Evaluation of the Effect of Erratic Demand on a Multi-Product Basestock Kanban-CONWIP Control Strategy

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1 STOCHASTIC MODELS OF MANUFACTURING AND SERVICE OPERATIONS SMMSO 2013 Evaluation of the Effect of Erratic Demand on a Multi-Product Basestock Kanban-CONWIP Control Strategy Onyeocha, Chukwunonyelum E. Enterprise Process Research Centre, School of Mechanical and Manufacturing Engineering Dublin City University, Dublin 9, Ireland chukwunonyelum.onyeocha2@mail.dcu.ie Khoury, Joseph Methode Electronics Malta Ltd. Industrial Estate Mriehel, Qormi, BKR 3000 joseph.khoury@methodegermany.com Geraghty, John Enterprise Process Research Centre, School of Mechanical and Manufacturing Engineering Dublin City University, Dublin 9, Ireland john.geraghty@dcu.ie Managing uncertainties in demand variability in a multi-product manufacturing environment is difficult and complex in nature. Organisations that adopt Pull-type Production Control Strategies, such as Kanban, for multi-product lines find that is necessary to plan high Kanban Card allocations in order to maintain volume flexibility to manage uncertainty in demand. This can result in line congestion, long lead times and low throughput rate. Recently, a Kanban Allocation Policy that allows product types to share Kanbans has been proposed in the literature. The benefit of this shared policy is that it is not necessary to maintain semi-finished and finished inventories of every product in the line. Rather, when demand/production volume shifts away from one product to another then the line can begin allocating Kanbans accordingly and therefore maintain volume flexibility. However, many Kanban-Like PCS that have been shown to be successful in single product environments e.g. Kanban, CONWIP and Basestock cannot operate S-KAP naturally, owing to tight coupling between the work authorisation cards and the demand information. Recently, we proposed a new PCS called Basestock Kanban-CONWIP (BK-CONWIP) Control Strategy (BK-CONWIP) that has the capability to operate S-KAP in a multi-product environment, minimising inventory and backlog while maintaining volume flexibility. This paper is aimed to test this strategy by application to a real manufacturing case study of a four-product system with erratic demand. Simulation based optimisation has been utilised to determine the optimal control parameters for BK-CONWIP. We then explore how the line performs under these settings to changing demand profiles. It is shown that BK- CONWIP operating S-KAP will outperform both Kanban and CONWIP, which both operate D-KAP. This research indicates that it is possible to design a robust Kanban-Like PCS for multi-product manufacturing systems with erratic demand. Key words : Kanban-Like Production Control Strategies; Multi-product Manufacturing Systems; Quick Response; Erratic Demand 1. Introduction Identifying optimal and robust settings for production control parameters for Pull strategies is difficult, especially in multi-product manufacturing environment with erratic demand profiles. For the case of single product manufacturing environments with linear demand profiles a distributional assumption is usually sufficient. However, organisations tend to move from single product manufacturing environments to multi-product manufacturing environments in order to satisfy customer 147

2 148 SMMSO 2013 demands. Customer demands often show irregular or intermittent patterns that are difficult to be represented by probability distribution. Selection and implementation of Pull Production Control Strategy (Kanban-Like PCS) in a manufacturing organisation determines the efficiency of inventory control and service level of the system. The challenge of selection of a Kanban-Like PCS is more prevalent and difficult in multiproduct manufacturing environment with erratic demand profiles. Significant research in Kanban- Like PCS has predominately concentrated on developing a simple model for control of a single product manufacturing system (see Satyam and Krishnamurthy (2008), Krejewski et al. (1987), Deleersnyder et al. (1992), Lee (1989), Spearman et al. (1990), Onyeocha and Geraghty (2012)). Furthermore, the majority of studies on multi-product manufacturing systems did not consider the effect of authorisation cards on