Planning and Designing of a Production Facility Layout: An Efficient Framework in a Manufacturing Process Setting

Transcription

1 Planning and Designing of a Production Facility Layout: An Efficient Framework in a Manufacturing Process Setting Kok-Min Cheng 1, Razamin Ramli 2, Chooi-Leng Ang 3 1 Knowles Electronics (M) Sdn. Bhd. Penang, Malaysia. 2,3 Division of Physical Sciences UUM College of Arts and Sciences Universiti Utara Malaysia, Kedah, Malaysia. aileng883@hotmail.com; {razamin, ang}@uum.edu.my Abstract: Manufacturing companies are continuously evaluating new concepts and strategies and changing their production systems to gain a competitive ed ge in domestic and global markets. One of the important areas of concern in order to obtain a competitive level of productivity in a manufacturing system is the layout design and material transportation system (conveyor system). Conveyor systems are used widespread across a range of industries due to their numerous benefits. However, changes in customer s requirements may trigger the need to design other alternatives by re-layout the existing production floor to minimize the space allocation. Hence, this paper discusses effective alternatives of manufacturing process layout. All constraints or criteria involve in the layout problem are identified and described. Subsequently, a framework of an effective and efficient layout design is proposed with the objective to increase the production output and minimize space allocation at the same time. This efficient planning and designing of facility layout in a particular manufacturing setting is expected to minimize material handling cost, minimize overall production time, minimize investment in equipment, and optimize utilization of space. Keywords: Facility layout problem, Manufacturing process layout, Conveyor systems, Business operations 1. Introduction Many manufacturing companies are continuously evaluating new concepts and strategies and changing their production systems to gain a competitive edge in domestic and global markets. In obtaining a competitive level of productivity in a manufacturing system, layout design and efficient material transportation system (conveyor system) is considered important (Gershwin, 1994; Sharifnia, 1995). This transportation system is also known as the delivery system. The delivery (transportation) system can be categorized in two types, i.e. non-manufacturing and manufacturing. In the former type, several classic conveyor systems exist in our daily activities including the railway system (Green, 1987), airport luggage delivery system (de Nuefville, 1994) and hospital transportation system (Rossetti & Selandari, 2001). In the later type, conveyor systems are used widespread across a range of industries including the automotive, agricultural, computer, electronic, food processing, aerospace, pharmaceutical, chemical, bottling and canning, print finishing and packaging. Although a wide variety of materials can be conveyed, some of the most common include food items, such as peanut processing system (Wrabacon Inc., 1986), and conveying and sorting system (Dematic Corp., 2008). The usage of conveyor systems are vast due to their numerous benefits, such as i) able to safely transport materials from one level to another, which when done by human labor would be strenuous and expensive, ii) able to be installed almost anywhere, and are much safer than using a forklift or other machine to move materials, iii) able to move loads of all shapes, sizes and weights, iv) possess advanced safety features that help prevent accidents, and iv) are less prone for mistake of mix parts. In order to gain a competitive edge in domestic and global markets, the efficient material delivery or conveyor system is the key to success. However, changes in customer s requirements may trigger the need

2 to design alternative conveyor layout by redesigning the existing production floor with the objective of minimizing the space allocation. Hence, the objectives of this paper are to discuss effective alternatives of manufacturing process layout, and to propose a framework of an effective and efficient layout design with the aim to increase the production output and minimize space allocation. In order to select the effective and efficient process layout, it is necessary to study and identify all constraints or criteria involve in the manufacturing process layout problems. It is also necessary to investigate previous work done relating to the layout problem, especially the methods involved. Thus, this paper presents the necessary discussions in the following sub-sections where the next section is regarding previous related work. Section 3 2. Methods of Designing Process Layout The focus of the study is the production process layout, specifically the conveyor system in an operation room or floor in the manufacturing environment space usage. The production or facility layout problem (Yaman & Balibek, 1999) is not an independent design problem. It relates to other manufacturing design processes as well, which are product design, process design and material transportation system design. The communication links among product, process, transportation system, and layout designs (Francis & White, 1974) can be summarized as shown in Figure 1 below. Product Design Layout Design Material Transportation System Design Process Design Figure 1. Relationship of a Layout Design The production layout problem may occur due to issues related to (i) material handling cost, (ii) excess production, (iii) overall production time, (iv) availability and investment in equipments, and (v) space utilization (Francis & White, 1974). The methods being attempted before in overcoming these issues are discussed below. 2.1 Integer Programming The production of fashion clothing made by specialized designers in small quantities involves a facility layout design problem in the fashion industry (Degraeve et al., 2002). In this problem, Degraeve & Vandebroek (1998) proposed an alternative Integer Programming (IP) model for solving the layout problem in cloth cutting in the fashion industry. Due to high cost of materials together with limited demand, it is worthwhile to produce with minimal excess production, which is defined as the number of clothing pieces which are produced above demand. Before cutting, several layers of cloth are put on a cutting table and several templates indicating how to cut all material for a specific size are fixed on top of the stack. The IP model is able to find the best combination of templates and the associated height of the stack of cloth to satisfy demand while minimizing total excess production.

