International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:03 68 Paired-cell Overlapping Loops of Cards with Authorization Simulation In Job shop Environment Chong Kuan Eng, How Whee Ching, Bong Cheng Siong Faculty of Manufacturing Engineering, Universiti Teknikal Melaka Malaysia, 76100 Durian Tunggal, Melaka. Email: kuaneng@utem.edu.my Abstract Companies with high product varieties are faced with challenges of finding suitable material control strategies on the shop floor. In such environment, Kanban or generic CONWIP systems are not helpful to meet the companies needs. An alternative is another material control strategy known as the Paired-cell Overlapping Loops of Cards with Authorization (POLCA) method. POLCA is a hybrid push-pull strategy and is designed for a high mix low volume production environment. In this paper, POLCA is applied in a job shop environment using real world data from the company s shop floor. The production system is modelled using a simulation model to compare the output from the current system. The performance of these two system are evaluated and compared by using work in process (WIP), throughput (TP) and flow time (FT). The results show that the POLCA system is performed better than the current practice. Index Term job shop, POLCA, Quick Response Manufacturing, Simulation I. INTRODUCTION Manufacturing environment today is faced with the challenges of highly customizable products and high demand variability. This scenario emanates from factors such as globalization of markets, higher customer expectations, and the changing of trading structure. In this scenario, also known as job shop, product mix is high, production volume varies and each product undergoes different set of process routes. Dominant flow pattern does not exists, but requires different workstation types with different product routes. However, one crucial implementation issue is the adoption of suitable production and material flow control (PMFC) mechanisms. A good combination of PMFC mechanism with advance technology can result in a great performance improvement and quality of manufacturing. Common issues focused by manufacturing todays is on the improvements of flow time, throughput and work in process. Over the years, various types of material control strategies are proposed to solve these issues. However, not all the material control strategies is workable for the job shop environment. For instance, Toyota Kanban System (TKS) is not applicable to job shop environment [1]. In order to respond to this highly dynamic market, a new approach known as Quick Response Manufacturing (QRM) was proposed by Rajan Suri [2]. QRM is a companywide approach to reduce lead times [2]. The principles of QRM emphasizes on lead time reduction unlike lean, which mainly focuses on standardizing the process and products, and reduction of waste [3]. A new material control strategy known as POLCA was introduced as a part of the overall strategy of QRM [2]. POLCA uses card capacity signal and it is almost same as Kanban but the main difference between both of these cards is that POLCA works as a capacity signal while Kanban works as inventory replenishment signal [4]. In this paper, a simulation model before and after implementation of POLCA in job shop environment is constructed by using real world data from a case company. The case company, located in Melaka, Malaysia is a high precision components manufacturer, producing precision tools, die, mold etc. The processes in the company include milling (MI), turning (TL), CNC turning (NCL), CNC milling (MC), Electric Discharge Machining (EDM), grinding (GF1), Profile Grinding (PG), CNC grinding (CNCG), Wire Cutting (WC) and Super Drilling (SD). Process routes and process times for each product are different, and volume of demand from customers are low. The environment of this company can be categorized as high mix low volume production environment. Figure 1 shows the layout of the various departments in the case company. In this study, simulation models are designed and constructed using the WITNESS simulation software. The performance of the current production system and a proposed POLCA system are compared and evaluated using FT, WIP and TP as performance measures. This paper is structured as follows. In Section II, the POLCA mechanism is described and previous work done by others are reviewed. In Section III, the development of POLCA simulation model is explained. This is followed by the results and discussion of the study in Section IV. Finally the conclusions and directions for future study are presented in Section V.