the performance of a system. These studies assumed dedication of production authorisation cards to each specific part-type is sufficient (Baynat et al. (2002)). Planning and scheduling issues in multi-product manufacturing systems has been given great attention in the literature (Akturk and Erhun 1999, Hum and Lee 1998). Another area with a good record of study in multi-product systems is the optimisation of production control parameters cards to minimise inventory and increase service level (Bard and Golany 1991). Studies like Satyam and Krishnamurthy (2008), Duenyas (1994), Ryan et al. (2000), Ryan and Vorasayan (2005) considered the effect of the WIP limit of CONWIP in multi-product manufacturing systems. Recently, increased interest has been observed in the literature on the effect of production authorisation card allocation policies (often referred to as Kanban allocation policies) in multi-product Kanban-Like PCS. Two Kanban allocation policies proposed by Baynat et al. (2002) for multi-product Kanban-Like PCS control mechanism are the Dedicated Kanban Allocation Policy (D-KAP) and the Shared Kanban Allocation Policy (S-KAP). D-KAP allocates a specific number of production authorisation cards to each part-type in a stage or system, such that a release of any part-type in a stage or system is accompanied by a corresponding authorisation card allocated to it. On the other hand, S-KAP, assigns a specific number of cards for all part-types in a stage or system, which are distributed among various part-types within a stage or system depending on the demand for a part-type and availability of a stage or system authorisation card (Baynat et al. 2002). The distribution of production authorisation cards among part-types according to demand for a part-type would respond to corresponding variations in demand of various part-types. Some of the Kanban-Like PCS such as Kanban Control Strategy (KCS), Constant Work-in- Process (CONWIP), Basestock Control Strategy (BSCS), and Hybrid Kanban CONWIP (HK- CONWIP) which have good performance with respect to WIP and service level in single product manufacturing environments were found not capable of operating S-KAP in multi-product manufacturing environment (Baynat et al. (2002), Onyeocha and Geraghty (2012)). The process of transmission of demand information in Kanban-Like PCS has a major influence on the capability of a control strategy to operate S-KAP (Onyeocha and Geraghty (2012)). If demand information is wholly or partly decoupled from the production authorisation card, the Kanban-Like PCS will be capable of operating S-KAP. If the demand information and the production authorisation cards are tightly coupled, the Kanban-Like PCS will not be capable of operating S-KAP. Onyeocha and Geraghty (2012) proposed an approach for modification of the control parameters of the HK-CONWIP, allowing it to operate S-KAP in a multi-product manufacturing environment. The modified HK-CONWIP, called the Basestock Kanban-CONWIP (BK-CONWIP) requires testing in a multi-product manufacturing environment to verify and validate the performance with respect to minimising WIP and backlog. This paper evaluates the performance of BK-CONWIP in a multi-product manufacturing system with erratic demand using real data. The system configuration values for the control parameters

3 SMMSO were determined based on simulation optimisation. Investigation into the behaviour of the system in response to changing demand profiles while maintaining the same settings for the control parameters was studied and results are presented in this paper. 2. Research Background This section provides a brief review of the literature; first on multi-product Kanban-Like control strategies, followed by dedicated and shared Kanban allocation policies and the control mechanisms of the Kanban-Like PCS under investigation Multi-product Kanban-Like Control Strategies Kanban Control Strategy (KCS) authorises parts into a manufacturing system in response to actual demand. It controls WIP while observing the throughput. In the literature several variations of KCS, (hereinafter referred to as Kanban-Like PCS) exist either to improve performance or to address manufacturing issues which are not satisfied by pure KCS. The issue of determining a simple and general framework for selecting, implementing