Analysis and Improvement in Plant Layout for Effective Production in Manufacturing Industries
Shailendra P. Daf1, Prof. D.R. Zanwar2
1M-Tech (Industrial Engineering 4th Sem), Shri. Ramdeobaba College of Engineering & Management, Nagpur, India
2Prof. Industrial Engineering Department, Shri. Ramdeobaba College of Engineering & Management, Nagpur, India
*Corresponding Author E-mail: shailu_daf@rediffmail.com, zanwardr@rknec.edu
ABSTRACT:
The various methods were proposed in the past to address the issue of selection of appropriate plant lay out selection for a given manufacturing industrial application, there is a need for a systematic and logical scientific method or mathematical tool to guide user organization in taking a proper decision. The objective of layout selection procedure is to identify the pertinent selection criteria (both quantitative and qualitative type) and to select proper lay out by eliminating the unproductive ones. This paper present an effective decision making framework for plant lay out selecting in manufacturing industries using a multiple criteria decision making method, Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE). The proposed decision making framework is practical for ranking layout design in terms of their overall performance with respect to multiple criteria.
INTRODUCTION:
With rapid increasing demand in production, industries need to increase their potential in production and effectiveness to compete against their rivals. At the same time, the production process needs to be equipped with the ability to have lower cost with higher effectiveness. Therefore, the way to solve the problem about production is very important. There are many ways i.e. Quality control (QC), total quality management (TQM), implementation of standard time, effective plant layout to solve the problems concerning productivity. According to many researchers, plant layout is one way to reduce the cost of manufacturing, increase productivity and improve workflow in production routes.
The efficiency of production depends on how well the various machines, production facilities and employee’s amenities are located in a plant. Only the properly laid out plant can ensure the smooth and rapid movement of material, from the raw material stage to the end product stage. Plant layout encompasses new layout as well as improvement in the existing layout. According to Riggs, “the overall objective of plant layout is to design a physical arrangement that most economically meets the required output – quantity and quality.”
Plant layout is an important decision as it represents long-term commitment. An ideal plant layout should provide the optimum relationship among output, floor area and manufacturing process. It facilitates the production process, minimizes material handling, time and cost. It allows flexibility of operations, easy production flow, makes economic use of the building and promotes effective utilization of manpower. It provides for employee’s convenience, safety, comfort at work, maximum exposure to natural light and ventilation. It is also important because it affects the flow of material and processes, labour efficiency, supervision and control, use of space and expansion possibilities etc.
Past researchers have already applied different techniques to solve facility location selection problems. But most of those techniques use complex mathematical formulations, while ignoring qualitative information regarding criteria values. Randhawa and West [6] proposed a solution approach to facility location selection problems while integrating analytical and multi-criteria decision-making models. Houshyar and White [7] developed a mathematical model and heuristics approach that assigns N machines to N equal-sized locations on a given site such that the total adjacency flow between the machines is maximized. Chu [8] presented a fuzzy topsis (technique for order preference by similarity to ideal solution) method-based approach for the plant location selection problems.
Karray et al. [9] proposed an integrated methodology using the fuzzy set theory and genetic algorithms to investigate the layout of temporary facilities in relation to the planned buildings in a construction site. It identifies the closeness relationship values between each pair of facilities in a construction site using fuzzy linguistic representation.
Grobelny [10] explored the use of a fuzzy approach to facilities layout problems using a fuzzy criterion to determine the closeness relationship among departments; and then to determine the final optimum design. Evans et al. [11] and Dweiri and Meier [12] used a similar concept that employed the theory of fuzzy sets to solve a block layout design problem.
Raoot and Rakshit [13] proposed a construction-type layout design heuristic based on fuzzy set theory. A linguistic variable was used to model various qualitative design criteria, and then to determine the closeness relationship among departments. The resulting closeness relationship matrix was used to construct a layout design. This approach allowed, in a qualitative manner, for the systematic treatment of uncertainty due to fuzziness.
