average to account for parts with differing production rates. Because the setup requirements are expected to be much less due to the low variety of products that will be manufactured in this cell, a much higher maximum utilization factor (Unc) can be incorporated into the equation. Equation (2) illustrates capacity for a flow line cell. Cflc = ~
e i ~ Aswgflc
(2)
where i = 123... n and n = number of parts assigned to flow line cell.. Once completed, compare the capacity to the flow line grouping. If the capacity is greater than the demand, then only one flow line cell is required. Otherwise, replicate the flow line. The number of cells needed to meet the demand can be calculated in (3) below:
Nile = Onc/Vnc
(3)
Capacity for the flow line cell was calculated using (2) yielding the following result: I"(28O.2) ~1+0 (7119015. 1 ) Cflc = L
~o,¢~,¢/j ](7.75)(2)(245)(0.95)
= 750368 parts/yr
Four of the 13 parts did not require one of the assembly operations. Their annual demand was 107 105 valves. When these parts are manufactured in the cell, only two people are required to operate the cell. This resulted in a bottleneck production rate of 280.2 parts per hour. When the other nine parts were being produced, the bottleneck production rate was 191.1 parts per hour. Based on the processing of these 13 parts, the cell's capacity (Cnc) was 750 368 parts per year. Annual demand was 564 847. The original manufacturing process had an 80% utilization rate; but it also had to manufacture the entire production demand distribution. The flow line cell has far fewer parts to manufacture compared to the original process, and as a result, there are fewer changeovers and a more consistent production schedule. Based on this information, an estimate of 95% utilization was used for Unc. Implementation of (3) showed that a single flow line cell easily had the capacity to meet this demand. As stated above, as the design team begins to balance the cell, the capacity of the flow line cell should be recalculated to assess the impact of balancing on the total number of flow line cells.
5.2.9. Step 9: determine the capacity of a manufacturing cell that has characteristics of a job shop and allocate low-volume part numbers. The remaining parts, characterized as "High-Variety, Low-Volume", will be assigned to a job shop cell. Capacity calculations are also carried out for these types of cells. Most often, the capacity in a job shop cell will be lower than that in the flow line cell due to the greater frequency of setups and changeovers that must occur in these cells. Equation (4) illustrates the formula used to calculate Cm.
Cjsc=
d' ASWUjs~ -D
(4)
where i = 123...n and n = number of parts assigned to job shop cell.. The number of cells needed to meet the demand can be determined from (5) below. Njsc = Djsc/Cjsc
(5)
The remaining 357 parts were processed in a job shop cell. It combined the four assembly operations into one operation called "Assembly Complete" (A/C). Even though this action reduced the production rate for the cell, it relieved the dependency of operations. The job shop cell was required to produce a high variety of products with numerous changeovers. Capacity for the job shop cell was calculated using (4) yielding the following result:
Cell design methodology
di
279840
Cjsc = Z Pip ASWUjsc = (59.5) ~
71
(7.75)(2)(245)(0.85) = 192058 parts/yr
The cell was designed to be operated by one worker who walked to each operation in the cell. This worker was solely responsible for completing the orders for the small- to medium-volume parts. The cell was capable of being set-up for all 357 parts. The bottleneck production rate was 59.5 parts per hour. Annual demand for these parts was 279 840. However, when trying to predict the utilization factor (Ujsc) to be applied in Equation (4), improvements over the original manufacturing system were anticipated since almost 67% of the volume is being processed in a different cell. A conservative estimate of 85% for Ujsc was used in the capacity estimate. The cell's capacity was 192 058 valves per year. Application of (5) above showed that two of these cells would be required. 5.2.10. Step 10: refine the layouts. At this point, the cell design team can utilize knowledge of the products and operations themselves to complete the cell design process. For example, they may wish to reassign parts based on similarities in setups. Final decisions on such factors as the physical configuration of machines, workstation arrangements, line balance, scheduling strategies, product movement and the supplying of components to the individual cells can be better made depending on whether a cell is to operate as a job shop or a flow line. In the case study, the cutoff point between the flow line cell and the job shop cell appeared to be a logical point to distinguish between the parts in either cell, and there was no reason to move the cutoff point. The utilization levels for each type of cell were satisfactory. These levels allowed for fluctuations in demands, and provided extra time for the workers to produce other product lines. Given the allocation of parts to these cells, the remaining tasks could now better be undertaken to refine the cell layout. These tasks included the addressing of setup and changeover requirements, balancing cell operations, determining material handling methods for each cell configuration, and detailing final cell layouts. The following subsections describe how the allocation of parts to the two types of cells impacted these remaining tasks.
5.2.10.1. Address Setup And Changeover Requirements A major advantage of the proposed cell design methodology is the efficiencies obtained in the flow line cell through a reduction in the frequency and number of setups/changeovers. Setup requirements for the cells in the case study consisted of (a) the supply of the right component materials for the different part numbers manufactured in the cells, and (b) changes in carrier fixtures and test bench fixtures. Due to the difference in the two cell configurations, the frequency of tooling and fixture changes required during part changeovers in the flow line cell were minimal. However, a system was still needed to avoid mix-ups in the changeover from one part to the next. Although most orders would be completed before new ones began, there was still a possibility of having two different parts in the flow line at one time. In order to avoid mixing orders together, the first carrier containing the new products contained a card identifying the product. The change was also orally communicated within the cell, by relaying the part number, quantity and the time at which the changeover would occur. The job shop cells did not add any complications to changeovers beyond what was done in the original process. The setup time for both of these tasks was minimal; however, due to the high variety of parts flowing through these cells, setups were more frequent than the flow line cells. In the job shop cells one person assembled, tested, and packaged an entire order before a new setup was initiated. A new order began when the tooling and fixtures were changed for the carriers. At this point, the leftover components were moved back to inventory, new components were taken from inventory, and fixtures in the test benches were changed.
