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Seminario Kanban y Scrumban
Wip Y Conwip Frente A Push & Pull
by Fernando Hinostroza
TOC ConWIP Kanban
Introducción a Lean y Kanban
Kanban tarjetas de instrucción paso...
by Rafael Carlos C...
by Daniel Dominguez
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1. International Journal of Production Technology and Management (IJPTM), TECHNOLOGY INTERNATIONAL JOURNAL OF PRODUCTION ISSN 0976 – 6383 (Print), ISSN 0976 – 6391 (Online), Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME AND MANAGEMENT (IJPTM) ISSN 0976- 6383 (Print) ISSN 0976 - 6391 (Online) Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME: www.iaeme.com/ijptm.asp Journal Impact Factor (2014): 1.8513 (Calculated by GISI) www.jifactor.com IJPTM ©IAEME A VIEW ON CONWIP CONTROL POLICY IN SUPPLY CHAIN USING HEURISTIC METHOD Ch. Srinivas Professor and Principal, Vaageswari Engineering College, Karimnagar, Andhra Pradesh, INDIA ABSTRACT This paper describes a methodology for a pull production inventory control strategy which is based on optimization using heuristic algorithm for a mathematical model and then it is evaluated using simulation. The approach is described through the examples of production lines that process a single part type and is planned according to demand and lead time. The pull system has drawn the attention of researchers due to substantial advantages of being able to directly control WIP using the CONWIP cards, and can be applied to a wider variety of manufacturing environments. The information sharing will help with the integrating the echelons but with some complexity. To analyze the CONWIP controlled production line mathematical modeling is often applied and also simulation study is another performance evaluation tools so that it gives a valuable aid for gaining insights into and making decisions about the manufacturing systems. Each node is considered as a machine in a CONWIP SC. Our objective in this paper is to extend CONWIP control to a production inventory control system setting with an emphasis on customer satisfaction. Given this goal, inventory levels must be set for the whole system and the product to satisfy demands fairly. We also address a secondary objective of minimizing inventory costs by designing our procedure to find the smallest effective inventory level. Formulating a solvable problem meant shifting the focus to throughput, but the allocation found by the throughput driven heuristic can be utilized to provide good customer service. CONWIP cards can be implemented with a simple visual control at almost any level, at a machine, a work center, plant, or even an entire supply chain treating each echelon as a work center. Key words: Pull and Push Production Control Systems, CONWIP, Supply Chain, Genetic Algorithm, Simulation. 1
International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383 (Print), ISSN 0976 – 6391 (Online), Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME 1. INTRODUCTION It has been noticed for the last several years that due to change in Globalized business and communication system a tremendous intensive competition being focussed in business environment let it be in manufacturing, healthcare, banking etc., in this situation enterprises must be able to quickly respond to the diverse needs of customers. The Production control systems can be generally be subdivided into two ways ie., push and pull systems (Spearman et al. 1990). The principle of pull system is implemented in several production control strategies such as KANBAN, CONWIP. In push system the production is initiated by a central planning system which makes use of forecasts for future demands. Production is initiated before the occurrence of demand, otherwise the goods cannot be delivered in time. Therefore the production lead times have to be known or approximated. Where as in Pull production system, production starts when demand actually occurs. The production is initiated by a decentralized control system. To avoid long waiting times for customers, parts and finished products must be stored in buffers. Therefore, the pull system is called minimum inventory level system and the push system is zero inventory system. Since it is very fact that production without some inventory can only be realized when the system works without any sort of failures or break downs. But this is very fictitious in industry scenario, always there will be certain amount of WIP. In pull system a certain inventory level of parts and finished products is planned to fulfill the customer demand. CONstant Work In Process (CONWIP) control system first proposed by Spearman et al. (1990) uses a single card type to control the total amount of WIP permitted in the entire line. It is a generalized of the Kanban system and exceptionally it can be viewed as a single stage Kanban system as a whole. A CONWIP system behaves as follow: when ever a job order arrives to a CONWIP production line at the beginning of line, a card is attached to the job, provided cards are available, otherwise, the job must have to wait in queue for allotment of card