and managing Kanban-Like PCS has not been clearly addressed in the literature. A number of the studies in Kanban-Like PCS focused on the optimisation of the control parameters (see (Krejewski et al. 1987, Deleersnyder et al. 1992, Spearman et al. 1990)). While those on multi-product systems concentrated mainly on issues relating to planning, scheduling and optimisation (see (Akturk and Erhun 1999, Hum and Lee 1998, Bard and Golany 1991, Satyam and Krishnamurthy 2008, Duenyas 1994, Ryan and Vorasayan 2005, Ryan et al. 2000)). Baynat et al. (2002) proposed two Kanban allocation policies; dedicated and shared Kanban allocation policies (D-KAP and S-KAP) for multi-product manufacturing systems. According to Baynat et al. (2002) the three transferable elements in a multi-product system which have significant effects on the control mechanism are (i) raw materials and semi-finish parts move downstream (ii) demand information is transmitted upstream and (iii) production authorisation card is attached either to demand information or parts. The method of transmitting the demand information and the production authorisation cards in a system is a vital characteristic element which distinguishes any Kanban-Like PCS (Onyeocha and Geraghty 2012). Bonvik and Gershwin (1996) suggested two methods for transmission of demand information in Kanban-Like PCS are (i) the global information flow and (ii) local information flow. KCS, CONWIP, BSCS and HK-CONWIP were found incapable of operating S-KAP in the literature (see (Baynat et al. 2002, Onyeocha and Geraghty 2012)). Onyeocha and Geraghty (2012) suggested a modification approach to enhance Kanban- Like PCs which were unable to operate S-KAP to operate S-KAP. Furthermore, they developed a conceptual model of BK-CONWIP from HK-CONWIP using the modification approach Dedicated and Shared Kanban Allocation Policies The two main approaches for allocation of production authorisation cards found in the literature are Dedicated and Shared Kanban-Allocation Policies. D-KAP is described as allocation of a defined number of authorisation cards to each part-type in a stage or system. For instance, when a defined number of production authorisation cards are assigned to a specific part-type in a stage or system, such that a part-type can only be released into a stage or system by a corresponding (assigned) production authorisation card (Baynat et al. 2002). The production authorisation cards are planned and assigned for each part-type in a stage or system. Multi-product systems operating D-KAP function as a series of single product systems with shared manufacturing process units (see (Baynat et al. 2002, Onyeocha and Geraghty 2012)). Onyeocha and Geraghty (2012) suggested that the close knit coupling existing between demand information and authorisation cards is a major factor causing D-KAP to behave as extended single product systems.

4 150 SMMSO 2013 In S-KAP, the production authorisation cards are distributed among part-types in a stage or system such that a defined number of production authorisation cards are allocated to a stage or system to be shared among part-types in that stage or system. The production authorisation cards are planned and scheduled for a stage or system for use by any part-type, such that authorisation cards are assigned to part-types based on the demand information (Baynat et al. 2002, Onyeocha and Geraghty 2012). Onyeocha and Geraghty (2012) suggested that S-KAP responds to demand variation in a stage or system such that if demand for a part-type increases proportionally to a decrease in demand for another part-type in same stage or system the policy should be valid for multi-product systems with erratic demand without reconfiguration of the control parameters. The paper noted that D-KAP does not have this capability to respond to such variations without reconfiguration of the control parameters. The two Kanban allocation policies (D-KAP and S-KAP) are represented in Figure 1. - Figure 1 D-KAP and S-KAP in a Multi-product Stage 2.3. Control Mechanism of Kanban-Like PCS under Investigation The three PCS studied in this paper are KCS, CONWIP and BK-CONWIP. KCS and CONWIP have previously been found to be incapable of operating S-KAP in a multi-product manufacturing environment (see Baynat et al. (2002), Onyeocha and Geraghty (2012)). Onyeocha and Geraghty (2012) proposed BK-CONWIP which is capable of operating both D-KAP and S-KAP in a multiproduct manufacturing environment. KCS uses signal cards known as Kanbans to release part-type or raw material into a stage. The Kanbans control work-in-process (WIP) of a stage in a system (Krejewski et al. (1987), Deleersnyder et al. (1992), Lee (1989)). The Kanbans and demand information are tightly coupled and transmitted upstream. The control mechanism of KCS transmits Kanbans and demand information using a local information flow approach. Figure 2 shows the control mechanism of a multi-product KCS with three stages. 