5.2.10.2. Balance Operations Balancing refers to the method of equalizing the work load at all the operations in a dependent process flow so as to minimize the idle time between operations [15]. Cell balancing is a simple task compared to the general line balancing problem. This is due to a much lower level
72
R i c h a r d E. Billo Table 3. Original balance of flow line cell Operation
Valves/hr
S G
484.9 280.2 191.1 291.5 1122.5 622.5
1
H T P
of complexity afforded by the fewer machines in the replicated cells. Black [13] provides guidelines for balancing manufacturing cells. The operating strategies associated with the two types of cells affected how each cell would be balanced. Because of the greater number of operators in the flow line cell and the separation of its assembly operations, balancing problems inevitably exist in this type of arrangement. In the present application, balancing was an important issue for justifying the flow line cell. If a machine or person was not performing some function on the product, then operational efficiency would suffer. Balancing labor, as opposed to machine usage, so as to minimize idle time was the main concern for the cell since the capital investment in machinery was small for the assembly, test, and package operations. The flow line production rates with no changes for balancing are shown in Table 3. If one person worked at each operation (six people in cell), the production rate for the process would have been 191.1 valves per hour and 17.6 production hours would have been lost each shift due to idle time. Only 63% of the available production hours would have been used. A better balance for the flow line cell has the operation production rates shown in Table 4. The rate for the process was 226.0 valves per hour, and only 1.6 production hours were lost each shift due to idle time. The percent of production hours improved to a satisfactory level of 95%. This option is made possible by using a U-shaped cell where the first and last operations are to be completed by the same person. Once the flow line cell was balanced, its capacity was recalculated to asses the impact of these changes on ultimate cell configuration. Balancing problems were not an issue in the job shop cell since only one person worked in the cell. This provided flexibility not easily afforded in the flow line cell. This flexibility was used to perform various special operations required for the high variety parts.
5.2.10.3. Determine Scheduling, Material Handling Method, And Batch S&es Major differences in cell scheduling can result from the two types of cell configuration. Because of the high, steady demand of a flow line cell, a simple mixed model schedule can be applied to both schedule and sequence parts within this configuration. However, the low, sporadic nature of parts within the job shop cell will require an appropriate group scheduling heuristic. With respect to material movement within a cell, Black [13] recommends pull production methods to minimize the WIP levels and to gain the additional factors of improved quality and reduced throughput time. To gain further reductions in product throughput time, a common technique is to reduce batch sizes of parts. In the case study, carrier size used in the flow line cell was reduced from 40 valves to 20 valves. A 20 valve carrier was chosen as a compromise to control WIP levels and still maintain the high production rates. In the job shop cell, the carrier size was maintained at 40 valves for those work orders that were greater than or equal to 40 valves. Smaller work orders still used the 40 valve carrier, but a single carrier only contained one work order. Table 4. Proposed balance for flow line cell Operation S&P G I H&T
Valves/hr 272.6 228.5 226 231.4
Cell design methodology
[~
---l~Pallet Lift
!1
---I~-Ball Conveyor
73
Stack of Incoming Valve Bodies
IHII]--
Roller Conveyor
Workbench Cell Worker Stack of Common Carriers Fig. 4. Physical layout of flow line cell.
5.2.10.4. Detail Cell Layouts The final step in the generalized cell design methodology is the detailing of the cell layouts. In this step, final equipment selection and placement decisions are made with the criteria of easing material handling and minimizing throughput time through the cell. As an example, the layouts for the flow line cell and the job shop cells in the case study are illustrated in Figs 4 and 5. In summary, the flow line cell was designed to improve the throughput time for the valves over the original layout. This cell was expected to produce a small number of parts but their demands were large and steady. The cell was to be operated by four workers. Two of the workers had to perform two operations thus necessitating a U-shaped serial configuration to keep operations close to one another. A common carrier was designed to move product through the cell. Roller conveyors were placed between operations to assist in the movement of material. The job shop cell was designed to maximize the flexibility required to produce 357 different valves. The cell did not resemble a job shop in all aspects since WlP was kept to a minimum and successive operations were kept in close proximity. Instead, the flexibility was maintained by assigning one worker to complete all functions (excluding the supplying of components to the cell) necessary to operate the cell. This was important with the numerous changeovers required within the cell. It was much easier for one person, rather than four people, to coordinate numerous changeovers. Besides changeover benefits, process flexibility was also enhanced by the fact that small orders would have short throughput times which allotted more time to the larger orders. Material movement was kept to a minimum in the cell since one worker was required to walk to each operation. Buffers between operations were not required. All.the assembly operations were performed on an Assembly Complete bench. This arrangement had a carrier fixture that
74
Richard E. Billo
$$ 0
[~
---l,,-PalletLift
~
~Workbench Q ~
i
@
m
Stackof Incoming ValveBodies RollerConveyor
CellWorker
Fig. 5. Physical layout of job shop cells.
held 40 valves at a time while the operations were performed. The test bench was reduced in size from that which was used in the flow line cell since it was required to hold a fewer number of valves. Along with a workbench, a conveyor was provided for operation "P", and the length of the conveyor allowed for one shifts worth of work to be stored before loading to a pallet.
6. R E S U L T S
To verify the usefulness of the manufacturing cells, metrics from the original facility were compared to those of the manufacturing cells. Table 5 shows some of the benefits resulting from this analysis. As can be seen from the table, the cells show the expected improvements in the metrics by which the factory is operated, including improvements in throughput time, WIP levels, and productivity per assembly operator. What is significant for the current discussion is the improvement in throughput time in the flow line cell versus the job shop cells. Due to the fewer number of setups and changeovers as well as the smaller carrier size, the flow line cell had a throughput time five minutes shorter per part than the job shop cell, thus validating the usefulness of the approach.