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:03 69 B-C. First, an order must receive an A/B POLCA card before starting the first operation in machine A. After finishing the work at A, A/B card will travel together with the order to machine B. At B, the order has to wait for the next required card, B/C card. The A/B card will be attached until operation at machine B is completed, only then will it be sent back to machine A.This process continues until all the process is completed. Equation (1) is used to calculate the numbers of POLCA cards required for each loop. L A and L B are the lead time(in days) of cell A and B. F A/B is the number of jobs which go from cell A to cell B over a period of time. D is the planning period. Fig. 1. Layout of the Departments in the Case Company II. LITERATURE REVIEW PMFC mechanism can be classified into two main functions: order release and material flow control [1]. Order release determines the time to release a particular job into the production to start for production [1]. Material flow control regulates the flow material throughout the production [5]. For material flow control, there are three types of manufacturing system, namely the pull system, push system and hybrid push/pull system [6]. Many material flow control have been developed such as base stock system, material requirements planning, just in time, workload control, constant work in process (CONWIP) and etc, which use to increase the performance of the shop floor [7]. However, the traditional pull control strategies such as Kanban and CONWIP has its own limitations when applied in a high mix and low volume environment [8]. Kanban requires a minimum inventory in each workstation [4]. Once the inventory is taken by the downstream process, an immediate signals will send to the upstream to begin work to replenish this quantity [4]. If this concept is applied to the high mix and low volume environment, it will result in high WIP at each stage [4]. To resolve this issues, POLCA was developed by Suri in 1998 [2]. The basic concept of POLCA is to use cards to show the free capacity between two cells [2]. The operation of shop floor is controlled by the combination of authorization of release and POLCA card [4]. The release authorization system is different from the regular MRP system [4]. Authorization release system in POLCA assumes every cell as a black box and plans the material flow between cells [4]. POLCA not only works at the shop floor however it is also decide on when to release of orders to the shop floor [9]. According to Land and Gaalman [10], shop floor throughput time and on time delivery performance are greatly affected by the decision what and when to release to the shop floor. Figure 2 shows how POLCA cards flow according to a particular order. The order is processed according to flow: A- Fig. 2. POLCA card flow for an order N AB = (L A + L B ) X (F A/B / D) (1) In last decade, only a few studies have been done on the POLCA mechanism. Initial research was about the planning and implementation of POLCA. Operations and features of POLCA, procedure for using POLCA in a factory were described by Krishnamurthy and Suri [4]. Results from implementation show that POLCA improve the efficiency of their operations together with employee satisfaction [4]. Reiezebos [9] focused on the design of POLCA material control system. This paper proposed a quick scan approach for a POLCA system design [9]. First, an analysis is performed to check whether the objectives of the company match with the capabilities of a POLCA system [9]. Next, POLCA cells, loops and lead times are determined [9]. Lastly, effectiveness of applying POLCA is evaluated [9]. In 2006, a new modified POLCA material control system called Generic POLCA (GPOLCA) was developed by Fernandas and Silvo [1]. The main difference feature is that GPOLCA start to process the job when all the desired GPOLCA cards are available [8]. The comparison of GPOLCA, POLCA and material requirement planning (MRP) indicate that GPOLCA is the best production control strategy for multi-product systems or make-to-order (MTO) manufacturing environment [1]. Another research about increasing effectiveness of POLCA strategy called as Load-Based POLCA (LB-POLCA) was developed by Vandaele in 2008 [11]. LB- POLCA system is a POLCA system which combine with an advance resource planning (ARP) system, which recognizes the stochastic nature of manufacturing system and acts as high-level tuning and planning tool [11]. Preliminary result shows that this system is