meanwhile the job order can be treated as backlog order. When a job is processed at the final station, the card is removed and sent back to the beginning of the line, where it might be attached to the next job waiting in the backlog, no work order can enter the line without its corresponding work permitted card ie., CONWIP card. Along the production line the total WIP is constant when the system is sufficiently loaded to work non stop (thus the name CONWIP). The primary difference between CONWIP and Kanban systems is that CONWIP pulls a job into the beginning of the line and the job goes with a card between the workstations, while Kanban pulls jobs between all stations (Hopp and Speraman 2001). The basic rule assigned to each station in a Kanban model applies to the whole line in the CONWIP model. Under the CONWIP system, the materials are pulled into the production system by the completion of products in order to restrict the level of inventory, then the pulled materials are pushed from one station to another through the whole production system. CONWIP does not send signal from the bottleneck, but sends only form the final step in the line, however in CONWIP with fully integrated supply chain it is quite possible in sharing information between the echelons. Inventory is one of the most widely discussed areas for improving supply chain echelon efficiency. Since the holding of inventories can cost anywhere between 20% to 40% of product value, hence an effective management of inventory is critical and most essential (Ballou, 1992). Supply chain integration has become the focus and goal of many firms and it is used as strategy through which such integration can be achieved. In this environment, 2
International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383 (Print), ISSN 0976 – 6391 (Online), Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME ‘supply chain management’ has become effective business tool to reduce supply chain echelon inventory. Many researchers have approached the management of inventory in supply chain from operational perspective. The main issues that have been addressed include deployment of strategies, and control policies. Information and communication technology (electronic data interchange, internet), globalization, intensifying worldwide production and competition, shorter product life cycles, higher innovation rates or higher customer requirements are all the reasons for evolution of SCM. There are two types of inventory control in supply chain management. In a stage based inventory control system, inventory is managed at each stage only and inventory limits are determined by individual stage. However, in an echelon based inventory control system, inventory is counted from the current stage to the last stages inventory and the inventory limits are determined by considering different stages altogether rather than a single stage. CONWIP supply chain (CONWIP SC), is an approach by which we attempt to improve the supply chain (SC) performance, through an extension of the closed production control system. CONWIP SC is defined in this paper as a production–distribution system, in which the production line of each firm has a similarity to a ‘‘work center’’ being a part of a ‘‘global line’’ of supply. The set of cards mentioned in the description of CONWIP system, extends now to a virtual center of control that governs the SC and manages the parts flow and the inventories along the chain. When orders arrive at the final node, the production orders and required materials are released to the first node considering its production capacity constraints. There is a unique and centralized control of the backorders of the SC. Thus, the centralized information control through Internet type of tools is critical in this context (Ovalle and Marquez, 2003). Distinctive heuristic procedures such as customer dispatching rules, local search and Meta heuristics procedures such as Tabu Search, Simulated Annealing, and Genetic Algorithm (GA) have been applied to solve the production control problems and find the optimal. Genetic Algorithm (Holland, 1975, Goldberg, 1989 and Michalewicz, 1996, Srinivas and Rao, 2010) belongs to the class of evolutionary computation that was based on Darwin’s principle of the survival of the fittest. It is a stochastic global search technique which can effectively search good feasible solutions by mimicking the natural process of evolution and by using genetic operators. In order to apply the genetic algorithm to solve the inventory problem, the first step is to encode the candidate solutions with a system of concatenated, multi parameter, mapped, fixed point coding (Goldberg, 1989). Individuals which represent the candidate solutions are then grouped into a set called population, and the number of individuals contained in the population is called the population size. Hence, individuals form a population and strive for survival in accordance with their fitness function values. The fittest individuals are selected to undergo a sequence of perturbations (by using crossover and mutation operations) to breed a new population of individuals for the next generation. During the search process, the topological information of the solution space is extracted, and the most promising regions of the solution space are enumerated to locate the optimum. After a number of generations, the search converges. The overall best individual is then decoded to identify the final optimal or sub optimal solution. 2. LITERATURE REVIEW There are many studies on control policies for manufacturing systems, Spearman et al.(1990) proposed that the CONWIP concept could be applied to an assembly system fed by 3