Table 5. Comparison of original layout with cellular configuration
Productivity/person WIP Throughput time
Original layout
Cells
81 848 valves/yr 12 858 valves 7 days
99 953 valves/yr 260 valves Flow line: 40 min Job shop: 44.7 min
Cell design methodology
75
7. CONCLUSIONS
Traditionally, a cellular design is only concerned with grouping products according to their physical characteristics or processing requirements, although of late there has been an increased interest in taking into consideration various cost factors. If these were the only grouping parameters, the same cell would have been used to make both a part with demand over 100 000 per year and one requiring less than 100 per year. The result would have been less predictable lead times and an ineffective use of labor. It was shown here that using manufacturing parameters for further grouping of parts can have substantial improvements in operational efficiency. Large orders for the high-demand products are processed in a different cell than the low-demand products. They are no longer hindrances to one another in the process flow. The operational needs for each part can be addressed by the different cellular layouts. The high-volume parts show a large customer need, and the large order sizes that ensue can result in longer throughput times. However, by producing these parts in cells operating more as flow lines, throughput time can be reduced. The lowto medium-volume parts put large strains on the manufacturing process. The process must be able to adapt to the individual needs of each part, and this is made difficult by the large variety of these parts. They require the flexibility and labor skills found in a job shop. By integrating manufacturing parameters such as demand and processing time into cell design and replication, the benefits specifically associated with these two layout options can be improved. To date, the cell design methodology described in this work has been successfully employed for development of at least 16 manufacturing cells in five different discrete parts manufacturing industries over a three year period. These applications have provided the author many opportunities to improve and validate the methodology described in this work. At the author's University affiliation, it has been incorporated into the industrial and manufacturing systems engineering courses. The methodology has received additional usage as our engineering graduates have applied it to their places of employment. REFERENCES 1. Apple, J. M. Plant Layout and Material Handling. Wiley, New York, 1977. 2. Cleland, D. 1. and Bidanda, B., The Automated Factor)" Handbook. Technologies and Management. TAB Professional and Reference Books, Blue Ridge Summit, PA, 1990. 3. King, J. R. and Nakornchai, V. Machine-component group formation in group technology: review and extension. International Journal of Production Research, 20, 1982, 117-133. 4. Chandrasekharan, M. P. and Rajagopalan, R. Zodiac: an algorithm for concurrent formulation of part-families and machine-cells. International Journal of Production Research, 25, 1987, 835-850. 5. Kusiak, A. and Chow, W. S. Efficient solving of the group technology problem. Journal of Manufacturing Systems, 6, 1987, 117-124. 6. Gupta, T. and Seifoddini, H. Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system. International Journal of Production Research, 28(7), 1990, 1247. 7. Srinivasan, G. and Narendran, T. T. Grafics: a nonhierarchical clustering algorithm for group technology. International Journal of Production Research, 29, 1991, 463-478. 8. Kumar, K. R. and Vannelli, A. Strategic subcontracting for efficient disaggregated manufacturing. International Journal of Production Research, 25, 1987, 1715-1728. 9. Askin, R. G. and Chiu, K. S. A graph partitioning procedure for machine assignment and cell formation in group technology. International Journal of Production Research, 28, 1990, 1555-1572. 10. Joines, J., Culbreth, C. T. and King, R. E., Manufacturing cell design using an integer-based genetic algorithm. In Flexible Automation and Intelligent Manufacturing, ed. R. D. Schraft, M. M. Ahmad, W. G. Sullivan and H. F. Jacobi. Begell House, New York, 1995. 11. Kazerooni, M., Luong, L. H. S. and Abhary, K. Cell formation using genetic algorithms. In Flexible Automation and Intelligent Manufacturing, ed. R. D. Schraft, M. M. Ahmad, W. G. Sullivan and H. F. Jacobi. Begell House, New York, 1995. 12. Tompkins, J. A. and White, J. A. Facilities Planning. Wiley, New York, 1984. 13. Black, J. T. The Design of the Factory with a Future. McGraw-Hill, New York, 1991. 14. Wemmerlov, U. and Vakharia, A. J. On the impact of family scheduling procedures, liE Transactions, 25(4), 1993, 102. 15. Salvendy, G. (ed.), Handbook oflndustrial Engineering. Wiley, New York, 1982.
A DESIGN
Computers ind. Engng Vol. 34, No. 1, pp. 63-75, 1998 © 1998 Published by Elsevier Science Ltd. All rights reserved PII: S0360-8352(97)00151-4 Printed in Great Britain 0360-8352/98 $19.00 + 0.00
METHODOLOGY FOR CONFIGURATION MANUFACTURING CELLS
OF
RICHARD E. BILLO Department of Industrial Engineering, 1037 Benedum Hall, University of Pittsburgh, Pittsburgh, PA 15 261, U.S.A. Abstract--This paper presents a general design methodology for manufacturing cells. The approach makes use of the observation that 85% of the production demand of a manufacturing facility can be attributed to 15% of the products manufactured in the facility. This logic was extended to manufacturing cell design. Specifically, within a part family, those parts that have a high steady demand should be placed in cells that are configured and operated similar to a flow line. Those part numbers within the family that have little demand should be assigned to cells designed to operate more as a job shop. In this way, a manufacturing cell that is designed to serve both the high and low demand components will not be impeded by imposed constraints resulting from demand or processing time considerations for individual parts within the family. The author presents a ten step approach for analysis and design of such cells after initial machine-component groupings have been formed. © 1998 Published by Elsevier Science Ltd. All rights reserved.
1. INTRODUCTION
Cellular Manufacturing is manufacturing based on groups of processes, people and machines to produce a specific family of products with similar manufacturing characteristics [1]. Manufacturing cells have shown benefits in reducing throughput times, making the manufacturing system more flexible, and improving quality [2]. A review of the literature in cellular manufacturing reveals that the majority of research focuses on the cell formation process. Array-based methods [3-5], cluster analysis methods [6, 7], graph theoretic methods [8,9], and mathematical programming methods [10, 11] have all been developed to group parts and machines into cellular configurations. Although much progress has been made in these cell formation techniques, they represent only the beginnings of the cell configuration process, and are not sufficient for the design of a robust cellular manufacturing system. Once initial machine-component groupings are identified, the design team is still faced with a myriad of other decisions including number of cells, number of machines in each cell, physical layout, material movement strategy, setup/changeover design, cell balancing, and scheduling. 2. PARETO'S LAW AND FACILITIES PLANNING
Many factors can influence the configuration of a manufacturing facility, and ultimately the efficiency of its operation. These may include product variety, operation times, production demand, process costs, number of moves, and so forth. For example, Tompkins and White [12] observed that Pareto's law often applies to the product mix of a facility. They found that 85% of the production demand can be attributed to 15% of the products manufactured in the facility. They then concluded that the facilities plan should consist of a mass production line for the 15% of high-volume items and a job shop arrangement for the remaining 85% of the product mix. These conclusions were based on the common knowledge that a flow line layout is most appropriate for parts with high demand and low variety, while a functional arrangement is best suited for a product mix characterized by high variety and low demand. This logic can be extended to manufacturing cell design. For example, Fig. 1 illustrates a comparison of different kinds of manufacturing systems based on their production rate and product variety [13]. From this figure, the reader may notice that the operational coverage of manufacturing cells overlaps portions of both job shop and flow line configurations. The high variety constraint associated with the job shop is eliminated through the usage of the manufacturing 63
64
Richard E. Billo
m~ volume 1000
100
Production rates
Parts per Hour
10
Low
volume
1
1
10
100
1000
10000
Variety (Number of different parts per system) Fig. 1. Comparison of different kinds of manufacturing systems with ceils [13].