All of the above fuzzy-based layout design algorithms modeled the fuzzy or linguistic closeness relationship among departments. The resulting fuzzy scores that represent the desired closeness are then used for a layout design criterion along as part of the layout improvement process. In these methods, the fuzzy closeness determines the order of entry of departments into the layout; but the department placement and departmental dimensions are not explicitly considered.
Badiru and Arif proposed a fuzzy linguistic expert system in solving a layout design problem. It incorporated an existing layout algorithm, Blocplan, to efficiently create design alternatives. Their proposed expert system is an integrated system with three major components— fuzzy algorithm, Blocplan and expert system (knowledge-based rules). The interactions among the three components have the merits of computational efficiency and fuzzy linguistic modeling capability for a layout design problem. The system is fundamentally an improvement type layout design algorithm.
In the study of decision making, terms such as multiple objective, multiple attribute and multiple criteria are often used interchangeably. Here, we provide the conceptual distinctions leading to the definition of the proposed MADM methods.
Multiple objective decision making (MODM) consists of a set of conflicting goals that cannot be achieved simultaneously. It invariably concentrates on continuous decision spaces and can be solved with mathematical programming techniques. MODM generally deals with (i) preferences relating to the decision maker’s objectives and (ii) the relationships between objectives and attributes. An alternative could be described either in terms of its attributes or in terms of the attainment of the decision maker’s objectives.
MADM deals with the problem of choosing an option from a set of alternatives which are characterized in terms of their attributes. MADM is a qualitative approach due to the existence of criteria subjectivity. It requires information on the preferences among the instances of an attribute, and the preferences across the existing attributes. The decision maker may express or define a ranking for the attributes as importance/weights. The aim of the MADM is to obtain the optimum alternative that has the highest degree of satisfaction for all of the relevant attributes. Topsis and fuzzy topsis have been applied to solve a variety of applications, and are proven methodology in solving MADM problems.
Plant layout problems have already been solved using different MCDM techniques. This research makes an attempt to implement another appropriate MCDM approach with SLP, and simulation. The research is carried out at “KEC International Pvt Ltd, Butibori Nagpur”.
Figure.01
Systematic Layout Planning (SLP) was developed by Richard Muther in 1973 with 2 major purposes; high frequency and logical relationship. There are 6 main procedures as follows
1. Making Relationship Chart and from-to chart: In this procedure, relationship of each pair of activities is determined and evaluated in relationship chart. A material flow analysis is done in from-to chart.
2. Relationships Diagram: It is a diagram which symbols of proximity for all activities in the layout are shown how activities in each area are related to others.
3. Space Requirements and space available: Resulted from measuring the space of manufacturing process, machinery, and other manufacturing equipments of current manufacturing plant and analyzing space required.
4. Space Relationship diagram: Utilized as a guideline for design alternative layouts.
5. Alternative layouts Evaluation: Developed alternatives are evaluated based on specific criteria of each manufacturing plant.
6. Layout Selection and Installation: This final procedure is to select and to implement the most prefer alternative.
Based on mathematics and sociology, it was developed at the beginning of the 1980s and has been extensively studied and refined since then. It has particular application in decision making, and is used around the world in a wide variety of decision scenarios, in fields such as business, governmental institutions, transportation, healthcare and education. Rather than pointing out a "right" decision, the PROMETHEE & GAIA method helps decision makers find the alternative that best suits their goal and their understanding of the problem. It provides a comprehensive and rational framework for structuring a decision problem, for identifying and quantifying its conflicts and synergies, clusters of actions and highlight the main alternatives and the structured reasoning behind.
The PROMETHEE Method The PROMETHEE (Preference Ranking Organization method for Enrichment Evaluation) is a multi-criteria decision- making method developed by Brans et al. (Brans and Vincke 1985; Brans et al. 1986). It is a quite simple ranking method in conception and application compared with other methods used for multi-criteria analysis. It is well adapted to problems where a finite number of alternatives are to be ranked according to several, sometimes conflicting criteria (Albadvi et al. 2007). The evaluation table is the starting point of the PROMETHEE method. In this table, the alternatives are evaluated on the different criteria. The implementation of PROMETHEE requires two additional types of information, namely:
(1) Information on the relative importance that is the weights of the criteria considered.