International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383 (Print), ISSN 0976 – 6391 (Online), Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME two fabrication lines. Hopp and Roof (1998) studied such assembly systems using statistical throughput control method, for setting WIP levels to meet target production rates in the CONWIP system. Duri et al (2000) developed an approximation method to obtain some performance measures in three stage production lines under CONWIP control policy with random processing time and random inspection. Framinan et al. (2001, 2003, 2006) studied the input control and dispatching rules that might be used in a flow shop controlled by the CONWIP system within a make-to-stock environment. Christelle et al.(2000) analyzed a CONWIP system which consists of three stations in series. They proposed an analytical method to evaluate performance of CONWIP systems with inspection for the two following cases: saturated systems and system with external demands. Yang (2000) investigated the performance of single kanban, Dual kanban, and CONWIP for the production of different parts on a single flow line. CONWIP is a well known production control system, and some papers have shown it has better performance than the KANBAN system (Yaghoub, 2009) but however Kanban is more flexible. Houlihan (1985) is credited for coining the term supply chain (SC) with insight concepts and a strong case for viewing it as a strategy for global business decisions. Many definitions of SCM have been mentioned in the literature and in practice, although the underlying philosophy is the same. The lack of a universal definition for SCM is because of the multidisciplinary origin and evolution of the concept. Simchi-Levi et al.(2000) defined SCM as a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouse and stores, so that merchandise is produced and distributed at the right quantities, to the right location and at the right time in order to minimize system wise cost, while satisfying service level requirements. On the other hand, Christopher (2000) defined SCM as the management of upstream and downstream relationships with suppliers and customers to deliver superior customer value at minimal cost in the supply chain as a whole. Each echelon of SC perform independent business with integrated information sharing among all the echelons and it holds some inventories which may be unavoidable due to existing uncertainty in the business (Srinivas and Rao, 2004). 3. CONWIP SC MODELING Customers orders have to be analyzed in detailed for the purpose of segregation of items that allows work orders are to be placed in to list of orders based on dispatching rules or any customer production priority rule. These can be placed in visual master plan. The development of CONWIP control has highlighted the benefits of control policies that pull work into the facility in response to demand while limiting inventory. The proposed solution procedure consists of two stages; the first stage is a heuristic for solving the problem, and the second stage is using simulation to analyze the characteristic behavior of system. We assumed that, authorized card will never wait for raw material at the input station. Product mix is allowed, it is possible because a sole card for each lot, ie., each lot will be having a independent card. Starting from an initial allocation of a small number of cards for each product type, each successive card is given on a trial basis to each of the product types. The type that makes the best use of the additional card, by moving the total system throughput closest to the target is allowed to keep the card. The process continues until each type’s throughput attains its requirement. To overcome bottleneck situation in CONWIP production system a simple and logical procedure may be applied. Cards must be available for material to be 4