cell with its similar process flow feature allowing operational efficiencies to take place. However, variation in production demand within a cell is typically not controlled through the application of manufacturing cells. Some parts within the family may have high, consistent demand, while others have low, sporadic demand requirements. Knowledge of this constraint at the outset of a manufacturing cell design project can positively impact many succeeding cellular design considerations including setup/changeover strategies, scheduling, cell replication, and so forth. 3. RESEARCH PURPOSE
The purpose of this research was to develop a general methodology for cell design. The methodology employs common manufacturing data such as production demand, production rates, and capacity, all of which are deemed critical for making further decisions on cellular configuration. Production demand refers to the volume of units required to meet the market's requirements over a given time period. Production rate is the number of parts per hour that can be completed at the bottleneck operation in a manufacturing cell. Capacity is the total production time available in a manufacturing cell as measured from the bottleneck operation in the cell. By formally and systematically utilizing such additional production information in the cell design process, designers can achieve greater efficiencies in cellular operation. Within a part family, better cell operation can occur when parts are further grouped and cells replicated according to similar production demand or processing time requirements. Demand for individual parts within a family can provide insight into the most appropriate layout and operation of cells. High-demand products may need a cellular layout operating like a flow line, while medium- and low-demand parts may need a layout that is also cellular in structure, but operate more like a job shop. Although the physical arrangement of the cells would remain in the classic " U " or "C" type serial configuration commonly associated with manufacturing cells, operation of the cells may be quite different depending on the product mix. By analyzing the production demand or processing time requirements for each part within the part family, cells can be designed to operate
Cell designmethodology
65
in a manner that would gain the greatest efficiencies. For example, manufacturing cells designed to operate as a flow line would tend to have a smaller product mix with high volumes. In this arrangement, fewer setups and changeovers would be required and production could be controlled through a pull manufacturing technique and a simple mixed model schedule. In contrast, those cells designed to operate as a job shop may have a lower throughput rate due to the need for more frequent setups. Product through these configurations may best be scheduled using a group scheduling heuristic [14]. 4. PREMISESOF A CELLDESIGN METHODOLOGY The methodology to be described in this research is based on three underlying assumptions: 1. More than one cell per part family may be needed to satisfy demand. We have termed this situation as cell replication. 2. Pareto analysis of demand versus product mix for a part family shows distinct cutoff points justifying different cell strategies. 3. There are operational advantages for configuring and operating cells as flow lines or job shops.
4.1. The need for cell replication Once initial part and machine groupings are formulated, the design team must determine whether there is a need to replicate the cells. Based upon the bottleneck operation, defined here as the operation within the cell with the lowest production rate (or its reciprocal--longest operation time), the capacity of the original cell must be determined and compared against the demand for the parts in the cell.
4.2. Pareto analysis for cells Once it is determined that the cell should be replicated to meet overall demand requirements of the part family, the design team must determine the expected distribution of production demand for the part family. It would be naive to believe that all demand distributions would result in a perfect match with a Pareto distribution. Even so, any analysis, whether Pareto distributions result or not, can provide additional insight into an effective means for assigning parts to replicated cells. A Pareto analysis is typically used as an "aid" in identifying groups and not as an inflexible set of rules. The Pareto distribution identifies the following categories according to the 80-20 rule: (a) the low-mix, high-volume parts, and (b) the medium-to-high-mix, low-volume parts. The first category represents 15-20% of the part numbers in the product family that constitute 80-85% of the demand of the family. The second category represents the remaining 80-85% of the part numbers that constitute only a very small portion of the demand for the product family. In the present study, distribution of demand for individual part numbers in a family was analyzed, although total processing time for a part family may be easily substituted. Demand for individual part numbers was first sorted in descending order, then graphed. The design team determines points on the graph where the distribution significantly fluctuates from a smooth distribution. Given these points and known capacity constraints of the cell, the design team may gain a better understanding of the number of cells needed, the most appropriate assignment of individual part numbers to cells and the operating strategy of the cell.
4.3. Operational advantages of different configurations The third premise for the cell design methodology is that the production demand for a part reveals operational advantages for the cell configuration problem. When a Pareto chart reveals significant differences among part numbers based on differences in their demand and/or their total processing time (demand × operation time), then further grouping of parts into replicated cells can result in an opportune production system.
66
R i c h a r d E. Billo
The high-volume parts constitute the commodity items for which there is a large steady demand. The market in which these products compete are generally established markets. Manufacturing cost is a n important performance metric for these products. The current production system may have hindered these products from reaching their lowest cost potential by not allowing them to have a continuous, uninterrupted flow. Grouping these parts into a replicated cell that operates as a flow line allows many improvements to be obtained. Consistent and repetitive production schedules can be delivered through simple mixed model scheduling techniques. In addition, more effective product movement techniques such as pull production can be employed, changeovers or setups need occur less frequently, inventories can be stabilized, throughput times can be shortened, and a low manufacturing cost per unit can be achieved. Finally, mechanization or automation is more feasible for these products. This is due to the fact that shorter throughput times, less direct labor, fewer product variations, and a relatively small number of changeovers provide an easier justification for automation and mechanization on these high-volume products. The low- and medium-volume parts tend to be special order items for which the demand is typically unsteady. The low- and medium-volume parts have generally resulted from a proliferation of a product line into niche markets. These parts usually result from the need to meet the needs of a large, varied customer base. Batch processing using a functional layout has the flexible processing characteristics to efficiently manufacture these products. Manufacturing operations or machines in a job shop have the capability to make a variety of products, and machines are less dependent on one another. However, in a job shop, operations are typically separated by large distances causing long throughput times and excess inventory. A cell that provides a continuous flow of material through machines co-located can help overcome the shortcomings of a functional layout by reducing move time, and simplifying scheduling of machines through usage of a group scheduling system. 5. C E L L D E S I G N
METHODOLOGY
In this section, the method for cell design is described along with definitions of terms used for capacity and cell requirement calculations. The methodology incorporates several common manufacturing attributes such as demand, production rate, operation time, utilization, and capacity.