2 Figure 2 RM: Raw Material, MP: Manufacturing Process, I: Buffer Inventory, K: Kanbans, D: Demand, Kanban Flow, Part Flow Control Mechanism of a Multi-product KCS with Three Stage

5 SMMSO CONWIP control strategy combines pull and push control strategies in its control mechanism. Its ability to release parts or raw material based on actual market demand makes it a Kanban-Like PCS. CONWIP uses a set of signal cards to control the total WIP in a system, referred to as the WIP Cap (Spearman et al. 1990). When a demand for part-type is made, a set of signal cards (CONWIP cards) is attached to raw material which is released into a system in response to actual demand. This set of cards (CONWIP cards) moves with the parts to the finish goods buffer as illustrated in Figure 3. It is removed when the finished goods satisfies a demand such that the detached CONWIP card is tightly coupled to demand information and transmitted upstream to the first stage for replacement of the part-type. 2 Figure 3 RM: Raw Material, MP: Manufacturing Process, I: Buffer Inventory, C: CONWIP, D: Demand, CONWIP Flow Part Flow Control Mechanism of a Multi-product CONWIP with Three Stages BK-CONWIP is a modification of HK-CONWIP proposed by Onyeocha and Geraghty (2012). Like HK-CONWIP it uses two production authorisation cards. CONWIP cards are used to control the Total WIP of a system and Kanban cards for the stage WIP. The last stage has no Kanbans. In a D-KAP BK-CONWIP system; a defined number of CONWIP cards are dedicated to each part-type for the entire system production authorisation. Also, Kanbans are dedicated to each part-type at each stage for the production authorisation of a part-type within the stage. In a S- KAP BK-CONWIP system; a specific number of CONWIP cards are assigned for the release of any part-types into a system. Also, a stages Kanbans are assigned for authorisation of any parttypes within a stage. The CONWIP and the Kanban cards are shared by the various part-types in a system and the stage respectively. The demand information of BK-CONWIP is transmitted using a global information service to all the stages and to the raw material supply store. Both the CONWIP and the Kanban cards are required for the production authorisation of the part-type. Figure 4 and 5 present the control mechanisms of multi-product BK-CONWIP with three stages operating D-KAP and S-KAP, respectively. Part-Type 1 Part-Type 1 Part-Type 2 Part-Type 2 Figure 4 RM: Raw Material, MP: Manufacturing Process, I: Buffer Inventory, K: Kanbans, D: Demand, C: CONWIP, Kanban Flow, Part Flow Control Mechanism of Multi-product D-KAP BK-CONWIP with Three Stages

6 152 SMMSO 2013 Part- Type 1 Part- Type 2 Figure 5 RM: Raw Material, MP: Manufacturing Process, I: Buffer Inventory, K: Kanbans, D: Demand, C: CONWIP, Kanban Flow, Part Flow Control Mechanism of Multi-product S-KAP BK-CONWIP with Three Stages 3. Experimental Structure This section presents a simulation study on a multi-product manufacturing facility with an erratic demand profile. First, a brief description of the demand data is provided, followed by a description of the system configuration data and the optimal values of the control parameters of the three PCS and two policies under investigation Description of Data and Model The demand profile is irregular and infrequent. There are six weeks demand data for each of the four products. Table 1 provides details of the weekly demand in boxes (90 parts in a box for products 1 and 2 and 120 parts in a box for products 3 and 4). If demand has not been fully satisfied the shipment will be carried out based on the quantity acquired. The unsatisfied demand is considered as a backlog