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:03 70 much simpler and robust compare with finite scheduling system although there are several issues have not yet studied [11]. There are some previous work to compare POLCA with other material control system. One of the studies is to determine the workload balancing capability of CONWIP, POLCA and m-conwip [5]. The simulation results showed that CONWIP has no workload balancing capability. The control workload and achieve same utilization level in a CONWIP controlled make-to-order environment has increased the total throughput time of orders. POLCA shows a better workload balancing capability than CONWIP, but they are not perfectly notice signal and sometimes imbalance in workload [5]. This paper shows that m-conwip has the best working balancing capability. Another studies was done on the analysis of POLCA and GPOLCA material control strategies [8]. This study reconfirm the result of previous studies, which describe GPOLCA has low WIP than POLCA in order to achieve the same throughput on the shop floor. However, the results reveals that to achieve the same service level, GPOLCA gains a higher total inventory and longer response time than POLCA. In brief, GPOLCA increase the average delay of time and total inventory of a product that have a higher number of operation due to the wrong prioritization of orders [8]. Recently, another study was conducted on the applicability of three different variants of POLCA and m-cowip in automotive environment with high variety and divergent line. The three different types of POLCA known as POLCA second control loop, POLCA first control loop and basic POLCA. Result shows that m-conwip outperformed in this environment. It leads to more reduction in throughput time and WIP amount without decrease in throughput value [12]. There is another research investigate whether variability such as arrival and service process will affect the effectiveness of POLCA [13]. This research also state that work in progress and shop floor throughput time will reduce if number of POLCA cards is reduced [13]. Genetic Algorithm was implemented to compute the right amount of cards in order to reduce the overall average Work in Process and Total Throughput Time. The proposed simulation-driven genetic algorithm become a valid support tool and making the most of POLCA s capability [14]. Most studies conducted as described above were focused on the POLCA mechanisms and variants. In this paper, the implementation of POLCA was studied based on real data collected from the shop floor of a job shop company. The benefits of the proposed mechanism were assessed using simulation models. The methodology of this study is explained in the following section. III. METHOD Fig. 3. Overall procedure of POLCA simulation The overall procedure of POLCA simulation as shown in Figure 3. First, data is collected from shop floor. A conceptual model to represent the current scenario in the case company was developed. This model was verified and the computer model was developed using the WITNESS simulation software. After the base model was statistically validated, the POLCA model was built. Parameters for the POLCA model were first calculated. Table I illustrates some examples of the parameters calculation.
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:03 71 Table I Parameters Calculation for POLCA simulation Step Description 1. Determine POLCA loop For example Job1 is undergo process A, process B and process C. The POLCA loop will be A-B, B-C. 2. Calculate POLCA paired loop. POLCA paired loop Numbers of orders A-B 5 B-C 2 3. Determine Planning Period and quantum size Quantum size is the size of POLCA cards. For example, quantum size of 1 represents each POLCA cards able to carry only 1 job. In this example, planning period is 2 days. Quantum size is 1. 