5.1. Definition of terms C = capacity of original cell (parts/yr); Cnc = capacity of flow line cell (parts/yr); Cisc = capacity of job shop cell (parts/yr); P = production rate of original cell (parts/hr); Ot = operation time of original cell (parts/hr); Pi= production rate for part i (parts/hr); A = available hours (hours/shift); S = number of shifts (shifts/day); W = number of available workdays; U = utilization factor due to manufacturing inefficiencies; Unc = utilization in flow line cell; Uj~ = utilization in job shop cell; D = d e m a n d for all parts in the part family (parts/yr); Dnc = demand for flow line cell (parts/yr); Disc = demand for job shop cell (parts/yr); di= demand for parts i (parts/yr); Nnc = number of flow line cells; Nj~c= number of job shop cells.
5.2. Description of methodology Table 1 lists the major steps of the cell design methodology. It is initiated after the part family and the machine grouping have been identified. A brief description of the major steps of the method follows with a short explanation. The methodology is iterative in nature. As Step 10 (Refine the Cell) is carried out, capacity checks of each proposed cellular arrangement should be repeated. This is especially important during the cell balancing task as proposed changes to the work methods can impact production rates and operation times.
67
Cell design m e t h o d o l o g y Table 1. Cell design method I. 2. 3. 4. 5. 6. 7.
9.
10.
Determine the production demand for the product family and the cell's capacity If necessary, replicate the cell. Sort the product family's demand in descending order. Identify breakpoints on a Pareto chart of the demand distribution. Identify the "High Volume Point" on the graph. Identify a Flow Line Cutoff (FLC). Review the parts above and around the FLC label. Determine the capacity of a cell that has characteristics of a flow line and allocate highvolume part numbers. Determine the capacity of a manufacturing cell that has characteristics of a job shop and allocate low-volume part numbers. Refine the layouts - Address setup/changeover requirements Balance operations - Determine scheduling, material handling, batch size Detail final layout -
-
The equations and presentation graphics discussed in the methodology were implemented in an electronic spreadsheet operating on a 486DX personal computer. This application environment was chosen because production data, equations, and presentation graphics--all necessary components of the proposed cell design methodology--can easily be implemented and modified for different cell design requirements. The cell design methodology is explained and validated through a case study of a local manufacturer of gas valves. The company was attempting to design a series of cells to assemble, test, and package their products. Table 2 depicts the process with relevant manufacturing data. The three departments were originally designed in a functional layout with each department and its operations physically separated from the others. Within each department, independent workstations were located in close proximity. The material flow proceeded in batches of parts and the batches did not follow a clearly defined path. Schedules were generated through a Manufacturing Resources Planning (MRP II) System. As a result of this configuration, work-inprocess (WlP) levels were excessive at each operation within each department. Paperwork to track material was redundant and unnecessary, being tracked after each operation and between departments. The process flow for the part family used for illustration in this work was similar for most parts in the family with the only deviations being the omission of one of the assembly operations for several part numbers in the family. What follows is a description of how the cell design methodology was applied in the analysis, replication and subsequent detailed design of the cellular layout for this family of products. 5.2.l. Step 1: determine the production demand for the family and the cell's capacity. This first task is a gross assessment of capacity requirements for the cell. Its only purpose is to allow the designer to initially determine whether the cell must be replicated or not. The demand may be based on a one- or two-year historical period or could be based on future projections. The capacity is based on a manufacturing cell layout assuming only one machine for each operation. The capacity calculation considers production rate or its reciprocal--operation time--of the bottleneck operation, available working hours, and a utilization factor that included allowances for setup time, move time, and other inefficiencies resulting from the functional layout. Equation (1) depicts this initial estimate of capacity.
Table 2. Case study process data Operaton S G I H T P
Operation description
Operation time (hr/valve)
Assemble safety nut, disc & washer Gage & assemble safety nut to valve body Assemble lower seat, stem, packing & packing nut Assemble gasket, handwheel, spring, ferrule & screw Test Package
0.0021 0.0035 0.0052 0.0034 0.0009 0.0016
Production rate (valves/hr) 484.9 280.2 191.1 291.5 1122.5 622.5
68
Richard E. Billo c = PaSWU
(1)
If operation time (Ot) is used instead of production rate, then Equation (1.1) can be substituted: C = (1/Ot)ASWU (1.1) The production demand for the family of cylinder valves was determined by reviewing a oneyear historical time period. The previous calendar year production demand for the family of 370 different valves was 844 687. Initially, it had to be determined if only one cell could meet the demand for the entire product family, or if the cell must be replicated. Application of (1) for the present case provided the following capacity for the cell: C = (191.1 parts/hr) x (7.75 hr/shift) x (2shifts/day) x (245 days/yr)(0.8) = 580562 parts/yr Utilization (U) was determined through historical records of operation utilization using the original configuration of equipment. Utilization was a metric that reflected the time available for production usage of operations. In the original configuration, due to manufacturing inefficiencies such as equipment downtime, frequent setups and changeovers, unbalanced material flow, etc., most operations did not always operate at full capacity. The assumption was made that if a cell had to process the entire part family, then U would not change significantly from the original manufacturing system. 5.2.2. Step 2: if necessary, replicate the cell. If any of the following statements are true, then replicate the cell and continue with Step 3: 1. The demand for the products in the cell is greater than the cell's capacity. 2. Even though the capacity is greater than the demand, there is an anticipated growth in the customer demand for the parts in the cell. 3. The operational advantages of grouping parts by their demand provide benefits that outweigh the lower equipment utilization that could possibly occur (i.e., shorter throughput times, better processing flexibility, higher production rates, shorter changeovers, etc.). Given an annual demand of 844 687 and a cell capacity of 580 562, the demand for the parts in the cell would have been greater than its capacity. Therefore, the cell must be replicated. 5.2.3. Step 3: sort the family's demand in descending order. This task marks the beginning of the Pareto analysis of the family. Using a commercial spreadsheet application, the cell design team can sort the product either by demand (D) or required processing time (D x Ot). Figure 2 illustrates the Pareto chart of the demand for the 370 parts comprising the family. The demand 140,000
..=
120,000 A D 100,000 ne n m 80,000 u a an I d
60,0oo 40,000 2o,ooo 0
J'"
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Parts within Cell
Fig. 2. Pareto chart of demand distribution.