(2) Information on the decision-makers preference function, which he/she uses when comparing the contribution of the alternatives in terms of each separate criterion.
The information on the relative importance that is the weights of criteria (wj) can be determined by various methods (Nijkamp et al. 1990; Mergias et al. 2007).Numerical scale method is used to determine the criteria weights in this study. After calculating the weights of the criteria, the next step is to have the information on the decision maker preference function, which he/she uses when comparing the contribution of the alternatives in terms of each separate criterion.
The preference function (Pj) translates the difference between the evaluations obtained by two alternatives (a and b) in terms of a particular criterion, into a preference degree ranging from 0 to 1. Let Pj(a,b) be the preference function associated to the criterion fj(i).
... (1)
....(2)
Where Gj is a non-decreasing function of the observed deviation (d) between two alternatives a and b over the criterion fj. In order to facilitate the selection of a specific preference function, six basic types were proposed. These include “usual function”, “linear function”, “U-shape function”, “V-shape function”, “level function” and “Gaussian function”. Preference “usual function” which is equal to the simple difference between the values of the criterion fj for alternatives ‘a’ and ‘b’ is adapted in this paper because of its simplicity. PROMETHEE permits the computation of the following quantities for each alternative a and b:
.. (3)
….(4)
.... (5)
.... (6)
For each alternative a, belonging to the set A of alternatives, π (a, b) is an overall preference index of a over b. The leaving flow φ+ (a) is the measure of the outranking character of a (how a dominates all the other alternatives of A). Symmetrically, the entering flow φ− (a) gives the outranked character of a (how a is dominated by all the other alternatives of A). φ (a) represents a value function, whereby a higher value reflects a higher attractiveness of alternative a and is called net flow. PROMTHEE provides a complete ranking of the alternatives from the best to the worst one using the net flows.
The research is carrying out at KEC International Pvt. Ltd. It is tower manufacturing industry which provides design, manufacture, testing, and distribution. There are variety of products which depend on customer demand, and manufacturing process. KEC production facility has been expanding during the past years. This has happened in several steps and the philosophy has been very similar to: Where can we free space for this workstation? This has led to many short-term solutions, which in turn has affected the material flow in the facility. The present layout, with the different departments marked is presented in figure1. The workshop of industries (kec international pvt ltd) is distributed in various departments such as fabrication, Galva, welding shop etc.
KEC has divided all fabrication workers into five different departments (bay) or teams. In each team approximate 25-30 workers. It is common that the different teams have to borrow workers from each other if one team has a lot to do. The locations of these teams are, like the storages, spread over the facility, material handling. There are no clear boundaries for each workstation within the departments. The workers may work with separate station, but are sharing areas for tools and inventories are same. The company has some departments with preparatory work, to increase the throughput of the finished product. The fabrication department includes the five different bays which are saraswati, yamuna, kaveri, godawari and narmada out of these narmada deals with plate shop and other are deals with the angles.
Fundamental data of the factory such as product data, manufacturing process data, flow process (routing), layout pattern, manufacturing facilities and relationship between each process are collected. The current factory layout can be shown in Fig. 2. Regarding to the results from the fundamental data, the product group, two subgroups of products angle and plate can be formed by process flow analysis (PFA). The 1st group contains operation O1 to O2 and the 2nd group contains operation O7-O10. Currently, the layout of the factory is a combination layout. There are 7 major manufacturing processes for these two groups. The angles for the tower are mostly made to customer’s order. The manufacturing of tower broadly classified in to two operations:
1) Primary operation.
2) Secondary operation.
Primary operation consists of punching; cutting, stamping and secondary operations consist of bending, heel cutting, notching, grinding.