4. Determine Lead Time Parameters For example, Process A Job Order Pre- Processin g Time Processin g Time Post- Processin g Time Job 1 1 0.1 0.5 Job 2 2 0.5 1 Total 5.1 days MCT Average MCT per jobs 5.1/2= 2.55 days Lead time for Process A= 2.55 days Assume lead time for Process B = 2 days Same calculation for Process B and C. 5. Calculate Number of POLCA Cards For example : A B loop No. of Orders No. of No. of POLCA Cards Needed per Order POLCA Cards Flowing between Cells 5 5 5 Total No. of Cards Flowing 5 from A to B in 2 days. Numbers of A/B cards= [LT(A)+ (LT(B)X NUM(A,B)/D)] = (2.55 +2) x (5 /2) = 11.4 cards 12 cards TP, WIP and FT used as the performance measures. The objective is to evaluate the TP, WIP and flow time of current situation simulation model with POLCA simulation model. WIP is defined as the numbers of jobs stay between first work center and end point of product routing [15]. TP is defined as the number of completed products per time unit [15]. Flow time is the referred to time between the job release and its end of routing [15]. Sample size of 10 replications were conducted for both the current and POLCA simulation models. The performance measures were calculated based on the means of 10 independent identically distributed replications with a run length of 10080 time units (equivalent to 7 days) and preloaded conditions. Results are presented and discussed in the following section. IV. RESULTS AND DISCUSSION Tables II and III show the TP replication results of the current and POLCA system simulation models while Tables IV and V show the WIP replication results of the current system and POLCA system simulation model. The results from Tables II and III are plotted as shown in Figure 4. Throughput was higher in 5 out of the 7 days. Results from Tables IV and V, show that WIP of current system is significantly higher than POLCA system for all replications. The results from Table IV and V are plotted as shown in Figure 5. The paired-t confidence interval was used to determine the difference between the results from the two scenarios [16]. The results of the t-test with 95% confidence interval is shown by using the Minitab software as shown in Figure 6 and 7. Figure 6 and 7 shows the paired t- test confidence interval of TP and WIP respectively. The confidence interval of TP is (-1.921, - 1.365) while the confidence interval of WIP is (3.880, 5.834). Since the TP confidence interval is to the left, it proves that POLCA system is greater than current system. This indicates that the POLCA system has higher throughput than current system. On the other, the confidence interval of WIP to the right of zero indicating that current system has higher WIP than the POLCA system. Table II TP Replication Results of Current System Replication Day1 Day2 Day3 Day4 Day5 Day6 Day7 1 41 49 40 52 48 38 4 2 40 50 39 58 44 38 2 3 38 53 35 57 48 35 3 4 40 50 36 59 47 37 4 5 38 52 39 58 44 39 3 6 38 51 41 59 43 37 4 7 40 49 39 56 49 37 4 8 38 51 38 58 47 34 2 9 39 51 40 56 47 35 4 10 38 51 37 60 45 38 3 TP (per 39 50.7 38.4 57.3 46.2 36.8 3.30 Average TP (per 38.81
WIP (unit) Throughput (per International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:03 72 Table III TP Replication Results of POCLA System Replication Day1 Day2 Day3 Day4 Day5 Day6 Day 7 1 43 47 43 56 37 53 4 2 42 49 41 56 49 39 5 3 42 49 40 60 47 40 4 4 43 49 43 56 36 52 8 5 43 48 40 58 39 53 3 6 43 45 44 59 46 44 7 7 42 49 44 59 37 46 6 8 42 50 39 60 39 47 5 9 43 47 42 60 41 46 5 10 42 48 42 56 37 44 9 TP (per 42.5 48.1 41.8 58 40.8 46.4 5.6 Average TP (per 40.46 Table IV WIP Replication Results of Current System Replication Day1 Day2 Day3 Day4 Day5 Day6 Day7 1 309 260 220 168 120 82 78 2 310 260 221 163 119 81 79 3 312 259 224 167 119 84 81 4 310 260 224 165 118 81 77 5 312 260 221 163 119 80 77 6 312 261 220 161 118 81 77 7 310 261 222 166 117 80 76 8 312 261 223 165 118 84 82 9 311 260 220 164 117 82 78 10 312 261 224 164 119 81 78 WIP (per 311 260.3 221.9 164.6 118.4 81.6 78.3 Average WIP (per 176.59 Table V WIP Replication Results of POCLA System Replication Day1 Day2 Day3 Day4 Day5 Day6 Day7 1 307 260 217 161 124 71 67 2 308 259 218 162 113 74 69 3 308 259 219 159 112 72 68 4 307 258 215 159 123 71 63 5 307 259 219 161 122 69 66 6 307 262 218 159 113 69 62 7 308 259 215 156 119 73 67 8 308 258 219 159 120 73 68 9 307 260 218 158 117 71 66 10 308 260 218 162 125 81 72 WIP (per Average WIP (per 307.5 259.4 217.6 159.6 118.8 72.4 66.8 171.73 70 60 50 40 30 20 10 0 Day 330 315 300 285 270 255 240 225 210 195 180 165 150 135 120 105 90 75 60 45 30 15 0 Current System Throughput POLCA System 1 2 3 4 5 6 7 Fig. 4. TP vs Day of both system WIP Current System POLCA System 1 2 3 4 5 6 7 Day Fig. 5. WIP vs Day of both systems