,
Cell designmethodology
69
140,000 120,000 A D lOO,OOO n e n m 80,000 u a a n I d
6o,o0o
40,000 20,000
Parts within Cell
Fig. 3. Pareto chart of demand distributionfor first 27 parts. distribution varied from 125 441 parts per year to 2 parts per year. From the chart, it is obvious that the demand distribution for the parts closely resembles a Pareto distribution. 5.2.4. Step 4: identify breakpoints. Breakpoints (BP) are marked on the chart. These will be points in which two consecutive parts in the distribution can be readily distinguished from one another based upon the differences in their demands. Figure 3 illustrates a Pareto chart for the first 27 parts of the part family which caused the largest slope in the distribution. Judgments were made by the design team as to where significant breakpoints in demand occur for various products. All breakpoints in Fig. 3 occurred at the upper end of the distribution representing high-volume products. Low- and medium-volume parts tended to show a consistently smooth and shallow slope with no real breakpoints among successive parts. 5.2.5. Step 5: identify the "high volume point". Given the total demand for the part family, identify the "High Volume Point" on the graph where 80% of the demand is associated with the high volume parts. This is accomplished by summing the demand beginning at the highest volume part and ending at the part that results in over 80% of the production demand being accounted for in the grouping. Figure 3 displays the High Volume Point on the graph. 5.2.6. Step 6: identify a flow line cutoff (FLC). Identify the breakpoint that is nearest to the High Volume Point and label it "FLC". This step provides a rule to help the design team quickly identify the most appropriate breakpoint for those parts to be assigned to a flow line cell. If a distribution does not follow the 80-20 rule exactly, then the FLC serves to identify the breakpoint nearest to 80% of the volume. Figure 3 displays the FLC label for the current distribution of data. This corresponds to the point labeled BP-7. 5.2.7. Step 7: review the parts above and around the FLC label. This task often requires knowledge of the product line and the manufacturing process. Address the following questions: Do all the parts above the label have large stable demands? Should the FLC label be moved up or down in the demand distribution? Should parts below the FLC label be grouped into the cell resembling the flow line layout for a layout alternative? Thirteen of the 370 parts in the part family, approximately 3.5% of the parts, comprised the grouping above the FLC label. These 13 parts accounted for 564 847 parts per year. This was 67% of the total 844 687 parts per year for the entire part family. All of the parts above the FLC label showed a steady demand over the year. In this application, the FLC label fell on the SV-7 point. This point was over 90% higher than the next part in the distribution. Therefore, this appeared to be a good cutoff point to separate those parts which would be assigned to a flow line cell and a job shop cell. Moving the FLC label to one part lower in the production demand level would have included an odd size valve inlet which would have resulted in special material handling for the flow line. If the FLC label had been moved three parts lower in the distribution, a part that is custom-manufactured rather than built-to-stock is found which could be reflective of an unstable customer demand.
70
Richard E. Billo
5.2.8. Step 8." determine the capacity of a cell that has characteristics of a flow line and allocate high volume part numbers. This is a capacity determination for a cell that will resemble a flow line in its configuration and operation. The equation depicted in (2) represents a weighted average to account for parts with differing production rates. Because the setup requirements are expected to be much less due to the low variety of products that will be manufactured in this cell, a much higher maximum utilization factor (Unc) can be incorporated into the equation. Equation (2) illustrates capacity for a flow line cell. Cflc = ~
e i ~ Aswgflc
(2)
where i = 123... n and n = number of parts assigned to flow line cell.. Once completed, compare the capacity to the flow line grouping. If the capacity is greater than the demand, then only one flow line cell is required. Otherwise, replicate the flow line. The number of cells needed to meet the demand can be calculated in (3) below:
Nile = Onc/Vnc
(3)
Capacity for the flow line cell was calculated using (2) yielding the following result: I"(28O.2) ~1+0 (7119015. 1 ) Cflc = L
~o,¢~,¢/j ](7.75)(2)(245)(0.95)
= 750368 parts/yr
Four of the 13 parts did not require one of the assembly operations. Their annual demand was 107 105 valves. When these parts are manufactured in the cell, only two people are required to operate the cell. This resulted in a bottleneck production rate of 280.2 parts per hour. When the other nine parts were being produced, the bottleneck production rate was 191.1 parts per hour. Based on the processing of these 13 parts, the cell's capacity (Cnc) was 750 368 parts per year. Annual demand was 564 847. The original manufacturing process had an 80% utilization rate; but it also had to manufacture the entire production demand distribution. The flow line cell has far fewer parts to manufacture compared to the original process, and as a result, there are fewer changeovers and a more consistent production schedule. Based on this information, an estimate of 95% utilization was used for Unc. Implementation of (3) showed that a single flow line cell easily had the capacity to meet this demand. As stated above, as the design team begins to balance the cell, the capacity of the flow line cell should be recalculated to assess the impact of balancing on the total number of flow line cells.
5.2.9. Step 9: determine the capacity of a manufacturing cell that has characteristics of a job shop and allocate low-volume part numbers. The remaining parts, characterized as "High-Variety, Low-Volume", will be assigned to a job shop cell. Capacity calculations are also carried out for these types of cells. Most often, the capacity in a job shop cell will be lower than that in the flow line cell due to the greater frequency of setups and changeovers that must occur in these cells. Equation (4) illustrates the formula used to calculate Cm.