Various operation carried out for manufactured the tower are:
First group: (angleshop)
O1 Primary Operation + Bending (S.O)
O2 Primary Operation + Heel Cutting (S.O)
O3 Primary Operation + Notching (S.O)
O4 Primary Operation + Bending (S.O) +Drilling
O5 Primary Operation
O6 Primary Operation + Notching (S.O) + Bending (S.O)
Second group (plate shop)
O7 Primary Operation
O8 Primary Operation+ Grinding
O9 Primary Operation+ Bending
O10 Primary Operation+ Bending+ Drilling
According to the study of the manufacturing process, it was found that the long distance could be reduced for moving raw materials and the problem about useless area could be solved. The way to improve the plant was to apply SLP method to make the work flow continually by arranging the important sequence of the manufacturing. Then the relationship of each activity in closeness area was considered to make the relationship of each activity `in the graph from-to chart as shown in Fig 3 , and the closeness value are defined as A = absolutely, E = especially important, I = important, O= ordinary closeness, U= unimportant. The intensities of flow from each activity to another were developed. Based on modifying plant layout and practical limitations, 11 numbers of layouts were developed.
Figure.2 Exiting layout
a) Generation of various layouts:
A preliminary study was conducted to determine the design criteria among the area experts that subsequently led to two quantitative and four qualitative design attributes. The quantitative attributes included material handling distance (in ‘meters’), adjacency score. They are referred to as C1, C2, respectively, hereafter. The handling distance was measured by the sum of the products of flow volume and rectilinear distance between the centroids of two departments. The adjacency score is the sum of all positive relationships between adjacent departments. There is a positive relationship between each two consecutive departments along the process routing. For a layout design problem, we Endeavour to minimize the flow distance, while maximizing adjacency score there are three qualitative attributes—flexibility, accessibility, maintenance and safety. They are referred to as C3, C4, C5, C6 respectively, hereafter. Flexibility involves two aspects: the first is the capability to perform a variety of tasks under a variety of operating conditions; second is the flexibility of future expansion. Accessibility involves material handling and operator paths. Finally, the maintenance issue involves the required space for maintenance engineers and tool movement.
We adopt the numeric scale method proposed by ribeiro. It uses a five grade scale from “extremely important (the grade of 5)” to “extremely unimportant (the grade of 1)” The calculation algorithm is shown as Eq. (7):
j=1,2,3,4,……n ....(7)
Where, gradej is the grade scale for attribute Cj. According to experts’ opinion, the grade scales for the six attributes are {5, 4, 2, 4, 3, 5}. We collected a pretty unanimous conclusion during the weight data collection process, and thus, do not feel the compelling need to develop a more sophisticated approach. Then, the resulting numeric scale weights using Eq. (7).
W = {5/23, 4/23, 2/23, 4/23, 3/23, 5/23}
{0.22, 0.17, 0.09, 0.17, 0.13, 0.22}
In PROMETHEE, firstly alternative are evaluated based on the evaluation criteria and the evaluation matrix is formed. The evaluations of these 11 alternatives according to the previously stated criteria, i.e., evaluation matrix, the pair wise comparison of criteria C1 give the matrix show in Table 1.
Similarly, the pair wise comparisons of the 6 alternatives with respect to other criteria are made, but not show here for space reasons. following the equation 3to 6, table 2 is prepared which show the resulting preference indices as well as leaving, entering, and net flow of the alternatives.
From the values of layout ranking, layout A11 is understood as best choice among the considered plant layout alternatives for the given selection problem under consideration. Now considering the decision matrix again, in the case of criteria C4, C5, C6, all of them are beneficial attributes and highest value is preferred. It is clearly evident that A11 is having the highest value and A11 is the preferred alternative for these attributes. In the case of criteria C1and C3, both are non beneficial attributes and the lowest value is preferred. Here also A11 is the most preferred value with lowest value among the alternatives, so out of the six, in five criteria A11 is the most preferred value. It is clearly seen by just viewing the data. Thus, the result proposed by PROMETHEE method is justified and reliable.