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:03 73 ACKNOWLEDGMENT The authors are grateful for the support from the case company and special thanks to UTeM for the research funding to make this study possible. Fig. 6. Paired t-test confidence interval of TP Fig. 7. Paired t-test confidence interval of WIP Results under POLCA system are clearly better than under current system as flow time is reduced by 6.81%. Table VI shows that POCLA system needs less flow time to clear the WIP compare to current system which need a slightly longer flow time. Table VI also present TP percentage of POLCA system is 4.25% higher than TP of current system, and WIP percentage of current system is 2.75% higher than POLCA system. Table VI Performance Results of Current System and POCLA System Mechanism TP (no. of jobs per WIP (unit) FT ( Current System 38.81 176.59 4.55 POLCA 40.46 171.73 4.24 V. CONCLUSION A study was conducted in a job shop manufacturing company. Data was collected and simulation models were developed to compare the production control system as currently practiced in the company with a proposed POLCA system. The simulation models were developed using the WITNESS software and the results were statistically validated. The results showed that the POLCA system performed better than the current system by having shorter flow time and lower WIP level. For further studies, comparisons can made with other production and material control systems such as Kanban or CONWIP in the same environment. The study can also be extended to other similar job shop environments. REFERENCES [1] N. O. Fernandes and S.D. Carmo-Silvo. (2006, Feb). Generic POLCA- A production and materials flow control mechanism for quick response manufacturing. International Journal of Production Economics. 104 (1), pp.74-84. [2] R. Suri, Quick Response Manufacturing: A Companywide Approach to Reducing Lead Time, New York: Productivity Press, 1998, pp. 1-509. [3] R. Suri, It s About Time, United States: Productivity Press, 2011, pp. 3-4. [4] A. Krishnamurthy and R. Suri. (2009, Sep.). Planning and implementing POLCA: card-based control system for high variety or custom engineered products. Production Planning and Control: The Management of Operations. 20 (7), pp.596-610. [5] R. Germs and J. Riezebos. (2009, April.). Workload balancing capability of pull systems in MTO production. International Journal of Production Research. 48 (8), pp.2345-2360. [6] A. Krisnamurthy, R. Suri and M. Vernon. (2004, April.).Re-Examining the Performance of MRP and Kanban Material Control Strategies for Multi-Product Flexible Manufacturing Systems. International Journal of Flexible Manufaturing Systems. 16(3), pp.123-150. [7] N. Nahavandi. (2014, March). Comparison od SCONWIP, DCONWIP, TOC and CWIP II in Job Shop. Universal Journal of Industrial and Business Management. 2 (3), pp.61-68. [8] V. Bhatewara, Analysis of POLCA and GPOLCA Material Control Strategies, M.S. thesis, Dept. Mechanical Eng., Pune Univ., Pune, Maharashtra, 2010. [9] J. Riezebos. (2008, Oct.). Design of POLCA material control system. International Journal of Production Research. 48 (5), pp.1455-1477. [10] M. J. Land and G.J.C. Gaalman. (1998). The performance of workload control concepts in job shops: Improving the release method. International Journal of Operations & Production Economics. 56-57, pp.347-364. [11] N. Vandale, I.V. Nieuwenhuyse, D. Claerhout and R.Cremmery. (2007, Dec.).Load-Based POLCA: An Integrated Material Control System for Multiproduct, Multimachine Job Shops. Manufacturng & Servic Operations Management. 10 (2), pp.181-197. [12] A. Farnoush and M. Wiktorsson. (2013, July.).POLCA and CONWIP performance in a divergent production line: an automotive case study. Journal of Management Control. 24 (2), pp.159-186. [13] J. Riezebos, POLCA simulation of a unidirectional flow system, presented at the 3th World conference on Group Technology/ Cellular Manufacturing, Groningen, The Netherlands, Jun, 2006. [14] M. Braglia, D. Castellano and M. Frosolini. (2013, Sept.). Optimization of POLCA-controlled production systems with a simulation-driven genetic algorithm. The International Journal of Advanced Manufacturing Technology. 70 (1), pp.385-395. [15] W.J. Hopp and M.L. Spearman, Basic Factory Dynamics, in Factory Physics, 3rd ed., New York: McGraw-Hill, 2008, pp. 229 230. [16] S. Robinson, Simulation: The Practical of Model Development and Use. England: John Wiley & Sons, 2004, pp. 177-180.