Cjsc=
d' ASWUjs~ -D
(4)
where i = 123...n and n = number of parts assigned to job shop cell.. The number of cells needed to meet the demand can be determined from (5) below. Njsc = Djsc/Cjsc
(5)
The remaining 357 parts were processed in a job shop cell. It combined the four assembly operations into one operation called "Assembly Complete" (A/C). Even though this action reduced the production rate for the cell, it relieved the dependency of operations. The job shop cell was required to produce a high variety of products with numerous changeovers. Capacity for the job shop cell was calculated using (4) yielding the following result:
Cell design methodology
di
279840
Cjsc = Z Pip ASWUjsc = (59.5) ~
71
(7.75)(2)(245)(0.85) = 192058 parts/yr
The cell was designed to be operated by one worker who walked to each operation in the cell. This worker was solely responsible for completing the orders for the small- to medium-volume parts. The cell was capable of being set-up for all 357 parts. The bottleneck production rate was 59.5 parts per hour. Annual demand for these parts was 279 840. However, when trying to predict the utilization factor (Ujsc) to be applied in Equation (4), improvements over the original manufacturing system were anticipated since almost 67% of the volume is being processed in a different cell. A conservative estimate of 85% for Ujsc was used in the capacity estimate. The cell's capacity was 192 058 valves per year. Application of (5) above showed that two of these cells would be required. 5.2.10. Step 10: refine the layouts. At this point, the cell design team can utilize knowledge of the products and operations themselves to complete the cell design process. For example, they may wish to reassign parts based on similarities in setups. Final decisions on such factors as the physical configuration of machines, workstation arrangements, line balance, scheduling strategies, product movement and the supplying of components to the individual cells can be better made depending on whether a cell is to operate as a job shop or a flow line. In the case study, the cutoff point between the flow line cell and the job shop cell appeared to be a logical point to distinguish between the parts in either cell, and there was no reason to move the cutoff point. The utilization levels for each type of cell were satisfactory. These levels allowed for fluctuations in demands, and provided extra time for the workers to produce other product lines. Given the allocation of parts to these cells, the remaining tasks could now better be undertaken to refine the cell layout. These tasks included the addressing of setup and changeover requirements, balancing cell operations, determining material handling methods for each cell configuration, and detailing final cell layouts. The following subsections describe how the allocation of parts to the two types of cells impacted these remaining tasks.
5.2.10.1. Address Setup And Changeover Requirements A major advantage of the proposed cell design methodology is the efficiencies obtained in the flow line cell through a reduction in the frequency and number of setups/changeovers. Setup requirements for the cells in the case study consisted of (a) the supply of the right component materials for the different part numbers manufactured in the cells, and (b) changes in carrier fixtures and test bench fixtures. Due to the difference in the two cell configurations, the frequency of tooling and fixture changes required during part changeovers in the flow line cell were minimal. However, a system was still needed to avoid mix-ups in the changeover from one part to the next. Although most orders would be completed before new ones began, there was still a possibility of having two different parts in the flow line at one time. In order to avoid mixing orders together, the first carrier containing the new products contained a card identifying the product. The change was also orally communicated within the cell, by relaying the part number, quantity and the time at which the changeover would occur. The job shop cells did not add any complications to changeovers beyond what was done in the original process. The setup time for both of these tasks was minimal; however, due to the high variety of parts flowing through these cells, setups were more frequent than the flow line cells. In the job shop cells one person assembled, tested, and packaged an entire order before a new setup was initiated. A new order began when the tooling and fixtures were changed for the carriers. At this point, the leftover components were moved back to inventory, new components were taken from inventory, and fixtures in the test benches were changed.
5.2.10.2. Balance Operations Balancing refers to the method of equalizing the work load at all the operations in a dependent process flow so as to minimize the idle time between operations [15]. Cell balancing is a simple task compared to the general line balancing problem. This is due to a much lower level
72
R i c h a r d E. Billo Table 3. Original balance of flow line cell Operation
Valves/hr
S G
484.9 280.2 191.1 291.5 1122.5 622.5
1
H T P
of complexity afforded by the fewer machines in the replicated cells. Black [13] provides guidelines for balancing manufacturing cells. The operating strategies associated with the two types of cells affected how each cell would be balanced. Because of the greater number of operators in the flow line cell and the separation of its assembly operations, balancing problems inevitably exist in this type of arrangement. In the present application, balancing was an important issue for justifying the flow line cell. If a machine or person was not performing some function on the product, then operational efficiency would suffer. Balancing labor, as opposed to machine usage, so as to minimize idle time was the main concern for the cell since the capital investment in machinery was small for the assembly, test, and package operations. The flow line production rates with no changes for balancing are shown in Table 3. If one person worked at each operation (six people in cell), the production rate for the process would have been 191.1 valves per hour and 17.6 production hours would have been lost each shift due to idle time. Only 63% of the available production hours would have been used. A better balance for the flow line cell has the operation production rates shown in Table 4. The rate for the process was 226.0 valves per hour, and only 1.6 production hours were lost each shift due to idle time. The percent of production hours improved to a satisfactory level of 95%. This option is made possible by using a U-shaped cell where the first and last operations are to be completed by the same person. Once the flow line cell was balanced, its capacity was recalculated to asses the impact of these changes on ultimate cell configuration. Balancing problems were not an issue in the job shop cell since only one person worked in the cell. This provided flexibility not easily afforded in the flow line cell. This flexibility was used to perform various special operations required for the high variety parts.
5.2.10.3. Determine Scheduling, Material Handling Method, And Batch S&es Major differences in cell scheduling can result from the two types of cell configuration. Because of the high, steady demand of a flow line cell, a simple mixed model schedule can be applied to both schedule and sequence parts within this configuration. However, the low, sporadic nature of parts within the job shop cell will require an appropriate group scheduling heuristic. With respect to material movement within a cell, Black [13] recommends pull production methods to minimize the WIP levels and to gain the additional factors of improved quality and reduced throughput time. To gain further reductions in product throughput time, a common technique is to reduce batch sizes of parts. In the case study, carrier size used in the flow line cell was reduced from 40 valves to 20 valves. A 20 valve carrier was chosen as a compromise to control WIP levels and still maintain the high production rates. In the job shop cell, the carrier size was maintained at 40 valves for those work orders that were greater than or equal to 40 valves. Smaller work orders still used the 40 valve carrier, but a single carrier only contained one work order. Table 4. Proposed balance for flow line cell Operation S&P G I H&T
Valves/hr 272.6 228.5 226 231.4
Cell design methodology
[~
---l~Pallet Lift
!1
---I~-Ball Conveyor
73
Stack of Incoming Valve Bodies
IHII]--
Roller Conveyor
Workbench Cell Worker Stack of Common Carriers Fig. 4. Physical layout of flow line cell.