3 2.2 Genetic Algorithm In ensuring operational effectiveness, a Genetic Algorithm (GA) tool for designing manufacturing facilities in a capital goods industry is proposed by Hicks (2006). The development of the GA based method is to minimize the materials movement for a given schedule of work. The GA model includes geometric information on resources and building constraints. The tool may be used for brown-field or green-field layout design problems. It has been applied using data obtained from an engineer-to-order/make-to-order capital goods company. The algorithm produces layouts that significantly reduce the total distance traveled by components in both green field and brown field situations. 2.3 Simulation Simulation technique is applied in many different problems. Azadeh et al. (2008) applied computer simulation for railway system improvement and optimization, where it involves scheduling of the movement of trains in the most complex non-linear programming problem. For most complex situations, a closed form expression does not exist due to versatility and complexity of the imposed operations and constraints. Hence, simulation modeling technique is the only ideal tool for solving complicated priorities of cargo and passenger train scheduling. The advantage of computer simulation technique is that it provides feasible solutions within a short period and that they can be used to prepare, correct and modify periodical timetable for movement of trains. However, in an earlier work, Nedeljkavic &Narton (1984) used a heuristic method to generate the main program for movement of trains based on human-computer interaction in the western Australian railway system. 3. Methods of Layout Evaluation Layout model is normally a multi-objective problem (Apple,1997) and evaluating the multi-objective problem involves six components (Keeney & Raiffa, 1976; Pitz and McKillip, 1984). The six components that should be considered are: (i) A goal or a set of goals the decision makers want to achieve, (ii) The decision maker or a group of decision makers involved in the decision making process with their preferences with respect to the evaluation criteria, (iii) A set of evaluation criteria (objectives and/or physical attributes), (iv) The set of decision alternatives, (v) The set of uncontrollable (independent) variables or states of nature (decision environment), (vi) The set of outcomes or consequences associated with each alternative attribute pair. There are several Multi-Criteria Decision Analysis (MCDA) techniques which are normally used to evaluate the performance of the proposed layout as discussed below. These analysis techniques can be used to identify a single most preferred option, to rank options, to list a limited number of options for subsequent detailed evaluation, or to distinguish acceptable from unacceptable possibilities (Dodgson, 2000). 3.1 Value or Utility Function Method The value or utility function method is based on the multi-attribute utility theory (Keeney and Raiffa, 1976). The method enables the decision maker to focus initially on deriving utility function for one attribute at a time. The method provides a better theoretical foundation for describing the utilities. However, this method is quite impractical since it is usually difficult to obtain a mathematical representation of the decision maker s preferences. In addition, it is difficult to assess utility functions with even a moderate number of criteria due to it is very time consuming and tedious. This method also neglects the existence of spatial relationships among spatial alternatives (Malczewski, 1999). 3.2 Goal Programming The mathematical formulation of Goal Programming (GP) attempts to best satisfy a set of goals in the decision problem, which aims to minimize the deviations from the expected goals. However, the GP technique requires the decision maker to specify fairly detailed information about priorities and the