International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383 (Print), ISSN 0976 – 6391 (Online), Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME pulled from upstream to downstream workstations and must have enough cards to pull processes material from the bottleneck, so that the bottleneck is not blocked. The key to the card reduction heuristic is to reduce system WIP by reducing the number of cards while still meeting or exceeding a desired throughput goal. Estimate the total number of cards needed for the CONWIP system using the analytical formula Th = wrbTo wrb , Th = w + W0 − 1 w + W0 − 1 Where, rb is the rate of bottleneck workstation in job per minute. W0 is the WIP level attained for a line with maximum throughput operating at the rate of the bottleneck = rbTo , where To is the sum of the average processing time availability, blocking, machine breakdowns, supply chain failures. The CONWIP system acts inside as the push system, so the throughput rate may be taken to be equal to the arrival rate of jobs per minute. Using the estimated global card level, find the current workstation utilization and system output levels. Whenever the number of cards reduces, the time in queue increase, and the time in the physical system decreases. As the system WIP is reduced, the order spend more time to get card in the first stage where as the raw material spends less time in shop floor. Thus card dealing heuristic is designed to efficiently search for the smallest WIP with an effective allocation. Since, the fitness function is “max. throughput”, and hence the maximum balanced throughput represents the target for the card allocation. The proposed CONWIPSC model is shown in Fig. 1. and the heuristic model is shown in Fig. 2. Information Card Flow Material Flow Cards flow Figure. 1 Cards flow and material flow in CONWIP SC In the present work, initial population is generated having a fixed number of chromosomes and it is called population size (pop_size). Initial population contains suitable number of solutions for the problem. The GA Chromosomes structure is: (n,bj,os,k,bn,i) n: bj : os : k : orders backlog which can be joined with regular order shipped orders available production cards 5
International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383 (Print), ISSN 0976 – 6391 (Online), Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME bn : i : production backlog cannot enter in production available total FGI in the node Input parameters: Job type Order quantity Previous backorder Due date Number of cards Customer dispatching priority conditions Start Production order details Encoding Generation of Initial population Calculation of objective and Fitness evaluation no yes is termination criterion fulfilled? Population after regeneration Apply regeneration scheme Stop Results no yes is regeneration required? no Selection Pi+I = PI Crossover Mutation Repair Fig.2 Flow chart showing the Heuristic method The present work considers pop_size equal to 150 and it is generated randomly. If the optimization criterion is not fulfilled, then creation of new generation begins. Parent strings are selected according to their fitness for the production of offspring and combined to produce superior offspring chromosomes using crossover and mutation operations with a certain probability. This process is performed with number of generations as 300 which is the termination criteria. The probability of cross over is 0.7 and probability of mutation is 0.05. Simulation is used before an existing system is altered or a new system is built, to reduce the chances of failure to meet specification, to eliminate unforeseen bottlenecks, to prevent under or over utilization of resources, and to optimize system performance (Geoffrey, 1978). Thus, simulation modeling can be used both as an analysis tool for predicting the effect of changes to existing systems, and as a design tool to predict the performance of new systems under varying sets of circumstances. A performance evaluation is carried out based on the throughput rates and inventory levels. The CONWIP SC simulation model developed using Planimate™ is described in Fig.3. 6
International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383 (Print), ISSN 0976 – 6391 (Online), Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME Figure 3. CONWIP SC simulation model 4. RESULTS Throughputx10000 The evolutionary algorithms proposed are coded using VC++ on Pentium 4 with 1.60 GHZ, 2 GB RAM and SP3 and for simulation technique a Planimate™ simulation software is used. The simulation model has been simulated for 52 weeks with a warm up period of 5% of simulation time ie., during the first three weeks (warm up time) it is observed that a steady state is reached. Starting from the moment in which WIP and finished goods inventory in the chain reaches the steady state, as new orders arrive, sufficient number of cards were always available for releasing the necessary orders, in order to produce and meet customer demand. The average throughput rate is reached a steady state after warm up time of initial simulation period (Fig.5). and the mean delivery times are shown in Table 1. Figure 5. Average throughput 7