Table 1.Pair wise comparison of criteria C1
|
C1 |
A1 |
A2 |
A3 |
A4 |
A5 |
A6 |
A7 |
A8 |
A9 |
A10 |
A11 |
|
A1 |
0 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
|
A2 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
|
A3 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
|
A4 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
|
A5 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
|
A6 |
0 |
1 |
1 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
0 |
|
A7 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
|
A8 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
|
A9 |
0 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
0 |
0 |
0 |
|
A10 |
0 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
0 |
0 |
|
A11 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
Table 2. Net flow data of alternatives and their ranking
|
Π |
A1 |
A2 |
A3 |
A4 |
A5 |
A6 |
A7 |
A8 |
A9 |
A10 |
A11 |
φ+(a) |
φ(a) |
|
A1 |
0 |
0.22 |
0.39 |
0.17 |
0.48 |
0.22 |
0.22 |
0.31 |
0.22 |
0.22 |
0 |
2.45 |
-3.99 |
|
A2 |
0.69 |
0 |
0.39 |
0.39 |
0.26 |
0 |
0 |
0.31 |
0.17 |
0 |
0 |
2.21 |
-2.13 |
|
A3 |
0.52 |
0 |
0 |
0.39 |
0.48 |
0 |
0 |
0.31 |
0.17 |
0 |
0 |
1.87 |
-1.2 |
|
A4 |
0.52 |
0.22 |
0.22 |
0 |
0.31 |
0.22 |
0.22 |
0.31 |
0.39 |
0.22 |
0 |
2.83 |
-2.34 |
|
A5 |
0.52 |
0.52 |
0.52 |
0.69 |
0 |
0.13 |
0.52 |
0.35 |
0.52 |
0.52 |
0.13 |
4.42 |
0.32 |
|
A6 |
0.69 |
0.78 |
0.78 |
0.56 |
0.48 |
0 |
0.78 |
0.48 |
0.39 |
0.56 |
0 |
5.5 |
3.84(2) |
|
A7 |
0.69 |
0.22 |
0.39 |
0.39 |
0.48 |
0 |
0 |
0.31 |
0.17 |
0 |
0 |
2.65 |
-1.47 |
|
A8 |
0.69 |
0.69 |
0.69 |
0.69 |
0.17 |
0.13 |
0.69 |
0 |
0.52 |
0.52 |
0 |
4.79 |
1.14(3) |
|
A9 |
0.52 |
0.39 |
0.39 |
0.39 |
0.48 |
0.22 |
0.39 |
0.48 |
0 |
0.17 |
0 |
3.43 |
-0.42 |
|
A10 |
0.69 |
0.39 |
0.39 |
0.39 |
0.48 |
0.22 |
0.39 |
0.31 |
0.39 |
0 |
0 |
4.04 |
0.92 |
|
A11 |
0.91 |
0.91 |
0.91 |
0.91 |
0.48 |
0.52 |
0.91 |
0.48 |
0.91 |
0.91 |
0 |
7.85 |
7.72 (1) |
|
φ-(a) |
6.44 |
4.34 |
3.07 |
4.97 |
4.1 |
1.66 |
4.12 |
3.65 |
3.85 |
3.12 |
0.13 |
|
|
With the help of travel chart technique [14] analysis of the result given in table.3. By implementing improved layout, the material handling cost is reduced by Rs. 551.38/day for hand trolley and Rs. 381.82/day for bridge crane.
Table3.Material Handling Cost
|
Layout |
Total material handling cost |
|
|
For hand trolley |
For bridge crane |
|
|
Exiting |
2998.38 per day |
Rs. 3999.94per day |
|
Improved layout |
2447.0/day |
Rs.3618.12 /day |
The selection of suitable layout will drastically improve the productivity of an industry. This paper has presented the detail of a decision making frame work for plant layout improvement and selection in manufacturing industries using SLP integrating with PROMETHEE method. The proposed PROMETHEE method is a fairly simple and quite easy for decision maker’s application who are often non-experts.the proposed decision making framework using PROMETHEE method can be extended to any type of decision making problem involving any number of criteria and alternatives.
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Received on 24.05.2013 Accepted on 11.06.2013
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