4 importance of goals in the form of weights, which is a difficult task to provide meaningful preference weights on a cardinal scale (Nijkamp, 1979). The cardinal weight and lexicographic methods have a strong tendency to generate inefficient solutions. This inefficiency problem seriously limits the utility of goal programming methods (Malczewski, 1997). 3.3 Analytic Hierarchy Process The Analytic Hierarchy Process (AHP) (Saaty, 1980) is a method available for use in an MCDA problem. AHP is based on pairwise comparisons between attributes, and can be used to evaluate the relative performance of decision alternatives with respect to a decision hierarchy consisting of one or several criteria. The method is flexible, easy to use and can be used to derive weights and combine them with attribute, and to aggregate the priority for all levels of the hierarchy structures. AHP requires four steps in its procedure: (i) Structuring the hierarchy of criteria and alternatives for evaluation, (ii) Assessing the decision makers evaluations by way of pairwise comparisons, (iii) Using the eigenvector method to yield priorities for criteria and for alternatives by criteria, (iv) Synthesizing the priorities of the alternatives by criteria into composite measures to arrive at a set of ratings for the alternatives. The scale of importance from 1-9 as defined by Saaty (1980) has to be used. Foulds & Partovi (1998) applied AHP to evaluate a closeness relationship among planning departments for a layout problem. Their goal was to generate a block plan based on the resulting closeness relationship. Subsequently, Cambron & Evans (1991) used different computer-aided layout design methods to generate a set of design alternatives that were then evaluated by AHP against a set of design criteria. On the other hand, Yang et al. (2000) used AHP to evaluate multiple-objective layout design alternatives generated from Muther s systematic layout planning (SLP) procedure (Muther, 1973). AHP is found to be capable of providing objective weights against a set of qualitative layout evaluation criteria. 4. A Framework on the Layout Design Based on the review of literature, computer simulation modeling is most suitable to be adopted in designing the layout and predicting the output. The operators utilization rates in different layouts could be compared even without the new layout being practically implemented. Output and ROI could be obtained through computer simulation modeling prior to implementation. On the other hand, when the selection of the available alternative designs need to be done, the AHP technique is deemed appropriate to be adopted in evaluating the layout alternatives. AHP stands out since it is able to compare qualitatively and quantitatively based on the indentified relevant criteria. In addition, AHP could be employed in problems which involve spatial relationships among spatial alternatives. The simulation and selection processes are given in a framework as shown in Figure Simulation Modeling The focus of the study is on conveyor system with consideration of conveyor oven for product assembly in a particular operation room in a manufacturing environment. Due to ever changing customers requirements, alternative layout designs are seek for and thus, the need is to re-layout the existing production floor in order to minimize the space allocation along with the department s criteria. In consequent, two alternative designs for the conveyor system are proposed. Each of these manufacturing process layouts is designed in such a way to increase the output. Computer simulation technique will be employed to design the new facility or make changes to the existing one. Simulation experiments will be conducted to understand the performance of each conveyor layout design based on the operational characteristics. The experiment is also able to predict the output of layout. Subsequently, the performance of the two designs will be compared before selection is done.

5 Data collection for the proposal line layout: 1. Usage Space especially for 2. the additional equipment (conveyor oven or conveyor). Cost of equipment Perform pairwise comparison Comparing Alternatives 3 UPH for simulate output Model Building Make a decision Verified & validated Run Experiments More Run? Report results Decompose their decision problem into a hierachy Prioritize and numerical weight Judge the priority setting Figure 2. A Framework on the Layout Design Involving Simulation and Selection Processes 4.2 Selection Modeling The two alternative layout designs need to be evaluated before final selection is done. In selection modeling, best layout design is selected using the AHP technique. Relevant criteria involved in this layout design problem are identified as (i) usage of space of the alternative design, (ii) operator s utilization rates, (iii) return of investment (ROI) of the layout, and (iv) output of the layout. In the process of selecting the best layout design based on these criteria, the AHP conducts pairwise comparisons and prioritization of numerical weight for each alternative design. 5. Expected Result The study on the manufacturing process layout design is still ongoing. Certain meaningful information or data has been obtained, such as (i) space usage of the alternative layouts, (ii) equipment cost, (iii) speed of the conveyor, and (iv) processing rate of the operator (existing process) in unit per hour (UPH). The simulation model and experiments are expected to provide results, such as (i) the output of the proposed alternative layouts, (ii) operators utilization rates, and (iii) ROI values calculated based on equipment cost and output. Finally, the AHP is expected to recommend the best alternative layout design either with or without the installation of conveyor or conveyor oven to be constructed in the particular manufacturing room based on priority.