and is added to the demand in the following week. The company provided data on their view of how demand was changing from four weeks (Week 20 in their production Calendar) before production was due to commence (Week 24 in their production calendar) to one week after production began (Week 25 in their production calendar). Due to space limitations only the first and fifth data sets are presented in Table 1. Table 1 Demand profiles for Week 20 and Week 24 views of demand Week 20 Demand Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Product Product Product Product Week 24 Demand Week 24 Week 25 Week 26 Week 27 Week 28 Week 29 Product Product Product Product There are five production stages in the system modelled. The system has two product families, with two products in each. The products of the second family enter the line at production stage 3. A simulation model of the system was developed using ExtendSim. In developing the models for the three Kanban-Like PCS, raw materials for production and assembly were considered as always available. It is the availability of the authorisation cards or the production capacity that delays the authorisation of any of the products. All stages are subject to random failure and three stages of the system have significant set-up times. Finished goods are held in the supermarket area in box quantities. Shipments of satisfied demands are performed on a two-hour interval. Figure 6 illustrates a schematic diagram of the system and the system configuration is provided in Table 2. A four week warm-up period was found to be sufficient. Ten simulation replications were performed for each weekly demand profile. The performance of a Kanban-Like PCS controlled system depends greatly on the settings of the control parameters. The control parameters of KCS,

7 SMMSO Super Market Shipment Part-types (P) RM: Raw Material, MP: Manufacturing Process, I: Buffer Inventory, D: Demands, Demand Flow Part Flow Figure 6 Schematic diagram of the system Table 2 The configuration of the manufacturing system for modelling Lead Time/box (Hours) Maintenance: Exponential Distribution Mean Stage Product 1 Product 2 Product 3 Product 4 MTBF (Hours) MTTR (Hours) Setup Time (Hours) N/A N/A N/A N/A N/A N/A N (0.327, 0.109) N (0.327, 0.109) N (0.327, 0.109) CONWIP and BK-CONWIP were optimised for the Week 20 view of the demand profiles (which represents the order lead-time to suppliers) using the Genetic Algorithm optimisation block built into ExtendSim. Table 3 provide details of the search range and the optimal values for each PCS examined. Table 3 Solution space and optimal configuration Product KCS CONWIP D-KAP BK-CONWIP S-KAP BK-CONWIP K1 Kanbans K2 Kanbans CONWIP CONWIP K1 Kanbans K2 Kanbans CONWIP K1 Kanbans K2 Kanbans [O.S] Pallet [O.S] Box cards [O.S] Box cards [O.S] Box [O.S] pallet [O.S] Box cards [O.S] Box [O.S] pallet [O.S] Box Quantity Quantity Quantity Quantity Quantity Quantity Quantity Quantity Quantity [8] [8] 9 16 [14] [81] [121] [196] [3] [4] 7 10 [8] [6] [12] [62] [89] [58] 3 N/A [27] N/A 4 7 [6] [212] N/A 4 7 [74] [89] 4 N/A [22] N/A 4 7 [6] N/A [6] [47] [68] 4. Experimental Results and Discussion This section presents the outcome of the experiments. The weekly WIP level and the Backlog were examined. Again only the first and the fifth weeks results are presented due to space restriction. The Total weekly WIP and Backlog of each PCS are graphically represented in Figure 7. The results show that BK-CONWIP was consistently the best performer of the three PCS examined. Whereas, the least effective PCS in terms of WIP and Backlog is KCS. Significant difference was observed in the WIP and backlog of KCS and CONWIP. In general, BK-CONWIP out-performed KCS and CONWIP. The performance of BK-CONWIP is attributed to the relationship existing between the demand information, part-types, production authorisation cards and distribution. Demand information in KCS is transmitted upstream, one stage each time, until it reaches the initial stages. CONWIP transmits demand information instantaneously to the initial stage as soon as demand event occurs. This gave CONWIP superior performance over KCS. BK-CONWIP performed better than KCS and CONWIP because it uses the global information approach found in Basestock Control Strategy (BSCS) to eliminate information delay, the WIP Cap of CONWIP to control the total WIP in a system and stage WIP control using Kanban cards. The two policies D-KAP