5.2.10.4. Detail Cell Layouts The final step in the generalized cell design methodology is the detailing of the cell layouts. In this step, final equipment selection and placement decisions are made with the criteria of easing material handling and minimizing throughput time through the cell. As an example, the layouts for the flow line cell and the job shop cells in the case study are illustrated in Figs 4 and 5. In summary, the flow line cell was designed to improve the throughput time for the valves over the original layout. This cell was expected to produce a small number of parts but their demands were large and steady. The cell was to be operated by four workers. Two of the workers had to perform two operations thus necessitating a U-shaped serial configuration to keep operations close to one another. A common carrier was designed to move product through the cell. Roller conveyors were placed between operations to assist in the movement of material. The job shop cell was designed to maximize the flexibility required to produce 357 different valves. The cell did not resemble a job shop in all aspects since WlP was kept to a minimum and successive operations were kept in close proximity. Instead, the flexibility was maintained by assigning one worker to complete all functions (excluding the supplying of components to the cell) necessary to operate the cell. This was important with the numerous changeovers required within the cell. It was much easier for one person, rather than four people, to coordinate numerous changeovers. Besides changeover benefits, process flexibility was also enhanced by the fact that small orders would have short throughput times which allotted more time to the larger orders. Material movement was kept to a minimum in the cell since one worker was required to walk to each operation. Buffers between operations were not required. All.the assembly operations were performed on an Assembly Complete bench. This arrangement had a carrier fixture that
74
Richard E. Billo
$$ 0
[~
---l,,-PalletLift
~
~Workbench Q ~
i
@
m
Stackof Incoming ValveBodies RollerConveyor
CellWorker
Fig. 5. Physical layout of job shop cells.
held 40 valves at a time while the operations were performed. The test bench was reduced in size from that which was used in the flow line cell since it was required to hold a fewer number of valves. Along with a workbench, a conveyor was provided for operation "P", and the length of the conveyor allowed for one shifts worth of work to be stored before loading to a pallet.
6. R E S U L T S
To verify the usefulness of the manufacturing cells, metrics from the original facility were compared to those of the manufacturing cells. Table 5 shows some of the benefits resulting from this analysis. As can be seen from the table, the cells show the expected improvements in the metrics by which the factory is operated, including improvements in throughput time, WIP levels, and productivity per assembly operator. What is significant for the current discussion is the improvement in throughput time in the flow line cell versus the job shop cells. Due to the fewer number of setups and changeovers as well as the smaller carrier size, the flow line cell had a throughput time five minutes shorter per part than the job shop cell, thus validating the usefulness of the approach.
Table 5. Comparison of original layout with cellular configuration
Productivity/person WIP Throughput time
Original layout
Cells
81 848 valves/yr 12 858 valves 7 days
99 953 valves/yr 260 valves Flow line: 40 min Job shop: 44.7 min
Cell design methodology
75
7. CONCLUSIONS
Traditionally, a cellular design is only concerned with grouping products according to their physical characteristics or processing requirements, although of late there has been an increased interest in taking into consideration various cost factors. If these were the only grouping parameters, the same cell would have been used to make both a part with demand over 100 000 per year and one requiring less than 100 per year. The result would have been less predictable lead times and an ineffective use of labor. It was shown here that using manufacturing parameters for further grouping of parts can have substantial improvements in operational efficiency. Large orders for the high-demand products are processed in a different cell than the low-demand products. They are no longer hindrances to one another in the process flow. The operational needs for each part can be addressed by the different cellular layouts. The high-volume parts show a large customer need, and the large order sizes that ensue can result in longer throughput times. However, by producing these parts in cells operating more as flow lines, throughput time can be reduced. The lowto medium-volume parts put large strains on the manufacturing process. The process must be able to adapt to the individual needs of each part, and this is made difficult by the large variety of these parts. They require the flexibility and labor skills found in a job shop. By integrating manufacturing parameters such as demand and processing time into cell design and replication, the benefits specifically associated with these two layout options can be improved. To date, the cell design methodology described in this work has been successfully employed for development of at least 16 manufacturing cells in five different discrete parts manufacturing industries over a three year period. These applications have provided the author many opportunities to improve and validate the methodology described in this work. At the author's University affiliation, it has been incorporated into the industrial and manufacturing systems engineering courses. The methodology has received additional usage as our engineering graduates have applied it to their places of employment. REFERENCES 1. Apple, J. M. Plant Layout and Material Handling. Wiley, New York, 1977. 2. Cleland, D. 1. and Bidanda, B., The Automated Factor)" Handbook. Technologies and Management. TAB Professional and Reference Books, Blue Ridge Summit, PA, 1990. 3. King, J. R. and Nakornchai, V. Machine-component group formation in group technology: review and extension. International Journal of Production Research, 20, 1982, 117-133. 4. Chandrasekharan, M. P. and Rajagopalan, R. Zodiac: an algorithm for concurrent formulation of part-families and machine-cells. International Journal of Production Research, 25, 1987, 835-850. 5. Kusiak, A. and Chow, W. S. Efficient solving of the group technology problem. Journal of Manufacturing Systems, 6, 1987, 117-124. 6. Gupta, T. and Seifoddini, H. Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system. International Journal of Production Research, 28(7), 1990, 1247. 7. Srinivasan, G. and Narendran, T. T. Grafics: a nonhierarchical clustering algorithm for group technology. International Journal of Production Research, 29, 1991, 463-478. 8. Kumar, K. R. and Vannelli, A. Strategic subcontracting for efficient disaggregated manufacturing. International Journal of Production Research, 25, 1987, 1715-1728. 9. Askin, R. G. and Chiu, K. S. A graph partitioning procedure for machine assignment and cell formation in group technology. International Journal of Production Research, 28, 1990, 1555-1572. 10. Joines, J., Culbreth, C. T. and King, R. E., Manufacturing cell design using an integer-based genetic algorithm. In Flexible Automation and Intelligent Manufacturing, ed. R. D. Schraft, M. M. Ahmad, W. G. Sullivan and H. F. Jacobi. Begell House, New York, 1995. 11. Kazerooni, M., Luong, L. H. S. and Abhary, K. Cell formation using genetic algorithms. In Flexible Automation and Intelligent Manufacturing, ed. R. D. Schraft, M. M. Ahmad, W. G. Sullivan and H. F. Jacobi. Begell House, New York, 1995. 12. Tompkins, J. A. and White, J. A. Facilities Planning. Wiley, New York, 1984. 13. Black, J. T. The Design of the Factory with a Future. McGraw-Hill, New York, 1991. 14. Wemmerlov, U. and Vakharia, A. J. On the impact of family scheduling procedures, liE Transactions, 25(4), 1993, 102. 15. Salvendy, G. (ed.), Handbook oflndustrial Engineering. Wiley, New York, 1982.