International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383 (Print), ISSN 0976 – 6391 (Online), Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME Table 1. Mean delivery times (minutes) From To Time (minutes) Supplier 1 Manufacturer 48 Supplier 2 Manufacturer 52 Manufacturer Customer 1 48 Manufacturer Customer 1 72 5. CONCLUSIONS This research paper intends to develop the ways for applying CONWIP production mechanism to supply chain system and to establish a suitable inventory management scheme. The proposed CONWIP supply chain is evaluated by a GA and Simulation. The simulation results show that CONWIP supply chain reduces the fluctuation and system inventory. However, we derived a throughput target that balances the production system. The card dealing is based on the given conditions which derived from the heuristics, the stopping criterion is based on the number of generations which is fulfilled each product type throughput. Our computational analysis suggests that equitable customer service can be provided by finding an allocation that achieves a throughput reasonably close to the target. The proposed model is validated through analytical and simulation study. The proposed system can derive the multi echelon WIP inventory limits effectively compared to the traditional stage based inventory monitoring scheme and can also obtain higher service levels with lowest inventory. In the future, the studies can be analyzed for multi echelons with a product mix complex CONWIP SC network with more realistic demand data to get realistic simulated results using Radio-frequency identification (RFID) as the source of information sharing. REFERENCES 1. 2. 3. 4. 5. 6. 7. M.L.Spearman, D.L.Woodruff and W.J.Hopp (1990), “CONWIP: a pull alternative to kanban”, International Journal of Production Research, vol.28, pp.879-894. Hopp, W.J. and M.L. Spearman (2001), “Factory Physics: Foundations of Manufacturing Management”, McGraw-Hill, New York. Ballou, R.H. (1992), “Business Logistics Management”, Prentice-Hal, Englewood Cliffs, New Jersey, 3rd ed. O.R.Ovalle and A.C.Marquez, (2003), “Exploring the utilization of a CONWIP system for supply chain management.A comparison with fully integrated supply chains”, International Journal of Production Economics, vol.83, pp. 195-215. Holland, J.H. (1975), “Adaptation in Nature and Artificial Systems”, University of Michigan press, Ann Arbor, MI. Goldberg, D. E. (1989), “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison Wesley. Michalewicz, Z. (1996), “Genetic Algorithms + Data Structures = Evolution Programs”, 3ed, New York, Springer Publishers. 8
International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383 (Print), ISSN 0976 – 6391 (Online), Volume 5, Issue 1, January - February (2014), pp. 01-09 © IAEME 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. Ch.Srinivas and Rao, (2010), “Optimization of supply chains for single-vendormultibuyer consignment stock policy with genetic algorithm”, International Journla of Advanced Manufacturing Technol, vol.48, pp.407-420. Hopp, W.J and Roof, M.L (1998), “ Setting WIP level with stastitical throughput control (STC) in CONWIP production lines”, Internatio nal journal of production research vol.36, pp.867-882. Duri, C., Frein, Y., & Lee, H.-S. (2000), “Performance evaluation and design of a CONWIP system with inspections”, International Journal of Production Economics, vol.64, pp.219-229. Framinan, J. M., Ruiz-Usano, R., & Leisten, R. (2001). “Sequencing CONWIP flowshops: analysis and heuristics”, International Journal of Production Research, vol.39, pp.2735-2749. Framinan, J.M., Gonzàlez, P.L., & Ruiz-Usano, R. (2003), “The CONWIP production control system: review and research issues”, Production Planning & Control, vol.14, pp.255-265. Framinan, J.M., Gonzàlez, P.L., & Ruiz-Usano, R. (2006). “Dynamic card controlling in a Conwip system”, International Journal of Production Economics, vol.99, pp.102116. Christelle D, Yannick F, Lee H-S (2000), “Performance evaluation and design of CONWIP system with inspection”, International journal of production economics, vol.64, pp.219-229. Kum Khiong Yang (2000), “Managing A Flow Line With Single-Kanban, DualKanban Or Conwip”, Production and Operations Management, vol.9, pp.349-366. Yaghoub Khojasteh-Ghamari (2009), “A performance comparison between Kanban and CONWIP controlled assembly systems”, Journal of Intelligent Manufacturing, vol. 20, pp. 751-760. J.B.Houlihan,(1985), “International supply chain”, International Journal of Physcial distribution and Materials Management, vol.15, pp.22-38. Simchi-Levi, D., Kaminsky, P., and Simchi-Levi, E. (2000), “Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies” McGraw-Hill, New York. Christopher, M. (2000), “Logistics and Supply Chain Management, Financial Times, Pitman publishing”, Pauls Press, New Delhi. Ch.Srinivas and Rao,(2004), “Simulation of Supply Chains under uncertainty inventory levels”, Proceedings 33rd International Conference on Computers & Industrial Engineering, Jeju, South Korea, 25 – 27th March, pp. 1-4. Geoffrey, G. (1978), “Systems simulation”, Prentice Hall, Englewood Cliffs, New Jersey. C. P. Aruna Kumari and Dr. Y. Vijaya Kumar, “An Effective Way to Optimize Key Performance Factors of Supply Chain Management (SCM)”, International Journal of Management (IJM), Volume 4, Issue 3, 2013, pp. 8 - 13, ISSN Print: 0976-6502, ISSN Online: 0976-6510. Amit Raj Varshney, Sanjay Paliwal and Yogesh Atray, “A Systematic Review of Existing Supply Chain Management: Definition, Framework and Key Factor” International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 2, 2013, pp. 298 - 309, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 9