8 Quantity Quantity Onyeocha, Khoury, Geraghty: Effect of Erratic Demand on BK-CONWIP 154 SMMSO 2013 WIP Vs Backlog from Week 20 WIP Vs Backlog from Week CONWIP Total WIP 800 CONWIP Total WIP 700 CONWIP Total Backlog 700 CONWIP Total Backlog 600 Kanban Total Backlog 600 Kanban Total Backlog Wk 24 Wk 25 Wk 26 Wk 27 Wk 28 Wk 29 Kanban Total WIP D-KAP BK-CONWIP Total WIP D-KAP BK-CONWIP Total Backlog S-KAP BK-CONWIP Total WIP S-KAP BK-CONWIP Total Backlog Figure 7 WIP vs. Backlog from Week 20 and Week Wk 24 Wk 25 Wk 26 Wk 27 Wk 28 Wk 29 Kanban Total WIP D-KAP BK-CONWIP Total WIP D-KAP BK-CONWIP Total Backlog S-KAP BK-CONWIP Total WIP S-KAP BK-CONWIP Total Backlog and S-KAP in BK-CONWIP recorded high backlog when the demand volume increased above their capacity. It was observed that S-KAP performed better than D-KAP. S-KAP used a lower number authorisation cards in its control mechanism and recorded lower WIP and lower backlogs compared to D-KAP. The large increase in product volume, from the Week 20 view the Week 24 view of demand profiles, affected the performance of the three strategies. However, the WIP level of BK-CONWIP remained relatively low in comparison to the WIP of CONWIP and KCS. S-KAP BK-CONWIP was found to have lower WIP level and lower backlog level when compared to D-KAP BK-CONWIP. The good performance of S-KAP BK-CONWIP PCS over D-KAP BK-CONWIP is attributed largely to the sharing of authorisation cards. 5. Conclusions In this paper we have discussed the performance of BK-CONWIP (D-KAP and S-KAP) in a multiproduct environment under erratic demand. We have shown that S-KAP uses lower configuration settings than D-KAP and illustrated the behaviour of BK-CONWIP (D-KAP and S-KAP) control parameters and their performance with respect to WIP and backlog in a multi-product system under erratic demand. Further study on BK-CONWIP would be of great value to provide better understanding of BK-CONWIP performance in comparison with other Kanban-Like PCS. Acknowledgments The authors wish to acknowledge the engineers at Methode Electronics Malta Ltd who assisted with the development and validation of the conceptual and simulation models, especially Edward Chetcuti. References Akturk, M.S., F. Erhun An overview of design and operational issues of kanban systems. International Journal of Production Research Bard, J.F., B. Golany Determining the number of kanbans in a multi-product, multi-stage production system. International Journal of Production Research Baynat, B., J.A. Buzacott, Y. Dallery Multi-product kanban-like control systems. International Journal of Production Research 40(16) Bonvik, A.M., S.B. Gershwin Beyond kanban: Creating and analyzing lean shop floor control policies. Proceedings of the Manufacturing and Service Operations Management Conference. Dartmouth College, The Amos Tuck School Hanover, NH, USA, Deleersnyder, J.L., T.J. Hodgson, R.E. King, P.J. OGrady, A. Savva Integrating kanban type pull systems and mrp type push systems: insights from a markovian model. IIE Transactions Duenyas, I A simple release policy for networks of queues with controllable inputs. Operations Research

9 SMMSO Hum, S.H., C.K. Lee Jit scheduling rules: a simulation evaluation. Omega Krejewski, L.J., B.E. King, L.P. Ritzman, D.S. Wong Kanban, mrp, and shaping the manufacturing environment. Management Science Lee, L.C A comparative study of the push and pull productions systems. Journal of Operations and Production Management Onyeocha, C.E., J. Geraghty A modification of the hybrid kanban-conwip production control strategy for multi-product manufacturing systems. Proceedings of the International Manufacturing Conference IMC29. University of Ulster, Belfast, UK. Ryan, S.M., B. Baynat, F. Choobineh Determining inventory levels in a conwip controlled job shop. IIE Transactions Ryan, S.M., J. Vorasayan Allocating work in process in multi-product conwip system with lost sales. International Journal of Production Research Satyam, K., A. Krishnamurthy Performance evaluation of a multi-product system under conwip control. IIE Transactions Spearman, M.L., D. Woodruff, W. Hopp Conwip: A pull alternative to kanban. International Journal of Production Research