6 6. Conclusions This efficient planning and designing of a facility layout in a particular manufacturing room is expected to minimize material handling cost, minimize overall production time, minimize investment in equipment, and optimize utilization or usage of space. This optimal utilization of space is able to ensure safety for employee, provide comfort, provide flexibility for rearrangement and operations, and facilitate the manufacturing process. The new automation equipment, such as the conveyor or conveyor oven could increase output and reduce cost in long term. References Apple, J.M. (1997). Plant Layout and Material Handling (3 rd ed.), New York: Wiley. Azadeh, S.F., Ghaderi, H. & Izadbakhsh. (2008). Integration of DEA and AHP with computer simulation for railway system improvement and optimization, Applied Mathematics and Computation, 195 (2), Cambron, K.E. & Evans, G.W. (1991). Layout design using the analytic hierarchy process, Computers and Industrial Engineering, 20 (2), Degraeve, Z., Gochet, W. & Jans, R. (2002). Alternative formulations for a layout problem in the fashion Industry. European Journal of Operations Research, Vol. 143, Issue 1, Degraeve, Z. & Vandebroek, M. (1998). A mixed integer programming model for solving a layout problem in the fashion industry. Journal of Management Science, 44, Dematic. (2008). Dematic Introduces New Conveyor System, Retrieved on 19 June 2009 from Dodgson, J., Spackman, Pearman A., Phillips L., (2000). Multi-criteria analysis manual. NERA, UK. Foulds, L.R. & Partovi, F.Y. (1998). Integrating the analytic hierarchy process and graph theory to model facilitates layout, Annals of Operations Research, 82, Francis, R.L. & White, J.A. (1974). Facilities Layout and Location: An Analytical Approach, Englewood Cliffs, NJ: Prentice Hall. Gershwin, S.B. (1994). Manufacturing systems engineering. Englewood Cliffs, NJ: Prentice Hall. Green, O. (1987). The London Underground - An illustrated history. London: Ian Allan Ltd. Hicks, C. (2004). A genetic algorithm tool for designing manufacturing facilities in the capital goods industry, International Journal of Production Economics, 90 (2), Keeney, R.L. & Raiffa, H. (1976). Decisions with multiple objectives. New York: Wiley. Rosetti, M.D. & Selandari, F. (2001). Multi-objective analysis of hospital delivery systems. Computers & Industrial Engineering, Vol. 41, No. 3, (25). Malczewski, J. (1999). GIS and Multicriteria decision analysis. New York: John Wiley & Sons. Muther, R. (1973). Systematic Layout Planning (2 nd ed.). Boston, MA: Cahners Books. Nedeljkovic, N.B. & Narton, N.C. (1984). Computerized train scheduling, Western Australian Government Railway Report, de Neufville, R. (1994). The baggage system at Denver: Prospects and lessons, Journal of Air Transport Management, Vol. 1, No. 4, Dec., Nijkamp, P. (1979). Multidimensional spatial data and decision analysis. Chichester: Wiley. Pitz, G.F. & McKillip, J. (1984). Decision analysis for program evaluators. Beverly Hills : Sage Publications Saaty, T.L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill. Sharifnia, A. (1995). Optimal production control based on continuous flow models, In: P. Brandimarte and A. Villa (Eds.), Optimization models and concepts in production management, , New York: Gordon and Breach Science Publishers. Wrabacon Inc. (1986). Conveyor case studies: Peanut processor. Retrieved on 19 June 2009 from Yaman, R. (1993). A Knowledge Based Approach for Facility Layout Design, Unpublished PhD Thesis, University of Wales. Yaman, R. & Balibek, E. (1999). Decision Making for Facility Layout Problem Solutions. Computers & Industrial Engineering, Vol. 37, Issues 1-2, Yang, T., Su, C.T. & Hsu, Y.R. (2000). Systematic layout planning: A study on semiconductor wafer fabrication facilities. Int. Journal of Operations and Production Management 20 (11),

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